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define(factory) :\n (factory());\n}(this, (function () { 'use strict';\n\n /**\n * Applies the :focus-visible polyfill at the given scope.\n * A scope in this case is either the top-level Document or a Shadow Root.\n *\n * @param {(Document|ShadowRoot)} scope\n * @see https://github.com/WICG/focus-visible\n */\n function applyFocusVisiblePolyfill(scope) {\n var hadKeyboardEvent = true;\n var hadFocusVisibleRecently = false;\n var hadFocusVisibleRecentlyTimeout = null;\n\n var inputTypesAllowlist = {\n text: true,\n search: true,\n url: true,\n tel: true,\n email: true,\n password: true,\n number: true,\n date: true,\n month: true,\n week: true,\n time: true,\n datetime: true,\n 'datetime-local': true\n };\n\n /**\n * Helper function for legacy browsers and iframes which sometimes focus\n * elements like document, body, and non-interactive SVG.\n * @param {Element} el\n */\n function isValidFocusTarget(el) {\n if (\n el &&\n el !== document &&\n el.nodeName !== 'HTML' &&\n el.nodeName !== 'BODY' &&\n 'classList' in el &&\n 'contains' in el.classList\n ) {\n return true;\n }\n return false;\n }\n\n /**\n * Computes whether the given element should automatically trigger the\n * `focus-visible` class being added, i.e. whether it should always match\n * `:focus-visible` when focused.\n * @param {Element} el\n * @return {boolean}\n */\n function focusTriggersKeyboardModality(el) {\n var type = el.type;\n var tagName = el.tagName;\n\n if (tagName === 'INPUT' && inputTypesAllowlist[type] && !el.readOnly) {\n return true;\n }\n\n if (tagName === 'TEXTAREA' && !el.readOnly) {\n return true;\n }\n\n if (el.isContentEditable) {\n return true;\n }\n\n return false;\n }\n\n /**\n * Add the `focus-visible` class to the given element if it was not added by\n * the author.\n * @param {Element} el\n */\n function addFocusVisibleClass(el) {\n if (el.classList.contains('focus-visible')) {\n return;\n }\n el.classList.add('focus-visible');\n el.setAttribute('data-focus-visible-added', '');\n }\n\n /**\n * Remove the `focus-visible` class from the given element if it was not\n * originally added by the author.\n * @param {Element} el\n */\n function removeFocusVisibleClass(el) {\n if (!el.hasAttribute('data-focus-visible-added')) {\n return;\n }\n el.classList.remove('focus-visible');\n el.removeAttribute('data-focus-visible-added');\n }\n\n /**\n * If the most recent user interaction was via the keyboard;\n * and the key press did not include a meta, alt/option, or control key;\n * then the modality is keyboard. Otherwise, the modality is not keyboard.\n * Apply `focus-visible` to any current active element and keep track\n * of our keyboard modality state with `hadKeyboardEvent`.\n * @param {KeyboardEvent} e\n */\n function onKeyDown(e) {\n if (e.metaKey || e.altKey || e.ctrlKey) {\n return;\n }\n\n if (isValidFocusTarget(scope.activeElement)) {\n addFocusVisibleClass(scope.activeElement);\n }\n\n hadKeyboardEvent = true;\n }\n\n /**\n * If at any point a user clicks with a pointing device, ensure that we change\n * the modality away from keyboard.\n * This avoids the situation where a user presses a key on an already focused\n * element, and then clicks on a different element, focusing it with a\n * pointing device, while we still think we're in keyboard modality.\n * @param {Event} e\n */\n function onPointerDown(e) {\n hadKeyboardEvent = false;\n }\n\n /**\n * On `focus`, add the `focus-visible` class to the target if:\n * - the target received focus as a result of keyboard navigation, or\n * - the event target is an element that will likely require interaction\n * via the keyboard (e.g. a text box)\n * @param {Event} e\n */\n function onFocus(e) {\n // Prevent IE from focusing the document or HTML element.\n if (!isValidFocusTarget(e.target)) {\n return;\n }\n\n if (hadKeyboardEvent || focusTriggersKeyboardModality(e.target)) {\n addFocusVisibleClass(e.target);\n }\n }\n\n /**\n * On `blur`, remove the `focus-visible` class from the target.\n * @param {Event} e\n */\n function onBlur(e) {\n if (!isValidFocusTarget(e.target)) {\n return;\n }\n\n if (\n e.target.classList.contains('focus-visible') ||\n e.target.hasAttribute('data-focus-visible-added')\n ) {\n // To detect a tab/window switch, we look for a blur event followed\n // rapidly by a visibility change.\n // If we don't see a visibility change within 100ms, it's probably a\n // regular focus change.\n hadFocusVisibleRecently = true;\n window.clearTimeout(hadFocusVisibleRecentlyTimeout);\n hadFocusVisibleRecentlyTimeout = window.setTimeout(function() {\n hadFocusVisibleRecently = false;\n }, 100);\n removeFocusVisibleClass(e.target);\n }\n }\n\n /**\n * If the user changes tabs, keep track of whether or not the previously\n * focused element had .focus-visible.\n * @param {Event} e\n */\n function onVisibilityChange(e) {\n if (document.visibilityState === 'hidden') {\n // If the tab becomes active again, the browser will handle calling focus\n // on the element (Safari actually calls it twice).\n // If this tab change caused a blur on an element with focus-visible,\n // re-apply the class when the user switches back to the tab.\n if (hadFocusVisibleRecently) {\n hadKeyboardEvent = true;\n }\n addInitialPointerMoveListeners();\n }\n }\n\n /**\n * Add a group of listeners to detect usage of any pointing devices.\n * These listeners will be added when the polyfill first loads, and anytime\n * the window is blurred, so that they are active when the window regains\n * focus.\n */\n function addInitialPointerMoveListeners() {\n document.addEventListener('mousemove', onInitialPointerMove);\n document.addEventListener('mousedown', onInitialPointerMove);\n document.addEventListener('mouseup', onInitialPointerMove);\n document.addEventListener('pointermove', onInitialPointerMove);\n document.addEventListener('pointerdown', onInitialPointerMove);\n document.addEventListener('pointerup', onInitialPointerMove);\n document.addEventListener('touchmove', onInitialPointerMove);\n document.addEventListener('touchstart', onInitialPointerMove);\n document.addEventListener('touchend', onInitialPointerMove);\n }\n\n function removeInitialPointerMoveListeners() {\n document.removeEventListener('mousemove', onInitialPointerMove);\n document.removeEventListener('mousedown', onInitialPointerMove);\n document.removeEventListener('mouseup', onInitialPointerMove);\n document.removeEventListener('pointermove', onInitialPointerMove);\n document.removeEventListener('pointerdown', onInitialPointerMove);\n document.removeEventListener('pointerup', onInitialPointerMove);\n document.removeEventListener('touchmove', onInitialPointerMove);\n document.removeEventListener('touchstart', onInitialPointerMove);\n document.removeEventListener('touchend', onInitialPointerMove);\n }\n\n /**\n * When the polfyill first loads, assume the user is in keyboard modality.\n * If any event is received from a pointing device (e.g. mouse, pointer,\n * touch), turn off keyboard modality.\n * This accounts for situations where focus enters the page from the URL bar.\n * @param {Event} e\n */\n function onInitialPointerMove(e) {\n // Work around a Safari quirk that fires a mousemove on whenever the\n // window blurs, even if you're tabbing out of the page. \u00AF\\_(\u30C4)_/\u00AF\n if (e.target.nodeName && e.target.nodeName.toLowerCase() === 'html') {\n return;\n }\n\n hadKeyboardEvent = false;\n removeInitialPointerMoveListeners();\n }\n\n // For some kinds of state, we are interested in changes at the global scope\n // only. For example, global pointer input, global key presses and global\n // visibility change should affect the state at every scope:\n document.addEventListener('keydown', onKeyDown, true);\n document.addEventListener('mousedown', onPointerDown, true);\n document.addEventListener('pointerdown', onPointerDown, true);\n document.addEventListener('touchstart', onPointerDown, true);\n document.addEventListener('visibilitychange', onVisibilityChange, true);\n\n addInitialPointerMoveListeners();\n\n // For focus and blur, we specifically care about state changes in the local\n // scope. This is because focus / blur events that originate from within a\n // shadow root are not re-dispatched from the host element if it was already\n // the active element in its own scope:\n scope.addEventListener('focus', onFocus, true);\n scope.addEventListener('blur', onBlur, true);\n\n // We detect that a node is a ShadowRoot by ensuring that it is a\n // DocumentFragment and also has a host property. This check covers native\n // implementation and polyfill implementation transparently. If we only cared\n // about the native implementation, we could just check if the scope was\n // an instance of a ShadowRoot.\n if (scope.nodeType === Node.DOCUMENT_FRAGMENT_NODE && scope.host) {\n // Since a ShadowRoot is a special kind of DocumentFragment, it does not\n // have a root element to add a class to. So, we add this attribute to the\n // host element instead:\n scope.host.setAttribute('data-js-focus-visible', '');\n } else if (scope.nodeType === Node.DOCUMENT_NODE) {\n document.documentElement.classList.add('js-focus-visible');\n document.documentElement.setAttribute('data-js-focus-visible', '');\n }\n }\n\n // It is important to wrap all references to global window and document in\n // these checks to support server-side rendering use cases\n // @see https://github.com/WICG/focus-visible/issues/199\n if (typeof window !== 'undefined' && typeof document !== 'undefined') {\n // Make the polyfill helper globally available. This can be used as a signal\n // to interested libraries that wish to coordinate with the polyfill for e.g.,\n // applying the polyfill to a shadow root:\n window.applyFocusVisiblePolyfill = applyFocusVisiblePolyfill;\n\n // Notify interested libraries of the polyfill's presence, in case the\n // polyfill was loaded lazily:\n var event;\n\n try {\n event = new CustomEvent('focus-visible-polyfill-ready');\n } catch (error) {\n // IE11 does not support using CustomEvent as a constructor directly:\n event = document.createEvent('CustomEvent');\n event.initCustomEvent('focus-visible-polyfill-ready', false, false, {});\n }\n\n window.dispatchEvent(event);\n }\n\n if (typeof document !== 'undefined') {\n // Apply the polyfill to the global document, so that no JavaScript\n // coordination is required to use the polyfill in the top-level document:\n applyFocusVisiblePolyfill(document);\n }\n\n})));\n", "(function(global) {\r\n /**\r\n * Polyfill URLSearchParams\r\n *\r\n * Inspired from : https://github.com/WebReflection/url-search-params/blob/master/src/url-search-params.js\r\n */\r\n\r\n var checkIfIteratorIsSupported = function() {\r\n try {\r\n return !!Symbol.iterator;\r\n } catch (error) {\r\n return false;\r\n }\r\n };\r\n\r\n\r\n var iteratorSupported = checkIfIteratorIsSupported();\r\n\r\n var createIterator = function(items) {\r\n var iterator = {\r\n next: function() {\r\n var value = items.shift();\r\n return { done: value === void 0, value: value };\r\n }\r\n };\r\n\r\n if (iteratorSupported) {\r\n iterator[Symbol.iterator] = function() {\r\n return iterator;\r\n };\r\n }\r\n\r\n return iterator;\r\n };\r\n\r\n /**\r\n * Search param name and values should be encoded according to https://url.spec.whatwg.org/#urlencoded-serializing\r\n * encodeURIComponent() produces the same result except encoding spaces as `%20` instead of `+`.\r\n */\r\n var serializeParam = function(value) {\r\n return encodeURIComponent(value).replace(/%20/g, '+');\r\n };\r\n\r\n var deserializeParam = function(value) {\r\n return decodeURIComponent(String(value).replace(/\\+/g, ' '));\r\n };\r\n\r\n var polyfillURLSearchParams = function() {\r\n\r\n var URLSearchParams = function(searchString) {\r\n Object.defineProperty(this, '_entries', { writable: true, value: {} });\r\n var typeofSearchString = typeof searchString;\r\n\r\n if (typeofSearchString === 'undefined') {\r\n // do nothing\r\n } else if (typeofSearchString === 'string') {\r\n if (searchString !== '') {\r\n this._fromString(searchString);\r\n }\r\n } else if (searchString instanceof URLSearchParams) {\r\n var _this = this;\r\n searchString.forEach(function(value, name) {\r\n _this.append(name, value);\r\n });\r\n } else if ((searchString !== null) && (typeofSearchString === 'object')) {\r\n if (Object.prototype.toString.call(searchString) === '[object Array]') {\r\n for (var i = 0; i < searchString.length; i++) {\r\n var entry = searchString[i];\r\n if ((Object.prototype.toString.call(entry) === '[object Array]') || (entry.length !== 2)) {\r\n this.append(entry[0], entry[1]);\r\n } else {\r\n throw new TypeError('Expected [string, any] as entry at index ' + i + ' of URLSearchParams\\'s input');\r\n }\r\n }\r\n } else {\r\n for (var key in searchString) {\r\n if (searchString.hasOwnProperty(key)) {\r\n this.append(key, searchString[key]);\r\n }\r\n }\r\n }\r\n } else {\r\n throw new TypeError('Unsupported input\\'s type for URLSearchParams');\r\n }\r\n };\r\n\r\n var proto = URLSearchParams.prototype;\r\n\r\n proto.append = function(name, value) {\r\n if (name in this._entries) {\r\n this._entries[name].push(String(value));\r\n } else {\r\n this._entries[name] = [String(value)];\r\n }\r\n };\r\n\r\n proto.delete = function(name) {\r\n delete this._entries[name];\r\n };\r\n\r\n proto.get = function(name) {\r\n return (name in this._entries) ? this._entries[name][0] : null;\r\n };\r\n\r\n proto.getAll = function(name) {\r\n return (name in this._entries) ? this._entries[name].slice(0) : [];\r\n };\r\n\r\n proto.has = function(name) {\r\n return (name in this._entries);\r\n };\r\n\r\n proto.set = function(name, value) {\r\n this._entries[name] = [String(value)];\r\n };\r\n\r\n proto.forEach = function(callback, thisArg) {\r\n var entries;\r\n for (var name in this._entries) {\r\n if (this._entries.hasOwnProperty(name)) {\r\n entries = this._entries[name];\r\n for (var i = 0; i < entries.length; i++) {\r\n callback.call(thisArg, entries[i], name, this);\r\n }\r\n }\r\n }\r\n };\r\n\r\n proto.keys = function() {\r\n var items = [];\r\n this.forEach(function(value, name) {\r\n items.push(name);\r\n });\r\n return createIterator(items);\r\n };\r\n\r\n proto.values = function() {\r\n var items = [];\r\n this.forEach(function(value) {\r\n items.push(value);\r\n });\r\n return createIterator(items);\r\n };\r\n\r\n proto.entries = function() {\r\n var items = [];\r\n this.forEach(function(value, name) {\r\n items.push([name, value]);\r\n });\r\n return createIterator(items);\r\n };\r\n\r\n if (iteratorSupported) {\r\n proto[Symbol.iterator] = proto.entries;\r\n }\r\n\r\n proto.toString = function() {\r\n var searchArray = [];\r\n this.forEach(function(value, name) {\r\n searchArray.push(serializeParam(name) + '=' + serializeParam(value));\r\n });\r\n return searchArray.join('&');\r\n };\r\n\r\n\r\n global.URLSearchParams = URLSearchParams;\r\n };\r\n\r\n var checkIfURLSearchParamsSupported = function() {\r\n try {\r\n var URLSearchParams = global.URLSearchParams;\r\n\r\n return (\r\n (new URLSearchParams('?a=1').toString() === 'a=1') &&\r\n (typeof URLSearchParams.prototype.set === 'function') &&\r\n (typeof URLSearchParams.prototype.entries === 'function')\r\n );\r\n } catch (e) {\r\n return false;\r\n }\r\n };\r\n\r\n if (!checkIfURLSearchParamsSupported()) {\r\n polyfillURLSearchParams();\r\n }\r\n\r\n var proto = global.URLSearchParams.prototype;\r\n\r\n if (typeof proto.sort !== 'function') {\r\n proto.sort = function() {\r\n var _this = this;\r\n var items = [];\r\n this.forEach(function(value, name) {\r\n items.push([name, value]);\r\n if (!_this._entries) {\r\n _this.delete(name);\r\n }\r\n });\r\n items.sort(function(a, b) {\r\n if (a[0] < b[0]) {\r\n return -1;\r\n } else if (a[0] > b[0]) {\r\n return +1;\r\n } else {\r\n return 0;\r\n }\r\n });\r\n if (_this._entries) { // force reset because IE keeps keys index\r\n _this._entries = {};\r\n }\r\n for (var i = 0; i < items.length; i++) {\r\n this.append(items[i][0], items[i][1]);\r\n }\r\n };\r\n }\r\n\r\n if (typeof proto._fromString !== 'function') {\r\n Object.defineProperty(proto, '_fromString', {\r\n enumerable: false,\r\n configurable: false,\r\n writable: false,\r\n value: function(searchString) {\r\n if (this._entries) {\r\n this._entries = {};\r\n } else {\r\n var keys = [];\r\n this.forEach(function(value, name) {\r\n keys.push(name);\r\n });\r\n for (var i = 0; i < keys.length; i++) {\r\n this.delete(keys[i]);\r\n }\r\n }\r\n\r\n searchString = searchString.replace(/^\\?/, '');\r\n var attributes = searchString.split('&');\r\n var attribute;\r\n for (var i = 0; i < attributes.length; i++) {\r\n attribute = attributes[i].split('=');\r\n this.append(\r\n deserializeParam(attribute[0]),\r\n (attribute.length > 1) ? deserializeParam(attribute[1]) : ''\r\n );\r\n }\r\n }\r\n });\r\n }\r\n\r\n // HTMLAnchorElement\r\n\r\n})(\r\n (typeof global !== 'undefined') ? global\r\n : ((typeof window !== 'undefined') ? window\r\n : ((typeof self !== 'undefined') ? self : this))\r\n);\r\n\r\n(function(global) {\r\n /**\r\n * Polyfill URL\r\n *\r\n * Inspired from : https://github.com/arv/DOM-URL-Polyfill/blob/master/src/url.js\r\n */\r\n\r\n var checkIfURLIsSupported = function() {\r\n try {\r\n var u = new global.URL('b', 'http://a');\r\n u.pathname = 'c d';\r\n return (u.href === 'http://a/c%20d') && u.searchParams;\r\n } catch (e) {\r\n return false;\r\n }\r\n };\r\n\r\n\r\n var polyfillURL = function() {\r\n var _URL = global.URL;\r\n\r\n var URL = function(url, base) {\r\n if (typeof url !== 'string') url = String(url);\r\n if (base && typeof base !== 'string') base = String(base);\r\n\r\n // Only create another document if the base is different from current location.\r\n var doc = document, baseElement;\r\n if (base && (global.location === void 0 || base !== global.location.href)) {\r\n base = base.toLowerCase();\r\n doc = document.implementation.createHTMLDocument('');\r\n baseElement = doc.createElement('base');\r\n baseElement.href = base;\r\n doc.head.appendChild(baseElement);\r\n try {\r\n if (baseElement.href.indexOf(base) !== 0) throw new Error(baseElement.href);\r\n } catch (err) {\r\n throw new Error('URL unable to set base ' + base + ' due to ' + err);\r\n }\r\n }\r\n\r\n var anchorElement = doc.createElement('a');\r\n anchorElement.href = url;\r\n if (baseElement) {\r\n doc.body.appendChild(anchorElement);\r\n anchorElement.href = anchorElement.href; // force href to refresh\r\n }\r\n\r\n var inputElement = doc.createElement('input');\r\n inputElement.type = 'url';\r\n inputElement.value = url;\r\n\r\n if (anchorElement.protocol === ':' || !/:/.test(anchorElement.href) || (!inputElement.checkValidity() && !base)) {\r\n throw new TypeError('Invalid URL');\r\n }\r\n\r\n Object.defineProperty(this, '_anchorElement', {\r\n value: anchorElement\r\n });\r\n\r\n\r\n // create a linked searchParams which reflect its changes on URL\r\n var searchParams = new global.URLSearchParams(this.search);\r\n var enableSearchUpdate = true;\r\n var enableSearchParamsUpdate = true;\r\n var _this = this;\r\n ['append', 'delete', 'set'].forEach(function(methodName) {\r\n var method = searchParams[methodName];\r\n searchParams[methodName] = function() {\r\n method.apply(searchParams, arguments);\r\n if (enableSearchUpdate) {\r\n enableSearchParamsUpdate = false;\r\n _this.search = searchParams.toString();\r\n enableSearchParamsUpdate = true;\r\n }\r\n };\r\n });\r\n\r\n Object.defineProperty(this, 'searchParams', {\r\n value: searchParams,\r\n enumerable: true\r\n });\r\n\r\n var search = void 0;\r\n Object.defineProperty(this, '_updateSearchParams', {\r\n enumerable: false,\r\n configurable: false,\r\n writable: false,\r\n value: function() {\r\n if (this.search !== search) {\r\n search = this.search;\r\n if (enableSearchParamsUpdate) {\r\n enableSearchUpdate = false;\r\n this.searchParams._fromString(this.search);\r\n enableSearchUpdate = true;\r\n }\r\n }\r\n }\r\n });\r\n };\r\n\r\n var proto = URL.prototype;\r\n\r\n var linkURLWithAnchorAttribute = function(attributeName) {\r\n Object.defineProperty(proto, attributeName, {\r\n get: function() {\r\n return this._anchorElement[attributeName];\r\n },\r\n set: function(value) {\r\n this._anchorElement[attributeName] = value;\r\n },\r\n enumerable: true\r\n });\r\n };\r\n\r\n ['hash', 'host', 'hostname', 'port', 'protocol']\r\n .forEach(function(attributeName) {\r\n linkURLWithAnchorAttribute(attributeName);\r\n });\r\n\r\n Object.defineProperty(proto, 'search', {\r\n get: function() {\r\n return this._anchorElement['search'];\r\n },\r\n set: function(value) {\r\n this._anchorElement['search'] = value;\r\n this._updateSearchParams();\r\n },\r\n enumerable: true\r\n });\r\n\r\n Object.defineProperties(proto, {\r\n\r\n 'toString': {\r\n get: function() {\r\n var _this = this;\r\n return function() {\r\n return _this.href;\r\n };\r\n }\r\n },\r\n\r\n 'href': {\r\n get: function() {\r\n return this._anchorElement.href.replace(/\\?$/, '');\r\n },\r\n set: function(value) {\r\n this._anchorElement.href = value;\r\n this._updateSearchParams();\r\n },\r\n enumerable: true\r\n },\r\n\r\n 'pathname': {\r\n get: function() {\r\n return this._anchorElement.pathname.replace(/(^\\/?)/, '/');\r\n },\r\n set: function(value) {\r\n this._anchorElement.pathname = value;\r\n },\r\n enumerable: true\r\n },\r\n\r\n 'origin': {\r\n get: function() {\r\n // get expected port from protocol\r\n var expectedPort = { 'http:': 80, 'https:': 443, 'ftp:': 21 }[this._anchorElement.protocol];\r\n // add port to origin if, expected port is different than actual port\r\n // and it is not empty f.e http://foo:8080\r\n // 8080 != 80 && 8080 != ''\r\n var addPortToOrigin = this._anchorElement.port != expectedPort &&\r\n this._anchorElement.port !== '';\r\n\r\n return this._anchorElement.protocol +\r\n '//' +\r\n this._anchorElement.hostname +\r\n (addPortToOrigin ? (':' + this._anchorElement.port) : '');\r\n },\r\n enumerable: true\r\n },\r\n\r\n 'password': { // TODO\r\n get: function() {\r\n return '';\r\n },\r\n set: function(value) {\r\n },\r\n enumerable: true\r\n },\r\n\r\n 'username': { // TODO\r\n get: function() {\r\n return '';\r\n },\r\n set: function(value) {\r\n },\r\n enumerable: true\r\n },\r\n });\r\n\r\n URL.createObjectURL = function(blob) {\r\n return _URL.createObjectURL.apply(_URL, arguments);\r\n };\r\n\r\n URL.revokeObjectURL = function(url) {\r\n return _URL.revokeObjectURL.apply(_URL, arguments);\r\n };\r\n\r\n global.URL = URL;\r\n\r\n };\r\n\r\n if (!checkIfURLIsSupported()) {\r\n polyfillURL();\r\n }\r\n\r\n if ((global.location !== void 0) && !('origin' in global.location)) {\r\n var getOrigin = function() {\r\n return global.location.protocol + '//' + global.location.hostname + (global.location.port ? 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'copy' : _options$action,\n container = options.container,\n target = options.target,\n text = options.text; // Sets the `action` to be performed which can be either 'copy' or 'cut'.\n\n if (action !== 'copy' && action !== 'cut') {\n throw new Error('Invalid \"action\" value, use either \"copy\" or \"cut\"');\n } // Sets the `target` property using an element that will be have its content copied.\n\n\n if (target !== undefined) {\n if (target && _typeof(target) === 'object' && target.nodeType === 1) {\n if (action === 'copy' && target.hasAttribute('disabled')) {\n throw new Error('Invalid \"target\" attribute. Please use \"readonly\" instead of \"disabled\" attribute');\n }\n\n if (action === 'cut' && (target.hasAttribute('readonly') || target.hasAttribute('disabled'))) {\n throw new Error('Invalid \"target\" attribute. You can\\'t cut text from elements with \"readonly\" or \"disabled\" attributes');\n }\n } else {\n throw new Error('Invalid \"target\" value, use a valid Element');\n }\n } // Define selection strategy based on `text` property.\n\n\n if (text) {\n return actions_copy(text, {\n container: container\n });\n } // Defines which selection strategy based on `target` property.\n\n\n if (target) {\n return action === 'cut' ? actions_cut(target) : actions_copy(target, {\n container: container\n });\n }\n};\n\n/* harmony default export */ var actions_default = (ClipboardActionDefault);\n;// CONCATENATED MODULE: ./src/clipboard.js\nfunction clipboard_typeof(obj) { \"@babel/helpers - typeof\"; if (typeof Symbol === \"function\" && typeof Symbol.iterator === \"symbol\") { clipboard_typeof = function _typeof(obj) { return typeof obj; }; } else { clipboard_typeof = function _typeof(obj) { return obj && typeof Symbol === \"function\" && obj.constructor === Symbol && obj !== Symbol.prototype ? \"symbol\" : typeof obj; }; } return clipboard_typeof(obj); }\n\nfunction _classCallCheck(instance, Constructor) { if (!(instance instanceof Constructor)) { throw new TypeError(\"Cannot call a class as a function\"); } }\n\nfunction _defineProperties(target, props) { for (var i = 0; i < props.length; i++) { var descriptor = props[i]; descriptor.enumerable = descriptor.enumerable || false; descriptor.configurable = true; if (\"value\" in descriptor) descriptor.writable = true; Object.defineProperty(target, descriptor.key, descriptor); } }\n\nfunction _createClass(Constructor, protoProps, staticProps) { if (protoProps) _defineProperties(Constructor.prototype, protoProps); if (staticProps) _defineProperties(Constructor, staticProps); return Constructor; }\n\nfunction _inherits(subClass, superClass) { if (typeof superClass !== \"function\" && superClass !== null) { throw new TypeError(\"Super expression must either be null or a function\"); } subClass.prototype = Object.create(superClass && superClass.prototype, { constructor: { value: subClass, writable: true, configurable: true } }); if (superClass) _setPrototypeOf(subClass, superClass); }\n\nfunction _setPrototypeOf(o, p) { _setPrototypeOf = Object.setPrototypeOf || function _setPrototypeOf(o, p) { o.__proto__ = p; return o; }; return _setPrototypeOf(o, p); }\n\nfunction _createSuper(Derived) { var hasNativeReflectConstruct = _isNativeReflectConstruct(); return function _createSuperInternal() { var Super = _getPrototypeOf(Derived), result; if (hasNativeReflectConstruct) { var NewTarget = _getPrototypeOf(this).constructor; result = Reflect.construct(Super, arguments, NewTarget); } else { result = Super.apply(this, arguments); } return _possibleConstructorReturn(this, result); }; }\n\nfunction _possibleConstructorReturn(self, call) { if (call && (clipboard_typeof(call) === \"object\" || typeof call === \"function\")) { return call; } return _assertThisInitialized(self); }\n\nfunction _assertThisInitialized(self) { if (self === void 0) { throw new ReferenceError(\"this hasn't been initialised - super() hasn't been called\"); } return self; }\n\nfunction _isNativeReflectConstruct() { if (typeof Reflect === \"undefined\" || !Reflect.construct) return false; if (Reflect.construct.sham) return false; if (typeof Proxy === \"function\") return true; try { Date.prototype.toString.call(Reflect.construct(Date, [], function () {})); return true; } catch (e) { return false; } }\n\nfunction _getPrototypeOf(o) { _getPrototypeOf = Object.setPrototypeOf ? Object.getPrototypeOf : function _getPrototypeOf(o) { return o.__proto__ || Object.getPrototypeOf(o); }; return _getPrototypeOf(o); }\n\n\n\n\n\n\n/**\n * Helper function to retrieve attribute value.\n * @param {String} suffix\n * @param {Element} element\n */\n\nfunction getAttributeValue(suffix, element) {\n var attribute = \"data-clipboard-\".concat(suffix);\n\n if (!element.hasAttribute(attribute)) {\n return;\n }\n\n return element.getAttribute(attribute);\n}\n/**\n * Base class which takes one or more elements, adds event listeners to them,\n * and instantiates a new `ClipboardAction` on each click.\n */\n\n\nvar Clipboard = /*#__PURE__*/function (_Emitter) {\n _inherits(Clipboard, _Emitter);\n\n var _super = _createSuper(Clipboard);\n\n /**\n * @param {String|HTMLElement|HTMLCollection|NodeList} trigger\n * @param {Object} options\n */\n function Clipboard(trigger, options) {\n var _this;\n\n _classCallCheck(this, Clipboard);\n\n _this = _super.call(this);\n\n _this.resolveOptions(options);\n\n _this.listenClick(trigger);\n\n return _this;\n }\n /**\n * Defines if attributes would be resolved using internal setter functions\n * or custom functions that were passed in the constructor.\n * @param {Object} options\n */\n\n\n _createClass(Clipboard, [{\n key: \"resolveOptions\",\n value: function resolveOptions() {\n var options = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : {};\n this.action = typeof options.action === 'function' ? options.action : this.defaultAction;\n this.target = typeof options.target === 'function' ? options.target : this.defaultTarget;\n this.text = typeof options.text === 'function' ? options.text : this.defaultText;\n this.container = clipboard_typeof(options.container) === 'object' ? options.container : document.body;\n }\n /**\n * Adds a click event listener to the passed trigger.\n * @param {String|HTMLElement|HTMLCollection|NodeList} trigger\n */\n\n }, {\n key: \"listenClick\",\n value: function listenClick(trigger) {\n var _this2 = this;\n\n this.listener = listen_default()(trigger, 'click', function (e) {\n return _this2.onClick(e);\n });\n }\n /**\n * Defines a new `ClipboardAction` on each click event.\n * @param {Event} e\n */\n\n }, {\n key: \"onClick\",\n value: function onClick(e) {\n var trigger = e.delegateTarget || e.currentTarget;\n var action = this.action(trigger) || 'copy';\n var text = actions_default({\n action: action,\n container: this.container,\n target: this.target(trigger),\n text: this.text(trigger)\n }); // Fires an event based on the copy operation result.\n\n this.emit(text ? 'success' : 'error', {\n action: action,\n text: text,\n trigger: trigger,\n clearSelection: function clearSelection() {\n if (trigger) {\n trigger.focus();\n }\n\n window.getSelection().removeAllRanges();\n }\n });\n }\n /**\n * Default `action` lookup function.\n * @param {Element} trigger\n */\n\n }, {\n key: \"defaultAction\",\n value: function defaultAction(trigger) {\n return getAttributeValue('action', trigger);\n }\n /**\n * Default `target` lookup function.\n * @param {Element} trigger\n */\n\n }, {\n key: \"defaultTarget\",\n value: function defaultTarget(trigger) {\n var selector = getAttributeValue('target', trigger);\n\n if (selector) {\n return document.querySelector(selector);\n }\n }\n /**\n * Allow fire programmatically a copy action\n * @param {String|HTMLElement} target\n * @param {Object} options\n * @returns Text copied.\n */\n\n }, {\n key: \"defaultText\",\n\n /**\n * Default `text` lookup function.\n * @param {Element} trigger\n */\n value: function defaultText(trigger) {\n return getAttributeValue('text', trigger);\n }\n /**\n * Destroy lifecycle.\n */\n\n }, {\n key: \"destroy\",\n value: function destroy() {\n this.listener.destroy();\n }\n }], [{\n key: \"copy\",\n value: function copy(target) {\n var options = arguments.length > 1 && arguments[1] !== undefined ? arguments[1] : {\n container: document.body\n };\n return actions_copy(target, options);\n }\n /**\n * Allow fire programmatically a cut action\n * @param {String|HTMLElement} target\n * @returns Text cutted.\n */\n\n }, {\n key: \"cut\",\n value: function cut(target) {\n return actions_cut(target);\n }\n /**\n * Returns the support of the given action, or all actions if no action is\n * given.\n * @param {String} [action]\n */\n\n }, {\n key: \"isSupported\",\n value: function isSupported() {\n var action = arguments.length > 0 && arguments[0] !== undefined ? arguments[0] : ['copy', 'cut'];\n var actions = typeof action === 'string' ? [action] : action;\n var support = !!document.queryCommandSupported;\n actions.forEach(function (action) {\n support = support && !!document.queryCommandSupported(action);\n });\n return support;\n }\n }]);\n\n return Clipboard;\n}((tiny_emitter_default()));\n\n/* harmony default export */ var clipboard = (Clipboard);\n\n/***/ }),\n\n/***/ 828:\n/***/ (function(module) {\n\nvar DOCUMENT_NODE_TYPE = 9;\n\n/**\n * A polyfill for Element.matches()\n */\nif (typeof Element !== 'undefined' && !Element.prototype.matches) {\n var proto = Element.prototype;\n\n proto.matches = proto.matchesSelector ||\n proto.mozMatchesSelector ||\n proto.msMatchesSelector ||\n proto.oMatchesSelector ||\n proto.webkitMatchesSelector;\n}\n\n/**\n * Finds the closest parent that matches a selector.\n *\n * @param {Element} element\n * @param {String} selector\n * @return {Function}\n */\nfunction closest (element, selector) {\n while (element && element.nodeType !== DOCUMENT_NODE_TYPE) {\n if (typeof element.matches === 'function' &&\n element.matches(selector)) {\n return element;\n }\n element = element.parentNode;\n }\n}\n\nmodule.exports = closest;\n\n\n/***/ }),\n\n/***/ 438:\n/***/ (function(module, __unused_webpack_exports, __webpack_require__) {\n\nvar closest = __webpack_require__(828);\n\n/**\n * Delegates event to a selector.\n *\n * @param {Element} element\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @param {Boolean} useCapture\n * @return {Object}\n */\nfunction _delegate(element, selector, type, callback, useCapture) {\n var listenerFn = listener.apply(this, arguments);\n\n element.addEventListener(type, listenerFn, useCapture);\n\n return {\n destroy: function() {\n element.removeEventListener(type, listenerFn, useCapture);\n }\n }\n}\n\n/**\n * Delegates event to a selector.\n *\n * @param {Element|String|Array} [elements]\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @param {Boolean} useCapture\n * @return {Object}\n */\nfunction delegate(elements, selector, type, callback, useCapture) {\n // Handle the regular Element usage\n if (typeof elements.addEventListener === 'function') {\n return _delegate.apply(null, arguments);\n }\n\n // Handle Element-less usage, it defaults to global delegation\n if (typeof type === 'function') {\n // Use `document` as the first parameter, then apply arguments\n // This is a short way to .unshift `arguments` without running into deoptimizations\n return _delegate.bind(null, document).apply(null, arguments);\n }\n\n // Handle Selector-based usage\n if (typeof elements === 'string') {\n elements = document.querySelectorAll(elements);\n }\n\n // Handle Array-like based usage\n return Array.prototype.map.call(elements, function (element) {\n return _delegate(element, selector, type, callback, useCapture);\n });\n}\n\n/**\n * Finds closest match and invokes callback.\n *\n * @param {Element} element\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @return {Function}\n */\nfunction listener(element, selector, type, callback) {\n return function(e) {\n e.delegateTarget = closest(e.target, selector);\n\n if (e.delegateTarget) {\n callback.call(element, e);\n }\n }\n}\n\nmodule.exports = delegate;\n\n\n/***/ }),\n\n/***/ 879:\n/***/ (function(__unused_webpack_module, exports) {\n\n/**\n * Check if argument is a HTML element.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.node = function(value) {\n return value !== undefined\n && value instanceof HTMLElement\n && value.nodeType === 1;\n};\n\n/**\n * Check if argument is a list of HTML elements.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.nodeList = function(value) {\n var type = Object.prototype.toString.call(value);\n\n return value !== undefined\n && (type === '[object NodeList]' || type === '[object HTMLCollection]')\n && ('length' in value)\n && (value.length === 0 || exports.node(value[0]));\n};\n\n/**\n * Check if argument is a string.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.string = function(value) {\n return typeof value === 'string'\n || value instanceof String;\n};\n\n/**\n * Check if argument is a function.\n *\n * @param {Object} value\n * @return {Boolean}\n */\nexports.fn = function(value) {\n var type = Object.prototype.toString.call(value);\n\n return type === '[object Function]';\n};\n\n\n/***/ }),\n\n/***/ 370:\n/***/ (function(module, __unused_webpack_exports, __webpack_require__) {\n\nvar is = __webpack_require__(879);\nvar delegate = __webpack_require__(438);\n\n/**\n * Validates all params and calls the right\n * listener function based on its target type.\n *\n * @param {String|HTMLElement|HTMLCollection|NodeList} target\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listen(target, type, callback) {\n if (!target && !type && !callback) {\n throw new Error('Missing required arguments');\n }\n\n if (!is.string(type)) {\n throw new TypeError('Second argument must be a String');\n }\n\n if (!is.fn(callback)) {\n throw new TypeError('Third argument must be a Function');\n }\n\n if (is.node(target)) {\n return listenNode(target, type, callback);\n }\n else if (is.nodeList(target)) {\n return listenNodeList(target, type, callback);\n }\n else if (is.string(target)) {\n return listenSelector(target, type, callback);\n }\n else {\n throw new TypeError('First argument must be a String, HTMLElement, HTMLCollection, or NodeList');\n }\n}\n\n/**\n * Adds an event listener to a HTML element\n * and returns a remove listener function.\n *\n * @param {HTMLElement} node\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenNode(node, type, callback) {\n node.addEventListener(type, callback);\n\n return {\n destroy: function() {\n node.removeEventListener(type, callback);\n }\n }\n}\n\n/**\n * Add an event listener to a list of HTML elements\n * and returns a remove listener function.\n *\n * @param {NodeList|HTMLCollection} nodeList\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenNodeList(nodeList, type, callback) {\n Array.prototype.forEach.call(nodeList, function(node) {\n node.addEventListener(type, callback);\n });\n\n return {\n destroy: function() {\n Array.prototype.forEach.call(nodeList, function(node) {\n node.removeEventListener(type, callback);\n });\n }\n }\n}\n\n/**\n * Add an event listener to a selector\n * and returns a remove listener function.\n *\n * @param {String} selector\n * @param {String} type\n * @param {Function} callback\n * @return {Object}\n */\nfunction listenSelector(selector, type, callback) {\n return delegate(document.body, selector, type, callback);\n}\n\nmodule.exports = listen;\n\n\n/***/ }),\n\n/***/ 817:\n/***/ (function(module) {\n\nfunction select(element) {\n var selectedText;\n\n if (element.nodeName === 'SELECT') {\n element.focus();\n\n selectedText = element.value;\n }\n else if (element.nodeName === 'INPUT' || element.nodeName === 'TEXTAREA') {\n var isReadOnly = element.hasAttribute('readonly');\n\n if (!isReadOnly) {\n element.setAttribute('readonly', '');\n }\n\n element.select();\n element.setSelectionRange(0, element.value.length);\n\n if (!isReadOnly) {\n element.removeAttribute('readonly');\n }\n\n selectedText = element.value;\n }\n else {\n if (element.hasAttribute('contenteditable')) {\n element.focus();\n }\n\n var selection = window.getSelection();\n var range = document.createRange();\n\n range.selectNodeContents(element);\n selection.removeAllRanges();\n selection.addRange(range);\n\n selectedText = selection.toString();\n }\n\n return selectedText;\n}\n\nmodule.exports = select;\n\n\n/***/ }),\n\n/***/ 279:\n/***/ (function(module) {\n\nfunction E () {\n // Keep this empty so it's easier to inherit from\n // (via https://github.com/lipsmack from https://github.com/scottcorgan/tiny-emitter/issues/3)\n}\n\nE.prototype = {\n on: function (name, callback, ctx) {\n var e = this.e || (this.e = {});\n\n (e[name] || (e[name] = [])).push({\n fn: callback,\n ctx: ctx\n });\n\n return this;\n },\n\n once: function (name, callback, ctx) {\n var self = this;\n function listener () {\n self.off(name, listener);\n callback.apply(ctx, arguments);\n };\n\n listener._ = callback\n return this.on(name, listener, ctx);\n },\n\n emit: function (name) {\n var data = [].slice.call(arguments, 1);\n var evtArr = ((this.e || (this.e = {}))[name] || []).slice();\n var i = 0;\n var len = evtArr.length;\n\n for (i; i < len; i++) {\n evtArr[i].fn.apply(evtArr[i].ctx, data);\n }\n\n return this;\n },\n\n off: function (name, callback) {\n var e = this.e || (this.e = {});\n var evts = e[name];\n var liveEvents = [];\n\n if (evts && callback) {\n for (var i = 0, len = evts.length; i < len; i++) {\n if (evts[i].fn !== callback && evts[i].fn._ !== callback)\n liveEvents.push(evts[i]);\n }\n }\n\n // Remove event from queue to prevent memory leak\n // Suggested by https://github.com/lazd\n // Ref: https://github.com/scottcorgan/tiny-emitter/commit/c6ebfaa9bc973b33d110a84a307742b7cf94c953#commitcomment-5024910\n\n (liveEvents.length)\n ? e[name] = liveEvents\n : delete e[name];\n\n return this;\n }\n};\n\nmodule.exports = E;\nmodule.exports.TinyEmitter = E;\n\n\n/***/ })\n\n/******/ \t});\n/************************************************************************/\n/******/ \t// The module cache\n/******/ \tvar __webpack_module_cache__ = {};\n/******/ \t\n/******/ \t// The require function\n/******/ \tfunction __webpack_require__(moduleId) {\n/******/ \t\t// Check if module is in cache\n/******/ \t\tif(__webpack_module_cache__[moduleId]) {\n/******/ \t\t\treturn __webpack_module_cache__[moduleId].exports;\n/******/ \t\t}\n/******/ \t\t// Create a new module (and put it into the cache)\n/******/ \t\tvar module = __webpack_module_cache__[moduleId] = {\n/******/ \t\t\t// no module.id needed\n/******/ \t\t\t// no module.loaded needed\n/******/ \t\t\texports: {}\n/******/ \t\t};\n/******/ \t\n/******/ \t\t// Execute the module function\n/******/ \t\t__webpack_modules__[moduleId](module, module.exports, __webpack_require__);\n/******/ \t\n/******/ \t\t// Return the exports of the module\n/******/ \t\treturn module.exports;\n/******/ \t}\n/******/ \t\n/************************************************************************/\n/******/ \t/* webpack/runtime/compat get default export */\n/******/ \t!function() {\n/******/ \t\t// getDefaultExport function for compatibility with non-harmony modules\n/******/ \t\t__webpack_require__.n = function(module) {\n/******/ \t\t\tvar getter = module && module.__esModule ?\n/******/ \t\t\t\tfunction() { return module['default']; } :\n/******/ \t\t\t\tfunction() { return module; };\n/******/ \t\t\t__webpack_require__.d(getter, { a: getter });\n/******/ \t\t\treturn getter;\n/******/ \t\t};\n/******/ \t}();\n/******/ \t\n/******/ \t/* webpack/runtime/define property getters */\n/******/ \t!function() {\n/******/ \t\t// define getter functions for harmony exports\n/******/ \t\t__webpack_require__.d = function(exports, definition) {\n/******/ \t\t\tfor(var key in definition) {\n/******/ \t\t\t\tif(__webpack_require__.o(definition, key) && !__webpack_require__.o(exports, key)) {\n/******/ \t\t\t\t\tObject.defineProperty(exports, key, { enumerable: true, get: definition[key] });\n/******/ \t\t\t\t}\n/******/ \t\t\t}\n/******/ \t\t};\n/******/ \t}();\n/******/ \t\n/******/ \t/* webpack/runtime/hasOwnProperty shorthand */\n/******/ \t!function() {\n/******/ \t\t__webpack_require__.o = function(obj, prop) { return Object.prototype.hasOwnProperty.call(obj, prop); }\n/******/ \t}();\n/******/ \t\n/************************************************************************/\n/******/ \t// module exports must be returned from runtime so entry inlining is disabled\n/******/ \t// startup\n/******/ \t// Load entry module and return exports\n/******/ \treturn __webpack_require__(686);\n/******/ })()\n.default;\n});", "/*!\n * escape-html\n * Copyright(c) 2012-2013 TJ Holowaychuk\n * Copyright(c) 2015 Andreas Lubbe\n * Copyright(c) 2015 Tiancheng \"Timothy\" Gu\n * MIT Licensed\n */\n\n'use strict';\n\n/**\n * Module variables.\n * @private\n */\n\nvar matchHtmlRegExp = /[\"'&<>]/;\n\n/**\n * Module exports.\n * @public\n */\n\nmodule.exports = escapeHtml;\n\n/**\n * Escape special characters in the given string of html.\n *\n * @param {string} string The string to escape for inserting into HTML\n * @return {string}\n * @public\n */\n\nfunction escapeHtml(string) {\n var str = '' + string;\n var match = matchHtmlRegExp.exec(str);\n\n if (!match) {\n return str;\n }\n\n var escape;\n var html = '';\n var index = 0;\n var lastIndex = 0;\n\n for (index = match.index; index < str.length; index++) {\n switch (str.charCodeAt(index)) {\n case 34: // \"\n escape = '"';\n break;\n case 38: // &\n escape = '&';\n break;\n case 39: // '\n escape = ''';\n break;\n case 60: // <\n escape = '<';\n break;\n case 62: // >\n escape = '>';\n break;\n default:\n continue;\n }\n\n if (lastIndex !== index) {\n html += str.substring(lastIndex, index);\n }\n\n lastIndex = index + 1;\n html += escape;\n }\n\n return lastIndex !== index\n ? html + str.substring(lastIndex, index)\n : html;\n}\n", "Array.prototype.flat||Object.defineProperty(Array.prototype,\"flat\",{configurable:!0,value:function r(){var t=isNaN(arguments[0])?1:Number(arguments[0]);return t?Array.prototype.reduce.call(this,function(a,e){return Array.isArray(e)?a.push.apply(a,r.call(e,t-1)):a.push(e),a},[]):Array.prototype.slice.call(this)},writable:!0}),Array.prototype.flatMap||Object.defineProperty(Array.prototype,\"flatMap\",{configurable:!0,value:function(r){return Array.prototype.map.apply(this,arguments).flat()},writable:!0})\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport \"array-flat-polyfill\"\nimport \"focus-visible\"\nimport \"unfetch/polyfill\"\nimport \"url-polyfill\"\n\nimport {\n EMPTY,\n NEVER,\n Subject,\n defer,\n delay,\n filter,\n map,\n merge,\n mergeWith,\n shareReplay,\n switchMap\n} from \"rxjs\"\n\nimport { configuration, feature } from \"./_\"\nimport {\n at,\n getOptionalElement,\n requestJSON,\n setToggle,\n watchDocument,\n watchKeyboard,\n watchLocation,\n watchLocationTarget,\n watchMedia,\n watchPrint,\n watchViewport\n} from \"./browser\"\nimport {\n getComponentElement,\n getComponentElements,\n mountBackToTop,\n mountContent,\n mountDialog,\n mountHeader,\n mountHeaderTitle,\n mountPalette,\n mountSearch,\n mountSearchHiglight,\n mountSidebar,\n mountSource,\n mountTableOfContents,\n mountTabs,\n watchHeader,\n watchMain\n} from \"./components\"\nimport {\n SearchIndex,\n setupClipboardJS,\n setupInstantLoading,\n setupVersionSelector\n} from \"./integrations\"\nimport {\n patchIndeterminate,\n patchScrollfix,\n patchScrolllock\n} from \"./patches\"\nimport \"./polyfills\"\n\n/* ----------------------------------------------------------------------------\n * Application\n * ------------------------------------------------------------------------- */\n\n/* Yay, JavaScript is available */\ndocument.documentElement.classList.remove(\"no-js\")\ndocument.documentElement.classList.add(\"js\")\n\n/* Set up navigation observables and subjects */\nconst document$ = watchDocument()\nconst location$ = watchLocation()\nconst target$ = watchLocationTarget()\nconst keyboard$ = watchKeyboard()\n\n/* Set up media observables */\nconst viewport$ = watchViewport()\nconst tablet$ = watchMedia(\"(min-width: 960px)\")\nconst screen$ = watchMedia(\"(min-width: 1220px)\")\nconst print$ = watchPrint()\n\n/* Retrieve search index, if search is enabled */\nconst config = configuration()\nconst index$ = document.forms.namedItem(\"search\")\n ? __search?.index || requestJSON(\n new URL(\"search/search_index.json\", config.base)\n )\n : NEVER\n\n/* Set up Clipboard.js integration */\nconst alert$ = new Subject()\nsetupClipboardJS({ alert$ })\n\n/* Set up instant loading, if enabled */\nif (feature(\"navigation.instant\"))\n setupInstantLoading({ document$, location$, viewport$ })\n\n/* Set up version selector */\nif (config.version?.provider === \"mike\")\n setupVersionSelector({ document$ })\n\n/* Always close drawer and search on navigation */\nmerge(location$, target$)\n .pipe(\n delay(125)\n )\n .subscribe(() => {\n setToggle(\"drawer\", false)\n setToggle(\"search\", false)\n })\n\n/* Set up global keyboard handlers */\nkeyboard$\n .pipe(\n filter(({ mode }) => mode === \"global\")\n )\n .subscribe(key => {\n switch (key.type) {\n\n /* Go to previous page */\n case \"p\":\n case \",\":\n const prev = getOptionalElement(\"[href][rel=prev]\")\n if (typeof prev !== \"undefined\")\n prev.click()\n break\n\n /* Go to next page */\n case \"n\":\n case \".\":\n const next = getOptionalElement(\"[href][rel=next]\")\n if (typeof next !== \"undefined\")\n next.click()\n break\n }\n })\n\n/* Set up patches */\npatchIndeterminate({ document$, tablet$ })\npatchScrollfix({ document$ })\npatchScrolllock({ viewport$, tablet$ })\n\n/* Set up header and main area observable */\nconst header$ = watchHeader(getComponentElement(\"header\"), { viewport$ })\nconst main$ = document$\n .pipe(\n map(() => getComponentElement(\"main\")),\n switchMap(el => watchMain(el, { viewport$, header$ })),\n shareReplay(1)\n )\n\n/* Set up control component observables */\nconst control$ = merge(\n\n /* Dialog */\n ...getComponentElements(\"dialog\")\n .map(el => mountDialog(el, { alert$ })),\n\n /* Header */\n ...getComponentElements(\"header\")\n .map(el => mountHeader(el, { viewport$, header$, main$ })),\n\n /* Color palette */\n ...getComponentElements(\"palette\")\n .map(el => mountPalette(el)),\n\n /* Search */\n ...getComponentElements(\"search\")\n .map(el => mountSearch(el, { index$, keyboard$ })),\n\n /* Repository information */\n ...getComponentElements(\"source\")\n .map(el => mountSource(el))\n)\n\n/* Set up content component observables */\nconst content$ = defer(() => merge(\n\n /* Content */\n ...getComponentElements(\"content\")\n .map(el => mountContent(el, { target$, print$ })),\n\n /* Search highlighting */\n ...getComponentElements(\"content\")\n .map(el => feature(\"search.highlight\")\n ? mountSearchHiglight(el, { index$, location$ })\n : EMPTY\n ),\n\n /* Header title */\n ...getComponentElements(\"header-title\")\n .map(el => mountHeaderTitle(el, { viewport$, header$ })),\n\n /* Sidebar */\n ...getComponentElements(\"sidebar\")\n .map(el => el.getAttribute(\"data-md-type\") === \"navigation\"\n ? at(screen$, () => mountSidebar(el, { viewport$, header$, main$ }))\n : at(tablet$, () => mountSidebar(el, { viewport$, header$, main$ }))\n ),\n\n /* Navigation tabs */\n ...getComponentElements(\"tabs\")\n .map(el => mountTabs(el, { viewport$, header$ })),\n\n /* Table of contents */\n ...getComponentElements(\"toc\")\n .map(el => mountTableOfContents(el, { viewport$, header$, target$ })),\n\n /* Back-to-top button */\n ...getComponentElements(\"top\")\n .map(el => mountBackToTop(el, { viewport$, header$, main$, target$ }))\n))\n\n/* Set up component observables */\nconst component$ = document$\n .pipe(\n switchMap(() => content$),\n mergeWith(control$),\n shareReplay(1)\n )\n\n/* Subscribe to all components */\ncomponent$.subscribe()\n\n/* ----------------------------------------------------------------------------\n * Exports\n * ------------------------------------------------------------------------- */\n\nwindow.document$ = document$ /* Document observable */\nwindow.location$ = location$ /* Location subject */\nwindow.target$ = target$ /* Location target observable */\nwindow.keyboard$ = keyboard$ /* Keyboard observable */\nwindow.viewport$ = viewport$ /* Viewport observable */\nwindow.tablet$ = tablet$ /* Media tablet observable */\nwindow.screen$ = screen$ /* Media screen observable */\nwindow.print$ = print$ /* Media print observable */\nwindow.alert$ = alert$ /* Alert subject */\nwindow.component$ = component$ /* Component observable */\n", "self.fetch||(self.fetch=function(e,n){return n=n||{},new Promise(function(t,s){var r=new XMLHttpRequest,o=[],u=[],i={},a=function(){return{ok:2==(r.status/100|0),statusText:r.statusText,status:r.status,url:r.responseURL,text:function(){return Promise.resolve(r.responseText)},json:function(){return Promise.resolve(r.responseText).then(JSON.parse)},blob:function(){return Promise.resolve(new Blob([r.response]))},clone:a,headers:{keys:function(){return o},entries:function(){return u},get:function(e){return i[e.toLowerCase()]},has:function(e){return e.toLowerCase()in i}}}};for(var c in r.open(n.method||\"get\",e,!0),r.onload=function(){r.getAllResponseHeaders().replace(/^(.*?):[^\\S\\n]*([\\s\\S]*?)$/gm,function(e,n,t){o.push(n=n.toLowerCase()),u.push([n,t]),i[n]=i[n]?i[n]+\",\"+t:t}),t(a())},r.onerror=s,r.withCredentials=\"include\"==n.credentials,n.headers)r.setRequestHeader(c,n.headers[c]);r.send(n.body||null)})});\n", "import tslib from '../tslib.js';\r\nconst {\r\n __extends,\r\n __assign,\r\n __rest,\r\n __decorate,\r\n __param,\r\n __metadata,\r\n __awaiter,\r\n __generator,\r\n __exportStar,\r\n __createBinding,\r\n __values,\r\n __read,\r\n __spread,\r\n __spreadArrays,\r\n __spreadArray,\r\n __await,\r\n __asyncGenerator,\r\n __asyncDelegator,\r\n __asyncValues,\r\n __makeTemplateObject,\r\n __importStar,\r\n __importDefault,\r\n __classPrivateFieldGet,\r\n __classPrivateFieldSet,\r\n} = tslib;\r\nexport {\r\n __extends,\r\n __assign,\r\n __rest,\r\n __decorate,\r\n __param,\r\n __metadata,\r\n __awaiter,\r\n __generator,\r\n __exportStar,\r\n __createBinding,\r\n __values,\r\n __read,\r\n __spread,\r\n __spreadArrays,\r\n __spreadArray,\r\n __await,\r\n __asyncGenerator,\r\n __asyncDelegator,\r\n __asyncValues,\r\n __makeTemplateObject,\r\n __importStar,\r\n __importDefault,\r\n __classPrivateFieldGet,\r\n __classPrivateFieldSet,\r\n};\r\n", null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, null, "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n ReplaySubject,\n Subject,\n fromEvent\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch document\n *\n * Documents are implemented as subjects, so all downstream observables are\n * automatically updated when a new document is emitted.\n *\n * @returns Document subject\n */\nexport function watchDocument(): Subject {\n const document$ = new ReplaySubject(1)\n fromEvent(document, \"DOMContentLoaded\", { once: true })\n .subscribe(() => document$.next(document))\n\n /* Return document */\n return document$\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve all elements matching the query selector\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Elements\n */\nexport function getElements(\n selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T][]\n\nexport function getElements(\n selector: string, node?: ParentNode\n): T[]\n\nexport function getElements(\n selector: string, node: ParentNode = document\n): T[] {\n return Array.from(node.querySelectorAll(selector))\n}\n\n/**\n * Retrieve an element matching a query selector or throw a reference error\n *\n * Note that this function assumes that the element is present. If unsure if an\n * element is existent, use the `getOptionalElement` function instead.\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Element\n */\nexport function getElement(\n selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T]\n\nexport function getElement(\n selector: string, node?: ParentNode\n): T\n\nexport function getElement(\n selector: string, node: ParentNode = document\n): T {\n const el = getOptionalElement(selector, node)\n if (typeof el === \"undefined\")\n throw new ReferenceError(\n `Missing element: expected \"${selector}\" to be present`\n )\n\n /* Return element */\n return el\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Retrieve an optional element matching the query selector\n *\n * @template T - Element type\n *\n * @param selector - Query selector\n * @param node - Node of reference\n *\n * @returns Element or nothing\n */\nexport function getOptionalElement(\n selector: T, node?: ParentNode\n): HTMLElementTagNameMap[T] | undefined\n\nexport function getOptionalElement(\n selector: string, node?: ParentNode\n): T | undefined\n\nexport function getOptionalElement(\n selector: string, node: ParentNode = document\n): T | undefined {\n return node.querySelector(selector) || undefined\n}\n\n/**\n * Retrieve the currently active element\n *\n * @returns Element or nothing\n */\nexport function getActiveElement(): HTMLElement | undefined {\n return document.activeElement instanceof HTMLElement\n ? document.activeElement || undefined\n : undefined\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n debounceTime,\n distinctUntilChanged,\n fromEvent,\n map,\n merge,\n startWith\n} from \"rxjs\"\n\nimport { getActiveElement } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch element focus\n *\n * Previously, this function used `focus` and `blur` events to determine whether\n * an element is focused, but this doesn't work if there are focusable elements\n * within the elements itself. A better solutions are `focusin` and `focusout`\n * events, which bubble up the tree and allow for more fine-grained control.\n *\n * `debounceTime` is necessary, because when a focus change happens inside an\n * element, the observable would first emit `false` and then `true` again.\n *\n * @param el - Element\n *\n * @returns Element focus observable\n */\nexport function watchElementFocus(\n el: HTMLElement\n): Observable {\n return merge(\n fromEvent(document.body, \"focusin\"),\n fromEvent(document.body, \"focusout\")\n )\n .pipe(\n debounceTime(1),\n map(() => {\n const active = getActiveElement()\n return typeof active !== \"undefined\"\n ? el.contains(active)\n : false\n }),\n startWith(el === getActiveElement()),\n distinctUntilChanged()\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n animationFrameScheduler,\n auditTime,\n fromEvent,\n map,\n merge,\n startWith\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Element offset\n */\nexport interface ElementOffset {\n x: number /* Horizontal offset */\n y: number /* Vertical offset */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve element offset\n *\n * @param el - Element\n *\n * @returns Element offset\n */\nexport function getElementOffset(\n el: HTMLElement\n): ElementOffset {\n return {\n x: el.offsetLeft,\n y: el.offsetTop\n }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch element offset\n *\n * @param el - Element\n *\n * @returns Element offset observable\n */\nexport function watchElementOffset(\n el: HTMLElement\n): Observable {\n return merge(\n fromEvent(window, \"load\"),\n fromEvent(window, \"resize\")\n )\n .pipe(\n auditTime(0, animationFrameScheduler),\n map(() => getElementOffset(el)),\n startWith(getElementOffset(el))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n animationFrameScheduler,\n auditTime,\n fromEvent,\n map,\n merge,\n startWith\n} from \"rxjs\"\n\nimport { ElementOffset } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve element content offset (= scroll offset)\n *\n * @param el - Element\n *\n * @returns Element content offset\n */\nexport function getElementContentOffset(\n el: HTMLElement\n): ElementOffset {\n return {\n x: el.scrollLeft,\n y: el.scrollTop\n }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch element content offset\n *\n * @param el - Element\n *\n * @returns Element content offset observable\n */\nexport function watchElementContentOffset(\n el: HTMLElement\n): Observable {\n return merge(\n fromEvent(el, \"scroll\"),\n fromEvent(window, \"resize\")\n )\n .pipe(\n auditTime(0, animationFrameScheduler),\n map(() => getElementContentOffset(el)),\n startWith(getElementContentOffset(el))\n )\n}\n", "/**\r\n * A collection of shims that provide minimal functionality of the ES6 collections.\r\n *\r\n * These implementations are not meant to be used outside of the ResizeObserver\r\n * modules as they cover only a limited range of use cases.\r\n */\r\n/* eslint-disable require-jsdoc, valid-jsdoc */\r\nvar MapShim = (function () {\r\n if (typeof Map !== 'undefined') {\r\n return Map;\r\n }\r\n /**\r\n * Returns index in provided array that matches the specified key.\r\n *\r\n * @param {Array} arr\r\n * @param {*} key\r\n * @returns {number}\r\n */\r\n function getIndex(arr, key) {\r\n var result = -1;\r\n arr.some(function (entry, index) {\r\n if (entry[0] === key) {\r\n result = index;\r\n return true;\r\n }\r\n return false;\r\n });\r\n return result;\r\n }\r\n return /** @class */ (function () {\r\n function class_1() {\r\n this.__entries__ = [];\r\n }\r\n Object.defineProperty(class_1.prototype, \"size\", {\r\n /**\r\n * @returns {boolean}\r\n */\r\n get: function () {\r\n return this.__entries__.length;\r\n },\r\n enumerable: true,\r\n configurable: true\r\n });\r\n /**\r\n * @param {*} key\r\n * @returns {*}\r\n */\r\n class_1.prototype.get = function (key) {\r\n var index = getIndex(this.__entries__, key);\r\n var entry = this.__entries__[index];\r\n return entry && entry[1];\r\n };\r\n /**\r\n * @param {*} key\r\n * @param {*} value\r\n * @returns {void}\r\n */\r\n class_1.prototype.set = function (key, value) {\r\n var index = getIndex(this.__entries__, key);\r\n if (~index) {\r\n this.__entries__[index][1] = value;\r\n }\r\n else {\r\n this.__entries__.push([key, value]);\r\n }\r\n };\r\n /**\r\n * @param {*} key\r\n * @returns {void}\r\n */\r\n class_1.prototype.delete = function (key) {\r\n var entries = this.__entries__;\r\n var index = getIndex(entries, key);\r\n if (~index) {\r\n entries.splice(index, 1);\r\n }\r\n };\r\n /**\r\n * @param {*} key\r\n * @returns {void}\r\n */\r\n class_1.prototype.has = function (key) {\r\n return !!~getIndex(this.__entries__, key);\r\n };\r\n /**\r\n * @returns {void}\r\n */\r\n class_1.prototype.clear = function () {\r\n this.__entries__.splice(0);\r\n };\r\n /**\r\n * @param {Function} callback\r\n * @param {*} [ctx=null]\r\n * @returns {void}\r\n */\r\n class_1.prototype.forEach = function (callback, ctx) {\r\n if (ctx === void 0) { ctx = null; }\r\n for (var _i = 0, _a = this.__entries__; _i < _a.length; _i++) {\r\n var entry = _a[_i];\r\n callback.call(ctx, entry[1], entry[0]);\r\n }\r\n };\r\n return class_1;\r\n }());\r\n})();\n\n/**\r\n * Detects whether window and document objects are available in current environment.\r\n */\r\nvar isBrowser = typeof window !== 'undefined' && typeof document !== 'undefined' && window.document === document;\n\n// Returns global object of a current environment.\r\nvar global$1 = (function () {\r\n if (typeof global !== 'undefined' && global.Math === Math) {\r\n return global;\r\n }\r\n if (typeof self !== 'undefined' && self.Math === Math) {\r\n return self;\r\n }\r\n if (typeof window !== 'undefined' && window.Math === Math) {\r\n return window;\r\n }\r\n // eslint-disable-next-line no-new-func\r\n return Function('return this')();\r\n})();\n\n/**\r\n * A shim for the requestAnimationFrame which falls back to the setTimeout if\r\n * first one is not supported.\r\n *\r\n * @returns {number} Requests' identifier.\r\n */\r\nvar requestAnimationFrame$1 = (function () {\r\n if (typeof requestAnimationFrame === 'function') {\r\n // It's required to use a bounded function because IE sometimes throws\r\n // an \"Invalid calling object\" error if rAF is invoked without the global\r\n // object on the left hand side.\r\n return requestAnimationFrame.bind(global$1);\r\n }\r\n return function (callback) { return setTimeout(function () { return callback(Date.now()); }, 1000 / 60); };\r\n})();\n\n// Defines minimum timeout before adding a trailing call.\r\nvar trailingTimeout = 2;\r\n/**\r\n * Creates a wrapper function which ensures that provided callback will be\r\n * invoked only once during the specified delay period.\r\n *\r\n * @param {Function} callback - Function to be invoked after the delay period.\r\n * @param {number} delay - Delay after which to invoke callback.\r\n * @returns {Function}\r\n */\r\nfunction throttle (callback, delay) {\r\n var leadingCall = false, trailingCall = false, lastCallTime = 0;\r\n /**\r\n * Invokes the original callback function and schedules new invocation if\r\n * the \"proxy\" was called during current request.\r\n *\r\n * @returns {void}\r\n */\r\n function resolvePending() {\r\n if (leadingCall) {\r\n leadingCall = false;\r\n callback();\r\n }\r\n if (trailingCall) {\r\n proxy();\r\n }\r\n }\r\n /**\r\n * Callback invoked after the specified delay. It will further postpone\r\n * invocation of the original function delegating it to the\r\n * requestAnimationFrame.\r\n *\r\n * @returns {void}\r\n */\r\n function timeoutCallback() {\r\n requestAnimationFrame$1(resolvePending);\r\n }\r\n /**\r\n * Schedules invocation of the original function.\r\n *\r\n * @returns {void}\r\n */\r\n function proxy() {\r\n var timeStamp = Date.now();\r\n if (leadingCall) {\r\n // Reject immediately following calls.\r\n if (timeStamp - lastCallTime < trailingTimeout) {\r\n return;\r\n }\r\n // Schedule new call to be in invoked when the pending one is resolved.\r\n // This is important for \"transitions\" which never actually start\r\n // immediately so there is a chance that we might miss one if change\r\n // happens amids the pending invocation.\r\n trailingCall = true;\r\n }\r\n else {\r\n leadingCall = true;\r\n trailingCall = false;\r\n setTimeout(timeoutCallback, delay);\r\n }\r\n lastCallTime = timeStamp;\r\n }\r\n return proxy;\r\n}\n\n// Minimum delay before invoking the update of observers.\r\nvar REFRESH_DELAY = 20;\r\n// A list of substrings of CSS properties used to find transition events that\r\n// might affect dimensions of observed elements.\r\nvar transitionKeys = ['top', 'right', 'bottom', 'left', 'width', 'height', 'size', 'weight'];\r\n// Check if MutationObserver is available.\r\nvar mutationObserverSupported = typeof MutationObserver !== 'undefined';\r\n/**\r\n * Singleton controller class which handles updates of ResizeObserver instances.\r\n */\r\nvar ResizeObserverController = /** @class */ (function () {\r\n /**\r\n * Creates a new instance of ResizeObserverController.\r\n *\r\n * @private\r\n */\r\n function ResizeObserverController() {\r\n /**\r\n * Indicates whether DOM listeners have been added.\r\n *\r\n * @private {boolean}\r\n */\r\n this.connected_ = false;\r\n /**\r\n * Tells that controller has subscribed for Mutation Events.\r\n *\r\n * @private {boolean}\r\n */\r\n this.mutationEventsAdded_ = false;\r\n /**\r\n * Keeps reference to the instance of MutationObserver.\r\n *\r\n * @private {MutationObserver}\r\n */\r\n this.mutationsObserver_ = null;\r\n /**\r\n * A list of connected observers.\r\n *\r\n * @private {Array}\r\n */\r\n this.observers_ = [];\r\n this.onTransitionEnd_ = this.onTransitionEnd_.bind(this);\r\n this.refresh = throttle(this.refresh.bind(this), REFRESH_DELAY);\r\n }\r\n /**\r\n * Adds observer to observers list.\r\n *\r\n * @param {ResizeObserverSPI} observer - Observer to be added.\r\n * @returns {void}\r\n */\r\n ResizeObserverController.prototype.addObserver = function (observer) {\r\n if (!~this.observers_.indexOf(observer)) {\r\n this.observers_.push(observer);\r\n }\r\n // Add listeners if they haven't been added yet.\r\n if (!this.connected_) {\r\n this.connect_();\r\n }\r\n };\r\n /**\r\n * Removes observer from observers list.\r\n *\r\n * @param {ResizeObserverSPI} observer - Observer to be removed.\r\n * @returns {void}\r\n */\r\n ResizeObserverController.prototype.removeObserver = function (observer) {\r\n var observers = this.observers_;\r\n var index = observers.indexOf(observer);\r\n // Remove observer if it's present in registry.\r\n if (~index) {\r\n observers.splice(index, 1);\r\n }\r\n // Remove listeners if controller has no connected observers.\r\n if (!observers.length && this.connected_) {\r\n this.disconnect_();\r\n }\r\n };\r\n /**\r\n * Invokes the update of observers. It will continue running updates insofar\r\n * it detects changes.\r\n *\r\n * @returns {void}\r\n */\r\n ResizeObserverController.prototype.refresh = function () {\r\n var changesDetected = this.updateObservers_();\r\n // Continue running updates if changes have been detected as there might\r\n // be future ones caused by CSS transitions.\r\n if (changesDetected) {\r\n this.refresh();\r\n }\r\n };\r\n /**\r\n * Updates every observer from observers list and notifies them of queued\r\n * entries.\r\n *\r\n * @private\r\n * @returns {boolean} Returns \"true\" if any observer has detected changes in\r\n * dimensions of it's elements.\r\n */\r\n ResizeObserverController.prototype.updateObservers_ = function () {\r\n // Collect observers that have active observations.\r\n var activeObservers = this.observers_.filter(function (observer) {\r\n return observer.gatherActive(), observer.hasActive();\r\n });\r\n // Deliver notifications in a separate cycle in order to avoid any\r\n // collisions between observers, e.g. when multiple instances of\r\n // ResizeObserver are tracking the same element and the callback of one\r\n // of them changes content dimensions of the observed target. Sometimes\r\n // this may result in notifications being blocked for the rest of observers.\r\n activeObservers.forEach(function (observer) { return observer.broadcastActive(); });\r\n return activeObservers.length > 0;\r\n };\r\n /**\r\n * Initializes DOM listeners.\r\n *\r\n * @private\r\n * @returns {void}\r\n */\r\n ResizeObserverController.prototype.connect_ = function () {\r\n // Do nothing if running in a non-browser environment or if listeners\r\n // have been already added.\r\n if (!isBrowser || this.connected_) {\r\n return;\r\n }\r\n // Subscription to the \"Transitionend\" event is used as a workaround for\r\n // delayed transitions. This way it's possible to capture at least the\r\n // final state of an element.\r\n document.addEventListener('transitionend', this.onTransitionEnd_);\r\n window.addEventListener('resize', this.refresh);\r\n if (mutationObserverSupported) {\r\n this.mutationsObserver_ = new MutationObserver(this.refresh);\r\n this.mutationsObserver_.observe(document, {\r\n attributes: true,\r\n childList: true,\r\n characterData: true,\r\n subtree: true\r\n });\r\n }\r\n else {\r\n document.addEventListener('DOMSubtreeModified', this.refresh);\r\n this.mutationEventsAdded_ = true;\r\n }\r\n this.connected_ = true;\r\n };\r\n /**\r\n * Removes DOM listeners.\r\n *\r\n * @private\r\n * @returns {void}\r\n */\r\n ResizeObserverController.prototype.disconnect_ = function () {\r\n // Do nothing if running in a non-browser environment or if listeners\r\n // have been already removed.\r\n if (!isBrowser || !this.connected_) {\r\n return;\r\n }\r\n document.removeEventListener('transitionend', this.onTransitionEnd_);\r\n window.removeEventListener('resize', this.refresh);\r\n if (this.mutationsObserver_) {\r\n this.mutationsObserver_.disconnect();\r\n }\r\n if (this.mutationEventsAdded_) {\r\n document.removeEventListener('DOMSubtreeModified', this.refresh);\r\n }\r\n this.mutationsObserver_ = null;\r\n this.mutationEventsAdded_ = false;\r\n this.connected_ = false;\r\n };\r\n /**\r\n * \"Transitionend\" event handler.\r\n *\r\n * @private\r\n * @param {TransitionEvent} event\r\n * @returns {void}\r\n */\r\n ResizeObserverController.prototype.onTransitionEnd_ = function (_a) {\r\n var _b = _a.propertyName, propertyName = _b === void 0 ? '' : _b;\r\n // Detect whether transition may affect dimensions of an element.\r\n var isReflowProperty = transitionKeys.some(function (key) {\r\n return !!~propertyName.indexOf(key);\r\n });\r\n if (isReflowProperty) {\r\n this.refresh();\r\n }\r\n };\r\n /**\r\n * Returns instance of the ResizeObserverController.\r\n *\r\n * @returns {ResizeObserverController}\r\n */\r\n ResizeObserverController.getInstance = function () {\r\n if (!this.instance_) {\r\n this.instance_ = new ResizeObserverController();\r\n }\r\n return this.instance_;\r\n };\r\n /**\r\n * Holds reference to the controller's instance.\r\n *\r\n * @private {ResizeObserverController}\r\n */\r\n ResizeObserverController.instance_ = null;\r\n return ResizeObserverController;\r\n}());\n\n/**\r\n * Defines non-writable/enumerable properties of the provided target object.\r\n *\r\n * @param {Object} target - Object for which to define properties.\r\n * @param {Object} props - Properties to be defined.\r\n * @returns {Object} Target object.\r\n */\r\nvar defineConfigurable = (function (target, props) {\r\n for (var _i = 0, _a = Object.keys(props); _i < _a.length; _i++) {\r\n var key = _a[_i];\r\n Object.defineProperty(target, key, {\r\n value: props[key],\r\n enumerable: false,\r\n writable: false,\r\n configurable: true\r\n });\r\n }\r\n return target;\r\n});\n\n/**\r\n * Returns the global object associated with provided element.\r\n *\r\n * @param {Object} target\r\n * @returns {Object}\r\n */\r\nvar getWindowOf = (function (target) {\r\n // Assume that the element is an instance of Node, which means that it\r\n // has the \"ownerDocument\" property from which we can retrieve a\r\n // corresponding global object.\r\n var ownerGlobal = target && target.ownerDocument && target.ownerDocument.defaultView;\r\n // Return the local global object if it's not possible extract one from\r\n // provided element.\r\n return ownerGlobal || global$1;\r\n});\n\n// Placeholder of an empty content rectangle.\r\nvar emptyRect = createRectInit(0, 0, 0, 0);\r\n/**\r\n * Converts provided string to a number.\r\n *\r\n * @param {number|string} value\r\n * @returns {number}\r\n */\r\nfunction toFloat(value) {\r\n return parseFloat(value) || 0;\r\n}\r\n/**\r\n * Extracts borders size from provided styles.\r\n *\r\n * @param {CSSStyleDeclaration} styles\r\n * @param {...string} positions - Borders positions (top, right, ...)\r\n * @returns {number}\r\n */\r\nfunction getBordersSize(styles) {\r\n var positions = [];\r\n for (var _i = 1; _i < arguments.length; _i++) {\r\n positions[_i - 1] = arguments[_i];\r\n }\r\n return positions.reduce(function (size, position) {\r\n var value = styles['border-' + position + '-width'];\r\n return size + toFloat(value);\r\n }, 0);\r\n}\r\n/**\r\n * Extracts paddings sizes from provided styles.\r\n *\r\n * @param {CSSStyleDeclaration} styles\r\n * @returns {Object} Paddings box.\r\n */\r\nfunction getPaddings(styles) {\r\n var positions = ['top', 'right', 'bottom', 'left'];\r\n var paddings = {};\r\n for (var _i = 0, positions_1 = positions; _i < positions_1.length; _i++) {\r\n var position = positions_1[_i];\r\n var value = styles['padding-' + position];\r\n paddings[position] = toFloat(value);\r\n }\r\n return paddings;\r\n}\r\n/**\r\n * Calculates content rectangle of provided SVG element.\r\n *\r\n * @param {SVGGraphicsElement} target - Element content rectangle of which needs\r\n * to be calculated.\r\n * @returns {DOMRectInit}\r\n */\r\nfunction getSVGContentRect(target) {\r\n var bbox = target.getBBox();\r\n return createRectInit(0, 0, bbox.width, bbox.height);\r\n}\r\n/**\r\n * Calculates content rectangle of provided HTMLElement.\r\n *\r\n * @param {HTMLElement} target - Element for which to calculate the content rectangle.\r\n * @returns {DOMRectInit}\r\n */\r\nfunction getHTMLElementContentRect(target) {\r\n // Client width & height properties can't be\r\n // used exclusively as they provide rounded values.\r\n var clientWidth = target.clientWidth, clientHeight = target.clientHeight;\r\n // By this condition we can catch all non-replaced inline, hidden and\r\n // detached elements. Though elements with width & height properties less\r\n // than 0.5 will be discarded as well.\r\n //\r\n // Without it we would need to implement separate methods for each of\r\n // those cases and it's not possible to perform a precise and performance\r\n // effective test for hidden elements. E.g. even jQuery's ':visible' filter\r\n // gives wrong results for elements with width & height less than 0.5.\r\n if (!clientWidth && !clientHeight) {\r\n return emptyRect;\r\n }\r\n var styles = getWindowOf(target).getComputedStyle(target);\r\n var paddings = getPaddings(styles);\r\n var horizPad = paddings.left + paddings.right;\r\n var vertPad = paddings.top + paddings.bottom;\r\n // Computed styles of width & height are being used because they are the\r\n // only dimensions available to JS that contain non-rounded values. It could\r\n // be possible to utilize the getBoundingClientRect if only it's data wasn't\r\n // affected by CSS transformations let alone paddings, borders and scroll bars.\r\n var width = toFloat(styles.width), height = toFloat(styles.height);\r\n // Width & height include paddings and borders when the 'border-box' box\r\n // model is applied (except for IE).\r\n if (styles.boxSizing === 'border-box') {\r\n // Following conditions are required to handle Internet Explorer which\r\n // doesn't include paddings and borders to computed CSS dimensions.\r\n //\r\n // We can say that if CSS dimensions + paddings are equal to the \"client\"\r\n // properties then it's either IE, and thus we don't need to subtract\r\n // anything, or an element merely doesn't have paddings/borders styles.\r\n if (Math.round(width + horizPad) !== clientWidth) {\r\n width -= getBordersSize(styles, 'left', 'right') + horizPad;\r\n }\r\n if (Math.round(height + vertPad) !== clientHeight) {\r\n height -= getBordersSize(styles, 'top', 'bottom') + vertPad;\r\n }\r\n }\r\n // Following steps can't be applied to the document's root element as its\r\n // client[Width/Height] properties represent viewport area of the window.\r\n // Besides, it's as well not necessary as the itself neither has\r\n // rendered scroll bars nor it can be clipped.\r\n if (!isDocumentElement(target)) {\r\n // In some browsers (only in Firefox, actually) CSS width & height\r\n // include scroll bars size which can be removed at this step as scroll\r\n // bars are the only difference between rounded dimensions + paddings\r\n // and \"client\" properties, though that is not always true in Chrome.\r\n var vertScrollbar = Math.round(width + horizPad) - clientWidth;\r\n var horizScrollbar = Math.round(height + vertPad) - clientHeight;\r\n // Chrome has a rather weird rounding of \"client\" properties.\r\n // E.g. for an element with content width of 314.2px it sometimes gives\r\n // the client width of 315px and for the width of 314.7px it may give\r\n // 314px. And it doesn't happen all the time. So just ignore this delta\r\n // as a non-relevant.\r\n if (Math.abs(vertScrollbar) !== 1) {\r\n width -= vertScrollbar;\r\n }\r\n if (Math.abs(horizScrollbar) !== 1) {\r\n height -= horizScrollbar;\r\n }\r\n }\r\n return createRectInit(paddings.left, paddings.top, width, height);\r\n}\r\n/**\r\n * Checks whether provided element is an instance of the SVGGraphicsElement.\r\n *\r\n * @param {Element} target - Element to be checked.\r\n * @returns {boolean}\r\n */\r\nvar isSVGGraphicsElement = (function () {\r\n // Some browsers, namely IE and Edge, don't have the SVGGraphicsElement\r\n // interface.\r\n if (typeof SVGGraphicsElement !== 'undefined') {\r\n return function (target) { return target instanceof getWindowOf(target).SVGGraphicsElement; };\r\n }\r\n // If it's so, then check that element is at least an instance of the\r\n // SVGElement and that it has the \"getBBox\" method.\r\n // eslint-disable-next-line no-extra-parens\r\n return function (target) { return (target instanceof getWindowOf(target).SVGElement &&\r\n typeof target.getBBox === 'function'); };\r\n})();\r\n/**\r\n * Checks whether provided element is a document element ().\r\n *\r\n * @param {Element} target - Element to be checked.\r\n * @returns {boolean}\r\n */\r\nfunction isDocumentElement(target) {\r\n return target === getWindowOf(target).document.documentElement;\r\n}\r\n/**\r\n * Calculates an appropriate content rectangle for provided html or svg element.\r\n *\r\n * @param {Element} target - Element content rectangle of which needs to be calculated.\r\n * @returns {DOMRectInit}\r\n */\r\nfunction getContentRect(target) {\r\n if (!isBrowser) {\r\n return emptyRect;\r\n }\r\n if (isSVGGraphicsElement(target)) {\r\n return getSVGContentRect(target);\r\n }\r\n return getHTMLElementContentRect(target);\r\n}\r\n/**\r\n * Creates rectangle with an interface of the DOMRectReadOnly.\r\n * Spec: https://drafts.fxtf.org/geometry/#domrectreadonly\r\n *\r\n * @param {DOMRectInit} rectInit - Object with rectangle's x/y coordinates and dimensions.\r\n * @returns {DOMRectReadOnly}\r\n */\r\nfunction createReadOnlyRect(_a) {\r\n var x = _a.x, y = _a.y, width = _a.width, height = _a.height;\r\n // If DOMRectReadOnly is available use it as a prototype for the rectangle.\r\n var Constr = typeof DOMRectReadOnly !== 'undefined' ? DOMRectReadOnly : Object;\r\n var rect = Object.create(Constr.prototype);\r\n // Rectangle's properties are not writable and non-enumerable.\r\n defineConfigurable(rect, {\r\n x: x, y: y, width: width, height: height,\r\n top: y,\r\n right: x + width,\r\n bottom: height + y,\r\n left: x\r\n });\r\n return rect;\r\n}\r\n/**\r\n * Creates DOMRectInit object based on the provided dimensions and the x/y coordinates.\r\n * Spec: https://drafts.fxtf.org/geometry/#dictdef-domrectinit\r\n *\r\n * @param {number} x - X coordinate.\r\n * @param {number} y - Y coordinate.\r\n * @param {number} width - Rectangle's width.\r\n * @param {number} height - Rectangle's height.\r\n * @returns {DOMRectInit}\r\n */\r\nfunction createRectInit(x, y, width, height) {\r\n return { x: x, y: y, width: width, height: height };\r\n}\n\n/**\r\n * Class that is responsible for computations of the content rectangle of\r\n * provided DOM element and for keeping track of it's changes.\r\n */\r\nvar ResizeObservation = /** @class */ (function () {\r\n /**\r\n * Creates an instance of ResizeObservation.\r\n *\r\n * @param {Element} target - Element to be observed.\r\n */\r\n function ResizeObservation(target) {\r\n /**\r\n * Broadcasted width of content rectangle.\r\n *\r\n * @type {number}\r\n */\r\n this.broadcastWidth = 0;\r\n /**\r\n * Broadcasted height of content rectangle.\r\n *\r\n * @type {number}\r\n */\r\n this.broadcastHeight = 0;\r\n /**\r\n * Reference to the last observed content rectangle.\r\n *\r\n * @private {DOMRectInit}\r\n */\r\n this.contentRect_ = createRectInit(0, 0, 0, 0);\r\n this.target = target;\r\n }\r\n /**\r\n * Updates content rectangle and tells whether it's width or height properties\r\n * have changed since the last broadcast.\r\n *\r\n * @returns {boolean}\r\n */\r\n ResizeObservation.prototype.isActive = function () {\r\n var rect = getContentRect(this.target);\r\n this.contentRect_ = rect;\r\n return (rect.width !== this.broadcastWidth ||\r\n rect.height !== this.broadcastHeight);\r\n };\r\n /**\r\n * Updates 'broadcastWidth' and 'broadcastHeight' properties with a data\r\n * from the corresponding properties of the last observed content rectangle.\r\n *\r\n * @returns {DOMRectInit} Last observed content rectangle.\r\n */\r\n ResizeObservation.prototype.broadcastRect = function () {\r\n var rect = this.contentRect_;\r\n this.broadcastWidth = rect.width;\r\n this.broadcastHeight = rect.height;\r\n return rect;\r\n };\r\n return ResizeObservation;\r\n}());\n\nvar ResizeObserverEntry = /** @class */ (function () {\r\n /**\r\n * Creates an instance of ResizeObserverEntry.\r\n *\r\n * @param {Element} target - Element that is being observed.\r\n * @param {DOMRectInit} rectInit - Data of the element's content rectangle.\r\n */\r\n function ResizeObserverEntry(target, rectInit) {\r\n var contentRect = createReadOnlyRect(rectInit);\r\n // According to the specification following properties are not writable\r\n // and are also not enumerable in the native implementation.\r\n //\r\n // Property accessors are not being used as they'd require to define a\r\n // private WeakMap storage which may cause memory leaks in browsers that\r\n // don't support this type of collections.\r\n defineConfigurable(this, { target: target, contentRect: contentRect });\r\n }\r\n return ResizeObserverEntry;\r\n}());\n\nvar ResizeObserverSPI = /** @class */ (function () {\r\n /**\r\n * Creates a new instance of ResizeObserver.\r\n *\r\n * @param {ResizeObserverCallback} callback - Callback function that is invoked\r\n * when one of the observed elements changes it's content dimensions.\r\n * @param {ResizeObserverController} controller - Controller instance which\r\n * is responsible for the updates of observer.\r\n * @param {ResizeObserver} callbackCtx - Reference to the public\r\n * ResizeObserver instance which will be passed to callback function.\r\n */\r\n function ResizeObserverSPI(callback, controller, callbackCtx) {\r\n /**\r\n * Collection of resize observations that have detected changes in dimensions\r\n * of elements.\r\n *\r\n * @private {Array}\r\n */\r\n this.activeObservations_ = [];\r\n /**\r\n * Registry of the ResizeObservation instances.\r\n *\r\n * @private {Map}\r\n */\r\n this.observations_ = new MapShim();\r\n if (typeof callback !== 'function') {\r\n throw new TypeError('The callback provided as parameter 1 is not a function.');\r\n }\r\n this.callback_ = callback;\r\n this.controller_ = controller;\r\n this.callbackCtx_ = callbackCtx;\r\n }\r\n /**\r\n * Starts observing provided element.\r\n *\r\n * @param {Element} target - Element to be observed.\r\n * @returns {void}\r\n */\r\n ResizeObserverSPI.prototype.observe = function (target) {\r\n if (!arguments.length) {\r\n throw new TypeError('1 argument required, but only 0 present.');\r\n }\r\n // Do nothing if current environment doesn't have the Element interface.\r\n if (typeof Element === 'undefined' || !(Element instanceof Object)) {\r\n return;\r\n }\r\n if (!(target instanceof getWindowOf(target).Element)) {\r\n throw new TypeError('parameter 1 is not of type \"Element\".');\r\n }\r\n var observations = this.observations_;\r\n // Do nothing if element is already being observed.\r\n if (observations.has(target)) {\r\n return;\r\n }\r\n observations.set(target, new ResizeObservation(target));\r\n this.controller_.addObserver(this);\r\n // Force the update of observations.\r\n this.controller_.refresh();\r\n };\r\n /**\r\n * Stops observing provided element.\r\n *\r\n * @param {Element} target - Element to stop observing.\r\n * @returns {void}\r\n */\r\n ResizeObserverSPI.prototype.unobserve = function (target) {\r\n if (!arguments.length) {\r\n throw new TypeError('1 argument required, but only 0 present.');\r\n }\r\n // Do nothing if current environment doesn't have the Element interface.\r\n if (typeof Element === 'undefined' || !(Element instanceof Object)) {\r\n return;\r\n }\r\n if (!(target instanceof getWindowOf(target).Element)) {\r\n throw new TypeError('parameter 1 is not of type \"Element\".');\r\n }\r\n var observations = this.observations_;\r\n // Do nothing if element is not being observed.\r\n if (!observations.has(target)) {\r\n return;\r\n }\r\n observations.delete(target);\r\n if (!observations.size) {\r\n this.controller_.removeObserver(this);\r\n }\r\n };\r\n /**\r\n * Stops observing all elements.\r\n *\r\n * @returns {void}\r\n */\r\n ResizeObserverSPI.prototype.disconnect = function () {\r\n this.clearActive();\r\n this.observations_.clear();\r\n this.controller_.removeObserver(this);\r\n };\r\n /**\r\n * Collects observation instances the associated element of which has changed\r\n * it's content rectangle.\r\n *\r\n * @returns {void}\r\n */\r\n ResizeObserverSPI.prototype.gatherActive = function () {\r\n var _this = this;\r\n this.clearActive();\r\n this.observations_.forEach(function (observation) {\r\n if (observation.isActive()) {\r\n _this.activeObservations_.push(observation);\r\n }\r\n });\r\n };\r\n /**\r\n * Invokes initial callback function with a list of ResizeObserverEntry\r\n * instances collected from active resize observations.\r\n *\r\n * @returns {void}\r\n */\r\n ResizeObserverSPI.prototype.broadcastActive = function () {\r\n // Do nothing if observer doesn't have active observations.\r\n if (!this.hasActive()) {\r\n return;\r\n }\r\n var ctx = this.callbackCtx_;\r\n // Create ResizeObserverEntry instance for every active observation.\r\n var entries = this.activeObservations_.map(function (observation) {\r\n return new ResizeObserverEntry(observation.target, observation.broadcastRect());\r\n });\r\n this.callback_.call(ctx, entries, ctx);\r\n this.clearActive();\r\n };\r\n /**\r\n * Clears the collection of active observations.\r\n *\r\n * @returns {void}\r\n */\r\n ResizeObserverSPI.prototype.clearActive = function () {\r\n this.activeObservations_.splice(0);\r\n };\r\n /**\r\n * Tells whether observer has active observations.\r\n *\r\n * @returns {boolean}\r\n */\r\n ResizeObserverSPI.prototype.hasActive = function () {\r\n return this.activeObservations_.length > 0;\r\n };\r\n return ResizeObserverSPI;\r\n}());\n\n// Registry of internal observers. If WeakMap is not available use current shim\r\n// for the Map collection as it has all required methods and because WeakMap\r\n// can't be fully polyfilled anyway.\r\nvar observers = typeof WeakMap !== 'undefined' ? new WeakMap() : new MapShim();\r\n/**\r\n * ResizeObserver API. Encapsulates the ResizeObserver SPI implementation\r\n * exposing only those methods and properties that are defined in the spec.\r\n */\r\nvar ResizeObserver = /** @class */ (function () {\r\n /**\r\n * Creates a new instance of ResizeObserver.\r\n *\r\n * @param {ResizeObserverCallback} callback - Callback that is invoked when\r\n * dimensions of the observed elements change.\r\n */\r\n function ResizeObserver(callback) {\r\n if (!(this instanceof ResizeObserver)) {\r\n throw new TypeError('Cannot call a class as a function.');\r\n }\r\n if (!arguments.length) {\r\n throw new TypeError('1 argument required, but only 0 present.');\r\n }\r\n var controller = ResizeObserverController.getInstance();\r\n var observer = new ResizeObserverSPI(callback, controller, this);\r\n observers.set(this, observer);\r\n }\r\n return ResizeObserver;\r\n}());\r\n// Expose public methods of ResizeObserver.\r\n[\r\n 'observe',\r\n 'unobserve',\r\n 'disconnect'\r\n].forEach(function (method) {\r\n ResizeObserver.prototype[method] = function () {\r\n var _a;\r\n return (_a = observers.get(this))[method].apply(_a, arguments);\r\n };\r\n});\n\nvar index = (function () {\r\n // Export existing implementation if available.\r\n if (typeof global$1.ResizeObserver !== 'undefined') {\r\n return global$1.ResizeObserver;\r\n }\r\n return ResizeObserver;\r\n})();\n\nexport default index;\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport ResizeObserver from \"resize-observer-polyfill\"\nimport {\n NEVER,\n Observable,\n Subject,\n defer,\n filter,\n finalize,\n map,\n merge,\n of,\n shareReplay,\n startWith,\n switchMap,\n tap\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Element offset\n */\nexport interface ElementSize {\n width: number /* Element width */\n height: number /* Element height */\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Resize observer entry subject\n */\nconst entry$ = new Subject()\n\n/**\n * Resize observer observable\n *\n * This observable will create a `ResizeObserver` on the first subscription\n * and will automatically terminate it when there are no more subscribers.\n * It's quite important to centralize observation in a single `ResizeObserver`,\n * as the performance difference can be quite dramatic, as the link shows.\n *\n * @see https://bit.ly/3iIYfEm - Google Groups on performance\n */\nconst observer$ = defer(() => of(\n new ResizeObserver(entries => {\n for (const entry of entries)\n entry$.next(entry)\n })\n))\n .pipe(\n switchMap(observer => merge(NEVER, of(observer))\n .pipe(\n finalize(() => observer.disconnect())\n )\n ),\n shareReplay(1)\n )\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve element size\n *\n * @param el - Element\n *\n * @returns Element size\n */\nexport function getElementSize(\n el: HTMLElement\n): ElementSize {\n return {\n width: el.offsetWidth,\n height: el.offsetHeight\n }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch element size\n *\n * This function returns an observable that subscribes to a single internal\n * instance of `ResizeObserver` upon subscription, and emit resize events until\n * termination. Note that this function should not be called with the same\n * element twice, as the first unsubscription will terminate observation.\n *\n * Sadly, we can't use the `DOMRect` objects returned by the observer, because\n * we need the emitted values to be consistent with `getElementSize`, which will\n * return the used values (rounded) and not actual values (unrounded). Thus, we\n * use the `offset*` properties. See the linked GitHub issue.\n *\n * @see https://bit.ly/3m0k3he - GitHub issue\n *\n * @param el - Element\n *\n * @returns Element size observable\n */\nexport function watchElementSize(\n el: HTMLElement\n): Observable {\n return observer$\n .pipe(\n tap(observer => observer.observe(el)),\n switchMap(observer => entry$\n .pipe(\n filter(({ target }) => target === el),\n finalize(() => observer.unobserve(el)),\n map(() => getElementSize(el))\n )\n ),\n startWith(getElementSize(el))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { ElementSize } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve element content size (= scroll width and height)\n *\n * @param el - Element\n *\n * @returns Element content size\n */\nexport function getElementContentSize(\n el: HTMLElement\n): ElementSize {\n return {\n width: el.scrollWidth,\n height: el.scrollHeight\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n NEVER,\n Observable,\n Subject,\n defer,\n distinctUntilChanged,\n filter,\n finalize,\n map,\n merge,\n of,\n shareReplay,\n switchMap,\n tap\n} from \"rxjs\"\n\nimport {\n getElementContentSize,\n getElementSize,\n watchElementContentOffset\n} from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Intersection observer entry subject\n */\nconst entry$ = new Subject()\n\n/**\n * Intersection observer observable\n *\n * This observable will create an `IntersectionObserver` on first subscription\n * and will automatically terminate it when there are no more subscribers.\n *\n * @see https://bit.ly/3iIYfEm - Google Groups on performance\n */\nconst observer$ = defer(() => of(\n new IntersectionObserver(entries => {\n for (const entry of entries)\n entry$.next(entry)\n }, {\n threshold: 0\n })\n))\n .pipe(\n switchMap(observer => merge(NEVER, of(observer))\n .pipe(\n finalize(() => observer.disconnect())\n )\n ),\n shareReplay(1)\n )\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch element visibility\n *\n * @param el - Element\n *\n * @returns Element visibility observable\n */\nexport function watchElementVisibility(\n el: HTMLElement\n): Observable {\n return observer$\n .pipe(\n tap(observer => observer.observe(el)),\n switchMap(observer => entry$\n .pipe(\n filter(({ target }) => target === el),\n finalize(() => observer.unobserve(el)),\n map(({ isIntersecting }) => isIntersecting)\n )\n )\n )\n}\n\n/**\n * Watch element boundary\n *\n * This function returns an observable which emits whether the bottom content\n * boundary (= scroll offset) of an element is within a certain threshold.\n *\n * @param el - Element\n * @param threshold - Threshold\n *\n * @returns Element boundary observable\n */\nexport function watchElementBoundary(\n el: HTMLElement, threshold = 16\n): Observable {\n return watchElementContentOffset(el)\n .pipe(\n map(({ y }) => {\n const visible = getElementSize(el)\n const content = getElementContentSize(el)\n return y >= (\n content.height - visible.height - threshold\n )\n }),\n distinctUntilChanged()\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n fromEvent,\n map,\n startWith\n} from \"rxjs\"\n\nimport { getElement } from \"../element\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Toggle\n */\nexport type Toggle =\n | \"drawer\" /* Toggle for drawer */\n | \"search\" /* Toggle for search */\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Toggle map\n */\nconst toggles: Record = {\n drawer: getElement(\"[data-md-toggle=drawer]\"),\n search: getElement(\"[data-md-toggle=search]\")\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve the value of a toggle\n *\n * @param name - Toggle\n *\n * @returns Toggle value\n */\nexport function getToggle(name: Toggle): boolean {\n return toggles[name].checked\n}\n\n/**\n * Set toggle\n *\n * Simulating a click event seems to be the most cross-browser compatible way\n * of changing the value while also emitting a `change` event. Before, Material\n * used `CustomEvent` to programmatically change the value of a toggle, but this\n * is a much simpler and cleaner solution which doesn't require a polyfill.\n *\n * @param name - Toggle\n * @param value - Toggle value\n */\nexport function setToggle(name: Toggle, value: boolean): void {\n if (toggles[name].checked !== value)\n toggles[name].click()\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch toggle\n *\n * @param name - Toggle\n *\n * @returns Toggle value observable\n */\nexport function watchToggle(name: Toggle): Observable {\n const el = toggles[name]\n return fromEvent(el, \"change\")\n .pipe(\n map(() => el.checked),\n startWith(el.checked)\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n filter,\n fromEvent,\n map,\n share\n} from \"rxjs\"\n\nimport { getActiveElement } from \"../element\"\nimport { getToggle } from \"../toggle\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Keyboard mode\n */\nexport type KeyboardMode =\n | \"global\" /* Global */\n | \"search\" /* Search is open */\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Keyboard\n */\nexport interface Keyboard {\n mode: KeyboardMode /* Keyboard mode */\n type: string /* Key type */\n claim(): void /* Key claim */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Check whether an element may receive keyboard input\n *\n * @param el - Element\n * @param type - Key type\n *\n * @returns Test result\n */\nfunction isSusceptibleToKeyboard(\n el: HTMLElement, type: string\n): boolean {\n switch (el.constructor) {\n\n /* Input elements */\n case HTMLInputElement:\n /* @ts-expect-error - omit unnecessary type cast */\n if (el.type === \"radio\")\n return /^Arrow/.test(type)\n else\n return true\n\n /* Select element and textarea */\n case HTMLSelectElement:\n case HTMLTextAreaElement:\n return true\n\n /* Everything else */\n default:\n return el.isContentEditable\n }\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch keyboard\n *\n * @returns Keyboard observable\n */\nexport function watchKeyboard(): Observable {\n return fromEvent(window, \"keydown\")\n .pipe(\n filter(ev => !(ev.metaKey || ev.ctrlKey)),\n map(ev => ({\n mode: getToggle(\"search\") ? \"search\" : \"global\",\n type: ev.key,\n claim() {\n ev.preventDefault()\n ev.stopPropagation()\n }\n } as Keyboard)),\n filter(({ mode, type }) => {\n if (mode === \"global\") {\n const active = getActiveElement()\n if (typeof active !== \"undefined\")\n return !isSusceptibleToKeyboard(active, type)\n }\n return true\n }),\n share()\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { Subject } from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve location\n *\n * This function returns a `URL` object (and not `Location`) to normalize the\n * typings across the application. Furthermore, locations need to be tracked\n * without setting them and `Location` is a singleton which represents the\n * current location.\n *\n * @returns URL\n */\nexport function getLocation(): URL {\n return new URL(location.href)\n}\n\n/**\n * Set location\n *\n * @param url - URL to change to\n */\nexport function setLocation(url: URL): void {\n location.href = url.href\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch location\n *\n * @returns Location subject\n */\nexport function watchLocation(): Subject {\n return new Subject()\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { JSX as JSXInternal } from \"preact\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * HTML attributes\n */\ntype Attributes =\n & JSXInternal.HTMLAttributes\n & JSXInternal.SVGAttributes\n & Record\n\n/**\n * Child element\n */\ntype Child =\n | HTMLElement\n | Text\n | string\n | number\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Append a child node to an element\n *\n * @param el - Element\n * @param child - Child node(s)\n */\nfunction appendChild(el: HTMLElement, child: Child | Child[]): void {\n\n /* Handle primitive types (including raw HTML) */\n if (typeof child === \"string\" || typeof child === \"number\") {\n el.innerHTML += child.toString()\n\n /* Handle nodes */\n } else if (child instanceof Node) {\n el.appendChild(child)\n\n /* Handle nested children */\n } else if (Array.isArray(child)) {\n for (const node of child)\n appendChild(el, node)\n }\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * JSX factory\n *\n * @template T - Element type\n *\n * @param tag - HTML tag\n * @param attributes - HTML attributes\n * @param children - Child elements\n *\n * @returns Element\n */\nexport function h(\n tag: T, attributes?: Attributes | null, ...children: Child[]\n): HTMLElementTagNameMap[T]\n\nexport function h(\n tag: string, attributes?: Attributes | null, ...children: Child[]\n): T\n\nexport function h(\n tag: string, attributes?: Attributes | null, ...children: Child[]\n): T {\n const el = document.createElement(tag)\n\n /* Set attributes, if any */\n if (attributes)\n for (const attr of Object.keys(attributes)) {\n if (typeof attributes[attr] === \"undefined\")\n continue\n\n /* Set default attribute or boolean */\n if (typeof attributes[attr] !== \"boolean\")\n el.setAttribute(attr, attributes[attr])\n else\n el.setAttribute(attr, \"\")\n }\n\n /* Append child nodes */\n for (const child of children)\n appendChild(el, child)\n\n /* Return element */\n return el as T\n}\n\n/* ----------------------------------------------------------------------------\n * Namespace\n * ------------------------------------------------------------------------- */\n\nexport declare namespace h {\n namespace JSX {\n type Element = HTMLElement\n type IntrinsicElements = JSXInternal.IntrinsicElements\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Truncate a string after the given number of characters\n *\n * This is not a very reasonable approach, since the summaries kind of suck.\n * It would be better to create something more intelligent, highlighting the\n * search occurrences and making a better summary out of it, but this note was\n * written three years ago, so who knows if we'll ever fix it.\n *\n * @param value - Value to be truncated\n * @param n - Number of characters\n *\n * @returns Truncated value\n */\nexport function truncate(value: string, n: number): string {\n let i = n\n if (value.length > i) {\n while (value[i] !== \" \" && --i > 0) { /* keep eating */ }\n return `${value.substring(0, i)}...`\n }\n return value\n}\n\n/**\n * Round a number for display with repository facts\n *\n * This is a reverse-engineered version of GitHub's weird rounding algorithm\n * for stars, forks and all other numbers. While all numbers below `1,000` are\n * returned as-is, bigger numbers are converted to fixed numbers:\n *\n * - `1,049` => `1k`\n * - `1,050` => `1.1k`\n * - `1,949` => `1.9k`\n * - `1,950` => `2k`\n *\n * @param value - Original value\n *\n * @returns Rounded value\n */\nexport function round(value: number): string {\n if (value > 999) {\n const digits = +((value - 950) % 1000 > 99)\n return `${((value + 0.000001) / 1000).toFixed(digits)}k`\n } else {\n return value.toString()\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n filter,\n fromEvent,\n map,\n shareReplay,\n startWith\n} from \"rxjs\"\n\nimport { getOptionalElement } from \"~/browser\"\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve location hash\n *\n * @returns Location hash\n */\nexport function getLocationHash(): string {\n return location.hash.substring(1)\n}\n\n/**\n * Set location hash\n *\n * Setting a new fragment identifier via `location.hash` will have no effect\n * if the value doesn't change. When a new fragment identifier is set, we want\n * the browser to target the respective element at all times, which is why we\n * use this dirty little trick.\n *\n * @param hash - Location hash\n */\nexport function setLocationHash(hash: string): void {\n const el = h(\"a\", { href: hash })\n el.addEventListener(\"click\", ev => ev.stopPropagation())\n el.click()\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch location hash\n *\n * @returns Location hash observable\n */\nexport function watchLocationHash(): Observable {\n return fromEvent(window, \"hashchange\")\n .pipe(\n map(getLocationHash),\n startWith(getLocationHash()),\n filter(hash => hash.length > 0),\n shareReplay(1)\n )\n}\n\n/**\n * Watch location target\n *\n * @returns Location target observable\n */\nexport function watchLocationTarget(): Observable {\n return watchLocationHash()\n .pipe(\n map(id => getOptionalElement(`[id=\"${id}\"]`)!),\n filter(el => typeof el !== \"undefined\")\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n EMPTY,\n Observable,\n fromEvent,\n fromEventPattern,\n map,\n merge,\n startWith,\n switchMap\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch media query\n *\n * Note that although `MediaQueryList.addListener` is deprecated we have to\n * use it, because it's the only way to ensure proper downward compatibility.\n *\n * @see https://bit.ly/3dUBH2m - GitHub issue\n *\n * @param query - Media query\n *\n * @returns Media observable\n */\nexport function watchMedia(query: string): Observable {\n const media = matchMedia(query)\n return fromEventPattern(next => (\n media.addListener(() => next(media.matches))\n ))\n .pipe(\n startWith(media.matches)\n )\n}\n\n/**\n * Watch print mode\n *\n * @returns Print observable\n */\nexport function watchPrint(): Observable {\n const media = matchMedia(\"print\")\n return merge(\n fromEvent(window, \"beforeprint\").pipe(map(() => true)),\n fromEvent(window, \"afterprint\").pipe(map(() => false))\n )\n .pipe(\n startWith(media.matches)\n )\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Toggle an observable with a media observable\n *\n * @template T - Data type\n *\n * @param query$ - Media observable\n * @param factory - Observable factory\n *\n * @returns Toggled observable\n */\nexport function at(\n query$: Observable, factory: () => Observable\n): Observable {\n return query$\n .pipe(\n switchMap(active => active ? factory() : EMPTY)\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n EMPTY,\n Observable,\n catchError,\n from,\n map,\n of,\n shareReplay,\n switchMap,\n throwError\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch the given URL\n *\n * If the request fails (e.g. when dispatched from `file://` locations), the\n * observable will complete without emitting a value.\n *\n * @param url - Request URL\n * @param options - Options\n *\n * @returns Response observable\n */\nexport function request(\n url: URL | string, options: RequestInit = { credentials: \"same-origin\" }\n): Observable {\n return from(fetch(`${url}`, options))\n .pipe(\n catchError(() => EMPTY),\n switchMap(res => res.status !== 200\n ? throwError(() => new Error(res.statusText))\n : of(res)\n )\n )\n}\n\n/**\n * Fetch JSON from the given URL\n *\n * @template T - Data type\n *\n * @param url - Request URL\n * @param options - Options\n *\n * @returns Data observable\n */\nexport function requestJSON(\n url: URL | string, options?: RequestInit\n): Observable {\n return request(url, options)\n .pipe(\n switchMap(res => res.json()),\n shareReplay(1)\n )\n}\n\n/**\n * Fetch XML from the given URL\n *\n * @param url - Request URL\n * @param options - Options\n *\n * @returns Data observable\n */\nexport function requestXML(\n url: URL | string, options?: RequestInit\n): Observable {\n const dom = new DOMParser()\n return request(url, options)\n .pipe(\n switchMap(res => res.text()),\n map(res => dom.parseFromString(res, \"text/xml\")),\n shareReplay(1)\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n defer,\n finalize,\n fromEvent,\n map,\n merge,\n switchMap,\n take,\n throwError\n} from \"rxjs\"\n\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create and load a `script` element\n *\n * This function returns an observable that will emit when the script was\n * successfully loaded, or throw an error if it didn't.\n *\n * @param src - Script URL\n *\n * @returns Script observable\n */\nexport function watchScript(src: string): Observable {\n const script = h(\"script\", { src })\n return defer(() => {\n document.head.appendChild(script)\n return merge(\n fromEvent(script, \"load\"),\n fromEvent(script, \"error\")\n .pipe(\n switchMap(() => (\n throwError(() => new ReferenceError(`Invalid script: ${src}`))\n ))\n )\n )\n .pipe(\n map(() => undefined),\n finalize(() => document.head.removeChild(script)),\n take(1)\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n fromEvent,\n map,\n merge,\n startWith\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Viewport offset\n */\nexport interface ViewportOffset {\n x: number /* Horizontal offset */\n y: number /* Vertical offset */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve viewport offset\n *\n * On iOS Safari, viewport offset can be negative due to overflow scrolling.\n * As this may induce strange behaviors downstream, we'll just limit it to 0.\n *\n * @returns Viewport offset\n */\nexport function getViewportOffset(): ViewportOffset {\n return {\n x: Math.max(0, scrollX),\n y: Math.max(0, scrollY)\n }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch viewport offset\n *\n * @returns Viewport offset observable\n */\nexport function watchViewportOffset(): Observable {\n return merge(\n fromEvent(window, \"scroll\", { passive: true }),\n fromEvent(window, \"resize\", { passive: true })\n )\n .pipe(\n map(getViewportOffset),\n startWith(getViewportOffset())\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n fromEvent,\n map,\n startWith\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Viewport size\n */\nexport interface ViewportSize {\n width: number /* Viewport width */\n height: number /* Viewport height */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve viewport size\n *\n * @returns Viewport size\n */\nexport function getViewportSize(): ViewportSize {\n return {\n width: innerWidth,\n height: innerHeight\n }\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Watch viewport size\n *\n * @returns Viewport size observable\n */\nexport function watchViewportSize(): Observable {\n return fromEvent(window, \"resize\", { passive: true })\n .pipe(\n map(getViewportSize),\n startWith(getViewportSize())\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n combineLatest,\n map,\n shareReplay\n} from \"rxjs\"\n\nimport {\n ViewportOffset,\n watchViewportOffset\n} from \"../offset\"\nimport {\n ViewportSize,\n watchViewportSize\n} from \"../size\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Viewport\n */\nexport interface Viewport {\n offset: ViewportOffset /* Viewport offset */\n size: ViewportSize /* Viewport size */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch viewport\n *\n * @returns Viewport observable\n */\nexport function watchViewport(): Observable {\n return combineLatest([\n watchViewportOffset(),\n watchViewportSize()\n ])\n .pipe(\n map(([offset, size]) => ({ offset, size })),\n shareReplay(1)\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n combineLatest,\n distinctUntilKeyChanged,\n map\n} from \"rxjs\"\n\nimport { Header } from \"~/components\"\n\nimport { getElementOffset } from \"../../element\"\nimport { Viewport } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
/* Header observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch viewport relative to element\n *\n * @param el - Element\n * @param options - Options\n *\n * @returns Viewport observable\n */\nexport function watchViewportAt(\n el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable {\n const size$ = viewport$\n .pipe(\n distinctUntilKeyChanged(\"size\")\n )\n\n /* Compute element offset */\n const offset$ = combineLatest([size$, header$])\n .pipe(\n map(() => getElementOffset(el))\n )\n\n /* Compute relative viewport, return hot observable */\n return combineLatest([header$, viewport$, offset$])\n .pipe(\n map(([{ height }, { offset, size }, { x, y }]) => ({\n offset: {\n x: offset.x - x,\n y: offset.y - y + height\n },\n size\n }))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n fromEvent,\n map,\n share,\n switchMap,\n tap,\n throttle\n} from \"rxjs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Worker message\n */\nexport interface WorkerMessage {\n type: unknown /* Message type */\n data?: unknown /* Message data */\n}\n\n/**\n * Worker handler\n *\n * @template T - Message type\n */\nexport interface WorkerHandler<\n T extends WorkerMessage\n> {\n tx$: Subject /* Message transmission subject */\n rx$: Observable /* Message receive observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n *\n * @template T - Worker message type\n */\ninterface WatchOptions {\n tx$: Observable /* Message transmission observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch a web worker\n *\n * This function returns an observable that sends all values emitted by the\n * message observable to the web worker. Web worker communication is expected\n * to be bidirectional (request-response) and synchronous. Messages that are\n * emitted during a pending request are throttled, the last one is emitted.\n *\n * @param worker - Web worker\n * @param options - Options\n *\n * @returns Worker message observable\n */\nexport function watchWorker(\n worker: Worker, { tx$ }: WatchOptions\n): Observable {\n\n /* Intercept messages from worker-like objects */\n const rx$ = fromEvent(worker, \"message\")\n .pipe(\n map(({ data }) => data as T)\n )\n\n /* Send and receive messages, return hot observable */\n return tx$\n .pipe(\n throttle(() => rx$, { leading: true, trailing: true }),\n tap(message => worker.postMessage(message)),\n switchMap(() => rx$),\n share()\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { getElement, getLocation } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Feature flag\n */\nexport type Flag =\n | \"content.code.annotate\" /* Code annotations */\n | \"content.tabs.link\" /* Link content tabs */\n | \"header.autohide\" /* Hide header */\n | \"navigation.expand\" /* Automatic expansion */\n | \"navigation.indexes\" /* Section pages */\n | \"navigation.instant\" /* Instant loading */\n | \"navigation.sections\" /* Section navigation */\n | \"navigation.tabs\" /* Tabs navigation */\n | \"navigation.tabs.sticky\" /* Tabs navigation (sticky) */\n | \"navigation.top\" /* Back-to-top button */\n | \"navigation.tracking\" /* Anchor tracking */\n | \"search.highlight\" /* Search highlighting */\n | \"search.share\" /* Search sharing */\n | \"search.suggest\" /* Search suggestions */\n | \"toc.integrate\" /* Integrated table of contents */\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Translation\n */\nexport type Translation =\n | \"clipboard.copy\" /* Copy to clipboard */\n | \"clipboard.copied\" /* Copied to clipboard */\n | \"search.config.lang\" /* Search language */\n | \"search.config.pipeline\" /* Search pipeline */\n | \"search.config.separator\" /* Search separator */\n | \"search.placeholder\" /* Search */\n | \"search.result.placeholder\" /* Type to start searching */\n | \"search.result.none\" /* No matching documents */\n | \"search.result.one\" /* 1 matching document */\n | \"search.result.other\" /* # matching documents */\n | \"search.result.more.one\" /* 1 more on this page */\n | \"search.result.more.other\" /* # more on this page */\n | \"search.result.term.missing\" /* Missing */\n | \"select.version.title\" /* Version selector */\n\n/**\n * Translations\n */\nexport type Translations = Record\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Versioning\n */\nexport interface Versioning {\n provider: \"mike\" /* Version provider */\n default?: string /* Default version */\n}\n\n/**\n * Configuration\n */\nexport interface Config {\n base: string /* Base URL */\n features: Flag[] /* Feature flags */\n translations: Translations /* Translations */\n search: string /* Search worker URL */\n version?: Versioning /* Versioning */\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve global configuration and make base URL absolute\n */\nconst script = getElement(\"#__config\")\nconst config: Config = JSON.parse(script.textContent!)\nconfig.base = `${new URL(config.base, getLocation())}`\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve global configuration\n *\n * @returns Global configuration\n */\nexport function configuration(): Config {\n return config\n}\n\n/**\n * Check whether a feature flag is enabled\n *\n * @param flag - Feature flag\n *\n * @returns Test result\n */\nexport function feature(flag: Flag): boolean {\n return config.features.includes(flag)\n}\n\n/**\n * Retrieve the translation for the given key\n *\n * @param key - Key to be translated\n * @param value - Positional value, if any\n *\n * @returns Translation\n */\nexport function translation(\n key: Translation, value?: string | number\n): string {\n return typeof value !== \"undefined\"\n ? config.translations[key].replace(\"#\", value.toString())\n : config.translations[key]\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { getElement, getElements } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Component type\n */\nexport type ComponentType =\n | \"announce\" /* Announcement bar */\n | \"container\" /* Container */\n | \"content\" /* Content */\n | \"dialog\" /* Dialog */\n | \"header\" /* Header */\n | \"header-title\" /* Header title */\n | \"header-topic\" /* Header topic */\n | \"main\" /* Main area */\n | \"outdated\" /* Version warning */\n | \"palette\" /* Color palette */\n | \"search\" /* Search */\n | \"search-query\" /* Search input */\n | \"search-result\" /* Search results */\n | \"search-share\" /* Search sharing */\n | \"search-suggest\" /* Search suggestions */\n | \"sidebar\" /* Sidebar */\n | \"skip\" /* Skip link */\n | \"source\" /* Repository information */\n | \"tabs\" /* Navigation tabs */\n | \"toc\" /* Table of contents */\n | \"top\" /* Back-to-top button */\n\n/**\n * Component\n *\n * @template T - Component type\n * @template U - Reference type\n */\nexport type Component<\n T extends {} = {},\n U extends HTMLElement = HTMLElement\n> =\n T & {\n ref: U /* Component reference */\n }\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Component type map\n */\ninterface ComponentTypeMap {\n \"announce\": HTMLElement /* Announcement bar */\n \"container\": HTMLElement /* Container */\n \"content\": HTMLElement /* Content */\n \"dialog\": HTMLElement /* Dialog */\n \"header\": HTMLElement /* Header */\n \"header-title\": HTMLElement /* Header title */\n \"header-topic\": HTMLElement /* Header topic */\n \"main\": HTMLElement /* Main area */\n \"outdated\": HTMLElement /* Version warning */\n \"palette\": HTMLElement /* Color palette */\n \"search\": HTMLElement /* Search */\n \"search-query\": HTMLInputElement /* Search input */\n \"search-result\": HTMLElement /* Search results */\n \"search-share\": HTMLAnchorElement /* Search sharing */\n \"search-suggest\": HTMLElement /* Search suggestions */\n \"sidebar\": HTMLElement /* Sidebar */\n \"skip\": HTMLAnchorElement /* Skip link */\n \"source\": HTMLAnchorElement /* Repository information */\n \"tabs\": HTMLElement /* Navigation tabs */\n \"toc\": HTMLElement /* Table of contents */\n \"top\": HTMLAnchorElement /* Back-to-top button */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Retrieve the element for a given component or throw a reference error\n *\n * @template T - Component type\n *\n * @param type - Component type\n * @param node - Node of reference\n *\n * @returns Element\n */\nexport function getComponentElement(\n type: T, node: ParentNode = document\n): ComponentTypeMap[T] {\n return getElement(`[data-md-component=${type}]`, node)\n}\n\n/**\n * Retrieve all elements for a given component\n *\n * @template T - Component type\n *\n * @param type - Component type\n * @param node - Node of reference\n *\n * @returns Elements\n */\nexport function getComponentElements(\n type: T, node: ParentNode = document\n): ComponentTypeMap[T][] {\n return getElements(`[data-md-component=${type}]`, node)\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport ClipboardJS from \"clipboard\"\nimport {\n EMPTY,\n Observable,\n Subject,\n defer,\n distinctUntilChanged,\n distinctUntilKeyChanged,\n filter,\n finalize,\n map,\n mergeWith,\n switchMap,\n take,\n takeLast,\n takeUntil,\n tap\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n getElementContentSize,\n watchElementSize,\n watchElementVisibility\n} from \"~/browser\"\nimport { renderClipboardButton } from \"~/templates\"\n\nimport { Component } from \"../../../_\"\nimport {\n Annotation,\n mountAnnotationList\n} from \"../../annotation\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Code block\n */\nexport interface CodeBlock {\n scrollable: boolean /* Code block overflows */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n print$: Observable /* Media print observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Global sequence number for Clipboard.js integration\n */\nlet sequence = 0\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Find candidate list element directly following a code block\n *\n * @param el - Code block element\n *\n * @returns List element or nothing\n */\nfunction findCandidateList(el: HTMLElement): HTMLElement | undefined {\n if (el.nextElementSibling) {\n const sibling = el.nextElementSibling as HTMLElement\n if (sibling.tagName === \"OL\")\n return sibling\n\n /* Skip empty paragraphs - see https://bit.ly/3r4ZJ2O */\n else if (sibling.tagName === \"P\" && !sibling.children.length)\n return findCandidateList(sibling)\n }\n\n /* Everything else */\n return undefined\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch code block\n *\n * This function monitors size changes of the viewport, as well as switches of\n * content tabs with embedded code blocks, as both may trigger overflow.\n *\n * @param el - Code block element\n *\n * @returns Code block observable\n */\nexport function watchCodeBlock(\n el: HTMLElement\n): Observable {\n return watchElementSize(el)\n .pipe(\n map(({ width }) => {\n const content = getElementContentSize(el)\n return {\n scrollable: content.width > width\n }\n }),\n distinctUntilKeyChanged(\"scrollable\")\n )\n}\n\n/**\n * Mount code block\n *\n * This function ensures that an overflowing code block is focusable through\n * keyboard, so it can be scrolled without a mouse to improve on accessibility.\n * Furthermore, if code annotations are enabled, they are mounted if and only\n * if the code block is currently visible, e.g., not in a hidden content tab.\n *\n * @param el - Code block element\n * @param options - Options\n *\n * @returns Code block and annotation component observable\n */\nexport function mountCodeBlock(\n el: HTMLElement, options: MountOptions\n): Observable> {\n const { matches: hover } = matchMedia(\"(hover)\")\n\n /* Defer mounting of code block - see https://bit.ly/3vHVoVD */\n const factory$ = defer(() => {\n const push$ = new Subject()\n push$.subscribe(({ scrollable }) => {\n if (scrollable && hover)\n el.setAttribute(\"tabindex\", \"0\")\n else\n el.removeAttribute(\"tabindex\")\n })\n\n /* Render button for Clipboard.js integration */\n if (ClipboardJS.isSupported()) {\n const parent = el.closest(\"pre\")!\n parent.id = `__code_${++sequence}`\n parent.insertBefore(\n renderClipboardButton(parent.id),\n el\n )\n }\n\n /* Handle code annotations */\n const container = el.closest(\".highlight\")\n if (container instanceof HTMLElement) {\n const list = findCandidateList(container)\n\n /* Mount code annotations, if enabled */\n if (typeof list !== \"undefined\" && (\n container.classList.contains(\"annotate\") ||\n feature(\"content.code.annotate\")\n )) {\n const annotations$ = mountAnnotationList(list, el, options)\n\n /* Create and return component */\n return watchCodeBlock(el)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state })),\n mergeWith(\n watchElementSize(container)\n .pipe(\n takeUntil(push$.pipe(takeLast(1))),\n map(({ width, height }) => width && height),\n distinctUntilChanged(),\n switchMap(active => active ? annotations$ : EMPTY)\n )\n )\n )\n }\n }\n\n /* Create and return component */\n return watchCodeBlock(el)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n\n /* Mount code block on first sight */\n return watchElementVisibility(el)\n .pipe(\n filter(visible => visible),\n take(1),\n switchMap(() => factory$)\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render an empty annotation\n *\n * @param id - Annotation identifier\n *\n * @returns Element\n */\nexport function renderAnnotation(id: number): HTMLElement {\n return (\n \n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { translation } from \"~/_\"\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a 'copy-to-clipboard' button\n *\n * @param id - Unique identifier\n *\n * @returns Element\n */\nexport function renderClipboardButton(id: string): HTMLElement {\n return (\n code`}\n >\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { ComponentChild } from \"preact\"\n\nimport { feature, translation } from \"~/_\"\nimport {\n SearchDocument,\n SearchMetadata,\n SearchResultItem\n} from \"~/integrations/search\"\nimport { h, truncate } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Render flag\n */\nconst enum Flag {\n TEASER = 1, /* Render teaser */\n PARENT = 2 /* Render as parent */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper function\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a search document\n *\n * @param document - Search document\n * @param flag - Render flags\n *\n * @returns Element\n */\nfunction renderSearchDocument(\n document: SearchDocument & SearchMetadata, flag: Flag\n): HTMLElement {\n const parent = flag & Flag.PARENT\n const teaser = flag & Flag.TEASER\n\n /* Render missing query terms */\n const missing = Object.keys(document.terms)\n .filter(key => !document.terms[key])\n .reduce((list, key) => [\n ...list, {key}, \" \"\n ], [])\n .slice(0, -1)\n\n /* Assemble query string for highlighting */\n const url = new URL(document.location)\n if (feature(\"search.highlight\"))\n url.searchParams.set(\"h\", Object.entries(document.terms)\n .filter(([, match]) => match)\n .reduce((highlight, [value]) => `${highlight} ${value}`.trim(), \"\")\n )\n\n /* Render article or section, depending on flags */\n return (\n \n \n {parent > 0 &&
}\n

{document.title}

\n {teaser > 0 && document.text.length > 0 &&\n

\n {truncate(document.text, 320)}\n

\n }\n {document.tags && document.tags.map(tag => (\n {tag}\n ))}\n {teaser > 0 && missing.length > 0 &&\n

\n {translation(\"search.result.term.missing\")}: {...missing}\n

\n }\n \n \n )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a search result\n *\n * @param result - Search result\n *\n * @returns Element\n */\nexport function renderSearchResultItem(\n result: SearchResultItem\n): HTMLElement {\n const threshold = result[0].score\n const docs = [...result]\n\n /* Find and extract parent article */\n const parent = docs.findIndex(doc => !doc.location.includes(\"#\"))\n const [article] = docs.splice(parent, 1)\n\n /* Determine last index above threshold */\n let index = docs.findIndex(doc => doc.score < threshold)\n if (index === -1)\n index = docs.length\n\n /* Partition sections */\n const best = docs.slice(0, index)\n const more = docs.slice(index)\n\n /* Render children */\n const children = [\n renderSearchDocument(article, Flag.PARENT | +(!parent && index === 0)),\n ...best.map(section => renderSearchDocument(section, Flag.TEASER)),\n ...more.length ? [\n
\n \n {more.length > 0 && more.length === 1\n ? translation(\"search.result.more.one\")\n : translation(\"search.result.more.other\", more.length)\n }\n \n {...more.map(section => renderSearchDocument(section, Flag.TEASER))}\n
\n ] : []\n ]\n\n /* Render search result */\n return (\n
  • \n {children}\n
    • \n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { SourceFacts } from \"~/components\"\nimport { h, round } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render repository facts\n *\n * @param facts - Repository facts\n *\n * @returns Element\n */\nexport function renderSourceFacts(facts: SourceFacts): HTMLElement {\n return (\n
      \n {Object.entries(facts).map(([key, value]) => (\n
    • \n {typeof value === \"number\" ? round(value) : value}\n
      • \n ))}\n
    \n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Tabbed control type\n */\ntype TabbedControlType =\n | \"prev\"\n | \"next\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render control for content tabs\n *\n * @param type - Control type\n *\n * @returns Element\n */\nexport function renderTabbedControl(\n type: TabbedControlType\n): HTMLElement {\n const classes = `tabbed-control tabbed-control--${type}`\n return (\n \n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a table inside a wrapper to improve scrolling on mobile\n *\n * @param table - Table element\n *\n * @returns Element\n */\nexport function renderTable(table: HTMLElement): HTMLElement {\n return (\n
    \n
    \n {table}\n
    \n
    \n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { configuration, translation } from \"~/_\"\nimport { h } from \"~/utilities\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Version\n */\nexport interface Version {\n version: string /* Version identifier */\n title: string /* Version title */\n aliases: string[] /* Version aliases */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a version\n *\n * @param version - Version\n *\n * @returns Element\n */\nfunction renderVersion(version: Version): HTMLElement {\n const config = configuration()\n\n /* Ensure trailing slash, see https://bit.ly/3rL5u3f */\n const url = new URL(`../${version.version}/`, config.base)\n return (\n
  • \n \n {version.title}\n \n
    • \n )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Render a version selector\n *\n * @param versions - Versions\n * @param active - Active version\n *\n * @returns Element\n */\nexport function renderVersionSelector(\n versions: Version[], active: Version\n): HTMLElement {\n return (\n
    \n \n {active.title}\n \n
      \n {versions.map(renderVersion)}\n
    \n
    \n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n EMPTY,\n Observable,\n Subject,\n animationFrameScheduler,\n combineLatest,\n defer,\n finalize,\n fromEvent,\n map,\n switchMap,\n take,\n takeLast,\n takeUntil,\n tap,\n throttleTime\n} from \"rxjs\"\n\nimport {\n ElementOffset,\n getElement,\n getElementSize,\n watchElementContentOffset,\n watchElementFocus,\n watchElementOffset,\n watchElementVisibility\n} from \"~/browser\"\n\nimport { Component } from \"../../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Annotation\n */\nexport interface Annotation {\n active: boolean /* Annotation is active */\n offset: ElementOffset /* Annotation offset */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch annotation\n *\n * @param el - Annotation element\n * @param container - Containing element\n *\n * @returns Annotation observable\n */\nexport function watchAnnotation(\n el: HTMLElement, container: HTMLElement\n): Observable {\n const offset$ = defer(() => combineLatest([\n watchElementOffset(el),\n watchElementContentOffset(container)\n ]))\n .pipe(\n map(([{ x, y }, scroll]) => {\n const { width } = getElementSize(el)\n return ({\n x: x - scroll.x + width / 2,\n y: y - scroll.y\n })\n })\n )\n\n /* Actively watch annotation on focus */\n return watchElementFocus(el)\n .pipe(\n switchMap(active => offset$\n .pipe(\n map(offset => ({ active, offset })),\n take(+!active || Infinity)\n )\n )\n )\n}\n\n/**\n * Mount annotation\n *\n * @param el - Annotation element\n * @param container - Containing element\n *\n * @returns Annotation component observable\n */\nexport function mountAnnotation(\n el: HTMLElement, container: HTMLElement\n): Observable> {\n return defer(() => {\n const push$ = new Subject()\n push$.subscribe({\n\n /* Handle emission */\n next({ offset }) {\n el.style.setProperty(\"--md-tooltip-x\", `${offset.x}px`)\n el.style.setProperty(\"--md-tooltip-y\", `${offset.y}px`)\n },\n\n /* Handle complete */\n complete() {\n el.style.removeProperty(\"--md-tooltip-x\")\n el.style.removeProperty(\"--md-tooltip-y\")\n }\n })\n\n /* Start animation only when annotation is visible */\n const done$ = push$.pipe(takeLast(1))\n watchElementVisibility(el)\n .pipe(\n takeUntil(done$)\n )\n .subscribe(visible => {\n el.toggleAttribute(\"data-md-visible\", visible)\n })\n\n /* Track relative origin of tooltip */\n push$\n .pipe(\n throttleTime(500, animationFrameScheduler),\n map(() => container.getBoundingClientRect()),\n map(({ x }) => x)\n )\n .subscribe({\n\n /* Handle emission */\n next(origin) {\n if (origin)\n el.style.setProperty(\"--md-tooltip-0\", `${-origin}px`)\n else\n el.style.removeProperty(\"--md-tooltip-0\")\n },\n\n /* Handle complete */\n complete() {\n el.style.removeProperty(\"--md-tooltip-0\")\n }\n })\n\n /* Close open annotation on click */\n const index = getElement(\":scope > :last-child\", el)\n const blur$ = fromEvent(index, \"mousedown\", { once: true })\n push$\n .pipe(\n switchMap(({ active }) => active ? blur$ : EMPTY),\n tap(ev => ev.preventDefault())\n )\n .subscribe(() => el.blur())\n\n /* Create and return component */\n return watchAnnotation(el, container)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n EMPTY,\n Observable,\n Subject,\n defer,\n finalize,\n merge,\n share,\n takeLast,\n takeUntil\n} from \"rxjs\"\n\nimport {\n getElement,\n getElements,\n getOptionalElement\n} from \"~/browser\"\nimport { renderAnnotation } from \"~/templates\"\n\nimport { Component } from \"../../../_\"\nimport {\n Annotation,\n mountAnnotation\n} from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n print$: Observable /* Media print observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Find all annotation markers in the given code block\n *\n * @param container - Containing element\n *\n * @returns Annotation markers\n */\nfunction findAnnotationMarkers(container: HTMLElement): Text[] {\n const markers: Text[] = []\n for (const comment of getElements(\".c, .c1, .cm\", container)) {\n let match: RegExpExecArray | null\n\n /* Split text at marker and add to list */\n let text = comment.firstChild as Text\n if (text instanceof Text)\n while ((match = /\\((\\d+)\\)/.exec(text.textContent!))) {\n const marker = text.splitText(match.index)\n text = marker.splitText(match[0].length)\n markers.push(marker)\n }\n }\n return markers\n}\n\n/**\n * Swap the child nodes of two elements\n *\n * @param source - Source element\n * @param target - Target element\n */\nfunction swap(source: HTMLElement, target: HTMLElement): void {\n target.append(...Array.from(source.childNodes))\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount annotation list\n *\n * This function analyzes the containing code block and checks for markers\n * referring to elements in the given annotation list. If no markers are found,\n * the list is left untouched. Otherwise, list elements are rendered as\n * annotations inside the code block.\n *\n * @param el - Annotation list element\n * @param container - Containing element\n * @param options - Options\n *\n * @returns Annotation component observable\n */\nexport function mountAnnotationList(\n el: HTMLElement, container: HTMLElement, { print$ }: MountOptions\n): Observable> {\n\n /* Find and replace all markers with empty annotations */\n const annotations = new Map()\n for (const marker of findAnnotationMarkers(container)) {\n const [, id] = marker.textContent!.match(/\\((\\d+)\\)/)!\n if (getOptionalElement(`li:nth-child(${id})`, el)) {\n annotations.set(+id, renderAnnotation(+id))\n marker.replaceWith(annotations.get(+id)!)\n }\n }\n\n /* Keep list if there are no annotations to render */\n if (annotations.size === 0)\n return EMPTY\n\n /* Create and return component */\n return defer(() => {\n const done$ = new Subject()\n\n /* Handle print mode - see https://bit.ly/3rgPdpt */\n print$\n .pipe(\n takeUntil(done$.pipe(takeLast(1)))\n )\n .subscribe(active => {\n el.hidden = !active\n\n /* Show annotations in code block or list (print) */\n for (const [id, annotation] of annotations) {\n const inner = getElement(\".md-typeset\", annotation)\n const child = getElement(`li:nth-child(${id})`, el)\n if (!active)\n swap(child, inner)\n else\n swap(inner, child)\n }\n })\n\n /* Create and return component */\n return merge(...[...annotations]\n .map(([, annotation]) => (\n mountAnnotation(annotation, container)\n ))\n )\n .pipe(\n finalize(() => done$.complete()),\n share()\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n map,\n of,\n shareReplay,\n tap\n} from \"rxjs\"\n\nimport { watchScript } from \"~/browser\"\nimport { h } from \"~/utilities\"\n\nimport { Component } from \"../../../_\"\n\nimport themeCSS from \"./index.css\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mermaid diagram\n */\nexport interface Mermaid {}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Mermaid instance observable\n */\nlet mermaid$: Observable\n\n/**\n * Global sequence number for diagrams\n */\nlet sequence = 0\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch Mermaid script\n *\n * @returns Mermaid scripts observable\n */\nfunction fetchScripts(): Observable {\n return typeof mermaid === \"undefined\" || mermaid instanceof Element\n ? watchScript(\"https://unpkg.com/mermaid@9.0.1/dist/mermaid.min.js\")\n : of(undefined)\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount Mermaid diagram\n *\n * @param el - Code block element\n *\n * @returns Mermaid diagram component observable\n */\nexport function mountMermaid(\n el: HTMLElement\n): Observable> {\n el.classList.remove(\"mermaid\") // Hack: mitigate https://bit.ly/3CiN6Du\n mermaid$ ||= fetchScripts()\n .pipe(\n tap(() => mermaid.initialize({\n startOnLoad: false,\n themeCSS\n })),\n map(() => undefined),\n shareReplay(1)\n )\n\n /* Render diagram */\n mermaid$.subscribe(() => {\n el.classList.add(\"mermaid\") // Hack: mitigate https://bit.ly/3CiN6Du\n const id = `__mermaid_${sequence++}`\n const host = h(\"div\", { class: \"mermaid\" })\n mermaid.mermaidAPI.render(id, el.textContent, (svg: string) => {\n\n /* Create a shadow root and inject diagram */\n const shadow = host.attachShadow({ mode: \"closed\" })\n shadow.innerHTML = svg\n\n /* Replace code block with diagram */\n el.replaceWith(host)\n })\n })\n\n /* Create and return component */\n return mermaid$\n .pipe(\n map(() => ({ ref: el }))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n defer,\n filter,\n finalize,\n map,\n merge,\n tap\n} from \"rxjs\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Details\n */\nexport interface Details {\n action: \"open\" | \"close\" /* Details state */\n reveal?: boolean /* Details is revealed */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n target$: Observable /* Location target observable */\n print$: Observable /* Media print observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n target$: Observable /* Location target observable */\n print$: Observable /* Media print observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch details\n *\n * @param el - Details element\n * @param options - Options\n *\n * @returns Details observable\n */\nexport function watchDetails(\n el: HTMLDetailsElement, { target$, print$ }: WatchOptions\n): Observable
    {\n let open = true\n return merge(\n\n /* Open and focus details on location target */\n target$\n .pipe(\n map(target => target.closest(\"details:not([open])\")!),\n filter(details => el === details),\n map(() => ({\n action: \"open\", reveal: true\n }) as Details)\n ),\n\n /* Open details on print and close afterwards */\n print$\n .pipe(\n filter(active => active || !open),\n tap(() => open = el.open),\n map(active => ({\n action: active ? \"open\" : \"close\"\n }) as Details)\n )\n )\n}\n\n/**\n * Mount details\n *\n * This function ensures that `details` tags are opened on anchor jumps and\n * prior to printing, so the whole content of the page is visible.\n *\n * @param el - Details element\n * @param options - Options\n *\n * @returns Details component observable\n */\nexport function mountDetails(\n el: HTMLDetailsElement, options: MountOptions\n): Observable> {\n return defer(() => {\n const push$ = new Subject
    ()\n push$.subscribe(({ action, reveal }) => {\n if (action === \"open\")\n el.setAttribute(\"open\", \"\")\n else\n el.removeAttribute(\"open\")\n if (reveal)\n el.scrollIntoView()\n })\n\n /* Create and return component */\n return watchDetails(el, options)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { Observable, of } from \"rxjs\"\n\nimport { renderTable } from \"~/templates\"\nimport { h } from \"~/utilities\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Data table\n */\nexport interface DataTable {}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Sentinel for replacement\n */\nconst sentinel = h(\"table\")\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount data table\n *\n * This function wraps a data table in another scrollable container, so it can\n * be smoothly scrolled on smaller screen sizes and won't break the layout.\n *\n * @param el - Data table element\n *\n * @returns Data table component observable\n */\nexport function mountDataTable(\n el: HTMLElement\n): Observable> {\n el.replaceWith(sentinel)\n sentinel.replaceWith(renderTable(el))\n\n /* Create and return component */\n return of({ ref: el })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n animationFrameScheduler,\n asyncScheduler,\n auditTime,\n combineLatest,\n defer,\n finalize,\n fromEvent,\n map,\n merge,\n skip,\n startWith,\n subscribeOn,\n takeLast,\n takeUntil,\n tap\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n getElement,\n getElementContentOffset,\n getElementContentSize,\n getElementOffset,\n getElementSize,\n getElements,\n watchElementContentOffset,\n watchElementSize\n} from \"~/browser\"\nimport { renderTabbedControl } from \"~/templates\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Content tabs\n */\nexport interface ContentTabs {\n active: HTMLLabelElement /* Active tab label */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch content tabs\n *\n * @param el - Content tabs element\n *\n * @returns Content tabs observable\n */\nexport function watchContentTabs(\n el: HTMLElement\n): Observable {\n const inputs = getElements(\":scope > input\", el)\n const initial = inputs.find(input => input.checked) || inputs[0]\n return merge(...inputs.map(input => fromEvent(input, \"change\")\n .pipe(\n map(() => getElement(`label[for=${input.id}]`))\n )\n ))\n .pipe(\n startWith(getElement(`label[for=${initial.id}]`)),\n map(active => ({ active }))\n )\n}\n\n/**\n * Mount content tabs\n *\n * This function scrolls the active tab into view. While this functionality is\n * provided by browsers as part of `scrollInfoView`, browsers will always also\n * scroll the vertical axis, which we do not want. Thus, we decided to provide\n * this functionality ourselves.\n *\n * @param el - Content tabs element\n *\n * @returns Content tabs component observable\n */\nexport function mountContentTabs(\n el: HTMLElement\n): Observable> {\n\n /* Render content tab previous button for pagination */\n const prev = renderTabbedControl(\"prev\")\n el.append(prev)\n\n /* Render content tab next button for pagination */\n const next = renderTabbedControl(\"next\")\n el.append(next)\n\n /* Mount component on subscription */\n const container = getElement(\".tabbed-labels\", el)\n return defer(() => {\n const push$ = new Subject()\n const done$ = push$.pipe(takeLast(1))\n combineLatest([push$, watchElementSize(el)])\n .pipe(\n auditTime(1, animationFrameScheduler),\n takeUntil(done$)\n )\n .subscribe({\n\n /* Handle emission */\n next([{ active }, size]) {\n const offset = getElementOffset(active)\n const { width } = getElementSize(active)\n\n /* Set tab indicator offset and width */\n el.style.setProperty(\"--md-indicator-x\", `${offset.x}px`)\n el.style.setProperty(\"--md-indicator-width\", `${width}px`)\n\n /* Scroll container to active content tab */\n const content = getElementContentOffset(container)\n if (\n offset.x < content.x ||\n offset.x + width > content.x + size.width\n )\n container.scrollTo({\n left: Math.max(0, offset.x - 16),\n behavior: \"smooth\"\n })\n },\n\n /* Handle complete */\n complete() {\n el.style.removeProperty(\"--md-indicator-x\")\n el.style.removeProperty(\"--md-indicator-width\")\n }\n })\n\n /* Hide content tab buttons on borders */\n combineLatest([\n watchElementContentOffset(container),\n watchElementSize(container)\n ])\n .pipe(\n takeUntil(done$)\n )\n .subscribe(([offset, size]) => {\n const content = getElementContentSize(container)\n prev.hidden = offset.x < 16\n next.hidden = offset.x > content.width - size.width - 16\n })\n\n /* Paginate content tab container on click */\n merge(\n fromEvent(prev, \"click\").pipe(map(() => -1)),\n fromEvent(next, \"click\").pipe(map(() => +1))\n )\n .pipe(\n takeUntil(done$)\n )\n .subscribe(direction => {\n const { width } = getElementSize(container)\n container.scrollBy({\n left: width * direction,\n behavior: \"smooth\"\n })\n })\n\n /* Set up linking of content tabs, if enabled */\n if (feature(\"content.tabs.link\"))\n push$.pipe(skip(1))\n .subscribe(({ active }) => {\n const tab = active.innerText.trim()\n for (const set of getElements(\"[data-tabs]\"))\n for (const input of getElements(\n \":scope > input\", set\n )) {\n const label = getElement(`label[for=${input.id}]`)\n if (label.innerText.trim() === tab) {\n input.click()\n break\n }\n }\n\n /* Persist active tabs in local storage */\n const tabs = __md_get(\"__tabs\") || []\n __md_set(\"__tabs\", [...new Set([tab, ...tabs])])\n })\n\n /* Create and return component */\n return watchContentTabs(el)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n .pipe(\n subscribeOn(asyncScheduler)\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { Observable, merge } from \"rxjs\"\n\nimport { getElements } from \"~/browser\"\n\nimport { Component } from \"../../_\"\nimport { Annotation } from \"../annotation\"\nimport {\n CodeBlock,\n Mermaid,\n mountCodeBlock,\n mountMermaid\n} from \"../code\"\nimport {\n Details,\n mountDetails\n} from \"../details\"\nimport {\n DataTable,\n mountDataTable\n} from \"../table\"\nimport {\n ContentTabs,\n mountContentTabs\n} from \"../tabs\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Content\n */\nexport type Content =\n | Annotation\n | ContentTabs\n | CodeBlock\n | Mermaid\n | DataTable\n | Details\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n target$: Observable /* Location target observable */\n print$: Observable /* Media print observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount content\n *\n * This function mounts all components that are found in the content of the\n * actual article, including code blocks, data tables and details.\n *\n * @param el - Content element\n * @param options - Options\n *\n * @returns Content component observable\n */\nexport function mountContent(\n el: HTMLElement, { target$, print$ }: MountOptions\n): Observable> {\n return merge(\n\n /* Code blocks */\n ...getElements(\"pre:not(.mermaid) > code\", el)\n .map(child => mountCodeBlock(child, { print$ })),\n\n /* Mermaid diagrams */\n ...getElements(\"pre.mermaid\", el)\n .map(child => mountMermaid(child)),\n\n /* Data tables */\n ...getElements(\"table:not([class])\", el)\n .map(child => mountDataTable(child)),\n\n /* Details */\n ...getElements(\"details\", el)\n .map(child => mountDetails(child, { target$, print$ })),\n\n /* Content tabs */\n ...getElements(\"[data-tabs]\", el)\n .map(child => mountContentTabs(child))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n defer,\n delay,\n finalize,\n map,\n merge,\n of,\n switchMap,\n tap\n} from \"rxjs\"\n\nimport { getElement } from \"~/browser\"\n\nimport { Component } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Dialog\n */\nexport interface Dialog {\n message: string /* Dialog message */\n active: boolean /* Dialog is active */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n alert$: Subject /* Alert subject */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n alert$: Subject /* Alert subject */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch dialog\n *\n * @param _el - Dialog element\n * @param options - Options\n *\n * @returns Dialog observable\n */\nexport function watchDialog(\n _el: HTMLElement, { alert$ }: WatchOptions\n): Observable {\n return alert$\n .pipe(\n switchMap(message => merge(\n of(true),\n of(false).pipe(delay(2000))\n )\n .pipe(\n map(active => ({ message, active }))\n )\n )\n )\n}\n\n/**\n * Mount dialog\n *\n * This function reveals the dialog in the right corner when a new alert is\n * emitted through the subject that is passed as part of the options.\n *\n * @param el - Dialog element\n * @param options - Options\n *\n * @returns Dialog component observable\n */\nexport function mountDialog(\n el: HTMLElement, options: MountOptions\n): Observable> {\n const inner = getElement(\".md-typeset\", el)\n return defer(() => {\n const push$ = new Subject()\n push$.subscribe(({ message, active }) => {\n el.classList.toggle(\"md-dialog--active\", active)\n inner.textContent = message\n })\n\n /* Create and return component */\n return watchDialog(el, options)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n bufferCount,\n combineLatest,\n combineLatestWith,\n defer,\n distinctUntilChanged,\n distinctUntilKeyChanged,\n filter,\n map,\n of,\n shareReplay,\n startWith,\n switchMap,\n takeLast,\n takeUntil\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n Viewport,\n watchElementSize,\n watchToggle\n} from \"~/browser\"\n\nimport { Component } from \"../../_\"\nimport { Main } from \"../../main\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Header\n */\nexport interface Header {\n height: number /* Header visible height */\n hidden: boolean /* Header is hidden */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n viewport$: Observable /* Viewport observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n main$: Observable
    /* Main area observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Compute whether the header is hidden\n *\n * If the user scrolls past a certain threshold, the header can be hidden when\n * scrolling down, and shown when scrolling up.\n *\n * @param options - Options\n *\n * @returns Toggle observable\n */\nfunction isHidden({ viewport$ }: WatchOptions): Observable {\n if (!feature(\"header.autohide\"))\n return of(false)\n\n /* Compute direction and turning point */\n const direction$ = viewport$\n .pipe(\n map(({ offset: { y } }) => y),\n bufferCount(2, 1),\n map(([a, b]) => [a < b, b] as const),\n distinctUntilKeyChanged(0)\n )\n\n /* Compute whether header should be hidden */\n const hidden$ = combineLatest([viewport$, direction$])\n .pipe(\n filter(([{ offset }, [, y]]) => Math.abs(y - offset.y) > 100),\n map(([, [direction]]) => direction),\n distinctUntilChanged()\n )\n\n /* Compute threshold for hiding */\n const search$ = watchToggle(\"search\")\n return combineLatest([viewport$, search$])\n .pipe(\n map(([{ offset }, search]) => offset.y > 400 && !search),\n distinctUntilChanged(),\n switchMap(active => active ? hidden$ : of(false)),\n startWith(false)\n )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch header\n *\n * @param el - Header element\n * @param options - Options\n *\n * @returns Header observable\n */\nexport function watchHeader(\n el: HTMLElement, options: WatchOptions\n): Observable
    {\n return defer(() => combineLatest([\n watchElementSize(el),\n isHidden(options)\n ]))\n .pipe(\n map(([{ height }, hidden]) => ({\n height,\n hidden\n })),\n distinctUntilChanged((a, b) => (\n a.height === b.height &&\n a.hidden === b.hidden\n )),\n shareReplay(1)\n )\n}\n\n/**\n * Mount header\n *\n * This function manages the different states of the header, i.e. whether it's\n * hidden or rendered with a shadow. This depends heavily on the main area.\n *\n * @param el - Header element\n * @param options - Options\n *\n * @returns Header component observable\n */\nexport function mountHeader(\n el: HTMLElement, { header$, main$ }: MountOptions\n): Observable> {\n return defer(() => {\n const push$ = new Subject
    ()\n const done$ = push$.pipe(takeLast(1))\n push$\n .pipe(\n distinctUntilKeyChanged(\"active\"),\n combineLatestWith(header$)\n )\n .subscribe(([{ active }, { hidden }]) => {\n el.classList.toggle(\"md-header--shadow\", active && !hidden)\n el.hidden = hidden\n })\n\n /* Link to main area */\n main$.subscribe(push$)\n\n /* Create and return component */\n return header$\n .pipe(\n takeUntil(done$),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n EMPTY,\n Observable,\n Subject,\n defer,\n distinctUntilKeyChanged,\n finalize,\n map,\n tap\n} from \"rxjs\"\n\nimport {\n Viewport,\n getElementSize,\n getOptionalElement,\n watchViewportAt\n} from \"~/browser\"\n\nimport { Component } from \"../../_\"\nimport { Header } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Header\n */\nexport interface HeaderTitle {\n active: boolean /* Header title is active */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch header title\n *\n * @param el - Heading element\n * @param options - Options\n *\n * @returns Header title observable\n */\nexport function watchHeaderTitle(\n el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable {\n return watchViewportAt(el, { viewport$, header$ })\n .pipe(\n map(({ offset: { y } }) => {\n const { height } = getElementSize(el)\n return {\n active: y >= height\n }\n }),\n distinctUntilKeyChanged(\"active\")\n )\n}\n\n/**\n * Mount header title\n *\n * This function swaps the header title from the site title to the title of the\n * current page when the user scrolls past the first headline.\n *\n * @param el - Header title element\n * @param options - Options\n *\n * @returns Header title component observable\n */\nexport function mountHeaderTitle(\n el: HTMLElement, options: MountOptions\n): Observable> {\n return defer(() => {\n const push$ = new Subject()\n push$.subscribe(({ active }) => {\n el.classList.toggle(\"md-header__title--active\", active)\n })\n\n /* Obtain headline, if any */\n const heading = getOptionalElement(\"article h1\")\n if (typeof heading === \"undefined\")\n return EMPTY\n\n /* Create and return component */\n return watchHeaderTitle(heading, options)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n combineLatest,\n distinctUntilChanged,\n distinctUntilKeyChanged,\n map,\n switchMap\n} from \"rxjs\"\n\nimport {\n Viewport,\n watchElementSize\n} from \"~/browser\"\n\nimport { Header } from \"../header\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Main area\n */\nexport interface Main {\n offset: number /* Main area top offset */\n height: number /* Main area visible height */\n active: boolean /* Main area is active */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch main area\n *\n * This function returns an observable that computes the visual parameters of\n * the main area which depends on the viewport vertical offset and height, as\n * well as the height of the header element, if the header is fixed.\n *\n * @param el - Main area element\n * @param options - Options\n *\n * @returns Main area observable\n */\nexport function watchMain(\n el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable
    {\n\n /* Compute necessary adjustment for header */\n const adjust$ = header$\n .pipe(\n map(({ height }) => height),\n distinctUntilChanged()\n )\n\n /* Compute the main area's top and bottom borders */\n const border$ = adjust$\n .pipe(\n switchMap(() => watchElementSize(el)\n .pipe(\n map(({ height }) => ({\n top: el.offsetTop,\n bottom: el.offsetTop + height\n })),\n distinctUntilKeyChanged(\"bottom\")\n )\n )\n )\n\n /* Compute the main area's offset, visible height and if we scrolled past */\n return combineLatest([adjust$, border$, viewport$])\n .pipe(\n map(([header, { top, bottom }, { offset: { y }, size: { height } }]) => {\n height = Math.max(0, height\n - Math.max(0, top - y, header)\n - Math.max(0, height + y - bottom)\n )\n return {\n offset: top - header,\n height,\n active: top - header <= y\n }\n }),\n distinctUntilChanged((a, b) => (\n a.offset === b.offset &&\n a.height === b.height &&\n a.active === b.active\n ))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n asyncScheduler,\n defer,\n finalize,\n fromEvent,\n map,\n mergeMap,\n observeOn,\n of,\n shareReplay,\n startWith,\n tap\n} from \"rxjs\"\n\nimport { getElements } from \"~/browser\"\n\nimport { Component } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Palette colors\n */\nexport interface PaletteColor {\n scheme?: string /* Color scheme */\n primary?: string /* Primary color */\n accent?: string /* Accent color */\n}\n\n/**\n * Palette\n */\nexport interface Palette {\n index: number /* Palette index */\n color: PaletteColor /* Palette colors */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch color palette\n *\n * @param inputs - Color palette element\n *\n * @returns Color palette observable\n */\nexport function watchPalette(\n inputs: HTMLInputElement[]\n): Observable {\n const current = __md_get(\"__palette\") || {\n index: inputs.findIndex(input => matchMedia(\n input.getAttribute(\"data-md-color-media\")!\n ).matches)\n }\n\n /* Emit changes in color palette */\n return of(...inputs)\n .pipe(\n mergeMap(input => fromEvent(input, \"change\")\n .pipe(\n map(() => input)\n )\n ),\n startWith(inputs[Math.max(0, current.index)]),\n map(input => ({\n index: inputs.indexOf(input),\n color: {\n scheme: input.getAttribute(\"data-md-color-scheme\"),\n primary: input.getAttribute(\"data-md-color-primary\"),\n accent: input.getAttribute(\"data-md-color-accent\")\n }\n } as Palette)),\n shareReplay(1)\n )\n}\n\n/**\n * Mount color palette\n *\n * @param el - Color palette element\n *\n * @returns Color palette component observable\n */\nexport function mountPalette(\n el: HTMLElement\n): Observable> {\n return defer(() => {\n const push$ = new Subject()\n push$.subscribe(palette => {\n document.body.setAttribute(\"data-md-color-switching\", \"\")\n\n /* Set color palette */\n for (const [key, value] of Object.entries(palette.color))\n document.body.setAttribute(`data-md-color-${key}`, value)\n\n /* Toggle visibility */\n for (let index = 0; index < inputs.length; index++) {\n const label = inputs[index].nextElementSibling\n if (label instanceof HTMLElement)\n label.hidden = palette.index !== index\n }\n\n /* Persist preference in local storage */\n __md_set(\"__palette\", palette)\n })\n\n /* Revert transition durations after color switch */\n push$.pipe(observeOn(asyncScheduler))\n .subscribe(() => {\n document.body.removeAttribute(\"data-md-color-switching\")\n })\n\n /* Create and return component */\n const inputs = getElements(\"input\", el)\n return watchPalette(inputs)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport ClipboardJS from \"clipboard\"\nimport {\n Observable,\n Subject,\n map,\n tap\n} from \"rxjs\"\n\nimport { translation } from \"~/_\"\nimport { getElement } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Setup options\n */\ninterface SetupOptions {\n alert$: Subject /* Alert subject */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Extract text to copy\n *\n * @param el - HTML element\n *\n * @returns Extracted text\n */\nfunction extract(el: HTMLElement): string {\n el.setAttribute(\"data-md-copying\", \"\")\n const text = el.innerText\n el.removeAttribute(\"data-md-copying\")\n return text\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up Clipboard.js integration\n *\n * @param options - Options\n */\nexport function setupClipboardJS(\n { alert$ }: SetupOptions\n): void {\n if (ClipboardJS.isSupported()) {\n new Observable(subscriber => {\n new ClipboardJS(\"[data-clipboard-target], [data-clipboard-text]\", {\n text: el => (\n el.getAttribute(\"data-clipboard-text\")! ||\n extract(getElement(\n el.getAttribute(\"data-clipboard-target\")!\n ))\n )\n })\n .on(\"success\", ev => subscriber.next(ev))\n })\n .pipe(\n tap(ev => {\n const trigger = ev.trigger as HTMLElement\n trigger.focus()\n }),\n map(() => translation(\"clipboard.copied\"))\n )\n .subscribe(alert$)\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n EMPTY,\n Observable,\n catchError,\n defaultIfEmpty,\n map,\n of,\n tap\n} from \"rxjs\"\n\nimport { configuration } from \"~/_\"\nimport { getElements, requestXML } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Sitemap, i.e. a list of URLs\n */\nexport type Sitemap = string[]\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Preprocess a list of URLs\n *\n * This function replaces the `site_url` in the sitemap with the actual base\n * URL, to allow instant loading to work in occasions like Netlify previews.\n *\n * @param urls - URLs\n *\n * @returns URL path parts\n */\nfunction preprocess(urls: Sitemap): Sitemap {\n if (urls.length < 2)\n return [\"\"]\n\n /* Take the first two URLs and remove everything after the last slash */\n const [root, next] = [...urls]\n .sort((a, b) => a.length - b.length)\n .map(url => url.replace(/[^/]+$/, \"\"))\n\n /* Compute common prefix */\n let index = 0\n if (root === next)\n index = root.length\n else\n while (root.charCodeAt(index) === next.charCodeAt(index))\n index++\n\n /* Remove common prefix and return in original order */\n return urls.map(url => url.replace(root.slice(0, index), \"\"))\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch the sitemap for the given base URL\n *\n * @param base - Base URL\n *\n * @returns Sitemap observable\n */\nexport function fetchSitemap(base?: URL): Observable {\n const cached = __md_get(\"__sitemap\", sessionStorage, base)\n if (cached) {\n return of(cached)\n } else {\n const config = configuration()\n return requestXML(new URL(\"sitemap.xml\", base || config.base))\n .pipe(\n map(sitemap => preprocess(getElements(\"loc\", sitemap)\n .map(node => node.textContent!)\n )),\n catchError(() => EMPTY), // @todo refactor instant loading\n defaultIfEmpty([]),\n tap(sitemap => __md_set(\"__sitemap\", sitemap, sessionStorage, base))\n )\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n EMPTY,\n NEVER,\n Observable,\n Subject,\n bufferCount,\n catchError,\n concatMap,\n debounceTime,\n distinctUntilChanged,\n distinctUntilKeyChanged,\n filter,\n fromEvent,\n map,\n merge,\n of,\n sample,\n share,\n skip,\n skipUntil,\n switchMap\n} from \"rxjs\"\n\nimport { configuration, feature } from \"~/_\"\nimport {\n Viewport,\n ViewportOffset,\n getElements,\n getOptionalElement,\n request,\n setLocation,\n setLocationHash\n} from \"~/browser\"\nimport { getComponentElement } from \"~/components\"\nimport { h } from \"~/utilities\"\n\nimport { fetchSitemap } from \"../sitemap\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * History state\n */\nexport interface HistoryState {\n url: URL /* State URL */\n offset?: ViewportOffset /* State viewport offset */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Setup options\n */\ninterface SetupOptions {\n document$: Subject /* Document subject */\n location$: Subject /* Location subject */\n viewport$: Observable /* Viewport observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up instant loading\n *\n * When fetching, theoretically, we could use `responseType: \"document\"`, but\n * since all MkDocs links are relative, we need to make sure that the current\n * location matches the document we just loaded. Otherwise any relative links\n * in the document could use the old location.\n *\n * This is the reason why we need to synchronize history events and the process\n * of fetching the document for navigation changes (except `popstate` events):\n *\n * 1. Fetch document via `XMLHTTPRequest`\n * 2. Set new location via `history.pushState`\n * 3. Parse and emit fetched document\n *\n * For `popstate` events, we must not use `history.pushState`, or the forward\n * history will be irreversibly overwritten. In case the request fails, the\n * location change is dispatched regularly.\n *\n * @param options - Options\n */\nexport function setupInstantLoading(\n { document$, location$, viewport$ }: SetupOptions\n): void {\n const config = configuration()\n if (location.protocol === \"file:\")\n return\n\n /* Disable automatic scroll restoration */\n if (\"scrollRestoration\" in history) {\n history.scrollRestoration = \"manual\"\n\n /* Hack: ensure that reloads restore viewport offset */\n fromEvent(window, \"beforeunload\")\n .subscribe(() => {\n history.scrollRestoration = \"auto\"\n })\n }\n\n /* Hack: ensure absolute favicon link to omit 404s when switching */\n const favicon = getOptionalElement(\"link[rel=icon]\")\n if (typeof favicon !== \"undefined\")\n favicon.href = favicon.href\n\n /* Intercept internal navigation */\n const push$ = fetchSitemap()\n .pipe(\n map(paths => paths.map(path => `${new URL(path, config.base)}`)),\n switchMap(urls => fromEvent(document.body, \"click\")\n .pipe(\n filter(ev => !ev.metaKey && !ev.ctrlKey),\n switchMap(ev => {\n if (ev.target instanceof Element) {\n const el = ev.target.closest(\"a\")\n if (el && !el.target) {\n const url = new URL(el.href)\n\n /* Canonicalize URL */\n url.search = \"\"\n url.hash = \"\"\n\n /* Check if URL should be intercepted */\n if (\n url.pathname !== location.pathname &&\n urls.includes(url.toString())\n ) {\n ev.preventDefault()\n return of({\n url: new URL(el.href)\n })\n }\n }\n }\n return NEVER\n })\n )\n ),\n share()\n )\n\n /* Intercept history back and forward */\n const pop$ = fromEvent(window, \"popstate\")\n .pipe(\n filter(ev => ev.state !== null),\n map(ev => ({\n url: new URL(location.href),\n offset: ev.state\n })),\n share()\n )\n\n /* Emit location change */\n merge(push$, pop$)\n .pipe(\n distinctUntilChanged((a, b) => a.url.href === b.url.href),\n map(({ url }) => url)\n )\n .subscribe(location$)\n\n /* Fetch document via `XMLHTTPRequest` */\n const response$ = location$\n .pipe(\n distinctUntilKeyChanged(\"pathname\"),\n switchMap(url => request(url.href)\n .pipe(\n catchError(() => {\n setLocation(url)\n return NEVER\n })\n )\n ),\n share()\n )\n\n /* Set new location via `history.pushState` */\n push$\n .pipe(\n sample(response$)\n )\n .subscribe(({ url }) => {\n history.pushState({}, \"\", `${url}`)\n })\n\n /* Parse and emit fetched document */\n const dom = new DOMParser()\n response$\n .pipe(\n switchMap(res => res.text()),\n map(res => dom.parseFromString(res, \"text/html\"))\n )\n .subscribe(document$)\n\n /* Replace meta tags and components */\n document$\n .pipe(\n skip(1)\n )\n .subscribe(replacement => {\n for (const selector of [\n\n /* Meta tags */\n \"title\",\n \"link[rel=canonical]\",\n \"meta[name=author]\",\n \"meta[name=description]\",\n\n /* Components */\n \"[data-md-component=announce]\",\n \"[data-md-component=container]\",\n \"[data-md-component=header-topic]\",\n \"[data-md-component=outdated]\",\n \"[data-md-component=logo]\",\n \"[data-md-component=skip]\",\n ...feature(\"navigation.tabs.sticky\")\n ? [\"[data-md-component=tabs]\"]\n : []\n ]) {\n const source = getOptionalElement(selector)\n const target = getOptionalElement(selector, replacement)\n if (\n typeof source !== \"undefined\" &&\n typeof target !== \"undefined\"\n ) {\n source.replaceWith(target)\n }\n }\n })\n\n /* Re-evaluate scripts */\n document$\n .pipe(\n skip(1),\n map(() => getComponentElement(\"container\")),\n switchMap(el => getElements(\"script\", el)),\n concatMap(el => {\n const script = h(\"script\")\n if (el.src) {\n for (const name of el.getAttributeNames())\n script.setAttribute(name, el.getAttribute(name)!)\n el.replaceWith(script)\n\n /* Complete when script is loaded */\n return new Observable(observer => {\n script.onload = () => observer.complete()\n })\n\n /* Complete immediately */\n } else {\n script.textContent = el.textContent\n el.replaceWith(script)\n return EMPTY\n }\n })\n )\n .subscribe()\n\n /* Emit history state change */\n merge(push$, pop$)\n .pipe(\n sample(document$)\n )\n .subscribe(({ url, offset }) => {\n if (url.hash && !offset) {\n setLocationHash(url.hash)\n } else {\n window.scrollTo(0, offset?.y || 0)\n }\n })\n\n /* Debounce update of viewport offset */\n viewport$\n .pipe(\n skipUntil(push$),\n debounceTime(250),\n distinctUntilKeyChanged(\"offset\")\n )\n .subscribe(({ offset }) => {\n history.replaceState(offset, \"\")\n })\n\n /* Set viewport offset from history */\n merge(push$, pop$)\n .pipe(\n bufferCount(2, 1),\n filter(([a, b]) => a.url.pathname === b.url.pathname),\n map(([, state]) => state)\n )\n .subscribe(({ offset }) => {\n window.scrollTo(0, offset?.y || 0)\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport escapeHTML from \"escape-html\"\n\nimport { SearchIndexDocument } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search document\n */\nexport interface SearchDocument extends SearchIndexDocument {\n parent?: SearchIndexDocument /* Parent article */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search document mapping\n */\nexport type SearchDocumentMap = Map\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create a search document mapping\n *\n * @param docs - Search index documents\n *\n * @returns Search document map\n */\nexport function setupSearchDocumentMap(\n docs: SearchIndexDocument[]\n): SearchDocumentMap {\n const documents = new Map()\n const parents = new Set()\n for (const doc of docs) {\n const [path, hash] = doc.location.split(\"#\")\n\n /* Extract location, title and tags */\n const location = doc.location\n const title = doc.title\n const tags = doc.tags\n\n /* Escape and cleanup text */\n const text = escapeHTML(doc.text)\n .replace(/\\s+(?=[,.:;!?])/g, \"\")\n .replace(/\\s+/g, \" \")\n\n /* Handle section */\n if (hash) {\n const parent = documents.get(path)!\n\n /* Ignore first section, override article */\n if (!parents.has(parent)) {\n parent.title = doc.title\n parent.text = text\n\n /* Remember that we processed the article */\n parents.add(parent)\n\n /* Add subsequent section */\n } else {\n documents.set(location, {\n location,\n title,\n text,\n parent\n })\n }\n\n /* Add article */\n } else {\n documents.set(location, {\n location,\n title,\n text,\n ...tags && { tags }\n })\n }\n }\n return documents\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport escapeHTML from \"escape-html\"\n\nimport { SearchIndexConfig } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search highlight function\n *\n * @param value - Value\n *\n * @returns Highlighted value\n */\nexport type SearchHighlightFn = (value: string) => string\n\n/**\n * Search highlight factory function\n *\n * @param query - Query value\n *\n * @returns Search highlight function\n */\nexport type SearchHighlightFactoryFn = (query: string) => SearchHighlightFn\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create a search highlighter\n *\n * @param config - Search index configuration\n * @param escape - Whether to escape HTML\n *\n * @returns Search highlight factory function\n */\nexport function setupSearchHighlighter(\n config: SearchIndexConfig, escape: boolean\n): SearchHighlightFactoryFn {\n const separator = new RegExp(config.separator, \"img\")\n const highlight = (_: unknown, data: string, term: string) => {\n return `${data}${term}`\n }\n\n /* Return factory function */\n return (query: string) => {\n query = query\n .replace(/[\\s*+\\-:~^]+/g, \" \")\n .trim()\n\n /* Create search term match expression */\n const match = new RegExp(`(^|${config.separator})(${\n query\n .replace(/[|\\\\{}()[\\]^$+*?.-]/g, \"\\\\$&\")\n .replace(separator, \"|\")\n })`, \"img\")\n\n /* Highlight string value */\n return value => (\n escape\n ? escapeHTML(value)\n : value\n )\n .replace(match, highlight)\n .replace(/<\\/mark>(\\s+)]*>/img, \"$1\")\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search transformation function\n *\n * @param value - Query value\n *\n * @returns Transformed query value\n */\nexport type SearchTransformFn = (value: string) => string\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Default transformation function\n *\n * 1. Search for terms in quotation marks and prepend a `+` modifier to denote\n * that the resulting document must contain all terms, converting the query\n * to an `AND` query (as opposed to the default `OR` behavior). While users\n * may expect terms enclosed in quotation marks to map to span queries, i.e.\n * for which order is important, Lunr.js doesn't support them, so the best\n * we can do is to convert the terms to an `AND` query.\n *\n * 2. Replace control characters which are not located at the beginning of the\n * query or preceded by white space, or are not followed by a non-whitespace\n * character or are at the end of the query string. Furthermore, filter\n * unmatched quotation marks.\n *\n * 3. Trim excess whitespace from left and right.\n *\n * @param query - Query value\n *\n * @returns Transformed query value\n */\nexport function defaultTransform(query: string): string {\n return query\n .split(/\"([^\"]+)\"/g) /* => 1 */\n .map((terms, index) => index & 1\n ? terms.replace(/^\\b|^(?![^\\x00-\\x7F]|$)|\\s+/g, \" +\")\n : terms\n )\n .join(\"\")\n .replace(/\"|(?:^|\\s+)[*+\\-:^~]+(?=\\s+|$)/g, \"\") /* => 2 */\n .trim() /* => 3 */\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A RTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { SearchIndex, SearchResult } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search message type\n */\nexport const enum SearchMessageType {\n SETUP, /* Search index setup */\n READY, /* Search index ready */\n QUERY, /* Search query */\n RESULT /* Search results */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Message containing the data necessary to setup the search index\n */\nexport interface SearchSetupMessage {\n type: SearchMessageType.SETUP /* Message type */\n data: SearchIndex /* Message data */\n}\n\n/**\n * Message indicating the search index is ready\n */\nexport interface SearchReadyMessage {\n type: SearchMessageType.READY /* Message type */\n}\n\n/**\n * Message containing a search query\n */\nexport interface SearchQueryMessage {\n type: SearchMessageType.QUERY /* Message type */\n data: string /* Message data */\n}\n\n/**\n * Message containing results for a search query\n */\nexport interface SearchResultMessage {\n type: SearchMessageType.RESULT /* Message type */\n data: SearchResult /* Message data */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Message exchanged with the search worker\n */\nexport type SearchMessage =\n | SearchSetupMessage\n | SearchReadyMessage\n | SearchQueryMessage\n | SearchResultMessage\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Type guard for search setup messages\n *\n * @param message - Search worker message\n *\n * @returns Test result\n */\nexport function isSearchSetupMessage(\n message: SearchMessage\n): message is SearchSetupMessage {\n return message.type === SearchMessageType.SETUP\n}\n\n/**\n * Type guard for search ready messages\n *\n * @param message - Search worker message\n *\n * @returns Test result\n */\nexport function isSearchReadyMessage(\n message: SearchMessage\n): message is SearchReadyMessage {\n return message.type === SearchMessageType.READY\n}\n\n/**\n * Type guard for search query messages\n *\n * @param message - Search worker message\n *\n * @returns Test result\n */\nexport function isSearchQueryMessage(\n message: SearchMessage\n): message is SearchQueryMessage {\n return message.type === SearchMessageType.QUERY\n}\n\n/**\n * Type guard for search result messages\n *\n * @param message - Search worker message\n *\n * @returns Test result\n */\nexport function isSearchResultMessage(\n message: SearchMessage\n): message is SearchResultMessage {\n return message.type === SearchMessageType.RESULT\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A RTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n ObservableInput,\n Subject,\n from,\n map,\n share\n} from \"rxjs\"\n\nimport { configuration, feature, translation } from \"~/_\"\nimport { WorkerHandler, watchWorker } from \"~/browser\"\n\nimport { SearchIndex } from \"../../_\"\nimport {\n SearchOptions,\n SearchPipeline\n} from \"../../options\"\nimport {\n SearchMessage,\n SearchMessageType,\n SearchSetupMessage,\n isSearchResultMessage\n} from \"../message\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search worker\n */\nexport type SearchWorker = WorkerHandler\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up search index\n *\n * @param data - Search index\n *\n * @returns Search index\n */\nfunction setupSearchIndex({ config, docs }: SearchIndex): SearchIndex {\n\n /* Override default language with value from translation */\n if (config.lang.length === 1 && config.lang[0] === \"en\")\n config.lang = [\n translation(\"search.config.lang\")\n ]\n\n /* Override default separator with value from translation */\n if (config.separator === \"[\\\\s\\\\-]+\")\n config.separator = translation(\"search.config.separator\")\n\n /* Set pipeline from translation */\n const pipeline = translation(\"search.config.pipeline\")\n .split(/\\s*,\\s*/)\n .filter(Boolean) as SearchPipeline\n\n /* Determine search options */\n const options: SearchOptions = {\n pipeline,\n suggestions: feature(\"search.suggest\")\n }\n\n /* Return search index after defaulting */\n return { config, docs, options }\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up search worker\n *\n * This function creates a web worker to set up and query the search index,\n * which is done using Lunr.js. The index must be passed as an observable to\n * enable hacks like _localsearch_ via search index embedding as JSON.\n *\n * @param url - Worker URL\n * @param index - Search index observable input\n *\n * @returns Search worker\n */\nexport function setupSearchWorker(\n url: string, index: ObservableInput\n): SearchWorker {\n const config = configuration()\n const worker = new Worker(url)\n\n /* Create communication channels and resolve relative links */\n const tx$ = new Subject()\n const rx$ = watchWorker(worker, { tx$ })\n .pipe(\n map(message => {\n if (isSearchResultMessage(message)) {\n for (const result of message.data.items)\n for (const document of result)\n document.location = `${new URL(document.location, config.base)}`\n }\n return message\n }),\n share()\n )\n\n /* Set up search index */\n from(index)\n .pipe(\n map(data => ({\n type: SearchMessageType.SETUP,\n data: setupSearchIndex(data)\n } as SearchSetupMessage))\n )\n .subscribe(tx$.next.bind(tx$))\n\n /* Return search worker */\n return { tx$, rx$ }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n EMPTY,\n Subject,\n catchError,\n combineLatest,\n filter,\n fromEvent,\n map,\n of,\n switchMap\n} from \"rxjs\"\n\nimport { configuration } from \"~/_\"\nimport {\n getElement,\n getLocation,\n requestJSON,\n setLocation\n} from \"~/browser\"\nimport { getComponentElements } from \"~/components\"\nimport {\n Version,\n renderVersionSelector\n} from \"~/templates\"\n\nimport { fetchSitemap } from \"../sitemap\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Setup options\n */\ninterface SetupOptions {\n document$: Subject /* Document subject */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Set up version selector\n *\n * @param options - Options\n */\nexport function setupVersionSelector(\n { document$ }: SetupOptions\n): void {\n const config = configuration()\n const versions$ = requestJSON(\n new URL(\"../versions.json\", config.base)\n )\n .pipe(\n catchError(() => EMPTY) // @todo refactor instant loading\n )\n\n /* Determine current version */\n const current$ = versions$\n .pipe(\n map(versions => {\n const [, current] = config.base.match(/([^/]+)\\/?$/)!\n return versions.find(({ version, aliases }) => (\n version === current || aliases.includes(current)\n )) || versions[0]\n })\n )\n\n /* Intercept inter-version navigation */\n combineLatest([versions$, current$])\n .pipe(\n map(([versions, current]) => new Map(versions\n .filter(version => version !== current)\n .map(version => [\n `${new URL(`../${version.version}/`, config.base)}`,\n version\n ])\n )),\n switchMap(urls => fromEvent(document.body, \"click\")\n .pipe(\n filter(ev => !ev.metaKey && !ev.ctrlKey),\n switchMap(ev => {\n if (ev.target instanceof Element) {\n const el = ev.target.closest(\"a\")\n if (el && !el.target && urls.has(el.href)) {\n ev.preventDefault()\n return of(el.href)\n }\n }\n return EMPTY\n }),\n switchMap(url => {\n const { version } = urls.get(url)!\n return fetchSitemap(new URL(url))\n .pipe(\n map(sitemap => {\n const location = getLocation()\n const path = location.href.replace(config.base, \"\")\n return sitemap.includes(path)\n ? new URL(`../${version}/${path}`, config.base)\n : new URL(url)\n })\n )\n })\n )\n )\n )\n .subscribe(url => setLocation(url))\n\n /* Render version selector and warning */\n combineLatest([versions$, current$])\n .subscribe(([versions, current]) => {\n const topic = getElement(\".md-header__topic\")\n topic.appendChild(renderVersionSelector(versions, current))\n })\n\n /* Integrate outdated version banner with instant loading */\n document$.pipe(switchMap(() => current$))\n .subscribe(current => {\n\n /* Check if version state was already determined */\n let outdated = __md_get(\"__outdated\", sessionStorage)\n if (outdated === null) {\n const latest = config.version?.default || \"latest\"\n outdated = !current.aliases.includes(latest)\n\n /* Persist version state in session storage */\n __md_set(\"__outdated\", outdated, sessionStorage)\n }\n\n /* Unhide outdated version banner */\n if (outdated)\n for (const warning of getComponentElements(\"outdated\"))\n warning.hidden = false\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n combineLatest,\n delay,\n distinctUntilChanged,\n distinctUntilKeyChanged,\n filter,\n finalize,\n fromEvent,\n map,\n merge,\n share,\n shareReplay,\n startWith,\n take,\n takeLast,\n takeUntil,\n tap\n} from \"rxjs\"\n\nimport { translation } from \"~/_\"\nimport {\n getLocation,\n setToggle,\n watchElementFocus,\n watchToggle\n} from \"~/browser\"\nimport {\n SearchMessageType,\n SearchQueryMessage,\n SearchWorker,\n defaultTransform,\n isSearchReadyMessage\n} from \"~/integrations\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search query\n */\nexport interface SearchQuery {\n value: string /* Query value */\n focus: boolean /* Query focus */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch search query\n *\n * Note that the focus event which triggers re-reading the current query value\n * is delayed by `1ms` so the input's empty state is allowed to propagate.\n *\n * @param el - Search query element\n * @param worker - Search worker\n *\n * @returns Search query observable\n */\nexport function watchSearchQuery(\n el: HTMLInputElement, { rx$ }: SearchWorker\n): Observable {\n const fn = __search?.transform || defaultTransform\n\n /* Immediately show search dialog */\n const { searchParams } = getLocation()\n if (searchParams.has(\"q\"))\n setToggle(\"search\", true)\n\n /* Intercept query parameter (deep link) */\n const param$ = rx$\n .pipe(\n filter(isSearchReadyMessage),\n take(1),\n map(() => searchParams.get(\"q\") || \"\")\n )\n\n /* Remove query parameter when search is closed */\n watchToggle(\"search\")\n .pipe(\n filter(active => !active),\n take(1)\n )\n .subscribe(() => {\n const url = new URL(location.href)\n url.searchParams.delete(\"q\")\n history.replaceState({}, \"\", `${url}`)\n })\n\n /* Set query from parameter */\n param$.subscribe(value => { // TODO: not ideal - find a better way\n if (value) {\n el.value = value\n el.focus()\n }\n })\n\n /* Intercept focus and input events */\n const focus$ = watchElementFocus(el)\n const value$ = merge(\n fromEvent(el, \"keyup\"),\n fromEvent(el, \"focus\").pipe(delay(1)),\n param$\n )\n .pipe(\n map(() => fn(el.value)),\n startWith(\"\"),\n distinctUntilChanged(),\n )\n\n /* Combine into single observable */\n return combineLatest([value$, focus$])\n .pipe(\n map(([value, focus]) => ({ value, focus })),\n shareReplay(1)\n )\n}\n\n/**\n * Mount search query\n *\n * @param el - Search query element\n * @param worker - Search worker\n *\n * @returns Search query component observable\n */\nexport function mountSearchQuery(\n el: HTMLInputElement, { tx$, rx$ }: SearchWorker\n): Observable> {\n const push$ = new Subject()\n const done$ = push$.pipe(takeLast(1))\n\n /* Handle value changes */\n push$\n .pipe(\n distinctUntilKeyChanged(\"value\"),\n map(({ value }): SearchQueryMessage => ({\n type: SearchMessageType.QUERY,\n data: value\n }))\n )\n .subscribe(tx$.next.bind(tx$))\n\n /* Handle focus changes */\n push$\n .pipe(\n distinctUntilKeyChanged(\"focus\")\n )\n .subscribe(({ focus }) => {\n if (focus) {\n setToggle(\"search\", focus)\n el.placeholder = \"\"\n } else {\n el.placeholder = translation(\"search.placeholder\")\n }\n })\n\n /* Handle reset */\n fromEvent(el.form!, \"reset\")\n .pipe(\n takeUntil(done$)\n )\n .subscribe(() => el.focus())\n\n /* Create and return component */\n return watchSearchQuery(el, { tx$, rx$ })\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state })),\n share()\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n bufferCount,\n filter,\n finalize,\n map,\n merge,\n of,\n skipUntil,\n switchMap,\n take,\n tap,\n withLatestFrom,\n zipWith\n} from \"rxjs\"\n\nimport { translation } from \"~/_\"\nimport {\n getElement,\n watchElementBoundary\n} from \"~/browser\"\nimport {\n SearchResult,\n SearchWorker,\n isSearchReadyMessage,\n isSearchResultMessage\n} from \"~/integrations\"\nimport { renderSearchResultItem } from \"~/templates\"\nimport { round } from \"~/utilities\"\n\nimport { Component } from \"../../_\"\nimport { SearchQuery } from \"../query\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n query$: Observable /* Search query observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search result list\n *\n * This function performs a lazy rendering of the search results, depending on\n * the vertical offset of the search result container.\n *\n * @param el - Search result list element\n * @param worker - Search worker\n * @param options - Options\n *\n * @returns Search result list component observable\n */\nexport function mountSearchResult(\n el: HTMLElement, { rx$ }: SearchWorker, { query$ }: MountOptions\n): Observable> {\n const push$ = new Subject()\n const boundary$ = watchElementBoundary(el.parentElement!)\n .pipe(\n filter(Boolean)\n )\n\n /* Retrieve nested components */\n const meta = getElement(\":scope > :first-child\", el)\n const list = getElement(\":scope > :last-child\", el)\n\n /* Wait until search is ready */\n const ready$ = rx$\n .pipe(\n filter(isSearchReadyMessage),\n take(1)\n )\n\n /* Update search result metadata */\n push$\n .pipe(\n withLatestFrom(query$),\n skipUntil(ready$)\n )\n .subscribe(([{ items }, { value }]) => {\n if (value) {\n switch (items.length) {\n\n /* No results */\n case 0:\n meta.textContent = translation(\"search.result.none\")\n break\n\n /* One result */\n case 1:\n meta.textContent = translation(\"search.result.one\")\n break\n\n /* Multiple result */\n default:\n meta.textContent = translation(\n \"search.result.other\",\n round(items.length)\n )\n }\n } else {\n meta.textContent = translation(\"search.result.placeholder\")\n }\n })\n\n /* Update search result list */\n push$\n .pipe(\n tap(() => list.innerHTML = \"\"),\n switchMap(({ items }) => merge(\n of(...items.slice(0, 10)),\n of(...items.slice(10))\n .pipe(\n bufferCount(4),\n zipWith(boundary$),\n switchMap(([chunk]) => chunk)\n )\n ))\n )\n .subscribe(result => list.appendChild(\n renderSearchResultItem(result)\n ))\n\n /* Filter search result message */\n const result$ = rx$\n .pipe(\n filter(isSearchResultMessage),\n map(({ data }) => data)\n )\n\n /* Create and return component */\n return result$\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n finalize,\n fromEvent,\n map,\n tap\n} from \"rxjs\"\n\nimport { getLocation } from \"~/browser\"\n\nimport { Component } from \"../../_\"\nimport { SearchQuery } from \"../query\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search sharing\n */\nexport interface SearchShare {\n url: URL /* Deep link for sharing */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n query$: Observable /* Search query observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n query$: Observable /* Search query observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search sharing\n *\n * @param _el - Search sharing element\n * @param options - Options\n *\n * @returns Search sharing observable\n */\nexport function watchSearchShare(\n _el: HTMLElement, { query$ }: WatchOptions\n): Observable {\n return query$\n .pipe(\n map(({ value }) => {\n const url = getLocation()\n url.hash = \"\"\n url.searchParams.delete(\"h\")\n url.searchParams.set(\"q\", value)\n return { url }\n })\n )\n}\n\n/**\n * Mount search sharing\n *\n * @param el - Search sharing element\n * @param options - Options\n *\n * @returns Search sharing component observable\n */\nexport function mountSearchShare(\n el: HTMLAnchorElement, options: MountOptions\n): Observable> {\n const push$ = new Subject()\n push$.subscribe(({ url }) => {\n el.setAttribute(\"data-clipboard-text\", el.href)\n el.href = `${url}`\n })\n\n /* Prevent following of link */\n fromEvent(el, \"click\")\n .subscribe(ev => ev.preventDefault())\n\n /* Create and return component */\n return watchSearchShare(el, options)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n asyncScheduler,\n combineLatestWith,\n distinctUntilChanged,\n filter,\n finalize,\n fromEvent,\n map,\n merge,\n observeOn,\n tap\n} from \"rxjs\"\n\nimport { Keyboard } from \"~/browser\"\nimport {\n SearchResult,\n SearchWorker,\n isSearchResultMessage\n} from \"~/integrations\"\n\nimport { Component, getComponentElement } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search suggestions\n */\nexport interface SearchSuggest {}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n keyboard$: Observable /* Keyboard observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search suggestions\n *\n * This function will perform a lazy rendering of the search results, depending\n * on the vertical offset of the search result container.\n *\n * @param el - Search result list element\n * @param worker - Search worker\n * @param options - Options\n *\n * @returns Search result list component observable\n */\nexport function mountSearchSuggest(\n el: HTMLElement, { rx$ }: SearchWorker, { keyboard$ }: MountOptions\n): Observable> {\n const push$ = new Subject()\n\n /* Retrieve query component and track all changes */\n const query = getComponentElement(\"search-query\")\n const query$ = merge(\n fromEvent(query, \"keydown\"),\n fromEvent(query, \"focus\")\n )\n .pipe(\n observeOn(asyncScheduler),\n map(() => query.value),\n distinctUntilChanged(),\n )\n\n /* Update search suggestions */\n push$\n .pipe(\n combineLatestWith(query$),\n map(([{ suggestions }, value]) => {\n const words = value.split(/([\\s-]+)/)\n if (suggestions?.length && words[words.length - 1]) {\n const last = suggestions[suggestions.length - 1]\n if (last.startsWith(words[words.length - 1]))\n words[words.length - 1] = last\n } else {\n words.length = 0\n }\n return words\n })\n )\n .subscribe(words => el.innerHTML = words\n .join(\"\")\n .replace(/\\s/g, \" \")\n )\n\n /* Set up search keyboard handlers */\n keyboard$\n .pipe(\n filter(({ mode }) => mode === \"search\")\n )\n .subscribe(key => {\n switch (key.type) {\n\n /* Right arrow: accept current suggestion */\n case \"ArrowRight\":\n if (\n el.innerText.length &&\n query.selectionStart === query.value.length\n )\n query.value = el.innerText\n break\n }\n })\n\n /* Filter search result message */\n const result$ = rx$\n .pipe(\n filter(isSearchResultMessage),\n map(({ data }) => data)\n )\n\n /* Create and return component */\n return result$\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(() => ({ ref: el }))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n NEVER,\n Observable,\n ObservableInput,\n filter,\n merge,\n mergeWith,\n sample,\n take\n} from \"rxjs\"\n\nimport { configuration } from \"~/_\"\nimport {\n Keyboard,\n getActiveElement,\n getElements,\n setToggle\n} from \"~/browser\"\nimport {\n SearchIndex,\n SearchResult,\n isSearchQueryMessage,\n isSearchReadyMessage,\n setupSearchWorker\n} from \"~/integrations\"\n\nimport {\n Component,\n getComponentElement,\n getComponentElements\n} from \"../../_\"\nimport {\n SearchQuery,\n mountSearchQuery\n} from \"../query\"\nimport { mountSearchResult } from \"../result\"\nimport {\n SearchShare,\n mountSearchShare\n} from \"../share\"\nimport {\n SearchSuggest,\n mountSearchSuggest\n} from \"../suggest\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search\n */\nexport type Search =\n | SearchQuery\n | SearchResult\n | SearchShare\n | SearchSuggest\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n index$: ObservableInput /* Search index observable */\n keyboard$: Observable /* Keyboard observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search\n *\n * This function sets up the search functionality, including the underlying\n * web worker and all keyboard bindings.\n *\n * @param el - Search element\n * @param options - Options\n *\n * @returns Search component observable\n */\nexport function mountSearch(\n el: HTMLElement, { index$, keyboard$ }: MountOptions\n): Observable> {\n const config = configuration()\n try {\n const url = __search?.worker || config.search\n const worker = setupSearchWorker(url, index$)\n\n /* Retrieve query and result components */\n const query = getComponentElement(\"search-query\", el)\n const result = getComponentElement(\"search-result\", el)\n\n /* Re-emit query when search is ready */\n const { tx$, rx$ } = worker\n tx$\n .pipe(\n filter(isSearchQueryMessage),\n sample(rx$.pipe(filter(isSearchReadyMessage))),\n take(1)\n )\n .subscribe(tx$.next.bind(tx$))\n\n /* Set up search keyboard handlers */\n keyboard$\n .pipe(\n filter(({ mode }) => mode === \"search\")\n )\n .subscribe(key => {\n const active = getActiveElement()\n switch (key.type) {\n\n /* Enter: go to first (best) result */\n case \"Enter\":\n if (active === query) {\n const anchors = new Map()\n for (const anchor of getElements(\n \":first-child [href]\", result\n )) {\n const article = anchor.firstElementChild!\n anchors.set(anchor, parseFloat(\n article.getAttribute(\"data-md-score\")!\n ))\n }\n\n /* Go to result with highest score, if any */\n if (anchors.size) {\n const [[best]] = [...anchors].sort(([, a], [, b]) => b - a)\n best.click()\n }\n\n /* Otherwise omit form submission */\n key.claim()\n }\n break\n\n /* Escape or Tab: close search */\n case \"Escape\":\n case \"Tab\":\n setToggle(\"search\", false)\n query.blur()\n break\n\n /* Vertical arrows: select previous or next search result */\n case \"ArrowUp\":\n case \"ArrowDown\":\n if (typeof active === \"undefined\") {\n query.focus()\n } else {\n const els = [query, ...getElements(\n \":not(details) > [href], summary, details[open] [href]\",\n result\n )]\n const i = Math.max(0, (\n Math.max(0, els.indexOf(active)) + els.length + (\n key.type === \"ArrowUp\" ? -1 : +1\n )\n ) % els.length)\n els[i].focus()\n }\n\n /* Prevent scrolling of page */\n key.claim()\n break\n\n /* All other keys: hand to search query */\n default:\n if (query !== getActiveElement())\n query.focus()\n }\n })\n\n /* Set up global keyboard handlers */\n keyboard$\n .pipe(\n filter(({ mode }) => mode === \"global\"),\n )\n .subscribe(key => {\n switch (key.type) {\n\n /* Open search and select query */\n case \"f\":\n case \"s\":\n case \"/\":\n query.focus()\n query.select()\n\n /* Prevent scrolling of page */\n key.claim()\n break\n }\n })\n\n /* Create and return component */\n const query$ = mountSearchQuery(query, worker)\n const result$ = mountSearchResult(result, worker, { query$ })\n return merge(query$, result$)\n .pipe(\n mergeWith(\n\n /* Search sharing */\n ...getComponentElements(\"search-share\", el)\n .map(child => mountSearchShare(child, { query$ })),\n\n /* Search suggestions */\n ...getComponentElements(\"search-suggest\", el)\n .map(child => mountSearchSuggest(child, worker, { keyboard$ }))\n )\n )\n\n /* Gracefully handle broken search */\n } catch (err) {\n el.hidden = true\n return NEVER\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n ObservableInput,\n combineLatest,\n filter,\n map,\n startWith\n} from \"rxjs\"\n\nimport { getLocation } from \"~/browser\"\nimport {\n SearchIndex,\n setupSearchHighlighter\n} from \"~/integrations\"\nimport { h } from \"~/utilities\"\n\nimport { Component } from \"../../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search highlighting\n */\nexport interface SearchHighlight {\n nodes: Map /* Map of replacements */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount options\n */\ninterface MountOptions {\n index$: ObservableInput /* Search index observable */\n location$: Observable /* Location observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Mount search highlighting\n *\n * @param el - Content element\n * @param options - Options\n *\n * @returns Search highlighting component observable\n */\nexport function mountSearchHiglight(\n el: HTMLElement, { index$, location$ }: MountOptions\n): Observable> {\n return combineLatest([\n index$,\n location$\n .pipe(\n startWith(getLocation()),\n filter(url => !!url.searchParams.get(\"h\"))\n )\n ])\n .pipe(\n map(([index, url]) => setupSearchHighlighter(index.config, true)(\n url.searchParams.get(\"h\")!\n )),\n map(fn => {\n const nodes = new Map()\n\n /* Traverse text nodes and collect matches */\n const it = document.createNodeIterator(el, NodeFilter.SHOW_TEXT)\n for (let node = it.nextNode(); node; node = it.nextNode()) {\n if (node.parentElement?.offsetHeight) {\n const original = node.textContent!\n const replaced = fn(original)\n if (replaced.length > original.length)\n nodes.set(node as ChildNode, replaced)\n }\n }\n\n /* Replace original nodes with matches */\n for (const [node, text] of nodes) {\n const { childNodes } = h(\"span\", null, text)\n node.replaceWith(...Array.from(childNodes))\n }\n\n /* Return component */\n return { ref: el, nodes }\n })\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n animationFrameScheduler,\n auditTime,\n combineLatest,\n defer,\n distinctUntilChanged,\n finalize,\n map,\n tap,\n withLatestFrom\n} from \"rxjs\"\n\nimport {\n Viewport,\n getElement,\n getElementOffset\n} from \"~/browser\"\n\nimport { Component } from \"../_\"\nimport { Header } from \"../header\"\nimport { Main } from \"../main\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Sidebar\n */\nexport interface Sidebar {\n height: number /* Sidebar height */\n locked: boolean /* Sidebar is locked */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n viewport$: Observable /* Viewport observable */\n main$: Observable
    /* Main area observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n main$: Observable
    /* Main area observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch sidebar\n *\n * This function returns an observable that computes the visual parameters of\n * the sidebar which depends on the vertical viewport offset, as well as the\n * height of the main area. When the page is scrolled beyond the header, the\n * sidebar is locked and fills the remaining space.\n *\n * @param el - Sidebar element\n * @param options - Options\n *\n * @returns Sidebar observable\n */\nexport function watchSidebar(\n el: HTMLElement, { viewport$, main$ }: WatchOptions\n): Observable {\n const parent = el.parentElement!\n const adjust =\n parent.offsetTop -\n parent.parentElement!.offsetTop\n\n /* Compute the sidebar's available height and if it should be locked */\n return combineLatest([main$, viewport$])\n .pipe(\n map(([{ offset, height }, { offset: { y } }]) => {\n height = height\n + Math.min(adjust, Math.max(0, y - offset))\n - adjust\n return {\n height,\n locked: y >= offset + adjust\n }\n }),\n distinctUntilChanged((a, b) => (\n a.height === b.height &&\n a.locked === b.locked\n ))\n )\n}\n\n/**\n * Mount sidebar\n *\n * This function doesn't set the height of the actual sidebar, but of its first\n * child \u2013 the `.md-sidebar__scrollwrap` element in order to mitigiate jittery\n * sidebars when the footer is scrolled into view. At some point we switched\n * from `absolute` / `fixed` positioning to `sticky` positioning, significantly\n * reducing jitter in some browsers (respectively Firefox and Safari) when\n * scrolling from the top. However, top-aligned sticky positioning means that\n * the sidebar snaps to the bottom when the end of the container is reached.\n * This is what leads to the mentioned jitter, as the sidebar's height may be\n * updated too slowly.\n *\n * This behaviour can be mitigiated by setting the height of the sidebar to `0`\n * while preserving the padding, and the height on its first element.\n *\n * @param el - Sidebar element\n * @param options - Options\n *\n * @returns Sidebar component observable\n */\nexport function mountSidebar(\n el: HTMLElement, { header$, ...options }: MountOptions\n): Observable> {\n const inner = getElement(\".md-sidebar__scrollwrap\", el)\n const { y } = getElementOffset(inner)\n return defer(() => {\n const push$ = new Subject()\n push$\n .pipe(\n auditTime(0, animationFrameScheduler),\n withLatestFrom(header$)\n )\n .subscribe({\n\n /* Handle emission */\n next([{ height }, { height: offset }]) {\n inner.style.height = `${height - 2 * y}px`\n el.style.top = `${offset}px`\n },\n\n /* Handle complete */\n complete() {\n inner.style.height = \"\"\n el.style.top = \"\"\n }\n })\n\n /* Create and return component */\n return watchSidebar(el, options)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { Repo, User } from \"github-types\"\nimport {\n EMPTY,\n Observable,\n catchError,\n defaultIfEmpty,\n map,\n zip\n} from \"rxjs\"\n\nimport { requestJSON } from \"~/browser\"\n\nimport { SourceFacts } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * GitHub release (partial)\n */\ninterface Release {\n tag_name: string /* Tag name */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch GitHub repository facts\n *\n * @param user - GitHub user or organization\n * @param repo - GitHub repository\n *\n * @returns Repository facts observable\n */\nexport function fetchSourceFactsFromGitHub(\n user: string, repo?: string\n): Observable {\n if (typeof repo !== \"undefined\") {\n const url = `https://api.github.com/repos/${user}/${repo}`\n return zip(\n\n /* Fetch version */\n requestJSON(`${url}/releases/latest`)\n .pipe(\n catchError(() => EMPTY), // @todo refactor instant loading\n map(release => ({\n version: release.tag_name\n })),\n defaultIfEmpty({})\n ),\n\n /* Fetch stars and forks */\n requestJSON(url)\n .pipe(\n catchError(() => EMPTY), // @todo refactor instant loading\n map(info => ({\n stars: info.stargazers_count,\n forks: info.forks_count\n })),\n defaultIfEmpty({})\n )\n )\n .pipe(\n map(([release, info]) => ({ ...release, ...info }))\n )\n\n /* User or organization */\n } else {\n const url = `https://api.github.com/users/${user}`\n return requestJSON(url)\n .pipe(\n map(info => ({\n repositories: info.public_repos\n })),\n defaultIfEmpty({})\n )\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { ProjectSchema } from \"gitlab\"\nimport {\n EMPTY,\n Observable,\n catchError,\n defaultIfEmpty,\n map\n} from \"rxjs\"\n\nimport { requestJSON } from \"~/browser\"\n\nimport { SourceFacts } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch GitLab repository facts\n *\n * @param base - GitLab base\n * @param project - GitLab project\n *\n * @returns Repository facts observable\n */\nexport function fetchSourceFactsFromGitLab(\n base: string, project: string\n): Observable {\n const url = `https://${base}/api/v4/projects/${encodeURIComponent(project)}`\n return requestJSON(url)\n .pipe(\n catchError(() => EMPTY), // @todo refactor instant loading\n map(({ star_count, forks_count }) => ({\n stars: star_count,\n forks: forks_count\n })),\n defaultIfEmpty({})\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport { EMPTY, Observable } from \"rxjs\"\n\nimport { fetchSourceFactsFromGitHub } from \"../github\"\nimport { fetchSourceFactsFromGitLab } from \"../gitlab\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Repository facts for repositories\n */\nexport interface RepositoryFacts {\n stars?: number /* Number of stars */\n forks?: number /* Number of forks */\n version?: string /* Latest version */\n}\n\n/**\n * Repository facts for organizations\n */\nexport interface OrganizationFacts {\n repositories?: number /* Number of repositories */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Repository facts\n */\nexport type SourceFacts =\n | RepositoryFacts\n | OrganizationFacts\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch repository facts\n *\n * @param url - Repository URL\n *\n * @returns Repository facts observable\n */\nexport function fetchSourceFacts(\n url: string\n): Observable {\n const [type] = url.match(/(git(?:hub|lab))/i) || []\n switch (type.toLowerCase()) {\n\n /* GitHub repository */\n case \"github\":\n const [, user, repo] = url.match(/^.+github\\.com\\/([^/]+)\\/?([^/]+)?/i)!\n return fetchSourceFactsFromGitHub(user, repo)\n\n /* GitLab repository */\n case \"gitlab\":\n const [, base, slug] = url.match(/^.+?([^/]*gitlab[^/]+)\\/(.+?)\\/?$/i)!\n return fetchSourceFactsFromGitLab(base, slug)\n\n /* Everything else */\n default:\n return EMPTY\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n EMPTY,\n Observable,\n Subject,\n catchError,\n defer,\n filter,\n finalize,\n map,\n of,\n shareReplay,\n tap\n} from \"rxjs\"\n\nimport { getElement } from \"~/browser\"\nimport { renderSourceFacts } from \"~/templates\"\n\nimport { Component } from \"../../_\"\nimport {\n SourceFacts,\n fetchSourceFacts\n} from \"../facts\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Repository information\n */\nexport interface Source {\n facts: SourceFacts /* Repository facts */\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Repository information observable\n */\nlet fetch$: Observable\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch repository information\n *\n * This function tries to read the repository facts from session storage, and\n * if unsuccessful, fetches them from the underlying provider.\n *\n * @param el - Repository information element\n *\n * @returns Repository information observable\n */\nexport function watchSource(\n el: HTMLAnchorElement\n): Observable {\n return fetch$ ||= defer(() => {\n const cached = __md_get(\"__source\", sessionStorage)\n if (cached)\n return of(cached)\n else\n return fetchSourceFacts(el.href)\n .pipe(\n tap(facts => __md_set(\"__source\", facts, sessionStorage))\n )\n })\n .pipe(\n catchError(() => EMPTY),\n filter(facts => Object.keys(facts).length > 0),\n map(facts => ({ facts })),\n shareReplay(1)\n )\n}\n\n/**\n * Mount repository information\n *\n * @param el - Repository information element\n *\n * @returns Repository information component observable\n */\nexport function mountSource(\n el: HTMLAnchorElement\n): Observable> {\n const inner = getElement(\":scope > :last-child\", el)\n return defer(() => {\n const push$ = new Subject()\n push$.subscribe(({ facts }) => {\n inner.appendChild(renderSourceFacts(facts))\n inner.classList.add(\"md-source__repository--active\")\n })\n\n /* Create and return component */\n return watchSource(el)\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n defer,\n distinctUntilKeyChanged,\n finalize,\n map,\n of,\n switchMap,\n tap\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n Viewport,\n watchElementSize,\n watchViewportAt\n} from \"~/browser\"\n\nimport { Component } from \"../_\"\nimport { Header } from \"../header\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Navigation tabs\n */\nexport interface Tabs {\n hidden: boolean /* Navigation tabs are hidden */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch navigation tabs\n *\n * @param el - Navigation tabs element\n * @param options - Options\n *\n * @returns Navigation tabs observable\n */\nexport function watchTabs(\n el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable {\n return watchElementSize(document.body)\n .pipe(\n switchMap(() => watchViewportAt(el, { header$, viewport$ })),\n map(({ offset: { y } }) => {\n return {\n hidden: y >= 10\n }\n }),\n distinctUntilKeyChanged(\"hidden\")\n )\n}\n\n/**\n * Mount navigation tabs\n *\n * This function hides the navigation tabs when scrolling past the threshold\n * and makes them reappear in a nice CSS animation when scrolling back up.\n *\n * @param el - Navigation tabs element\n * @param options - Options\n *\n * @returns Navigation tabs component observable\n */\nexport function mountTabs(\n el: HTMLElement, options: MountOptions\n): Observable> {\n return defer(() => {\n const push$ = new Subject()\n push$.subscribe({\n\n /* Handle emission */\n next({ hidden }) {\n el.hidden = hidden\n },\n\n /* Handle complete */\n complete() {\n el.hidden = false\n }\n })\n\n /* Create and return component */\n return (\n feature(\"navigation.tabs.sticky\")\n ? of({ hidden: false })\n : watchTabs(el, options)\n )\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n bufferCount,\n combineLatestWith,\n debounceTime,\n defer,\n distinctUntilChanged,\n distinctUntilKeyChanged,\n finalize,\n map,\n of,\n repeat,\n scan,\n share,\n skip,\n startWith,\n switchMap,\n takeLast,\n takeUntil,\n tap,\n withLatestFrom\n} from \"rxjs\"\n\nimport { feature } from \"~/_\"\nimport {\n Viewport,\n getElement,\n getElements,\n getLocation,\n getOptionalElement,\n watchElementSize\n} from \"~/browser\"\n\nimport {\n Component,\n getComponentElement\n} from \"../_\"\nimport { Header } from \"../header\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Table of contents\n */\nexport interface TableOfContents {\n prev: HTMLAnchorElement[][] /* Anchors (previous) */\n next: HTMLAnchorElement[][] /* Anchors (next) */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n target$: Observable /* Location target observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch table of contents\n *\n * This is effectively a scroll spy implementation which will account for the\n * fixed header and automatically re-calculate anchor offsets when the viewport\n * is resized. The returned observable will only emit if the table of contents\n * needs to be repainted.\n *\n * This implementation tracks an anchor element's entire path starting from its\n * level up to the top-most anchor element, e.g. `[h3, h2, h1]`. Although the\n * Material theme currently doesn't make use of this information, it enables\n * the styling of the entire hierarchy through customization.\n *\n * Note that the current anchor is the last item of the `prev` anchor list.\n *\n * @param el - Table of contents element\n * @param options - Options\n *\n * @returns Table of contents observable\n */\nexport function watchTableOfContents(\n el: HTMLElement, { viewport$, header$ }: WatchOptions\n): Observable {\n const table = new Map()\n\n /* Compute anchor-to-target mapping */\n const anchors = getElements(\"[href^=\\\\#]\", el)\n for (const anchor of anchors) {\n const id = decodeURIComponent(anchor.hash.substring(1))\n const target = getOptionalElement(`[id=\"${id}\"]`)\n if (typeof target !== \"undefined\")\n table.set(anchor, target)\n }\n\n /* Compute necessary adjustment for header */\n const adjust$ = header$\n .pipe(\n distinctUntilKeyChanged(\"height\"),\n map(({ height }) => {\n const main = getComponentElement(\"main\")\n const grid = getElement(\":scope > :first-child\", main)\n return height + 0.8 * (\n grid.offsetTop -\n main.offsetTop\n )\n }),\n share()\n )\n\n /* Compute partition of previous and next anchors */\n const partition$ = watchElementSize(document.body)\n .pipe(\n distinctUntilKeyChanged(\"height\"),\n\n /* Build index to map anchor paths to vertical offsets */\n switchMap(body => defer(() => {\n let path: HTMLAnchorElement[] = []\n return of([...table].reduce((index, [anchor, target]) => {\n while (path.length) {\n const last = table.get(path[path.length - 1])!\n if (last.tagName >= target.tagName) {\n path.pop()\n } else {\n break\n }\n }\n\n /* If the current anchor is hidden, continue with its parent */\n let offset = target.offsetTop\n while (!offset && target.parentElement) {\n target = target.parentElement\n offset = target.offsetTop\n }\n\n /* Map reversed anchor path to vertical offset */\n return index.set(\n [...path = [...path, anchor]].reverse(),\n offset\n )\n }, new Map()))\n })\n .pipe(\n\n /* Sort index by vertical offset (see https://bit.ly/30z6QSO) */\n map(index => new Map([...index].sort(([, a], [, b]) => a - b))),\n combineLatestWith(adjust$),\n\n /* Re-compute partition when viewport offset changes */\n switchMap(([index, adjust]) => viewport$\n .pipe(\n scan(([prev, next], { offset: { y }, size }) => {\n const last = y + size.height >= Math.floor(body.height)\n\n /* Look forward */\n while (next.length) {\n const [, offset] = next[0]\n if (offset - adjust < y || last) {\n prev = [...prev, next.shift()!]\n } else {\n break\n }\n }\n\n /* Look backward */\n while (prev.length) {\n const [, offset] = prev[prev.length - 1]\n if (offset - adjust >= y && !last) {\n next = [prev.pop()!, ...next]\n } else {\n break\n }\n }\n\n /* Return partition */\n return [prev, next]\n }, [[], [...index]]),\n distinctUntilChanged((a, b) => (\n a[0] === b[0] &&\n a[1] === b[1]\n ))\n )\n )\n )\n )\n )\n\n /* Compute and return anchor list migrations */\n return partition$\n .pipe(\n map(([prev, next]) => ({\n prev: prev.map(([path]) => path),\n next: next.map(([path]) => path)\n })),\n\n /* Extract anchor list migrations */\n startWith({ prev: [], next: [] }),\n bufferCount(2, 1),\n map(([a, b]) => {\n\n /* Moving down */\n if (a.prev.length < b.prev.length) {\n return {\n prev: b.prev.slice(Math.max(0, a.prev.length - 1), b.prev.length),\n next: []\n }\n\n /* Moving up */\n } else {\n return {\n prev: b.prev.slice(-1),\n next: b.next.slice(0, b.next.length - a.next.length)\n }\n }\n })\n )\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Mount table of contents\n *\n * @param el - Table of contents element\n * @param options - Options\n *\n * @returns Table of contents component observable\n */\nexport function mountTableOfContents(\n el: HTMLElement, { viewport$, header$, target$ }: MountOptions\n): Observable> {\n return defer(() => {\n const push$ = new Subject()\n const done$ = push$.pipe(takeLast(1))\n push$.subscribe(({ prev, next }) => {\n\n /* Look forward */\n for (const [anchor] of next) {\n anchor.classList.remove(\"md-nav__link--passed\")\n anchor.classList.remove(\"md-nav__link--active\")\n }\n\n /* Look backward */\n for (const [index, [anchor]] of prev.entries()) {\n anchor.classList.add(\"md-nav__link--passed\")\n anchor.classList.toggle(\n \"md-nav__link--active\",\n index === prev.length - 1\n )\n }\n })\n\n /* Set up anchor tracking, if enabled */\n if (feature(\"navigation.tracking\"))\n viewport$\n .pipe(\n takeUntil(done$),\n distinctUntilKeyChanged(\"offset\"),\n debounceTime(250),\n skip(1),\n takeUntil(target$.pipe(skip(1))),\n repeat({ delay: 250 }),\n withLatestFrom(push$)\n )\n .subscribe(([, { prev }]) => {\n const url = getLocation()\n\n /* Set hash fragment to active anchor */\n const anchor = prev[prev.length - 1]\n if (anchor && anchor.length) {\n const [active] = anchor\n const { hash } = new URL(active.href)\n if (url.hash !== hash) {\n url.hash = hash\n history.replaceState({}, \"\", `${url}`)\n }\n\n /* Reset anchor when at the top */\n } else {\n url.hash = \"\"\n history.replaceState({}, \"\", `${url}`)\n }\n })\n\n /* Create and return component */\n return watchTableOfContents(el, { viewport$, header$ })\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n Subject,\n bufferCount,\n combineLatest,\n distinctUntilChanged,\n distinctUntilKeyChanged,\n endWith,\n finalize,\n map,\n repeat,\n skip,\n takeLast,\n takeUntil,\n tap\n} from \"rxjs\"\n\nimport { Viewport } from \"~/browser\"\n\nimport { Component } from \"../_\"\nimport { Header } from \"../header\"\nimport { Main } from \"../main\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Back-to-top button\n */\nexport interface BackToTop {\n hidden: boolean /* Back-to-top button is hidden */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch options\n */\ninterface WatchOptions {\n viewport$: Observable /* Viewport observable */\n main$: Observable
    /* Main area observable */\n target$: Observable /* Location target observable */\n}\n\n/**\n * Mount options\n */\ninterface MountOptions {\n viewport$: Observable /* Viewport observable */\n header$: Observable
    /* Header observable */\n main$: Observable
    /* Main area observable */\n target$: Observable /* Location target observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Watch back-to-top\n *\n * @param _el - Back-to-top element\n * @param options - Options\n *\n * @returns Back-to-top observable\n */\nexport function watchBackToTop(\n _el: HTMLElement, { viewport$, main$, target$ }: WatchOptions\n): Observable {\n\n /* Compute direction */\n const direction$ = viewport$\n .pipe(\n map(({ offset: { y } }) => y),\n bufferCount(2, 1),\n map(([a, b]) => a > b && b > 0),\n distinctUntilChanged()\n )\n\n /* Compute whether main area is active */\n const active$ = main$\n .pipe(\n map(({ active }) => active)\n )\n\n /* Compute threshold for hiding */\n return combineLatest([active$, direction$])\n .pipe(\n map(([active, direction]) => !(active && direction)),\n distinctUntilChanged(),\n takeUntil(target$.pipe(skip(1))),\n endWith(true),\n repeat({ delay: 250 }),\n map(hidden => ({ hidden }))\n )\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Mount back-to-top\n *\n * @param el - Back-to-top element\n * @param options - Options\n *\n * @returns Back-to-top component observable\n */\nexport function mountBackToTop(\n el: HTMLElement, { viewport$, header$, main$, target$ }: MountOptions\n): Observable> {\n const push$ = new Subject()\n const done$ = push$.pipe(takeLast(1))\n push$.subscribe({\n\n /* Handle emission */\n next({ hidden }) {\n el.hidden = hidden\n if (hidden) {\n el.setAttribute(\"tabindex\", \"-1\")\n el.blur()\n } else {\n el.removeAttribute(\"tabindex\")\n }\n },\n\n /* Handle complete */\n complete() {\n el.style.top = \"\"\n el.hidden = true\n el.removeAttribute(\"tabindex\")\n }\n })\n\n /* Watch header height */\n header$\n .pipe(\n takeUntil(done$),\n distinctUntilKeyChanged(\"height\")\n )\n .subscribe(({ height }) => {\n el.style.top = `${height + 16}px`\n })\n\n /* Create and return component */\n return watchBackToTop(el, { viewport$, main$, target$ })\n .pipe(\n tap(state => push$.next(state)),\n finalize(() => push$.complete()),\n map(state => ({ ref: el, ...state }))\n )\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n fromEvent,\n map,\n mergeMap,\n switchMap,\n takeWhile,\n tap,\n withLatestFrom\n} from \"rxjs\"\n\nimport { getElements } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch options\n */\ninterface PatchOptions {\n document$: Observable /* Document observable */\n tablet$: Observable /* Media tablet observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch indeterminate checkboxes\n *\n * This function replaces the indeterminate \"pseudo state\" with the actual\n * indeterminate state, which is used to keep navigation always expanded.\n *\n * @param options - Options\n */\nexport function patchIndeterminate(\n { document$, tablet$ }: PatchOptions\n): void {\n document$\n .pipe(\n switchMap(() => getElements(\n // @todo `data-md-state` is deprecated and removed in v9\n \".md-toggle--indeterminate, [data-md-state=indeterminate]\"\n )),\n tap(el => {\n el.indeterminate = true\n el.checked = false\n }),\n mergeMap(el => fromEvent(el, \"change\")\n .pipe(\n takeWhile(() => el.classList.contains(\"md-toggle--indeterminate\")),\n map(() => el)\n )\n ),\n withLatestFrom(tablet$)\n )\n .subscribe(([el, tablet]) => {\n el.classList.remove(\"md-toggle--indeterminate\")\n if (tablet)\n el.checked = false\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n filter,\n fromEvent,\n map,\n mergeMap,\n switchMap,\n tap\n} from \"rxjs\"\n\nimport { getElements } from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch options\n */\ninterface PatchOptions {\n document$: Observable /* Document observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Check whether the given device is an Apple device\n *\n * @returns Test result\n */\nfunction isAppleDevice(): boolean {\n return /(iPad|iPhone|iPod)/.test(navigator.userAgent)\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch all elements with `data-md-scrollfix` attributes\n *\n * This is a year-old patch which ensures that overflow scrolling works at the\n * top and bottom of containers on iOS by ensuring a `1px` scroll offset upon\n * the start of a touch event.\n *\n * @see https://bit.ly/2SCtAOO - Original source\n *\n * @param options - Options\n */\nexport function patchScrollfix(\n { document$ }: PatchOptions\n): void {\n document$\n .pipe(\n switchMap(() => getElements(\"[data-md-scrollfix]\")),\n tap(el => el.removeAttribute(\"data-md-scrollfix\")),\n filter(isAppleDevice),\n mergeMap(el => fromEvent(el, \"touchstart\")\n .pipe(\n map(() => el)\n )\n )\n )\n .subscribe(el => {\n const top = el.scrollTop\n\n /* We're at the top of the container */\n if (top === 0) {\n el.scrollTop = 1\n\n /* We're at the bottom of the container */\n } else if (top + el.offsetHeight === el.scrollHeight) {\n el.scrollTop = top - 1\n }\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n Observable,\n combineLatest,\n delay,\n map,\n of,\n switchMap,\n withLatestFrom\n} from \"rxjs\"\n\nimport {\n Viewport,\n watchToggle\n} from \"~/browser\"\n\n/* ----------------------------------------------------------------------------\n * Helper types\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch options\n */\ninterface PatchOptions {\n viewport$: Observable /* Viewport observable */\n tablet$: Observable /* Media tablet observable */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Patch the document body to lock when search is open\n *\n * For mobile and tablet viewports, the search is rendered full screen, which\n * leads to scroll leaking when at the top or bottom of the search result. This\n * function locks the body when the search is in full screen mode, and restores\n * the scroll position when leaving.\n *\n * @param options - Options\n */\nexport function patchScrolllock(\n { viewport$, tablet$ }: PatchOptions\n): void {\n combineLatest([watchToggle(\"search\"), tablet$])\n .pipe(\n map(([active, tablet]) => active && !tablet),\n switchMap(active => of(active)\n .pipe(\n delay(active ? 400 : 100)\n )\n ),\n withLatestFrom(viewport$)\n )\n .subscribe(([active, { offset: { y }}]) => {\n if (active) {\n document.body.setAttribute(\"data-md-scrolllock\", \"\")\n document.body.style.top = `-${y}px`\n } else {\n const value = -1 * parseInt(document.body.style.top, 10)\n document.body.removeAttribute(\"data-md-scrolllock\")\n document.body.style.top = \"\"\n if (value)\n window.scrollTo(0, value)\n }\n })\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Polyfills\n * ------------------------------------------------------------------------- */\n\n/* Polyfill `Object.entries` */\nif (!Object.entries)\n Object.entries = function (obj: object) {\n const data: [string, string][] = []\n for (const key of Object.keys(obj))\n // @ts-expect-error - ignore property access warning\n data.push([key, obj[key]])\n\n /* Return entries */\n return data\n }\n\n/* Polyfill `Object.values` */\nif (!Object.values)\n Object.values = function (obj: object) {\n const data: string[] = []\n for (const key of Object.keys(obj))\n // @ts-expect-error - ignore property access warning\n data.push(obj[key])\n\n /* Return values */\n return data\n }\n\n/* ------------------------------------------------------------------------- */\n\n/* Polyfills for `Element` */\nif (typeof Element !== \"undefined\") {\n\n /* Polyfill `Element.scrollTo` */\n if (!Element.prototype.scrollTo)\n Element.prototype.scrollTo = function (\n x?: ScrollToOptions | number, y?: number\n ): void {\n if (typeof x === \"object\") {\n this.scrollLeft = x.left!\n this.scrollTop = x.top!\n } else {\n this.scrollLeft = x!\n this.scrollTop = y!\n }\n }\n\n /* Polyfill `Element.replaceWith` */\n if (!Element.prototype.replaceWith)\n Element.prototype.replaceWith = function (\n ...nodes: Array\n ): void {\n const parent = this.parentNode\n if (parent) {\n if (nodes.length === 0)\n parent.removeChild(this)\n\n /* Replace children and create text nodes */\n for (let i = nodes.length - 1; i >= 0; i--) {\n let node = nodes[i]\n if (typeof node !== \"object\")\n node = document.createTextNode(node)\n else if 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also am an ander andere anderem anderen anderer anderes anderm andern anderr anders auch auf aus bei bin bis bist da damit dann das dasselbe dazu daƟ dein deine deinem deinen deiner deines dem demselben den denn denselben der derer derselbe derselben des desselben dessen dich die dies diese dieselbe dieselben diesem diesen dieser dieses dir doch dort du durch ein eine einem einen einer eines einig einige einigem einigen einiger einiges einmal er es etwas euch euer eure eurem euren eurer eures fĆ¼r gegen gewesen hab habe haben hat hatte hatten hier hin hinter ich ihm ihn ihnen ihr ihre ihrem ihren ihrer ihres im in indem ins ist jede jedem jeden jeder jedes jene jenem jenen jener jenes jetzt kann kein keine keinem keinen keiner keines kƶnnen kƶnnte machen man manche manchem manchen mancher manches mein meine meinem meinen meiner meines mich mir mit muss musste nach nicht nichts noch nun nur ob oder ohne sehr sein seine seinem seinen seiner seines selbst sich sie sind so solche solchem solchen solcher solches soll sollte sondern sonst um und uns unse unsem unsen unser unses unter viel vom von vor war waren warst was weg weil weiter welche welchem welchen welcher welches wenn werde werden wie wieder will wir wird wirst wo wollen wollte wƤhrend wĆ¼rde wĆ¼rden zu zum zur zwar zwischen Ć¼ber".split(" ")),e.Pipeline.registerFunction(e.de.stopWordFilter,"stopWordFilter-de")}}); \ No newline at end of file diff --git a/assets/javascripts/lunr/min/lunr.du.min.js b/assets/javascripts/lunr/min/lunr.du.min.js new file mode 100644 index 00000000..49a0f3f0 --- /dev/null +++ b/assets/javascripts/lunr/min/lunr.du.min.js @@ -0,0 +1,18 @@ +/*! + * Lunr languages, `Dutch` language + * https://github.com/MihaiValentin/lunr-languages + * + * Copyright 2014, Mihai Valentin + * http://www.mozilla.org/MPL/ + */ +/*! + * based on + * Snowball JavaScript Library v0.3 + * http://code.google.com/p/urim/ + * http://snowball.tartarus.org/ + * + * Copyright 2010, Oleg Mazko + * http://www.mozilla.org/MPL/ + */ + +!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. Please include / require Lunr before this script.");if(void 0===e.stemmerSupport)throw new Error("Lunr stemmer support is not present. Please include / require Lunr stemmer support before this script.");console.warn('[Lunr Languages] Please use the "nl" instead of the "du". 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r("",-1,6),new r("Ć”",0,1),new r("Ƥ",0,1),new r("Ć©",0,2),new r("Ć«",0,2),new r("Ć­",0,3),new r("ĆÆ",0,3),new r("Ć³",0,4),new r("ƶ",0,4),new r("Ćŗ",0,5),new r("Ć¼",0,5)],p=[new r("",-1,3),new r("I",0,2),new r("Y",0,1)],g=[new r("dd",-1,-1),new r("kk",-1,-1),new r("tt",-1,-1)],h=[new r("ene",-1,2),new r("se",-1,3),new r("en",-1,2),new r("heden",2,1),new r("s",-1,3)],k=[new r("end",-1,1),new r("ig",-1,2),new r("ing",-1,1),new r("lijk",-1,3),new r("baar",-1,4),new r("bar",-1,5)],v=[new r("aa",-1,-1),new r("ee",-1,-1),new r("oo",-1,-1),new r("uu",-1,-1)],q=[17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,128],j=[1,0,0,17,65,16,1,0,0,0,0,0,0,0,0,0,0,0,0,128],z=[17,67,16,1,0,0,0,0,0,0,0,0,0,0,0,0,128],C=new i;this.setCurrent=function(e){C.setCurrent(e)},this.getCurrent=function(){return C.getCurrent()},this.stem=function(){var r=C.cursor;return e(),C.cursor=r,o(),C.limit_backward=r,C.cursor=C.limit,d(),C.cursor=C.limit_backward,s(),!0}};return function(e){return"function"==typeof e.update?e.update(function(e){return n.setCurrent(e),n.stem(),n.getCurrent()}):(n.setCurrent(e),n.stem(),n.getCurrent())}}(),e.Pipeline.registerFunction(e.du.stemmer,"stemmer-du"),e.du.stopWordFilter=e.generateStopWordFilter(" aan al alles als altijd andere ben bij daar dan dat de der deze die dit doch doen door dus een eens en er ge geen geweest haar had heb hebben heeft hem het hier hij hoe hun iemand iets ik in is ja je kan kon kunnen maar me meer men met mij mijn moet na naar niet niets nog nu of om omdat onder ons ook op over reeds te tegen toch toen tot u uit uw van veel voor want waren was wat werd wezen wie wil worden wordt zal ze zelf zich zij zijn zo zonder zou".split(" ")),e.Pipeline.registerFunction(e.du.stopWordFilter,"stopWordFilter-du")}}); \ No newline at end of file diff --git a/assets/javascripts/lunr/min/lunr.es.min.js b/assets/javascripts/lunr/min/lunr.es.min.js new file mode 100644 index 00000000..2989d342 --- /dev/null +++ b/assets/javascripts/lunr/min/lunr.es.min.js @@ -0,0 +1,18 @@ +/*! + * Lunr languages, `Spanish` language + * https://github.com/MihaiValentin/lunr-languages + * + * Copyright 2014, Mihai Valentin + * http://www.mozilla.org/MPL/ + */ +/*! + * based on + * Snowball JavaScript Library v0.3 + * http://code.google.com/p/urim/ + * http://snowball.tartarus.org/ + * + * Copyright 2010, Oleg Mazko + * http://www.mozilla.org/MPL/ + */ + +!function(e,s){"function"==typeof define&&define.amd?define(s):"object"==typeof exports?module.exports=s():s()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. 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erre ez ezek ezen ezt ezzel ezĆ©rt fel felĆ© hanem hiszen hogy hogyan igen ill ill. illetve ilyen ilyenkor ismĆ©t ison itt jobban jĆ³ jĆ³l kell kellett keressĆ¼nk keresztĆ¼l ki kĆ­vĆ¼l kƶzƶtt kƶzĆ¼l legalĆ”bb legyen lehet lehetett lenne lenni lesz lett maga magĆ”t majd majd meg mellett mely melyek mert mi mikor milyen minden mindenki mindent mindig mint mintha mit mivel miĆ©rt most mĆ”r mĆ”s mĆ”sik mĆ©g mĆ­g nagy nagyobb nagyon ne nekem neki nem nincs nĆ©ha nĆ©hĆ”ny nĆ©lkĆ¼l olyan ott pedig persze rĆ” s sajĆ”t sem semmi sok sokat sokkal szemben szerint szinte szĆ”mĆ”ra talĆ”n tehĆ”t teljes tovĆ”bb tovĆ”bbĆ” tƶbb ugyanis utolsĆ³ utĆ”n utĆ”na vagy vagyis vagyok valaki valami valamint valĆ³ van vannak vele vissza viszont volna volt voltak voltam voltunk Ć”ltal Ć”ltalĆ”ban Ć”t Ć©n Ć©ppen Ć©s Ć­gy Ƶ Ƶk Ƶket ƶssze Ćŗgy Ćŗj Ćŗjabb Ćŗjra".split(" ")),e.Pipeline.registerFunction(e.hu.stopWordFilter,"stopWordFilter-hu")}}); \ No newline at end of file diff --git a/assets/javascripts/lunr/min/lunr.it.min.js b/assets/javascripts/lunr/min/lunr.it.min.js new file mode 100644 index 00000000..344b6a3c --- /dev/null +++ b/assets/javascripts/lunr/min/lunr.it.min.js @@ -0,0 +1,18 @@ +/*! + * Lunr languages, `Italian` language + * https://github.com/MihaiValentin/lunr-languages + * + * Copyright 2014, Mihai Valentin + * http://www.mozilla.org/MPL/ + */ +/*! + * based on + * Snowball JavaScript Library v0.3 + * http://code.google.com/p/urim/ + * http://snowball.tartarus.org/ + * + * Copyright 2010, Oleg Mazko + * http://www.mozilla.org/MPL/ + */ + +!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. 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avevi avevo avrai avranno avrebbe avrebbero avrei avremmo avremo avreste avresti avrete avrĆ  avrĆ² avuta avute avuti avuto c che chi ci coi col come con contro cui da dagl dagli dai dal dall dalla dalle dallo degl degli dei del dell della delle dello di dov dove e ebbe ebbero ebbi ed era erano eravamo eravate eri ero essendo faccia facciamo facciano facciate faccio facemmo facendo facesse facessero facessi facessimo faceste facesti faceva facevamo facevano facevate facevi facevo fai fanno farai faranno farebbe farebbero farei faremmo faremo fareste faresti farete farĆ  farĆ² fece fecero feci fosse fossero fossi fossimo foste fosti fu fui fummo furono gli ha hai hanno ho i il in io l la le lei li lo loro lui ma mi mia mie miei mio ne negl negli nei nel nell nella nelle nello noi non nostra nostre nostri nostro o per perchĆ© piĆ¹ quale quanta quante quanti quanto quella quelle quelli quello questa queste questi questo sarai saranno sarebbe sarebbero sarei saremmo saremo sareste saresti sarete sarĆ  sarĆ² se sei si sia siamo siano siate siete sono sta stai stando stanno starai staranno starebbe starebbero starei staremmo staremo stareste staresti starete starĆ  starĆ² stava stavamo stavano stavate stavi stavo stemmo stesse stessero stessi stessimo steste stesti stette stettero stetti stia stiamo stiano stiate sto su sua sue sugl sugli sui sul sull sulla sulle sullo suo suoi ti tra tu tua tue tuo tuoi tutti tutto un una uno vi voi vostra vostre vostri vostro ĆØ".split(" ")),e.Pipeline.registerFunction(e.it.stopWordFilter,"stopWordFilter-it")}}); \ No newline at end of file diff --git a/assets/javascripts/lunr/min/lunr.ja.min.js b/assets/javascripts/lunr/min/lunr.ja.min.js new file mode 100644 index 00000000..5f254ebe --- /dev/null +++ b/assets/javascripts/lunr/min/lunr.ja.min.js @@ -0,0 +1 @@ +!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. 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Please include / require Lunr stemmer support before this script.");var r="2"==e.version[0];e.ja=function(){this.pipeline.reset(),this.pipeline.add(e.ja.trimmer,e.ja.stopWordFilter,e.ja.stemmer),r?this.tokenizer=e.ja.tokenizer:(e.tokenizer&&(e.tokenizer=e.ja.tokenizer),this.tokenizerFn&&(this.tokenizerFn=e.ja.tokenizer))};var t=new e.TinySegmenter;e.ja.tokenizer=function(i){var n,o,s,p,a,u,m,l,c,f;if(!arguments.length||null==i||void 0==i)return[];if(Array.isArray(i))return i.map(function(t){return r?new 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00000000..3e5eb640 --- /dev/null +++ b/assets/javascripts/lunr/min/lunr.sv.min.js @@ -0,0 +1,18 @@ +/*! + * Lunr languages, `Swedish` language + * https://github.com/MihaiValentin/lunr-languages + * + * Copyright 2014, Mihai Valentin + * http://www.mozilla.org/MPL/ + */ +/*! + * based on + * Snowball JavaScript Library v0.3 + * http://code.google.com/p/urim/ + * http://snowball.tartarus.org/ + * + * Copyright 2010, Oleg Mazko + * http://www.mozilla.org/MPL/ + */ + +!function(e,r){"function"==typeof define&&define.amd?define(r):"object"==typeof exports?module.exports=r():r()(e.lunr)}(this,function(){return function(e){if(void 0===e)throw new Error("Lunr is not present. 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+ }) + this.addWords(words, false) + } + if(finalize){ + this.finalizeDict(); + } + }, + + dictSeek: function (l, r, ch, strOffset, pos) { + var ans = null; + while (l <= r) { + var m = Math.floor((l + r) / 2), + dict_item = this.dict[m], + len = dict_item.length; + if (len <= strOffset) { + l = m + 1; + } else { + var ch_ = dict_item[strOffset]; + if (ch_ < ch) { + l = m + 1; + } else if (ch_ > ch) { + r = m - 1; + } else { + ans = m; + if (pos == LEFT) { + r = m - 1; + } else { + l = m + 1; + } + } + } + } + return ans; + }, + + isFinal: function (acceptor) { + return this.dict[acceptor.l].length == acceptor.strOffset; + }, + + createAcceptor: function () { + return { + l: 0, + r: this.dict.length - 1, + strOffset: 0, + isFinal: false, + dict: this, + transit: function (ch) { + return this.dict.transit(this, ch); + }, + isError: false, + tag: "DICT", + w: 1, + type: "DICT" + }; + }, + + transit: function (acceptor, ch) { + var l = this.dictSeek(acceptor.l, + acceptor.r, + ch, + acceptor.strOffset, + LEFT); + if (l !== null) { + var r = this.dictSeek(l, + acceptor.r, + ch, + acceptor.strOffset, + RIGHT); + acceptor.l = l; + acceptor.r = r; + acceptor.strOffset++; + acceptor.isFinal = this.isFinal(acceptor); + } else { + acceptor.isError = true; + } + return acceptor; + }, + + sortuniq: function(a){ + return a.sort().filter(function(item, pos, arr){ + return !pos || item != arr[pos - 1]; + }) + }, + + flatten: function(a){ + //[[1,2],[3]] -> [1,2,3] + return [].concat.apply([], a); + } +}; +module.exports = WordcutDict; + +}).call(this,"/dist/tmp") +},{"glob":16,"path":22}],3:[function(require,module,exports){ +var WordRule = { + createAcceptor: function(tag) { + if (tag["WORD_RULE"]) + return null; + + return {strOffset: 0, + isFinal: false, + transit: function(ch) { + var lch = ch.toLowerCase(); + if (lch >= "a" && lch <= "z") { + this.isFinal = true; + this.strOffset++; + } else { + this.isError = true; + } + return this; + }, + isError: false, + tag: "WORD_RULE", + type: "WORD_RULE", + w: 1}; + } +}; + +var NumberRule = { + createAcceptor: function(tag) { + if (tag["NUMBER_RULE"]) + return null; + + return {strOffset: 0, + isFinal: false, + transit: function(ch) { + if (ch >= "0" && ch <= "9") { + this.isFinal = true; + this.strOffset++; + } else { + this.isError = true; + } + return this; + }, + isError: false, + tag: "NUMBER_RULE", + type: "NUMBER_RULE", + w: 1}; + } +}; + +var SpaceRule = { + tag: "SPACE_RULE", + createAcceptor: function(tag) { + + if (tag["SPACE_RULE"]) + return null; + + return {strOffset: 0, + isFinal: false, + transit: function(ch) { + if (ch == " " || ch == "\t" || ch == "\r" || ch == "\n" || + ch == "\u00A0" || ch=="\u2003"//nbsp and emsp + ) { + this.isFinal = true; + this.strOffset++; + } else { + this.isError = true; + } + return this; + }, + isError: false, + tag: SpaceRule.tag, + w: 1, + type: "SPACE_RULE"}; + } +} + +var SingleSymbolRule = { + tag: "SINSYM", + createAcceptor: function(tag) { + return {strOffset: 0, + isFinal: false, + transit: function(ch) { + if (this.strOffset == 0 && ch.match(/^[\@\(\)\/\,\-\."`]$/)) { + this.isFinal = true; + this.strOffset++; + } else { + this.isError = true; + } + return this; + }, + isError: false, + tag: "SINSYM", + w: 1, + type: "SINSYM"}; + } +} + + +var LatinRules = [WordRule, SpaceRule, SingleSymbolRule, NumberRule]; + +module.exports = LatinRules; + +},{}],4:[function(require,module,exports){ +var _ = require("underscore") + , WordcutCore = require("./wordcut_core"); +var PathInfoBuilder = { + + /* + buildByPartAcceptors: function(path, acceptors, i) { + var + var genInfos = partAcceptors.reduce(function(genInfos, acceptor) { + + }, []); + + return genInfos; + } + */ + + buildByAcceptors: function(path, finalAcceptors, i) { + var self = this; + var infos = finalAcceptors.map(function(acceptor) { + var p = i - acceptor.strOffset + 1 + , _info = path[p]; + + var info = {p: p, + mw: _info.mw + (acceptor.mw === undefined ? 0 : acceptor.mw), + w: acceptor.w + _info.w, + unk: (acceptor.unk ? acceptor.unk : 0) + _info.unk, + type: acceptor.type}; + + if (acceptor.type == "PART") { + for(var j = p + 1; j <= i; j++) { + path[j].merge = p; + } + info.merge = p; + } + + return info; + }); + return infos.filter(function(info) { return info; }); + }, + + fallback: function(path, leftBoundary, text, i) { + var _info = path[leftBoundary]; + if (text[i].match(/[\u0E48-\u0E4E]/)) { + if (leftBoundary != 0) + leftBoundary = path[leftBoundary].p; + return {p: leftBoundary, + mw: 0, + w: 1 + _info.w, + unk: 1 + _info.unk, + type: "UNK"}; +/* } else if(leftBoundary > 0 && path[leftBoundary].type !== "UNK") { + leftBoundary = path[leftBoundary].p; + return {p: leftBoundary, + w: 1 + _info.w, + unk: 1 + _info.unk, + type: "UNK"}; */ + } else { + return {p: leftBoundary, + mw: _info.mw, + w: 1 + _info.w, + unk: 1 + _info.unk, + type: "UNK"}; + } + }, + + build: function(path, finalAcceptors, i, leftBoundary, text) { + var basicPathInfos = this.buildByAcceptors(path, finalAcceptors, i); + if (basicPathInfos.length > 0) { + return basicPathInfos; + } else { + return [this.fallback(path, leftBoundary, text, i)]; + } + } +}; + +module.exports = function() { + return _.clone(PathInfoBuilder); +} + +},{"./wordcut_core":8,"underscore":25}],5:[function(require,module,exports){ +var _ = require("underscore"); + + +var PathSelector = { + selectPath: function(paths) { + var path = paths.reduce(function(selectedPath, path) { + if (selectedPath == null) { + return path; + } else { + if (path.unk < selectedPath.unk) + return path; + if (path.unk == selectedPath.unk) { + if (path.mw < selectedPath.mw) + return path + if (path.mw == selectedPath.mw) { + if (path.w < selectedPath.w) + return path; + } + } + return selectedPath; + } + }, null); + return path; + }, + + createPath: function() { + return [{p:null, w:0, unk:0, type: "INIT", mw:0}]; + } +}; + +module.exports = function() { + return _.clone(PathSelector); +}; + +},{"underscore":25}],6:[function(require,module,exports){ +function isMatch(pat, offset, ch) { + if (pat.length <= offset) + return false; + var _ch = pat[offset]; + return _ch == ch || + (_ch.match(/[ąøąø‚]/) && ch.match(/[ąø-ąø®]/)) || + (_ch.match(/[ąø”ąøš]/) && ch.match(/[ąø-ąø®]/)) || + (_ch.match(/\u0E49/) && ch.match(/[\u0E48-\u0E4B]/)); +} + +var Rule0 = { + pat: "ą¹€ąø«ąøą¹‡ąø”", + createAcceptor: function(tag) { + return {strOffset: 0, + isFinal: false, + transit: function(ch) { + if (isMatch(Rule0.pat, this.strOffset,ch)) { + this.isFinal = (this.strOffset + 1 == Rule0.pat.length); + this.strOffset++; + } else { + this.isError = true; + } + return this; + }, + isError: false, + tag: "THAI_RULE", + type: "THAI_RULE", + w: 1}; + } +}; + +var PartRule = { + createAcceptor: function(tag) { + return {strOffset: 0, + patterns: [ + "ą¹ąø", "ą¹€ąø", "ąøą¹‰", "ąøąøą¹Œ", "ąøąø²", "ąøąøµ", "ąøąø“", "ąøąø·ąø" + ], + isFinal: false, + transit: function(ch) { + var offset = this.strOffset; + this.patterns = this.patterns.filter(function(pat) { + return isMatch(pat, offset, ch); + }); + + if (this.patterns.length > 0) { + var len = 1 + offset; + this.isFinal = this.patterns.some(function(pat) { + return pat.length == len; + }); + this.strOffset++; + } else { + this.isError = true; + } + return this; + }, + isError: false, + tag: "PART", + type: "PART", + unk: 1, + w: 1}; + } +}; + +var ThaiRules = [Rule0, PartRule]; + +module.exports = ThaiRules; + +},{}],7:[function(require,module,exports){ +var sys = require("sys") + , WordcutDict = require("./dict") + , WordcutCore = require("./wordcut_core") + , PathInfoBuilder = require("./path_info_builder") + , PathSelector = require("./path_selector") + , Acceptors = require("./acceptors") + , latinRules = require("./latin_rules") + , thaiRules = require("./thai_rules") + , _ = require("underscore"); + + +var Wordcut = Object.create(WordcutCore); +Wordcut.defaultPathInfoBuilder = PathInfoBuilder; +Wordcut.defaultPathSelector = PathSelector; +Wordcut.defaultAcceptors = Acceptors; +Wordcut.defaultLatinRules = latinRules; +Wordcut.defaultThaiRules = thaiRules; +Wordcut.defaultDict = WordcutDict; + + +Wordcut.initNoDict = function(dict_path) { + var self = this; + self.pathInfoBuilder = new self.defaultPathInfoBuilder; + self.pathSelector = new self.defaultPathSelector; + self.acceptors = new self.defaultAcceptors; + self.defaultLatinRules.forEach(function(rule) { + self.acceptors.creators.push(rule); + }); + self.defaultThaiRules.forEach(function(rule) { + self.acceptors.creators.push(rule); + }); +}; + +Wordcut.init = function(dict_path, withDefault, additionalWords) { + withDefault = withDefault || false; + this.initNoDict(); + var dict = _.clone(this.defaultDict); + dict.init(dict_path, withDefault, additionalWords); + this.acceptors.creators.push(dict); +}; + +module.exports = Wordcut; + +},{"./acceptors":1,"./dict":2,"./latin_rules":3,"./path_info_builder":4,"./path_selector":5,"./thai_rules":6,"./wordcut_core":8,"sys":28,"underscore":25}],8:[function(require,module,exports){ +var WordcutCore = { + + buildPath: function(text) { + var self = this + , path = self.pathSelector.createPath() + , leftBoundary = 0; + self.acceptors.reset(); + for (var i = 0; i < text.length; i++) { + var ch = text[i]; + self.acceptors.transit(ch); + + var possiblePathInfos = self + .pathInfoBuilder + .build(path, + self.acceptors.getFinalAcceptors(), + i, + leftBoundary, + text); + var selectedPath = self.pathSelector.selectPath(possiblePathInfos) + + path.push(selectedPath); + if (selectedPath.type !== "UNK") { + leftBoundary = i; + } + } + return path; + }, + + pathToRanges: function(path) { + var e = path.length - 1 + , ranges = []; + + while (e > 0) { + var info = path[e] + , s = info.p; + + if (info.merge !== undefined && ranges.length > 0) { + var r = ranges[ranges.length - 1]; + r.s = info.merge; + s = r.s; + } else { + ranges.push({s:s, e:e}); + } + e = s; + } + return ranges.reverse(); + }, + + rangesToText: function(text, ranges, delimiter) { + return ranges.map(function(r) { + return text.substring(r.s, r.e); + }).join(delimiter); + }, + + cut: function(text, delimiter) { + var path = this.buildPath(text) + , ranges = this.pathToRanges(path); + return this + .rangesToText(text, ranges, + (delimiter === undefined ? "|" : delimiter)); + }, + + cutIntoRanges: function(text, noText) { + var path = this.buildPath(text) + , ranges = this.pathToRanges(path); + + if (!noText) { + ranges.forEach(function(r) { + r.text = text.substring(r.s, r.e); + }); + } + return ranges; + }, + + cutIntoArray: function(text) { + var path = this.buildPath(text) + , ranges = this.pathToRanges(path); + + return ranges.map(function(r) { + return text.substring(r.s, r.e) + }); + } +}; + +module.exports = WordcutCore; + +},{}],9:[function(require,module,exports){ +// http://wiki.commonjs.org/wiki/Unit_Testing/1.0 +// +// THIS IS NOT TESTED NOR LIKELY TO WORK OUTSIDE V8! +// +// Originally from narwhal.js (http://narwhaljs.org) +// Copyright (c) 2009 Thomas Robinson <280north.com> +// +// Permission is hereby granted, free of charge, to any person obtaining a copy +// of this software and associated documentation files (the 'Software'), to +// deal in the Software without restriction, including without limitation the +// rights to use, copy, modify, merge, publish, distribute, sublicense, and/or +// sell copies of the Software, and to permit persons to whom the Software is +// furnished to do so, subject to the following conditions: +// +// The above copyright notice and this permission notice shall be included in +// all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED 'AS IS', WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +// IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +// FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +// AUTHORS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN +// ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION +// WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. + +// when used in node, this will actually load the util module we depend on +// versus loading the builtin util module as happens otherwise +// this is a bug in node module loading as far as I am concerned +var util = require('util/'); + +var pSlice = Array.prototype.slice; +var hasOwn = Object.prototype.hasOwnProperty; + +// 1. The assert module provides functions that throw +// AssertionError's when particular conditions are not met. The +// assert module must conform to the following interface. + +var assert = module.exports = ok; + +// 2. The AssertionError is defined in assert. +// new assert.AssertionError({ message: message, +// actual: actual, +// expected: expected }) + +assert.AssertionError = function AssertionError(options) { + this.name = 'AssertionError'; + this.actual = options.actual; + this.expected = options.expected; + this.operator = options.operator; + if (options.message) { + this.message = options.message; + this.generatedMessage = false; + } else { + this.message = getMessage(this); + this.generatedMessage = true; + } + var stackStartFunction = options.stackStartFunction || fail; + + if (Error.captureStackTrace) { + Error.captureStackTrace(this, stackStartFunction); + } + else { + // non v8 browsers so we can have a stacktrace + var err = new Error(); + if (err.stack) { + var out = err.stack; + + // try to strip useless frames + var fn_name = stackStartFunction.name; + var idx = out.indexOf('\n' + fn_name); + if (idx >= 0) { + // once we have located the function frame + // we need to strip out everything before it (and its line) + var next_line = out.indexOf('\n', idx + 1); + out = out.substring(next_line + 1); + } + + this.stack = out; + } + } +}; + +// assert.AssertionError instanceof Error +util.inherits(assert.AssertionError, Error); + +function replacer(key, value) { + if (util.isUndefined(value)) { + return '' + value; + } + if (util.isNumber(value) && !isFinite(value)) { + return value.toString(); + } + if (util.isFunction(value) || util.isRegExp(value)) { + return value.toString(); + } + return value; +} + +function truncate(s, n) { + if (util.isString(s)) { + return s.length < n ? s : s.slice(0, n); + } else { + return s; + } +} + +function getMessage(self) { + return truncate(JSON.stringify(self.actual, replacer), 128) + ' ' + + self.operator + ' ' + + truncate(JSON.stringify(self.expected, replacer), 128); +} + +// At present only the three keys mentioned above are used and +// understood by the spec. Implementations or sub modules can pass +// other keys to the AssertionError's constructor - they will be +// ignored. + +// 3. All of the following functions must throw an AssertionError +// when a corresponding condition is not met, with a message that +// may be undefined if not provided. All assertion methods provide +// both the actual and expected values to the assertion error for +// display purposes. + +function fail(actual, expected, message, operator, stackStartFunction) { + throw new assert.AssertionError({ + message: message, + actual: actual, + expected: expected, + operator: operator, + stackStartFunction: stackStartFunction + }); +} + +// EXTENSION! allows for well behaved errors defined elsewhere. +assert.fail = fail; + +// 4. Pure assertion tests whether a value is truthy, as determined +// by !!guard. +// assert.ok(guard, message_opt); +// This statement is equivalent to assert.equal(true, !!guard, +// message_opt);. To test strictly for the value true, use +// assert.strictEqual(true, guard, message_opt);. + +function ok(value, message) { + if (!value) fail(value, true, message, '==', assert.ok); +} +assert.ok = ok; + +// 5. The equality assertion tests shallow, coercive equality with +// ==. +// assert.equal(actual, expected, message_opt); + +assert.equal = function equal(actual, expected, message) { + if (actual != expected) fail(actual, expected, message, '==', assert.equal); +}; + +// 6. The non-equality assertion tests for whether two objects are not equal +// with != assert.notEqual(actual, expected, message_opt); + +assert.notEqual = function notEqual(actual, expected, message) { + if (actual == expected) { + fail(actual, expected, message, '!=', assert.notEqual); + } +}; + +// 7. The equivalence assertion tests a deep equality relation. +// assert.deepEqual(actual, expected, message_opt); + +assert.deepEqual = function deepEqual(actual, expected, message) { + if (!_deepEqual(actual, expected)) { + fail(actual, expected, message, 'deepEqual', assert.deepEqual); + } +}; + +function _deepEqual(actual, expected) { + // 7.1. All identical values are equivalent, as determined by ===. + if (actual === expected) { + return true; + + } else if (util.isBuffer(actual) && util.isBuffer(expected)) { + if (actual.length != expected.length) return false; + + for (var i = 0; i < actual.length; i++) { + if (actual[i] !== expected[i]) return false; + } + + return true; + + // 7.2. If the expected value is a Date object, the actual value is + // equivalent if it is also a Date object that refers to the same time. + } else if (util.isDate(actual) && util.isDate(expected)) { + return actual.getTime() === expected.getTime(); + + // 7.3 If the expected value is a RegExp object, the actual value is + // equivalent if it is also a RegExp object with the same source and + // properties (`global`, `multiline`, `lastIndex`, `ignoreCase`). + } else if (util.isRegExp(actual) && util.isRegExp(expected)) { + return actual.source === expected.source && + actual.global === expected.global && + actual.multiline === expected.multiline && + actual.lastIndex === expected.lastIndex && + actual.ignoreCase === expected.ignoreCase; + + // 7.4. Other pairs that do not both pass typeof value == 'object', + // equivalence is determined by ==. + } else if (!util.isObject(actual) && !util.isObject(expected)) { + return actual == expected; + + // 7.5 For all other Object pairs, including Array objects, equivalence is + // determined by having the same number of owned properties (as verified + // with Object.prototype.hasOwnProperty.call), the same set of keys + // (although not necessarily the same order), equivalent values for every + // corresponding key, and an identical 'prototype' property. Note: this + // accounts for both named and indexed properties on Arrays. + } else { + return objEquiv(actual, expected); + } +} + +function isArguments(object) { + return Object.prototype.toString.call(object) == '[object Arguments]'; +} + +function objEquiv(a, b) { + if (util.isNullOrUndefined(a) || util.isNullOrUndefined(b)) + return false; + // an identical 'prototype' property. + if (a.prototype !== b.prototype) return false; + // if one is a primitive, the other must be same + if (util.isPrimitive(a) || util.isPrimitive(b)) { + return a === b; + } + var aIsArgs = isArguments(a), + bIsArgs = isArguments(b); + if ((aIsArgs && !bIsArgs) || (!aIsArgs && bIsArgs)) + return false; + if (aIsArgs) { + a = pSlice.call(a); + b = pSlice.call(b); + return _deepEqual(a, b); + } + var ka = objectKeys(a), + kb = objectKeys(b), + key, i; + // having the same number of owned properties (keys incorporates + // hasOwnProperty) + if (ka.length != kb.length) + return false; + //the same set of keys (although not necessarily the same order), + ka.sort(); + kb.sort(); + //~~~cheap key test + for (i = ka.length - 1; i >= 0; i--) { + if (ka[i] != kb[i]) + return false; + } + //equivalent values for every corresponding key, and + //~~~possibly expensive deep test + for (i = ka.length - 1; i >= 0; i--) { + key = ka[i]; + if (!_deepEqual(a[key], b[key])) return false; + } + return true; +} + +// 8. The non-equivalence assertion tests for any deep inequality. +// assert.notDeepEqual(actual, expected, message_opt); + +assert.notDeepEqual = function notDeepEqual(actual, expected, message) { + if (_deepEqual(actual, expected)) { + fail(actual, expected, message, 'notDeepEqual', assert.notDeepEqual); + } +}; + +// 9. The strict equality assertion tests strict equality, as determined by ===. +// assert.strictEqual(actual, expected, message_opt); + +assert.strictEqual = function strictEqual(actual, expected, message) { + if (actual !== expected) { + fail(actual, expected, message, '===', assert.strictEqual); + } +}; + +// 10. The strict non-equality assertion tests for strict inequality, as +// determined by !==. assert.notStrictEqual(actual, expected, message_opt); + +assert.notStrictEqual = function notStrictEqual(actual, expected, message) { + if (actual === expected) { + fail(actual, expected, message, '!==', assert.notStrictEqual); + } +}; + +function expectedException(actual, expected) { + if (!actual || !expected) { + return false; + } + + if (Object.prototype.toString.call(expected) == '[object RegExp]') { + return expected.test(actual); + } else if (actual instanceof expected) { + return true; + } else if (expected.call({}, actual) === true) { + return true; + } + + return false; +} + +function _throws(shouldThrow, block, expected, message) { + var actual; + + if (util.isString(expected)) { + message = expected; + expected = null; + } + + try { + block(); + } catch (e) { + actual = e; + } + + message = (expected && expected.name ? ' (' + expected.name + ').' : '.') + + (message ? ' ' + message : '.'); + + if (shouldThrow && !actual) { + fail(actual, expected, 'Missing expected exception' + message); + } + + if (!shouldThrow && expectedException(actual, expected)) { + fail(actual, expected, 'Got unwanted exception' + message); + } + + if ((shouldThrow && actual && expected && + !expectedException(actual, expected)) || (!shouldThrow && actual)) { + throw actual; + } +} + +// 11. Expected to throw an error: +// assert.throws(block, Error_opt, message_opt); + +assert.throws = function(block, /*optional*/error, /*optional*/message) { + _throws.apply(this, [true].concat(pSlice.call(arguments))); +}; + +// EXTENSION! This is annoying to write outside this module. +assert.doesNotThrow = function(block, /*optional*/message) { + _throws.apply(this, [false].concat(pSlice.call(arguments))); +}; + +assert.ifError = function(err) { if (err) {throw err;}}; + +var objectKeys = Object.keys || function (obj) { + var keys = []; + for (var key in obj) { + if (hasOwn.call(obj, key)) keys.push(key); + } + return keys; +}; + +},{"util/":28}],10:[function(require,module,exports){ +'use strict'; +module.exports = balanced; +function balanced(a, b, str) { + if (a instanceof RegExp) a = maybeMatch(a, str); + if (b instanceof RegExp) b = maybeMatch(b, str); + + var r = range(a, b, str); + + return r && { + start: r[0], + end: r[1], + pre: str.slice(0, r[0]), + body: str.slice(r[0] + a.length, r[1]), + post: str.slice(r[1] + b.length) + }; +} + +function maybeMatch(reg, str) { + var m = str.match(reg); + return m ? m[0] : null; +} + +balanced.range = range; +function range(a, b, str) { + var begs, beg, left, right, result; + var ai = str.indexOf(a); + var bi = str.indexOf(b, ai + 1); + var i = ai; + + if (ai >= 0 && bi > 0) { + begs = []; + left = str.length; + + while (i >= 0 && !result) { + if (i == ai) { + begs.push(i); + ai = str.indexOf(a, i + 1); + } else if (begs.length == 1) { + result = [ begs.pop(), bi ]; + } else { + beg = begs.pop(); + if (beg < left) { + left = beg; + right = bi; + } + + bi = str.indexOf(b, i + 1); + } + + i = ai < bi && ai >= 0 ? ai : bi; + } + + if (begs.length) { + result = [ left, right ]; + } + } + + return result; +} + +},{}],11:[function(require,module,exports){ +var concatMap = require('concat-map'); +var balanced = require('balanced-match'); + +module.exports = expandTop; + +var escSlash = '\0SLASH'+Math.random()+'\0'; +var escOpen = '\0OPEN'+Math.random()+'\0'; +var escClose = '\0CLOSE'+Math.random()+'\0'; +var escComma = '\0COMMA'+Math.random()+'\0'; +var escPeriod = '\0PERIOD'+Math.random()+'\0'; + +function numeric(str) { + return parseInt(str, 10) == str + ? parseInt(str, 10) + : str.charCodeAt(0); +} + +function escapeBraces(str) { + return str.split('\\\\').join(escSlash) + .split('\\{').join(escOpen) + .split('\\}').join(escClose) + .split('\\,').join(escComma) + .split('\\.').join(escPeriod); +} + +function unescapeBraces(str) { + return str.split(escSlash).join('\\') + .split(escOpen).join('{') + .split(escClose).join('}') + .split(escComma).join(',') + .split(escPeriod).join('.'); +} + + +// Basically just str.split(","), but handling cases +// where we have nested braced sections, which should be +// treated as individual members, like {a,{b,c},d} +function parseCommaParts(str) { + if (!str) + return ['']; + + var parts = []; + var m = balanced('{', '}', str); + + if (!m) + return str.split(','); + + var pre = m.pre; + var body = m.body; + var post = m.post; + var p = pre.split(','); + + p[p.length-1] += '{' + body + '}'; + var postParts = parseCommaParts(post); + if (post.length) { + p[p.length-1] += postParts.shift(); + p.push.apply(p, postParts); + } + + parts.push.apply(parts, p); + + return parts; +} + +function expandTop(str) { + if (!str) + return []; + + // I don't know why Bash 4.3 does this, but it does. + // Anything starting with {} will have the first two bytes preserved + // but *only* at the top level, so {},a}b will not expand to anything, + // but a{},b}c will be expanded to [a}c,abc]. + // One could argue that this is a bug in Bash, but since the goal of + // this module is to match Bash's rules, we escape a leading {} + if (str.substr(0, 2) === '{}') { + str = '\\{\\}' + str.substr(2); + } + + return expand(escapeBraces(str), true).map(unescapeBraces); +} + +function identity(e) { + return e; +} + +function embrace(str) { + return '{' + str + '}'; +} +function isPadded(el) { + return /^-?0\d/.test(el); +} + +function lte(i, y) { + return i <= y; +} +function gte(i, y) { + return i >= y; +} + +function expand(str, isTop) { + var expansions = []; + + var m = balanced('{', '}', str); + if (!m || /\$$/.test(m.pre)) return [str]; + + var isNumericSequence = /^-?\d+\.\.-?\d+(?:\.\.-?\d+)?$/.test(m.body); + var isAlphaSequence = /^[a-zA-Z]\.\.[a-zA-Z](?:\.\.-?\d+)?$/.test(m.body); + var isSequence = isNumericSequence || isAlphaSequence; + var isOptions = m.body.indexOf(',') >= 0; + if (!isSequence && !isOptions) { + // {a},b} + if (m.post.match(/,.*\}/)) { + str = m.pre + '{' + m.body + escClose + m.post; + return expand(str); + } + return [str]; + } + + var n; + if (isSequence) { + n = m.body.split(/\.\./); + } else { + n = parseCommaParts(m.body); + if (n.length === 1) { + // x{{a,b}}y ==> x{a}y x{b}y + n = expand(n[0], false).map(embrace); + if (n.length === 1) { + var post = m.post.length + ? expand(m.post, false) + : ['']; + return post.map(function(p) { + return m.pre + n[0] + p; + }); + } + } + } + + // at this point, n is the parts, and we know it's not a comma set + // with a single entry. + + // no need to expand pre, since it is guaranteed to be free of brace-sets + var pre = m.pre; + var post = m.post.length + ? expand(m.post, false) + : ['']; + + var N; + + if (isSequence) { + var x = numeric(n[0]); + var y = numeric(n[1]); + var width = Math.max(n[0].length, n[1].length) + var incr = n.length == 3 + ? Math.abs(numeric(n[2])) + : 1; + var test = lte; + var reverse = y < x; + if (reverse) { + incr *= -1; + test = gte; + } + var pad = n.some(isPadded); + + N = []; + + for (var i = x; test(i, y); i += incr) { + var c; + if (isAlphaSequence) { + c = String.fromCharCode(i); + if (c === '\\') + c = ''; + } else { + c = String(i); + if (pad) { + var need = width - c.length; + if (need > 0) { + var z = new Array(need + 1).join('0'); + if (i < 0) + c = '-' + z + c.slice(1); + else + c = z + c; + } + } + } + N.push(c); + } + } else { + N = concatMap(n, function(el) { return expand(el, false) }); + } + + for (var j = 0; j < N.length; j++) { + for (var k = 0; k < post.length; k++) { + var expansion = pre + N[j] + post[k]; + if (!isTop || isSequence || expansion) + expansions.push(expansion); + } + } + + return expansions; +} + + +},{"balanced-match":10,"concat-map":13}],12:[function(require,module,exports){ + +},{}],13:[function(require,module,exports){ +module.exports = function (xs, fn) { + var res = []; + for (var i = 0; i < xs.length; i++) { + var x = fn(xs[i], i); + if (isArray(x)) res.push.apply(res, x); + else res.push(x); + } + return res; +}; + +var isArray = Array.isArray || function (xs) { + return Object.prototype.toString.call(xs) === '[object Array]'; +}; + +},{}],14:[function(require,module,exports){ +// Copyright Joyent, Inc. and other Node contributors. +// +// Permission is hereby granted, free of charge, to any person obtaining a +// copy of this software and associated documentation files (the +// "Software"), to deal in the Software without restriction, including +// without limitation the rights to use, copy, modify, merge, publish, +// distribute, sublicense, and/or sell copies of the Software, and to permit +// persons to whom the Software is furnished to do so, subject to the +// following conditions: +// +// The above copyright notice and this permission notice shall be included +// in all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS +// OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +// MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN +// NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, +// DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR +// OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE +// USE OR OTHER DEALINGS IN THE SOFTWARE. + +function EventEmitter() { + this._events = this._events || {}; + this._maxListeners = this._maxListeners || undefined; +} +module.exports = EventEmitter; + +// Backwards-compat with node 0.10.x +EventEmitter.EventEmitter = EventEmitter; + +EventEmitter.prototype._events = undefined; +EventEmitter.prototype._maxListeners = undefined; + +// By default EventEmitters will print a warning if more than 10 listeners are +// added to it. This is a useful default which helps finding memory leaks. +EventEmitter.defaultMaxListeners = 10; + +// Obviously not all Emitters should be limited to 10. This function allows +// that to be increased. Set to zero for unlimited. +EventEmitter.prototype.setMaxListeners = function(n) { + if (!isNumber(n) || n < 0 || isNaN(n)) + throw TypeError('n must be a positive number'); + this._maxListeners = n; + return this; +}; + +EventEmitter.prototype.emit = function(type) { + var er, handler, len, args, i, listeners; + + if (!this._events) + this._events = {}; + + // If there is no 'error' event listener then throw. + if (type === 'error') { + if (!this._events.error || + (isObject(this._events.error) && !this._events.error.length)) { + er = arguments[1]; + if (er instanceof Error) { + throw er; // Unhandled 'error' event + } + throw TypeError('Uncaught, unspecified "error" event.'); + } + } + + handler = this._events[type]; + + if (isUndefined(handler)) + return false; + + if (isFunction(handler)) { + switch (arguments.length) { + // fast cases + case 1: + handler.call(this); + break; + case 2: + handler.call(this, arguments[1]); + break; + case 3: + handler.call(this, arguments[1], arguments[2]); + break; + // slower + default: + len = arguments.length; + args = new Array(len - 1); + for (i = 1; i < len; i++) + args[i - 1] = arguments[i]; + handler.apply(this, args); + } + } else if (isObject(handler)) { + len = arguments.length; + args = new Array(len - 1); + for (i = 1; i < len; i++) + args[i - 1] = arguments[i]; + + listeners = handler.slice(); + len = listeners.length; + for (i = 0; i < len; i++) + listeners[i].apply(this, args); + } + + return true; +}; + +EventEmitter.prototype.addListener = function(type, listener) { + var m; + + if (!isFunction(listener)) + throw TypeError('listener must be a function'); + + if (!this._events) + this._events = {}; + + // To avoid recursion in the case that type === "newListener"! Before + // adding it to the listeners, first emit "newListener". + if (this._events.newListener) + this.emit('newListener', type, + isFunction(listener.listener) ? + listener.listener : listener); + + if (!this._events[type]) + // Optimize the case of one listener. Don't need the extra array object. + this._events[type] = listener; + else if (isObject(this._events[type])) + // If we've already got an array, just append. + this._events[type].push(listener); + else + // Adding the second element, need to change to array. + this._events[type] = [this._events[type], listener]; + + // Check for listener leak + if (isObject(this._events[type]) && !this._events[type].warned) { + var m; + if (!isUndefined(this._maxListeners)) { + m = this._maxListeners; + } else { + m = EventEmitter.defaultMaxListeners; + } + + if (m && m > 0 && this._events[type].length > m) { + this._events[type].warned = true; + console.error('(node) warning: possible EventEmitter memory ' + + 'leak detected. %d listeners added. ' + + 'Use emitter.setMaxListeners() to increase limit.', + this._events[type].length); + if (typeof console.trace === 'function') { + // not supported in IE 10 + console.trace(); + } + } + } + + return this; +}; + +EventEmitter.prototype.on = EventEmitter.prototype.addListener; + +EventEmitter.prototype.once = function(type, listener) { + if (!isFunction(listener)) + throw TypeError('listener must be a function'); + + var fired = false; + + function g() { + this.removeListener(type, g); + + if (!fired) { + fired = true; + listener.apply(this, arguments); + } + } + + g.listener = listener; + this.on(type, g); + + return this; +}; + +// emits a 'removeListener' event iff the listener was removed +EventEmitter.prototype.removeListener = function(type, listener) { + var list, position, length, i; + + if (!isFunction(listener)) + throw TypeError('listener must be a function'); + + if (!this._events || !this._events[type]) + return this; + + list = this._events[type]; + length = list.length; + position = -1; + + if (list === listener || + (isFunction(list.listener) && list.listener === listener)) { + delete this._events[type]; + if (this._events.removeListener) + this.emit('removeListener', type, listener); + + } else if (isObject(list)) { + for (i = length; i-- > 0;) { + if (list[i] === listener || + (list[i].listener && list[i].listener === listener)) { + position = i; + break; + } + } + + if (position < 0) + return this; + + if (list.length === 1) { + list.length = 0; + delete this._events[type]; + } else { + list.splice(position, 1); + } + + if (this._events.removeListener) + this.emit('removeListener', type, listener); + } + + return this; +}; + +EventEmitter.prototype.removeAllListeners = function(type) { + var key, listeners; + + if (!this._events) + return this; + + // not listening for removeListener, no need to emit + if (!this._events.removeListener) { + if (arguments.length === 0) + this._events = {}; + else if (this._events[type]) + delete this._events[type]; + return this; + } + + // emit removeListener for all listeners on all events + if (arguments.length === 0) { + for (key in this._events) { + if (key === 'removeListener') continue; + this.removeAllListeners(key); + } + this.removeAllListeners('removeListener'); + this._events = {}; + return this; + } + + listeners = this._events[type]; + + if (isFunction(listeners)) { + this.removeListener(type, listeners); + } else { + // LIFO order + while (listeners.length) + this.removeListener(type, listeners[listeners.length - 1]); + } + delete this._events[type]; + + return this; +}; + +EventEmitter.prototype.listeners = function(type) { + var ret; + if (!this._events || !this._events[type]) + ret = []; + else if (isFunction(this._events[type])) + ret = [this._events[type]]; + else + ret = this._events[type].slice(); + return ret; +}; + +EventEmitter.listenerCount = function(emitter, type) { + var ret; + if (!emitter._events || !emitter._events[type]) + ret = 0; + else if (isFunction(emitter._events[type])) + ret = 1; + else + ret = emitter._events[type].length; + return ret; +}; + +function isFunction(arg) { + return typeof arg === 'function'; +} + +function isNumber(arg) { + return typeof arg === 'number'; +} + +function isObject(arg) { + return typeof arg === 'object' && arg !== null; +} + +function isUndefined(arg) { + return arg === void 0; +} + +},{}],15:[function(require,module,exports){ +(function (process){ +exports.alphasort = alphasort +exports.alphasorti = alphasorti +exports.setopts = setopts +exports.ownProp = ownProp +exports.makeAbs = makeAbs +exports.finish = finish +exports.mark = mark +exports.isIgnored = isIgnored +exports.childrenIgnored = childrenIgnored + +function ownProp (obj, field) { + return Object.prototype.hasOwnProperty.call(obj, field) +} + +var path = require("path") +var minimatch = require("minimatch") +var isAbsolute = require("path-is-absolute") +var Minimatch = minimatch.Minimatch + +function alphasorti (a, b) { + return a.toLowerCase().localeCompare(b.toLowerCase()) +} + +function alphasort (a, b) { + return a.localeCompare(b) +} + +function setupIgnores (self, options) { + self.ignore = options.ignore || [] + + if (!Array.isArray(self.ignore)) + self.ignore = [self.ignore] + + if (self.ignore.length) { + self.ignore = self.ignore.map(ignoreMap) + } +} + +function ignoreMap (pattern) { + var gmatcher = null + if (pattern.slice(-3) === '/**') { + var gpattern = pattern.replace(/(\/\*\*)+$/, '') + gmatcher = new Minimatch(gpattern) + } + + return { + matcher: new Minimatch(pattern), + gmatcher: gmatcher + } +} + +function setopts (self, pattern, options) { + if (!options) + options = {} + + // base-matching: just use globstar for that. + if (options.matchBase && -1 === pattern.indexOf("/")) { + if (options.noglobstar) { + throw new Error("base matching requires globstar") + } + pattern = "**/" + pattern + } + + self.silent = !!options.silent + self.pattern = pattern + self.strict = options.strict !== false + self.realpath = !!options.realpath + self.realpathCache = options.realpathCache || Object.create(null) + self.follow = !!options.follow + self.dot = !!options.dot + self.mark = !!options.mark + self.nodir = !!options.nodir + if (self.nodir) + self.mark = true + self.sync = !!options.sync + self.nounique = !!options.nounique + self.nonull = !!options.nonull + self.nosort = !!options.nosort + self.nocase = !!options.nocase + self.stat = !!options.stat + self.noprocess = !!options.noprocess + + self.maxLength = options.maxLength || Infinity + self.cache = options.cache || Object.create(null) + self.statCache = options.statCache || Object.create(null) + self.symlinks = options.symlinks || Object.create(null) + + setupIgnores(self, options) + + self.changedCwd = false + var cwd = process.cwd() + if (!ownProp(options, "cwd")) + self.cwd = cwd + else { + self.cwd = options.cwd + self.changedCwd = path.resolve(options.cwd) !== cwd + } + + self.root = options.root || path.resolve(self.cwd, "/") + self.root = path.resolve(self.root) + if (process.platform === "win32") + self.root = self.root.replace(/\\/g, "/") + + self.nomount = !!options.nomount + + // disable comments and negation unless the user explicitly + // passes in false as the option. + options.nonegate = options.nonegate === false ? false : true + options.nocomment = options.nocomment === false ? false : true + deprecationWarning(options) + + self.minimatch = new Minimatch(pattern, options) + self.options = self.minimatch.options +} + +// TODO(isaacs): remove entirely in v6 +// exported to reset in tests +exports.deprecationWarned +function deprecationWarning(options) { + if (!options.nonegate || !options.nocomment) { + if (process.noDeprecation !== true && !exports.deprecationWarned) { + var msg = 'glob WARNING: comments and negation will be disabled in v6' + if (process.throwDeprecation) + throw new Error(msg) + else if (process.traceDeprecation) + console.trace(msg) + else + console.error(msg) + + exports.deprecationWarned = true + } + } +} + +function finish (self) { + var nou = self.nounique + var all = nou ? [] : Object.create(null) + + for (var i = 0, l = self.matches.length; i < l; i ++) { + var matches = self.matches[i] + if (!matches || Object.keys(matches).length === 0) { + if (self.nonull) { + // do like the shell, and spit out the literal glob + var literal = self.minimatch.globSet[i] + if (nou) + all.push(literal) + else + all[literal] = true + } + } else { + // had matches + var m = Object.keys(matches) + if (nou) + all.push.apply(all, m) + else + m.forEach(function (m) { + all[m] = true + }) + } + } + + if (!nou) + all = Object.keys(all) + + if (!self.nosort) + all = all.sort(self.nocase ? alphasorti : alphasort) + + // at *some* point we statted all of these + if (self.mark) { + for (var i = 0; i < all.length; i++) { + all[i] = self._mark(all[i]) + } + if (self.nodir) { + all = all.filter(function (e) { + return !(/\/$/.test(e)) + }) + } + } + + if (self.ignore.length) + all = all.filter(function(m) { + return !isIgnored(self, m) + }) + + self.found = all +} + +function mark (self, p) { + var abs = makeAbs(self, p) + var c = self.cache[abs] + var m = p + if (c) { + var isDir = c === 'DIR' || Array.isArray(c) + var slash = p.slice(-1) === '/' + + if (isDir && !slash) + m += '/' + else if (!isDir && slash) + m = m.slice(0, -1) + + if (m !== p) { + var mabs = makeAbs(self, m) + self.statCache[mabs] = self.statCache[abs] + self.cache[mabs] = self.cache[abs] + } + } + + return m +} + +// lotta situps... +function makeAbs (self, f) { + var abs = f + if (f.charAt(0) === '/') { + abs = path.join(self.root, f) + } else if (isAbsolute(f) || f === '') { + abs = f + } else if (self.changedCwd) { + abs = path.resolve(self.cwd, f) + } else { + abs = path.resolve(f) + } + return abs +} + + +// Return true, if pattern ends with globstar '**', for the accompanying parent directory. +// Ex:- If node_modules/** is the pattern, add 'node_modules' to ignore list along with it's contents +function isIgnored (self, path) { + if (!self.ignore.length) + return false + + return self.ignore.some(function(item) { + return item.matcher.match(path) || !!(item.gmatcher && item.gmatcher.match(path)) + }) +} + +function childrenIgnored (self, path) { + if (!self.ignore.length) + return false + + return self.ignore.some(function(item) { + return !!(item.gmatcher && item.gmatcher.match(path)) + }) +} + +}).call(this,require('_process')) +},{"_process":24,"minimatch":20,"path":22,"path-is-absolute":23}],16:[function(require,module,exports){ +(function (process){ +// Approach: +// +// 1. Get the minimatch set +// 2. For each pattern in the set, PROCESS(pattern, false) +// 3. Store matches per-set, then uniq them +// +// PROCESS(pattern, inGlobStar) +// Get the first [n] items from pattern that are all strings +// Join these together. This is PREFIX. +// If there is no more remaining, then stat(PREFIX) and +// add to matches if it succeeds. END. +// +// If inGlobStar and PREFIX is symlink and points to dir +// set ENTRIES = [] +// else readdir(PREFIX) as ENTRIES +// If fail, END +// +// with ENTRIES +// If pattern[n] is GLOBSTAR +// // handle the case where the globstar match is empty +// // by pruning it out, and testing the resulting pattern +// PROCESS(pattern[0..n] + pattern[n+1 .. $], false) +// // handle other cases. +// for ENTRY in ENTRIES (not dotfiles) +// // attach globstar + tail onto the entry +// // Mark that this entry is a globstar match +// PROCESS(pattern[0..n] + ENTRY + pattern[n .. $], true) +// +// else // not globstar +// for ENTRY in ENTRIES (not dotfiles, unless pattern[n] is dot) +// Test ENTRY against pattern[n] +// If fails, continue +// If passes, PROCESS(pattern[0..n] + item + pattern[n+1 .. $]) +// +// Caveat: +// Cache all stats and readdirs results to minimize syscall. Since all +// we ever care about is existence and directory-ness, we can just keep +// `true` for files, and [children,...] for directories, or `false` for +// things that don't exist. + +module.exports = glob + +var fs = require('fs') +var minimatch = require('minimatch') +var Minimatch = minimatch.Minimatch +var inherits = require('inherits') +var EE = require('events').EventEmitter +var path = require('path') +var assert = require('assert') +var isAbsolute = require('path-is-absolute') +var globSync = require('./sync.js') +var common = require('./common.js') +var alphasort = common.alphasort +var alphasorti = common.alphasorti +var setopts = common.setopts +var ownProp = common.ownProp +var inflight = require('inflight') +var util = require('util') +var childrenIgnored = common.childrenIgnored +var isIgnored = common.isIgnored + +var once = require('once') + +function glob (pattern, options, cb) { + if (typeof options === 'function') cb = options, options = {} + if (!options) options = {} + + if (options.sync) { + if (cb) + throw new TypeError('callback provided to sync glob') + return globSync(pattern, options) + } + + return new Glob(pattern, options, cb) +} + +glob.sync = globSync +var GlobSync = glob.GlobSync = globSync.GlobSync + +// old api surface +glob.glob = glob + +glob.hasMagic = function (pattern, options_) { + var options = util._extend({}, options_) + options.noprocess = true + + var g = new Glob(pattern, options) + var set = g.minimatch.set + if (set.length > 1) + return true + + for (var j = 0; j < set[0].length; j++) { + if (typeof set[0][j] !== 'string') + return true + } + + return false +} + +glob.Glob = Glob +inherits(Glob, EE) +function Glob (pattern, options, cb) { + if (typeof options === 'function') { + cb = options + options = null + } + + if (options && options.sync) { + if (cb) + throw new TypeError('callback provided to sync glob') + return new GlobSync(pattern, options) + } + + if (!(this instanceof Glob)) + return new Glob(pattern, options, cb) + + setopts(this, pattern, options) + this._didRealPath = false + + // process each pattern in the minimatch set + var n = this.minimatch.set.length + + // The matches are stored as {: true,...} so that + // duplicates are automagically pruned. + // Later, we do an Object.keys() on these. + // Keep them as a list so we can fill in when nonull is set. + this.matches = new Array(n) + + if (typeof cb === 'function') { + cb = once(cb) + this.on('error', cb) + this.on('end', function (matches) { + cb(null, matches) + }) + } + + var self = this + var n = this.minimatch.set.length + this._processing = 0 + this.matches = new Array(n) + + this._emitQueue = [] + this._processQueue = [] + this.paused = false + + if (this.noprocess) + return this + + if (n === 0) + return done() + + for (var i = 0; i < n; i ++) { + this._process(this.minimatch.set[i], i, false, done) + } + + function done () { + --self._processing + if (self._processing <= 0) + self._finish() + } +} + +Glob.prototype._finish = function () { + assert(this instanceof Glob) + if (this.aborted) + return + + if (this.realpath && !this._didRealpath) + return this._realpath() + + common.finish(this) + this.emit('end', this.found) +} + +Glob.prototype._realpath = function () { + if (this._didRealpath) + return + + this._didRealpath = true + + var n = this.matches.length + if (n === 0) + return this._finish() + + var self = this + for (var i = 0; i < this.matches.length; i++) + this._realpathSet(i, next) + + function next () { + if (--n === 0) + self._finish() + } +} + +Glob.prototype._realpathSet = function (index, cb) { + var matchset = this.matches[index] + if (!matchset) + return cb() + + var found = Object.keys(matchset) + var self = this + var n = found.length + + if (n === 0) + return cb() + + var set = this.matches[index] = Object.create(null) + found.forEach(function (p, i) { + // If there's a problem with the stat, then it means that + // one or more of the links in the realpath couldn't be + // resolved. just return the abs value in that case. + p = self._makeAbs(p) + fs.realpath(p, self.realpathCache, function (er, real) { + if (!er) + set[real] = true + else if (er.syscall === 'stat') + set[p] = true + else + self.emit('error', er) // srsly wtf right here + + if (--n === 0) { + self.matches[index] = set + cb() + } + }) + }) +} + +Glob.prototype._mark = function (p) { + return common.mark(this, p) +} + +Glob.prototype._makeAbs = function (f) { + return common.makeAbs(this, f) +} + +Glob.prototype.abort = function () { + this.aborted = true + this.emit('abort') +} + +Glob.prototype.pause = function () { + if (!this.paused) { + this.paused = true + this.emit('pause') + } +} + +Glob.prototype.resume = function () { + if (this.paused) { + this.emit('resume') + this.paused = false + if (this._emitQueue.length) { + var eq = this._emitQueue.slice(0) + this._emitQueue.length = 0 + for (var i = 0; i < eq.length; i ++) { + var e = eq[i] + this._emitMatch(e[0], e[1]) + } + } + if (this._processQueue.length) { + var pq = this._processQueue.slice(0) + this._processQueue.length = 0 + for (var i = 0; i < pq.length; i ++) { + var p = pq[i] + this._processing-- + this._process(p[0], p[1], p[2], p[3]) + } + } + } +} + +Glob.prototype._process = function (pattern, index, inGlobStar, cb) { + assert(this instanceof Glob) + assert(typeof cb === 'function') + + if (this.aborted) + return + + this._processing++ + if (this.paused) { + this._processQueue.push([pattern, index, inGlobStar, cb]) + return + } + + //console.error('PROCESS %d', this._processing, pattern) + + // Get the first [n] parts of pattern that are all strings. + var n = 0 + while (typeof pattern[n] === 'string') { + n ++ + } + // now n is the index of the first one that is *not* a string. + + // see if there's anything else + var prefix + switch (n) { + // if not, then this is rather simple + case pattern.length: + this._processSimple(pattern.join('/'), index, cb) + return + + case 0: + // pattern *starts* with some non-trivial item. + // going to readdir(cwd), but not include the prefix in matches. + prefix = null + break + + default: + // pattern has some string bits in the front. + // whatever it starts with, whether that's 'absolute' like /foo/bar, + // or 'relative' like '../baz' + prefix = pattern.slice(0, n).join('/') + break + } + + var remain = pattern.slice(n) + + // get the list of entries. + var read + if (prefix === null) + read = '.' + else if (isAbsolute(prefix) || isAbsolute(pattern.join('/'))) { + if (!prefix || !isAbsolute(prefix)) + prefix = '/' + prefix + read = prefix + } else + read = prefix + + var abs = this._makeAbs(read) + + //if ignored, skip _processing + if (childrenIgnored(this, read)) + return cb() + + var isGlobStar = remain[0] === minimatch.GLOBSTAR + if (isGlobStar) + this._processGlobStar(prefix, read, abs, remain, index, inGlobStar, cb) + else + this._processReaddir(prefix, read, abs, remain, index, inGlobStar, cb) +} + +Glob.prototype._processReaddir = function (prefix, read, abs, remain, index, inGlobStar, cb) { + var self = this + this._readdir(abs, inGlobStar, function (er, entries) { + return self._processReaddir2(prefix, read, abs, remain, index, inGlobStar, entries, cb) + }) +} + +Glob.prototype._processReaddir2 = function (prefix, read, abs, remain, index, inGlobStar, entries, cb) { + + // if the abs isn't a dir, then nothing can match! + if (!entries) + return cb() + + // It will only match dot entries if it starts with a dot, or if + // dot is set. Stuff like @(.foo|.bar) isn't allowed. + var pn = remain[0] + var negate = !!this.minimatch.negate + var rawGlob = pn._glob + var dotOk = this.dot || rawGlob.charAt(0) === '.' + + var matchedEntries = [] + for (var i = 0; i < entries.length; i++) { + var e = entries[i] + if (e.charAt(0) !== '.' || dotOk) { + var m + if (negate && !prefix) { + m = !e.match(pn) + } else { + m = e.match(pn) + } + if (m) + matchedEntries.push(e) + } + } + + //console.error('prd2', prefix, entries, remain[0]._glob, matchedEntries) + + var len = matchedEntries.length + // If there are no matched entries, then nothing matches. + if (len === 0) + return cb() + + // if this is the last remaining pattern bit, then no need for + // an additional stat *unless* the user has specified mark or + // stat explicitly. We know they exist, since readdir returned + // them. + + if (remain.length === 1 && !this.mark && !this.stat) { + if (!this.matches[index]) + this.matches[index] = Object.create(null) + + for (var i = 0; i < len; i ++) { + var e = matchedEntries[i] + if (prefix) { + if (prefix !== '/') + e = prefix + '/' + e + else + e = prefix + e + } + + if (e.charAt(0) === '/' && !this.nomount) { + e = path.join(this.root, e) + } + this._emitMatch(index, e) + } + // This was the last one, and no stats were needed + return cb() + } + + // now test all matched entries as stand-ins for that part + // of the pattern. + remain.shift() + for (var i = 0; i < len; i ++) { + var e = matchedEntries[i] + var newPattern + if (prefix) { + if (prefix !== '/') + e = prefix + '/' + e + else + e = prefix + e + } + this._process([e].concat(remain), index, inGlobStar, cb) + } + cb() +} + +Glob.prototype._emitMatch = function (index, e) { + if (this.aborted) + return + + if (this.matches[index][e]) + return + + if (isIgnored(this, e)) + return + + if (this.paused) { + this._emitQueue.push([index, e]) + return + } + + var abs = this._makeAbs(e) + + if (this.nodir) { + var c = this.cache[abs] + if (c === 'DIR' || Array.isArray(c)) + return + } + + if (this.mark) + e = this._mark(e) + + this.matches[index][e] = true + + var st = this.statCache[abs] + if (st) + this.emit('stat', e, st) + + this.emit('match', e) +} + +Glob.prototype._readdirInGlobStar = function (abs, cb) { + if (this.aborted) + return + + // follow all symlinked directories forever + // just proceed as if this is a non-globstar situation + if (this.follow) + return this._readdir(abs, false, cb) + + var lstatkey = 'lstat\0' + abs + var self = this + var lstatcb = inflight(lstatkey, lstatcb_) + + if (lstatcb) + fs.lstat(abs, lstatcb) + + function lstatcb_ (er, lstat) { + if (er) + return cb() + + var isSym = lstat.isSymbolicLink() + self.symlinks[abs] = isSym + + // If it's not a symlink or a dir, then it's definitely a regular file. + // don't bother doing a readdir in that case. + if (!isSym && !lstat.isDirectory()) { + self.cache[abs] = 'FILE' + cb() + } else + self._readdir(abs, false, cb) + } +} + +Glob.prototype._readdir = function (abs, inGlobStar, cb) { + if (this.aborted) + return + + cb = inflight('readdir\0'+abs+'\0'+inGlobStar, cb) + if (!cb) + return + + //console.error('RD %j %j', +inGlobStar, abs) + if (inGlobStar && !ownProp(this.symlinks, abs)) + return this._readdirInGlobStar(abs, cb) + + if (ownProp(this.cache, abs)) { + var c = this.cache[abs] + if (!c || c === 'FILE') + return cb() + + if (Array.isArray(c)) + return cb(null, c) + } + + var self = this + fs.readdir(abs, readdirCb(this, abs, cb)) +} + +function readdirCb (self, abs, cb) { + return function (er, entries) { + if (er) + self._readdirError(abs, er, cb) + else + self._readdirEntries(abs, entries, cb) + } +} + +Glob.prototype._readdirEntries = function (abs, entries, cb) { + if (this.aborted) + return + + // if we haven't asked to stat everything, then just + // assume that everything in there exists, so we can avoid + // having to stat it a second time. + if (!this.mark && !this.stat) { + for (var i = 0; i < entries.length; i ++) { + var e = entries[i] + if (abs === '/') + e = abs + e + else + e = abs + '/' + e + this.cache[e] = true + } + } + + this.cache[abs] = entries + return cb(null, entries) +} + +Glob.prototype._readdirError = function (f, er, cb) { + if (this.aborted) + return + + // handle errors, and cache the information + switch (er.code) { + case 'ENOTSUP': // https://github.com/isaacs/node-glob/issues/205 + case 'ENOTDIR': // totally normal. means it *does* exist. + this.cache[this._makeAbs(f)] = 'FILE' + break + + case 'ENOENT': // not terribly unusual + case 'ELOOP': + case 'ENAMETOOLONG': + case 'UNKNOWN': + this.cache[this._makeAbs(f)] = false + break + + default: // some unusual error. Treat as failure. + this.cache[this._makeAbs(f)] = false + if (this.strict) { + this.emit('error', er) + // If the error is handled, then we abort + // if not, we threw out of here + this.abort() + } + if (!this.silent) + console.error('glob error', er) + break + } + + return cb() +} + +Glob.prototype._processGlobStar = function (prefix, read, abs, remain, index, inGlobStar, cb) { + var self = this + this._readdir(abs, inGlobStar, function (er, entries) { + self._processGlobStar2(prefix, read, abs, remain, index, inGlobStar, entries, cb) + }) +} + + +Glob.prototype._processGlobStar2 = function (prefix, read, abs, remain, index, inGlobStar, entries, cb) { + //console.error('pgs2', prefix, remain[0], entries) + + // no entries means not a dir, so it can never have matches + // foo.txt/** doesn't match foo.txt + if (!entries) + return cb() + + // test without the globstar, and with every child both below + // and replacing the globstar. + var remainWithoutGlobStar = remain.slice(1) + var gspref = prefix ? [ prefix ] : [] + var noGlobStar = gspref.concat(remainWithoutGlobStar) + + // the noGlobStar pattern exits the inGlobStar state + this._process(noGlobStar, index, false, cb) + + var isSym = this.symlinks[abs] + var len = entries.length + + // If it's a symlink, and we're in a globstar, then stop + if (isSym && inGlobStar) + return cb() + + for (var i = 0; i < len; i++) { + var e = entries[i] + if (e.charAt(0) === '.' && !this.dot) + continue + + // these two cases enter the inGlobStar state + var instead = gspref.concat(entries[i], remainWithoutGlobStar) + this._process(instead, index, true, cb) + + var below = gspref.concat(entries[i], remain) + this._process(below, index, true, cb) + } + + cb() +} + +Glob.prototype._processSimple = function (prefix, index, cb) { + // XXX review this. Shouldn't it be doing the mounting etc + // before doing stat? kinda weird? + var self = this + this._stat(prefix, function (er, exists) { + self._processSimple2(prefix, index, er, exists, cb) + }) +} +Glob.prototype._processSimple2 = function (prefix, index, er, exists, cb) { + + //console.error('ps2', prefix, exists) + + if (!this.matches[index]) + this.matches[index] = Object.create(null) + + // If it doesn't exist, then just mark the lack of results + if (!exists) + return cb() + + if (prefix && isAbsolute(prefix) && !this.nomount) { + var trail = /[\/\\]$/.test(prefix) + if (prefix.charAt(0) === '/') { + prefix = path.join(this.root, prefix) + } else { + prefix = path.resolve(this.root, prefix) + if (trail) + prefix += '/' + } + } + + if (process.platform === 'win32') + prefix = prefix.replace(/\\/g, '/') + + // Mark this as a match + this._emitMatch(index, prefix) + cb() +} + +// Returns either 'DIR', 'FILE', or false +Glob.prototype._stat = function (f, cb) { + var abs = this._makeAbs(f) + var needDir = f.slice(-1) === '/' + + if (f.length > this.maxLength) + return cb() + + if (!this.stat && ownProp(this.cache, abs)) { + var c = this.cache[abs] + + if (Array.isArray(c)) + c = 'DIR' + + // It exists, but maybe not how we need it + if (!needDir || c === 'DIR') + return cb(null, c) + + if (needDir && c === 'FILE') + return cb() + + // otherwise we have to stat, because maybe c=true + // if we know it exists, but not what it is. + } + + var exists + var stat = this.statCache[abs] + if (stat !== undefined) { + if (stat === false) + return cb(null, stat) + else { + var type = stat.isDirectory() ? 'DIR' : 'FILE' + if (needDir && type === 'FILE') + return cb() + else + return cb(null, type, stat) + } + } + + var self = this + var statcb = inflight('stat\0' + abs, lstatcb_) + if (statcb) + fs.lstat(abs, statcb) + + function lstatcb_ (er, lstat) { + if (lstat && lstat.isSymbolicLink()) { + // If it's a symlink, then treat it as the target, unless + // the target does not exist, then treat it as a file. + return fs.stat(abs, function (er, stat) { + if (er) + self._stat2(f, abs, null, lstat, cb) + else + self._stat2(f, abs, er, stat, cb) + }) + } else { + self._stat2(f, abs, er, lstat, cb) + } + } +} + +Glob.prototype._stat2 = function (f, abs, er, stat, cb) { + if (er) { + this.statCache[abs] = false + return cb() + } + + var needDir = f.slice(-1) === '/' + this.statCache[abs] = stat + + if (abs.slice(-1) === '/' && !stat.isDirectory()) + return cb(null, false, stat) + + var c = stat.isDirectory() ? 'DIR' : 'FILE' + this.cache[abs] = this.cache[abs] || c + + if (needDir && c !== 'DIR') + return cb() + + return cb(null, c, stat) +} + +}).call(this,require('_process')) +},{"./common.js":15,"./sync.js":17,"_process":24,"assert":9,"events":14,"fs":12,"inflight":18,"inherits":19,"minimatch":20,"once":21,"path":22,"path-is-absolute":23,"util":28}],17:[function(require,module,exports){ +(function (process){ +module.exports = globSync +globSync.GlobSync = GlobSync + +var fs = require('fs') +var minimatch = require('minimatch') +var Minimatch = minimatch.Minimatch +var Glob = require('./glob.js').Glob +var util = require('util') +var path = require('path') +var assert = require('assert') +var isAbsolute = require('path-is-absolute') +var common = require('./common.js') +var alphasort = common.alphasort +var alphasorti = common.alphasorti +var setopts = common.setopts +var ownProp = common.ownProp +var childrenIgnored = common.childrenIgnored + +function globSync (pattern, options) { + if (typeof options === 'function' || arguments.length === 3) + throw new TypeError('callback provided to sync glob\n'+ + 'See: https://github.com/isaacs/node-glob/issues/167') + + return new GlobSync(pattern, options).found +} + +function GlobSync (pattern, options) { + if (!pattern) + throw new Error('must provide pattern') + + if (typeof options === 'function' || arguments.length === 3) + throw new TypeError('callback provided to sync glob\n'+ + 'See: https://github.com/isaacs/node-glob/issues/167') + + if (!(this instanceof GlobSync)) + return new GlobSync(pattern, options) + + setopts(this, pattern, options) + + if (this.noprocess) + return this + + var n = this.minimatch.set.length + this.matches = new Array(n) + for (var i = 0; i < n; i ++) { + this._process(this.minimatch.set[i], i, false) + } + this._finish() +} + +GlobSync.prototype._finish = function () { + assert(this instanceof GlobSync) + if (this.realpath) { + var self = this + this.matches.forEach(function (matchset, index) { + var set = self.matches[index] = Object.create(null) + for (var p in matchset) { + try { + p = self._makeAbs(p) + var real = fs.realpathSync(p, self.realpathCache) + set[real] = true + } catch (er) { + if (er.syscall === 'stat') + set[self._makeAbs(p)] = true + else + throw er + } + } + }) + } + common.finish(this) +} + + +GlobSync.prototype._process = function (pattern, index, inGlobStar) { + assert(this instanceof GlobSync) + + // Get the first [n] parts of pattern that are all strings. + var n = 0 + while (typeof pattern[n] === 'string') { + n ++ + } + // now n is the index of the first one that is *not* a string. + + // See if there's anything else + var prefix + switch (n) { + // if not, then this is rather simple + case pattern.length: + this._processSimple(pattern.join('/'), index) + return + + case 0: + // pattern *starts* with some non-trivial item. + // going to readdir(cwd), but not include the prefix in matches. + prefix = null + break + + default: + // pattern has some string bits in the front. + // whatever it starts with, whether that's 'absolute' like /foo/bar, + // or 'relative' like '../baz' + prefix = pattern.slice(0, n).join('/') + break + } + + var remain = pattern.slice(n) + + // get the list of entries. + var read + if (prefix === null) + read = '.' + else if (isAbsolute(prefix) || isAbsolute(pattern.join('/'))) { + if (!prefix || !isAbsolute(prefix)) + prefix = '/' + prefix + read = prefix + } else + read = prefix + + var abs = this._makeAbs(read) + + //if ignored, skip processing + if (childrenIgnored(this, read)) + return + + var isGlobStar = remain[0] === minimatch.GLOBSTAR + if (isGlobStar) + this._processGlobStar(prefix, read, abs, remain, index, inGlobStar) + else + this._processReaddir(prefix, read, abs, remain, index, inGlobStar) +} + + +GlobSync.prototype._processReaddir = function (prefix, read, abs, remain, index, inGlobStar) { + var entries = this._readdir(abs, inGlobStar) + + // if the abs isn't a dir, then nothing can match! + if (!entries) + return + + // It will only match dot entries if it starts with a dot, or if + // dot is set. Stuff like @(.foo|.bar) isn't allowed. + var pn = remain[0] + var negate = !!this.minimatch.negate + var rawGlob = pn._glob + var dotOk = this.dot || rawGlob.charAt(0) === '.' + + var matchedEntries = [] + for (var i = 0; i < entries.length; i++) { + var e = entries[i] + if (e.charAt(0) !== '.' || dotOk) { + var m + if (negate && !prefix) { + m = !e.match(pn) + } else { + m = e.match(pn) + } + if (m) + matchedEntries.push(e) + } + } + + var len = matchedEntries.length + // If there are no matched entries, then nothing matches. + if (len === 0) + return + + // if this is the last remaining pattern bit, then no need for + // an additional stat *unless* the user has specified mark or + // stat explicitly. We know they exist, since readdir returned + // them. + + if (remain.length === 1 && !this.mark && !this.stat) { + if (!this.matches[index]) + this.matches[index] = Object.create(null) + + for (var i = 0; i < len; i ++) { + var e = matchedEntries[i] + if (prefix) { + if (prefix.slice(-1) !== '/') + e = prefix + '/' + e + else + e = prefix + e + } + + if (e.charAt(0) === '/' && !this.nomount) { + e = path.join(this.root, e) + } + this.matches[index][e] = true + } + // This was the last one, and no stats were needed + return + } + + // now test all matched entries as stand-ins for that part + // of the pattern. + remain.shift() + for (var i = 0; i < len; i ++) { + var e = matchedEntries[i] + var newPattern + if (prefix) + newPattern = [prefix, e] + else + newPattern = [e] + this._process(newPattern.concat(remain), index, inGlobStar) + } +} + + +GlobSync.prototype._emitMatch = function (index, e) { + var abs = this._makeAbs(e) + if (this.mark) + e = this._mark(e) + + if (this.matches[index][e]) + return + + if (this.nodir) { + var c = this.cache[this._makeAbs(e)] + if (c === 'DIR' || Array.isArray(c)) + return + } + + this.matches[index][e] = true + if (this.stat) + this._stat(e) +} + + +GlobSync.prototype._readdirInGlobStar = function (abs) { + // follow all symlinked directories forever + // just proceed as if this is a non-globstar situation + if (this.follow) + return this._readdir(abs, false) + + var entries + var lstat + var stat + try { + lstat = fs.lstatSync(abs) + } catch (er) { + // lstat failed, doesn't exist + return null + } + + var isSym = lstat.isSymbolicLink() + this.symlinks[abs] = isSym + + // If it's not a symlink or a dir, then it's definitely a regular file. + // don't bother doing a readdir in that case. + if (!isSym && !lstat.isDirectory()) + this.cache[abs] = 'FILE' + else + entries = this._readdir(abs, false) + + return entries +} + +GlobSync.prototype._readdir = function (abs, inGlobStar) { + var entries + + if (inGlobStar && !ownProp(this.symlinks, abs)) + return this._readdirInGlobStar(abs) + + if (ownProp(this.cache, abs)) { + var c = this.cache[abs] + if (!c || c === 'FILE') + return null + + if (Array.isArray(c)) + return c + } + + try { + return this._readdirEntries(abs, fs.readdirSync(abs)) + } catch (er) { + this._readdirError(abs, er) + return null + } +} + +GlobSync.prototype._readdirEntries = function (abs, entries) { + // if we haven't asked to stat everything, then just + // assume that everything in there exists, so we can avoid + // having to stat it a second time. + if (!this.mark && !this.stat) { + for (var i = 0; i < entries.length; i ++) { + var e = entries[i] + if (abs === '/') + e = abs + e + else + e = abs + '/' + e + this.cache[e] = true + } + } + + this.cache[abs] = entries + + // mark and cache dir-ness + return entries +} + +GlobSync.prototype._readdirError = function (f, er) { + // handle errors, and cache the information + switch (er.code) { + case 'ENOTSUP': // https://github.com/isaacs/node-glob/issues/205 + case 'ENOTDIR': // totally normal. means it *does* exist. + this.cache[this._makeAbs(f)] = 'FILE' + break + + case 'ENOENT': // not terribly unusual + case 'ELOOP': + case 'ENAMETOOLONG': + case 'UNKNOWN': + this.cache[this._makeAbs(f)] = false + break + + default: // some unusual error. Treat as failure. + this.cache[this._makeAbs(f)] = false + if (this.strict) + throw er + if (!this.silent) + console.error('glob error', er) + break + } +} + +GlobSync.prototype._processGlobStar = function (prefix, read, abs, remain, index, inGlobStar) { + + var entries = this._readdir(abs, inGlobStar) + + // no entries means not a dir, so it can never have matches + // foo.txt/** doesn't match foo.txt + if (!entries) + return + + // test without the globstar, and with every child both below + // and replacing the globstar. + var remainWithoutGlobStar = remain.slice(1) + var gspref = prefix ? [ prefix ] : [] + var noGlobStar = gspref.concat(remainWithoutGlobStar) + + // the noGlobStar pattern exits the inGlobStar state + this._process(noGlobStar, index, false) + + var len = entries.length + var isSym = this.symlinks[abs] + + // If it's a symlink, and we're in a globstar, then stop + if (isSym && inGlobStar) + return + + for (var i = 0; i < len; i++) { + var e = entries[i] + if (e.charAt(0) === '.' && !this.dot) + continue + + // these two cases enter the inGlobStar state + var instead = gspref.concat(entries[i], remainWithoutGlobStar) + this._process(instead, index, true) + + var below = gspref.concat(entries[i], remain) + this._process(below, index, true) + } +} + +GlobSync.prototype._processSimple = function (prefix, index) { + // XXX review this. Shouldn't it be doing the mounting etc + // before doing stat? kinda weird? + var exists = this._stat(prefix) + + if (!this.matches[index]) + this.matches[index] = Object.create(null) + + // If it doesn't exist, then just mark the lack of results + if (!exists) + return + + if (prefix && isAbsolute(prefix) && !this.nomount) { + var trail = /[\/\\]$/.test(prefix) + if (prefix.charAt(0) === '/') { + prefix = path.join(this.root, prefix) + } else { + prefix = path.resolve(this.root, prefix) + if (trail) + prefix += '/' + } + } + + if (process.platform === 'win32') + prefix = prefix.replace(/\\/g, '/') + + // Mark this as a match + this.matches[index][prefix] = true +} + +// Returns either 'DIR', 'FILE', or false +GlobSync.prototype._stat = function (f) { + var abs = this._makeAbs(f) + var needDir = f.slice(-1) === '/' + + if (f.length > this.maxLength) + return false + + if (!this.stat && ownProp(this.cache, abs)) { + var c = this.cache[abs] + + if (Array.isArray(c)) + c = 'DIR' + + // It exists, but maybe not how we need it + if (!needDir || c === 'DIR') + return c + + if (needDir && c === 'FILE') + return false + + // otherwise we have to stat, because maybe c=true + // if we know it exists, but not what it is. + } + + var exists + var stat = this.statCache[abs] + if (!stat) { + var lstat + try { + lstat = fs.lstatSync(abs) + } catch (er) { + return false + } + + if (lstat.isSymbolicLink()) { + try { + stat = fs.statSync(abs) + } catch (er) { + stat = lstat + } + } else { + stat = lstat + } + } + + this.statCache[abs] = stat + + var c = stat.isDirectory() ? 'DIR' : 'FILE' + this.cache[abs] = this.cache[abs] || c + + if (needDir && c !== 'DIR') + return false + + return c +} + +GlobSync.prototype._mark = function (p) { + return common.mark(this, p) +} + +GlobSync.prototype._makeAbs = function (f) { + return common.makeAbs(this, f) +} + +}).call(this,require('_process')) +},{"./common.js":15,"./glob.js":16,"_process":24,"assert":9,"fs":12,"minimatch":20,"path":22,"path-is-absolute":23,"util":28}],18:[function(require,module,exports){ +(function (process){ +var wrappy = require('wrappy') +var reqs = Object.create(null) +var once = require('once') + +module.exports = wrappy(inflight) + +function inflight (key, cb) { + if (reqs[key]) { + reqs[key].push(cb) + return null + } else { + reqs[key] = [cb] + return makeres(key) + } +} + +function makeres (key) { + return once(function RES () { + var cbs = reqs[key] + var len = cbs.length + var args = slice(arguments) + + // XXX It's somewhat ambiguous whether a new callback added in this + // pass should be queued for later execution if something in the + // list of callbacks throws, or if it should just be discarded. + // However, it's such an edge case that it hardly matters, and either + // choice is likely as surprising as the other. + // As it happens, we do go ahead and schedule it for later execution. + try { + for (var i = 0; i < len; i++) { + cbs[i].apply(null, args) + } + } finally { + if (cbs.length > len) { + // added more in the interim. + // de-zalgo, just in case, but don't call again. + cbs.splice(0, len) + process.nextTick(function () { + RES.apply(null, args) + }) + } else { + delete reqs[key] + } + } + }) +} + +function slice (args) { + var length = args.length + var array = [] + + for (var i = 0; i < length; i++) array[i] = args[i] + return array +} + +}).call(this,require('_process')) +},{"_process":24,"once":21,"wrappy":29}],19:[function(require,module,exports){ +if (typeof Object.create === 'function') { + // implementation from standard node.js 'util' module + module.exports = function inherits(ctor, superCtor) { + ctor.super_ = superCtor + ctor.prototype = Object.create(superCtor.prototype, { + constructor: { + value: ctor, + enumerable: false, + writable: true, + configurable: true + } + }); + }; +} else { + // old school shim for old browsers + module.exports = function inherits(ctor, superCtor) { + ctor.super_ = superCtor + var TempCtor = function () {} + TempCtor.prototype = superCtor.prototype + ctor.prototype = new TempCtor() + ctor.prototype.constructor = ctor + } +} + +},{}],20:[function(require,module,exports){ +module.exports = minimatch +minimatch.Minimatch = Minimatch + +var path = { sep: '/' } +try { + path = require('path') +} catch (er) {} + +var GLOBSTAR = minimatch.GLOBSTAR = Minimatch.GLOBSTAR = {} +var expand = require('brace-expansion') + +var plTypes = { + '!': { open: '(?:(?!(?:', close: '))[^/]*?)'}, + '?': { open: '(?:', close: ')?' }, + '+': { open: '(?:', close: ')+' }, + '*': { open: '(?:', close: ')*' }, + '@': { open: '(?:', close: ')' } +} + +// any single thing other than / +// don't need to escape / when using new RegExp() +var qmark = '[^/]' + +// * => any number of characters +var star = qmark + '*?' + +// ** when dots are allowed. Anything goes, except .. and . +// not (^ or / followed by one or two dots followed by $ or /), +// followed by anything, any number of times. +var twoStarDot = '(?:(?!(?:\\\/|^)(?:\\.{1,2})($|\\\/)).)*?' + +// not a ^ or / followed by a dot, +// followed by anything, any number of times. +var twoStarNoDot = '(?:(?!(?:\\\/|^)\\.).)*?' + +// characters that need to be escaped in RegExp. +var reSpecials = charSet('().*{}+?[]^$\\!') + +// "abc" -> { a:true, b:true, c:true } +function charSet (s) { + return s.split('').reduce(function (set, c) { + set[c] = true + return set + }, {}) +} + +// normalizes slashes. +var slashSplit = /\/+/ + +minimatch.filter = filter +function filter (pattern, options) { + options = options || {} + return function (p, i, list) { + return minimatch(p, pattern, options) + } +} + +function ext (a, b) { + a = a || {} + b = b || {} + var t = {} + Object.keys(b).forEach(function (k) { + t[k] = b[k] + }) + Object.keys(a).forEach(function (k) { + t[k] = a[k] + }) + return t +} + +minimatch.defaults = function (def) { + if (!def || !Object.keys(def).length) return minimatch + + var orig = minimatch + + var m = function minimatch (p, pattern, options) { + return orig.minimatch(p, pattern, ext(def, options)) + } + + m.Minimatch = function Minimatch (pattern, options) { + return new orig.Minimatch(pattern, ext(def, options)) + } + + return m +} + +Minimatch.defaults = function (def) { + if (!def || !Object.keys(def).length) return Minimatch + return minimatch.defaults(def).Minimatch +} + +function minimatch (p, pattern, options) { + if (typeof pattern !== 'string') { + throw new TypeError('glob pattern string required') + } + + if (!options) options = {} + + // shortcut: comments match nothing. + if (!options.nocomment && pattern.charAt(0) === '#') { + return false + } + + // "" only matches "" + if (pattern.trim() === '') return p === '' + + return new Minimatch(pattern, options).match(p) +} + +function Minimatch (pattern, options) { + if (!(this instanceof Minimatch)) { + return new Minimatch(pattern, options) + } + + if (typeof pattern !== 'string') { + throw new TypeError('glob pattern string required') + } + + if (!options) options = {} + pattern = pattern.trim() + + // windows support: need to use /, not \ + if (path.sep !== '/') { + pattern = pattern.split(path.sep).join('/') + } + + this.options = options + this.set = [] + this.pattern = pattern + this.regexp = null + this.negate = false + this.comment = false + this.empty = false + + // make the set of regexps etc. + this.make() +} + +Minimatch.prototype.debug = function () {} + +Minimatch.prototype.make = make +function make () { + // don't do it more than once. + if (this._made) return + + var pattern = this.pattern + var options = this.options + + // empty patterns and comments match nothing. + if (!options.nocomment && pattern.charAt(0) === '#') { + this.comment = true + return + } + if (!pattern) { + this.empty = true + return + } + + // step 1: figure out negation, etc. + this.parseNegate() + + // step 2: expand braces + var set = this.globSet = this.braceExpand() + + if (options.debug) this.debug = console.error + + this.debug(this.pattern, set) + + // step 3: now we have a set, so turn each one into a series of path-portion + // matching patterns. + // These will be regexps, except in the case of "**", which is + // set to the GLOBSTAR object for globstar behavior, + // and will not contain any / characters + set = this.globParts = set.map(function (s) { + return s.split(slashSplit) + }) + + this.debug(this.pattern, set) + + // glob --> regexps + set = set.map(function (s, si, set) { + return s.map(this.parse, this) + }, this) + + this.debug(this.pattern, set) + + // filter out everything that didn't compile properly. + set = set.filter(function (s) { + return s.indexOf(false) === -1 + }) + + this.debug(this.pattern, set) + + this.set = set +} + +Minimatch.prototype.parseNegate = parseNegate +function parseNegate () { + var pattern = this.pattern + var negate = false + var options = this.options + var negateOffset = 0 + + if (options.nonegate) return + + for (var i = 0, l = pattern.length + ; i < l && pattern.charAt(i) === '!' + ; i++) { + negate = !negate + negateOffset++ + } + + if (negateOffset) this.pattern = pattern.substr(negateOffset) + this.negate = negate +} + +// Brace expansion: +// a{b,c}d -> abd acd +// a{b,}c -> abc ac +// a{0..3}d -> a0d a1d a2d a3d +// a{b,c{d,e}f}g -> abg acdfg acefg +// a{b,c}d{e,f}g -> abdeg acdeg abdeg abdfg +// +// Invalid sets are not expanded. +// a{2..}b -> a{2..}b +// a{b}c -> a{b}c +minimatch.braceExpand = function (pattern, options) { + return braceExpand(pattern, options) +} + +Minimatch.prototype.braceExpand = braceExpand + +function braceExpand (pattern, options) { + if (!options) { + if (this instanceof Minimatch) { + options = this.options + } else { + options = {} + } + } + + pattern = typeof pattern === 'undefined' + ? this.pattern : pattern + + if (typeof pattern === 'undefined') { + throw new TypeError('undefined pattern') + } + + if (options.nobrace || + !pattern.match(/\{.*\}/)) { + // shortcut. no need to expand. + return [pattern] + } + + return expand(pattern) +} + +// parse a component of the expanded set. +// At this point, no pattern may contain "/" in it +// so we're going to return a 2d array, where each entry is the full +// pattern, split on '/', and then turned into a regular expression. +// A regexp is made at the end which joins each array with an +// escaped /, and another full one which joins each regexp with |. +// +// Following the lead of Bash 4.1, note that "**" only has special meaning +// when it is the *only* thing in a path portion. Otherwise, any series +// of * is equivalent to a single *. Globstar behavior is enabled by +// default, and can be disabled by setting options.noglobstar. +Minimatch.prototype.parse = parse +var SUBPARSE = {} +function parse (pattern, isSub) { + if (pattern.length > 1024 * 64) { + throw new TypeError('pattern is too long') + } + + var options = this.options + + // shortcuts + if (!options.noglobstar && pattern === '**') return GLOBSTAR + if (pattern === '') return '' + + var re = '' + var hasMagic = !!options.nocase + var escaping = false + // ? => one single character + var patternListStack = [] + var negativeLists = [] + var stateChar + var inClass = false + var reClassStart = -1 + var classStart = -1 + // . and .. never match anything that doesn't start with ., + // even when options.dot is set. + var patternStart = pattern.charAt(0) === '.' ? '' // anything + // not (start or / followed by . or .. followed by / or end) + : options.dot ? '(?!(?:^|\\\/)\\.{1,2}(?:$|\\\/))' + : '(?!\\.)' + var self = this + + function clearStateChar () { + if (stateChar) { + // we had some state-tracking character + // that wasn't consumed by this pass. + switch (stateChar) { + case '*': + re += star + hasMagic = true + break + case '?': + re += qmark + hasMagic = true + break + default: + re += '\\' + stateChar + break + } + self.debug('clearStateChar %j %j', stateChar, re) + stateChar = false + } + } + + for (var i = 0, len = pattern.length, c + ; (i < len) && (c = pattern.charAt(i)) + ; i++) { + this.debug('%s\t%s %s %j', pattern, i, re, c) + + // skip over any that are escaped. + if (escaping && reSpecials[c]) { + re += '\\' + c + escaping = false + continue + } + + switch (c) { + case '/': + // completely not allowed, even escaped. + // Should already be path-split by now. + return false + + case '\\': + clearStateChar() + escaping = true + continue + + // the various stateChar values + // for the "extglob" stuff. + case '?': + case '*': + case '+': + case '@': + case '!': + this.debug('%s\t%s %s %j <-- stateChar', pattern, i, re, c) + + // all of those are literals inside a class, except that + // the glob [!a] means [^a] in regexp + if (inClass) { + this.debug(' in class') + if (c === '!' && i === classStart + 1) c = '^' + re += c + continue + } + + // if we already have a stateChar, then it means + // that there was something like ** or +? in there. + // Handle the stateChar, then proceed with this one. + self.debug('call clearStateChar %j', stateChar) + clearStateChar() + stateChar = c + // if extglob is disabled, then +(asdf|foo) isn't a thing. + // just clear the statechar *now*, rather than even diving into + // the patternList stuff. + if (options.noext) clearStateChar() + continue + + case '(': + if (inClass) { + re += '(' + continue + } + + if (!stateChar) { + re += '\\(' + continue + } + + patternListStack.push({ + type: stateChar, + start: i - 1, + reStart: re.length, + open: plTypes[stateChar].open, + close: plTypes[stateChar].close + }) + // negation is (?:(?!js)[^/]*) + re += stateChar === '!' ? '(?:(?!(?:' : '(?:' + this.debug('plType %j %j', stateChar, re) + stateChar = false + continue + + case ')': + if (inClass || !patternListStack.length) { + re += '\\)' + continue + } + + clearStateChar() + hasMagic = true + var pl = patternListStack.pop() + // negation is (?:(?!js)[^/]*) + // The others are (?:) + re += pl.close + if (pl.type === '!') { + negativeLists.push(pl) + } + pl.reEnd = re.length + continue + + case '|': + if (inClass || !patternListStack.length || escaping) { + re += '\\|' + escaping = false + continue + } + + clearStateChar() + re += '|' + continue + + // these are mostly the same in regexp and glob + case '[': + // swallow any state-tracking char before the [ + clearStateChar() + + if (inClass) { + re += '\\' + c + continue + } + + inClass = true + classStart = i + reClassStart = re.length + re += c + continue + + case ']': + // a right bracket shall lose its special + // meaning and represent itself in + // a bracket expression if it occurs + // first in the list. -- POSIX.2 2.8.3.2 + if (i === classStart + 1 || !inClass) { + re += '\\' + c + escaping = false + continue + } + + // handle the case where we left a class open. + // "[z-a]" is valid, equivalent to "\[z-a\]" + if (inClass) { + // split where the last [ was, make sure we don't have + // an invalid re. if so, re-walk the contents of the + // would-be class to re-translate any characters that + // were passed through as-is + // TODO: It would probably be faster to determine this + // without a try/catch and a new RegExp, but it's tricky + // to do safely. For now, this is safe and works. + var cs = pattern.substring(classStart + 1, i) + try { + RegExp('[' + cs + ']') + } catch (er) { + // not a valid class! + var sp = this.parse(cs, SUBPARSE) + re = re.substr(0, reClassStart) + '\\[' + sp[0] + '\\]' + hasMagic = hasMagic || sp[1] + inClass = false + continue + } + } + + // finish up the class. + hasMagic = true + inClass = false + re += c + continue + + default: + // swallow any state char that wasn't consumed + clearStateChar() + + if (escaping) { + // no need + escaping = false + } else if (reSpecials[c] + && !(c === '^' && inClass)) { + re += '\\' + } + + re += c + + } // switch + } // for + + // handle the case where we left a class open. + // "[abc" is valid, equivalent to "\[abc" + if (inClass) { + // split where the last [ was, and escape it + // this is a huge pita. We now have to re-walk + // the contents of the would-be class to re-translate + // any characters that were passed through as-is + cs = pattern.substr(classStart + 1) + sp = this.parse(cs, SUBPARSE) + re = re.substr(0, reClassStart) + '\\[' + sp[0] + hasMagic = hasMagic || sp[1] + } + + // handle the case where we had a +( thing at the *end* + // of the pattern. + // each pattern list stack adds 3 chars, and we need to go through + // and escape any | chars that were passed through as-is for the regexp. + // Go through and escape them, taking care not to double-escape any + // | chars that were already escaped. + for (pl = patternListStack.pop(); pl; pl = patternListStack.pop()) { + var tail = re.slice(pl.reStart + pl.open.length) + this.debug('setting tail', re, pl) + // maybe some even number of \, then maybe 1 \, followed by a | + tail = tail.replace(/((?:\\{2}){0,64})(\\?)\|/g, function (_, $1, $2) { + if (!$2) { + // the | isn't already escaped, so escape it. + $2 = '\\' + } + + // need to escape all those slashes *again*, without escaping the + // one that we need for escaping the | character. As it works out, + // escaping an even number of slashes can be done by simply repeating + // it exactly after itself. That's why this trick works. + // + // I am sorry that you have to see this. + return $1 + $1 + $2 + '|' + }) + + this.debug('tail=%j\n %s', tail, tail, pl, re) + var t = pl.type === '*' ? star + : pl.type === '?' ? qmark + : '\\' + pl.type + + hasMagic = true + re = re.slice(0, pl.reStart) + t + '\\(' + tail + } + + // handle trailing things that only matter at the very end. + clearStateChar() + if (escaping) { + // trailing \\ + re += '\\\\' + } + + // only need to apply the nodot start if the re starts with + // something that could conceivably capture a dot + var addPatternStart = false + switch (re.charAt(0)) { + case '.': + case '[': + case '(': addPatternStart = true + } + + // Hack to work around lack of negative lookbehind in JS + // A pattern like: *.!(x).!(y|z) needs to ensure that a name + // like 'a.xyz.yz' doesn't match. So, the first negative + // lookahead, has to look ALL the way ahead, to the end of + // the pattern. + for (var n = negativeLists.length - 1; n > -1; n--) { + var nl = negativeLists[n] + + var nlBefore = re.slice(0, nl.reStart) + var nlFirst = re.slice(nl.reStart, nl.reEnd - 8) + var nlLast = re.slice(nl.reEnd - 8, nl.reEnd) + var nlAfter = re.slice(nl.reEnd) + + nlLast += nlAfter + + // Handle nested stuff like *(*.js|!(*.json)), where open parens + // mean that we should *not* include the ) in the bit that is considered + // "after" the negated section. + var openParensBefore = nlBefore.split('(').length - 1 + var cleanAfter = nlAfter + for (i = 0; i < openParensBefore; i++) { + cleanAfter = cleanAfter.replace(/\)[+*?]?/, '') + } + nlAfter = cleanAfter + + var dollar = '' + if (nlAfter === '' && isSub !== SUBPARSE) { + dollar = '$' + } + var newRe = nlBefore + nlFirst + nlAfter + dollar + nlLast + re = newRe + } + + // if the re is not "" at this point, then we need to make sure + // it doesn't match against an empty path part. + // Otherwise a/* will match a/, which it should not. + if (re !== '' && hasMagic) { + re = '(?=.)' + re + } + + if (addPatternStart) { + re = patternStart + re + } + + // parsing just a piece of a larger pattern. + if (isSub === SUBPARSE) { + return [re, hasMagic] + } + + // skip the regexp for non-magical patterns + // unescape anything in it, though, so that it'll be + // an exact match against a file etc. + if (!hasMagic) { + return globUnescape(pattern) + } + + var flags = options.nocase ? 'i' : '' + try { + var regExp = new RegExp('^' + re + '$', flags) + } catch (er) { + // If it was an invalid regular expression, then it can't match + // anything. This trick looks for a character after the end of + // the string, which is of course impossible, except in multi-line + // mode, but it's not a /m regex. + return new RegExp('$.') + } + + regExp._glob = pattern + regExp._src = re + + return regExp +} + +minimatch.makeRe = function (pattern, options) { + return new Minimatch(pattern, options || {}).makeRe() +} + +Minimatch.prototype.makeRe = makeRe +function makeRe () { + if (this.regexp || this.regexp === false) return this.regexp + + // at this point, this.set is a 2d array of partial + // pattern strings, or "**". + // + // It's better to use .match(). This function shouldn't + // be used, really, but it's pretty convenient sometimes, + // when you just want to work with a regex. + var set = this.set + + if (!set.length) { + this.regexp = false + return this.regexp + } + var options = this.options + + var twoStar = options.noglobstar ? star + : options.dot ? twoStarDot + : twoStarNoDot + var flags = options.nocase ? 'i' : '' + + var re = set.map(function (pattern) { + return pattern.map(function (p) { + return (p === GLOBSTAR) ? twoStar + : (typeof p === 'string') ? regExpEscape(p) + : p._src + }).join('\\\/') + }).join('|') + + // must match entire pattern + // ending in a * or ** will make it less strict. + re = '^(?:' + re + ')$' + + // can match anything, as long as it's not this. + if (this.negate) re = '^(?!' + re + ').*$' + + try { + this.regexp = new RegExp(re, flags) + } catch (ex) { + this.regexp = false + } + return this.regexp +} + +minimatch.match = function (list, pattern, options) { + options = options || {} + var mm = new Minimatch(pattern, options) + list = list.filter(function (f) { + return mm.match(f) + }) + if (mm.options.nonull && !list.length) { + list.push(pattern) + } + return list +} + +Minimatch.prototype.match = match +function match (f, partial) { + this.debug('match', f, this.pattern) + // short-circuit in the case of busted things. + // comments, etc. + if (this.comment) return false + if (this.empty) return f === '' + + if (f === '/' && partial) return true + + var options = this.options + + // windows: need to use /, not \ + if (path.sep !== '/') { + f = f.split(path.sep).join('/') + } + + // treat the test path as a set of pathparts. + f = f.split(slashSplit) + this.debug(this.pattern, 'split', f) + + // just ONE of the pattern sets in this.set needs to match + // in order for it to be valid. If negating, then just one + // match means that we have failed. + // Either way, return on the first hit. + + var set = this.set + this.debug(this.pattern, 'set', set) + + // Find the basename of the path by looking for the last non-empty segment + var filename + var i + for (i = f.length - 1; i >= 0; i--) { + filename = f[i] + if (filename) break + } + + for (i = 0; i < set.length; i++) { + var pattern = set[i] + var file = f + if (options.matchBase && pattern.length === 1) { + file = [filename] + } + var hit = this.matchOne(file, pattern, partial) + if (hit) { + if (options.flipNegate) return true + return !this.negate + } + } + + // didn't get any hits. this is success if it's a negative + // pattern, failure otherwise. + if (options.flipNegate) return false + return this.negate +} + +// set partial to true to test if, for example, +// "/a/b" matches the start of "/*/b/*/d" +// Partial means, if you run out of file before you run +// out of pattern, then that's fine, as long as all +// the parts match. +Minimatch.prototype.matchOne = function (file, pattern, partial) { + var options = this.options + + this.debug('matchOne', + { 'this': this, file: file, pattern: pattern }) + + this.debug('matchOne', file.length, pattern.length) + + for (var fi = 0, + pi = 0, + fl = file.length, + pl = pattern.length + ; (fi < fl) && (pi < pl) + ; fi++, pi++) { + this.debug('matchOne loop') + var p = pattern[pi] + var f = file[fi] + + this.debug(pattern, p, f) + + // should be impossible. + // some invalid regexp stuff in the set. + if (p === false) return false + + if (p === GLOBSTAR) { + this.debug('GLOBSTAR', [pattern, p, f]) + + // "**" + // a/**/b/**/c would match the following: + // a/b/x/y/z/c + // a/x/y/z/b/c + // a/b/x/b/x/c + // a/b/c + // To do this, take the rest of the pattern after + // the **, and see if it would match the file remainder. + // If so, return success. + // If not, the ** "swallows" a segment, and try again. + // This is recursively awful. + // + // a/**/b/**/c matching a/b/x/y/z/c + // - a matches a + // - doublestar + // - matchOne(b/x/y/z/c, b/**/c) + // - b matches b + // - doublestar + // - matchOne(x/y/z/c, c) -> no + // - matchOne(y/z/c, c) -> no + // - matchOne(z/c, c) -> no + // - matchOne(c, c) yes, hit + var fr = fi + var pr = pi + 1 + if (pr === pl) { + this.debug('** at the end') + // a ** at the end will just swallow the rest. + // We have found a match. + // however, it will not swallow /.x, unless + // options.dot is set. + // . and .. are *never* matched by **, for explosively + // exponential reasons. + for (; fi < fl; fi++) { + if (file[fi] === '.' || file[fi] === '..' || + (!options.dot && file[fi].charAt(0) === '.')) return false + } + return true + } + + // ok, let's see if we can swallow whatever we can. + while (fr < fl) { + var swallowee = file[fr] + + this.debug('\nglobstar while', file, fr, pattern, pr, swallowee) + + // XXX remove this slice. Just pass the start index. + if (this.matchOne(file.slice(fr), pattern.slice(pr), partial)) { + this.debug('globstar found match!', fr, fl, swallowee) + // found a match. + return true + } else { + // can't swallow "." or ".." ever. + // can only swallow ".foo" when explicitly asked. + if (swallowee === '.' || swallowee === '..' || + (!options.dot && swallowee.charAt(0) === '.')) { + this.debug('dot detected!', file, fr, pattern, pr) + break + } + + // ** swallows a segment, and continue. + this.debug('globstar swallow a segment, and continue') + fr++ + } + } + + // no match was found. + // However, in partial mode, we can't say this is necessarily over. + // If there's more *pattern* left, then + if (partial) { + // ran out of file + this.debug('\n>>> no match, partial?', file, fr, pattern, pr) + if (fr === fl) return true + } + return false + } + + // something other than ** + // non-magic patterns just have to match exactly + // patterns with magic have been turned into regexps. + var hit + if (typeof p === 'string') { + if (options.nocase) { + hit = f.toLowerCase() === p.toLowerCase() + } else { + hit = f === p + } + this.debug('string match', p, f, hit) + } else { + hit = f.match(p) + this.debug('pattern match', p, f, hit) + } + + if (!hit) return false + } + + // Note: ending in / means that we'll get a final "" + // at the end of the pattern. This can only match a + // corresponding "" at the end of the file. + // If the file ends in /, then it can only match a + // a pattern that ends in /, unless the pattern just + // doesn't have any more for it. But, a/b/ should *not* + // match "a/b/*", even though "" matches against the + // [^/]*? pattern, except in partial mode, where it might + // simply not be reached yet. + // However, a/b/ should still satisfy a/* + + // now either we fell off the end of the pattern, or we're done. + if (fi === fl && pi === pl) { + // ran out of pattern and filename at the same time. + // an exact hit! + return true + } else if (fi === fl) { + // ran out of file, but still had pattern left. + // this is ok if we're doing the match as part of + // a glob fs traversal. + return partial + } else if (pi === pl) { + // ran out of pattern, still have file left. + // this is only acceptable if we're on the very last + // empty segment of a file with a trailing slash. + // a/* should match a/b/ + var emptyFileEnd = (fi === fl - 1) && (file[fi] === '') + return emptyFileEnd + } + + // should be unreachable. + throw new Error('wtf?') +} + +// replace stuff like \* with * +function globUnescape (s) { + return s.replace(/\\(.)/g, '$1') +} + +function regExpEscape (s) { + return s.replace(/[-[\]{}()*+?.,\\^$|#\s]/g, '\\$&') +} + +},{"brace-expansion":11,"path":22}],21:[function(require,module,exports){ +var wrappy = require('wrappy') +module.exports = wrappy(once) +module.exports.strict = wrappy(onceStrict) + +once.proto = once(function () { + Object.defineProperty(Function.prototype, 'once', { + value: function () { + return once(this) + }, + configurable: true + }) + + Object.defineProperty(Function.prototype, 'onceStrict', { + value: function () { + return onceStrict(this) + }, + configurable: true + }) +}) + +function once (fn) { + var f = function () { + if (f.called) return f.value + f.called = true + return f.value = fn.apply(this, arguments) + } + f.called = false + return f +} + +function onceStrict (fn) { + var f = function () { + if (f.called) + throw new Error(f.onceError) + f.called = true + return f.value = fn.apply(this, arguments) + } + var name = fn.name || 'Function wrapped with `once`' + f.onceError = name + " shouldn't be called more than once" + f.called = false + return f +} + +},{"wrappy":29}],22:[function(require,module,exports){ +(function (process){ +// Copyright Joyent, Inc. and other Node contributors. +// +// Permission is hereby granted, free of charge, to any person obtaining a +// copy of this software and associated documentation files (the +// "Software"), to deal in the Software without restriction, including +// without limitation the rights to use, copy, modify, merge, publish, +// distribute, sublicense, and/or sell copies of the Software, and to permit +// persons to whom the Software is furnished to do so, subject to the +// following conditions: +// +// The above copyright notice and this permission notice shall be included +// in all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS +// OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +// MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN +// NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, +// DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR +// OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE +// USE OR OTHER DEALINGS IN THE SOFTWARE. + +// resolves . and .. elements in a path array with directory names there +// must be no slashes, empty elements, or device names (c:\) in the array +// (so also no leading and trailing slashes - it does not distinguish +// relative and absolute paths) +function normalizeArray(parts, allowAboveRoot) { + // if the path tries to go above the root, `up` ends up > 0 + var up = 0; + for (var i = parts.length - 1; i >= 0; i--) { + var last = parts[i]; + if (last === '.') { + parts.splice(i, 1); + } else if (last === '..') { + parts.splice(i, 1); + up++; + } else if (up) { + parts.splice(i, 1); + up--; + } + } + + // if the path is allowed to go above the root, restore leading ..s + if (allowAboveRoot) { + for (; up--; up) { + parts.unshift('..'); + } + } + + return parts; +} + +// Split a filename into [root, dir, basename, ext], unix version +// 'root' is just a slash, or nothing. +var splitPathRe = + /^(\/?|)([\s\S]*?)((?:\.{1,2}|[^\/]+?|)(\.[^.\/]*|))(?:[\/]*)$/; +var splitPath = function(filename) { + return splitPathRe.exec(filename).slice(1); +}; + +// path.resolve([from ...], to) +// posix version +exports.resolve = function() { + var resolvedPath = '', + resolvedAbsolute = false; + + for (var i = arguments.length - 1; i >= -1 && !resolvedAbsolute; i--) { + var path = (i >= 0) ? arguments[i] : process.cwd(); + + // Skip empty and invalid entries + if (typeof path !== 'string') { + throw new TypeError('Arguments to path.resolve must be strings'); + } else if (!path) { + continue; + } + + resolvedPath = path + '/' + resolvedPath; + resolvedAbsolute = path.charAt(0) === '/'; + } + + // At this point the path should be resolved to a full absolute path, but + // handle relative paths to be safe (might happen when process.cwd() fails) + + // Normalize the path + resolvedPath = normalizeArray(filter(resolvedPath.split('/'), function(p) { + return !!p; + }), !resolvedAbsolute).join('/'); + + return ((resolvedAbsolute ? '/' : '') + resolvedPath) || '.'; +}; + +// path.normalize(path) +// posix version +exports.normalize = function(path) { + var isAbsolute = exports.isAbsolute(path), + trailingSlash = substr(path, -1) === '/'; + + // Normalize the path + path = normalizeArray(filter(path.split('/'), function(p) { + return !!p; + }), !isAbsolute).join('/'); + + if (!path && !isAbsolute) { + path = '.'; + } + if (path && trailingSlash) { + path += '/'; + } + + return (isAbsolute ? '/' : '') + path; +}; + +// posix version +exports.isAbsolute = function(path) { + return path.charAt(0) === '/'; +}; + +// posix version +exports.join = function() { + var paths = Array.prototype.slice.call(arguments, 0); + return exports.normalize(filter(paths, function(p, index) { + if (typeof p !== 'string') { + throw new TypeError('Arguments to path.join must be strings'); + } + return p; + }).join('/')); +}; + + +// path.relative(from, to) +// posix version +exports.relative = function(from, to) { + from = exports.resolve(from).substr(1); + to = exports.resolve(to).substr(1); + + function trim(arr) { + var start = 0; + for (; start < arr.length; start++) { + if (arr[start] !== '') break; + } + + var end = arr.length - 1; + for (; end >= 0; end--) { + if (arr[end] !== '') break; + } + + if (start > end) return []; + return arr.slice(start, end - start + 1); + } + + var fromParts = trim(from.split('/')); + var toParts = trim(to.split('/')); + + var length = Math.min(fromParts.length, toParts.length); + var samePartsLength = length; + for (var i = 0; i < length; i++) { + if (fromParts[i] !== toParts[i]) { + samePartsLength = i; + break; + } + } + + var outputParts = []; + for (var i = samePartsLength; i < fromParts.length; i++) { + outputParts.push('..'); + } + + outputParts = outputParts.concat(toParts.slice(samePartsLength)); + + return outputParts.join('/'); +}; + +exports.sep = '/'; +exports.delimiter = ':'; + +exports.dirname = function(path) { + var result = splitPath(path), + root = result[0], + dir = result[1]; + + if (!root && !dir) { + // No dirname whatsoever + return '.'; + } + + if (dir) { + // It has a dirname, strip trailing slash + dir = dir.substr(0, dir.length - 1); + } + + return root + dir; +}; + + +exports.basename = function(path, ext) { + var f = splitPath(path)[2]; + // TODO: make this comparison case-insensitive on windows? + if (ext && f.substr(-1 * ext.length) === ext) { + f = f.substr(0, f.length - ext.length); + } + return f; +}; + + +exports.extname = function(path) { + return splitPath(path)[3]; +}; + +function filter (xs, f) { + if (xs.filter) return xs.filter(f); + var res = []; + for (var i = 0; i < xs.length; i++) { + if (f(xs[i], i, xs)) res.push(xs[i]); + } + return res; +} + +// String.prototype.substr - negative index don't work in IE8 +var substr = 'ab'.substr(-1) === 'b' + ? function (str, start, len) { return str.substr(start, len) } + : function (str, start, len) { + if (start < 0) start = str.length + start; + return str.substr(start, len); + } +; + +}).call(this,require('_process')) +},{"_process":24}],23:[function(require,module,exports){ +(function (process){ +'use strict'; + +function posix(path) { + return path.charAt(0) === '/'; +} + +function win32(path) { + // https://github.com/nodejs/node/blob/b3fcc245fb25539909ef1d5eaa01dbf92e168633/lib/path.js#L56 + var splitDeviceRe = /^([a-zA-Z]:|[\\\/]{2}[^\\\/]+[\\\/]+[^\\\/]+)?([\\\/])?([\s\S]*?)$/; + var result = splitDeviceRe.exec(path); + var device = result[1] || ''; + var isUnc = Boolean(device && device.charAt(1) !== ':'); + + // UNC paths are always absolute + return Boolean(result[2] || isUnc); +} + +module.exports = process.platform === 'win32' ? win32 : posix; +module.exports.posix = posix; +module.exports.win32 = win32; + +}).call(this,require('_process')) +},{"_process":24}],24:[function(require,module,exports){ +// shim for using process in browser +var process = module.exports = {}; + +// cached from whatever global is present so that test runners that stub it +// don't break things. But we need to wrap it in a try catch in case it is +// wrapped in strict mode code which doesn't define any globals. It's inside a +// function because try/catches deoptimize in certain engines. + +var cachedSetTimeout; +var cachedClearTimeout; + +function defaultSetTimout() { + throw new Error('setTimeout has not been defined'); +} +function defaultClearTimeout () { + throw new Error('clearTimeout has not been defined'); +} +(function () { + try { + if (typeof setTimeout === 'function') { + cachedSetTimeout = setTimeout; + } else { + cachedSetTimeout = defaultSetTimout; + } + } catch (e) { + cachedSetTimeout = defaultSetTimout; + } + try { + if (typeof clearTimeout === 'function') { + cachedClearTimeout = clearTimeout; + } else { + cachedClearTimeout = defaultClearTimeout; + } + } catch (e) { + cachedClearTimeout = defaultClearTimeout; + } +} ()) +function runTimeout(fun) { + if (cachedSetTimeout === setTimeout) { + //normal enviroments in sane situations + return setTimeout(fun, 0); + } + // if setTimeout wasn't available but was latter defined + if ((cachedSetTimeout === defaultSetTimout || !cachedSetTimeout) && setTimeout) { + cachedSetTimeout = setTimeout; + return setTimeout(fun, 0); + } + try { + // when when somebody has screwed with setTimeout but no I.E. maddness + return cachedSetTimeout(fun, 0); + } catch(e){ + try { + // When we are in I.E. but the script has been evaled so I.E. doesn't trust the global object when called normally + return cachedSetTimeout.call(null, fun, 0); + } catch(e){ + // same as above but when it's a version of I.E. that must have the global object for 'this', hopfully our context correct otherwise it will throw a global error + return cachedSetTimeout.call(this, fun, 0); + } + } + + +} +function runClearTimeout(marker) { + if (cachedClearTimeout === clearTimeout) { + //normal enviroments in sane situations + return clearTimeout(marker); + } + // if clearTimeout wasn't available but was latter defined + if ((cachedClearTimeout === defaultClearTimeout || !cachedClearTimeout) && clearTimeout) { + cachedClearTimeout = clearTimeout; + return clearTimeout(marker); + } + try { + // when when somebody has screwed with setTimeout but no I.E. maddness + return cachedClearTimeout(marker); + } catch (e){ + try { + // When we are in I.E. but the script has been evaled so I.E. doesn't trust the global object when called normally + return cachedClearTimeout.call(null, marker); + } catch (e){ + // same as above but when it's a version of I.E. that must have the global object for 'this', hopfully our context correct otherwise it will throw a global error. + // Some versions of I.E. have different rules for clearTimeout vs setTimeout + return cachedClearTimeout.call(this, marker); + } + } + + + +} +var queue = []; +var draining = false; +var currentQueue; +var queueIndex = -1; + +function cleanUpNextTick() { + if (!draining || !currentQueue) { + return; + } + draining = false; + if (currentQueue.length) { + queue = currentQueue.concat(queue); + } else { + queueIndex = -1; + } + if (queue.length) { + drainQueue(); + } +} + +function drainQueue() { + if (draining) { + return; + } + var timeout = runTimeout(cleanUpNextTick); + draining = true; + + var len = queue.length; + while(len) { + currentQueue = queue; + queue = []; + while (++queueIndex < len) { + if (currentQueue) { + currentQueue[queueIndex].run(); + } + } + queueIndex = -1; + len = queue.length; + } + currentQueue = null; + draining = false; + runClearTimeout(timeout); +} + +process.nextTick = function (fun) { + var args = new Array(arguments.length - 1); + if (arguments.length > 1) { + for (var i = 1; i < arguments.length; i++) { + args[i - 1] = arguments[i]; + } + } + queue.push(new Item(fun, args)); + if (queue.length === 1 && !draining) { + runTimeout(drainQueue); + } +}; + +// v8 likes predictible objects +function Item(fun, array) { + this.fun = fun; + this.array = array; +} +Item.prototype.run = function () { + this.fun.apply(null, this.array); +}; +process.title = 'browser'; +process.browser = true; +process.env = {}; +process.argv = []; +process.version = ''; // empty string to avoid regexp issues +process.versions = {}; + +function noop() {} + +process.on = noop; +process.addListener = noop; +process.once = noop; +process.off = noop; +process.removeListener = noop; +process.removeAllListeners = noop; +process.emit = noop; +process.prependListener = noop; +process.prependOnceListener = noop; + +process.listeners = function (name) { return [] } + +process.binding = function (name) { + throw new Error('process.binding is not supported'); +}; + +process.cwd = function () { return '/' }; +process.chdir = function (dir) { + throw new Error('process.chdir is not supported'); +}; +process.umask = function() { return 0; }; + +},{}],25:[function(require,module,exports){ +// Underscore.js 1.8.3 +// http://underscorejs.org +// (c) 2009-2015 Jeremy Ashkenas, DocumentCloud and Investigative Reporters & Editors +// Underscore may be freely distributed under the MIT license. + +(function() { + + // Baseline setup + // -------------- + + // Establish the root object, `window` in the browser, or `exports` on the server. + var root = this; + + // Save the previous value of the `_` variable. + var previousUnderscore = root._; + + // Save bytes in the minified (but not gzipped) version: + var ArrayProto = Array.prototype, ObjProto = Object.prototype, FuncProto = Function.prototype; + + // Create quick reference variables for speed access to core prototypes. + var + push = ArrayProto.push, + slice = ArrayProto.slice, + toString = ObjProto.toString, + hasOwnProperty = ObjProto.hasOwnProperty; + + // All **ECMAScript 5** native function implementations that we hope to use + // are declared here. + var + nativeIsArray = Array.isArray, + nativeKeys = Object.keys, + nativeBind = FuncProto.bind, + nativeCreate = Object.create; + + // Naked function reference for surrogate-prototype-swapping. + var Ctor = function(){}; + + // Create a safe reference to the Underscore object for use below. + var _ = function(obj) { + if (obj instanceof _) return obj; + if (!(this instanceof _)) return new _(obj); + this._wrapped = obj; + }; + + // Export the Underscore object for **Node.js**, with + // backwards-compatibility for the old `require()` API. If we're in + // the browser, add `_` as a global object. + if (typeof exports !== 'undefined') { + if (typeof module !== 'undefined' && module.exports) { + exports = module.exports = _; + } + exports._ = _; + } else { + root._ = _; + } + + // Current version. + _.VERSION = '1.8.3'; + + // Internal function that returns an efficient (for current engines) version + // of the passed-in callback, to be repeatedly applied in other Underscore + // functions. + var optimizeCb = function(func, context, argCount) { + if (context === void 0) return func; + switch (argCount == null ? 3 : argCount) { + case 1: return function(value) { + return func.call(context, value); + }; + case 2: return function(value, other) { + return func.call(context, value, other); + }; + case 3: return function(value, index, collection) { + return func.call(context, value, index, collection); + }; + case 4: return function(accumulator, value, index, collection) { + return func.call(context, accumulator, value, index, collection); + }; + } + return function() { + return func.apply(context, arguments); + }; + }; + + // A mostly-internal function to generate callbacks that can be applied + // to each element in a collection, returning the desired result ā€” either + // identity, an arbitrary callback, a property matcher, or a property accessor. + var cb = function(value, context, argCount) { + if (value == null) return _.identity; + if (_.isFunction(value)) return optimizeCb(value, context, argCount); + if (_.isObject(value)) return _.matcher(value); + return _.property(value); + }; + _.iteratee = function(value, context) { + return cb(value, context, Infinity); + }; + + // An internal function for creating assigner functions. + var createAssigner = function(keysFunc, undefinedOnly) { + return function(obj) { + var length = arguments.length; + if (length < 2 || obj == null) return obj; + for (var index = 1; index < length; index++) { + var source = arguments[index], + keys = keysFunc(source), + l = keys.length; + for (var i = 0; i < l; i++) { + var key = keys[i]; + if (!undefinedOnly || obj[key] === void 0) obj[key] = source[key]; + } + } + return obj; + }; + }; + + // An internal function for creating a new object that inherits from another. + var baseCreate = function(prototype) { + if (!_.isObject(prototype)) return {}; + if (nativeCreate) return nativeCreate(prototype); + Ctor.prototype = prototype; + var result = new Ctor; + Ctor.prototype = null; + return result; + }; + + var property = function(key) { + return function(obj) { + return obj == null ? void 0 : obj[key]; + }; + }; + + // Helper for collection methods to determine whether a collection + // should be iterated as an array or as an object + // Related: http://people.mozilla.org/~jorendorff/es6-draft.html#sec-tolength + // Avoids a very nasty iOS 8 JIT bug on ARM-64. #2094 + var MAX_ARRAY_INDEX = Math.pow(2, 53) - 1; + var getLength = property('length'); + var isArrayLike = function(collection) { + var length = getLength(collection); + return typeof length == 'number' && length >= 0 && length <= MAX_ARRAY_INDEX; + }; + + // Collection Functions + // -------------------- + + // The cornerstone, an `each` implementation, aka `forEach`. + // Handles raw objects in addition to array-likes. Treats all + // sparse array-likes as if they were dense. + _.each = _.forEach = function(obj, iteratee, context) { + iteratee = optimizeCb(iteratee, context); + var i, length; + if (isArrayLike(obj)) { + for (i = 0, length = obj.length; i < length; i++) { + iteratee(obj[i], i, obj); + } + } else { + var keys = _.keys(obj); + for (i = 0, length = keys.length; i < length; i++) { + iteratee(obj[keys[i]], keys[i], obj); + } + } + return obj; + }; + + // Return the results of applying the iteratee to each element. + _.map = _.collect = function(obj, iteratee, context) { + iteratee = cb(iteratee, context); + var keys = !isArrayLike(obj) && _.keys(obj), + length = (keys || obj).length, + results = Array(length); + for (var index = 0; index < length; index++) { + var currentKey = keys ? keys[index] : index; + results[index] = iteratee(obj[currentKey], currentKey, obj); + } + return results; + }; + + // Create a reducing function iterating left or right. + function createReduce(dir) { + // Optimized iterator function as using arguments.length + // in the main function will deoptimize the, see #1991. + function iterator(obj, iteratee, memo, keys, index, length) { + for (; index >= 0 && index < length; index += dir) { + var currentKey = keys ? keys[index] : index; + memo = iteratee(memo, obj[currentKey], currentKey, obj); + } + return memo; + } + + return function(obj, iteratee, memo, context) { + iteratee = optimizeCb(iteratee, context, 4); + var keys = !isArrayLike(obj) && _.keys(obj), + length = (keys || obj).length, + index = dir > 0 ? 0 : length - 1; + // Determine the initial value if none is provided. + if (arguments.length < 3) { + memo = obj[keys ? keys[index] : index]; + index += dir; + } + return iterator(obj, iteratee, memo, keys, index, length); + }; + } + + // **Reduce** builds up a single result from a list of values, aka `inject`, + // or `foldl`. + _.reduce = _.foldl = _.inject = createReduce(1); + + // The right-associative version of reduce, also known as `foldr`. + _.reduceRight = _.foldr = createReduce(-1); + + // Return the first value which passes a truth test. Aliased as `detect`. + _.find = _.detect = function(obj, predicate, context) { + var key; + if (isArrayLike(obj)) { + key = _.findIndex(obj, predicate, context); + } else { + key = _.findKey(obj, predicate, context); + } + if (key !== void 0 && key !== -1) return obj[key]; + }; + + // Return all the elements that pass a truth test. + // Aliased as `select`. + _.filter = _.select = function(obj, predicate, context) { + var results = []; + predicate = cb(predicate, context); + _.each(obj, function(value, index, list) { + if (predicate(value, index, list)) results.push(value); + }); + return results; + }; + + // Return all the elements for which a truth test fails. + _.reject = function(obj, predicate, context) { + return _.filter(obj, _.negate(cb(predicate)), context); + }; + + // Determine whether all of the elements match a truth test. + // Aliased as `all`. + _.every = _.all = function(obj, predicate, context) { + predicate = cb(predicate, context); + var keys = !isArrayLike(obj) && _.keys(obj), + length = (keys || obj).length; + for (var index = 0; index < length; index++) { + var currentKey = keys ? keys[index] : index; + if (!predicate(obj[currentKey], currentKey, obj)) return false; + } + return true; + }; + + // Determine if at least one element in the object matches a truth test. + // Aliased as `any`. + _.some = _.any = function(obj, predicate, context) { + predicate = cb(predicate, context); + var keys = !isArrayLike(obj) && _.keys(obj), + length = (keys || obj).length; + for (var index = 0; index < length; index++) { + var currentKey = keys ? keys[index] : index; + if (predicate(obj[currentKey], currentKey, obj)) return true; + } + return false; + }; + + // Determine if the array or object contains a given item (using `===`). + // Aliased as `includes` and `include`. + _.contains = _.includes = _.include = function(obj, item, fromIndex, guard) { + if (!isArrayLike(obj)) obj = _.values(obj); + if (typeof fromIndex != 'number' || guard) fromIndex = 0; + return _.indexOf(obj, item, fromIndex) >= 0; + }; + + // Invoke a method (with arguments) on every item in a collection. + _.invoke = function(obj, method) { + var args = slice.call(arguments, 2); + var isFunc = _.isFunction(method); + return _.map(obj, function(value) { + var func = isFunc ? method : value[method]; + return func == null ? func : func.apply(value, args); + }); + }; + + // Convenience version of a common use case of `map`: fetching a property. + _.pluck = function(obj, key) { + return _.map(obj, _.property(key)); + }; + + // Convenience version of a common use case of `filter`: selecting only objects + // containing specific `key:value` pairs. + _.where = function(obj, attrs) { + return _.filter(obj, _.matcher(attrs)); + }; + + // Convenience version of a common use case of `find`: getting the first object + // containing specific `key:value` pairs. + _.findWhere = function(obj, attrs) { + return _.find(obj, _.matcher(attrs)); + }; + + // Return the maximum element (or element-based computation). + _.max = function(obj, iteratee, context) { + var result = -Infinity, lastComputed = -Infinity, + value, computed; + if (iteratee == null && obj != null) { + obj = isArrayLike(obj) ? obj : _.values(obj); + for (var i = 0, length = obj.length; i < length; i++) { + value = obj[i]; + if (value > result) { + result = value; + } + } + } else { + iteratee = cb(iteratee, context); + _.each(obj, function(value, index, list) { + computed = iteratee(value, index, list); + if (computed > lastComputed || computed === -Infinity && result === -Infinity) { + result = value; + lastComputed = computed; + } + }); + } + return result; + }; + + // Return the minimum element (or element-based computation). + _.min = function(obj, iteratee, context) { + var result = Infinity, lastComputed = Infinity, + value, computed; + if (iteratee == null && obj != null) { + obj = isArrayLike(obj) ? obj : _.values(obj); + for (var i = 0, length = obj.length; i < length; i++) { + value = obj[i]; + if (value < result) { + result = value; + } + } + } else { + iteratee = cb(iteratee, context); + _.each(obj, function(value, index, list) { + computed = iteratee(value, index, list); + if (computed < lastComputed || computed === Infinity && result === Infinity) { + result = value; + lastComputed = computed; + } + }); + } + return result; + }; + + // Shuffle a collection, using the modern version of the + // [Fisher-Yates shuffle](http://en.wikipedia.org/wiki/Fisherā€“Yates_shuffle). + _.shuffle = function(obj) { + var set = isArrayLike(obj) ? obj : _.values(obj); + var length = set.length; + var shuffled = Array(length); + for (var index = 0, rand; index < length; index++) { + rand = _.random(0, index); + if (rand !== index) shuffled[index] = shuffled[rand]; + shuffled[rand] = set[index]; + } + return shuffled; + }; + + // Sample **n** random values from a collection. + // If **n** is not specified, returns a single random element. + // The internal `guard` argument allows it to work with `map`. + _.sample = function(obj, n, guard) { + if (n == null || guard) { + if (!isArrayLike(obj)) obj = _.values(obj); + return obj[_.random(obj.length - 1)]; + } + return _.shuffle(obj).slice(0, Math.max(0, n)); + }; + + // Sort the object's values by a criterion produced by an iteratee. + _.sortBy = function(obj, iteratee, context) { + iteratee = cb(iteratee, context); + return _.pluck(_.map(obj, function(value, index, list) { + return { + value: value, + index: index, + criteria: iteratee(value, index, list) + }; + }).sort(function(left, right) { + var a = left.criteria; + var b = right.criteria; + if (a !== b) { + if (a > b || a === void 0) return 1; + if (a < b || b === void 0) return -1; + } + return left.index - right.index; + }), 'value'); + }; + + // An internal function used for aggregate "group by" operations. + var group = function(behavior) { + return function(obj, iteratee, context) { + var result = {}; + iteratee = cb(iteratee, context); + _.each(obj, function(value, index) { + var key = iteratee(value, index, obj); + behavior(result, value, key); + }); + return result; + }; + }; + + // Groups the object's values by a criterion. Pass either a string attribute + // to group by, or a function that returns the criterion. + _.groupBy = group(function(result, value, key) { + if (_.has(result, key)) result[key].push(value); else result[key] = [value]; + }); + + // Indexes the object's values by a criterion, similar to `groupBy`, but for + // when you know that your index values will be unique. + _.indexBy = group(function(result, value, key) { + result[key] = value; + }); + + // Counts instances of an object that group by a certain criterion. Pass + // either a string attribute to count by, or a function that returns the + // criterion. + _.countBy = group(function(result, value, key) { + if (_.has(result, key)) result[key]++; else result[key] = 1; + }); + + // Safely create a real, live array from anything iterable. + _.toArray = function(obj) { + if (!obj) return []; + if (_.isArray(obj)) return slice.call(obj); + if (isArrayLike(obj)) return _.map(obj, _.identity); + return _.values(obj); + }; + + // Return the number of elements in an object. + _.size = function(obj) { + if (obj == null) return 0; + return isArrayLike(obj) ? obj.length : _.keys(obj).length; + }; + + // Split a collection into two arrays: one whose elements all satisfy the given + // predicate, and one whose elements all do not satisfy the predicate. + _.partition = function(obj, predicate, context) { + predicate = cb(predicate, context); + var pass = [], fail = []; + _.each(obj, function(value, key, obj) { + (predicate(value, key, obj) ? pass : fail).push(value); + }); + return [pass, fail]; + }; + + // Array Functions + // --------------- + + // Get the first element of an array. Passing **n** will return the first N + // values in the array. Aliased as `head` and `take`. The **guard** check + // allows it to work with `_.map`. + _.first = _.head = _.take = function(array, n, guard) { + if (array == null) return void 0; + if (n == null || guard) return array[0]; + return _.initial(array, array.length - n); + }; + + // Returns everything but the last entry of the array. Especially useful on + // the arguments object. Passing **n** will return all the values in + // the array, excluding the last N. + _.initial = function(array, n, guard) { + return slice.call(array, 0, Math.max(0, array.length - (n == null || guard ? 1 : n))); + }; + + // Get the last element of an array. Passing **n** will return the last N + // values in the array. + _.last = function(array, n, guard) { + if (array == null) return void 0; + if (n == null || guard) return array[array.length - 1]; + return _.rest(array, Math.max(0, array.length - n)); + }; + + // Returns everything but the first entry of the array. Aliased as `tail` and `drop`. + // Especially useful on the arguments object. Passing an **n** will return + // the rest N values in the array. + _.rest = _.tail = _.drop = function(array, n, guard) { + return slice.call(array, n == null || guard ? 1 : n); + }; + + // Trim out all falsy values from an array. + _.compact = function(array) { + return _.filter(array, _.identity); + }; + + // Internal implementation of a recursive `flatten` function. + var flatten = function(input, shallow, strict, startIndex) { + var output = [], idx = 0; + for (var i = startIndex || 0, length = getLength(input); i < length; i++) { + var value = input[i]; + if (isArrayLike(value) && (_.isArray(value) || _.isArguments(value))) { + //flatten current level of array or arguments object + if (!shallow) value = flatten(value, shallow, strict); + var j = 0, len = value.length; + output.length += len; + while (j < len) { + output[idx++] = value[j++]; + } + } else if (!strict) { + output[idx++] = value; + } + } + return output; + }; + + // Flatten out an array, either recursively (by default), or just one level. + _.flatten = function(array, shallow) { + return flatten(array, shallow, false); + }; + + // Return a version of the array that does not contain the specified value(s). + _.without = function(array) { + return _.difference(array, slice.call(arguments, 1)); + }; + + // Produce a duplicate-free version of the array. If the array has already + // been sorted, you have the option of using a faster algorithm. + // Aliased as `unique`. + _.uniq = _.unique = function(array, isSorted, iteratee, context) { + if (!_.isBoolean(isSorted)) { + context = iteratee; + iteratee = isSorted; + isSorted = false; + } + if (iteratee != null) iteratee = cb(iteratee, context); + var result = []; + var seen = []; + for (var i = 0, length = getLength(array); i < length; i++) { + var value = array[i], + computed = iteratee ? iteratee(value, i, array) : value; + if (isSorted) { + if (!i || seen !== computed) result.push(value); + seen = computed; + } else if (iteratee) { + if (!_.contains(seen, computed)) { + seen.push(computed); + result.push(value); + } + } else if (!_.contains(result, value)) { + result.push(value); + } + } + return result; + }; + + // Produce an array that contains the union: each distinct element from all of + // the passed-in arrays. + _.union = function() { + return _.uniq(flatten(arguments, true, true)); + }; + + // Produce an array that contains every item shared between all the + // passed-in arrays. + _.intersection = function(array) { + var result = []; + var argsLength = arguments.length; + for (var i = 0, length = getLength(array); i < length; i++) { + var item = array[i]; + if (_.contains(result, item)) continue; + for (var j = 1; j < argsLength; j++) { + if (!_.contains(arguments[j], item)) break; + } + if (j === argsLength) result.push(item); + } + return result; + }; + + // Take the difference between one array and a number of other arrays. + // Only the elements present in just the first array will remain. + _.difference = function(array) { + var rest = flatten(arguments, true, true, 1); + return _.filter(array, function(value){ + return !_.contains(rest, value); + }); + }; + + // Zip together multiple lists into a single array -- elements that share + // an index go together. + _.zip = function() { + return _.unzip(arguments); + }; + + // Complement of _.zip. Unzip accepts an array of arrays and groups + // each array's elements on shared indices + _.unzip = function(array) { + var length = array && _.max(array, getLength).length || 0; + var result = Array(length); + + for (var index = 0; index < length; index++) { + result[index] = _.pluck(array, index); + } + return result; + }; + + // Converts lists into objects. Pass either a single array of `[key, value]` + // pairs, or two parallel arrays of the same length -- one of keys, and one of + // the corresponding values. + _.object = function(list, values) { + var result = {}; + for (var i = 0, length = getLength(list); i < length; i++) { + if (values) { + result[list[i]] = values[i]; + } else { + result[list[i][0]] = list[i][1]; + } + } + return result; + }; + + // Generator function to create the findIndex and findLastIndex functions + function createPredicateIndexFinder(dir) { + return function(array, predicate, context) { + predicate = cb(predicate, context); + var length = getLength(array); + var index = dir > 0 ? 0 : length - 1; + for (; index >= 0 && index < length; index += dir) { + if (predicate(array[index], index, array)) return index; + } + return -1; + }; + } + + // Returns the first index on an array-like that passes a predicate test + _.findIndex = createPredicateIndexFinder(1); + _.findLastIndex = createPredicateIndexFinder(-1); + + // Use a comparator function to figure out the smallest index at which + // an object should be inserted so as to maintain order. Uses binary search. + _.sortedIndex = function(array, obj, iteratee, context) { + iteratee = cb(iteratee, context, 1); + var value = iteratee(obj); + var low = 0, high = getLength(array); + while (low < high) { + var mid = Math.floor((low + high) / 2); + if (iteratee(array[mid]) < value) low = mid + 1; else high = mid; + } + return low; + }; + + // Generator function to create the indexOf and lastIndexOf functions + function createIndexFinder(dir, predicateFind, sortedIndex) { + return function(array, item, idx) { + var i = 0, length = getLength(array); + if (typeof idx == 'number') { + if (dir > 0) { + i = idx >= 0 ? idx : Math.max(idx + length, i); + } else { + length = idx >= 0 ? Math.min(idx + 1, length) : idx + length + 1; + } + } else if (sortedIndex && idx && length) { + idx = sortedIndex(array, item); + return array[idx] === item ? idx : -1; + } + if (item !== item) { + idx = predicateFind(slice.call(array, i, length), _.isNaN); + return idx >= 0 ? idx + i : -1; + } + for (idx = dir > 0 ? i : length - 1; idx >= 0 && idx < length; idx += dir) { + if (array[idx] === item) return idx; + } + return -1; + }; + } + + // Return the position of the first occurrence of an item in an array, + // or -1 if the item is not included in the array. + // If the array is large and already in sort order, pass `true` + // for **isSorted** to use binary search. + _.indexOf = createIndexFinder(1, _.findIndex, _.sortedIndex); + _.lastIndexOf = createIndexFinder(-1, _.findLastIndex); + + // Generate an integer Array containing an arithmetic progression. A port of + // the native Python `range()` function. See + // [the Python documentation](http://docs.python.org/library/functions.html#range). + _.range = function(start, stop, step) { + if (stop == null) { + stop = start || 0; + start = 0; + } + step = step || 1; + + var length = Math.max(Math.ceil((stop - start) / step), 0); + var range = Array(length); + + for (var idx = 0; idx < length; idx++, start += step) { + range[idx] = start; + } + + return range; + }; + + // Function (ahem) Functions + // ------------------ + + // Determines whether to execute a function as a constructor + // or a normal function with the provided arguments + var executeBound = function(sourceFunc, boundFunc, context, callingContext, args) { + if (!(callingContext instanceof boundFunc)) return sourceFunc.apply(context, args); + var self = baseCreate(sourceFunc.prototype); + var result = sourceFunc.apply(self, args); + if (_.isObject(result)) return result; + return self; + }; + + // Create a function bound to a given object (assigning `this`, and arguments, + // optionally). Delegates to **ECMAScript 5**'s native `Function.bind` if + // available. + _.bind = function(func, context) { + if (nativeBind && func.bind === nativeBind) return nativeBind.apply(func, slice.call(arguments, 1)); + if (!_.isFunction(func)) throw new TypeError('Bind must be called on a function'); + var args = slice.call(arguments, 2); + var bound = function() { + return executeBound(func, bound, context, this, args.concat(slice.call(arguments))); + }; + return bound; + }; + + // Partially apply a function by creating a version that has had some of its + // arguments pre-filled, without changing its dynamic `this` context. _ acts + // as a placeholder, allowing any combination of arguments to be pre-filled. + _.partial = function(func) { + var boundArgs = slice.call(arguments, 1); + var bound = function() { + var position = 0, length = boundArgs.length; + var args = Array(length); + for (var i = 0; i < length; i++) { + args[i] = boundArgs[i] === _ ? arguments[position++] : boundArgs[i]; + } + while (position < arguments.length) args.push(arguments[position++]); + return executeBound(func, bound, this, this, args); + }; + return bound; + }; + + // Bind a number of an object's methods to that object. Remaining arguments + // are the method names to be bound. Useful for ensuring that all callbacks + // defined on an object belong to it. + _.bindAll = function(obj) { + var i, length = arguments.length, key; + if (length <= 1) throw new Error('bindAll must be passed function names'); + for (i = 1; i < length; i++) { + key = arguments[i]; + obj[key] = _.bind(obj[key], obj); + } + return obj; + }; + + // Memoize an expensive function by storing its results. + _.memoize = function(func, hasher) { + var memoize = function(key) { + var cache = memoize.cache; + var address = '' + (hasher ? hasher.apply(this, arguments) : key); + if (!_.has(cache, address)) cache[address] = func.apply(this, arguments); + return cache[address]; + }; + memoize.cache = {}; + return memoize; + }; + + // Delays a function for the given number of milliseconds, and then calls + // it with the arguments supplied. + _.delay = function(func, wait) { + var args = slice.call(arguments, 2); + return setTimeout(function(){ + return func.apply(null, args); + }, wait); + }; + + // Defers a function, scheduling it to run after the current call stack has + // cleared. + _.defer = _.partial(_.delay, _, 1); + + // Returns a function, that, when invoked, will only be triggered at most once + // during a given window of time. Normally, the throttled function will run + // as much as it can, without ever going more than once per `wait` duration; + // but if you'd like to disable the execution on the leading edge, pass + // `{leading: false}`. To disable execution on the trailing edge, ditto. + _.throttle = function(func, wait, options) { + var context, args, result; + var timeout = null; + var previous = 0; + if (!options) options = {}; + var later = function() { + previous = options.leading === false ? 0 : _.now(); + timeout = null; + result = func.apply(context, args); + if (!timeout) context = args = null; + }; + return function() { + var now = _.now(); + if (!previous && options.leading === false) previous = now; + var remaining = wait - (now - previous); + context = this; + args = arguments; + if (remaining <= 0 || remaining > wait) { + if (timeout) { + clearTimeout(timeout); + timeout = null; + } + previous = now; + result = func.apply(context, args); + if (!timeout) context = args = null; + } else if (!timeout && options.trailing !== false) { + timeout = setTimeout(later, remaining); + } + return result; + }; + }; + + // Returns a function, that, as long as it continues to be invoked, will not + // be triggered. The function will be called after it stops being called for + // N milliseconds. If `immediate` is passed, trigger the function on the + // leading edge, instead of the trailing. + _.debounce = function(func, wait, immediate) { + var timeout, args, context, timestamp, result; + + var later = function() { + var last = _.now() - timestamp; + + if (last < wait && last >= 0) { + timeout = setTimeout(later, wait - last); + } else { + timeout = null; + if (!immediate) { + result = func.apply(context, args); + if (!timeout) context = args = null; + } + } + }; + + return function() { + context = this; + args = arguments; + timestamp = _.now(); + var callNow = immediate && !timeout; + if (!timeout) timeout = setTimeout(later, wait); + if (callNow) { + result = func.apply(context, args); + context = args = null; + } + + return result; + }; + }; + + // Returns the first function passed as an argument to the second, + // allowing you to adjust arguments, run code before and after, and + // conditionally execute the original function. + _.wrap = function(func, wrapper) { + return _.partial(wrapper, func); + }; + + // Returns a negated version of the passed-in predicate. + _.negate = function(predicate) { + return function() { + return !predicate.apply(this, arguments); + }; + }; + + // Returns a function that is the composition of a list of functions, each + // consuming the return value of the function that follows. + _.compose = function() { + var args = arguments; + var start = args.length - 1; + return function() { + var i = start; + var result = args[start].apply(this, arguments); + while (i--) result = args[i].call(this, result); + return result; + }; + }; + + // Returns a function that will only be executed on and after the Nth call. + _.after = function(times, func) { + return function() { + if (--times < 1) { + return func.apply(this, arguments); + } + }; + }; + + // Returns a function that will only be executed up to (but not including) the Nth call. + _.before = function(times, func) { + var memo; + return function() { + if (--times > 0) { + memo = func.apply(this, arguments); + } + if (times <= 1) func = null; + return memo; + }; + }; + + // Returns a function that will be executed at most one time, no matter how + // often you call it. Useful for lazy initialization. + _.once = _.partial(_.before, 2); + + // Object Functions + // ---------------- + + // Keys in IE < 9 that won't be iterated by `for key in ...` and thus missed. + var hasEnumBug = !{toString: null}.propertyIsEnumerable('toString'); + var nonEnumerableProps = ['valueOf', 'isPrototypeOf', 'toString', + 'propertyIsEnumerable', 'hasOwnProperty', 'toLocaleString']; + + function collectNonEnumProps(obj, keys) { + var nonEnumIdx = nonEnumerableProps.length; + var constructor = obj.constructor; + var proto = (_.isFunction(constructor) && constructor.prototype) || ObjProto; + + // Constructor is a special case. + var prop = 'constructor'; + if (_.has(obj, prop) && !_.contains(keys, prop)) keys.push(prop); + + while (nonEnumIdx--) { + prop = nonEnumerableProps[nonEnumIdx]; + if (prop in obj && obj[prop] !== proto[prop] && !_.contains(keys, prop)) { + keys.push(prop); + } + } + } + + // Retrieve the names of an object's own properties. + // Delegates to **ECMAScript 5**'s native `Object.keys` + _.keys = function(obj) { + if (!_.isObject(obj)) return []; + if (nativeKeys) return nativeKeys(obj); + var keys = []; + for (var key in obj) if (_.has(obj, key)) keys.push(key); + // Ahem, IE < 9. + if (hasEnumBug) collectNonEnumProps(obj, keys); + return keys; + }; + + // Retrieve all the property names of an object. + _.allKeys = function(obj) { + if (!_.isObject(obj)) return []; + var keys = []; + for (var key in obj) keys.push(key); + // Ahem, IE < 9. + if (hasEnumBug) collectNonEnumProps(obj, keys); + return keys; + }; + + // Retrieve the values of an object's properties. + _.values = function(obj) { + var keys = _.keys(obj); + var length = keys.length; + var values = Array(length); + for (var i = 0; i < length; i++) { + values[i] = obj[keys[i]]; + } + return values; + }; + + // Returns the results of applying the iteratee to each element of the object + // In contrast to _.map it returns an object + _.mapObject = function(obj, iteratee, context) { + iteratee = cb(iteratee, context); + var keys = _.keys(obj), + length = keys.length, + results = {}, + currentKey; + for (var index = 0; index < length; index++) { + currentKey = keys[index]; + results[currentKey] = iteratee(obj[currentKey], currentKey, obj); + } + return results; + }; + + // Convert an object into a list of `[key, value]` pairs. + _.pairs = function(obj) { + var keys = _.keys(obj); + var length = keys.length; + var pairs = Array(length); + for (var i = 0; i < length; i++) { + pairs[i] = [keys[i], obj[keys[i]]]; + } + return pairs; + }; + + // Invert the keys and values of an object. The values must be serializable. + _.invert = function(obj) { + var result = {}; + var keys = _.keys(obj); + for (var i = 0, length = keys.length; i < length; i++) { + result[obj[keys[i]]] = keys[i]; + } + return result; + }; + + // Return a sorted list of the function names available on the object. + // Aliased as `methods` + _.functions = _.methods = function(obj) { + var names = []; + for (var key in obj) { + if (_.isFunction(obj[key])) names.push(key); + } + return names.sort(); + }; + + // Extend a given object with all the properties in passed-in object(s). + _.extend = createAssigner(_.allKeys); + + // Assigns a given object with all the own properties in the passed-in object(s) + // (https://developer.mozilla.org/docs/Web/JavaScript/Reference/Global_Objects/Object/assign) + _.extendOwn = _.assign = createAssigner(_.keys); + + // Returns the first key on an object that passes a predicate test + _.findKey = function(obj, predicate, context) { + predicate = cb(predicate, context); + var keys = _.keys(obj), key; + for (var i = 0, length = keys.length; i < length; i++) { + key = keys[i]; + if (predicate(obj[key], key, obj)) return key; + } + }; + + // Return a copy of the object only containing the whitelisted properties. + _.pick = function(object, oiteratee, context) { + var result = {}, obj = object, iteratee, keys; + if (obj == null) return result; + if (_.isFunction(oiteratee)) { + keys = _.allKeys(obj); + iteratee = optimizeCb(oiteratee, context); + } else { + keys = flatten(arguments, false, false, 1); + iteratee = function(value, key, obj) { return key in obj; }; + obj = Object(obj); + } + for (var i = 0, length = keys.length; i < length; i++) { + var key = keys[i]; + var value = obj[key]; + if (iteratee(value, key, obj)) result[key] = value; + } + return result; + }; + + // Return a copy of the object without the blacklisted properties. + _.omit = function(obj, iteratee, context) { + if (_.isFunction(iteratee)) { + iteratee = _.negate(iteratee); + } else { + var keys = _.map(flatten(arguments, false, false, 1), String); + iteratee = function(value, key) { + return !_.contains(keys, key); + }; + } + return _.pick(obj, iteratee, context); + }; + + // Fill in a given object with default properties. + _.defaults = createAssigner(_.allKeys, true); + + // Creates an object that inherits from the given prototype object. + // If additional properties are provided then they will be added to the + // created object. + _.create = function(prototype, props) { + var result = baseCreate(prototype); + if (props) _.extendOwn(result, props); + return result; + }; + + // Create a (shallow-cloned) duplicate of an object. + _.clone = function(obj) { + if (!_.isObject(obj)) return obj; + return _.isArray(obj) ? obj.slice() : _.extend({}, obj); + }; + + // Invokes interceptor with the obj, and then returns obj. + // The primary purpose of this method is to "tap into" a method chain, in + // order to perform operations on intermediate results within the chain. + _.tap = function(obj, interceptor) { + interceptor(obj); + return obj; + }; + + // Returns whether an object has a given set of `key:value` pairs. + _.isMatch = function(object, attrs) { + var keys = _.keys(attrs), length = keys.length; + if (object == null) return !length; + var obj = Object(object); + for (var i = 0; i < length; i++) { + var key = keys[i]; + if (attrs[key] !== obj[key] || !(key in obj)) return false; + } + return true; + }; + + + // Internal recursive comparison function for `isEqual`. + var eq = function(a, b, aStack, bStack) { + // Identical objects are equal. `0 === -0`, but they aren't identical. + // See the [Harmony `egal` proposal](http://wiki.ecmascript.org/doku.php?id=harmony:egal). + if (a === b) return a !== 0 || 1 / a === 1 / b; + // A strict comparison is necessary because `null == undefined`. + if (a == null || b == null) return a === b; + // Unwrap any wrapped objects. + if (a instanceof _) a = a._wrapped; + if (b instanceof _) b = b._wrapped; + // Compare `[[Class]]` names. + var className = toString.call(a); + if (className !== toString.call(b)) return false; + switch (className) { + // Strings, numbers, regular expressions, dates, and booleans are compared by value. + case '[object RegExp]': + // RegExps are coerced to strings for comparison (Note: '' + /a/i === '/a/i') + case '[object String]': + // Primitives and their corresponding object wrappers are equivalent; thus, `"5"` is + // equivalent to `new String("5")`. + return '' + a === '' + b; + case '[object Number]': + // `NaN`s are equivalent, but non-reflexive. + // Object(NaN) is equivalent to NaN + if (+a !== +a) return +b !== +b; + // An `egal` comparison is performed for other numeric values. + return +a === 0 ? 1 / +a === 1 / b : +a === +b; + case '[object Date]': + case '[object Boolean]': + // Coerce dates and booleans to numeric primitive values. Dates are compared by their + // millisecond representations. Note that invalid dates with millisecond representations + // of `NaN` are not equivalent. + return +a === +b; + } + + var areArrays = className === '[object Array]'; + if (!areArrays) { + if (typeof a != 'object' || typeof b != 'object') return false; + + // Objects with different constructors are not equivalent, but `Object`s or `Array`s + // from different frames are. + var aCtor = a.constructor, bCtor = b.constructor; + if (aCtor !== bCtor && !(_.isFunction(aCtor) && aCtor instanceof aCtor && + _.isFunction(bCtor) && bCtor instanceof bCtor) + && ('constructor' in a && 'constructor' in b)) { + return false; + } + } + // Assume equality for cyclic structures. The algorithm for detecting cyclic + // structures is adapted from ES 5.1 section 15.12.3, abstract operation `JO`. + + // Initializing stack of traversed objects. + // It's done here since we only need them for objects and arrays comparison. + aStack = aStack || []; + bStack = bStack || []; + var length = aStack.length; + while (length--) { + // Linear search. Performance is inversely proportional to the number of + // unique nested structures. + if (aStack[length] === a) return bStack[length] === b; + } + + // Add the first object to the stack of traversed objects. + aStack.push(a); + bStack.push(b); + + // Recursively compare objects and arrays. + if (areArrays) { + // Compare array lengths to determine if a deep comparison is necessary. + length = a.length; + if (length !== b.length) return false; + // Deep compare the contents, ignoring non-numeric properties. + while (length--) { + if (!eq(a[length], b[length], aStack, bStack)) return false; + } + } else { + // Deep compare objects. + var keys = _.keys(a), key; + length = keys.length; + // Ensure that both objects contain the same number of properties before comparing deep equality. + if (_.keys(b).length !== length) return false; + while (length--) { + // Deep compare each member + key = keys[length]; + if (!(_.has(b, key) && eq(a[key], b[key], aStack, bStack))) return false; + } + } + // Remove the first object from the stack of traversed objects. + aStack.pop(); + bStack.pop(); + return true; + }; + + // Perform a deep comparison to check if two objects are equal. + _.isEqual = function(a, b) { + return eq(a, b); + }; + + // Is a given array, string, or object empty? + // An "empty" object has no enumerable own-properties. + _.isEmpty = function(obj) { + if (obj == null) return true; + if (isArrayLike(obj) && (_.isArray(obj) || _.isString(obj) || _.isArguments(obj))) return obj.length === 0; + return _.keys(obj).length === 0; + }; + + // Is a given value a DOM element? + _.isElement = function(obj) { + return !!(obj && obj.nodeType === 1); + }; + + // Is a given value an array? + // Delegates to ECMA5's native Array.isArray + _.isArray = nativeIsArray || function(obj) { + return toString.call(obj) === '[object Array]'; + }; + + // Is a given variable an object? + _.isObject = function(obj) { + var type = typeof obj; + return type === 'function' || type === 'object' && !!obj; + }; + + // Add some isType methods: isArguments, isFunction, isString, isNumber, isDate, isRegExp, isError. + _.each(['Arguments', 'Function', 'String', 'Number', 'Date', 'RegExp', 'Error'], function(name) { + _['is' + name] = function(obj) { + return toString.call(obj) === '[object ' + name + ']'; + }; + }); + + // Define a fallback version of the method in browsers (ahem, IE < 9), where + // there isn't any inspectable "Arguments" type. + if (!_.isArguments(arguments)) { + _.isArguments = function(obj) { + return _.has(obj, 'callee'); + }; + } + + // Optimize `isFunction` if appropriate. Work around some typeof bugs in old v8, + // IE 11 (#1621), and in Safari 8 (#1929). + if (typeof /./ != 'function' && typeof Int8Array != 'object') { + _.isFunction = function(obj) { + return typeof obj == 'function' || false; + }; + } + + // Is a given object a finite number? + _.isFinite = function(obj) { + return isFinite(obj) && !isNaN(parseFloat(obj)); + }; + + // Is the given value `NaN`? (NaN is the only number which does not equal itself). + _.isNaN = function(obj) { + return _.isNumber(obj) && obj !== +obj; + }; + + // Is a given value a boolean? + _.isBoolean = function(obj) { + return obj === true || obj === false || toString.call(obj) === '[object Boolean]'; + }; + + // Is a given value equal to null? + _.isNull = function(obj) { + return obj === null; + }; + + // Is a given variable undefined? + _.isUndefined = function(obj) { + return obj === void 0; + }; + + // Shortcut function for checking if an object has a given property directly + // on itself (in other words, not on a prototype). + _.has = function(obj, key) { + return obj != null && hasOwnProperty.call(obj, key); + }; + + // Utility Functions + // ----------------- + + // Run Underscore.js in *noConflict* mode, returning the `_` variable to its + // previous owner. Returns a reference to the Underscore object. + _.noConflict = function() { + root._ = previousUnderscore; + return this; + }; + + // Keep the identity function around for default iteratees. + _.identity = function(value) { + return value; + }; + + // Predicate-generating functions. Often useful outside of Underscore. + _.constant = function(value) { + return function() { + return value; + }; + }; + + _.noop = function(){}; + + _.property = property; + + // Generates a function for a given object that returns a given property. + _.propertyOf = function(obj) { + return obj == null ? function(){} : function(key) { + return obj[key]; + }; + }; + + // Returns a predicate for checking whether an object has a given set of + // `key:value` pairs. + _.matcher = _.matches = function(attrs) { + attrs = _.extendOwn({}, attrs); + return function(obj) { + return _.isMatch(obj, attrs); + }; + }; + + // Run a function **n** times. + _.times = function(n, iteratee, context) { + var accum = Array(Math.max(0, n)); + iteratee = optimizeCb(iteratee, context, 1); + for (var i = 0; i < n; i++) accum[i] = iteratee(i); + return accum; + }; + + // Return a random integer between min and max (inclusive). + _.random = function(min, max) { + if (max == null) { + max = min; + min = 0; + } + return min + Math.floor(Math.random() * (max - min + 1)); + }; + + // A (possibly faster) way to get the current timestamp as an integer. + _.now = Date.now || function() { + return new Date().getTime(); + }; + + // List of HTML entities for escaping. + var escapeMap = { + '&': '&', + '<': '<', + '>': '>', + '"': '"', + "'": ''', + '`': '`' + }; + var unescapeMap = _.invert(escapeMap); + + // Functions for escaping and unescaping strings to/from HTML interpolation. + var createEscaper = function(map) { + var escaper = function(match) { + return map[match]; + }; + // Regexes for identifying a key that needs to be escaped + var source = '(?:' + _.keys(map).join('|') + ')'; + var testRegexp = RegExp(source); + var replaceRegexp = RegExp(source, 'g'); + return function(string) { + string = string == null ? '' : '' + string; + return testRegexp.test(string) ? string.replace(replaceRegexp, escaper) : string; + }; + }; + _.escape = createEscaper(escapeMap); + _.unescape = createEscaper(unescapeMap); + + // If the value of the named `property` is a function then invoke it with the + // `object` as context; otherwise, return it. + _.result = function(object, property, fallback) { + var value = object == null ? void 0 : object[property]; + if (value === void 0) { + value = fallback; + } + return _.isFunction(value) ? value.call(object) : value; + }; + + // Generate a unique integer id (unique within the entire client session). + // Useful for temporary DOM ids. + var idCounter = 0; + _.uniqueId = function(prefix) { + var id = ++idCounter + ''; + return prefix ? prefix + id : id; + }; + + // By default, Underscore uses ERB-style template delimiters, change the + // following template settings to use alternative delimiters. + _.templateSettings = { + evaluate : /<%([\s\S]+?)%>/g, + interpolate : /<%=([\s\S]+?)%>/g, + escape : /<%-([\s\S]+?)%>/g + }; + + // When customizing `templateSettings`, if you don't want to define an + // interpolation, evaluation or escaping regex, we need one that is + // guaranteed not to match. + var noMatch = /(.)^/; + + // Certain characters need to be escaped so that they can be put into a + // string literal. + var escapes = { + "'": "'", + '\\': '\\', + '\r': 'r', + '\n': 'n', + '\u2028': 'u2028', + '\u2029': 'u2029' + }; + + var escaper = /\\|'|\r|\n|\u2028|\u2029/g; + + var escapeChar = function(match) { + return '\\' + escapes[match]; + }; + + // JavaScript micro-templating, similar to John Resig's implementation. + // Underscore templating handles arbitrary delimiters, preserves whitespace, + // and correctly escapes quotes within interpolated code. + // NB: `oldSettings` only exists for backwards compatibility. + _.template = function(text, settings, oldSettings) { + if (!settings && oldSettings) settings = oldSettings; + settings = _.defaults({}, settings, _.templateSettings); + + // Combine delimiters into one regular expression via alternation. + var matcher = RegExp([ + (settings.escape || noMatch).source, + (settings.interpolate || noMatch).source, + (settings.evaluate || noMatch).source + ].join('|') + '|$', 'g'); + + // Compile the template source, escaping string literals appropriately. + var index = 0; + var source = "__p+='"; + text.replace(matcher, function(match, escape, interpolate, evaluate, offset) { + source += text.slice(index, offset).replace(escaper, escapeChar); + index = offset + match.length; + + if (escape) { + source += "'+\n((__t=(" + escape + "))==null?'':_.escape(__t))+\n'"; + } else if (interpolate) { + source += "'+\n((__t=(" + interpolate + "))==null?'':__t)+\n'"; + } else if (evaluate) { + source += "';\n" + evaluate + "\n__p+='"; + } + + // Adobe VMs need the match returned to produce the correct offest. + return match; + }); + source += "';\n"; + + // If a variable is not specified, place data values in local scope. + if (!settings.variable) source = 'with(obj||{}){\n' + source + '}\n'; + + source = "var __t,__p='',__j=Array.prototype.join," + + "print=function(){__p+=__j.call(arguments,'');};\n" + + source + 'return __p;\n'; + + try { + var render = new Function(settings.variable || 'obj', '_', source); + } catch (e) { + e.source = source; + throw e; + } + + var template = function(data) { + return render.call(this, data, _); + }; + + // Provide the compiled source as a convenience for precompilation. + var argument = settings.variable || 'obj'; + template.source = 'function(' + argument + '){\n' + source + '}'; + + return template; + }; + + // Add a "chain" function. Start chaining a wrapped Underscore object. + _.chain = function(obj) { + var instance = _(obj); + instance._chain = true; + return instance; + }; + + // OOP + // --------------- + // If Underscore is called as a function, it returns a wrapped object that + // can be used OO-style. This wrapper holds altered versions of all the + // underscore functions. Wrapped objects may be chained. + + // Helper function to continue chaining intermediate results. + var result = function(instance, obj) { + return instance._chain ? _(obj).chain() : obj; + }; + + // Add your own custom functions to the Underscore object. + _.mixin = function(obj) { + _.each(_.functions(obj), function(name) { + var func = _[name] = obj[name]; + _.prototype[name] = function() { + var args = [this._wrapped]; + push.apply(args, arguments); + return result(this, func.apply(_, args)); + }; + }); + }; + + // Add all of the Underscore functions to the wrapper object. + _.mixin(_); + + // Add all mutator Array functions to the wrapper. + _.each(['pop', 'push', 'reverse', 'shift', 'sort', 'splice', 'unshift'], function(name) { + var method = ArrayProto[name]; + _.prototype[name] = function() { + var obj = this._wrapped; + method.apply(obj, arguments); + if ((name === 'shift' || name === 'splice') && obj.length === 0) delete obj[0]; + return result(this, obj); + }; + }); + + // Add all accessor Array functions to the wrapper. + _.each(['concat', 'join', 'slice'], function(name) { + var method = ArrayProto[name]; + _.prototype[name] = function() { + return result(this, method.apply(this._wrapped, arguments)); + }; + }); + + // Extracts the result from a wrapped and chained object. + _.prototype.value = function() { + return this._wrapped; + }; + + // Provide unwrapping proxy for some methods used in engine operations + // such as arithmetic and JSON stringification. + _.prototype.valueOf = _.prototype.toJSON = _.prototype.value; + + _.prototype.toString = function() { + return '' + this._wrapped; + }; + + // AMD registration happens at the end for compatibility with AMD loaders + // that may not enforce next-turn semantics on modules. Even though general + // practice for AMD registration is to be anonymous, underscore registers + // as a named module because, like jQuery, it is a base library that is + // popular enough to be bundled in a third party lib, but not be part of + // an AMD load request. Those cases could generate an error when an + // anonymous define() is called outside of a loader request. + if (typeof define === 'function' && define.amd) { + define('underscore', [], function() { + return _; + }); + } +}.call(this)); + +},{}],26:[function(require,module,exports){ +arguments[4][19][0].apply(exports,arguments) +},{"dup":19}],27:[function(require,module,exports){ +module.exports = function isBuffer(arg) { + return arg && typeof arg === 'object' + && typeof arg.copy === 'function' + && typeof arg.fill === 'function' + && typeof arg.readUInt8 === 'function'; +} +},{}],28:[function(require,module,exports){ +(function (process,global){ +// Copyright Joyent, Inc. and other Node contributors. +// +// Permission is hereby granted, free of charge, to any person obtaining a +// copy of this software and associated documentation files (the +// "Software"), to deal in the Software without restriction, including +// without limitation the rights to use, copy, modify, merge, publish, +// distribute, sublicense, and/or sell copies of the Software, and to permit +// persons to whom the Software is furnished to do so, subject to the +// following conditions: +// +// The above copyright notice and this permission notice shall be included +// in all copies or substantial portions of the Software. +// +// THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS +// OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF +// MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN +// NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, +// DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR +// OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE +// USE OR OTHER DEALINGS IN THE SOFTWARE. + +var formatRegExp = /%[sdj%]/g; +exports.format = function(f) { + if (!isString(f)) { + var objects = []; + for (var i = 0; i < arguments.length; i++) { + objects.push(inspect(arguments[i])); + } + return objects.join(' '); + } + + var i = 1; + var args = arguments; + var len = args.length; + var str = String(f).replace(formatRegExp, function(x) { + if (x === '%%') return '%'; + if (i >= len) return x; + switch (x) { + case '%s': return String(args[i++]); + case '%d': return Number(args[i++]); + case '%j': + try { + return JSON.stringify(args[i++]); + } catch (_) { + return '[Circular]'; + } + default: + return x; + } + }); + for (var x = args[i]; i < len; x = args[++i]) { + if (isNull(x) || !isObject(x)) { + str += ' ' + x; + } else { + str += ' ' + inspect(x); + } + } + return str; +}; + + +// Mark that a method should not be used. +// Returns a modified function which warns once by default. +// If --no-deprecation is set, then it is a no-op. +exports.deprecate = function(fn, msg) { + // Allow for deprecating things in the process of starting up. + if (isUndefined(global.process)) { + return function() { + return exports.deprecate(fn, msg).apply(this, arguments); + }; + } + + if (process.noDeprecation === true) { + return fn; + } + + var warned = false; + function deprecated() { + if (!warned) { + if (process.throwDeprecation) { + throw new Error(msg); + } else if (process.traceDeprecation) { + console.trace(msg); + } else { + console.error(msg); + } + warned = true; + } + return fn.apply(this, arguments); + } + + return deprecated; +}; + + +var debugs = {}; +var debugEnviron; +exports.debuglog = function(set) { + if (isUndefined(debugEnviron)) + debugEnviron = process.env.NODE_DEBUG || ''; + set = set.toUpperCase(); + if (!debugs[set]) { + if (new RegExp('\\b' + set + '\\b', 'i').test(debugEnviron)) { + var pid = process.pid; + debugs[set] = function() { + var msg = exports.format.apply(exports, arguments); + console.error('%s %d: %s', set, pid, msg); + }; + } else { + debugs[set] = function() {}; + } + } + return debugs[set]; +}; + + +/** + * Echos the value of a value. Trys to print the value out + * in the best way possible given the different types. + * + * @param {Object} obj The object to print out. + * @param {Object} opts Optional options object that alters the output. + */ +/* legacy: obj, showHidden, depth, colors*/ +function inspect(obj, opts) { + // default options + var ctx = { + seen: [], + stylize: stylizeNoColor + }; + // legacy... + if (arguments.length >= 3) ctx.depth = arguments[2]; + if (arguments.length >= 4) ctx.colors = arguments[3]; + if (isBoolean(opts)) { + // legacy... + ctx.showHidden = opts; + } else if (opts) { + // got an "options" object + exports._extend(ctx, opts); + } + // set default options + if (isUndefined(ctx.showHidden)) ctx.showHidden = false; + if (isUndefined(ctx.depth)) ctx.depth = 2; + if (isUndefined(ctx.colors)) ctx.colors = false; + if (isUndefined(ctx.customInspect)) ctx.customInspect = true; + if (ctx.colors) ctx.stylize = stylizeWithColor; + return formatValue(ctx, obj, ctx.depth); +} +exports.inspect = inspect; + + +// http://en.wikipedia.org/wiki/ANSI_escape_code#graphics +inspect.colors = { + 'bold' : [1, 22], + 'italic' : [3, 23], + 'underline' : [4, 24], + 'inverse' : [7, 27], + 'white' : [37, 39], + 'grey' : [90, 39], + 'black' : [30, 39], + 'blue' : [34, 39], + 'cyan' : [36, 39], + 'green' : [32, 39], + 'magenta' : [35, 39], + 'red' : [31, 39], + 'yellow' : [33, 39] +}; + +// Don't use 'blue' not visible on cmd.exe +inspect.styles = { + 'special': 'cyan', + 'number': 'yellow', + 'boolean': 'yellow', + 'undefined': 'grey', + 'null': 'bold', + 'string': 'green', + 'date': 'magenta', + // "name": intentionally not styling + 'regexp': 'red' +}; + + +function stylizeWithColor(str, styleType) { + var style = inspect.styles[styleType]; + + if (style) { + return '\u001b[' + inspect.colors[style][0] + 'm' + str + + '\u001b[' + inspect.colors[style][1] + 'm'; + } else { + return str; + } +} + + +function stylizeNoColor(str, styleType) { + return str; +} + + +function arrayToHash(array) { + var hash = {}; + + array.forEach(function(val, idx) { + hash[val] = true; + }); + + return hash; +} + + +function formatValue(ctx, value, recurseTimes) { + // Provide a hook for user-specified inspect functions. + // Check that value is an object with an inspect function on it + if (ctx.customInspect && + value && + isFunction(value.inspect) && + // Filter out the util module, it's inspect function is special + value.inspect !== exports.inspect && + // Also filter out any prototype objects using the circular check. + !(value.constructor && value.constructor.prototype === value)) { + var ret = value.inspect(recurseTimes, ctx); + if (!isString(ret)) { + ret = formatValue(ctx, ret, recurseTimes); + } + return ret; + } + + // Primitive types cannot have properties + var primitive = formatPrimitive(ctx, value); + if (primitive) { + return primitive; + } + + // Look up the keys of the object. + var keys = Object.keys(value); + var visibleKeys = arrayToHash(keys); + + if (ctx.showHidden) { + keys = Object.getOwnPropertyNames(value); + } + + // IE doesn't make error fields non-enumerable + // http://msdn.microsoft.com/en-us/library/ie/dww52sbt(v=vs.94).aspx + if (isError(value) + && (keys.indexOf('message') >= 0 || keys.indexOf('description') >= 0)) { + return formatError(value); + } + + // Some type of object without properties can be shortcutted. + if (keys.length === 0) { + if (isFunction(value)) { + var name = value.name ? ': ' + value.name : ''; + return ctx.stylize('[Function' + name + ']', 'special'); + } + if (isRegExp(value)) { + return ctx.stylize(RegExp.prototype.toString.call(value), 'regexp'); + } + if (isDate(value)) { + return ctx.stylize(Date.prototype.toString.call(value), 'date'); + } + if (isError(value)) { + return formatError(value); + } + } + + var base = '', array = false, braces = ['{', '}']; + + // Make Array say that they are Array + if (isArray(value)) { + array = true; + braces = ['[', ']']; + } + + // Make functions say that they are functions + if (isFunction(value)) { + var n = value.name ? ': ' + value.name : ''; + base = ' [Function' + n + ']'; + } + + // Make RegExps say that they are RegExps + if (isRegExp(value)) { + base = ' ' + RegExp.prototype.toString.call(value); + } + + // Make dates with properties first say the date + if (isDate(value)) { + base = ' ' + Date.prototype.toUTCString.call(value); + } + + // Make error with message first say the error + if (isError(value)) { + base = ' ' + formatError(value); + } + + if (keys.length === 0 && (!array || value.length == 0)) { + return braces[0] + base + braces[1]; + } + + if (recurseTimes < 0) { + if (isRegExp(value)) { + return ctx.stylize(RegExp.prototype.toString.call(value), 'regexp'); + } else { + return ctx.stylize('[Object]', 'special'); + } + } + + ctx.seen.push(value); + + var output; + if (array) { + output = formatArray(ctx, value, recurseTimes, visibleKeys, keys); + } else { + output = keys.map(function(key) { + return formatProperty(ctx, value, recurseTimes, visibleKeys, key, array); + }); + } + + ctx.seen.pop(); + + return reduceToSingleString(output, base, braces); +} + + +function formatPrimitive(ctx, value) { + if (isUndefined(value)) + return ctx.stylize('undefined', 'undefined'); + if (isString(value)) { + var simple = '\'' + JSON.stringify(value).replace(/^"|"$/g, '') + .replace(/'/g, "\\'") + .replace(/\\"/g, '"') + '\''; + return ctx.stylize(simple, 'string'); + } + if (isNumber(value)) + return ctx.stylize('' + value, 'number'); + if (isBoolean(value)) + return ctx.stylize('' + value, 'boolean'); + // For some reason typeof null is "object", so special case here. + if (isNull(value)) + return ctx.stylize('null', 'null'); +} + + +function formatError(value) { + return '[' + Error.prototype.toString.call(value) + ']'; +} + + +function formatArray(ctx, value, recurseTimes, visibleKeys, keys) { + var output = []; + for (var i = 0, l = value.length; i < l; ++i) { + if (hasOwnProperty(value, String(i))) { + output.push(formatProperty(ctx, value, recurseTimes, visibleKeys, + String(i), true)); + } else { + output.push(''); + } + } + keys.forEach(function(key) { + if (!key.match(/^\d+$/)) { + output.push(formatProperty(ctx, value, recurseTimes, visibleKeys, + key, true)); + } + }); + return output; +} + + +function formatProperty(ctx, value, recurseTimes, visibleKeys, key, array) { + var name, str, desc; + desc = Object.getOwnPropertyDescriptor(value, key) || { value: value[key] }; + if (desc.get) { + if (desc.set) { + str = ctx.stylize('[Getter/Setter]', 'special'); + } else { + str = ctx.stylize('[Getter]', 'special'); + } + } else { + if (desc.set) { + str = ctx.stylize('[Setter]', 'special'); + } + } + if (!hasOwnProperty(visibleKeys, key)) { + name = '[' + key + ']'; + } + if (!str) { + if (ctx.seen.indexOf(desc.value) < 0) { + if (isNull(recurseTimes)) { + str = formatValue(ctx, desc.value, null); + } else { + str = formatValue(ctx, desc.value, recurseTimes - 1); + } + if (str.indexOf('\n') > -1) { + if (array) { + str = str.split('\n').map(function(line) { + return ' ' + line; + }).join('\n').substr(2); + } else { + str = '\n' + str.split('\n').map(function(line) { + return ' ' + line; + }).join('\n'); + } + } + } else { + str = ctx.stylize('[Circular]', 'special'); + } + } + if (isUndefined(name)) { + if (array && key.match(/^\d+$/)) { + return str; + } + name = JSON.stringify('' + key); + if (name.match(/^"([a-zA-Z_][a-zA-Z_0-9]*)"$/)) { + name = name.substr(1, name.length - 2); + name = ctx.stylize(name, 'name'); + } else { + name = name.replace(/'/g, "\\'") + .replace(/\\"/g, '"') + .replace(/(^"|"$)/g, "'"); + name = ctx.stylize(name, 'string'); + } + } + + return name + ': ' + str; +} + + +function reduceToSingleString(output, base, braces) { + var numLinesEst = 0; + var length = output.reduce(function(prev, cur) { + numLinesEst++; + if (cur.indexOf('\n') >= 0) numLinesEst++; + return prev + cur.replace(/\u001b\[\d\d?m/g, '').length + 1; + }, 0); + + if (length > 60) { + return braces[0] + + (base === '' ? '' : base + '\n ') + + ' ' + + output.join(',\n ') + + ' ' + + braces[1]; + } + + return braces[0] + base + ' ' + output.join(', ') + ' ' + braces[1]; +} + + +// NOTE: These type checking functions intentionally don't use `instanceof` +// because it is fragile and can be easily faked with `Object.create()`. +function isArray(ar) { + return Array.isArray(ar); +} +exports.isArray = isArray; + +function isBoolean(arg) { + return typeof arg === 'boolean'; +} +exports.isBoolean = isBoolean; + +function isNull(arg) { + return arg === null; +} +exports.isNull = isNull; + +function isNullOrUndefined(arg) { + return arg == null; +} +exports.isNullOrUndefined = isNullOrUndefined; + +function isNumber(arg) { + return typeof arg === 'number'; +} +exports.isNumber = isNumber; + +function isString(arg) { + return typeof arg === 'string'; +} +exports.isString = isString; + +function isSymbol(arg) { + return typeof arg === 'symbol'; +} +exports.isSymbol = isSymbol; + +function isUndefined(arg) { + return arg === void 0; +} +exports.isUndefined = isUndefined; + +function isRegExp(re) { + return isObject(re) && objectToString(re) === '[object RegExp]'; +} +exports.isRegExp = isRegExp; + +function isObject(arg) { + return typeof arg === 'object' && arg !== null; +} +exports.isObject = isObject; + +function isDate(d) { + return isObject(d) && objectToString(d) === '[object Date]'; +} +exports.isDate = isDate; + +function isError(e) { + return isObject(e) && + (objectToString(e) === '[object Error]' || e instanceof Error); +} +exports.isError = isError; + +function isFunction(arg) { + return typeof arg === 'function'; +} +exports.isFunction = isFunction; + +function isPrimitive(arg) { + return arg === null || + typeof arg === 'boolean' || + typeof arg === 'number' || + typeof arg === 'string' || + typeof arg === 'symbol' || // ES6 symbol + typeof arg === 'undefined'; +} +exports.isPrimitive = isPrimitive; + +exports.isBuffer = require('./support/isBuffer'); + +function objectToString(o) { + return Object.prototype.toString.call(o); +} + + +function pad(n) { + return n < 10 ? '0' + n.toString(10) : n.toString(10); +} + + +var months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', + 'Oct', 'Nov', 'Dec']; + +// 26 Feb 16:19:34 +function timestamp() { + var d = new Date(); + var time = [pad(d.getHours()), + pad(d.getMinutes()), + pad(d.getSeconds())].join(':'); + return [d.getDate(), months[d.getMonth()], time].join(' '); +} + + +// log is just a thin wrapper to console.log that prepends a timestamp +exports.log = function() { + console.log('%s - %s', timestamp(), exports.format.apply(exports, arguments)); +}; + + +/** + * Inherit the prototype methods from one constructor into another. + * + * The Function.prototype.inherits from lang.js rewritten as a standalone + * function (not on Function.prototype). NOTE: If this file is to be loaded + * during bootstrapping this function needs to be rewritten using some native + * functions as prototype setup using normal JavaScript does not work as + * expected during bootstrapping (see mirror.js in r114903). + * + * @param {function} ctor Constructor function which needs to inherit the + * prototype. + * @param {function} superCtor Constructor function to inherit prototype from. + */ +exports.inherits = require('inherits'); + +exports._extend = function(origin, add) { + // Don't do anything if add isn't an object + if (!add || !isObject(add)) return origin; + + var keys = Object.keys(add); + var i = keys.length; + while (i--) { + origin[keys[i]] = add[keys[i]]; + } + return origin; +}; + +function hasOwnProperty(obj, prop) { + return Object.prototype.hasOwnProperty.call(obj, prop); +} + +}).call(this,require('_process'),typeof global !== "undefined" ? global : typeof self !== "undefined" ? self : typeof window !== "undefined" ? window : {}) +},{"./support/isBuffer":27,"_process":24,"inherits":26}],29:[function(require,module,exports){ +// Returns a wrapper function that returns a wrapped callback +// The wrapper function should do some stuff, and return a +// presumably different callback function. +// This makes sure that own properties are retained, so that +// decorations and such are not lost along the way. +module.exports = wrappy +function wrappy (fn, cb) { + if (fn && cb) return wrappy(fn)(cb) + + if (typeof fn !== 'function') + throw new TypeError('need wrapper function') + + Object.keys(fn).forEach(function (k) { + wrapper[k] = fn[k] + }) + + return wrapper + + function wrapper() { + var args = new Array(arguments.length) + for (var i = 0; i < args.length; i++) { + args[i] = arguments[i] + } + var ret = fn.apply(this, args) + var cb = args[args.length-1] + if (typeof ret === 'function' && ret !== cb) { + Object.keys(cb).forEach(function (k) { + ret[k] = cb[k] + }) + } + return ret + } +} + +},{}]},{},[7])(7) +}); \ No newline at end of file diff --git a/assets/javascripts/workers/search.85cb4492.min.js b/assets/javascripts/workers/search.85cb4492.min.js new file mode 100644 index 00000000..0933cd2b --- /dev/null +++ b/assets/javascripts/workers/search.85cb4492.min.js @@ -0,0 +1,48 @@ +(()=>{var ge=Object.create;var W=Object.defineProperty,ye=Object.defineProperties,me=Object.getOwnPropertyDescriptor,ve=Object.getOwnPropertyDescriptors,xe=Object.getOwnPropertyNames,G=Object.getOwnPropertySymbols,Se=Object.getPrototypeOf,X=Object.prototype.hasOwnProperty,Qe=Object.prototype.propertyIsEnumerable;var J=(t,e,r)=>e in t?W(t,e,{enumerable:!0,configurable:!0,writable:!0,value:r}):t[e]=r,M=(t,e)=>{for(var r in e||(e={}))X.call(e,r)&&J(t,r,e[r]);if(G)for(var r of G(e))Qe.call(e,r)&&J(t,r,e[r]);return t},Z=(t,e)=>ye(t,ve(e));var K=(t,e)=>()=>(e||t((e={exports:{}}).exports,e),e.exports);var be=(t,e,r,n)=>{if(e&&typeof e=="object"||typeof e=="function")for(let i of xe(e))!X.call(t,i)&&i!==r&&W(t,i,{get:()=>e[i],enumerable:!(n=me(e,i))||n.enumerable});return t};var H=(t,e,r)=>(r=t!=null?ge(Se(t)):{},be(e||!t||!t.__esModule?W(r,"default",{value:t,enumerable:!0}):r,t));var z=(t,e,r)=>new Promise((n,i)=>{var s=u=>{try{a(r.next(u))}catch(c){i(c)}},o=u=>{try{a(r.throw(u))}catch(c){i(c)}},a=u=>u.done?n(u.value):Promise.resolve(u.value).then(s,o);a((r=r.apply(t,e)).next())});var re=K((ee,te)=>{/** + * lunr - http://lunrjs.com - A bit like Solr, but much smaller and not as bright - 2.3.9 + * Copyright (C) 2020 Oliver Nightingale + * @license MIT + */(function(){var t=function(e){var r=new t.Builder;return r.pipeline.add(t.trimmer,t.stopWordFilter,t.stemmer),r.searchPipeline.add(t.stemmer),e.call(r,r),r.build()};t.version="2.3.9";/*! + * lunr.utils + * Copyright (C) 2020 Oliver Nightingale + */t.utils={},t.utils.warn=function(e){return function(r){e.console&&console.warn&&console.warn(r)}}(this),t.utils.asString=function(e){return e==null?"":e.toString()},t.utils.clone=function(e){if(e==null)return e;for(var r=Object.create(null),n=Object.keys(e),i=0;i0){var h=t.utils.clone(r)||{};h.position=[a,c],h.index=s.length,s.push(new t.Token(n.slice(a,o),h))}a=o+1}}return s},t.tokenizer.separator=/[\s\-]+/;/*! + * lunr.Pipeline + * Copyright (C) 2020 Oliver Nightingale + */t.Pipeline=function(){this._stack=[]},t.Pipeline.registeredFunctions=Object.create(null),t.Pipeline.registerFunction=function(e,r){r in this.registeredFunctions&&t.utils.warn("Overwriting existing registered function: "+r),e.label=r,t.Pipeline.registeredFunctions[e.label]=e},t.Pipeline.warnIfFunctionNotRegistered=function(e){var r=e.label&&e.label in this.registeredFunctions;r||t.utils.warn(`Function is not registered with pipeline. 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this.pos1&&(e.backup(),e.emit(t.QueryLexer.TERM)),e.ignore(),e.more())return t.QueryLexer.lexText},t.QueryLexer.lexEditDistance=function(e){return e.ignore(),e.acceptDigitRun(),e.emit(t.QueryLexer.EDIT_DISTANCE),t.QueryLexer.lexText},t.QueryLexer.lexBoost=function(e){return e.ignore(),e.acceptDigitRun(),e.emit(t.QueryLexer.BOOST),t.QueryLexer.lexText},t.QueryLexer.lexEOS=function(e){e.width()>0&&e.emit(t.QueryLexer.TERM)},t.QueryLexer.termSeparator=t.tokenizer.separator,t.QueryLexer.lexText=function(e){for(;;){var r=e.next();if(r==t.QueryLexer.EOS)return t.QueryLexer.lexEOS;if(r.charCodeAt(0)==92){e.escapeCharacter();continue}if(r==":")return t.QueryLexer.lexField;if(r=="~")return e.backup(),e.width()>0&&e.emit(t.QueryLexer.TERM),t.QueryLexer.lexEditDistance;if(r=="^")return e.backup(),e.width()>0&&e.emit(t.QueryLexer.TERM),t.QueryLexer.lexBoost;if(r=="+"&&e.width()===1||r=="-"&&e.width()===1)return e.emit(t.QueryLexer.PRESENCE),t.QueryLexer.lexText;if(r.match(t.QueryLexer.termSeparator))return t.QueryLexer.lexTerm}},t.QueryParser=function(e,r){this.lexer=new t.QueryLexer(e),this.query=r,this.currentClause={},this.lexemeIdx=0},t.QueryParser.prototype.parse=function(){this.lexer.run(),this.lexemes=this.lexer.lexemes;for(var e=t.QueryParser.parseClause;e;)e=e(this);return this.query},t.QueryParser.prototype.peekLexeme=function(){return this.lexemes[this.lexemeIdx]},t.QueryParser.prototype.consumeLexeme=function(){var e=this.peekLexeme();return this.lexemeIdx+=1,e},t.QueryParser.prototype.nextClause=function(){var e=this.currentClause;this.query.clause(e),this.currentClause={}},t.QueryParser.parseClause=function(e){var r=e.peekLexeme();if(r!=null)switch(r.type){case t.QueryLexer.PRESENCE:return t.QueryParser.parsePresence;case t.QueryLexer.FIELD:return t.QueryParser.parseField;case t.QueryLexer.TERM:return t.QueryParser.parseTerm;default:var n="expected either a field or a term, found "+r.type;throw r.str.length>=1&&(n+=" with value '"+r.str+"'"),new t.QueryParseError(n,r.start,r.end)}},t.QueryParser.parsePresence=function(e){var r=e.consumeLexeme();if(r!=null){switch(r.str){case"-":e.currentClause.presence=t.Query.presence.PROHIBITED;break;case"+":e.currentClause.presence=t.Query.presence.REQUIRED;break;default:var n="unrecognised presence operator'"+r.str+"'";throw new t.QueryParseError(n,r.start,r.end)}var i=e.peekLexeme();if(i==null){var n="expecting term or field, found nothing";throw new t.QueryParseError(n,r.start,r.end)}switch(i.type){case t.QueryLexer.FIELD:return t.QueryParser.parseField;case t.QueryLexer.TERM:return t.QueryParser.parseTerm;default:var n="expecting term or field, found '"+i.type+"'";throw new t.QueryParseError(n,i.start,i.end)}}},t.QueryParser.parseField=function(e){var r=e.consumeLexeme();if(r!=null){if(e.query.allFields.indexOf(r.str)==-1){var n=e.query.allFields.map(function(o){return"'"+o+"'"}).join(", "),i="unrecognised field '"+r.str+"', possible fields: "+n;throw new t.QueryParseError(i,r.start,r.end)}e.currentClause.fields=[r.str];var s=e.peekLexeme();if(s==null){var i="expecting term, found nothing";throw new t.QueryParseError(i,r.start,r.end)}switch(s.type){case t.QueryLexer.TERM:return t.QueryParser.parseTerm;default:var i="expecting term, found '"+s.type+"'";throw new t.QueryParseError(i,s.start,s.end)}}},t.QueryParser.parseTerm=function(e){var r=e.consumeLexeme();if(r!=null){e.currentClause.term=r.str.toLowerCase(),r.str.indexOf("*")!=-1&&(e.currentClause.usePipeline=!1);var n=e.peekLexeme();if(n==null){e.nextClause();return}switch(n.type){case t.QueryLexer.TERM:return e.nextClause(),t.QueryParser.parseTerm;case t.QueryLexer.FIELD:return e.nextClause(),t.QueryParser.parseField;case t.QueryLexer.EDIT_DISTANCE:return t.QueryParser.parseEditDistance;case t.QueryLexer.BOOST:return t.QueryParser.parseBoost;case t.QueryLexer.PRESENCE:return e.nextClause(),t.QueryParser.parsePresence;default:var i="Unexpected lexeme type '"+n.type+"'";throw new t.QueryParseError(i,n.start,n.end)}}},t.QueryParser.parseEditDistance=function(e){var r=e.consumeLexeme();if(r!=null){var n=parseInt(r.str,10);if(isNaN(n)){var i="edit distance must be numeric";throw new t.QueryParseError(i,r.start,r.end)}e.currentClause.editDistance=n;var s=e.peekLexeme();if(s==null){e.nextClause();return}switch(s.type){case t.QueryLexer.TERM:return e.nextClause(),t.QueryParser.parseTerm;case t.QueryLexer.FIELD:return e.nextClause(),t.QueryParser.parseField;case t.QueryLexer.EDIT_DISTANCE:return t.QueryParser.parseEditDistance;case t.QueryLexer.BOOST:return t.QueryParser.parseBoost;case t.QueryLexer.PRESENCE:return e.nextClause(),t.QueryParser.parsePresence;default:var i="Unexpected lexeme type '"+s.type+"'";throw new t.QueryParseError(i,s.start,s.end)}}},t.QueryParser.parseBoost=function(e){var r=e.consumeLexeme();if(r!=null){var n=parseInt(r.str,10);if(isNaN(n)){var i="boost must be numeric";throw new t.QueryParseError(i,r.start,r.end)}e.currentClause.boost=n;var s=e.peekLexeme();if(s==null){e.nextClause();return}switch(s.type){case t.QueryLexer.TERM:return e.nextClause(),t.QueryParser.parseTerm;case t.QueryLexer.FIELD:return e.nextClause(),t.QueryParser.parseField;case t.QueryLexer.EDIT_DISTANCE:return t.QueryParser.parseEditDistance;case t.QueryLexer.BOOST:return t.QueryParser.parseBoost;case t.QueryLexer.PRESENCE:return e.nextClause(),t.QueryParser.parsePresence;default:var i="Unexpected lexeme type '"+s.type+"'";throw new t.QueryParseError(i,s.start,s.end)}}},function(e,r){typeof define=="function"&&define.amd?define(r):typeof ee=="object"?te.exports=r():e.lunr=r()}(this,function(){return t})})()});var q=K((Re,ne)=>{"use strict";/*! + * escape-html + * Copyright(c) 2012-2013 TJ Holowaychuk + * Copyright(c) 2015 Andreas Lubbe + * Copyright(c) 2015 Tiancheng "Timothy" Gu + * MIT Licensed + */var Le=/["'&<>]/;ne.exports=we;function we(t){var e=""+t,r=Le.exec(e);if(!r)return e;var n,i="",s=0,o=0;for(s=r.index;s=0;r--){let n=t[r];typeof n!="object"?n=document.createTextNode(n):n.parentNode&&n.parentNode.removeChild(n),r?e.insertBefore(this.previousSibling,n):e.replaceChild(n,this)}}}));var ie=H(q());function se(t){let e=new Map,r=new Set;for(let n of t){let[i,s]=n.location.split("#"),o=n.location,a=n.title,u=n.tags,c=(0,ie.default)(n.text).replace(/\s+(?=[,.:;!?])/g,"").replace(/\s+/g," ");if(s){let h=e.get(i);r.has(h)?e.set(o,{location:o,title:a,text:c,parent:h}):(h.title=n.title,h.text=c,r.add(h))}else e.set(o,M({location:o,title:a,text:c},u&&{tags:u}))}return e}var oe=H(q());function ae(t,e){let r=new RegExp(t.separator,"img"),n=(i,s,o)=>`${s}${o}`;return i=>{i=i.replace(/[\s*+\-:~^]+/g," ").trim();let s=new RegExp(`(^|${t.separator})(${i.replace(/[|\\{}()[\]^$+*?.-]/g,"\\$&").replace(r,"|")})`,"img");return o=>(e?(0,oe.default)(o):o).replace(s,n).replace(/<\/mark>(\s+)]*>/img,"$1")}}function ue(t){let e=new lunr.Query(["title","text"]);return new lunr.QueryParser(t,e).parse(),e.clauses}function ce(t,e){var i;let r=new Set(t),n={};for(let s=0;s!n.has(i)))]}var U=class{constructor({config:e,docs:r,options:n}){this.options=n,this.documents=se(r),this.highlight=ae(e,!1),lunr.tokenizer.separator=new RegExp(e.separator),this.index=lunr(function(){e.lang.length===1&&e.lang[0]!=="en"?this.use(lunr[e.lang[0]]):e.lang.length>1&&this.use(lunr.multiLanguage(...e.lang));let i=Ee(["trimmer","stopWordFilter","stemmer"],n.pipeline);for(let s of e.lang.map(o=>o==="en"?lunr:lunr[o]))for(let o of i)this.pipeline.remove(s[o]),this.searchPipeline.remove(s[o]);this.ref("location"),this.field("title",{boost:1e3}),this.field("text"),this.field("tags",{boost:1e6,extractor:s=>{let{tags:o=[]}=s;return o.flatMap(a=>lunr.tokenizer(a))}});for(let s of r)this.add(s,{boost:s.boost})})}search(e){if(e)try{let r=this.highlight(e),n=ue(e).filter(o=>o.presence!==lunr.Query.presence.PROHIBITED),i=this.index.search(`${e}*`).reduce((o,{ref:a,score:u,matchData:c})=>{let h=this.documents.get(a);if(typeof h!="undefined"){let{location:y,title:g,text:b,tags:m,parent:Q}=h,p=ce(n,Object.keys(c.metadata)),d=+!Q+ +Object.values(p).every(w=>w);o.push(Z(M({location:y,title:r(g),text:r(b)},m&&{tags:m.map(r)}),{score:u*(1+d),terms:p}))}return o},[]).sort((o,a)=>a.score-o.score).reduce((o,a)=>{let u=this.documents.get(a.location);if(typeof u!="undefined"){let c="parent"in u?u.parent.location:u.location;o.set(c,[...o.get(c)||[],a])}return o},new Map),s;if(this.options.suggestions){let o=this.index.query(a=>{for(let u of n)a.term(u.term,{fields:["title"],presence:lunr.Query.presence.REQUIRED,wildcard:lunr.Query.wildcard.TRAILING})});s=o.length?Object.keys(o[0].matchData.metadata):[]}return M({items:[...i.values()]},typeof s!="undefined"&&{suggestions:s})}catch(r){console.warn(`Invalid query: ${e} \u2013 see https://bit.ly/2s3ChXG`)}return{items:[]}}};var Y;function ke(t){return z(this,null,function*(){let e="../lunr";if(typeof parent!="undefined"&&"IFrameWorker"in parent){let n=document.querySelector("script[src]"),[i]=n.src.split("/worker");e=e.replace("..",i)}let r=[];for(let n of t.lang){switch(n){case"ja":r.push(`${e}/tinyseg.js`);break;case"hi":case"th":r.push(`${e}/wordcut.js`);break}n!=="en"&&r.push(`${e}/min/lunr.${n}.min.js`)}t.lang.length>1&&r.push(`${e}/min/lunr.multi.min.js`),r.length&&(yield importScripts(`${e}/min/lunr.stemmer.support.min.js`,...r))})}function Te(t){return z(this,null,function*(){switch(t.type){case 0:return yield ke(t.data.config),Y=new U(t.data),{type:1};case 2:return{type:3,data:Y?Y.search(t.data):{items:[]}};default:throw new TypeError("Invalid message type")}})}self.lunr=le.default;addEventListener("message",t=>z(void 0,null,function*(){postMessage(yield Te(t.data))}));})(); +//# sourceMappingURL=search.85cb4492.min.js.map + diff --git a/assets/javascripts/workers/search.85cb4492.min.js.map b/assets/javascripts/workers/search.85cb4492.min.js.map new file mode 100644 index 00000000..120ad249 --- /dev/null +++ b/assets/javascripts/workers/search.85cb4492.min.js.map @@ -0,0 +1,8 @@ +{ + "version": 3, + "sources": ["node_modules/lunr/lunr.js", "node_modules/escape-html/index.js", "src/assets/javascripts/integrations/search/worker/main/index.ts", "src/assets/javascripts/polyfills/index.ts", "src/assets/javascripts/integrations/search/document/index.ts", "src/assets/javascripts/integrations/search/highlighter/index.ts", "src/assets/javascripts/integrations/search/query/_/index.ts", "src/assets/javascripts/integrations/search/_/index.ts"], + "sourceRoot": "../../../..", + "sourcesContent": ["/**\n * lunr - http://lunrjs.com - A bit like Solr, but much smaller and not as bright - 2.3.9\n * Copyright (C) 2020 Oliver Nightingale\n * @license MIT\n */\n\n;(function(){\n\n/**\n * A convenience function for configuring and constructing\n * a new lunr Index.\n *\n * A lunr.Builder instance is created and the pipeline setup\n * with a trimmer, stop word filter and stemmer.\n *\n * This builder object is yielded to the configuration function\n * that is passed as a parameter, allowing the list of fields\n * and other builder parameters to be customised.\n *\n * All documents _must_ be added within the passed config function.\n *\n * @example\n * var idx = lunr(function () {\n * this.field('title')\n * this.field('body')\n * this.ref('id')\n *\n * documents.forEach(function (doc) {\n * this.add(doc)\n * }, this)\n * })\n *\n * @see {@link lunr.Builder}\n * @see {@link lunr.Pipeline}\n * @see {@link lunr.trimmer}\n * @see {@link lunr.stopWordFilter}\n * @see {@link lunr.stemmer}\n * @namespace {function} lunr\n */\nvar lunr = function (config) {\n var builder = new lunr.Builder\n\n builder.pipeline.add(\n lunr.trimmer,\n lunr.stopWordFilter,\n lunr.stemmer\n )\n\n builder.searchPipeline.add(\n lunr.stemmer\n )\n\n config.call(builder, builder)\n return builder.build()\n}\n\nlunr.version = \"2.3.9\"\n/*!\n * lunr.utils\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A namespace containing utils for the rest of the lunr library\n * @namespace lunr.utils\n */\nlunr.utils = {}\n\n/**\n * Print a warning message to the console.\n *\n * @param {String} message The message to be printed.\n * @memberOf lunr.utils\n * @function\n */\nlunr.utils.warn = (function (global) {\n /* eslint-disable no-console */\n return function (message) {\n if (global.console && console.warn) {\n console.warn(message)\n }\n }\n /* eslint-enable no-console */\n})(this)\n\n/**\n * Convert an object to a string.\n *\n * In the case of `null` and `undefined` the function returns\n * the empty string, in all other cases the result of calling\n * `toString` on the passed object is returned.\n *\n * @param {Any} obj The object to convert to a string.\n * @return {String} string representation of the passed object.\n * @memberOf lunr.utils\n */\nlunr.utils.asString = function (obj) {\n if (obj === void 0 || obj === null) {\n return \"\"\n } else {\n return obj.toString()\n }\n}\n\n/**\n * Clones an object.\n *\n * Will create a copy of an existing object such that any mutations\n * on the copy cannot affect the original.\n *\n * Only shallow objects are supported, passing a nested object to this\n * function will cause a TypeError.\n *\n * Objects with primitives, and arrays of primitives are supported.\n *\n * @param {Object} obj The object to clone.\n * @return {Object} a clone of the passed object.\n * @throws {TypeError} when a nested object is passed.\n * @memberOf Utils\n */\nlunr.utils.clone = function (obj) {\n if (obj === null || obj === undefined) {\n return obj\n }\n\n var clone = Object.create(null),\n keys = Object.keys(obj)\n\n for (var i = 0; i < keys.length; i++) {\n var key = keys[i],\n val = obj[key]\n\n if (Array.isArray(val)) {\n clone[key] = val.slice()\n continue\n }\n\n if (typeof val === 'string' ||\n typeof val === 'number' ||\n typeof val === 'boolean') {\n clone[key] = val\n continue\n }\n\n throw new TypeError(\"clone is not deep and does not support nested objects\")\n }\n\n return clone\n}\nlunr.FieldRef = function (docRef, fieldName, stringValue) {\n this.docRef = docRef\n this.fieldName = fieldName\n this._stringValue = stringValue\n}\n\nlunr.FieldRef.joiner = \"/\"\n\nlunr.FieldRef.fromString = function (s) {\n var n = s.indexOf(lunr.FieldRef.joiner)\n\n if (n === -1) {\n throw \"malformed field ref string\"\n }\n\n var fieldRef = s.slice(0, n),\n docRef = s.slice(n + 1)\n\n return new lunr.FieldRef (docRef, fieldRef, s)\n}\n\nlunr.FieldRef.prototype.toString = function () {\n if (this._stringValue == undefined) {\n this._stringValue = this.fieldName + lunr.FieldRef.joiner + this.docRef\n }\n\n return this._stringValue\n}\n/*!\n * lunr.Set\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A lunr set.\n *\n * @constructor\n */\nlunr.Set = function (elements) {\n this.elements = Object.create(null)\n\n if (elements) {\n this.length = elements.length\n\n for (var i = 0; i < this.length; i++) {\n this.elements[elements[i]] = true\n }\n } else {\n this.length = 0\n }\n}\n\n/**\n * A complete set that contains all elements.\n *\n * @static\n * @readonly\n * @type {lunr.Set}\n */\nlunr.Set.complete = {\n intersect: function (other) {\n return other\n },\n\n union: function () {\n return this\n },\n\n contains: function () {\n return true\n }\n}\n\n/**\n * An empty set that contains no elements.\n *\n * @static\n * @readonly\n * @type {lunr.Set}\n */\nlunr.Set.empty = {\n intersect: function () {\n return this\n },\n\n union: function (other) {\n return other\n },\n\n contains: function () {\n return false\n }\n}\n\n/**\n * Returns true if this set contains the specified object.\n *\n * @param {object} object - Object whose presence in this set is to be tested.\n * @returns {boolean} - True if this set contains the specified object.\n */\nlunr.Set.prototype.contains = function (object) {\n return !!this.elements[object]\n}\n\n/**\n * Returns a new set containing only the elements that are present in both\n * this set and the specified set.\n *\n * @param {lunr.Set} other - set to intersect with this set.\n * @returns {lunr.Set} a new set that is the intersection of this and the specified set.\n */\n\nlunr.Set.prototype.intersect = function (other) {\n var a, b, elements, intersection = []\n\n if (other === lunr.Set.complete) {\n return this\n }\n\n if (other === lunr.Set.empty) {\n return other\n }\n\n if (this.length < other.length) {\n a = this\n b = other\n } else {\n a = other\n b = this\n }\n\n elements = Object.keys(a.elements)\n\n for (var i = 0; i < elements.length; i++) {\n var element = elements[i]\n if (element in b.elements) {\n intersection.push(element)\n }\n }\n\n return new lunr.Set (intersection)\n}\n\n/**\n * Returns a new set combining the elements of this and the specified set.\n *\n * @param {lunr.Set} other - set to union with this set.\n * @return {lunr.Set} a new set that is the union of this and the specified set.\n */\n\nlunr.Set.prototype.union = function (other) {\n if (other === lunr.Set.complete) {\n return lunr.Set.complete\n }\n\n if (other === lunr.Set.empty) {\n return this\n }\n\n return new lunr.Set(Object.keys(this.elements).concat(Object.keys(other.elements)))\n}\n/**\n * A function to calculate the inverse document frequency for\n * a posting. This is shared between the builder and the index\n *\n * @private\n * @param {object} posting - The posting for a given term\n * @param {number} documentCount - The total number of documents.\n */\nlunr.idf = function (posting, documentCount) {\n var documentsWithTerm = 0\n\n for (var fieldName in posting) {\n if (fieldName == '_index') continue // Ignore the term index, its not a field\n documentsWithTerm += Object.keys(posting[fieldName]).length\n }\n\n var x = (documentCount - documentsWithTerm + 0.5) / (documentsWithTerm + 0.5)\n\n return Math.log(1 + Math.abs(x))\n}\n\n/**\n * A token wraps a string representation of a token\n * as it is passed through the text processing pipeline.\n *\n * @constructor\n * @param {string} [str=''] - The string token being wrapped.\n * @param {object} [metadata={}] - Metadata associated with this token.\n */\nlunr.Token = function (str, metadata) {\n this.str = str || \"\"\n this.metadata = metadata || {}\n}\n\n/**\n * Returns the token string that is being wrapped by this object.\n *\n * @returns {string}\n */\nlunr.Token.prototype.toString = function () {\n return this.str\n}\n\n/**\n * A token update function is used when updating or optionally\n * when cloning a token.\n *\n * @callback lunr.Token~updateFunction\n * @param {string} str - The string representation of the token.\n * @param {Object} metadata - All metadata associated with this token.\n */\n\n/**\n * Applies the given function to the wrapped string token.\n *\n * @example\n * token.update(function (str, metadata) {\n * return str.toUpperCase()\n * })\n *\n * @param {lunr.Token~updateFunction} fn - A function to apply to the token string.\n * @returns {lunr.Token}\n */\nlunr.Token.prototype.update = function (fn) {\n this.str = fn(this.str, this.metadata)\n return this\n}\n\n/**\n * Creates a clone of this token. Optionally a function can be\n * applied to the cloned token.\n *\n * @param {lunr.Token~updateFunction} [fn] - An optional function to apply to the cloned token.\n * @returns {lunr.Token}\n */\nlunr.Token.prototype.clone = function (fn) {\n fn = fn || function (s) { return s }\n return new lunr.Token (fn(this.str, this.metadata), this.metadata)\n}\n/*!\n * lunr.tokenizer\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A function for splitting a string into tokens ready to be inserted into\n * the search index. Uses `lunr.tokenizer.separator` to split strings, change\n * the value of this property to change how strings are split into tokens.\n *\n * This tokenizer will convert its parameter to a string by calling `toString` and\n * then will split this string on the character in `lunr.tokenizer.separator`.\n * Arrays will have their elements converted to strings and wrapped in a lunr.Token.\n *\n * Optional metadata can be passed to the tokenizer, this metadata will be cloned and\n * added as metadata to every token that is created from the object to be tokenized.\n *\n * @static\n * @param {?(string|object|object[])} obj - The object to convert into tokens\n * @param {?object} metadata - Optional metadata to associate with every token\n * @returns {lunr.Token[]}\n * @see {@link lunr.Pipeline}\n */\nlunr.tokenizer = function (obj, metadata) {\n if (obj == null || obj == undefined) {\n return []\n }\n\n if (Array.isArray(obj)) {\n return obj.map(function (t) {\n return new lunr.Token(\n lunr.utils.asString(t).toLowerCase(),\n lunr.utils.clone(metadata)\n )\n })\n }\n\n var str = obj.toString().toLowerCase(),\n len = str.length,\n tokens = []\n\n for (var sliceEnd = 0, sliceStart = 0; sliceEnd <= len; sliceEnd++) {\n var char = str.charAt(sliceEnd),\n sliceLength = sliceEnd - sliceStart\n\n if ((char.match(lunr.tokenizer.separator) || sliceEnd == len)) {\n\n if (sliceLength > 0) {\n var tokenMetadata = lunr.utils.clone(metadata) || {}\n tokenMetadata[\"position\"] = [sliceStart, sliceLength]\n tokenMetadata[\"index\"] = tokens.length\n\n tokens.push(\n new lunr.Token (\n str.slice(sliceStart, sliceEnd),\n tokenMetadata\n )\n )\n }\n\n sliceStart = sliceEnd + 1\n }\n\n }\n\n return tokens\n}\n\n/**\n * The separator used to split a string into tokens. Override this property to change the behaviour of\n * `lunr.tokenizer` behaviour when tokenizing strings. By default this splits on whitespace and hyphens.\n *\n * @static\n * @see lunr.tokenizer\n */\nlunr.tokenizer.separator = /[\\s\\-]+/\n/*!\n * lunr.Pipeline\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.Pipelines maintain an ordered list of functions to be applied to all\n * tokens in documents entering the search index and queries being ran against\n * the index.\n *\n * An instance of lunr.Index created with the lunr shortcut will contain a\n * pipeline with a stop word filter and an English language stemmer. Extra\n * functions can be added before or after either of these functions or these\n * default functions can be removed.\n *\n * When run the pipeline will call each function in turn, passing a token, the\n * index of that token in the original list of all tokens and finally a list of\n * all the original tokens.\n *\n * The output of functions in the pipeline will be passed to the next function\n * in the pipeline. To exclude a token from entering the index the function\n * should return undefined, the rest of the pipeline will not be called with\n * this token.\n *\n * For serialisation of pipelines to work, all functions used in an instance of\n * a pipeline should be registered with lunr.Pipeline. Registered functions can\n * then be loaded. If trying to load a serialised pipeline that uses functions\n * that are not registered an error will be thrown.\n *\n * If not planning on serialising the pipeline then registering pipeline functions\n * is not necessary.\n *\n * @constructor\n */\nlunr.Pipeline = function () {\n this._stack = []\n}\n\nlunr.Pipeline.registeredFunctions = Object.create(null)\n\n/**\n * A pipeline function maps lunr.Token to lunr.Token. A lunr.Token contains the token\n * string as well as all known metadata. A pipeline function can mutate the token string\n * or mutate (or add) metadata for a given token.\n *\n * A pipeline function can indicate that the passed token should be discarded by returning\n * null, undefined or an empty string. This token will not be passed to any downstream pipeline\n * functions and will not be added to the index.\n *\n * Multiple tokens can be returned by returning an array of tokens. Each token will be passed\n * to any downstream pipeline functions and all will returned tokens will be added to the index.\n *\n * Any number of pipeline functions may be chained together using a lunr.Pipeline.\n *\n * @interface lunr.PipelineFunction\n * @param {lunr.Token} token - A token from the document being processed.\n * @param {number} i - The index of this token in the complete list of tokens for this document/field.\n * @param {lunr.Token[]} tokens - All tokens for this document/field.\n * @returns {(?lunr.Token|lunr.Token[])}\n */\n\n/**\n * Register a function with the pipeline.\n *\n * Functions that are used in the pipeline should be registered if the pipeline\n * needs to be serialised, or a serialised pipeline needs to be loaded.\n *\n * Registering a function does not add it to a pipeline, functions must still be\n * added to instances of the pipeline for them to be used when running a pipeline.\n *\n * @param {lunr.PipelineFunction} fn - The function to check for.\n * @param {String} label - The label to register this function with\n */\nlunr.Pipeline.registerFunction = function (fn, label) {\n if (label in this.registeredFunctions) {\n lunr.utils.warn('Overwriting existing registered function: ' + label)\n }\n\n fn.label = label\n lunr.Pipeline.registeredFunctions[fn.label] = fn\n}\n\n/**\n * Warns if the function is not registered as a Pipeline function.\n *\n * @param {lunr.PipelineFunction} fn - The function to check for.\n * @private\n */\nlunr.Pipeline.warnIfFunctionNotRegistered = function (fn) {\n var isRegistered = fn.label && (fn.label in this.registeredFunctions)\n\n if (!isRegistered) {\n lunr.utils.warn('Function is not registered with pipeline. This may cause problems when serialising the index.\\n', fn)\n }\n}\n\n/**\n * Loads a previously serialised pipeline.\n *\n * All functions to be loaded must already be registered with lunr.Pipeline.\n * If any function from the serialised data has not been registered then an\n * error will be thrown.\n *\n * @param {Object} serialised - The serialised pipeline to load.\n * @returns {lunr.Pipeline}\n */\nlunr.Pipeline.load = function (serialised) {\n var pipeline = new lunr.Pipeline\n\n serialised.forEach(function (fnName) {\n var fn = lunr.Pipeline.registeredFunctions[fnName]\n\n if (fn) {\n pipeline.add(fn)\n } else {\n throw new Error('Cannot load unregistered function: ' + fnName)\n }\n })\n\n return pipeline\n}\n\n/**\n * Adds new functions to the end of the pipeline.\n *\n * Logs a warning if the function has not been registered.\n *\n * @param {lunr.PipelineFunction[]} functions - Any number of functions to add to the pipeline.\n */\nlunr.Pipeline.prototype.add = function () {\n var fns = Array.prototype.slice.call(arguments)\n\n fns.forEach(function (fn) {\n lunr.Pipeline.warnIfFunctionNotRegistered(fn)\n this._stack.push(fn)\n }, this)\n}\n\n/**\n * Adds a single function after a function that already exists in the\n * pipeline.\n *\n * Logs a warning if the function has not been registered.\n *\n * @param {lunr.PipelineFunction} existingFn - A function that already exists in the pipeline.\n * @param {lunr.PipelineFunction} newFn - The new function to add to the pipeline.\n */\nlunr.Pipeline.prototype.after = function (existingFn, newFn) {\n lunr.Pipeline.warnIfFunctionNotRegistered(newFn)\n\n var pos = this._stack.indexOf(existingFn)\n if (pos == -1) {\n throw new Error('Cannot find existingFn')\n }\n\n pos = pos + 1\n this._stack.splice(pos, 0, newFn)\n}\n\n/**\n * Adds a single function before a function that already exists in the\n * pipeline.\n *\n * Logs a warning if the function has not been registered.\n *\n * @param {lunr.PipelineFunction} existingFn - A function that already exists in the pipeline.\n * @param {lunr.PipelineFunction} newFn - The new function to add to the pipeline.\n */\nlunr.Pipeline.prototype.before = function (existingFn, newFn) {\n lunr.Pipeline.warnIfFunctionNotRegistered(newFn)\n\n var pos = this._stack.indexOf(existingFn)\n if (pos == -1) {\n throw new Error('Cannot find existingFn')\n }\n\n this._stack.splice(pos, 0, newFn)\n}\n\n/**\n * Removes a function from the pipeline.\n *\n * @param {lunr.PipelineFunction} fn The function to remove from the pipeline.\n */\nlunr.Pipeline.prototype.remove = function (fn) {\n var pos = this._stack.indexOf(fn)\n if (pos == -1) {\n return\n }\n\n this._stack.splice(pos, 1)\n}\n\n/**\n * Runs the current list of functions that make up the pipeline against the\n * passed tokens.\n *\n * @param {Array} tokens The tokens to run through the pipeline.\n * @returns {Array}\n */\nlunr.Pipeline.prototype.run = function (tokens) {\n var stackLength = this._stack.length\n\n for (var i = 0; i < stackLength; i++) {\n var fn = this._stack[i]\n var memo = []\n\n for (var j = 0; j < tokens.length; j++) {\n var result = fn(tokens[j], j, tokens)\n\n if (result === null || result === void 0 || result === '') continue\n\n if (Array.isArray(result)) {\n for (var k = 0; k < result.length; k++) {\n memo.push(result[k])\n }\n } else {\n memo.push(result)\n }\n }\n\n tokens = memo\n }\n\n return tokens\n}\n\n/**\n * Convenience method for passing a string through a pipeline and getting\n * strings out. This method takes care of wrapping the passed string in a\n * token and mapping the resulting tokens back to strings.\n *\n * @param {string} str - The string to pass through the pipeline.\n * @param {?object} metadata - Optional metadata to associate with the token\n * passed to the pipeline.\n * @returns {string[]}\n */\nlunr.Pipeline.prototype.runString = function (str, metadata) {\n var token = new lunr.Token (str, metadata)\n\n return this.run([token]).map(function (t) {\n return t.toString()\n })\n}\n\n/**\n * Resets the pipeline by removing any existing processors.\n *\n */\nlunr.Pipeline.prototype.reset = function () {\n this._stack = []\n}\n\n/**\n * Returns a representation of the pipeline ready for serialisation.\n *\n * Logs a warning if the function has not been registered.\n *\n * @returns {Array}\n */\nlunr.Pipeline.prototype.toJSON = function () {\n return this._stack.map(function (fn) {\n lunr.Pipeline.warnIfFunctionNotRegistered(fn)\n\n return fn.label\n })\n}\n/*!\n * lunr.Vector\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A vector is used to construct the vector space of documents and queries. These\n * vectors support operations to determine the similarity between two documents or\n * a document and a query.\n *\n * Normally no parameters are required for initializing a vector, but in the case of\n * loading a previously dumped vector the raw elements can be provided to the constructor.\n *\n * For performance reasons vectors are implemented with a flat array, where an elements\n * index is immediately followed by its value. E.g. [index, value, index, value]. This\n * allows the underlying array to be as sparse as possible and still offer decent\n * performance when being used for vector calculations.\n *\n * @constructor\n * @param {Number[]} [elements] - The flat list of element index and element value pairs.\n */\nlunr.Vector = function (elements) {\n this._magnitude = 0\n this.elements = elements || []\n}\n\n\n/**\n * Calculates the position within the vector to insert a given index.\n *\n * This is used internally by insert and upsert. If there are duplicate indexes then\n * the position is returned as if the value for that index were to be updated, but it\n * is the callers responsibility to check whether there is a duplicate at that index\n *\n * @param {Number} insertIdx - The index at which the element should be inserted.\n * @returns {Number}\n */\nlunr.Vector.prototype.positionForIndex = function (index) {\n // For an empty vector the tuple can be inserted at the beginning\n if (this.elements.length == 0) {\n return 0\n }\n\n var start = 0,\n end = this.elements.length / 2,\n sliceLength = end - start,\n pivotPoint = Math.floor(sliceLength / 2),\n pivotIndex = this.elements[pivotPoint * 2]\n\n while (sliceLength > 1) {\n if (pivotIndex < index) {\n start = pivotPoint\n }\n\n if (pivotIndex > index) {\n end = pivotPoint\n }\n\n if (pivotIndex == index) {\n break\n }\n\n sliceLength = end - start\n pivotPoint = start + Math.floor(sliceLength / 2)\n pivotIndex = this.elements[pivotPoint * 2]\n }\n\n if (pivotIndex == index) {\n return pivotPoint * 2\n }\n\n if (pivotIndex > index) {\n return pivotPoint * 2\n }\n\n if (pivotIndex < index) {\n return (pivotPoint + 1) * 2\n }\n}\n\n/**\n * Inserts an element at an index within the vector.\n *\n * Does not allow duplicates, will throw an error if there is already an entry\n * for this index.\n *\n * @param {Number} insertIdx - The index at which the element should be inserted.\n * @param {Number} val - The value to be inserted into the vector.\n */\nlunr.Vector.prototype.insert = function (insertIdx, val) {\n this.upsert(insertIdx, val, function () {\n throw \"duplicate index\"\n })\n}\n\n/**\n * Inserts or updates an existing index within the vector.\n *\n * @param {Number} insertIdx - The index at which the element should be inserted.\n * @param {Number} val - The value to be inserted into the vector.\n * @param {function} fn - A function that is called for updates, the existing value and the\n * requested value are passed as arguments\n */\nlunr.Vector.prototype.upsert = function (insertIdx, val, fn) {\n this._magnitude = 0\n var position = this.positionForIndex(insertIdx)\n\n if (this.elements[position] == insertIdx) {\n this.elements[position + 1] = fn(this.elements[position + 1], val)\n } else {\n this.elements.splice(position, 0, insertIdx, val)\n }\n}\n\n/**\n * Calculates the magnitude of this vector.\n *\n * @returns {Number}\n */\nlunr.Vector.prototype.magnitude = function () {\n if (this._magnitude) return this._magnitude\n\n var sumOfSquares = 0,\n elementsLength = this.elements.length\n\n for (var i = 1; i < elementsLength; i += 2) {\n var val = this.elements[i]\n sumOfSquares += val * val\n }\n\n return this._magnitude = Math.sqrt(sumOfSquares)\n}\n\n/**\n * Calculates the dot product of this vector and another vector.\n *\n * @param {lunr.Vector} otherVector - The vector to compute the dot product with.\n * @returns {Number}\n */\nlunr.Vector.prototype.dot = function (otherVector) {\n var dotProduct = 0,\n a = this.elements, b = otherVector.elements,\n aLen = a.length, bLen = b.length,\n aVal = 0, bVal = 0,\n i = 0, j = 0\n\n while (i < aLen && j < bLen) {\n aVal = a[i], bVal = b[j]\n if (aVal < bVal) {\n i += 2\n } else if (aVal > bVal) {\n j += 2\n } else if (aVal == bVal) {\n dotProduct += a[i + 1] * b[j + 1]\n i += 2\n j += 2\n }\n }\n\n return dotProduct\n}\n\n/**\n * Calculates the similarity between this vector and another vector.\n *\n * @param {lunr.Vector} otherVector - The other vector to calculate the\n * similarity with.\n * @returns {Number}\n */\nlunr.Vector.prototype.similarity = function (otherVector) {\n return this.dot(otherVector) / this.magnitude() || 0\n}\n\n/**\n * Converts the vector to an array of the elements within the vector.\n *\n * @returns {Number[]}\n */\nlunr.Vector.prototype.toArray = function () {\n var output = new Array (this.elements.length / 2)\n\n for (var i = 1, j = 0; i < this.elements.length; i += 2, j++) {\n output[j] = this.elements[i]\n }\n\n return output\n}\n\n/**\n * A JSON serializable representation of the vector.\n *\n * @returns {Number[]}\n */\nlunr.Vector.prototype.toJSON = function () {\n return this.elements\n}\n/* eslint-disable */\n/*!\n * lunr.stemmer\n * Copyright (C) 2020 Oliver Nightingale\n * Includes code from - http://tartarus.org/~martin/PorterStemmer/js.txt\n */\n\n/**\n * lunr.stemmer is an english language stemmer, this is a JavaScript\n * implementation of the PorterStemmer taken from http://tartarus.org/~martin\n *\n * @static\n * @implements {lunr.PipelineFunction}\n * @param {lunr.Token} token - The string to stem\n * @returns {lunr.Token}\n * @see {@link lunr.Pipeline}\n * @function\n */\nlunr.stemmer = (function(){\n var step2list = {\n \"ational\" : \"ate\",\n \"tional\" : \"tion\",\n \"enci\" : \"ence\",\n \"anci\" : \"ance\",\n \"izer\" : \"ize\",\n \"bli\" : \"ble\",\n \"alli\" : \"al\",\n \"entli\" : \"ent\",\n \"eli\" : \"e\",\n \"ousli\" : \"ous\",\n \"ization\" : \"ize\",\n \"ation\" : \"ate\",\n \"ator\" : \"ate\",\n \"alism\" : \"al\",\n \"iveness\" : \"ive\",\n \"fulness\" : \"ful\",\n \"ousness\" : \"ous\",\n \"aliti\" : \"al\",\n \"iviti\" : \"ive\",\n \"biliti\" : \"ble\",\n \"logi\" : \"log\"\n },\n\n step3list = {\n \"icate\" : \"ic\",\n \"ative\" : \"\",\n \"alize\" : \"al\",\n \"iciti\" : \"ic\",\n \"ical\" : \"ic\",\n \"ful\" : \"\",\n \"ness\" : \"\"\n },\n\n c = \"[^aeiou]\", // consonant\n v = \"[aeiouy]\", // vowel\n C = c + \"[^aeiouy]*\", // consonant sequence\n V = v + \"[aeiou]*\", // vowel sequence\n\n mgr0 = \"^(\" + C + \")?\" + V + C, // [C]VC... is m>0\n meq1 = \"^(\" + C + \")?\" + V + C + \"(\" + V + \")?$\", // [C]VC[V] is m=1\n mgr1 = \"^(\" + C + \")?\" + V + C + V + C, // [C]VCVC... is m>1\n s_v = \"^(\" + C + \")?\" + v; // vowel in stem\n\n var re_mgr0 = new RegExp(mgr0);\n var re_mgr1 = new RegExp(mgr1);\n var re_meq1 = new RegExp(meq1);\n var re_s_v = new RegExp(s_v);\n\n var re_1a = /^(.+?)(ss|i)es$/;\n var re2_1a = /^(.+?)([^s])s$/;\n var re_1b = /^(.+?)eed$/;\n var re2_1b = /^(.+?)(ed|ing)$/;\n var re_1b_2 = /.$/;\n var re2_1b_2 = /(at|bl|iz)$/;\n var re3_1b_2 = new RegExp(\"([^aeiouylsz])\\\\1$\");\n var re4_1b_2 = new RegExp(\"^\" + C + v + \"[^aeiouwxy]$\");\n\n var re_1c = /^(.+?[^aeiou])y$/;\n var re_2 = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;\n\n var re_3 = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;\n\n var re_4 = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;\n var re2_4 = /^(.+?)(s|t)(ion)$/;\n\n var re_5 = /^(.+?)e$/;\n var re_5_1 = /ll$/;\n var re3_5 = new RegExp(\"^\" + C + v + \"[^aeiouwxy]$\");\n\n var porterStemmer = function porterStemmer(w) {\n var stem,\n suffix,\n firstch,\n re,\n re2,\n re3,\n re4;\n\n if (w.length < 3) { return w; }\n\n firstch = w.substr(0,1);\n if (firstch == \"y\") {\n w = firstch.toUpperCase() + w.substr(1);\n }\n\n // Step 1a\n re = re_1a\n re2 = re2_1a;\n\n if (re.test(w)) { w = w.replace(re,\"$1$2\"); }\n else if (re2.test(w)) { w = w.replace(re2,\"$1$2\"); }\n\n // Step 1b\n re = re_1b;\n re2 = re2_1b;\n if (re.test(w)) {\n var fp = re.exec(w);\n re = re_mgr0;\n if (re.test(fp[1])) {\n re = re_1b_2;\n w = w.replace(re,\"\");\n }\n } else if (re2.test(w)) {\n var fp = re2.exec(w);\n stem = fp[1];\n re2 = re_s_v;\n if (re2.test(stem)) {\n w = stem;\n re2 = re2_1b_2;\n re3 = re3_1b_2;\n re4 = re4_1b_2;\n if (re2.test(w)) { w = w + \"e\"; }\n else if (re3.test(w)) { re = re_1b_2; w = w.replace(re,\"\"); }\n else if (re4.test(w)) { w = w + \"e\"; }\n }\n }\n\n // Step 1c - replace suffix y or Y by i if preceded by a non-vowel which is not the first letter of the word (so cry -> cri, by -> by, say -> say)\n re = re_1c;\n if (re.test(w)) {\n var fp = re.exec(w);\n stem = fp[1];\n w = stem + \"i\";\n }\n\n // Step 2\n re = re_2;\n if (re.test(w)) {\n var fp = re.exec(w);\n stem = fp[1];\n suffix = fp[2];\n re = re_mgr0;\n if (re.test(stem)) {\n w = stem + step2list[suffix];\n }\n }\n\n // Step 3\n re = re_3;\n if (re.test(w)) {\n var fp = re.exec(w);\n stem = fp[1];\n suffix = fp[2];\n re = re_mgr0;\n if (re.test(stem)) {\n w = stem + step3list[suffix];\n }\n }\n\n // Step 4\n re = re_4;\n re2 = re2_4;\n if (re.test(w)) {\n var fp = re.exec(w);\n stem = fp[1];\n re = re_mgr1;\n if (re.test(stem)) {\n w = stem;\n }\n } else if (re2.test(w)) {\n var fp = re2.exec(w);\n stem = fp[1] + fp[2];\n re2 = re_mgr1;\n if (re2.test(stem)) {\n w = stem;\n }\n }\n\n // Step 5\n re = re_5;\n if (re.test(w)) {\n var fp = re.exec(w);\n stem = fp[1];\n re = re_mgr1;\n re2 = re_meq1;\n re3 = re3_5;\n if (re.test(stem) || (re2.test(stem) && !(re3.test(stem)))) {\n w = stem;\n }\n }\n\n re = re_5_1;\n re2 = re_mgr1;\n if (re.test(w) && re2.test(w)) {\n re = re_1b_2;\n w = w.replace(re,\"\");\n }\n\n // and turn initial Y back to y\n\n if (firstch == \"y\") {\n w = firstch.toLowerCase() + w.substr(1);\n }\n\n return w;\n };\n\n return function (token) {\n return token.update(porterStemmer);\n }\n})();\n\nlunr.Pipeline.registerFunction(lunr.stemmer, 'stemmer')\n/*!\n * lunr.stopWordFilter\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.generateStopWordFilter builds a stopWordFilter function from the provided\n * list of stop words.\n *\n * The built in lunr.stopWordFilter is built using this generator and can be used\n * to generate custom stopWordFilters for applications or non English languages.\n *\n * @function\n * @param {Array} token The token to pass through the filter\n * @returns {lunr.PipelineFunction}\n * @see lunr.Pipeline\n * @see lunr.stopWordFilter\n */\nlunr.generateStopWordFilter = function (stopWords) {\n var words = stopWords.reduce(function (memo, stopWord) {\n memo[stopWord] = stopWord\n return memo\n }, {})\n\n return function (token) {\n if (token && words[token.toString()] !== token.toString()) return token\n }\n}\n\n/**\n * lunr.stopWordFilter is an English language stop word list filter, any words\n * contained in the list will not be passed through the filter.\n *\n * This is intended to be used in the Pipeline. If the token does not pass the\n * filter then undefined will be returned.\n *\n * @function\n * @implements {lunr.PipelineFunction}\n * @params {lunr.Token} token - A token to check for being a stop word.\n * @returns {lunr.Token}\n * @see {@link lunr.Pipeline}\n */\nlunr.stopWordFilter = lunr.generateStopWordFilter([\n 'a',\n 'able',\n 'about',\n 'across',\n 'after',\n 'all',\n 'almost',\n 'also',\n 'am',\n 'among',\n 'an',\n 'and',\n 'any',\n 'are',\n 'as',\n 'at',\n 'be',\n 'because',\n 'been',\n 'but',\n 'by',\n 'can',\n 'cannot',\n 'could',\n 'dear',\n 'did',\n 'do',\n 'does',\n 'either',\n 'else',\n 'ever',\n 'every',\n 'for',\n 'from',\n 'get',\n 'got',\n 'had',\n 'has',\n 'have',\n 'he',\n 'her',\n 'hers',\n 'him',\n 'his',\n 'how',\n 'however',\n 'i',\n 'if',\n 'in',\n 'into',\n 'is',\n 'it',\n 'its',\n 'just',\n 'least',\n 'let',\n 'like',\n 'likely',\n 'may',\n 'me',\n 'might',\n 'most',\n 'must',\n 'my',\n 'neither',\n 'no',\n 'nor',\n 'not',\n 'of',\n 'off',\n 'often',\n 'on',\n 'only',\n 'or',\n 'other',\n 'our',\n 'own',\n 'rather',\n 'said',\n 'say',\n 'says',\n 'she',\n 'should',\n 'since',\n 'so',\n 'some',\n 'than',\n 'that',\n 'the',\n 'their',\n 'them',\n 'then',\n 'there',\n 'these',\n 'they',\n 'this',\n 'tis',\n 'to',\n 'too',\n 'twas',\n 'us',\n 'wants',\n 'was',\n 'we',\n 'were',\n 'what',\n 'when',\n 'where',\n 'which',\n 'while',\n 'who',\n 'whom',\n 'why',\n 'will',\n 'with',\n 'would',\n 'yet',\n 'you',\n 'your'\n])\n\nlunr.Pipeline.registerFunction(lunr.stopWordFilter, 'stopWordFilter')\n/*!\n * lunr.trimmer\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.trimmer is a pipeline function for trimming non word\n * characters from the beginning and end of tokens before they\n * enter the index.\n *\n * This implementation may not work correctly for non latin\n * characters and should either be removed or adapted for use\n * with languages with non-latin characters.\n *\n * @static\n * @implements {lunr.PipelineFunction}\n * @param {lunr.Token} token The token to pass through the filter\n * @returns {lunr.Token}\n * @see lunr.Pipeline\n */\nlunr.trimmer = function (token) {\n return token.update(function (s) {\n return s.replace(/^\\W+/, '').replace(/\\W+$/, '')\n })\n}\n\nlunr.Pipeline.registerFunction(lunr.trimmer, 'trimmer')\n/*!\n * lunr.TokenSet\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * A token set is used to store the unique list of all tokens\n * within an index. Token sets are also used to represent an\n * incoming query to the index, this query token set and index\n * token set are then intersected to find which tokens to look\n * up in the inverted index.\n *\n * A token set can hold multiple tokens, as in the case of the\n * index token set, or it can hold a single token as in the\n * case of a simple query token set.\n *\n * Additionally token sets are used to perform wildcard matching.\n * Leading, contained and trailing wildcards are supported, and\n * from this edit distance matching can also be provided.\n *\n * Token sets are implemented as a minimal finite state automata,\n * where both common prefixes and suffixes are shared between tokens.\n * This helps to reduce the space used for storing the token set.\n *\n * @constructor\n */\nlunr.TokenSet = function () {\n this.final = false\n this.edges = {}\n this.id = lunr.TokenSet._nextId\n lunr.TokenSet._nextId += 1\n}\n\n/**\n * Keeps track of the next, auto increment, identifier to assign\n * to a new tokenSet.\n *\n * TokenSets require a unique identifier to be correctly minimised.\n *\n * @private\n */\nlunr.TokenSet._nextId = 1\n\n/**\n * Creates a TokenSet instance from the given sorted array of words.\n *\n * @param {String[]} arr - A sorted array of strings to create the set from.\n * @returns {lunr.TokenSet}\n * @throws Will throw an error if the input array is not sorted.\n */\nlunr.TokenSet.fromArray = function (arr) {\n var builder = new lunr.TokenSet.Builder\n\n for (var i = 0, len = arr.length; i < len; i++) {\n builder.insert(arr[i])\n }\n\n builder.finish()\n return builder.root\n}\n\n/**\n * Creates a token set from a query clause.\n *\n * @private\n * @param {Object} clause - A single clause from lunr.Query.\n * @param {string} clause.term - The query clause term.\n * @param {number} [clause.editDistance] - The optional edit distance for the term.\n * @returns {lunr.TokenSet}\n */\nlunr.TokenSet.fromClause = function (clause) {\n if ('editDistance' in clause) {\n return lunr.TokenSet.fromFuzzyString(clause.term, clause.editDistance)\n } else {\n return lunr.TokenSet.fromString(clause.term)\n }\n}\n\n/**\n * Creates a token set representing a single string with a specified\n * edit distance.\n *\n * Insertions, deletions, substitutions and transpositions are each\n * treated as an edit distance of 1.\n *\n * Increasing the allowed edit distance will have a dramatic impact\n * on the performance of both creating and intersecting these TokenSets.\n * It is advised to keep the edit distance less than 3.\n *\n * @param {string} str - The string to create the token set from.\n * @param {number} editDistance - The allowed edit distance to match.\n * @returns {lunr.Vector}\n */\nlunr.TokenSet.fromFuzzyString = function (str, editDistance) {\n var root = new lunr.TokenSet\n\n var stack = [{\n node: root,\n editsRemaining: editDistance,\n str: str\n }]\n\n while (stack.length) {\n var frame = stack.pop()\n\n // no edit\n if (frame.str.length > 0) {\n var char = frame.str.charAt(0),\n noEditNode\n\n if (char in frame.node.edges) {\n noEditNode = frame.node.edges[char]\n } else {\n noEditNode = new lunr.TokenSet\n frame.node.edges[char] = noEditNode\n }\n\n if (frame.str.length == 1) {\n noEditNode.final = true\n }\n\n stack.push({\n node: noEditNode,\n editsRemaining: frame.editsRemaining,\n str: frame.str.slice(1)\n })\n }\n\n if (frame.editsRemaining == 0) {\n continue\n }\n\n // insertion\n if (\"*\" in frame.node.edges) {\n var insertionNode = frame.node.edges[\"*\"]\n } else {\n var insertionNode = new lunr.TokenSet\n frame.node.edges[\"*\"] = insertionNode\n }\n\n if (frame.str.length == 0) {\n insertionNode.final = true\n }\n\n stack.push({\n node: insertionNode,\n editsRemaining: frame.editsRemaining - 1,\n str: frame.str\n })\n\n // deletion\n // can only do a deletion if we have enough edits remaining\n // and if there are characters left to delete in the string\n if (frame.str.length > 1) {\n stack.push({\n node: frame.node,\n editsRemaining: frame.editsRemaining - 1,\n str: frame.str.slice(1)\n })\n }\n\n // deletion\n // just removing the last character from the str\n if (frame.str.length == 1) {\n frame.node.final = true\n }\n\n // substitution\n // can only do a substitution if we have enough edits remaining\n // and if there are characters left to substitute\n if (frame.str.length >= 1) {\n if (\"*\" in frame.node.edges) {\n var substitutionNode = frame.node.edges[\"*\"]\n } else {\n var substitutionNode = new lunr.TokenSet\n frame.node.edges[\"*\"] = substitutionNode\n }\n\n if (frame.str.length == 1) {\n substitutionNode.final = true\n }\n\n stack.push({\n node: substitutionNode,\n editsRemaining: frame.editsRemaining - 1,\n str: frame.str.slice(1)\n })\n }\n\n // transposition\n // can only do a transposition if there are edits remaining\n // and there are enough characters to transpose\n if (frame.str.length > 1) {\n var charA = frame.str.charAt(0),\n charB = frame.str.charAt(1),\n transposeNode\n\n if (charB in frame.node.edges) {\n transposeNode = frame.node.edges[charB]\n } else {\n transposeNode = new lunr.TokenSet\n frame.node.edges[charB] = transposeNode\n }\n\n if (frame.str.length == 1) {\n transposeNode.final = true\n }\n\n stack.push({\n node: transposeNode,\n editsRemaining: frame.editsRemaining - 1,\n str: charA + frame.str.slice(2)\n })\n }\n }\n\n return root\n}\n\n/**\n * Creates a TokenSet from a string.\n *\n * The string may contain one or more wildcard characters (*)\n * that will allow wildcard matching when intersecting with\n * another TokenSet.\n *\n * @param {string} str - The string to create a TokenSet from.\n * @returns {lunr.TokenSet}\n */\nlunr.TokenSet.fromString = function (str) {\n var node = new lunr.TokenSet,\n root = node\n\n /*\n * Iterates through all characters within the passed string\n * appending a node for each character.\n *\n * When a wildcard character is found then a self\n * referencing edge is introduced to continually match\n * any number of any characters.\n */\n for (var i = 0, len = str.length; i < len; i++) {\n var char = str[i],\n final = (i == len - 1)\n\n if (char == \"*\") {\n node.edges[char] = node\n node.final = final\n\n } else {\n var next = new lunr.TokenSet\n next.final = final\n\n node.edges[char] = next\n node = next\n }\n }\n\n return root\n}\n\n/**\n * Converts this TokenSet into an array of strings\n * contained within the TokenSet.\n *\n * This is not intended to be used on a TokenSet that\n * contains wildcards, in these cases the results are\n * undefined and are likely to cause an infinite loop.\n *\n * @returns {string[]}\n */\nlunr.TokenSet.prototype.toArray = function () {\n var words = []\n\n var stack = [{\n prefix: \"\",\n node: this\n }]\n\n while (stack.length) {\n var frame = stack.pop(),\n edges = Object.keys(frame.node.edges),\n len = edges.length\n\n if (frame.node.final) {\n /* In Safari, at this point the prefix is sometimes corrupted, see:\n * https://github.com/olivernn/lunr.js/issues/279 Calling any\n * String.prototype method forces Safari to \"cast\" this string to what\n * it's supposed to be, fixing the bug. */\n frame.prefix.charAt(0)\n words.push(frame.prefix)\n }\n\n for (var i = 0; i < len; i++) {\n var edge = edges[i]\n\n stack.push({\n prefix: frame.prefix.concat(edge),\n node: frame.node.edges[edge]\n })\n }\n }\n\n return words\n}\n\n/**\n * Generates a string representation of a TokenSet.\n *\n * This is intended to allow TokenSets to be used as keys\n * in objects, largely to aid the construction and minimisation\n * of a TokenSet. As such it is not designed to be a human\n * friendly representation of the TokenSet.\n *\n * @returns {string}\n */\nlunr.TokenSet.prototype.toString = function () {\n // NOTE: Using Object.keys here as this.edges is very likely\n // to enter 'hash-mode' with many keys being added\n //\n // avoiding a for-in loop here as it leads to the function\n // being de-optimised (at least in V8). From some simple\n // benchmarks the performance is comparable, but allowing\n // V8 to optimize may mean easy performance wins in the future.\n\n if (this._str) {\n return this._str\n }\n\n var str = this.final ? '1' : '0',\n labels = Object.keys(this.edges).sort(),\n len = labels.length\n\n for (var i = 0; i < len; i++) {\n var label = labels[i],\n node = this.edges[label]\n\n str = str + label + node.id\n }\n\n return str\n}\n\n/**\n * Returns a new TokenSet that is the intersection of\n * this TokenSet and the passed TokenSet.\n *\n * This intersection will take into account any wildcards\n * contained within the TokenSet.\n *\n * @param {lunr.TokenSet} b - An other TokenSet to intersect with.\n * @returns {lunr.TokenSet}\n */\nlunr.TokenSet.prototype.intersect = function (b) {\n var output = new lunr.TokenSet,\n frame = undefined\n\n var stack = [{\n qNode: b,\n output: output,\n node: this\n }]\n\n while (stack.length) {\n frame = stack.pop()\n\n // NOTE: As with the #toString method, we are using\n // Object.keys and a for loop instead of a for-in loop\n // as both of these objects enter 'hash' mode, causing\n // the function to be de-optimised in V8\n var qEdges = Object.keys(frame.qNode.edges),\n qLen = qEdges.length,\n nEdges = Object.keys(frame.node.edges),\n nLen = nEdges.length\n\n for (var q = 0; q < qLen; q++) {\n var qEdge = qEdges[q]\n\n for (var n = 0; n < nLen; n++) {\n var nEdge = nEdges[n]\n\n if (nEdge == qEdge || qEdge == '*') {\n var node = frame.node.edges[nEdge],\n qNode = frame.qNode.edges[qEdge],\n final = node.final && qNode.final,\n next = undefined\n\n if (nEdge in frame.output.edges) {\n // an edge already exists for this character\n // no need to create a new node, just set the finality\n // bit unless this node is already final\n next = frame.output.edges[nEdge]\n next.final = next.final || final\n\n } else {\n // no edge exists yet, must create one\n // set the finality bit and insert it\n // into the output\n next = new lunr.TokenSet\n next.final = final\n frame.output.edges[nEdge] = next\n }\n\n stack.push({\n qNode: qNode,\n output: next,\n node: node\n })\n }\n }\n }\n }\n\n return output\n}\nlunr.TokenSet.Builder = function () {\n this.previousWord = \"\"\n this.root = new lunr.TokenSet\n this.uncheckedNodes = []\n this.minimizedNodes = {}\n}\n\nlunr.TokenSet.Builder.prototype.insert = function (word) {\n var node,\n commonPrefix = 0\n\n if (word < this.previousWord) {\n throw new Error (\"Out of order word insertion\")\n }\n\n for (var i = 0; i < word.length && i < this.previousWord.length; i++) {\n if (word[i] != this.previousWord[i]) break\n commonPrefix++\n }\n\n this.minimize(commonPrefix)\n\n if (this.uncheckedNodes.length == 0) {\n node = this.root\n } else {\n node = this.uncheckedNodes[this.uncheckedNodes.length - 1].child\n }\n\n for (var i = commonPrefix; i < word.length; i++) {\n var nextNode = new lunr.TokenSet,\n char = word[i]\n\n node.edges[char] = nextNode\n\n this.uncheckedNodes.push({\n parent: node,\n char: char,\n child: nextNode\n })\n\n node = nextNode\n }\n\n node.final = true\n this.previousWord = word\n}\n\nlunr.TokenSet.Builder.prototype.finish = function () {\n this.minimize(0)\n}\n\nlunr.TokenSet.Builder.prototype.minimize = function (downTo) {\n for (var i = this.uncheckedNodes.length - 1; i >= downTo; i--) {\n var node = this.uncheckedNodes[i],\n childKey = node.child.toString()\n\n if (childKey in this.minimizedNodes) {\n node.parent.edges[node.char] = this.minimizedNodes[childKey]\n } else {\n // Cache the key for this node since\n // we know it can't change anymore\n node.child._str = childKey\n\n this.minimizedNodes[childKey] = node.child\n }\n\n this.uncheckedNodes.pop()\n }\n}\n/*!\n * lunr.Index\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * An index contains the built index of all documents and provides a query interface\n * to the index.\n *\n * Usually instances of lunr.Index will not be created using this constructor, instead\n * lunr.Builder should be used to construct new indexes, or lunr.Index.load should be\n * used to load previously built and serialized indexes.\n *\n * @constructor\n * @param {Object} attrs - The attributes of the built search index.\n * @param {Object} attrs.invertedIndex - An index of term/field to document reference.\n * @param {Object} attrs.fieldVectors - Field vectors\n * @param {lunr.TokenSet} attrs.tokenSet - An set of all corpus tokens.\n * @param {string[]} attrs.fields - The names of indexed document fields.\n * @param {lunr.Pipeline} attrs.pipeline - The pipeline to use for search terms.\n */\nlunr.Index = function (attrs) {\n this.invertedIndex = attrs.invertedIndex\n this.fieldVectors = attrs.fieldVectors\n this.tokenSet = attrs.tokenSet\n this.fields = attrs.fields\n this.pipeline = attrs.pipeline\n}\n\n/**\n * A result contains details of a document matching a search query.\n * @typedef {Object} lunr.Index~Result\n * @property {string} ref - The reference of the document this result represents.\n * @property {number} score - A number between 0 and 1 representing how similar this document is to the query.\n * @property {lunr.MatchData} matchData - Contains metadata about this match including which term(s) caused the match.\n */\n\n/**\n * Although lunr provides the ability to create queries using lunr.Query, it also provides a simple\n * query language which itself is parsed into an instance of lunr.Query.\n *\n * For programmatically building queries it is advised to directly use lunr.Query, the query language\n * is best used for human entered text rather than program generated text.\n *\n * At its simplest queries can just be a single term, e.g. `hello`, multiple terms are also supported\n * and will be combined with OR, e.g `hello world` will match documents that contain either 'hello'\n * or 'world', though those that contain both will rank higher in the results.\n *\n * Wildcards can be included in terms to match one or more unspecified characters, these wildcards can\n * be inserted anywhere within the term, and more than one wildcard can exist in a single term. Adding\n * wildcards will increase the number of documents that will be found but can also have a negative\n * impact on query performance, especially with wildcards at the beginning of a term.\n *\n * Terms can be restricted to specific fields, e.g. `title:hello`, only documents with the term\n * hello in the title field will match this query. Using a field not present in the index will lead\n * to an error being thrown.\n *\n * Modifiers can also be added to terms, lunr supports edit distance and boost modifiers on terms. A term\n * boost will make documents matching that term score higher, e.g. `foo^5`. Edit distance is also supported\n * to provide fuzzy matching, e.g. 'hello~2' will match documents with hello with an edit distance of 2.\n * Avoid large values for edit distance to improve query performance.\n *\n * Each term also supports a presence modifier. By default a term's presence in document is optional, however\n * this can be changed to either required or prohibited. For a term's presence to be required in a document the\n * term should be prefixed with a '+', e.g. `+foo bar` is a search for documents that must contain 'foo' and\n * optionally contain 'bar'. Conversely a leading '-' sets the terms presence to prohibited, i.e. it must not\n * appear in a document, e.g. `-foo bar` is a search for documents that do not contain 'foo' but may contain 'bar'.\n *\n * To escape special characters the backslash character '\\' can be used, this allows searches to include\n * characters that would normally be considered modifiers, e.g. `foo\\~2` will search for a term \"foo~2\" instead\n * of attempting to apply a boost of 2 to the search term \"foo\".\n *\n * @typedef {string} lunr.Index~QueryString\n * @example Simple single term query\n * hello\n * @example Multiple term query\n * hello world\n * @example term scoped to a field\n * title:hello\n * @example term with a boost of 10\n * hello^10\n * @example term with an edit distance of 2\n * hello~2\n * @example terms with presence modifiers\n * -foo +bar baz\n */\n\n/**\n * Performs a search against the index using lunr query syntax.\n *\n * Results will be returned sorted by their score, the most relevant results\n * will be returned first. For details on how the score is calculated, please see\n * the {@link https://lunrjs.com/guides/searching.html#scoring|guide}.\n *\n * For more programmatic querying use lunr.Index#query.\n *\n * @param {lunr.Index~QueryString} queryString - A string containing a lunr query.\n * @throws {lunr.QueryParseError} If the passed query string cannot be parsed.\n * @returns {lunr.Index~Result[]}\n */\nlunr.Index.prototype.search = function (queryString) {\n return this.query(function (query) {\n var parser = new lunr.QueryParser(queryString, query)\n parser.parse()\n })\n}\n\n/**\n * A query builder callback provides a query object to be used to express\n * the query to perform on the index.\n *\n * @callback lunr.Index~queryBuilder\n * @param {lunr.Query} query - The query object to build up.\n * @this lunr.Query\n */\n\n/**\n * Performs a query against the index using the yielded lunr.Query object.\n *\n * If performing programmatic queries against the index, this method is preferred\n * over lunr.Index#search so as to avoid the additional query parsing overhead.\n *\n * A query object is yielded to the supplied function which should be used to\n * express the query to be run against the index.\n *\n * Note that although this function takes a callback parameter it is _not_ an\n * asynchronous operation, the callback is just yielded a query object to be\n * customized.\n *\n * @param {lunr.Index~queryBuilder} fn - A function that is used to build the query.\n * @returns {lunr.Index~Result[]}\n */\nlunr.Index.prototype.query = function (fn) {\n // for each query clause\n // * process terms\n // * expand terms from token set\n // * find matching documents and metadata\n // * get document vectors\n // * score documents\n\n var query = new lunr.Query(this.fields),\n matchingFields = Object.create(null),\n queryVectors = Object.create(null),\n termFieldCache = Object.create(null),\n requiredMatches = Object.create(null),\n prohibitedMatches = Object.create(null)\n\n /*\n * To support field level boosts a query vector is created per\n * field. An empty vector is eagerly created to support negated\n * queries.\n */\n for (var i = 0; i < this.fields.length; i++) {\n queryVectors[this.fields[i]] = new lunr.Vector\n }\n\n fn.call(query, query)\n\n for (var i = 0; i < query.clauses.length; i++) {\n /*\n * Unless the pipeline has been disabled for this term, which is\n * the case for terms with wildcards, we need to pass the clause\n * term through the search pipeline. A pipeline returns an array\n * of processed terms. Pipeline functions may expand the passed\n * term, which means we may end up performing multiple index lookups\n * for a single query term.\n */\n var clause = query.clauses[i],\n terms = null,\n clauseMatches = lunr.Set.empty\n\n if (clause.usePipeline) {\n terms = this.pipeline.runString(clause.term, {\n fields: clause.fields\n })\n } else {\n terms = [clause.term]\n }\n\n for (var m = 0; m < terms.length; m++) {\n var term = terms[m]\n\n /*\n * Each term returned from the pipeline needs to use the same query\n * clause object, e.g. the same boost and or edit distance. The\n * simplest way to do this is to re-use the clause object but mutate\n * its term property.\n */\n clause.term = term\n\n /*\n * From the term in the clause we create a token set which will then\n * be used to intersect the indexes token set to get a list of terms\n * to lookup in the inverted index\n */\n var termTokenSet = lunr.TokenSet.fromClause(clause),\n expandedTerms = this.tokenSet.intersect(termTokenSet).toArray()\n\n /*\n * If a term marked as required does not exist in the tokenSet it is\n * impossible for the search to return any matches. We set all the field\n * scoped required matches set to empty and stop examining any further\n * clauses.\n */\n if (expandedTerms.length === 0 && clause.presence === lunr.Query.presence.REQUIRED) {\n for (var k = 0; k < clause.fields.length; k++) {\n var field = clause.fields[k]\n requiredMatches[field] = lunr.Set.empty\n }\n\n break\n }\n\n for (var j = 0; j < expandedTerms.length; j++) {\n /*\n * For each term get the posting and termIndex, this is required for\n * building the query vector.\n */\n var expandedTerm = expandedTerms[j],\n posting = this.invertedIndex[expandedTerm],\n termIndex = posting._index\n\n for (var k = 0; k < clause.fields.length; k++) {\n /*\n * For each field that this query term is scoped by (by default\n * all fields are in scope) we need to get all the document refs\n * that have this term in that field.\n *\n * The posting is the entry in the invertedIndex for the matching\n * term from above.\n */\n var field = clause.fields[k],\n fieldPosting = posting[field],\n matchingDocumentRefs = Object.keys(fieldPosting),\n termField = expandedTerm + \"/\" + field,\n matchingDocumentsSet = new lunr.Set(matchingDocumentRefs)\n\n /*\n * if the presence of this term is required ensure that the matching\n * documents are added to the set of required matches for this clause.\n *\n */\n if (clause.presence == lunr.Query.presence.REQUIRED) {\n clauseMatches = clauseMatches.union(matchingDocumentsSet)\n\n if (requiredMatches[field] === undefined) {\n requiredMatches[field] = lunr.Set.complete\n }\n }\n\n /*\n * if the presence of this term is prohibited ensure that the matching\n * documents are added to the set of prohibited matches for this field,\n * creating that set if it does not yet exist.\n */\n if (clause.presence == lunr.Query.presence.PROHIBITED) {\n if (prohibitedMatches[field] === undefined) {\n prohibitedMatches[field] = lunr.Set.empty\n }\n\n prohibitedMatches[field] = prohibitedMatches[field].union(matchingDocumentsSet)\n\n /*\n * Prohibited matches should not be part of the query vector used for\n * similarity scoring and no metadata should be extracted so we continue\n * to the next field\n */\n continue\n }\n\n /*\n * The query field vector is populated using the termIndex found for\n * the term and a unit value with the appropriate boost applied.\n * Using upsert because there could already be an entry in the vector\n * for the term we are working with. In that case we just add the scores\n * together.\n */\n queryVectors[field].upsert(termIndex, clause.boost, function (a, b) { return a + b })\n\n /**\n * If we've already seen this term, field combo then we've already collected\n * the matching documents and metadata, no need to go through all that again\n */\n if (termFieldCache[termField]) {\n continue\n }\n\n for (var l = 0; l < matchingDocumentRefs.length; l++) {\n /*\n * All metadata for this term/field/document triple\n * are then extracted and collected into an instance\n * of lunr.MatchData ready to be returned in the query\n * results\n */\n var matchingDocumentRef = matchingDocumentRefs[l],\n matchingFieldRef = new lunr.FieldRef (matchingDocumentRef, field),\n metadata = fieldPosting[matchingDocumentRef],\n fieldMatch\n\n if ((fieldMatch = matchingFields[matchingFieldRef]) === undefined) {\n matchingFields[matchingFieldRef] = new lunr.MatchData (expandedTerm, field, metadata)\n } else {\n fieldMatch.add(expandedTerm, field, metadata)\n }\n\n }\n\n termFieldCache[termField] = true\n }\n }\n }\n\n /**\n * If the presence was required we need to update the requiredMatches field sets.\n * We do this after all fields for the term have collected their matches because\n * the clause terms presence is required in _any_ of the fields not _all_ of the\n * fields.\n */\n if (clause.presence === lunr.Query.presence.REQUIRED) {\n for (var k = 0; k < clause.fields.length; k++) {\n var field = clause.fields[k]\n requiredMatches[field] = requiredMatches[field].intersect(clauseMatches)\n }\n }\n }\n\n /**\n * Need to combine the field scoped required and prohibited\n * matching documents into a global set of required and prohibited\n * matches\n */\n var allRequiredMatches = lunr.Set.complete,\n allProhibitedMatches = lunr.Set.empty\n\n for (var i = 0; i < this.fields.length; i++) {\n var field = this.fields[i]\n\n if (requiredMatches[field]) {\n allRequiredMatches = allRequiredMatches.intersect(requiredMatches[field])\n }\n\n if (prohibitedMatches[field]) {\n allProhibitedMatches = allProhibitedMatches.union(prohibitedMatches[field])\n }\n }\n\n var matchingFieldRefs = Object.keys(matchingFields),\n results = [],\n matches = Object.create(null)\n\n /*\n * If the query is negated (contains only prohibited terms)\n * we need to get _all_ fieldRefs currently existing in the\n * index. This is only done when we know that the query is\n * entirely prohibited terms to avoid any cost of getting all\n * fieldRefs unnecessarily.\n *\n * Additionally, blank MatchData must be created to correctly\n * populate the results.\n */\n if (query.isNegated()) {\n matchingFieldRefs = Object.keys(this.fieldVectors)\n\n for (var i = 0; i < matchingFieldRefs.length; i++) {\n var matchingFieldRef = matchingFieldRefs[i]\n var fieldRef = lunr.FieldRef.fromString(matchingFieldRef)\n matchingFields[matchingFieldRef] = new lunr.MatchData\n }\n }\n\n for (var i = 0; i < matchingFieldRefs.length; i++) {\n /*\n * Currently we have document fields that match the query, but we\n * need to return documents. The matchData and scores are combined\n * from multiple fields belonging to the same document.\n *\n * Scores are calculated by field, using the query vectors created\n * above, and combined into a final document score using addition.\n */\n var fieldRef = lunr.FieldRef.fromString(matchingFieldRefs[i]),\n docRef = fieldRef.docRef\n\n if (!allRequiredMatches.contains(docRef)) {\n continue\n }\n\n if (allProhibitedMatches.contains(docRef)) {\n continue\n }\n\n var fieldVector = this.fieldVectors[fieldRef],\n score = queryVectors[fieldRef.fieldName].similarity(fieldVector),\n docMatch\n\n if ((docMatch = matches[docRef]) !== undefined) {\n docMatch.score += score\n docMatch.matchData.combine(matchingFields[fieldRef])\n } else {\n var match = {\n ref: docRef,\n score: score,\n matchData: matchingFields[fieldRef]\n }\n matches[docRef] = match\n results.push(match)\n }\n }\n\n /*\n * Sort the results objects by score, highest first.\n */\n return results.sort(function (a, b) {\n return b.score - a.score\n })\n}\n\n/**\n * Prepares the index for JSON serialization.\n *\n * The schema for this JSON blob will be described in a\n * separate JSON schema file.\n *\n * @returns {Object}\n */\nlunr.Index.prototype.toJSON = function () {\n var invertedIndex = Object.keys(this.invertedIndex)\n .sort()\n .map(function (term) {\n return [term, this.invertedIndex[term]]\n }, this)\n\n var fieldVectors = Object.keys(this.fieldVectors)\n .map(function (ref) {\n return [ref, this.fieldVectors[ref].toJSON()]\n }, this)\n\n return {\n version: lunr.version,\n fields: this.fields,\n fieldVectors: fieldVectors,\n invertedIndex: invertedIndex,\n pipeline: this.pipeline.toJSON()\n }\n}\n\n/**\n * Loads a previously serialized lunr.Index\n *\n * @param {Object} serializedIndex - A previously serialized lunr.Index\n * @returns {lunr.Index}\n */\nlunr.Index.load = function (serializedIndex) {\n var attrs = {},\n fieldVectors = {},\n serializedVectors = serializedIndex.fieldVectors,\n invertedIndex = Object.create(null),\n serializedInvertedIndex = serializedIndex.invertedIndex,\n tokenSetBuilder = new lunr.TokenSet.Builder,\n pipeline = lunr.Pipeline.load(serializedIndex.pipeline)\n\n if (serializedIndex.version != lunr.version) {\n lunr.utils.warn(\"Version mismatch when loading serialised index. Current version of lunr '\" + lunr.version + \"' does not match serialized index '\" + serializedIndex.version + \"'\")\n }\n\n for (var i = 0; i < serializedVectors.length; i++) {\n var tuple = serializedVectors[i],\n ref = tuple[0],\n elements = tuple[1]\n\n fieldVectors[ref] = new lunr.Vector(elements)\n }\n\n for (var i = 0; i < serializedInvertedIndex.length; i++) {\n var tuple = serializedInvertedIndex[i],\n term = tuple[0],\n posting = tuple[1]\n\n tokenSetBuilder.insert(term)\n invertedIndex[term] = posting\n }\n\n tokenSetBuilder.finish()\n\n attrs.fields = serializedIndex.fields\n\n attrs.fieldVectors = fieldVectors\n attrs.invertedIndex = invertedIndex\n attrs.tokenSet = tokenSetBuilder.root\n attrs.pipeline = pipeline\n\n return new lunr.Index(attrs)\n}\n/*!\n * lunr.Builder\n * Copyright (C) 2020 Oliver Nightingale\n */\n\n/**\n * lunr.Builder performs indexing on a set of documents and\n * returns instances of lunr.Index ready for querying.\n *\n * All configuration of the index is done via the builder, the\n * fields to index, the document reference, the text processing\n * pipeline and document scoring parameters are all set on the\n * builder before indexing.\n *\n * @constructor\n * @property {string} _ref - Internal reference to the document reference field.\n * @property {string[]} _fields - Internal reference to the document fields to index.\n * @property {object} invertedIndex - The inverted index maps terms to document fields.\n * @property {object} documentTermFrequencies - Keeps track of document term frequencies.\n * @property {object} documentLengths - Keeps track of the length of documents added to the index.\n * @property {lunr.tokenizer} tokenizer - Function for splitting strings into tokens for indexing.\n * @property {lunr.Pipeline} pipeline - The pipeline performs text processing on tokens before indexing.\n * @property {lunr.Pipeline} searchPipeline - A pipeline for processing search terms before querying the index.\n * @property {number} documentCount - Keeps track of the total number of documents indexed.\n * @property {number} _b - A parameter to control field length normalization, setting this to 0 disabled normalization, 1 fully normalizes field lengths, the default value is 0.75.\n * @property {number} _k1 - A parameter to control how quickly an increase in term frequency results in term frequency saturation, the default value is 1.2.\n * @property {number} termIndex - A counter incremented for each unique term, used to identify a terms position in the vector space.\n * @property {array} metadataWhitelist - A list of metadata keys that have been whitelisted for entry in the index.\n */\nlunr.Builder = function () {\n this._ref = \"id\"\n this._fields = Object.create(null)\n this._documents = Object.create(null)\n this.invertedIndex = Object.create(null)\n this.fieldTermFrequencies = {}\n this.fieldLengths = {}\n this.tokenizer = lunr.tokenizer\n this.pipeline = new lunr.Pipeline\n this.searchPipeline = new lunr.Pipeline\n this.documentCount = 0\n this._b = 0.75\n this._k1 = 1.2\n this.termIndex = 0\n this.metadataWhitelist = []\n}\n\n/**\n * Sets the document field used as the document reference. Every document must have this field.\n * The type of this field in the document should be a string, if it is not a string it will be\n * coerced into a string by calling toString.\n *\n * The default ref is 'id'.\n *\n * The ref should _not_ be changed during indexing, it should be set before any documents are\n * added to the index. Changing it during indexing can lead to inconsistent results.\n *\n * @param {string} ref - The name of the reference field in the document.\n */\nlunr.Builder.prototype.ref = function (ref) {\n this._ref = ref\n}\n\n/**\n * A function that is used to extract a field from a document.\n *\n * Lunr expects a field to be at the top level of a document, if however the field\n * is deeply nested within a document an extractor function can be used to extract\n * the right field for indexing.\n *\n * @callback fieldExtractor\n * @param {object} doc - The document being added to the index.\n * @returns {?(string|object|object[])} obj - The object that will be indexed for this field.\n * @example Extracting a nested field\n * function (doc) { return doc.nested.field }\n */\n\n/**\n * Adds a field to the list of document fields that will be indexed. Every document being\n * indexed should have this field. Null values for this field in indexed documents will\n * not cause errors but will limit the chance of that document being retrieved by searches.\n *\n * All fields should be added before adding documents to the index. Adding fields after\n * a document has been indexed will have no effect on already indexed documents.\n *\n * Fields can be boosted at build time. This allows terms within that field to have more\n * importance when ranking search results. Use a field boost to specify that matches within\n * one field are more important than other fields.\n *\n * @param {string} fieldName - The name of a field to index in all documents.\n * @param {object} attributes - Optional attributes associated with this field.\n * @param {number} [attributes.boost=1] - Boost applied to all terms within this field.\n * @param {fieldExtractor} [attributes.extractor] - Function to extract a field from a document.\n * @throws {RangeError} fieldName cannot contain unsupported characters '/'\n */\nlunr.Builder.prototype.field = function (fieldName, attributes) {\n if (/\\//.test(fieldName)) {\n throw new RangeError (\"Field '\" + fieldName + \"' contains illegal character '/'\")\n }\n\n this._fields[fieldName] = attributes || {}\n}\n\n/**\n * A parameter to tune the amount of field length normalisation that is applied when\n * calculating relevance scores. A value of 0 will completely disable any normalisation\n * and a value of 1 will fully normalise field lengths. The default is 0.75. Values of b\n * will be clamped to the range 0 - 1.\n *\n * @param {number} number - The value to set for this tuning parameter.\n */\nlunr.Builder.prototype.b = function (number) {\n if (number < 0) {\n this._b = 0\n } else if (number > 1) {\n this._b = 1\n } else {\n this._b = number\n }\n}\n\n/**\n * A parameter that controls the speed at which a rise in term frequency results in term\n * frequency saturation. The default value is 1.2. Setting this to a higher value will give\n * slower saturation levels, a lower value will result in quicker saturation.\n *\n * @param {number} number - The value to set for this tuning parameter.\n */\nlunr.Builder.prototype.k1 = function (number) {\n this._k1 = number\n}\n\n/**\n * Adds a document to the index.\n *\n * Before adding fields to the index the index should have been fully setup, with the document\n * ref and all fields to index already having been specified.\n *\n * The document must have a field name as specified by the ref (by default this is 'id') and\n * it should have all fields defined for indexing, though null or undefined values will not\n * cause errors.\n *\n * Entire documents can be boosted at build time. Applying a boost to a document indicates that\n * this document should rank higher in search results than other documents.\n *\n * @param {object} doc - The document to add to the index.\n * @param {object} attributes - Optional attributes associated with this document.\n * @param {number} [attributes.boost=1] - Boost applied to all terms within this document.\n */\nlunr.Builder.prototype.add = function (doc, attributes) {\n var docRef = doc[this._ref],\n fields = Object.keys(this._fields)\n\n this._documents[docRef] = attributes || {}\n this.documentCount += 1\n\n for (var i = 0; i < fields.length; i++) {\n var fieldName = fields[i],\n extractor = this._fields[fieldName].extractor,\n field = extractor ? extractor(doc) : doc[fieldName],\n tokens = this.tokenizer(field, {\n fields: [fieldName]\n }),\n terms = this.pipeline.run(tokens),\n fieldRef = new lunr.FieldRef (docRef, fieldName),\n fieldTerms = Object.create(null)\n\n this.fieldTermFrequencies[fieldRef] = fieldTerms\n this.fieldLengths[fieldRef] = 0\n\n // store the length of this field for this document\n this.fieldLengths[fieldRef] += terms.length\n\n // calculate term frequencies for this field\n for (var j = 0; j < terms.length; j++) {\n var term = terms[j]\n\n if (fieldTerms[term] == undefined) {\n fieldTerms[term] = 0\n }\n\n fieldTerms[term] += 1\n\n // add to inverted index\n // create an initial posting if one doesn't exist\n if (this.invertedIndex[term] == undefined) {\n var posting = Object.create(null)\n posting[\"_index\"] = this.termIndex\n this.termIndex += 1\n\n for (var k = 0; k < fields.length; k++) {\n posting[fields[k]] = Object.create(null)\n }\n\n this.invertedIndex[term] = posting\n }\n\n // add an entry for this term/fieldName/docRef to the invertedIndex\n if (this.invertedIndex[term][fieldName][docRef] == undefined) {\n this.invertedIndex[term][fieldName][docRef] = Object.create(null)\n }\n\n // store all whitelisted metadata about this token in the\n // inverted index\n for (var l = 0; l < this.metadataWhitelist.length; l++) {\n var metadataKey = this.metadataWhitelist[l],\n metadata = term.metadata[metadataKey]\n\n if (this.invertedIndex[term][fieldName][docRef][metadataKey] == undefined) {\n this.invertedIndex[term][fieldName][docRef][metadataKey] = []\n }\n\n this.invertedIndex[term][fieldName][docRef][metadataKey].push(metadata)\n }\n }\n\n }\n}\n\n/**\n * Calculates the average document length for this index\n *\n * @private\n */\nlunr.Builder.prototype.calculateAverageFieldLengths = function () {\n\n var fieldRefs = Object.keys(this.fieldLengths),\n numberOfFields = fieldRefs.length,\n accumulator = {},\n documentsWithField = {}\n\n for (var i = 0; i < numberOfFields; i++) {\n var fieldRef = lunr.FieldRef.fromString(fieldRefs[i]),\n field = fieldRef.fieldName\n\n documentsWithField[field] || (documentsWithField[field] = 0)\n documentsWithField[field] += 1\n\n accumulator[field] || (accumulator[field] = 0)\n accumulator[field] += this.fieldLengths[fieldRef]\n }\n\n var fields = Object.keys(this._fields)\n\n for (var i = 0; i < fields.length; i++) {\n var fieldName = fields[i]\n accumulator[fieldName] = accumulator[fieldName] / documentsWithField[fieldName]\n }\n\n this.averageFieldLength = accumulator\n}\n\n/**\n * Builds a vector space model of every document using lunr.Vector\n *\n * @private\n */\nlunr.Builder.prototype.createFieldVectors = function () {\n var fieldVectors = {},\n fieldRefs = Object.keys(this.fieldTermFrequencies),\n fieldRefsLength = fieldRefs.length,\n termIdfCache = Object.create(null)\n\n for (var i = 0; i < fieldRefsLength; i++) {\n var fieldRef = lunr.FieldRef.fromString(fieldRefs[i]),\n fieldName = fieldRef.fieldName,\n fieldLength = this.fieldLengths[fieldRef],\n fieldVector = new lunr.Vector,\n termFrequencies = this.fieldTermFrequencies[fieldRef],\n terms = Object.keys(termFrequencies),\n termsLength = terms.length\n\n\n var fieldBoost = this._fields[fieldName].boost || 1,\n docBoost = this._documents[fieldRef.docRef].boost || 1\n\n for (var j = 0; j < termsLength; j++) {\n var term = terms[j],\n tf = termFrequencies[term],\n termIndex = this.invertedIndex[term]._index,\n idf, score, scoreWithPrecision\n\n if (termIdfCache[term] === undefined) {\n idf = lunr.idf(this.invertedIndex[term], this.documentCount)\n termIdfCache[term] = idf\n } else {\n idf = termIdfCache[term]\n }\n\n score = idf * ((this._k1 + 1) * tf) / (this._k1 * (1 - this._b + this._b * (fieldLength / this.averageFieldLength[fieldName])) + tf)\n score *= fieldBoost\n score *= docBoost\n scoreWithPrecision = Math.round(score * 1000) / 1000\n // Converts 1.23456789 to 1.234.\n // Reducing the precision so that the vectors take up less\n // space when serialised. Doing it now so that they behave\n // the same before and after serialisation. Also, this is\n // the fastest approach to reducing a number's precision in\n // JavaScript.\n\n fieldVector.insert(termIndex, scoreWithPrecision)\n }\n\n fieldVectors[fieldRef] = fieldVector\n }\n\n this.fieldVectors = fieldVectors\n}\n\n/**\n * Creates a token set of all tokens in the index using lunr.TokenSet\n *\n * @private\n */\nlunr.Builder.prototype.createTokenSet = function () {\n this.tokenSet = lunr.TokenSet.fromArray(\n Object.keys(this.invertedIndex).sort()\n )\n}\n\n/**\n * Builds the index, creating an instance of lunr.Index.\n *\n * This completes the indexing process and should only be called\n * once all documents have been added to the index.\n *\n * @returns {lunr.Index}\n */\nlunr.Builder.prototype.build = function () {\n this.calculateAverageFieldLengths()\n this.createFieldVectors()\n this.createTokenSet()\n\n return new lunr.Index({\n invertedIndex: this.invertedIndex,\n fieldVectors: this.fieldVectors,\n tokenSet: this.tokenSet,\n fields: Object.keys(this._fields),\n pipeline: this.searchPipeline\n })\n}\n\n/**\n * Applies a plugin to the index builder.\n *\n * A plugin is a function that is called with the index builder as its context.\n * Plugins can be used to customise or extend the behaviour of the index\n * in some way. A plugin is just a function, that encapsulated the custom\n * behaviour that should be applied when building the index.\n *\n * The plugin function will be called with the index builder as its argument, additional\n * arguments can also be passed when calling use. The function will be called\n * with the index builder as its context.\n *\n * @param {Function} plugin The plugin to apply.\n */\nlunr.Builder.prototype.use = function (fn) {\n var args = Array.prototype.slice.call(arguments, 1)\n args.unshift(this)\n fn.apply(this, args)\n}\n/**\n * Contains and collects metadata about a matching document.\n * A single instance of lunr.MatchData is returned as part of every\n * lunr.Index~Result.\n *\n * @constructor\n * @param {string} term - The term this match data is associated with\n * @param {string} field - The field in which the term was found\n * @param {object} metadata - The metadata recorded about this term in this field\n * @property {object} metadata - A cloned collection of metadata associated with this document.\n * @see {@link lunr.Index~Result}\n */\nlunr.MatchData = function (term, field, metadata) {\n var clonedMetadata = Object.create(null),\n metadataKeys = Object.keys(metadata || {})\n\n // Cloning the metadata to prevent the original\n // being mutated during match data combination.\n // Metadata is kept in an array within the inverted\n // index so cloning the data can be done with\n // Array#slice\n for (var i = 0; i < metadataKeys.length; i++) {\n var key = metadataKeys[i]\n clonedMetadata[key] = metadata[key].slice()\n }\n\n this.metadata = Object.create(null)\n\n if (term !== undefined) {\n this.metadata[term] = Object.create(null)\n this.metadata[term][field] = clonedMetadata\n }\n}\n\n/**\n * An instance of lunr.MatchData will be created for every term that matches a\n * document. However only one instance is required in a lunr.Index~Result. This\n * method combines metadata from another instance of lunr.MatchData with this\n * objects metadata.\n *\n * @param {lunr.MatchData} otherMatchData - Another instance of match data to merge with this one.\n * @see {@link lunr.Index~Result}\n */\nlunr.MatchData.prototype.combine = function (otherMatchData) {\n var terms = Object.keys(otherMatchData.metadata)\n\n for (var i = 0; i < terms.length; i++) {\n var term = terms[i],\n fields = Object.keys(otherMatchData.metadata[term])\n\n if (this.metadata[term] == undefined) {\n this.metadata[term] = Object.create(null)\n }\n\n for (var j = 0; j < fields.length; j++) {\n var field = fields[j],\n keys = Object.keys(otherMatchData.metadata[term][field])\n\n if (this.metadata[term][field] == undefined) {\n this.metadata[term][field] = Object.create(null)\n }\n\n for (var k = 0; k < keys.length; k++) {\n var key = keys[k]\n\n if (this.metadata[term][field][key] == undefined) {\n this.metadata[term][field][key] = otherMatchData.metadata[term][field][key]\n } else {\n this.metadata[term][field][key] = this.metadata[term][field][key].concat(otherMatchData.metadata[term][field][key])\n }\n\n }\n }\n }\n}\n\n/**\n * Add metadata for a term/field pair to this instance of match data.\n *\n * @param {string} term - The term this match data is associated with\n * @param {string} field - The field in which the term was found\n * @param {object} metadata - The metadata recorded about this term in this field\n */\nlunr.MatchData.prototype.add = function (term, field, metadata) {\n if (!(term in this.metadata)) {\n this.metadata[term] = Object.create(null)\n this.metadata[term][field] = metadata\n return\n }\n\n if (!(field in this.metadata[term])) {\n this.metadata[term][field] = metadata\n return\n }\n\n var metadataKeys = Object.keys(metadata)\n\n for (var i = 0; i < metadataKeys.length; i++) {\n var key = metadataKeys[i]\n\n if (key in this.metadata[term][field]) {\n this.metadata[term][field][key] = this.metadata[term][field][key].concat(metadata[key])\n } else {\n this.metadata[term][field][key] = metadata[key]\n }\n }\n}\n/**\n * A lunr.Query provides a programmatic way of defining queries to be performed\n * against a {@link lunr.Index}.\n *\n * Prefer constructing a lunr.Query using the {@link lunr.Index#query} method\n * so the query object is pre-initialized with the right index fields.\n *\n * @constructor\n * @property {lunr.Query~Clause[]} clauses - An array of query clauses.\n * @property {string[]} allFields - An array of all available fields in a lunr.Index.\n */\nlunr.Query = function (allFields) {\n this.clauses = []\n this.allFields = allFields\n}\n\n/**\n * Constants for indicating what kind of automatic wildcard insertion will be used when constructing a query clause.\n *\n * This allows wildcards to be added to the beginning and end of a term without having to manually do any string\n * concatenation.\n *\n * The wildcard constants can be bitwise combined to select both leading and trailing wildcards.\n *\n * @constant\n * @default\n * @property {number} wildcard.NONE - The term will have no wildcards inserted, this is the default behaviour\n * @property {number} wildcard.LEADING - Prepend the term with a wildcard, unless a leading wildcard already exists\n * @property {number} wildcard.TRAILING - Append a wildcard to the term, unless a trailing wildcard already exists\n * @see lunr.Query~Clause\n * @see lunr.Query#clause\n * @see lunr.Query#term\n * @example query term with trailing wildcard\n * query.term('foo', { wildcard: lunr.Query.wildcard.TRAILING })\n * @example query term with leading and trailing wildcard\n * query.term('foo', {\n * wildcard: lunr.Query.wildcard.LEADING | lunr.Query.wildcard.TRAILING\n * })\n */\n\nlunr.Query.wildcard = new String (\"*\")\nlunr.Query.wildcard.NONE = 0\nlunr.Query.wildcard.LEADING = 1\nlunr.Query.wildcard.TRAILING = 2\n\n/**\n * Constants for indicating what kind of presence a term must have in matching documents.\n *\n * @constant\n * @enum {number}\n * @see lunr.Query~Clause\n * @see lunr.Query#clause\n * @see lunr.Query#term\n * @example query term with required presence\n * query.term('foo', { presence: lunr.Query.presence.REQUIRED })\n */\nlunr.Query.presence = {\n /**\n * Term's presence in a document is optional, this is the default value.\n */\n OPTIONAL: 1,\n\n /**\n * Term's presence in a document is required, documents that do not contain\n * this term will not be returned.\n */\n REQUIRED: 2,\n\n /**\n * Term's presence in a document is prohibited, documents that do contain\n * this term will not be returned.\n */\n PROHIBITED: 3\n}\n\n/**\n * A single clause in a {@link lunr.Query} contains a term and details on how to\n * match that term against a {@link lunr.Index}.\n *\n * @typedef {Object} lunr.Query~Clause\n * @property {string[]} fields - The fields in an index this clause should be matched against.\n * @property {number} [boost=1] - Any boost that should be applied when matching this clause.\n * @property {number} [editDistance] - Whether the term should have fuzzy matching applied, and how fuzzy the match should be.\n * @property {boolean} [usePipeline] - Whether the term should be passed through the search pipeline.\n * @property {number} [wildcard=lunr.Query.wildcard.NONE] - Whether the term should have wildcards appended or prepended.\n * @property {number} [presence=lunr.Query.presence.OPTIONAL] - The terms presence in any matching documents.\n */\n\n/**\n * Adds a {@link lunr.Query~Clause} to this query.\n *\n * Unless the clause contains the fields to be matched all fields will be matched. In addition\n * a default boost of 1 is applied to the clause.\n *\n * @param {lunr.Query~Clause} clause - The clause to add to this query.\n * @see lunr.Query~Clause\n * @returns {lunr.Query}\n */\nlunr.Query.prototype.clause = function (clause) {\n if (!('fields' in clause)) {\n clause.fields = this.allFields\n }\n\n if (!('boost' in clause)) {\n clause.boost = 1\n }\n\n if (!('usePipeline' in clause)) {\n clause.usePipeline = true\n }\n\n if (!('wildcard' in clause)) {\n clause.wildcard = lunr.Query.wildcard.NONE\n }\n\n if ((clause.wildcard & lunr.Query.wildcard.LEADING) && (clause.term.charAt(0) != lunr.Query.wildcard)) {\n clause.term = \"*\" + clause.term\n }\n\n if ((clause.wildcard & lunr.Query.wildcard.TRAILING) && (clause.term.slice(-1) != lunr.Query.wildcard)) {\n clause.term = \"\" + clause.term + \"*\"\n }\n\n if (!('presence' in clause)) {\n clause.presence = lunr.Query.presence.OPTIONAL\n }\n\n this.clauses.push(clause)\n\n return this\n}\n\n/**\n * A negated query is one in which every clause has a presence of\n * prohibited. These queries require some special processing to return\n * the expected results.\n *\n * @returns boolean\n */\nlunr.Query.prototype.isNegated = function () {\n for (var i = 0; i < this.clauses.length; i++) {\n if (this.clauses[i].presence != lunr.Query.presence.PROHIBITED) {\n return false\n }\n }\n\n return true\n}\n\n/**\n * Adds a term to the current query, under the covers this will create a {@link lunr.Query~Clause}\n * to the list of clauses that make up this query.\n *\n * The term is used as is, i.e. no tokenization will be performed by this method. Instead conversion\n * to a token or token-like string should be done before calling this method.\n *\n * The term will be converted to a string by calling `toString`. Multiple terms can be passed as an\n * array, each term in the array will share the same options.\n *\n * @param {object|object[]} term - The term(s) to add to the query.\n * @param {object} [options] - Any additional properties to add to the query clause.\n * @returns {lunr.Query}\n * @see lunr.Query#clause\n * @see lunr.Query~Clause\n * @example adding a single term to a query\n * query.term(\"foo\")\n * @example adding a single term to a query and specifying search fields, term boost and automatic trailing wildcard\n * query.term(\"foo\", {\n * fields: [\"title\"],\n * boost: 10,\n * wildcard: lunr.Query.wildcard.TRAILING\n * })\n * @example using lunr.tokenizer to convert a string to tokens before using them as terms\n * query.term(lunr.tokenizer(\"foo bar\"))\n */\nlunr.Query.prototype.term = function (term, options) {\n if (Array.isArray(term)) {\n term.forEach(function (t) { this.term(t, lunr.utils.clone(options)) }, this)\n return this\n }\n\n var clause = options || {}\n clause.term = term.toString()\n\n this.clause(clause)\n\n return this\n}\nlunr.QueryParseError = function (message, start, end) {\n this.name = \"QueryParseError\"\n this.message = message\n this.start = start\n this.end = end\n}\n\nlunr.QueryParseError.prototype = new Error\nlunr.QueryLexer = function (str) {\n this.lexemes = []\n this.str = str\n this.length = str.length\n this.pos = 0\n this.start = 0\n this.escapeCharPositions = []\n}\n\nlunr.QueryLexer.prototype.run = function () {\n var state = lunr.QueryLexer.lexText\n\n while (state) {\n state = state(this)\n }\n}\n\nlunr.QueryLexer.prototype.sliceString = function () {\n var subSlices = [],\n sliceStart = this.start,\n sliceEnd = this.pos\n\n for (var i = 0; i < this.escapeCharPositions.length; i++) {\n sliceEnd = this.escapeCharPositions[i]\n subSlices.push(this.str.slice(sliceStart, sliceEnd))\n sliceStart = sliceEnd + 1\n }\n\n subSlices.push(this.str.slice(sliceStart, this.pos))\n this.escapeCharPositions.length = 0\n\n return subSlices.join('')\n}\n\nlunr.QueryLexer.prototype.emit = function (type) {\n this.lexemes.push({\n type: type,\n str: this.sliceString(),\n start: this.start,\n end: this.pos\n })\n\n this.start = this.pos\n}\n\nlunr.QueryLexer.prototype.escapeCharacter = function () {\n this.escapeCharPositions.push(this.pos - 1)\n this.pos += 1\n}\n\nlunr.QueryLexer.prototype.next = function () {\n if (this.pos >= this.length) {\n return lunr.QueryLexer.EOS\n }\n\n var char = this.str.charAt(this.pos)\n this.pos += 1\n return char\n}\n\nlunr.QueryLexer.prototype.width = function () {\n return this.pos - this.start\n}\n\nlunr.QueryLexer.prototype.ignore = function () {\n if (this.start == this.pos) {\n this.pos += 1\n }\n\n this.start = this.pos\n}\n\nlunr.QueryLexer.prototype.backup = function () {\n this.pos -= 1\n}\n\nlunr.QueryLexer.prototype.acceptDigitRun = function () {\n var char, charCode\n\n do {\n char = this.next()\n charCode = char.charCodeAt(0)\n } while (charCode > 47 && charCode < 58)\n\n if (char != lunr.QueryLexer.EOS) {\n this.backup()\n }\n}\n\nlunr.QueryLexer.prototype.more = function () {\n return this.pos < this.length\n}\n\nlunr.QueryLexer.EOS = 'EOS'\nlunr.QueryLexer.FIELD = 'FIELD'\nlunr.QueryLexer.TERM = 'TERM'\nlunr.QueryLexer.EDIT_DISTANCE = 'EDIT_DISTANCE'\nlunr.QueryLexer.BOOST = 'BOOST'\nlunr.QueryLexer.PRESENCE = 'PRESENCE'\n\nlunr.QueryLexer.lexField = function (lexer) {\n lexer.backup()\n lexer.emit(lunr.QueryLexer.FIELD)\n lexer.ignore()\n return lunr.QueryLexer.lexText\n}\n\nlunr.QueryLexer.lexTerm = function (lexer) {\n if (lexer.width() > 1) {\n lexer.backup()\n lexer.emit(lunr.QueryLexer.TERM)\n }\n\n lexer.ignore()\n\n if (lexer.more()) {\n return lunr.QueryLexer.lexText\n }\n}\n\nlunr.QueryLexer.lexEditDistance = function (lexer) {\n lexer.ignore()\n lexer.acceptDigitRun()\n lexer.emit(lunr.QueryLexer.EDIT_DISTANCE)\n return lunr.QueryLexer.lexText\n}\n\nlunr.QueryLexer.lexBoost = function (lexer) {\n lexer.ignore()\n lexer.acceptDigitRun()\n lexer.emit(lunr.QueryLexer.BOOST)\n return lunr.QueryLexer.lexText\n}\n\nlunr.QueryLexer.lexEOS = function (lexer) {\n if (lexer.width() > 0) {\n lexer.emit(lunr.QueryLexer.TERM)\n }\n}\n\n// This matches the separator used when tokenising fields\n// within a document. These should match otherwise it is\n// not possible to search for some tokens within a document.\n//\n// It is possible for the user to change the separator on the\n// tokenizer so it _might_ clash with any other of the special\n// characters already used within the search string, e.g. :.\n//\n// This means that it is possible to change the separator in\n// such a way that makes some words unsearchable using a search\n// string.\nlunr.QueryLexer.termSeparator = lunr.tokenizer.separator\n\nlunr.QueryLexer.lexText = function (lexer) {\n while (true) {\n var char = lexer.next()\n\n if (char == lunr.QueryLexer.EOS) {\n return lunr.QueryLexer.lexEOS\n }\n\n // Escape character is '\\'\n if (char.charCodeAt(0) == 92) {\n lexer.escapeCharacter()\n continue\n }\n\n if (char == \":\") {\n return lunr.QueryLexer.lexField\n }\n\n if (char == \"~\") {\n lexer.backup()\n if (lexer.width() > 0) {\n lexer.emit(lunr.QueryLexer.TERM)\n }\n return lunr.QueryLexer.lexEditDistance\n }\n\n if (char == \"^\") {\n lexer.backup()\n if (lexer.width() > 0) {\n lexer.emit(lunr.QueryLexer.TERM)\n }\n return lunr.QueryLexer.lexBoost\n }\n\n // \"+\" indicates term presence is required\n // checking for length to ensure that only\n // leading \"+\" are considered\n if (char == \"+\" && lexer.width() === 1) {\n lexer.emit(lunr.QueryLexer.PRESENCE)\n return lunr.QueryLexer.lexText\n }\n\n // \"-\" indicates term presence is prohibited\n // checking for length to ensure that only\n // leading \"-\" are considered\n if (char == \"-\" && lexer.width() === 1) {\n lexer.emit(lunr.QueryLexer.PRESENCE)\n return lunr.QueryLexer.lexText\n }\n\n if (char.match(lunr.QueryLexer.termSeparator)) {\n return lunr.QueryLexer.lexTerm\n }\n }\n}\n\nlunr.QueryParser = function (str, query) {\n this.lexer = new lunr.QueryLexer (str)\n this.query = query\n this.currentClause = {}\n this.lexemeIdx = 0\n}\n\nlunr.QueryParser.prototype.parse = function () {\n this.lexer.run()\n this.lexemes = this.lexer.lexemes\n\n var state = lunr.QueryParser.parseClause\n\n while (state) {\n state = state(this)\n }\n\n return this.query\n}\n\nlunr.QueryParser.prototype.peekLexeme = function () {\n return this.lexemes[this.lexemeIdx]\n}\n\nlunr.QueryParser.prototype.consumeLexeme = function () {\n var lexeme = this.peekLexeme()\n this.lexemeIdx += 1\n return lexeme\n}\n\nlunr.QueryParser.prototype.nextClause = function () {\n var completedClause = this.currentClause\n this.query.clause(completedClause)\n this.currentClause = {}\n}\n\nlunr.QueryParser.parseClause = function (parser) {\n var lexeme = parser.peekLexeme()\n\n if (lexeme == undefined) {\n return\n }\n\n switch (lexeme.type) {\n case lunr.QueryLexer.PRESENCE:\n return lunr.QueryParser.parsePresence\n case lunr.QueryLexer.FIELD:\n return lunr.QueryParser.parseField\n case lunr.QueryLexer.TERM:\n return lunr.QueryParser.parseTerm\n default:\n var errorMessage = \"expected either a field or a term, found \" + lexeme.type\n\n if (lexeme.str.length >= 1) {\n errorMessage += \" with value '\" + lexeme.str + \"'\"\n }\n\n throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n }\n}\n\nlunr.QueryParser.parsePresence = function (parser) {\n var lexeme = parser.consumeLexeme()\n\n if (lexeme == undefined) {\n return\n }\n\n switch (lexeme.str) {\n case \"-\":\n parser.currentClause.presence = lunr.Query.presence.PROHIBITED\n break\n case \"+\":\n parser.currentClause.presence = lunr.Query.presence.REQUIRED\n break\n default:\n var errorMessage = \"unrecognised presence operator'\" + lexeme.str + \"'\"\n throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n }\n\n var nextLexeme = parser.peekLexeme()\n\n if (nextLexeme == undefined) {\n var errorMessage = \"expecting term or field, found nothing\"\n throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n }\n\n switch (nextLexeme.type) {\n case lunr.QueryLexer.FIELD:\n return lunr.QueryParser.parseField\n case lunr.QueryLexer.TERM:\n return lunr.QueryParser.parseTerm\n default:\n var errorMessage = \"expecting term or field, found '\" + nextLexeme.type + \"'\"\n throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n }\n}\n\nlunr.QueryParser.parseField = function (parser) {\n var lexeme = parser.consumeLexeme()\n\n if (lexeme == undefined) {\n return\n }\n\n if (parser.query.allFields.indexOf(lexeme.str) == -1) {\n var possibleFields = parser.query.allFields.map(function (f) { return \"'\" + f + \"'\" }).join(', '),\n errorMessage = \"unrecognised field '\" + lexeme.str + \"', possible fields: \" + possibleFields\n\n throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n }\n\n parser.currentClause.fields = [lexeme.str]\n\n var nextLexeme = parser.peekLexeme()\n\n if (nextLexeme == undefined) {\n var errorMessage = \"expecting term, found nothing\"\n throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n }\n\n switch (nextLexeme.type) {\n case lunr.QueryLexer.TERM:\n return lunr.QueryParser.parseTerm\n default:\n var errorMessage = \"expecting term, found '\" + nextLexeme.type + \"'\"\n throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n }\n}\n\nlunr.QueryParser.parseTerm = function (parser) {\n var lexeme = parser.consumeLexeme()\n\n if (lexeme == undefined) {\n return\n }\n\n parser.currentClause.term = lexeme.str.toLowerCase()\n\n if (lexeme.str.indexOf(\"*\") != -1) {\n parser.currentClause.usePipeline = false\n }\n\n var nextLexeme = parser.peekLexeme()\n\n if (nextLexeme == undefined) {\n parser.nextClause()\n return\n }\n\n switch (nextLexeme.type) {\n case lunr.QueryLexer.TERM:\n parser.nextClause()\n return lunr.QueryParser.parseTerm\n case lunr.QueryLexer.FIELD:\n parser.nextClause()\n return lunr.QueryParser.parseField\n case lunr.QueryLexer.EDIT_DISTANCE:\n return lunr.QueryParser.parseEditDistance\n case lunr.QueryLexer.BOOST:\n return lunr.QueryParser.parseBoost\n case lunr.QueryLexer.PRESENCE:\n parser.nextClause()\n return lunr.QueryParser.parsePresence\n default:\n var errorMessage = \"Unexpected lexeme type '\" + nextLexeme.type + \"'\"\n throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n }\n}\n\nlunr.QueryParser.parseEditDistance = function (parser) {\n var lexeme = parser.consumeLexeme()\n\n if (lexeme == undefined) {\n return\n }\n\n var editDistance = parseInt(lexeme.str, 10)\n\n if (isNaN(editDistance)) {\n var errorMessage = \"edit distance must be numeric\"\n throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n }\n\n parser.currentClause.editDistance = editDistance\n\n var nextLexeme = parser.peekLexeme()\n\n if (nextLexeme == undefined) {\n parser.nextClause()\n return\n }\n\n switch (nextLexeme.type) {\n case lunr.QueryLexer.TERM:\n parser.nextClause()\n return lunr.QueryParser.parseTerm\n case lunr.QueryLexer.FIELD:\n parser.nextClause()\n return lunr.QueryParser.parseField\n case lunr.QueryLexer.EDIT_DISTANCE:\n return lunr.QueryParser.parseEditDistance\n case lunr.QueryLexer.BOOST:\n return lunr.QueryParser.parseBoost\n case lunr.QueryLexer.PRESENCE:\n parser.nextClause()\n return lunr.QueryParser.parsePresence\n default:\n var errorMessage = \"Unexpected lexeme type '\" + nextLexeme.type + \"'\"\n throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n }\n}\n\nlunr.QueryParser.parseBoost = function (parser) {\n var lexeme = parser.consumeLexeme()\n\n if (lexeme == undefined) {\n return\n }\n\n var boost = parseInt(lexeme.str, 10)\n\n if (isNaN(boost)) {\n var errorMessage = \"boost must be numeric\"\n throw new lunr.QueryParseError (errorMessage, lexeme.start, lexeme.end)\n }\n\n parser.currentClause.boost = boost\n\n var nextLexeme = parser.peekLexeme()\n\n if (nextLexeme == undefined) {\n parser.nextClause()\n return\n }\n\n switch (nextLexeme.type) {\n case lunr.QueryLexer.TERM:\n parser.nextClause()\n return lunr.QueryParser.parseTerm\n case lunr.QueryLexer.FIELD:\n parser.nextClause()\n return lunr.QueryParser.parseField\n case lunr.QueryLexer.EDIT_DISTANCE:\n return lunr.QueryParser.parseEditDistance\n case lunr.QueryLexer.BOOST:\n return lunr.QueryParser.parseBoost\n case lunr.QueryLexer.PRESENCE:\n parser.nextClause()\n return lunr.QueryParser.parsePresence\n default:\n var errorMessage = \"Unexpected lexeme type '\" + nextLexeme.type + \"'\"\n throw new lunr.QueryParseError (errorMessage, nextLexeme.start, nextLexeme.end)\n }\n}\n\n /**\n * export the module via AMD, CommonJS or as a browser global\n * Export code from https://github.com/umdjs/umd/blob/master/returnExports.js\n */\n ;(function (root, factory) {\n if (typeof define === 'function' && define.amd) {\n // AMD. Register as an anonymous module.\n define(factory)\n } else if (typeof exports === 'object') {\n /**\n * Node. Does not work with strict CommonJS, but\n * only CommonJS-like enviroments that support module.exports,\n * like Node.\n */\n module.exports = factory()\n } else {\n // Browser globals (root is window)\n root.lunr = factory()\n }\n }(this, function () {\n /**\n * Just return a value to define the module export.\n * This example returns an object, but the module\n * can return a function as the exported value.\n */\n return lunr\n }))\n})();\n", "/*!\n * escape-html\n * Copyright(c) 2012-2013 TJ Holowaychuk\n * Copyright(c) 2015 Andreas Lubbe\n * Copyright(c) 2015 Tiancheng \"Timothy\" Gu\n * MIT Licensed\n */\n\n'use strict';\n\n/**\n * Module variables.\n * @private\n */\n\nvar matchHtmlRegExp = /[\"'&<>]/;\n\n/**\n * Module exports.\n * @public\n */\n\nmodule.exports = escapeHtml;\n\n/**\n * Escape special characters in the given string of html.\n *\n * @param {string} string The string to escape for inserting into HTML\n * @return {string}\n * @public\n */\n\nfunction escapeHtml(string) {\n var str = '' + string;\n var match = matchHtmlRegExp.exec(str);\n\n if (!match) {\n return str;\n }\n\n var escape;\n var html = '';\n var index = 0;\n var lastIndex = 0;\n\n for (index = match.index; index < str.length; index++) {\n switch (str.charCodeAt(index)) {\n case 34: // \"\n escape = '"';\n break;\n case 38: // &\n escape = '&';\n break;\n case 39: // '\n escape = ''';\n break;\n case 60: // <\n escape = '<';\n break;\n case 62: // >\n escape = '>';\n break;\n default:\n continue;\n }\n\n if (lastIndex !== index) {\n html += str.substring(lastIndex, index);\n }\n\n lastIndex = index + 1;\n html += escape;\n }\n\n return lastIndex !== index\n ? html + str.substring(lastIndex, index)\n : html;\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A RTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport lunr from \"lunr\"\n\nimport \"~/polyfills\"\n\nimport { Search, SearchIndexConfig } from \"../../_\"\nimport {\n SearchMessage,\n SearchMessageType\n} from \"../message\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Add support for usage with `iframe-worker` polyfill\n *\n * While `importScripts` is synchronous when executed inside of a web worker,\n * it's not possible to provide a synchronous polyfilled implementation. The\n * cool thing is that awaiting a non-Promise is a noop, so extending the type\n * definition to return a `Promise` shouldn't break anything.\n *\n * @see https://bit.ly/2PjDnXi - GitHub comment\n */\ndeclare global {\n function importScripts(...urls: string[]): Promise | void\n}\n\n/* ----------------------------------------------------------------------------\n * Data\n * ------------------------------------------------------------------------- */\n\n/**\n * Search index\n */\nlet index: Search\n\n/* ----------------------------------------------------------------------------\n * Helper functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Fetch (= import) multi-language support through `lunr-languages`\n *\n * This function automatically imports the stemmers necessary to process the\n * languages, which are defined through the search index configuration.\n *\n * If the worker runs inside of an `iframe` (when using `iframe-worker` as\n * a shim), the base URL for the stemmers to be loaded must be determined by\n * searching for the first `script` element with a `src` attribute, which will\n * contain the contents of this script.\n *\n * @param config - Search index configuration\n *\n * @returns Promise resolving with no result\n */\nasync function setupSearchLanguages(\n config: SearchIndexConfig\n): Promise {\n let base = \"../lunr\"\n\n /* Detect `iframe-worker` and fix base URL */\n if (typeof parent !== \"undefined\" && \"IFrameWorker\" in parent) {\n const worker = document.querySelector(\"script[src]\")!\n const [path] = worker.src.split(\"/worker\")\n\n /* Prefix base with path */\n base = base.replace(\"..\", path)\n }\n\n /* Add scripts for languages */\n const scripts = []\n for (const lang of config.lang) {\n switch (lang) {\n\n /* Add segmenter for Japanese */\n case \"ja\":\n scripts.push(`${base}/tinyseg.js`)\n break\n\n /* Add segmenter for Hindi and Thai */\n case \"hi\":\n case \"th\":\n scripts.push(`${base}/wordcut.js`)\n break\n }\n\n /* Add language support */\n if (lang !== \"en\")\n scripts.push(`${base}/min/lunr.${lang}.min.js`)\n }\n\n /* Add multi-language support */\n if (config.lang.length > 1)\n scripts.push(`${base}/min/lunr.multi.min.js`)\n\n /* Load scripts synchronously */\n if (scripts.length)\n await importScripts(\n `${base}/min/lunr.stemmer.support.min.js`,\n ...scripts\n )\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Message handler\n *\n * @param message - Source message\n *\n * @returns Target message\n */\nexport async function handler(\n message: SearchMessage\n): Promise {\n switch (message.type) {\n\n /* Search setup message */\n case SearchMessageType.SETUP:\n await setupSearchLanguages(message.data.config)\n index = new Search(message.data)\n return {\n type: SearchMessageType.READY\n }\n\n /* Search query message */\n case SearchMessageType.QUERY:\n return {\n type: SearchMessageType.RESULT,\n data: index ? index.search(message.data) : { items: [] }\n }\n\n /* All other messages */\n default:\n throw new TypeError(\"Invalid message type\")\n }\n}\n\n/* ----------------------------------------------------------------------------\n * Worker\n * ------------------------------------------------------------------------- */\n\n/* @ts-expect-error - expose Lunr.js in global scope, or stemmers won't work */\nself.lunr = lunr\n\n/* Handle messages */\naddEventListener(\"message\", async ev => {\n postMessage(await handler(ev.data))\n})\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Polyfills\n * ------------------------------------------------------------------------- */\n\n/* Polyfill `Object.entries` */\nif (!Object.entries)\n Object.entries = function (obj: object) {\n const data: [string, string][] = []\n for (const key of Object.keys(obj))\n // @ts-expect-error - ignore property access warning\n data.push([key, obj[key]])\n\n /* Return entries */\n return data\n }\n\n/* Polyfill `Object.values` */\nif (!Object.values)\n Object.values = function (obj: object) {\n const data: string[] = []\n for (const key of Object.keys(obj))\n // @ts-expect-error - ignore property access warning\n data.push(obj[key])\n\n /* Return values */\n return data\n }\n\n/* ------------------------------------------------------------------------- */\n\n/* Polyfills for `Element` */\nif (typeof Element !== \"undefined\") {\n\n /* Polyfill `Element.scrollTo` */\n if (!Element.prototype.scrollTo)\n Element.prototype.scrollTo = function (\n x?: ScrollToOptions | number, y?: number\n ): void {\n if (typeof x === \"object\") {\n this.scrollLeft = x.left!\n this.scrollTop = x.top!\n } else {\n this.scrollLeft = x!\n this.scrollTop = y!\n }\n }\n\n /* Polyfill `Element.replaceWith` */\n if (!Element.prototype.replaceWith)\n Element.prototype.replaceWith = function (\n ...nodes: Array\n ): void {\n const parent = this.parentNode\n if (parent) {\n if (nodes.length === 0)\n parent.removeChild(this)\n\n /* Replace children and create text nodes */\n for (let i = nodes.length - 1; i >= 0; i--) {\n let node = nodes[i]\n if (typeof node !== \"object\")\n node = document.createTextNode(node)\n else if (node.parentNode)\n node.parentNode.removeChild(node)\n\n /* Replace child or insert before previous sibling */\n if (!i)\n parent.replaceChild(node, this)\n else\n parent.insertBefore(this.previousSibling!, node)\n }\n }\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport escapeHTML from \"escape-html\"\n\nimport { SearchIndexDocument } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search document\n */\nexport interface SearchDocument extends SearchIndexDocument {\n parent?: SearchIndexDocument /* Parent article */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search document mapping\n */\nexport type SearchDocumentMap = Map\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create a search document mapping\n *\n * @param docs - Search index documents\n *\n * @returns Search document map\n */\nexport function setupSearchDocumentMap(\n docs: SearchIndexDocument[]\n): SearchDocumentMap {\n const documents = new Map()\n const parents = new Set()\n for (const doc of docs) {\n const [path, hash] = doc.location.split(\"#\")\n\n /* Extract location, title and tags */\n const location = doc.location\n const title = doc.title\n const tags = doc.tags\n\n /* Escape and cleanup text */\n const text = escapeHTML(doc.text)\n .replace(/\\s+(?=[,.:;!?])/g, \"\")\n .replace(/\\s+/g, \" \")\n\n /* Handle section */\n if (hash) {\n const parent = documents.get(path)!\n\n /* Ignore first section, override article */\n if (!parents.has(parent)) {\n parent.title = doc.title\n parent.text = text\n\n /* Remember that we processed the article */\n parents.add(parent)\n\n /* Add subsequent section */\n } else {\n documents.set(location, {\n location,\n title,\n text,\n parent\n })\n }\n\n /* Add article */\n } else {\n documents.set(location, {\n location,\n title,\n text,\n ...tags && { tags }\n })\n }\n }\n return documents\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport escapeHTML from \"escape-html\"\n\nimport { SearchIndexConfig } from \"../_\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search highlight function\n *\n * @param value - Value\n *\n * @returns Highlighted value\n */\nexport type SearchHighlightFn = (value: string) => string\n\n/**\n * Search highlight factory function\n *\n * @param query - Query value\n *\n * @returns Search highlight function\n */\nexport type SearchHighlightFactoryFn = (query: string) => SearchHighlightFn\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Create a search highlighter\n *\n * @param config - Search index configuration\n * @param escape - Whether to escape HTML\n *\n * @returns Search highlight factory function\n */\nexport function setupSearchHighlighter(\n config: SearchIndexConfig, escape: boolean\n): SearchHighlightFactoryFn {\n const separator = new RegExp(config.separator, \"img\")\n const highlight = (_: unknown, data: string, term: string) => {\n return `${data}${term}`\n }\n\n /* Return factory function */\n return (query: string) => {\n query = query\n .replace(/[\\s*+\\-:~^]+/g, \" \")\n .trim()\n\n /* Create search term match expression */\n const match = new RegExp(`(^|${config.separator})(${\n query\n .replace(/[|\\\\{}()[\\]^$+*?.-]/g, \"\\\\$&\")\n .replace(separator, \"|\")\n })`, \"img\")\n\n /* Highlight string value */\n return value => (\n escape\n ? escapeHTML(value)\n : value\n )\n .replace(match, highlight)\n .replace(/<\\/mark>(\\s+)]*>/img, \"$1\")\n }\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search query clause\n */\nexport interface SearchQueryClause {\n presence: lunr.Query.presence /* Clause presence */\n term: string /* Clause term */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search query terms\n */\nexport type SearchQueryTerms = Record\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Parse a search query for analysis\n *\n * @param value - Query value\n *\n * @returns Search query clauses\n */\nexport function parseSearchQuery(\n value: string\n): SearchQueryClause[] {\n const query = new (lunr as any).Query([\"title\", \"text\"])\n const parser = new (lunr as any).QueryParser(value, query)\n\n /* Parse and return query clauses */\n parser.parse()\n return query.clauses\n}\n\n/**\n * Analyze the search query clauses in regard to the search terms found\n *\n * @param query - Search query clauses\n * @param terms - Search terms\n *\n * @returns Search query terms\n */\nexport function getSearchQueryTerms(\n query: SearchQueryClause[], terms: string[]\n): SearchQueryTerms {\n const clauses = new Set(query)\n\n /* Match query clauses against terms */\n const result: SearchQueryTerms = {}\n for (let t = 0; t < terms.length; t++)\n for (const clause of clauses)\n if (terms[t].startsWith(clause.term)) {\n result[clause.term] = true\n clauses.delete(clause)\n }\n\n /* Annotate unmatched non-stopword query clauses */\n for (const clause of clauses)\n if (lunr.stopWordFilter?.(clause.term as any))\n result[clause.term] = false\n\n /* Return query terms */\n return result\n}\n", "/*\n * Copyright (c) 2016-2022 Martin Donath \n *\n * Permission is hereby granted, free of charge, to any person obtaining a copy\n * of this software and associated documentation files (the \"Software\"), to\n * deal in the Software without restriction, including without limitation the\n * rights to use, copy, modify, merge, publish, distribute, sublicense, and/or\n * sell copies of the Software, and to permit persons to whom the Software is\n * furnished to do so, subject to the following conditions:\n *\n * The above copyright notice and this permission notice shall be included in\n * all copies or substantial portions of the Software.\n *\n * THE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\n * IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\n * FITNESS FOR A PARTICULAR PURPOSE AND NON-INFRINGEMENT. IN NO EVENT SHALL THE\n * AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\n * LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING\n * FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS\n * IN THE SOFTWARE.\n */\n\nimport {\n SearchDocument,\n SearchDocumentMap,\n setupSearchDocumentMap\n} from \"../document\"\nimport {\n SearchHighlightFactoryFn,\n setupSearchHighlighter\n} from \"../highlighter\"\nimport { SearchOptions } from \"../options\"\nimport {\n SearchQueryTerms,\n getSearchQueryTerms,\n parseSearchQuery\n} from \"../query\"\n\n/* ----------------------------------------------------------------------------\n * Types\n * ------------------------------------------------------------------------- */\n\n/**\n * Search index configuration\n */\nexport interface SearchIndexConfig {\n lang: string[] /* Search languages */\n separator: string /* Search separator */\n}\n\n/**\n * Search index document\n */\nexport interface SearchIndexDocument {\n location: string /* Document location */\n title: string /* Document title */\n text: string /* Document text */\n tags?: string[] /* Document tags */\n boost?: number /* Document boost */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search index\n *\n * This interfaces describes the format of the `search_index.json` file which\n * is automatically built by the MkDocs search plugin.\n */\nexport interface SearchIndex {\n config: SearchIndexConfig /* Search index configuration */\n docs: SearchIndexDocument[] /* Search index documents */\n options: SearchOptions /* Search options */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search metadata\n */\nexport interface SearchMetadata {\n score: number /* Score (relevance) */\n terms: SearchQueryTerms /* Search query terms */\n}\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search result document\n */\nexport type SearchResultDocument = SearchDocument & SearchMetadata\n\n/**\n * Search result item\n */\nexport type SearchResultItem = SearchResultDocument[]\n\n/* ------------------------------------------------------------------------- */\n\n/**\n * Search result\n */\nexport interface SearchResult {\n items: SearchResultItem[] /* Search result items */\n suggestions?: string[] /* Search suggestions */\n}\n\n/* ----------------------------------------------------------------------------\n * Functions\n * ------------------------------------------------------------------------- */\n\n/**\n * Compute the difference of two lists of strings\n *\n * @param a - 1st list of strings\n * @param b - 2nd list of strings\n *\n * @returns Difference\n */\nfunction difference(a: string[], b: string[]): string[] {\n const [x, y] = [new Set(a), new Set(b)]\n return [\n ...new Set([...x].filter(value => !y.has(value)))\n ]\n}\n\n/* ----------------------------------------------------------------------------\n * Class\n * ------------------------------------------------------------------------- */\n\n/**\n * Search index\n */\nexport class Search {\n\n /**\n * Search document mapping\n *\n * A mapping of URLs (including hash fragments) to the actual articles and\n * sections of the documentation. The search document mapping must be created\n * regardless of whether the index was prebuilt or not, as Lunr.js itself\n * only stores the actual index.\n */\n protected documents: SearchDocumentMap\n\n /**\n * Search highlight factory function\n */\n protected highlight: SearchHighlightFactoryFn\n\n /**\n * The underlying Lunr.js search index\n */\n protected index: lunr.Index\n\n /**\n * Search options\n */\n protected options: SearchOptions\n\n /**\n * Create the search integration\n *\n * @param data - Search index\n */\n public constructor({ config, docs, options }: SearchIndex) {\n this.options = options\n\n /* Set up document map and highlighter factory */\n this.documents = setupSearchDocumentMap(docs)\n this.highlight = setupSearchHighlighter(config, false)\n\n /* Set separator for tokenizer */\n lunr.tokenizer.separator = new RegExp(config.separator)\n\n /* Create search index */\n this.index = lunr(function () {\n\n /* Set up multi-language support */\n if (config.lang.length === 1 && config.lang[0] !== \"en\") {\n this.use((lunr as any)[config.lang[0]])\n } else if (config.lang.length > 1) {\n this.use((lunr as any).multiLanguage(...config.lang))\n }\n\n /* Compute functions to be removed from the pipeline */\n const fns = difference([\n \"trimmer\", \"stopWordFilter\", \"stemmer\"\n ], options.pipeline)\n\n /* Remove functions from the pipeline for registered languages */\n for (const lang of config.lang.map(language => (\n language === \"en\" ? lunr : (lunr as any)[language]\n ))) {\n for (const fn of fns) {\n this.pipeline.remove(lang[fn])\n this.searchPipeline.remove(lang[fn])\n }\n }\n\n /* Set up reference */\n this.ref(\"location\")\n\n /* Set up fields */\n this.field(\"title\", { boost: 1e3 })\n this.field(\"text\")\n this.field(\"tags\", { boost: 1e6, extractor: doc => {\n const { tags = [] } = doc as SearchDocument\n return tags.flatMap(tag => lunr.tokenizer(tag))\n } })\n\n /* Index documents */\n for (const doc of docs)\n this.add(doc, { boost: doc.boost })\n })\n }\n\n /**\n * Search for matching documents\n *\n * The search index which MkDocs provides is divided up into articles, which\n * contain the whole content of the individual pages, and sections, which only\n * contain the contents of the subsections obtained by breaking the individual\n * pages up at `h1` ... `h6`. As there may be many sections on different pages\n * with identical titles (for example within this very project, e.g. \"Usage\"\n * or \"Installation\"), they need to be put into the context of the containing\n * page. For this reason, section results are grouped within their respective\n * articles which are the top-level results that are returned.\n *\n * @param query - Query value\n *\n * @returns Search results\n */\n public search(query: string): SearchResult {\n if (query) {\n try {\n const highlight = this.highlight(query)\n\n /* Parse query to extract clauses for analysis */\n const clauses = parseSearchQuery(query)\n .filter(clause => (\n clause.presence !== lunr.Query.presence.PROHIBITED\n ))\n\n /* Perform search and post-process results */\n const groups = this.index.search(`${query}*`)\n\n /* Apply post-query boosts based on title and search query terms */\n .reduce((item, { ref, score, matchData }) => {\n const document = this.documents.get(ref)\n if (typeof document !== \"undefined\") {\n const { location, title, text, tags, parent } = document\n\n /* Compute and analyze search query terms */\n const terms = getSearchQueryTerms(\n clauses,\n Object.keys(matchData.metadata)\n )\n\n /* Highlight title and text and apply post-query boosts */\n const boost = +!parent + +Object.values(terms).every(t => t)\n item.push({\n location,\n title: highlight(title),\n text: highlight(text),\n ...tags && { tags: tags.map(highlight) },\n score: score * (1 + boost),\n terms\n })\n }\n return item\n }, [])\n\n /* Sort search results again after applying boosts */\n .sort((a, b) => b.score - a.score)\n\n /* Group search results by page */\n .reduce((items, result) => {\n const document = this.documents.get(result.location)\n if (typeof document !== \"undefined\") {\n const ref = \"parent\" in document\n ? document.parent!.location\n : document.location\n items.set(ref, [...items.get(ref) || [], result])\n }\n return items\n }, new Map())\n\n /* Generate search suggestions, if desired */\n let suggestions: string[] | undefined\n if (this.options.suggestions) {\n const titles = this.index.query(builder => {\n for (const clause of clauses)\n builder.term(clause.term, {\n fields: [\"title\"],\n presence: lunr.Query.presence.REQUIRED,\n wildcard: lunr.Query.wildcard.TRAILING\n })\n })\n\n /* Retrieve suggestions for best match */\n suggestions = titles.length\n ? Object.keys(titles[0].matchData.metadata)\n : []\n }\n\n /* Return items and suggestions */\n return {\n items: [...groups.values()],\n ...typeof suggestions !== \"undefined\" && { suggestions }\n }\n\n /* Log errors to console (for now) */\n } catch {\n console.warn(`Invalid query: ${query} \u2013 see https://bit.ly/2s3ChXG`)\n }\n }\n\n /* Return nothing in case of error or empty query */\n return { items: [] }\n }\n}\n"], + "mappings": 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\ No newline at end of file diff --git a/audio/audio_tts_coquitts_speech.wav b/audio/audio_tts_coquitts_speech.wav new file mode 100644 index 00000000..9889ee92 Binary files /dev/null and b/audio/audio_tts_coquitts_speech.wav differ diff --git a/audio/audio_tts_opentts_speech.wav b/audio/audio_tts_opentts_speech.wav new file mode 100644 index 00000000..f3856212 Binary files /dev/null and b/audio/audio_tts_opentts_speech.wav differ diff --git a/audio_intelligence/connectionist_temporal_classification/index.html b/audio_intelligence/connectionist_temporal_classification/index.html new file mode 100644 index 00000000..1dfce722 --- /dev/null +++ b/audio_intelligence/connectionist_temporal_classification/index.html @@ -0,0 +1,2203 @@ + + + + + + + + + + + + + + + + + + Connectionist Temporal Classification - A Lazy Data Science Guide + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    Connectionist Temporal Classification

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    Introduction

    +
      +
    • Connectionist Temporal Classification (CTC) is the algorithm to assign probability score to an output Y given any input X. The main advantage is that the size of X and Y do not have to match!
      • +
    • This makes CTC an ideal algorithm for use cases like speech recogition and handwriting recoginition where the input and output do not usually match.
      • +
    • Take the example of speech recognition. The input is usually a waveform of audio that could contains 16,000 samples per second (if sampling rate is 16kHz). But in a second, we hardly speak a word that could be 5-6 characters. So in ASR we are trying to map a large amount of input to much smaller sized output. This is just one of the use cases where CTC shines.
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    Message collapsing using CTC. Created using the tool available here

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    Understanding CTC

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    • To understand the CTC algorithm, we need to understand three aspects. Let's go through them one by one.
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    Collapsing

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    • In ASR, while the output is much smaller, we start with normal classification on the input. For this, we first divide the input into multiple equal sized tokens. For example, we can take 400 samples at a time (for 16kHz sampled audio that is 25ms of speech). Now we need to classify each one of these samples into characters available in the vocabulary. For a normal english language we will have all the alphanumeric characters plus some special tokens (not special characters) as the vocabulary.
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    Note

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    In ASR vocab, we do not put any of the special characters for which there is no sound. For example there is no sound for .. But we could identify ' in It's, so it could be there.

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      +
    • This will give us a long sequence of characters, and this is where collapsing logic of CTC comes into picture. The idea is to combine consecutive repetitive characters together. For example, hhhhiiiii could be simply written as hi.
      • +
    • Now we also want to handle two special cases, (1) there could be spaces between two words, (2) there could be multiple repetitive characters in a valid word like double l in hello. For these cases, we can add two special tokens in the vocab, say [BRK] and [SEP] respectively.
      • +
    • So a word like hello could be decoded if we get the classification output as hhelll[SEP]llo. This means for a complicated task like ASR, we can continue with a simple classification task at the output layer and later let CTC decoding logic handle the rest. But the next question is, "how can we teach model to predict these outputs?" šŸ¤”
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    Note

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    The overall collapsing algorithm is like this -- (1) First, collapse the consecutive characters, (2) Next remove any [SEP] tokens, and (3) Finally replace [BRK] tokens with space.

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    Relevant Paths

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    • For any given sample (time step), the output will be a probability distribution for each character in the vocab (imagine using softmax).
      • +
    • Suppose we only have 3 samples (three time steps) and 3 different characters in the vocab, then we can have \(3^3=27\) possible paths to choose from. An example is shown below where you could imagine paths (dashed lines) going from any left circle to every circle in the next time step.
      • +
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    One possible path for transcribing hi

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      +
    • One interesting property of CTC is that there could be multiple true possible paths. For CTC to transcribe hi any of the following will do -- hhi, hii, h[SEP]i, hi[SEP] or [SEP]hi. Hence the true relevant paths here are 5 out of all 27 available ones.
      • +
    • Looking from the perspective of training neural networks, we want to penalize the irrelevant paths and increase the probability of the relevant ones. This is done by two ways,
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      • We can train to increase the probability of the characters in the relevant paths at the respective time step. In our case, we can increase the probability of h and [SEP] at the 1st time step as these are the only available choices in set of relevant paths! This can be repeated for the other time steps as well. But this approach has one major con - it is training at time step level and not path level. So even if the probabilities at each step improves, output paths could still not be a relevant one.
        • +
      • Another approach is to consider the context of the path by using models like RNN that can compute per step wise probabilities wrt the overall path probability. We take product of probability of all steps in a relevant path ( \(\prod_{t=1}^{T}p_t (a_t | X)\) ) and then sum the path probabilities of the relevant paths ( \(\sum_{A\epsilon A_{X,Y}}^{}\) ). This gives us the CTC conditional probability, which we want to maximize.
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    \[ +P(Y|X) = \sum_{A\epsilon A_{X,Y}}^{}\prod_{t=1}^{T}p_t (a_t | X) +\]
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    Note

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    This interesting property of CTC helps us to train ASR model without perfect data annotations, where we will have to assign labels to each individual tokens of input. We just need the input audio stream and the expected output transcription and CTC loss takes care of the rest. In fact, famous deep learning frameworks like PyTorch has CTCLoss available for easy use!

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    Inference

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      +
    • Now after training the model, we want it to work during the inference. Here, we won't know the correct transcription or the relevant paths, so we will have to find one. For this we can employ a couple of approaches,
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      • The easiest approach is to go greedy! At each time step, we pick the token with the highest probability. But this could lead to suboptimal outputs as some time steps could have high probability but incorrect predictions. Also remember we trained the model to improve the summation of probabilty for all relevant paths. Now there could be a scenario where one irrelevant path (say [SEP]ih) has more probability than all individual paths, but the summation of two relevant paths are higher (say hi[SEP] and hii). Apart from this, as the predictions are at time step level, they are independent of context and this could lead to other issues like spelling mistakes and wrong grammer.
        • +
      • The next approach could be to use Beam search where we keep exploring top N paths, where N is the beam size. To handle the above problem, we can modify the beam search where before selecting the next paths to explore, we consider the summation of the explored paths so far by applying CTC collapsing. More details can be found here and an implementation is here.
        • +
      • Another approach could be to utilise a language model for decoding. This will take care of the spelling mistakes and grammer issues. For this we can either use n-gram language model or a neural language model. While neural language model is more powerful, it is more complicated to implement and will be much slower, and the comparitive gain in improvement wrt n-gram is not that much (Wav2vec2). There are several open source packages that can be utilised to create a language model like KenML and then use it for decoding with pyctcdecode.
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    Additional Materials

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    Interview Questions

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    • Here are some questions and their answers to make you ready for your next interview. Best of luck šŸ‘‹
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    What is the difference between Sample Rate, Bit Depth and Bit Rate?

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    • Sample rate is the number of audio samples recorded per unit of time. For example, an audio with 16kHz sample rate, means that for one second, we have captured 16000 samples.
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    • Bit Depth measures how precisely the samples were encoded. Here for a 16kHz sample rate audio, if the bit depth is 8 bits, it means we are using 8 bits of data to store each 16k samples per second.
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    • Bit rate is the amount of bits that are recorded per unit of time. For the above example, it means we have 16k * 8 bits of data per second i.e. 128kbps
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    What is the difference between Frame, Frame rate, Number of Channels and Sample size?

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    • Frame: one sample of the audio data per channel.
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    • Frame rate: the number of times per unit time the sound data is sampled. Same as sample rate.
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    • Number of channels: indicates if the audio is mono, stereo, or quadro.
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    • The sample size: the size of each sample in bytes.
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    What is the difference between i-vector, d-vector and x-vector?

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    • All of these are vector representation of the audio to capture the speaker information. Let's go through them,
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      • i-vector extraction is essentially a dimensionality reduction of the GMM supervector. Refer SO Question
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      • d-vector use the Long Short-Term Memory (LSTM) model to the process each individual frame (along with its context) to obtain a frame-level embedding, and average all the frame-level embeddings to obtain the segment-level embedding which can be used as the speaker embedding. Refer paper
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      • x-vector take a sliding window of frames as input, and it uses Time Delay Neural Networks (TDNN) to handle the context, to get the frame-level representation. It then has a statistics pooling layer to get the mean and sd of the frame-level embeddings. And then pass the mean and sd to a linear layer to get the segment-level embedding. Refer the original Paper, OxfordWaveResearch Slides and post on r/speechtech
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    Speaker Diarization

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    Introduction

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    • Speaker Diarization is the process of segregating different speakers from an audio stream. It is used to answer the question "who spoke when?". So if the input is a audio stream with 5 speakers, the output will contain the timestamp in audio when different speakers spoke.
      • +
    • A sample output for a conversation between 3 people (differers) could be,
      • +
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    start=0.2s stop=1.5s speaker_A
+start=1.8s stop=3.9s speaker_B
+start=4.2s stop=5.7s speaker_A
+start=6.2s stop=7.8s speaker_C
+...
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    Note

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    In speaker diarization we separate the speakers (cluster) and not identify them (classify). Hence the output contains anonymous identifiers like speaker_A, speaker_B, etc and not the actual names of the persons.

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    Traditional Diarization Approach

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    +

    The generic approach for Speaker Diarization [1]

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    • Traditional Speaker Diarization systems can be generalised into a 5 step process. These are,
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      • Feature extraction: here we transform the raw waveform into audio features like mel spectrogram.
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      • Voice activity detection: here we identify the chunks in the audio where some voice activity was observed. As we are not interested in silence and noise, we ignore those irrelevant chunks.
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      • Speaker change detection: here we identify the speaker changepoints in the conversation present in the audio. It is either capture by heuristic approach, classical algorithms or modern neural blocks. It will further divide the chunks from last step into subchunks.
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      • Speech turn representation: here we encode each subchunk by creating feature representations. Recent trends gives preference to neural approach where subchunks are encoded into context aware vector representation.
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      Hint

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      We can use any audio representation algorithm or model for this task. Recently d or x vectors are preferred. One example using speechbrain package is

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      # import
+import torch
+from speechbrain.pretrained import EncoderClassifier
+# load the model
+encoder = EncoderClassifier.from_hparams(source="speechbrain/spkrec-ecapa-voxceleb")
+# encode the audio chunk
+embedding = encoder.encode_batch(torch.from_numpy(chunk))
+
      +
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        +
      • Speech turn clustering: here we cluster the subchunks based on their vector representation. Different clustering algorithms could be applied based on availability of cluster count (k) and embedding process of the previous step.
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      Hint

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      • While Kmeans is the defacto clustering algorithm, it might create some problems if we use it for speaker diarization. Here are a few problem cases mentioned in [5]
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        • Non-gaussian data: speech data are often non-gaussian
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        • Imbalanced data: one person may speak more than the other
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        • Gender/Age/Racial effects: inter-gender difference is large but intra-gender differences are small.
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        • Overlapping speech: overlapping speech creates connections between clusters
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      • Kmeans algorithms will not be able to handle these issues of the audio data and will perform incorrect clustering. Hence we can use Spectral Clustering that can overcome all of the mentioned issues!
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    • The final output will be the clusters of different subchunks from the audio stream. Each cluster can be given an anonymous identifier (speaker_a, ..) and then it can be mapped with the audio stream to create the speaker aware audio timeline.
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    Metrics

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    Diarization Error Rate (DER)

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    • Diarization error rate (DER) provides an estimation (O is good, higher is bad; max may exceed 100%) of diarization performance by calculating the sum of following individual metrics, [2]
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      1. Speaker error: percentage of scored time for which the wrong speaker id is assigned within a speech region
        2. +
      2. False alarm speech: percentage of scored time for which a nonspeech region is incorrectly marked as containing speech
        3. +
      3. Missed speech: percentage of scored time for which a speech region is incorrectly marked as not containing speech
        4. +
      +
      • +
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    \[\text{DER} = \frac{(\text{Speaker error} + \text{False alarm speech} + \text{Missed speech})}{\text{Total Duration}}\]
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    +

    Note

    +

    Many literature may only report the speaker error in DER. Be aware of such usage.

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      +
    • To better understand the individual metrics we can refer an example call timeline that is shown below,
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    +

    +

    Example timeline of an audio with two speakers A and B. We have the original and predicted diarization timeline for the complete duration of call denoted in green for A and pink for B. For each segment, we have mapping of 0, 1, 2, 3 and tick for overlap, speaker error, false alarm, missed speech and correct segment respectively.

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    Note

    +

    Here we assumed that the mapping of speakers A to A and B to B between the original and predicted transcript is available. But this doesn't happen many times. As diarization is a clustering task, the grouping might be correct but the naming of the groups could be different. One example is where the speaking order in original could be A, B, A, C, D but in predicted it could be X, Y, X, Z, M. While the diarization is correct, the naming is different which should be handeled separately. For this, we can use Hungarian Algorithm to find the optimal map between the speakers in original and predicted before computing the DER.

    +
    +
    +

    Hint

    +

    Some packages like [1] and [2] may request the data (both original and predicted timeline) in Rich Transcription Time Marked (RTTM) file format. It is a space-delimited text file that contains one line for each segment with details like start time, duration and speaker name. Refer this for more details. Here is the code to convert RTTM file to CSV file using pandas:

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    def convert_rttm_to_csv(file):
+"""
+Inputs:
+file: str
+    file path of the rttm file to be converted
+
+Outputs:
+df: dataframe
+    Dataframe containing the extracted information from the rttm file
+"""
+# read the file
+df = pd.read_csv(file, delimiter=" ", header=None)
+df = df[[3, 4, 7]]
+df.columns = ['start_time', 'duration', 'speaker_name']
+# compute the end time
+df['end_time'] = df['start_time'] + df['duration']
+# convert time to miliseconds
+df['start_time'] *= 1000
+df['end_time'] *= 1000
+# sort the df based on the start_time
+df.sort_values(by=['start_time'], inplace=True)
+# return
+return df
+
    +
    +

    Jaccard Error Rate (JER)

    +
      +
    • While DER is estimated on whole utterance, in JER, per-speaker error rates are computed and then averaged. In this way, JER tries to give equal weightage to each speaker. Below is the formulation of JER,
      • +
    +
    \[\text{JER} = \frac{1}{N} \sum_{i}^{N_{ref}} \frac{(\text{Speaker Error}_i + \text{False alarm speech}_i + \text{Missed speech}_i)}{Total_i}\]
    +
      +
    • Here, \(Total_i\) is the union of ith speaker's speaking time in reference and predicted transcript. \(N_{ref}\) is the number of speakers in reference script. Note, contrary to DER, JER never exceed 100%.
      • +
    +

    Code

    +

    PyAnnote

    +
      +
    • Pyannote Audio provides readymade models and neural building blocks for Speaker diarization and other speech related tasks. While the models are also available on HuggingFace, Pyannote is super easy to use. Below is an example from the github repository of the package:
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    # Install pyannote
+!pip install pyannote.audio
+# for mac if you get "OSError: sndfile library not found"
+# !conda install -c conda-forge libsndfile 
+
+# instantiate pretrained speaker diarization pipeline
+from pyannote.audio import Pipeline
+pipeline = Pipeline.from_pretrained("pyannote/speaker-diarization")
+
+# apply pretrained pipeline
+diarization = pipeline("audio.wav")
+
+# print the result
+for turn, _, speaker in diarization.itertracks(yield_label=True):
+    print(f"start={turn.start:.1f}s stop={turn.end:.1f}s speaker_{speaker}")
+# start=0.2s stop=1.5s speaker_A
+# start=1.8s stop=3.9s speaker_B
+# start=4.2s stop=5.7s speaker_A
+# ...
+
    +
    +

    Hint

    +

    As per my observation, PyAnnote is quite slow on CPU. Keep this in mind while experimenting with the package.

    +
    +

    References

    +

    [1] PyAnnoteAudio - Code | Paper

    +

    [2] Dscore - Code

    +

    [3] A Review of Speaker Diarization: Recent Advances with Deep Learning - Paper

    +

    [4] Low-dimensional speech representation based on Factor Analysis and its applications - Slides

    +

    [5] Speaker Diarization with LSTM - Paper | Code | Video

    + + +
    + +
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    + +
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    + + + + + + + + + + + + + + \ No newline at end of file diff --git a/audio_intelligence/stt/index.html b/audio_intelligence/stt/index.html new file mode 100644 index 00000000..a86d0e77 --- /dev/null +++ b/audio_intelligence/stt/index.html @@ -0,0 +1,2382 @@ + + + + + + + + + + + + + + + + + + Speech to Text - A Lazy Data Science Guide + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + + +
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    + + + + + + + + +

    Speech to Text

    + +

    Introduction

    +
      +
    • Speech to text task is known by many names, be it Audio Transcription or Automatic Speech Recognition (ASR). Basically, it's the process of generating textual transcription of a conversation, where speech is provided as input and we get text as output.
      • +
    • The task is quite complex as it has depedency on several other modules like Voice activity detection and speaker diarization. On top of this, the core transcription module should be able to handle multiple languages, accents and domain specific keywords.
      • +
    • We can perform transcriptions in either of the two ways,
        +
      • Online transcription: this is for live use cases where you want to stream ongoing data and perform transcription on the go. For example, an ongoing sales or customer support call or news event, etc. Usually a smaller and faster (hence less accurate) model is used for this.
        • +
      • Offline transcription: this is delayed transcription that happens after the call or recording is over. For example, recorded telecast or OTT series's caption, etc. Usually a relatively bigger and more accurate (hence slower) model is used for this.
        • +
      +
      • +
    +
    +

    +

    a painting of a woman hand to ear listening to some speech and writing it down in a paper; (Generated by DallE)

    +
    +

    Available Solutions (Paid)

    +
      +
    • There are a lot of ASR services available online. In fact, there are several startups that only does ASR šŸ¤Æ. We will try to go through some paid as well as open source options for ASR, so that you are ready to use ASR from day zero using paid option or built an inhouse option using the open source ones.
      • +
    + +
    +

    Note

    +

    Please consider accuracy, additional features and cost before selecting any of the above services for your use case. The accuracy of each of the above services are arguably on par, but may vary based on specific domain. For example, one may perform better for healthcare domain, while the other on sales. Be sure to try them out before selecting one.

    +
    +
      +
    • There are several advantages of using paid ASR services,
        +
      • They take care of the major headache in the transcription space like support for multiple languages and accents. Their model is constantly fine-tuned to improve the performance on existing language and even to add support for new ones.
        • +
      • They take care of output formatting like adding punctuations, sentence breaks, speaker diarization etc. Otherwise for a inhouse ASR system, we will have to build these as well.
        • +
      • They take care of hosting the ASR models. Depending on the use case (for example if you want live transcription which needs to be super fast), we may end up spending substantially on GPU computes and hardware management.
        • +
      • Finally, there has been a trend of providing audio intelligence features on top of core transcription services. For example, AssemblyAI provides add on services like sentiment analysis, entity detection, Auto Chapters, etc.
        • +
      +
      • +
    • The one and the major con of these services, as you might have guessed, is cost šŸ’²
      • +
    +

    Available Solutions (Open Source)

    +
      +
    • Apart from the paid services, there are a lot of open source ASR services available. They are available in form of packages, frameworks or even trained AI models. One major advantage of these models (apart for being free) is that we can further finetune them for our usecase (ex: for our domain and accent). But this comes at a cost of having to deploy and maintain the complete system model ourself.
      • +
    • Some of the widely used open-source ASR systems (šŸ“¦ is package and šŸ§  is model) are, +
      • +
    +

    Evaluation Metrics

    +
      +
    • While there are multiple options for ASR, it is important to know how to quantitively compare different ASR systems. To begin, we need the data -- (1) golden transcripts i.e. the source of truth generated by some accurate model or human annotators, and (2) system outputs i.e. output of the model you want to test.
      • +
    • Comparing golden transcripts against system output, at word level, we can observe following scenarios -- (1) hit (\(H\)): word matching in both, (2) insertion (\(I\)): words only present in system output but not in golden transcripts, (3) deletion (\(D\)): words only present in golden transcripts but not in system output, (4) substitution (\(S\)): words substitued in the system output.
      • +
    +
    +

    +

    Word classification of golden transcription (below) ā€œmy computerā€™s deaf inā€™he?ā€ against the system output (above) [1]

    +
    +
      +
    • To evaluate the performance, first we need to align the two sentences such that it minimises the errors i.e \(S+D+I\). For this Viterbi alignment is the preferred choice. Once done, we just need to use any metric to quantify the performance. They could be,
      • +
    +

    WER (Word error rate)

    +
      +
    • WER is the proportion of the word errors to the words processed. It's formula is as follows,
      • +
    +
    \[WER = \frac{S+D+I}{H+S+D}\]
    +
    +

    Note

    +

    The upper range of WER is not bounded by 1, but by \(max(N_g, N_s)/N_g\) where \(N_g\) is the number of words in the golden transcription and \(N_s\) is the number of words in the system output. So don't be suprised if you get WER > 1, it just means that the system output has a lot more insertions than required (which is bad btw āŒ)

    +
    +

    MER (Match error rate)

    +
      +
    • MER normalizes the WER by considering the insertions in the denominator. It's formula is as follows,
      • +
    +
    \[MER = \frac{S+D+I}{H+S+D+I}\]
    +
    +

    Hint

    +

    If for some transcription, \(MER = WER\), it means there are 0 insertions.

    +
    +

    Code

    +
      +
    • Recent advances in artificial intelligence and specially transfer learning has lead to release of several pre-trained ASR models that are ready to use from day one. In case you want to improve the accuracy for your domain, we can even fine tune the model further!
      • +
    • One of the most famous models right now is Facebook's Wav2Vec2. We will use this model for coding purposes. More models can be found here.
      • +
    +

    Offline transcription using Wav2Vec2 (CTC)

    +
      +
    • Here is the code to perform offline transcription using Wav2Vec2 model with transformer package.
      • +
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    # import 
+import torch
+import librosa
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Tokenizer
+
+# load the tokenizer and model
+tokenizer = Wav2Vec2Tokenizer.from_pretrained("facebook/wav2vec2-large-960h")
+model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h")
+
+# load the audio data (use your own wav file here!)
+input_audio, sr = librosa.load('my_wav_file.wav', sr=16000)
+
+# tokenize
+input_values = tokenizer(input_audio, return_tensors="pt", padding="longest").input_values
+
+# retrieve logits
+logits = model(input_values).logits
+
+# take argmax and decode
+predicted_ids = torch.argmax(logits, dim=-1)
+transcription = tokenizer.batch_decode(predicted_ids)
+
+# print the output
+print(transcription)
+
    +

    Online transcription using Wav2Vec2 (CTC)

    +
      +
    • For live transcription using Wav2Vec2, we can utilize wav2vec2-live package.
      • +
    • Once you have cloned the repo and installed the packages from requirements.txt, the live transcription can be started with (taken from the package readme and modified),
      • +
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    # import
+from live_asr import LiveWav2Vec2
+
+# load model
+english_model = "facebook/wav2vec2-large-960h-lv60-self"
+asr = LiveWav2Vec2(english_model,device_name="default")
+
+# start the live ASR
+asr.start()
+
+try:        
+    while True:
+        text,sample_length,inference_time = asr.get_last_text()                        
+        print(f"Duration: {sample_length:.3f}s\tSpeed: {inference_time:.3f}s\t{text}")
+
+except KeyboardInterrupt:   
+    asr.stop()  
+
    +
      +
    • This starts the Live ASR on your terminal. The code listen to the audio in your microphone, identifies the chunks with voice using VAD and then pass the voiced chunks to Wave2Vec2 for transcription. One sample output is shown below,
      • +
    +
    listening to your voice
+
+Duration: 0.780s    Speed: 0.205s   hello
+Duration: 0.780s    Speed: 0.190s   hello
+Duration: 0.960s    Speed: 0.223s   my name
+....
+
    +

    References

    +

    [1] From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition

    +

    Cheers šŸ‘‹

    + + +
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    +
    + + + + + + + + + + + + + + \ No newline at end of file diff --git a/audio_intelligence/tts/index.html b/audio_intelligence/tts/index.html new file mode 100644 index 00000000..87667b83 --- /dev/null +++ b/audio_intelligence/tts/index.html @@ -0,0 +1,2288 @@ + + + + + + + + + + + + + + + + + + Text to Speech - A Lazy Data Science Guide + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + + +
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    + + + + + + + + +

    Text to Speech

    + +

    Introduction

    +
      +
    • Text to Speech (TTS) is the process of generating synthesized speech for a given text input. This is a complicated task as the generating system has to consider context-based pronunciations, tone, rhythm, language, accent, etc. That said, recent research has achieved significant improvement in overall performance.
      • +
    • Speech synthesis is quite old; in fact, Wolfgang von Kempelen, a Hungarian scientist, constructed a speaking machine with a series of bellows, springs, and bagpipes in the second half of the 18th century.
      • +
    • Before moving forward, letā€™s get accustomed to some basic terms related to speech,
        +
      • Phoneme: It is the smallest unit of speech. In English language there are a total of 44 phonemes.
        • +
      • Grapheme: Group of letters that represent speech. Count is 250
        • +
      • Syllable: Combination of phonemes to create an intelligent pattern of speech. Count is ~15k
        • +
      +
      • +
    +
    +

    +

    Wolfgang von Kempelen ... second half of the 18th century (Created by Stable Diffusion 2.1)

    +
    +

    Types of TTS

    +
      +
    • Let's start with a quick detour of different important types of TTS systems, [1]
        +
      • Concatenative Synthesis: Concatenative synthesis is based on combining pieces of speech stored in a database. This database usually contains speech units ranging from full sentences to individual syllables, all recorded by voice actors. There are two major problems with this approach, (a) we require a huge database of recordings (which is nearly impossible if you consider all combinations), and (b) the generated voice is not that smooth.
        • +
      • Statistical Parametric Speech Synthesis: SPSS usually consists of three components: a text analysis module, a parameter prediction module (acoustic model), and a vocoder analysis/synthesis module (vocoder). The text analysis module first processes the text, and then extracts the linguistic features, such as phonemes, duration and POS tags from different granularities. The acoustic model process the linguistic features to generate acoustic features which is then processed by the vocoder to generate the waveform.
        • +
      • Neural Speech Synthesis: Recent enhancements in Neural Network (NN) based approaches have led to the use of NN in TTS. Usually, these models takes either of the two approaches, (a) replace one or many of the SPSS components with respective NN models, or (b) use an end-to-end NN based model that replaces all SPSS components with one NN.
        • +
      +
      • +
    +

    Components of TTS

    +
      +
    • Next, letā€™s discuss the components of the SPSS in more detail as they are the common denominator in TTS systems. We will go through them one by one. [1]
        +
      • Text analysis: It is used to extract linguistic features from the raw text data. Some common tasks are,
          +
        • Text normalization: We can start with converting non-standard words into spoken forms which can make the words easier to pronounce for the TTS models. Ex: year ā€œ1989ā€ into ā€œnineteen eighty-nineā€
          • +
        • Word Segmentation: Identifying different words in a text seems trivial in alphabet-based languages like English (use spaces) but it is quite tedious in character-based languages like Chinese.
          • +
        • POS tagging: Identifying part of speech (like noun, adjective, etc) is important as different words have different pronunciations based on where and how they are used.
          • +
        • Prosody prediction: Rhythm, stress, intonation corresponds to variations in syllable duration, loudness and pitch. This plays an important part on how human-like the speech is. Prosody prediction used tagging system to label each kind of prosody and ToBI (tagging and break indices) is a popular tagging system for English.
          • +
        • Grapheme to Phoneme Conversion: Converting characters to pronunciation can greatly help with the speech synthesis process. Ex: ā€œspeechā€ is converted to ā€œs p iy chā€.
          • +
        +
        • +
      • Acoustic Models: They generate acoustic features from linguistics features or directly from phonemes or characters. While there are several models used in SPSS systems, letā€™s focus on NN based approaches.
          +
        • RNN-based models: Tacotron leverages an encoder-attention-decoder framework and takes characters as input and outputs linear-spectrograms, and uses Griffin Lim algorithm to generate waveform. Tacotron 2 generates mel-spectrograms +and converts them into waveform using an additional WaveNet model.
          • +
        • CNN-based models: DeepVoice utilises convolutional neural networks to obtain linguistic features. Then it leverages a WaveNet based vocoder to generate waveform. DeepVoice 2 introduced multi-speaker modeling. DeepVoice 3 leverages a fully-convolutional network structure for speech synthesis, which generates mel-spectrograms from characters and can scale up to real-word multi-speaker datasets.
          • +
        • Transformers-based models: TransformerTTS leverage transformer based encoder-attention-decoder architecture to generate mel-spectrogram form phonemes. It tackles two flaws of RNN, (a) RNN based encoder and decoder cannot be trained in parallel due to their recurrent nature, and (b) RNN is not good for long generations. While the voice quality is on par with Tacotron, the generations are not that robust (ex: same word repeating multiple times or missing some words). FastSpeech mitigated the issues by adopting fast-forward Transformer network and removing the attention mechanism between text and speech. (It is deployed in AzureTTS services). FastSpeech 2 further improves the overall performance.
          • +
        +
        • +
      • Vocoder: Early neural vocoders such as WaveNet, Char2Wav, WaveRNN directly take linguistic features as input and generate waveform. Later versions take mel-spectrograms as input and generate waveform. Since speech waveform is very long, autoregressive waveform generation takes much inference time. Thus, generative models such as Flow, GAN, VAE, and DDPM (Denoising Diffusion Probabilistic Model, Diffusion for short) are used in waveform generation.
        • +
      +
      • +
    +
    +

    +

    Different process of TTS Systems. Source [1]

    +
    +

    Code

    + +

    Coqui TTS

    +
      +
    • For this tutorial, let's use Coqui TTS as it is one of the simplest package in terms of usability. In fact you just need to install the package with pip install TTS and then run the server with tts-server, and thats it! It will run a http dashboard on the localhost woth default model and vocoder like shown below,
      • +
    +
    +

    +

    Coqui TTS server dashboard

    +
    +
      +
    • I tried it for "my name is Mohit" text and the result is shared below. Btw you can switch to different models or speakers to get different sounding speech.
      • +
    +

    +
      +
    • You can check out other models and vocoder available in the package with tts-server --list_models. Note, not all models and vocoder pairs are comparable. On top of this, Coqui TTS also provides the option to train and finetune the models further!
      • +
    +

    OpenTTS

    +
      +
    • Another good package is OpenTTS that unifies access to multiple open source text to speech systems and voices for many languages.
      • +
    • One distinctive feature is the partial support to SSML i.e. Speech Synthesis Markup Language. It is a XML-based markup language for assisting the generation of synthetic speech in Web and other applications. One example as shared in their readme is shown below,
      • +
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    <speak>
+  The 1st thing to remember is that 27 languages are supported in Open TTS as of 10/13/2021 at 3pm.
+
+  <voice name="glow-speak:en-us_mary_ann">
+    <s>
+      The current voice can be changed, even to a different text to speech system!
+    </s>
+  </voice>
+
+  <voice name="coqui-tts:en_vctk#p228">
+    <s>Breaks are possible</s>
+    <break time="0.5s" />
+    <s>between sentences.</s>
+  </voice>
+
+  <s lang="en">
+    One language is never enough
+  </s>
+  <s lang="de">
+   Eine Sprache ist niemals genug
+  </s>
+  <s lang="ja">
+    č؀čŖžć‚’äø€ć¤ćÆę±ŗć—ć¦č¶³ć‚ŠćŖ恄
+  </s>
+  <s lang="sw">
+    Lugha moja haitoshi
+  </s>
+</speak>
+
    +
      +
    • SSML support can lead to generation of complex and realistic sound as you can add sentence breaks, pauses, handle spelling out of numbers or date, change model or even languages for a single generation!
      • +
    • The package is quite simple to run. First you need to install docker, and then download and run a docker image with docker run -it -p 5500:5500 synesthesiam/opentts:<LANGUAGE>, where <LANGUAGE> could be any of the 20 supported langauge. To begin with you can try en.
      • +
    • The downloading will take some time (more than 5GB is downloaded!) but once done, you can access the dashboard on http://localhost:5500 or hit the HTTP APIs on http://localhost:5500/openapi/. The endpoint details can be found here and the complete list of voices generated by the available models is shared here.
      • +
    +
    +

    +

    Dashboard of OpenTTS

    +
    +
      +
    • I tried it for text "Hello, how are you? My number is 7350." by selecting the coqui-tts: vctk model and ED (0) speaker. The output is quite good and shared below,
      • +
    +

    +

    Additional Materials

    +

    [1] Paper - A Survey on Neural Speech Synthesis

    +

    [2] Speech synthesis: A review of the best text to speech architectures with Deep Learning

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    + + + + + + + + + + + + + + \ No newline at end of file diff --git a/audio_intelligence/voice_activity_detection/index.html b/audio_intelligence/voice_activity_detection/index.html new file mode 100644 index 00000000..f4904cd5 --- /dev/null +++ b/audio_intelligence/voice_activity_detection/index.html @@ -0,0 +1,2273 @@ + + + + + + + + + + + + + + + + + + Voice Activity Detection - A Lazy Data Science Guide + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    Voice Activity Detection

    + +

    Introduction

    +
      +
    • šŸ—£ Voice activity detection (VAD) is the process of identifying the chunks or parts of an audio stream that contains certain "voiced activities".
      • +
    • There could be different types of activity detection modules depending on the type of voice we want to identify. It could be human voice (in a conversation) or animal voice (in forest) or something else entirely!
      • +
    +
    +

    +

    a drawing of head of human, dog and cat speaking something Created using DallE

    +
    +

    Steps in VAD

    +
      +
    • The complete VAD process can be broken down to two simple steps,
        +
      • Step 1: we start with dividing the audio into multiple chunks of small sizes. Usually these chunks are quite small like 10ms, 20ms or 30ms.
        • +
      • Step 2: we have a classifier or detector, that takes the chunk as input and predicts if the chunk has voice or not. The classifier could be a simple logic based algorithm or even neural network models. It depends on the acceptable tradeoff between accuracy and speed.
        • +
      +
      • +
    +

    Code

    +

    Py-WebRTC VAD

    +
      +
    • For practice, we will use Py-WebRTC VAD package that is a port to the WebRTC project by Google. It provides sufficiently good accuracy with lightening speed! āš”ļø The complete code is shown below,
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    # Python 3.8 on Macbook Pro M1 2020
+# import
+import struct 
+import librosa # librosa==0.9.1
+import webrtcvad # webrtcvad==2.0.10
+
+# load data
+file_path = 'hello_my_name_is_mohit.wav'
+
+# load wav file (librosa)
+y, sr = librosa.load(file_path, sr=16000)
+# convert the file to int if it is in float (Py-WebRTC requirement)
+if y.dtype.kind == 'f':
+    # convert to int16
+    y = np.array([ int(s*32768) for s in y])
+    # bound
+    y[y > 32767] = 32767
+    y[y < -32768] = -32768
+
+# create raw sample in bit
+raw_samples = struct.pack("%dh" % len(y), *y)
+
+# define webrtcvad VAD
+vad = webrtcvad.Vad(3) # set aggressiveness from 0 to 3
+window_duration = 0.03 # duration in seconds
+samples_per_window = int(window_duration * sr + 0.5)
+bytes_per_sample = 2 # for int16
+
+# Start classifying chunks of samples
+# var to hold segment wise report
+segments = []
+# iterate over the audio samples
+for i, start in enumerate(np.arange(0, len(y), samples_per_window)):
+    stop = min(start + samples_per_window, len(y))
+    loc_raw_sample = raw_samples[start * bytes_per_sample: stop * bytes_per_sample]
+    try:
+        is_speech = vad.is_speech(loc_raw_sample, 
+                              sample_rate = sr)
+        segments.append(dict(
+                start = start,
+                stop = stop,
+                is_speech = is_speech))
+    except Exception as e:
+        print(f"Failed for step {i}, reason: {e}")
+
+# import pandas as pd
+# pd.DataFrame(segments) # result of classification
+
    +
      +
    • Let's go through the code,
        +
      • Line 3-5: we import the required packages. Make sure to install them using pip before starting. In case you are facing some issue, please install the specificed python and package versions (mentioned in the code).
        • +
      • Line 7- 18: we read a sample wav file (use your own šŸ˜„) and then transform the bit depth of the audio into int16. One point to note here is that webrtcvad only works for sample rate = 16000 and bit depth = int16. And librosa loads an audio file in float. Because of all this requirement we need to perform the transformations.
        • +
      • Line 21: we transform the numpy array (format in which an audio file is loaded in librosa) to byte string. This will be required for chunking and VAD analysis.
        • +
      • Line 24-27: we initialize an instance of webrtcvad with aggressiveness parameter. Note, the range is form 0 to 3, and higher the value, the more strict VAD is in classification chunks as voice. This means, you can miss some relevant voice chunks for higher aggressiveness and on the other hand get some false positives with lower aggressiveness.
        • +
      • +

        Line 31-45: the code to first create chunks of the audio and then perform VAD classification at line 37-38. The final results is stored in segments variable and a sample output is shown below,

        + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
        startstopis_speech
        0480True
        480960True
        9601440False
        14401920False
        19202400False
        +

        Here, each row denotes one chunk. The start and stop columns contain the begin and end details of each chunk. Finally the is_speech column contains True or False value depedening on if the chunk was detected as voice chunk or not.

        +
        • +
      +
      • +
    +
      +
    • Here is the code's output visualized in form of waveform with vice chunks highlighted.
      • +
    +
    +

    +

    Waveform of audio with webrtcvad detected voice chunks highlighted with yellow line on top. The aggressiveness parameter value was 0, hence lot's of false positive (chunks with no voice) are detected as well.

    +
    +
    +

    +

    Same as above, but with aggressiveness parameter value set to 3. Hence the detection is quite strict (some voice parts are missed).

    +
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    + + + + + + + + + + + + + + \ No newline at end of file diff --git a/audio_intelligence/wav2vec2/index.html b/audio_intelligence/wav2vec2/index.html new file mode 100644 index 00000000..d40bfbbd --- /dev/null +++ b/audio_intelligence/wav2vec2/index.html @@ -0,0 +1,2456 @@ + + + + + + + + + + + + + + + + + + Wav2Vec2 - A Lazy Data Science Guide + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
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    + + +
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    + + + + + + + + +

    Wav2Vec2

    + +

    Introduction

    +
      +
    • Wav2Vec is a framework for self-supervised learning of representations from raw audio data. Basically it learns to efficiently represent the raw audio data as a vector space encoding.
      • +
    +
    +

    +

    Illustration of the Wav2vec2 framework (Wav2vec2 paper)

    +
    +
      +
    • A major advantage of this approach is that we end up training a generic audio model that could be used for multiple downtream tasks! And because of the self-supervised learning, we don't need access to huge amount of labeled data. In the paper, after pre-training on unlabeled speech, the model is fine-tuned on small labeled data with a Connectionist Temporal Classification (CTC) loss for speech recognition task.
      • +
    +

    Architecture

    +
      +
    • The complete architecture of the framework can be divided into 3 components, they are
        +
      • Feature encoder: This is the encoder part of the model. It takes the raw audio data as input and outputs feature vectors. Input size is limited to 400 samples which is 20ms for 16kHz sample rate. The raw audio is first standardized to have zero mean and unit variance. Then it is passed to 1D convolutional neural network (temporal convolution) followed by layer normalization and GELU activation function. There could be 7 such convolution blocks with constant channel size (512), decreasing kernel width (10, 3x4, 2x2) and stride (5, 2x6). The output is list of feature vectors each with 512 dimensions.
        • +
      • Transformers: The output of the feature encoder is passed on to a transformer layer. One differentiator is use of relative positional embedding by using convolution layers, rather than using fixed positional encoding as done in original Transformers paper. The block size differs, as 12 transformers block with model dimension of 768 is used in BASE model but 24 blocks with 1024 dimension in LARGE version.
        • +
      • Quantization module: For self-supervised learning, we need to work with discrete outputs. For this, there is a quantization module that converts the continous vector output to discrete representations, and on top of it, it automatically learns the discete speech units. This is done by maintaining multiple codebooks/groups (320 in size) and the units are sampled from each codebook are later concatenated (320x320=102400 possiblt speech units). The sampling is done using Gumbel-Softmax which is like argmax but differentiable.
        • +
      +
      • +
    +

    Training

    +
      +
    • To pre-train the model, Wav2Vec2 masks certain portions of time steps in the feature encoder which is similar to masked language model. The aim is to teach the model to predict the correct qunatized latent audio representation in a set of distractors for each time step.
      • +
    • The overall training objective is to minimize contrastive loss (\(L_m\)) and diversity loss (\(L_d\)) in \(L = L_m + \alpha L_d\). Contrastive loss is the performance on the self-supervised task. Diversity loss is designed to increase the use of the complete quantized codebook representations, and not only a select subset.
      • +
    • For pretraining, the datasets used were (1) Librispeech corpus with 960 hours of audio data, (2) LibriVox 60k hours of audio data that was later subset to 53.2k hours. Only unlabeled data was used for pretraining.
      • +
    • To make the model more robust to different tasks, we can finetune the model on a different task specific modifications and dataset. Here, the paper finetuned for ASR by adding a randomly initialized classification layer on top on Transformer layer with class size equal to the size of vocab. The model is optimized by minimizing the CTC loss.
      • +
    • Adam was used as optimization algorithm and the learning rate is warmed up till 10% of the training duration, then kept constant for next 40% and finally linearly decayed for the remaining duration. Also, for the first 60k updates only output classifier was trained after which Transformer is also updated. The feature encoder is kept frozen (not trained at all).
      • +
    +

    Results

    +
      +
    • There are two interesting points to note from the results of the Wav2Vec2 model,
        +
      • The model is able to learn ASR with as minimum as 10 mins of labeled data! As shown below, \(LARGE\) model pre-trained on LV-60k and finetuned on Librispeech with CTC loss is giving 4.6/7.9 WER! This is a very good news incase you want to finetune the model for your domain or accent!
        • +
      • The choice of decoder can lead to improvement in performance. As obvious from the results, Transformer decoder is giving best performance, followed by n-gram and then CTC decoding. But also note that the CTC decoding will gives the best inference speed. The suggested decoder could be 4-gram, as it provides huge improvement in performance by fixing the spellling mistakes and grammer issues of CTC and is still faster than Transformer decoders.
        • +
      +
      • +
    +
    +

    +

    WER on Librispeech dev/test data (Wav2vec2 paper)

    +
    +

    Code

    +

    Offline transcription using Wav2Vec2 (CTC)

    +
      +
    • Here is the code to perform offline transcription using Wav2Vec2 model with transformer package. Note the default decoder is CTC.
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    # import 
+import torch
+import librosa
+from transformers import Wav2Vec2ForCTC, Wav2Vec2Tokenizer
+
+# load the tokenizer and model
+tokenizer = Wav2Vec2Tokenizer.from_pretrained("facebook/wav2vec2-large-960h")
+model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h")
+
+# load the audio data (use your own wav file here!)
+input_audio, sr = librosa.load('my_wav_file.wav', sr=16000)
+
+# tokenize
+input_values = tokenizer(input_audio, return_tensors="pt", padding="longest").input_values
+
+# retrieve logits
+logits = model(input_values).logits
+
+# take argmax and decode
+predicted_ids = torch.argmax(logits, dim=-1)
+transcription = tokenizer.batch_decode(predicted_ids)
+
+# print the output
+print(transcription)
+
    +

    Offline transcription using Wav2Vec2 (N-gram)

    +
      +
    • We can also use n-gram language model as decoder using a pre-trained model available in Huggingface. The usage is very similar to the CTC model, we just have to change the model name. Note, this downloads the Wav2Vec2 model plus the N-gram language model which will be around 3.2 GBs!
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    # install dependencies
+!pip install pyctcdecode pypi-kenlm
+# import
+import librosa
+from transformers import Wav2Vec2ProcessorWithLM, Wav2Vec2ForCTC
+
+# load the processor
+processor = Wav2Vec2ProcessorWithLM.from_pretrained("patrickvonplaten/wav2vec2-base-100h-with-lm")
+model = Wav2Vec2ForCTC.from_pretrained("facebook/wav2vec2-large-960h")
+
+# load the audio data (use your own wav file here!)
+input_audio, sr = librosa.load('my_wav_file.wav', sr=16000)
+
+# tokenize
+input_values = processor(input_audio, return_tensors="pt", padding="longest").input_values
+
+# retrieve logits
+logits = model(input_values).logits
+
+# decode using n-gram
+transcription = processor.batch_decode(logits.detach().numpy()).text
+
+# print the output
+print(transcription)
+
    +

    Creating your own N-gram language model for Word2Vec2

    +
      +
    • +

      To use n-gram model we can KenML to create language model and then use pyctcdecode for decoding. This part is referenced from Huggingface blog on Wav2vec2 with n-gram. The steps are as follows,

      +
        +
      • First, we will select one text dataset. This dataset can be the transcript of train data (part of labeled data we used to finetune Wav2Vec2 model) or a related (same domain like medical, telecom, etc) collection of documents.
        • +
      • +

        Next we can perform data cleaning like removing special characters and then combine the individual sentences to a free flow text and save that into text file. After this we can run KenML to create a language model.

        +
        kenlm/build/bin/lmplz -o 3 <"text.txt" > "3-gram.arpa"
+
        +
        • +
      +
        +
      • The .arpa file contains the n-gram language model that is ready to go with just two minor modifications. As per the Huggingface blog, we need to add </s> end of sentence token as 1 gram as well, so we open the arpa file, duplicate the existing <s> start of sentence token, and just replace the <s> with </s>. Next we also increment the count of 1-gram (present at the top of the .arpa file) by 1, because of what we just did. Then we save the file.
        • +
      +
        +
      • Next, we load the a LM-less model and then we can use the pyctcdecode.
        • +
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    # Taken from Blog @ https://huggingface.co/blog/wav2vec2-with-ngram
+# import packages
+from transformers import AutoProcessor
+from pyctcdecode import build_ctcdecoder
+from transformers import Wav2Vec2ProcessorWithLM
+
+# load a LM-less model
+processor = AutoProcessor.from_pretrained("hf-test/xls-r-300m-sv")
+
+# get the vocabulary of the tokenizer
+vocab_dict = processor.tokenizer.get_vocab()
+sorted_vocab_dict = {k.lower(): v for k, v in sorted(vocab_dict.items(), key=lambda item: item[1])}
+
+# build the decoder
+decoder = build_ctcdecoder(
+    labels=list(sorted_vocab_dict.keys()),
+    kenlm_model_path="3-gram.arpa",
+)
+
+# create a processor with the decoder
+processor_with_lm = Wav2Vec2ProcessorWithLM(
+    feature_extractor=processor.feature_extractor,
+    tokenizer=processor.tokenizer,
+    decoder=decoder
+)
+
+# now the processor can be used for inference as shown in other above code sections.
+
    +
      +
    • We can even reduce the size of the LM-model by converting it to a binary file.
      • +
    +
    kenlm/build/bin/build_binary 3-gram.arpa 3-gram.bin
+
    +

    Online transcription using Wav2Vec2

    +
      +
    • For live transcription using Wav2Vec2, we can utilize wav2vec2-live package.
      • +
    • Once you have cloned the repo and installed the packages from requirements.txt, the live transcription can be started with (taken from the package readme and modified),
      • +
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    # import
+from live_asr import LiveWav2Vec2
+
+# load model
+english_model = "facebook/wav2vec2-large-960h-lv60-self"
+asr = LiveWav2Vec2(english_model,device_name="default")
+
+# start the live ASR
+asr.start()
+
+try:        
+    while True:
+        text,sample_length,inference_time = asr.get_last_text()                        
+        print(f"Duration: {sample_length:.3f}s\tSpeed: {inference_time:.3f}s\t{text}")
+
+except KeyboardInterrupt:   
+    asr.stop()  
+
    +
      +
    • This starts the Live ASR on your terminal. The code listen to the audio in your microphone, identifies the chunks with voice using VAD and then pass the voiced chunks to Wave2Vec2 for transcription. One sample output is shown below,
      • +
    +
    listening to your voice
+
+Duration: 0.780s    Speed: 0.205s   hello
+Duration: 0.780s    Speed: 0.190s   hello
+Duration: 0.960s    Speed: 0.223s   my name
+....
+
    +

    Additional Materials

    + + + +
    + +
    +
    + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + \ No newline at end of file diff --git a/audio_intelligence/whisper/index.html b/audio_intelligence/whisper/index.html new file mode 100644 index 00000000..db803cd3 --- /dev/null +++ b/audio_intelligence/whisper/index.html @@ -0,0 +1,2490 @@ + + + + + + + + + + + + + + + + + + Whisper - A Lazy Data Science Guide + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
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    + + +
    +
    + + + + + + + + +

    Whisper

    + +

    Introduction

    +
      +
    • Whisper is an open source multi-task audio model released by OpenAI. It is an ASR system that works on 97 different languages (including english) and can even perform translation from other languages to english.
      • +
    • The model was trained on 680,000 hours of multilingual and multitask data collected from the web. Whisper was trained using large scale weak supervision.
      • +
    • Here is an interesting perspective for weak supervision training,
        +
      • There are 3 types of data and training strategies - (1) golden standard data for supervised training, (2) silver standard data for weakly supervised training, and (3) unlabelled data for unsupervised training.
        • +
      • Now, it is difficult to get golden dataset due to human involvement which is costly and time consuming. And model trained on unlabelled datatset with unsupervised strategy lead to mediocre decoder part that needs further finetuning for downstream tasks.
        • +
      • This gives the silver standard dataset a huge advantage, as it is the middle ground with large size and high accuracy.
        • +
      +
      • +
    +
    +

    Note

    +

    As huge portion of silver standard dataset might have had no humans verfication, there is always a room for faulty data. Hence the name - weak supervision.

    +
    +

    Dataset

    +
      +
    • Author scraped the internet to collect huge and diverse transcription dataset. As this may also introduce noise, extra care was taken in pre-processing step to clean the data.
      • +
    • The intention was to only consider human annotated data, for this any audio-text pair that "seems" like machine generated was removed. For this, they removed normalised transcriptions (only upper case, only lower case, lack of punctuations, etc) as they are most likely machine generated (no human writes like that). They even trained language detection models to make sure that there is no mis-match in the audio and text pair's language. Finally, de-duplication was also done.
      • +
    • With this, overall 680,000 hours was dataset was collected. The breakdown is as follows,
        +
      • 117,000 hours of non-english 96 different language data.
        • +
      • 125,000 hours of X-->en translation data.
        • +
      • 438,000 hours of english transcription data.
        • +
      +
      • +
    • Every audio files was resampled at 16,000 Hz and broken in 30 secs chunks to be passed to model for training. Transcription were also broken in the same chunk size respectively.
      • +
    +
    +

    Note

    +

    The training dataset was not released by OpenAI. But they have open sourced the code and the pretrained models. Evaluation dataset details are shared here

    +
    +

    Architecture

    +
      +
    • Authors picked the Transformer model as it has been widely used since its inception in 2017 and it scales reliably. The audio chunk is first converted into 80-channel log-magnitude Mel spectrogram with 25ms window and stride of 10ms. The features are scaled between -1 and 1 with zero mean across the dataset.
      • +
    +
    +

    +

    Transformer inspired Whisper model [1]

    +
    +
      +
    • The input is first passed to two convolution layers with a filter width of 3 and GELU activation function. Sinusoidal position embeddings are added to the output and it is then passed to the encoder block of Transformer. The decoder block uses learned positional embedding and uses multiple cross-attention layers to apply encoder output. BPE text tokenizer was used like GPT-2.
      • +
    • The decoder utilises multiple special tokens to facilitate the multi-task output generation. They are,
        +
      • <|startoftranscript|> to denote start of prediction
        • +
      • <|nospeech|> to denote silence or even non-spoken voices (ex: background noise and music)
        • +
      • <|transcribe|> to denote 'transcription' task
        • +
      • <|translation|> to denote 'translation' task
        • +
      • <|notimestamps|> to denote absence of timestamps
        • +
      • <|endoftranscript|> to denote end of prediction
        • +
      +
      • +
    +
    +

    +

    Overview of Whisper [1]

    +
    +

    Results

    +
      +
    • The performance of whisper model is very good. On comparing with wav2vec2 large 960h model, whisper large models makes 55% less errors on average. This is huge! In fact in some cases, even the tiny model performs better than older large models!
      • +
    +
    +

    +

    Comparison of Whisper on various datasets [1]

    +
    +
    +

    Warning

    +

    Claims on bad performance of Whisper was made in this Twitter thread. Here, Ryan Hileman compared Whisper with NVIDIA and Talon model on several datasets to find Talon performing well against Whisper models. Also, he noted that Whisper models are quite slow in execution (even the tiny model). Finally, he provided samples for "catastrophic failures" of Whisper of following types -- (1) generating complete paragraphs for single word audio input (like dec, car, two, snake, other), (2) hallucinations and repetition in the output.

    +

    Anyone thinking of using Whisper for their project should consider these concerns and test them out themselves before deployment.

    +
    +

    Released Models

    +
      +
    • Authors released 5 variety of models based on size, going from 39M param tiny model to 1550M param large model. For each there are is an english only model {size}.en (ex: tiny.en) and a multilingual model {size} (ex: tiny).
      • +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    SizeParametersEnglish-only modelMultilingual modelRequired VRAMRelative speed
    tiny39 Mtiny.entiny~1 GB~32x
    base74 Mbase.enbase~1 GB~16x
    small244 Msmall.ensmall~2 GB~6x
    medium769 Mmedium.enmedium~5 GB~2x
    large1550 MN/Alarge~10 GB1x
    +

    Code

    +
      +
    • We will go through two ways to use Whisper model.
        +
      • Authors have released a Python package called whisper [1] that makes using the pretrained models as easy as writing 3 lines of code.
        • +
      • OpenAI recently released the API for Whisper model [2] While it a paid service (~$0.36 for 1 hour of audio transcription), they take care of constantly improving the model and hosting complexities.
        • +
      +
      • +
    +

    Python Package Inference

    +
      +
    • Below is the inference code shared in the Readme of official Github repo [1],
      • +
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    # Install
+!brew install ffmpeg # for mac
+!pip install git+https://github.com/openai/whisper.git 
+
+# Import
+import whisper
+
+# load the model
+model = whisper.load_model("base")
+# get transcription
+result = model.transcribe("audio.mp3", language="english")
+# result contains 3 output,
+# result['text'] --> complete transcription that with punctuations
+# result['segments'] --> segment wise transcription with timestamps and other details 
+# result['langauge'] --> detected language of the audio
+
+# can be used for translation as well (here, Japanese to English)
+result = model.transcribe("japanese.wav", language="Japanese", task="translate", beam_size=5, best_of=5)
+
    +
    +

    Note

    +

    Auto language detection only works if you don't specify it explicitly using language param in transcribe function. The package uses only the first 30 secs to detect the language. +Also, whisper's translation is not that accurate hence an alternative approach could be to perform transcription using Whisper but use another package to translate the transcription.

    +
    +
      +
    • The package also provides CLI support, here is an example,
      • +
    +
    whisper japanese.wav --language Japanese
+# generates --
+# txt file (transcription), and 
+# vtt file (segment wise transcription with timestamp) 
+
    +
      +
    • Finally for the brave souls, we can even play around with the individual modules and perform the transcription step by step, (from readme [1])
      • +
    +
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    # Install
+!brew install ffmpeg # for mac
+!pip install git+https://github.com/openai/whisper.git 
+
+# import
+import whisper
+
+model = whisper.load_model("base")
+
+# load audio and pad/trim it to fit 30 seconds
+audio = whisper.load_audio("audio.mp3")
+audio = whisper.pad_or_trim(audio)
+
+# make log-Mel spectrogram and move to the same device as the model
+mel = whisper.log_mel_spectrogram(audio).to(model.device)
+
+# detect the spoken language
+_, probs = model.detect_language(mel)
+print(f"Detected language: {max(probs, key=probs.get)}")
+
+# decode the audio
+options = whisper.DecodingOptions()
+result = whisper.decode(model, mel, options)
+
+# print the recognized text
+print(result.text)
+
    +

    OpenAI API

    +
      +
    • Before we code, here are some important points to be aware of,
        +
      • OpenAI hosted Whisper model is recommended to be used for ~59 languages for which the WER rate is <50%, as for others the accuracy will not be good. Refer
        • +
      • Whisper API only supports files that are less than 25 MB. For bigger files, we will have to split or used compressed audio.
        • +
      • Finally, we can even use prompt engineering to further improve the accuracy of transcription. It can be used for word boosting, enforcing punctuations, styles and more. Refer
        • +
      +
      • +
    +
    +

    Note

    +

    Use of prompt engineering to enhance the transcription accuracy is a relatively newer approach and needs research for more clarity.

    +
    +
      +
    • With that out of the way, using OpenAI API for audio transcription using Whisper is quite easy as shown below, [1]
      • +
    +
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+2
+3
+4
+5
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    # Install
+!pip install openai>=0.27.0
+#  import 
+import openai
+# load the audio file
+audio_file= open("/path/to/file/audio.mp3", "rb")
+# pass for transcription
+transcript = openai.Audio.transcribe("whisper-1", audio_file)
+
    +
    +

    Note

    +

    Apart from file and model, transcribe function supports multiple parameters like, (Refer API doc)

    +
      +
    • prompt: for prompt engineering (more details below)
      • +
    • response_format: to change the transcription output. We can use verbose_json to get timestamp and logits details.
      • +
    • temperature: to introduce randomness in generation (default is 0)
      • +
    • language: language of input audio
      • +
    +
    +
      +
    • For translation you can directly use
      • +
    +
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+2
+3
+4
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+6
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    # Install
+!pip install openai>=0.27.0
+#  import 
+import openai
+# load the audio file
+audio_file= open("/path/to/file/german_audio.mp3", "rb")
+# pass for translation 
+transcript = openai.Audio.translate("whisper-1", audio_file)
+
    +
      +
    • Finally, we can use prompt engineering to enhance the accuracy by using prompt param. Below are some examples,
      • +
    +
    1
+2
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    # Word Boosting name - 'Mohit' 
+transcript = openai.Audio.translate("whisper-1", audio_file, 
+    prompt="This transcript may contains people name like Mohit.")
+# Enforce puntuations and special characters
+transcript = openai.Audio.translate("whisper-1", audio_file, 
+    prompt="Hello, this transcript may contains people name like 'Mohit'.")
+# Enforce filler words
+transcript = openai.Audio.translate("whisper-1", audio_file, 
+    prompt="Hmm...this transcript may contains people name like Mohit.")
+
    +

    References

    +

    [1] Whisper by OpenAI - Blog | Paper | Code | API Doc

    + + +
    + +
    +
    + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + \ No newline at end of file diff --git a/data_science_tools/compute_and_ai_services/index.html b/data_science_tools/compute_and_ai_services/index.html new file mode 100644 index 00000000..6a254950 --- /dev/null +++ b/data_science_tools/compute_and_ai_services/index.html @@ -0,0 +1,2717 @@ + + + + + + + + + + + + + + + + + + Compute and AI Services - A Lazy Data Science Guide + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
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    +
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    + + + +
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    +
    + + + +
    +
    +
    + + +
    +
    + + + + + + + + +

    Compute and AI Services

    +
      +
    • Gone are the days when we needed to buy high end devices to do literally anything. Currently there are plethora of services available online (and many of them are free!) that provide not only compute to use as you feel, but also generic AI services.
      • +
    +
      +
    • Let's look into some of the famous and widely used compute and AI services.
      • +
    +

    CaaS: Compute as a Service

    +

    In this section we will cover some of the famous (and with some hint of free) platforms that provide compute-as-a-service (CaaS). These CaaS sometimes could be plain simple virtual machines, sometime they can be a cluster of nodes, while in other cases they can also be jupyter like coding environment. Let's go through some of the examples.

    +

    Google Colab

    +

    Introduction

    +
      +
    • Colaboratory or "Colab" in short, is a browser based jupyter notebook environment that is available for free. It requires no installation and even provides access to free GPU and TPU.
      • +
    • The main disadvantages of Colab is that you cannot run long-running jobs (limit to max 12 hrs), GPU is subject to availability and in case of consistent usage of Colab, it might take longer to get GPU access.
      • +
    • Google provides Pro and Pro+ options which are paid subscriptions to Colab (10$ and 50$ per month, respectively). While it provides longer background execution time and better compute (among others), they do not guarantee GPU and TPU access all the time. Remember, Colab is not an alternative to a full-blown cloud computing environment. It's just a place to test out some ideas quickly.
      • +
    +

    Google Colab Snippets

    +
    Run tensorboard to visualize embeddings
    + +
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    import numpy as np
+import tensorflow as tf
+import tensorboard as tb
+tf.io.gfile = tb.compat.tensorflow_stub.io.gfile
+from torch.utils.tensorboard import SummaryWriter
+
+vectors = np.array([[0,0,1], [0,1,0], [1,0,0], [1,1,1]])
+metadata = ['001', '010', '100', '111']  # labels
+writer = SummaryWriter()
+writer.add_embedding(vectors, metadata)
+writer.close()
+
+%load_ext tensorboard
+%tensorboard --logdir=runs
+
    +
    Connect with Google Drive and access files
    +
      +
    • This code will prompt you to provide authorization to access your Google Drive.
      • +
    +
    1
+2
    from google.colab import drive
+drive.mount('/content/drive')
+
    +

    Kaggle

    +
      +
    • Apart from being famous for hosting big AI/ML competitions, the next cool thing about the site is that it also provides free GPU/TPU computes! All you have to do is to sign up, create a new notebook and then you can start using it - import their datasets or your own, and start training you AI models!
      • +
    • All of this ofcourse has a limit, you get minimum 30 hours of GPU usage per week, and at max 20 hours of TPU per week. Another catch is that you can only use GPU/TPU for 9 hours continuously.
      • +
    • That said, Kaggle notebooks are a great place to perform your personal experiments or participate in new competitons to enhance your expertise. For more official work (industry or academics), do remember that you are putting your dataset in 3rd party's hands.
      • +
    +
    +

    +

    Code/Notebook page of Kaggle.
    +

    +
    +

    DeepNote

    +
      +
    • DeepNote provides a highly customised jupyter like notebook. It's one of the richest service in terms of features. Here goes some examples - you can create projects with multiple notebooks, you can create teams and collaborate with your colleagues live, you can quickly visualize datasets from notebooks, you can schedule notebooks, you can host reports, and best of all - they have free tier šŸ˜„
      • +
    +
      +
    • There are multiple pricing based tiers. To begin with you can try out the free tier and get upto 750 hours of Standard compute hours per month, that's like keeping one project (that could consist of multiple notebooks) open for the complete month! (offer subject to change; was valid at the time of writing)
      • +
    +
    +

    +

    Pricing tiers of Deepnote.
    +

    +
    +
    +

    Hint

    +

    Planning to try DeepNote out? Use the refer link to get free 20 Pro compute hours (thats upto 16GB RAM and v4vCPU)

    +
    +

    MLaaS: Machine Learning as a Service

    +

    In this section we will cover some of the famous platforms that provide Machine learning-as-a-Service (MLaaS). These MLaaS take care of infrastructure related aspect of data holding, data preparing, model training and model deployment. On top of this, they provide a repository of classical ML algorithms that can be leveraged to create data science solutions. The idea is to make data science as a plug and play solution creation activity, as they take care of most of the engineering aspect. Let's go through some of the examples.

    +

    AWS Sagemaker (Amazon)

    +
      +
    • AWS Sagemaker is a cloud-based servies that helps data scientists with the complete lifecycle of data science project.
      • +
    • They have specialised tools that cover following stages of data science projects,
        +
      • Prepare: It's the pre-processing step of the project. Some of the important services are "Gound Truth" that is used for data labeling/annotation and "Feature Store" that is used to provide consistence data transformation across teams and services like training and deployment.
        • +
      • Build: It's where an Data Scientists spends most of his time coding. "Studio Notebooks" provides jupyter notebooks that can be used to perform quick ideation check and build the model.
        • +
      • Train & Tune: It's where you can efficiently train and debug your models. "Automatic Model Training" can be used for hyper-parameter tuning of the model i.e. finding the best parameters that provides highest accuracy. "Experiments" can be used to run and track multiple experiments, its an absolute must if your projects requires multiple runs to find the best architecture or parameters.
        • +
      • Deploy & Manage: The final stage, where you deploy your model for the rest of the world to use. "One-Click Deployment" can be used to efficiently deploy your model to the cloud. "Model Monitor" can be used to manage your model, like deleting, updating, and so on.
        • +
      +
      • +
    +
    +

    +

    Services provided by AWS Sagemaker.
    +

    +
    +
      +
    • AWS charges a premium for providing all of these features under a single umbrella. For a more detailed pricing information, you can estimate the cost using this.
      • +
    +
    +

    Hint

    +

    As AWS Sagemaker is a costly affair, several DS teams try to find workarounds. Some of them are like using spot instances for training as they are cheaper & using AWS Lambda for deploying small models.

    +
    + + +

    Kubeflow

    +

    Introduction

    +
      +
    • Kubeflow is an open-source project that is dedicated to making development, tracking and deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. As per their website, "Anywhere you are running Kubernetes, you should be able to run Kubeflow."
      • +
    • While there are many paid MLaaS like Sagemaker, Azure ML Services and Google AI Platform, Kubeflow is an outlier that provides most of the features present in the paid platforms, but for free!
      • +
    • We can deploy Kubeflow on Kubernetes by following the guide on their website. Once done, you can boot it up and it should look as shown below,
      • +
    +
    +

    +

    Main page of Kubeflow.
    +

    +
    +
    +

    Hint

    +

    Go with Kubeflow if you are setting up a new AI team for your organziation or school, and don't want to commit to costly services like Sagemaker. But beware, it does require DevOps knowledge, as you will need to setup Kubernetes and manage it. While it is completly free, you will be charged for the compute you utilize. To cut down the cost, in case you are connecting Kubeflow with AWS, you can use Spot instances.

    +
    +

    Components

    +
      +
    • Kubeflow provides several individual components that will help with the ML lifecycle. Note, we can even pick and choose the components you want while installation. Some of them are,
        +
      • Notebook: here we can create jupyter notebook servers and perform quick experimentations. Each server is assigned its own volume (hard memory). On booting up a server, a new compute is procured and you will see Jupyter Lab page where you can create mulitple notebooks, scripts or terminals. The compute could be EC2 instance or Spot instance, incase of AWS connection and based on your configuration.
        • +
      • Pipeline: here we define one ML project. Kubeflow supports defining a pipeline in terms of a DAG (Directed Acyclic Graph), where each individual function or module is one node. Pipeline represents a graph of modules, where execution happens in a sequential or parallel manner while considering the inter-module dependencies , ex: module_2 requires output of module_1. While this leads to modularization of the code, the real intention is to make the pipeline execution traceable and independent from each other. This is achieved by containerizing each module and running them on different instances, making the process truly independent.
        • +
      • Experiments: On a single ML project, we may want to run multiple experiments, ex: (1) test_accuracy to try out a couple of parameters and compare accuracy, (2) test_performance to compare latency on different shape and size of data. This is where you define individual experiments.
        • +
      • Runs: One execution of an experiment for a pipeline is captured here, ex: for test_accuracy experiment of MNIST pipeline, perform one run with learning_rate = 0.001.
        • +
      • Experiments (AutoML): we cannot try all the parameters for the test_accuracy one by one. The obvious question, why not automate it by doing hyperparameter tuning? AutoML is what you are looking for!
        • +
      • Models: after all experimentations and model training, we would like to host/deploy the model. It can done using this component.
        • +
      +
      • +
    +

    Creating and running Pipeline

    +
      +
    • Let's start the coding šŸ˜„. So for this tutorial, we will create a simple Kubeflow pipeline with two steps,
        +
      • Step 1 - Download data: where we will download data from S3 bucket, pass the downloaded data to the next step for further analysis.
        • +
      • Step 2 - Perform analysis: we will perform some rudimentary analysis on the data and log the metrics.
        • +
      +
      • +
    • We will try to go through some basic and advanced scenarios, so that you can refer the code to create your own pipeline, even if it is completely different. After creating the pipeline, we will register it, create an experiment and then execute a run.
      • +
    +
      +
    • Lets start with importing the relevant packages. Make sure to install kfp with the latest version by using pip install kfp --upgrade
      • +
    +
    1
+2
+3
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+6
    # imports
+import kfp
+import kfp.dsl as dsl
+from typing import NamedTuple
+import kfp.components as comp
+from kfp.components import InputPath, OutputPath
+
    +
      +
    • Now we will create the first module that downloads data from S3 bucket. Note, Kubeflow takes care of the logistics of data availability between modules, but we need to share the path where data is downloaded. This is done by typecasing parameter with OutputPath(str) as done on line 2. The process will be similar for ML models as well. We can download a model in the first module and perform training in another, and perform performance check in the third module.
      • +
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    ## Step 1
+def download_data(data_path: OutputPath(str)):
+    # import the functions
+    import os
+    import boto3
+
+    # create the path if not exist
+    if not os.path.exists(data_path):
+        os.makedirs(data_path)
+
+    # setup boto3
+    s3 = boto3.resource('s3')
+    s3_client = boto3.client('s3')
+    bucket_name = 'my-bucket'
+    bucket = s3.Bucket(bucket_name)
+
+    # get list of all files at the s3 bucket prefix
+    prefix = "dataset/"
+    query = s3_client.list_objects(Bucket=bucket_name, Prefix=prefix, Delimiter='/')
+    files = []
+    if 'Contents' in query:
+        for obj in query['Contents']: 
+            files.append(obj['Key'])
+
+    # download each file into the folder
+    for file_path in files:
+        # get file name
+        file_name = file_path.split('/')[-1]
+        # download and save the file
+        s3_client.download_file(bucket_name, 
+                        file_path, 
+                        f'{data_path}/{file_name}')
+        print(f"Downloaded: '{file_path}' into '{data_path}/{file_name}'")
+
+    # done!
+    return print("Done")
+
+# download_data()
+# create kubeflow component
+download_data_comp = kfp.components.create_component_from_func(
+    func=download_data,
+    base_image='python:3.7',
+    packages_to_install=['boto3'])
+
    +
      +
    • From line 40 to line 43, we are converting the function to Kubeflow pipeline component. As the component will run on an independent instance, we need to provide the base_image and packages_to_install information as well.
      • +
    +
      +
    • Next, we will create the second module that loads the data from first module and just returns some dummy metrics. In reality, you can do a lot of things like data preprocessing or data transformation or EDA. For now, we will just stick with a dummy example.
      • +
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    ## Step 2
+from typing import NamedTuple
+def data_analysis(data_path: InputPath(str)) -> NamedTuple('Outputs', [
+  ('mlpipeline_metrics', 'Metrics'),
+]):
+    # import
+    import json
+    from glob import glob
+    from collections import namedtuple
+
+    # load each json file
+    for file_path in glob(f"{data_path}/*.json"):
+        # load the call data file and perform some analysis
+        data = json.load(open(file_path))
+        # print
+        print(f"Loaded {file_path}")
+        # --- do something fancy here ---
+
+    # create metrics that should be logged
+    metrics = {'metrics': [
+        {
+        'name': 'accuracy',
+        'numberValue': 89
+        },
+        {
+        'name': 'f1',
+        'numberValue': 89
+        }]}
+
+    return [json.dumps(metrics)]
+
+# create kubeflow component
+data_analysis_comp = kfp.components.create_component_from_func(
+    func=data_analysis,
+    base_image='python:3.7',
+    packages_to_install=[])
+
    +
      +
    • In the function we are defining the data_path as InputPath(str) and is later used directly on line 14, without the need of manually sharing the data across instances.
      • +
    +
      +
    • We define mlpipeline_metrics as output (by type casing) as this is mandatory if you want to log metrics. This is done on line 21 to line 29, where we log dummy accuracy and f1 metrics. Next we return the metrics. Finally, we also create Kubeflow component.
      • +
    +
      +
    • Next, we will combine all of the components together to create the pipeline.
      • +
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    ## Create pipeline
+from kubernetes.client.models import (V1Affinity, 
+                                      V1NodeAffinity, 
+                                      V1NodeSelector, 
+                                      V1NodeSelectorTerm, 
+                                      V1NodeSelectorRequirement)
+
+# define pipeline
+@dsl.pipeline(name="my_pipeline", 
+              description="A simple demo of kubeflow pipeline")
+
+# Define parameters to be fed into pipeline
+def my_pipeline(data_path='data/'):
+    # Step 1
+    download_data_container = download_data_comp()
+    # Step 2
+    data_analysis_container = data_analysis_comp(download_data_container.output)
+
+    # add affinity
+    aff = V1Affinity(node_affinity=V1NodeAffinity(
+        required_during_scheduling_ignored_during_execution=V1NodeSelector( 
+            node_selector_terms=[V1NodeSelectorTerm( 
+                match_expressions=[V1NodeSelectorRequirement(
+                    key="xxxx", 
+                    operator="In",
+                    values=['yyy'])])]))
+    )
+
+    download_data_container.add_affinity(aff)
+    data_analysis_container.add_affinity(aff)
+
+# create client that would enable communication with the Pipelines API server 
+client = kfp.Client()
+experiment_name = 'my_pipeline'
+# Compile pipeline to generate compressed YAML definition of the pipeline.
+kfp.compiler.Compiler().compile(my_pipeline,  
+  '{}.zip'.format(experiment_name))
+
    +
      +
    • We start with importing relevant modules and creating the pipeline function where we define the name and description of the pipeline. Next we connect the components together.
      • +
    • From line 20 to line 30, we are defining and setting the node wide affinity so that we only use spot instances for the computation. This will keep our cost to the minimum.
      • +
    • Finally we create a Kubeflow client and compile the complete pipeline. This will create a zip file of the compiled pipeline that we can upload from the pipeline tab in Kubeflow.
      • +
    • Next, we can create an experiment and the perform a run from the respective Kubeflow tabs. The process is quite simple and can be easily done from the UI. Once we have executed a run and the process is completed, we can see the individual modules and the status in the run page as shown below.
      • +
    +
    +

    +

    Run page after successful execution of a run of my_pipeline pipeline
    +

    +
    +
      +
    • And we have done it šŸ˜Ž
      • +
    + + +
    + +
    +
    + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + \ No newline at end of file diff --git a/data_science_tools/introduction/index.html b/data_science_tools/introduction/index.html new file mode 100644 index 00000000..353240ce --- /dev/null +++ b/data_science_tools/introduction/index.html @@ -0,0 +1,2068 @@ + + + + + + + + + + + + + + + + + + Introduction - A Lazy Data Science Guide + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + + +
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    + + + + + + + + +

    Introduction

    +
      +
    • Several aspiring data scientists think Data science is just about training fancy models. While it's part of the job, what we are missing here is the huge additional effort that is required to make sure that a model is trainable, executable and deployable. Add to it the complexity of working in a team and now we have to make sure the code is well formatted and structured as well. In all, life of a Data scientist is similar to any software engineer, just with a caveat of having the luxury to play with the state-of-the-art AI algorithms once in a while šŸ˜„
      • +
    +
      +
    • Now, the industry is trying (or realizing) the capabilities, limitations and responsibilities of professionals in AI or ML field. This is giving rise to increase in requirements for diverse job profiles like ML engineer, Data Engineer, Data Scientist, Research Scientist, etc. That said, Data scientist (DS) are reinventing themselves as well, giving rise to the interesting profile of "Full stack Data scientists" - who while researching and experiment with AI models, are not afraid to dabble into the old school engineering aspect of the project. This article is for such aspirants or practitioners.
      • +
    +

    MLOps tools

    +
      +
    • Just like a mechanic should know about all the tools at his disposal, a data scientist should be aware of different ready-made and possibly free services available. You can quote me on this,
      • +
    +
    +

    Quote

    +

    Never pay for what you can do for free, and never build something which has already been built! šŸ˜

    +
    +
      +
    • With this fortune cookie quote in mind, let's look into different tools and their segregation based on the where and how they are used,
      • +
    +
    +

    +

    Different MLOps tools covering Data, Training, Evaluation and Deployment. (fullstackdeeplearning)
    +

    +
    +
      +
    • The rest of this section will cover some of these tools in detail.
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    + + + + + + + + + + + + + + \ No newline at end of file diff --git a/data_science_tools/version_control/index.html b/data_science_tools/version_control/index.html new file mode 100644 index 00000000..ff4c5b51 --- /dev/null +++ b/data_science_tools/version_control/index.html @@ -0,0 +1,2704 @@ + + + + + + + + + + + + + + + + + + Version control - A Lazy Data Science Guide + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    Version control

    + +
      +
    • Version control (VC) is basically needed for any file(s) that will be maintained for a long time (read, multiple editing process) and/or accessed by multiple people (in collaboration). If you have such a file or a set of files (as a project), you will agree the necessity to track the changes. VC tries to help us with the same šŸ†’
      • +
    +
      +
    • Now comes the question of what can be tracked and how? A very simple distinction of different tools available for VC can be introduced efficiently, if we look at them from the lens of what type of data they can "version control". Such as,
        +
      • Code: if you want to maintain just the code files, GIT is the defacto answer. There are several GIT based service providers, such as GitHub, whose platform can be used (for free) to maintain a git repository.
        • +
      • Data and ML Model: in contrast to GIT, that was developed to maintain relatively small sized files, we need something different if we want to handle big files like datasets or ML/DL models. Enter DVC (Data Version Control), an GIT extension that directly connects the data storages (cloud or local) with the git to maintain data, model and code at the same time!
        • +
      +
      • +
    +
      +
    • We will now go through some of the tools/services for version control in detail.
      • +
    +

    GIT

    +

    Introduction

    +
      +
    • In simple words, GIT is a system designed to track changes in your file. True story, it was developed by none other but the creator of Linux, yes, Linus Torvalds in 2005! The story goes something like this -- while he was developing the linux kernel along with other kernel developers, he found it troublesome to maintain, track and handle conflicting (overlapping) pieces of codes. So he ended up coding the GIT system as a side project, just to help him and his fellow developers maintain linux kernel in a more efficient manner! Now, isn't that a cool side project šŸ˜Ž. You can read more about GIT and the history here.
      • +
    +
      +
    • Some of the popular and free services are GitHub, Gitlab and BitBucket. While they differ by UI and add-on functionalities, the core system (Git) used by all of them is the same. Hence if you learn the basic Git commands once, you can use any of the services mentioned above. That is why we will limit ourselves to learn Git the old school way i.e. via GIT bash commands, and leave the fancy UI or tool based operation as an exploration activity for the interested audience.
      • +
    +
      +
    • Before we go into the command and syntax, we need to be clear about certain topics to better appreciate Git. These are,
        +
      • Where to download Git? Git is free and available here. Download the latest stable version as per your OS.
        • +
      • How do we use Git? After downloading Git (and specifically in Windows), from any directory in file explorer, on right click, you should get the option to open either "Git bash" or "Git GUI". For this article, we will use Git bash, as that's how real developers roll šŸ˜¤ (jk)
        • +
      • What is Git Bash? It is something similar to command prompt in windows or terminal in linux, but something specifically designed for Git. To perform any Git related operation, we will use the Git bash.
        • +
      • What is the life cycle in Git? Basically any file in a git repository typically goes through three states. These are, (1) working state: the initial stage, where you have created a git repository and git is just looking at the files to note what has changed. (2) staging state: the second state where you mark certain files that should be commited, and (3) commit state: where you finalize the changes made to the files and commit them to the Git's history. Basically, this will create a version of your code and persist it for future reference.
        • +
      • What is local vs remote instances? Local instance is a git repository present in your local computer, and on the other hand, remote instance is the common server used to upload the modifications of the local instance. This is done so that there is a centralised repository from where everyone in the team can pull or push the latest code.
        • +
      • What are branches in Git? Branches are like parallel universes in Git. We can spawn off new branches anytime and from any other branch. By doing so we create a fork within that branch, where the developer can do whatever they want to do. Finally, after relevant commits/changes, the forked branch is merged back to their original branch using merge request.
        • +
      • What is a merge request and merge conflict in Git? Merge request is a request raised by the developer to the maintainer of the Git remote repository, asking them to merge the two branches. The maintainer may want to perform final set of validations before accepting the merge request. It may also happen that the same lines in the same file has been modified in both the branches, this will then lead to a merge conflict. Resolving merge conflict can range from easy to highly complex based on how many files has been affected. To resolve a merge conflict, we will modify the affected lines and then create a new commit.
        • +
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      • +
    +
      +
    • +

      Now, let's take a practical approach of learning Git by role-playing an example to learn the basics. Suppose you are working on a big project that requires mulitple modifications per day. TO efficiently maintain the project, you are looking for a tool that will help to keep track of the changes made in the project, by recording the line wise modification made in the files in the project. For this you want to explore GIT and test if it will make your life easier or not. So, let's get stated with the exploration! šŸ˜€

      +
        +
      • Initializing the Git repository: As we discussed, Git helps in tracking the changes made in a file. On top of it, it's easy to scale it up and track a complete folder that contains hundreds of files! For reference, suppose you have already created a python project and want to track the changes in any of the files present there. To do so, just go to the main directory of the project and initialize git by using command git init. This will mark that directory as a git repository. Next, if you run git status, it will show you an overview of the project and all of files. Note, by default Git keeps a look out at all of the files within the directory and show when any of the files have changed.
        • +
      +
        +
      • +

        Staging files: You are going through the project file by file, making modifications as needed. Once you are happy with any file (or think that it is done), you can add that file to the staging area by command git add <file_name>, or if you want to stage all of the files at one go, do git add .. Now if you run git status again, you can see the files names are in green. This means these files are staged!

        +

        + +
        Example for initializing the git repository to tracking the files.
        +

        +
        • +
      +
        +
      • Commit: Now, suppose we just completed one small task. It would be a good idea to take a snapshot of our current code and save it. This can be done by git commit -m "your message", wherein you are asking git to commit all of the changes added in the staging area. This commit can be thought of as a unique snapshot of your code with the commit message as your description. Note, Git also generates hex code that is unique to each commit, that acts as the commit identifier. Your description is just a human readable piece of informance for us mere mortals šŸ˜€
        • +
      +
        +
      • Push: Note, all of these modifications has been done on your local instance, and to publish these to the world, we need to push the code to the remote instance. We can push our latest commits to the remote server by git push origin master. Note, here git push signifies we want to push the code, origin denotes the remote server and master denotes the branch of origin on which we want to push.
        • +
      +
      • +
    +
      +
    • And that's it! We have covered most of the fundamental aspects of using git!
      • +
    • One important aspect to remember is that, we should refrain from committing directly to the master branch. Instead whenever we are planning to do some modifications, we should checkout to a new git branch (by using git checkout -b <branch_name>), do the modifications there, push that particular branch to remote and then create a merge request. This is just a good practice followed when working with a team!
      • +
    +
    +

    Note

    +

    It may so happens that you only want to certain few files and not all of them. This can be done by creating a .gitignore file and placing it in the root directory. Within the file, add the relative (from root directory) path of all the files or folders you want the Git to ignore. For example, data/input.csv or data/* are respectively the examples to exclude one file or the complete folder from Git's tracking system.

    +
    +

    GIT Snippets

    +
      +
    • A consolidation of some of the most helper code snippets for GIT.
      • +
    +

    The basic git commands

    +
      +
    • Listing down some of the most basic GIT commands, that you should definitely know about. Most of them are references from the above theory part.
      • +
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    # list all local branches
+git branch
+# list all remote branches
+git branch -r
+# create a local copy of a remote branch
+git checkout --track origin/branch_name  
+# show the remote links
+git remote -v
+# add a new remote
+git remote add new_remote git@github.com:User/UserRepo.git
+# pull the latest code from "master" branch of "origin" remote server
+git pull origin master
+# checkout to an existing branch
+git checkout main
+# checkout to a new branch
+git checkout -b use_bert_model
+# after performing some changes, add files to staging state 
+git add .
+# commit
+git commit -m "added bert model"
+# push the branch to remote
+git push origin use_bert_model
+
    +

    Modify config to add email and name

    +
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    # Check the value of the config
+git config --get user.email 
+git config --get user.name 
+
+# Add username
+git config --global user.name "FIRST_NAME LAST_NAME"
+# Add email
+git config --global user.email "MY_NAME@example.com"
+
+# For local modification (for a git within a directory), use --local instead of --global
+# mode details: https://support.atlassian.com/bitbucket-cloud/docs/configure-your-dvcs-username-for-commits/
+
    +

    Publishing to Github using APIs (Python)

    +
      +
    • Publishing (adding files for instance) to Git is quite easy using the local CLI tool and/or Github tools or UI. We have already discussed how to do this using CLI. But what if you are creating your own application and need to do the same using Github APIs? This snippet is a python based function that adds a new file to your git repo, commits and publishes it!
      • +
    • Note, this is not as simple as single API call, for detailed information on what happens behind the scene when you commit and push, I will suggest this article.
      • +
    • You will need to create a personal token from github using this link. Store that as config['Github']['personal_token'] for this function to work. Note, the function takes config as parameter input.
      • +
    • This snippet is inspired from this article and this stackoverflow answer.
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    # function to publish new content to Github
+def publish_to_github(config, content, remote_path, commit_message, repo='test', 
+                        user='imohitmayank', branch='main', name="Mohit Mayank", 
+                        email="mohitmayank1@gmail.com"):
+    """
+    Function to publish new content to Github
+
+    Parameters
+    -----------
+      config: dict with `config['Github']['personal_token']` containing your Github personal token
+      content: string content you want to push to Github
+      remote_path: path wrt to remote, where you want to save the content to; include the file name
+      commit_message: message for commit
+    """
+
+    # Step 1: Get the SHA of the branch 
+    url = f"https://api.github.com/repos/{user}/{repo}/branches/{branch}"
+    header = {'Authorization': f'token {config["Github"]["personal_token"]}'}
+    r = requests.get(url, headers=header)
+    last_commit_sha = r.json()['commit']['sha']
+    print("Step 1 Done: SHA fetched.")
+
+    # Step 2: Create a blob
+    url = f"https://api.github.com/repos/{user}/{repo}/git/blobs"
+    header = {'Authorization': f'token {config["Github"]["personal_token"]}'}
+    body =  {
+    "content": content,
+    "encoding": "utf-8"
+    }
+    r = requests.post(url, json=body, headers=header)
+    utf8_blob_sha = r.json()['sha']
+    print("Step 2 Done: Blob created.")
+
+    # Step 3: Create a tree
+    url = f"https://api.github.com/repos/{user}/{repo}/git/trees"
+    header = {'Authorization': f'token {config["Github"]["personal_token"]}'}
+    body = {
+    "base_tree": last_commit_sha,
+    "tree": [
+        {
+        "path": remote_path,
+        "mode": "100644",
+        "type": "blob",
+        "sha": utf8_blob_sha
+        }
+    ]
+    }
+    r = requests.post(url, json=body, headers=header)
+    tree_sha = r.json()['sha']
+    print("Step 3 Done: Tree created.")
+
+    ## Step 4: Commit the changes
+    url = f"https://api.github.com/repos/{user}/{repo}/git/commits"
+    header = {'Authorization': f'token {config["Github"]["personal_token"]}'}
+    body = {
+    "message": commit_message,
+    "author": {
+        "name": name,
+        "email": email
+    },
+    "parents": [
+        last_commit_sha
+    ],
+    "tree": tree_sha
+    }
+    r = requests.post(url, json=body, headers=header)
+    new_commit_sha = r.json()['sha']
+    print("Step 4 Done: Changes commited")
+
+    ## Step 5: update the HEAD
+    url = f"https://api.github.com/repos/{user}/{repo}/git/refs/heads/{branch}"
+    header = {'Authorization': f'token {config["Github"]["personal_token"]}'}
+    body = {
+        "ref": "refs/heads/{branch}",
+        "sha": new_commit_sha
+    }
+    r = requests.post(url, json=body, headers=header)
+    print("Step 5 Done: HEAD updated")
+    print("------ ALL DONE! ------")
+
    +

    Ignore files/folders

    +
      +
    • .gitignore file in the root directory, contains the name of files and folders which should not be tracked by GIT.
      • +
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    # ignore personal.txt
+personal
+
+# ignore everything within "pic" folder
+pic/*
+
+# ignore everything except a specific file within a folder
+!pic/logo.png
+pic/*
+
    +

    Untrack file/folder and delete them from GIT

    +
      +
    • To untrack the files or folders, we can create .gitignore file and add respective info.
      • +
    • To delete the files or folders form GIT (and not from local system), we can delete them from the cache as suggested here,
      • +
    +
    1
+2
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    # for a single file:
+git rm --cached mylogfile.log
+
+# for a single directory:
+git rm --cached -r mydirectory
+
    +

    Stash partial changes

    +
      +
    • Suppose you have made some partial changes and the remote is updated with a new commit. Now you cannot commit your local change (as its partial) and you need to pull the latest code from remote (as its update). git stash comes to the rescue, example below.
      • +
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    # stash away the  current partial changes
+git stash
+
+# pull the latest code (or any other operation)
+git pull origin master
+
+# pop the stashed partial changes
+git stash pop
+
    +

    Reset to the last commit

    +
      +
    • You may want to revert back to the very last commit, discarding every modification from then. This could be because you were playing around with the code or doing some minor experiments. In either cases, you can do this by,
      • +
    +
    1
    git reset --hard HEAD
+
    +
      +
    • Otherwise, to just unstage the files which were staged by git add,
      • +
    +
    1
    git reset
+
    + +

    Using large files

    +
      +
    • Several VC platform have a maximum file size limit, for example Github has 100MB file size limit.
      • +
    • That said, there are ways to bypass such limitations, once of those are Git LFS i.e. Large file system.
      • +
    • Using Git LFS we can track large files and it internally takes care of storing them as LFS objects. This way we can store our dataset or models in the git repositories as well.
      • +
    • Below is an example track a model in a repository using Git LFS.
      • +
    +
    # install git lfs
+git lfs install
+
+# track a file
+git lfs track "model/model.pkl"
+# or track a folder
+git lfs track "model/**"
+
+# add the git attribute to reflect changes
+git add .gitattribute
+
+# migrate the changes i.e. replace files with pointers (for current branch)
+git lfs migrate import --include=='model/**' --verbose 
+# or for all branches
+git lfs migrate import --everything --include=='model/**' --verbose 
+
+# show the files which are tracked
+git lfs ls-files
+
    +

    Setting up multiple Github accounts on a single machine using SSH

    +
      +
    • It is possible to setup multiple Github account on a single machine using SSH. This is useful when you want to work on multiple projects which are hosted on different Github accounts. For example, you might have one personal and one work account that you want to use on your machine. You can refer this for more details, and I will also summarize the steps below.
      • +
    • +

      The process is quite simple, first we will generate two SSH keys (for two accounts) and save them on our machine. Next, we will create a global ssh config file to map the two keys with github. Then, we will add the public keys to the respective github account. Finally, we will create a git config file for each local project and add the user details. Let's get started.

      +
        +
      1. +

        Create SSH keys using the command shared below, make sure to replace the email address with your own. You will be prompted to enter a file name, you can enter any name you want. For example, if you want to create a key for your personal account, you can enter id_rsa_personal. Repeat this process twice to generate two keys for two different accounts.

        +

        ssh-keygen -t rsa -b 4096 -C "your_email@example.com"

        +
        2. +
      2. +

        Next, create a config file in .ssh folder to map the two accounts with their keys. If the file is not present, you can create it using the following command.

        +

        touch ~/.ssh/config

        +

        After this, open the file and add the following content.

        +
        # Personal account
+Host github.com-personal
+  HostName github.com
+  User git
+  IdentityFile ~/.ssh/id_rsa_personal
+  IdentitiesOnly yes
+
+# Work account
+Host github.com-work
+  HostName github.com
+  User git
+  IdentityFile ~/.ssh/id_rsa_work
+  IdentitiesOnly yes
+
        +
        3. +
      3. +

        Now, we will add the public keys to the respective Github account. You can find the public key in the following location, ~/.ssh/id_rsa_personal.pub and ~/.ssh/id_rsa_work.pub. Copy the content of the file and add it to the respective Github account. You can refer this official doc for more details.

        +
        4. +
      4. +

        Finally go to the local project directory and create a config file in the .git folder. Add the following content to the file.

        +
        [user]
+name = personal # use work if this is for work account
+email = {email-address} # add the respective email address
+[remote "origin"]
+url = git@github.com-personal:imohitmayank/my-repo.git # use git@github.com-personal:.. for work
+fetch = +refs/heads/*:refs/remotes/origin/*
+
        +
        5. +
      +
        +
      • And that's it, now for any git related interactions initiated from this directory, personal github account will be used. šŸ˜„
        • +
      +
      +

      Note

      +

      Step a to c is only needed to be done once. After that, you can follow step d for any new project.

      +
      +
      • +
    +

    DVC

    +

    Coming soon!

    +

    References

    + + + +
    + +
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    + +
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b/index.html @@ -0,0 +1,1998 @@ + + + + + + + + + + + + + + + + + + Hello - A Lazy Data Science Guide + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    Hello šŸ‘‹

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    What is this? A guide book on data science for busy and equally lazy Data Scientists šŸ˜„

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    What does this include? While the book started as a collection of snippets, the plan is to overtime include details about the algorithms, applications, tools, best practices and much more to make this the ultimate guide for a budding or experienced Data Scientists.

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    Why this? While there are lots of good quality articles on several data science topics, the major downside is that they are scattered across the web. This guide tries to consolidate the most relevant topics into one place.

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    How to read? As the name suggests, this is a lazy book for lazy people šŸ˜„. On a serious note, there is no specific order. Feel free to search for the topic you are interested in and start reading.

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    How to contribute? Any contribution (typo, error, new topic request, etc) is appreciated. Just create an issue, or start a new discussion on github, and I will be happy to discuss the idea!

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    How to appreciate? Maybe you can do two things? -- (1) Say hello to me on LinkedIn, and/or (2) Click on the ā­ @ github? Thnx!

    +

    How to cite? Add the following to your bib file, +

    @misc{mohitlazydatascience,
+  title   = "A Lazy Data Science Guide",
+  author  = "Mohit Mayank",
+  journal = "mohitmayank.com",
+  year    = "2021",
+  url     = "http://mohitmayank.com/a_lazy_data_science_guide/"
+}
+

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    -- Mohit Mayank

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    Getting started with Data Science

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    Introduction

    +
      +
    • Did you recently decide to become a Data Scientist? But you don't know what to do next or how can you get started? -- Well it's common to be confused about these things and specially on how to begin your journey in this amazing world of Data Science šŸ¤– šŸŒŽ
      • +
    • Just like learning any other technique, it's better to approach learning Data Science from scratch and in multiple phases. Remember, this is by no means a magic formula to make you a Data Science expert overnight - we also don't provide any certifications šŸ“„ šŸ˜‰. The intention of this guide is to provide the essential knowledge and skills to get you started in the Data Science field. And most importantly, to help you plan your journey in the right direction.
      • +
    • One more thing, the field of Data Science is quite vast. The learning approach could vary based on what you want to become -- Data Engineer, Data Scientist, Research Scientist, ML Engineer, etc. In this guide, we will focus on Data Scientist designation while also touching some peripheral topics.
      • +
    • +

      But before we get started, let's have a look at the different designations in the Data Science field and what they are all about.

      +
        +
      • Data Engineer: Data Engineer handles data i.e. they are responsible for the data processing and data analysis.
        • +
      • Data Scientist: Data Scientist handles model training i.e. they are responsible to leverage the data to generate predictive and actionable models.
        • +
      • ML Engineer: ML Engineer deploys models i.e. they are responsible to take the model and perform accessible and scalable deployment.
        • +
      +
      • +
    +
    +

    Note

    +

    Research Scientist is another Data Science related designation that is usually more research and academics oriented, when compared to Data Scientist, which is more practical and industry oriented.

    +
    +
      +
    • Now, let's get started by dividing the herculean task of masting Data Science into three levels, namely L1, L2, and L3. L1 is the most basic level, and L3 is the most advanced level.
      • +
    +
    +

    Note

    +

    Advancing from one level to another is subject to your own understanding and progress. There is no time related or checkpoint related explicit grading system that I will recommend. I believe, that if it seems you are not learning anything new by following the steps of a certain level, you are qualified enough to move on to the next level.

    +
    +
      +
    • "To learn is to read, understand and apply." -- with this in mind, each level is further divided into two subtasks of theory and practice. The idea is to have at least one action item for each subtask in each level. More details are below,
      • +
    + + + + + + + + + + + + + + + + + + + + + + + + + +
    LevelsTheoryPractice
    L1Beginner Online CoursesCourse Assignments and Code replication
    L2Domain Specialization CoursesProjects and Competitions
    L3Research Papers and BooksCreate Products and Publish Papers
    +

    L1: the absolute beginners šŸ‘¦

    + +
      +
    • Practice: it can start with any assignments mentioned in the theory courses or materials. On top of it, we can also perform code replication i.e. to look into basic code snippets and try to replicate them. Pick any existing EDA script or ML code or any other code snippet and try to replicate it on your own. This provides the opportunity to perform guided practice as you can always refer back to the original code if you are stuck!
      • +
    +

    L2: the intermediate level šŸ‘Ø

    + +
      +
    • Practice: in terms of practices, code assignment is mandatory. But another piece of advice is to participate in competitions and hackathons published on platforms like Kaggle. The plan could be to first participate in old competitions and get a feel of coding and solving problems. And then to participate in live competitions and compete with some of the best Data Science enthusiasts out there! Another thing that you can do is to create a simple AI/ML project from scratch, this will give you a much-needed practical experience. Here is an article I wrote on how to ideate and publish your projects. You can also refer to this Github repo to get some inspirations or AI project ideas.
      • +
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    +

    Note

    +

    Please be aware that the intention of participation should be to learn. If you enter any competition without proper preparation and practice, you might not be able to get the best results. But that should never demotivate you. Your intention should be to learn, if it's that, I am sure you will come out of any competition with learning something new. Winning or Lossing is just the by-product. ā˜®

    +
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    L3: the to be experts šŸ˜Ž

    +
      +
    • We have reached the final and an never-ending level šŸ˜‰. By now you have already mastered the basics of Data Science and even some advanced topics. But learning is a never-ending process, as the knowledge is not constant. Every day there is new research that takes the cumulative knowledge one step forward. This progress is quite fast in AI/ML field, so while learning all the existing stuff is quite important, it is necessary that you keep learning the new things and not get left behind! With this in mind, let's look into the individual subtasks in detail.
      • +
    • Theory: here you should read about the latest trends in the field. The intention is to be up-to-date and even to be ahead of the curve. For this you can refer cited papers, trending blogs, and famous books. Lots of researchers publish their work on Arxiv which is free for all and with sites like Arxiv Sanity you can find the trending papers. Several top AI labs have their own website where they frequently publish their latest research like - Google AI, Meta AI, DeepMind AI, OpenAI, Uber AI, etc. Apart from this, people also follow influencers and groups to get the latest news. People even subscribe to AI newsletters for the same.
      • +
    +
    +

    Note

    +

    If this sounds like a challenge, then you are not alone. It's quite a task to follow a lot of these sites and people to get the latest news in the field of AI and ML. That is why I created The ML Dojo. ML Dojo is a daily report on the latest and upcoming research, experiments, topics, articles, papers, ā€¦ (you name it), in the field of Artificial Intelligence and Machine learning. Give it a try, it's completly free šŸ˜Ž

    +
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    • Practice: instead of just reading research papers, the intention should be to find your topic of interest, research, and publish your own work. For this, you can even collaborate with your colleagues or friends. Writing a paper of your own is a challenging but educating process, as you get to ideate, experiment, and write (to defend) the work. It gives you the ability to look at a problem from multiple perspectives and then solve it! Another thing to do is to take the project to the next level -- by creating (and maybe open-sourcing) your own products. While projects were created for the purpose of learning and your own usage, products are created for the sake of other people's usage. This will teach you to look at the problem from someone else's perspective.
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    + + + + + + + + + + + + + + \ No newline at end of file diff --git a/javascripts/mathjax.js b/javascripts/mathjax.js new file mode 100644 index 00000000..e105205e --- /dev/null +++ b/javascripts/mathjax.js @@ -0,0 +1,17 @@ +window.MathJax = { + tex: { + inlineMath: [["\\(", "\\)"]], + displayMath: [["\\[", "\\]"]], + processEscapes: true, + processEnvironments: true + }, + options: { + ignoreHtmlClass: ".*|", + processHtmlClass: "arithmatex" + } +}; + +document$.subscribe(() => { // + + MathJax.typesetPromise() +}) \ No newline at end of file diff --git a/machine_learning/ML_snippets/index.html b/machine_learning/ML_snippets/index.html new file mode 100644 index 00000000..5b908391 --- /dev/null +++ b/machine_learning/ML_snippets/index.html @@ -0,0 +1,2611 @@ + + + + + + + + + + + + + + + + + + ML snippets - A Lazy Data Science Guide + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    Machine Learning / Deep Learning Snippets

    +

    Sharing some of the most widely used and arguably not so famous Machine Learning snippets šŸ˜‰

    +

    Feature importance

    +
      +
    • Feature importance calculation is an important technique to identify the features which "helps" with the downstream classification or regression tasks.
      • +
    +
      +
    • Sklearn provides several options to infer the importance of a feature. Most importantly, many model automatically computed the importane and store it in model.feature_importances_, after you call .fit()
      • +
    +
      +
    • As an example, lets take the text based classification task and try to infer the following,
        +
      • Part 1: First use CountVectorizer for feature engineering and ExtraTreesClassifier for classification.
        • +
      • Part 2: Show the top N features.
        • +
      • Part 3: Show evidence of a feature (by value count over different class labels)
        • +
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      • +
    +
      +
    • Following dataset based assumtions have been made,
        +
      • We assume x_train and y_train contains the a list of sentences and labels repectively.
        • +
      • We assume a pandas dataframe of name train_df is present which contains x_train and y_train as columns with name title and label respectively.
        • +
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    index = 2282 # the feature's index 
+# the label's distribution if this word is present in sentence
+train_df.iloc[np.where(features[:, index].todense() >= 1)[0]]['label'].value_counts()
+
    +

    Cross validation

    +
      +
    • Cross validation is a technique in which at each iteration you create different split of train and dev data. At each such iteration, we train he model on the train split and validate on the remaining split. This way, event with small training data, we can perform multiple fold of validation.
      • +
    • If you repeat this operation (for \(N\) iterations) over the complete data such that (1) each data point belonged to the dev split at most once, (2) each data point belonged to train split \(N-1\) times - its cross-validation.
      • +
    • I have used Stratified K-Folds cross-validator, you can use any function from the complete list mentioned here - Sklearn Model selection
      • +
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    # import =======
+from sklearn.model_selection import StratifiedKFold
+
+# code =============
+# split the dataset into K fold test
+def split_dataset(dataset, return_fold=0, n_splits=3, shuffle=True, random_state=1):
+    """
+    dataset: pandas dataframe
+    return_fold: the fold out of `n_split` fold, which is to be returned
+    n_splits: # cross fold
+    """
+    # defined the KFOld function
+    skf = StratifiedKFold(n_splits=n_splits, shuffle=shuffle, random_state=random_state)
+
+    # defined the dataset
+    X = dataset
+    y = dataset['class'] # label/class
+
+    for i, (train_index, test_index) in enumerate(skf.split(X, y)):
+        if return_fold == i:
+            return dataset.loc[train_index], dataset.loc[test_index]
+
+# example call
+if __name__ == '__main__':
+    # read the dataset
+    df = pd.read_csv("....")
+    # get one specific fold out of
+    train, test = split_dataset(dataset=df, return_fold=0, n_splits=3)
+    # run for all folds
+    for fold in range(n_splits):
+        train, test = split_dataset(dataset=df, return_fold=fold, n_splits=n_splits)
+        # <perform actions here...>
+
    +

    Hyper-parameter tuning

    +
      +
    • Below is an example of hyperparameter tuning for SVR regression algorithm. There we specify the search space i.e. the list of algorithm parameters to try, and for each parameter combination perform a 5 fold CV test.
      • +
    • More details: Sklearn Hyperparameter tuning
      • +
    • More details: Sklearn SVR Algorithm
      • +
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    # import =======
+from sklearn.model_selection import GridSearchCV
+from sklearn.metrics import make_scorer
+from sklearn.metrics import mean_squared_error
+
+# DATA LOAD ============
+train_data = ...  # load the features and target on which to train
+
+# SEARCH SPACE ============
+search_space = [{'kernel': ['poly', 'rbf', 'sigmoid'],
+               'C': [1, 10, 100], 'epsilon': [10, 1, 0.1, 0.2, 0.01]}]
+
+# TUNING ============
+scorer = make_scorer(mean_squared_error, greater_is_better=False)
+svr_gs = GridSearchCV(SVR(), search_space, cv = 5, scoring=scorer, verbose=10, n_jobs=None)
+svr_gs.fit(train_data['features'], train_data['target'])
+
+
+# PRINT RESULT ============
+parameter_result = []
+print("Grid scores on training set:")
+means = svr_gs.cv_results_['mean_test_score']
+stds = svr_gs.cv_results_['std_test_score']
+for mean, std, params in zip(means, stds, svr_gs.cv_results_['params']):
+    print("%0.3f (+/-%0.03f) for %r"% (mean, std * 2, params))
+    parameter_result.append({'mean': abs(mean), 'std': std, **params})
+
+# SELECT BEST PARAMETERS ============
+# select the settings with smallest loss
+parameter_result = pd.DataFrame(parameter_result)
+parameter_result = parameter_result.sort_values(by=['mean'])
+best_settings = parameter_result.head(1).to_dict(orient='records')[0]
+
+# FIT WITH BEST PARAMETERS ============
+SVRModel = SVR(C=best_settings['C'],
+            epsilon=best_settings['epsilon'],
+            kernel= best_settings['kernel'])
+SVRModel.fit(train_data['features'], train_data['target'])
+
    +

    Callbacks

    +
      +
    • Callbacks are the hooks that you can attach to your deep learning training or validation process.
      • +
    • It can be used to affect the training process from simple logging metric to even terminating the training in case special conditions are met.
      • +
    • Below is an example of EarlyStopping and ModelCheckpoint callbacks.
      • +
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    # fit the model
+history = model.fit(train_data_gen, # training data generator
+#                     .... # put usual code here
+                    callbacks=[checkpoint, earlystopping]
+                )
+
    +
    +
    +
    +

    Mean pooling

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    # import
+import numpy as np
+import keras
+
+# create sample data
+A=np.array([[1,2,3],[4,5,6],[0,0,0],[0,0,0],[0,0,0]])
+B=np.array([[1,3,0],[4,0,0],[0,0,1],[0,0,0],[0,0,0]])
+C=np.array([A,B]).astype("float32")
+# expected answer (for temporal mean)
+np.mean(C, axis=1)
+
+"""
+The output is
+array([[1. , 1.4, 1.8],
+       [1. , 0.6, 0.2]], dtype=float32)
+Now using AveragePooling1D,
+"""
+
+model = keras.models.Sequential(
+        tf.keras.layers.AveragePooling1D(pool_size=5)
+        )
+model.predict(C)
+
+"""
+The output is,
+array([[[1. , 1.4, 1.8]],
+       [[1. , 0.6, 0.2]]], dtype=float32)
+"""
+
    +
      +
    • Some points to consider,
        +
      • The pool_size should be equal to the step/timesteps size of the recurrent layer.
        • +
      • The shape of the output is (batch_size, downsampled_steps, features), which contains one additional downsampled_steps dimension. This will be always 1 if you set the pool_size equal to timestep size in recurrent layer.
        • +
      +
      • +
    +

    Dataset and Dataloader

    +
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    • Dataset can be downloaded from Kaggle.
      • +
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    # init the train and test dataset
+train_dataset = IMDBDataset(X_train, y_train)
+test_dataset = IMDBDataset(X_test, y_test)
+# create the dataloader
+train_dataloader = DataLoader(train_dataset, batch_size=64, shuffle=True)
+test_dataloader = DataLoader(test_dataset, batch_size=64, shuffle=True)
+
    +

    Freeze Layers

    +
      +
    • Example on how to freeze certain layers while training
      • +
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    {code-block} python
+# Before defining the optimizer, we need to freeze the layers
+# In pytorch lightning, as optimizer is defined in configure_optimizers, we freeze layers there.
+def configure_optimizers(self):
+    # iterate through the layers and freeze the one with certain name (here all BERT models)
+    # note: the name of layer depends on the varibale name
+    for name, param in self.named_parameters():
+        if 'BERTModel' in name:
+            param.requires_grad = False
+    # only pass the non-frozen paramters to optimizer
+    optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, self.parameters()), lr=1e-3)
+    # return optimizer
+    return optimizer
+
    +

    Check for GPU availability

    +
      +
    • We need GPUs for deep learning, and before we start training or inference it's a good idea to check if GPU is available on the system or not.
      • +
    • The most basic way to check for GPUs (if it's a NVIDIA one) is to run nvidia-smi command. It will return a detailed output with driver's version, cuda version and the processes using GPU. Refer this for more details on individual components.
      • +
    +
    +-----------------------------------------------------------------------------+
+| NVIDIA-SMI 435.21       Driver Version: 435.21       CUDA Version: 10.1     |
+|-------------------------------+----------------------+----------------------+
+| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
+| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
+|===============================+======================+======================|
+|   0  GeForce MX110       Off  | 00000000:01:00.0 Off |                  N/A |
+| N/A   43C    P0    N/A /  N/A |    164MiB /  2004MiB |      0%      Default |
++-------------------------------+----------------------+----------------------+
+
++-----------------------------------------------------------------------------+
+| Processes:                                                       GPU Memory |
+|  GPU       PID   Type   Process name                             Usage      |
+|=============================================================================|
+|    0      6348      G   /usr/lib/xorg                                 53MiB |
+|    0     13360      G   ...BBBBBaxsxsuxbssxsxs --shared-files         28MiB |
++-----------------------------------------------------------------------------+
+
    +
      +
    • You can even use deep learning frameworks like Pytorch to check for the GPU availability. In fact, this is where you will most probably use them.
      • +
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    # import 
+import torch
+# checks
+torch.cuda.is_available()
+## Output: True
+torch.cuda.device_count()
+## Output: 1
+torch.cuda.current_device()
+## Output: 0
+torch.cuda.device(0)
+## Output: <torch.cuda.device at 0x7efce0b03be0>
+torch.cuda.get_device_name(0)
+## Output: 'GeForce MX110'
+
    +

    HuggingFace Tokenizer

    +
      +
    • Tokenizer is a pre-processing step that converts the text into a sequence of tokens. HuggingFace tokenizer is a wrapper around the tokenizers library, that contains multiple base algorithms for fast tokenization.
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    # import
+from transformers import AutoTokenizer
+
+# load a tokenizer (use the model of your choice)
+tokenizer = AutoTokenizer.from_pretrained('sentence-transformers/all-MiniLM-L6-v2')
+
+# create an dummy text
+text = "Hello my Name is Mohit"
+
+# this will tokenize the text and return a list of tokens
+tokenizer.tokenize(text)
+# Output: ['hello', 'my', 'name', 'is', 'mo', '##hit']
+
+# this will tokenize the text and return a list of token ids
+tokenizer.encode(text)
+# Output: [101, 7592, 2026, 2171, 2003, 9587, 16584, 102]
+
+# this will return the decoded text (from token ids)
+tokenizer.decode(tokenizer.encode(text))
+# Output: [CLS] hello my name is mohit [SEP]
+
+# get the token and id details in key value pair
+vocabulary = tokenizer.get_vocab()
+# length of vocab here is 30522
+# vocabulary['hello'] returns 7592
+
    + + + +
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    + + + + + + + + + + + + + + \ No newline at end of file diff --git a/machine_learning/classification/index.html b/machine_learning/classification/index.html new file mode 100644 index 00000000..e9e364ec --- /dev/null +++ b/machine_learning/classification/index.html @@ -0,0 +1,2225 @@ + + + + + + + + + + + + + + + + + + Classification - A Lazy Data Science Guide + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    Classification

    + +
    +

    Note

    +

    This page is still not complete and new sections might get added later. That said, the existing content is ready to be consumed. šŸ‘

    +
    +

    Introduction

    +

    Classification is a fundamental task in supervised machine learning, where the goal is to assign predefined labels or categories to input data points based on their features. Unlike clustering, which deals with unsupervised learning and aims to discover inherent patterns or groupings in data, classification relies on a labeled dataset to learn a model that can make predictions on new, unseen data. The primary objective of classification is to create a decision boundary or model that can accurately differentiate between different classes or categories within the data. Some use cases of classification could be bank loan defaulter prediction, spam email detection, image recognition, medical diagnosis, sentiment analysis, etc.

    +

    Metrics

    +

    Classification metrics are used to evaluate the performance of a classification model by comparing its predictions to the actual ground truth labels. Here are some common classification metrics,

    +
    +

    +

    An extensive view of Confusion Matrix and different metrics. Source: Wikipedia

    +
    +
      +
    1. +

      Accuracy: Accuracy is the most basic classification metric, measuring the ratio of correctly predicted instances to the total number of instances. It provides an overall measure of the model's correctness. However, it may not be suitable for imbalanced datasets, where one class significantly outnumbers the others.

      +
      2. +
    2. +

      Precision: Precision is the ratio of true positive predictions to the total number of positive predictions made by the model. High precision indicates that when the model predicts a positive class, it is likely to be correct.

      +
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    3. +

      Recall (Sensitivity or True Positive Rate): Recall is the ratio of true positive predictions to the total number of actual positive instances in the dataset. It measures the model's ability to capture all positive instances. High recall means that the model can find most of the positive cases.

      +
      4. +
    4. +

      F1-Score: The F1-Score is the harmonic mean of precision and recall. It balances both metrics and is particularly useful when you need to consider the trade-off between precision and recall. It's a good overall measure of a model's performance. Please be aware of average params in the Sklearn implementation. Set the param to macro in case of imbalanced dataset, as it will compute the score for each class and then perform unweighted average i.e. giving each class equal importance, no matter their frequency. Setting it to weighted is similar to macro, but now the average will be weighted. Setting to micro will lead to computing the numbers for complete data without considering any class.

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      Specificity (True Negative Rate): Specificity measures the model's ability to correctly identify negative instances. It is the ratio of true negative predictions to the total number of actual negative instances. It is particularly relevant when false negatives are costly.

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      6. +
    6. +

      ROC Curve and AUC: The Receiver Operating Characteristic (ROC) curve is a graphical representation of the model's performance across different thresholds. The Area Under the ROC Curve (AUC) quantifies the overall performance of the model, with a higher AUC indicating better discrimination between classes.

      +
      7. +
    7. +

      Confusion Matrix: A confusion matrix is a table that summarizes the model's predictions compared to the actual labels, breaking down true positives, true negatives, false positives, and false negatives. It provides detailed insights into the model's performance.

      +
      8. +
    + + +

    The choice of which metric to use depends on the specific problem, the nature of the dataset, and the business or application requirements. It's essential to consider the context and goals when selecting the appropriate classification metrics for evaluating a machine learning model.

    +

    Classification Algorithms

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    While there are many classification algorithms, here are some of the most common and widely used ones,

    +

    Logistic Regression

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      +
    • Logistic Regression is a widely used classification model that is particularly effective for binary classification problems. It works by modeling the relationship between the input features and the probability of belonging to a particular class. It does this by fitting a logistic curve to the data, which allows it to output probabilities that an instance belongs to a specific class. Logistic Regression is a linear model, which means it assumes a linear relationship between the input features and the log-odds of the class probabilities. It's simple, interpretable, and computationally efficient, making it a good choice for problems with a large number of features.
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    Decision Tree

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      +
    • A Decision Tree is a versatile and interpretable machine learning model used for both classification and regression tasks. It is a tree-like structure where each internal node represents a feature, each branch represents a decision rule based on that feature, and each leaf node represents the predicted outcome or value. Decision Trees are particularly well-suited for tasks where the decision-making process can be represented as a series of logical if-then-else conditions.
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    • One of the significant advantages of Decision Trees is their transparency and interpretability. The model's decision rules can be easily visualized, understood, and explained to non-technical stakeholders, making it a valuable tool in domains where interpretability is crucial, such as healthcare and finance. However, Decision Trees can be prone to overfitting, especially when they are deep and the dataset is small. To mitigate this issue, techniques like pruning, limiting tree depth, and using ensemble methods like Random Forests or Gradient Boosting Trees are often employed. Decision Trees also provide feature importance scores, helping analysts identify the most critical factors driving the model's predictions, which can inform feature selection and data exploration efforts in a broader context.
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    Clustering

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    Note

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    This page is still not complete and new sections might get added later. That said, the existing content is ready to be consumed. šŸ‘

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    Introduction

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      +
    • Clustering is an unsupervised task of grouping a set of items based on itemā€™s features where the final grouping should minimize certain cost function. While finding optimum grouping is very hard, there are several algorithms that can help us find sub-optimal solutions. Also note that as the data is not labeled, the grouping could be completely different from the userā€™s expectation. Each clustering algorithm has its own internal similarity function and grouping strategy by which the clusters are formed.
      • +
    • Clustering algorithms can be categorised based on different perspectives. Below are some examples (not a complete list),
        +
      • Considering time of application, we can have online (streaming data) vs offline (all data present) clustering algorithms.
        • +
      • Considering input data type, we have algorithms that consider item's features while others that considers item-item similarity matrix.
        • +
      • Considering input parameter, there are algorithms that require no of clusters as input while others do not.
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      +
      • +
    +

    Metrics

    +

    Before starting with Clustering algorithms, let's understand the metrics that can be used to compare the clustering performance.

    +

    Silhouette Score

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      +
    • Silhouette score considers the intra-cluster \(a(i)\) and inter-cluster \(b(i)\) distances to generate a performance metric for the clustering of a dataset. The score can range between -1 (bad clustering) and 1 (good clustering), with higher number denoting better clustering. We can choose different distance functions (euclidean, manhattan, cosine, etc) based on the data. The formulation wrt each data point is shown below, where we can average the value to get dataset level scores.
      • +
    +
    \[ +{\displaystyle a(i)={\frac {1}{|C_{I}|-1}}\sum _{j\in C_{I},i\neq j}d(i,j)} +\]
    +
    \[ +{\displaystyle b(i)=\min _{J\neq I}{\frac {1}{|C_{J}|}}\sum _{j\in C_{J}}d(i,j)} +\]
    +
    \[ +{\displaystyle s(i)={\frac {b(i)-a(i)}{\max\{a(i),b(i)\}}}} +\]
    +
      +
    • In \(a(i)\) formulation, we compare each data point against all the other points in the same cluster. We use \(\frac {1}{|C_{I}|-1}\) as we ignore the \(d(i, i)\) computation. In \(b(i)\) formulation, we compare one data point against all data points from other clusters one at a time, and pick the value for the cluster with members having minimum average distance from the data point (think of it like the next best cluster for the data point). Finally, the silhouette score for that data point is computed in \(s(i)\). It is further simplified below,
      • +
    +
    \[ +s(i) = \begin{cases} + 1-a(i)/b(i), & \mbox{if } a(i) < b(i) \\ + 0, & \mbox{if } a(i) = b(i) \\ + b(i)/a(i)-1, & \mbox{if } a(i) > b(i) \\ +\end{cases} +\]
    +
      +
    • Sklearn package provides a function to compute silhouette score. The inputs are data point features, the cluster labels and the distance metric. Example call is shown below,
      • +
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    1
+2
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    # import 
+from sklearn.metrics import silhouette_score
+
+# compute the score
+score = silhouette_score(X, labels, metric='cosine')
+
    +

    Homogeneity, Completeness and V measure

    +
      +
    • +

      For a given set of datapoints, the original labels are called "Class" and the output of clustering algorithms is called "Clusters". To measure performance of the algorithm, we can check how well the cluster and class align. This alignment can be captured by creating a contingency table that contains distribution of data points falling under different combinations of class and clusters. For example, consider 6 datapoints for which we have the class label [1, 1, 1, 2, 2, 2] and cluster label [1, 1, 2, 2, 3, 3]. The contingency table will contains \(a_{ck}\) elements where \(c\) denotes the class and \(k\) the clusters. It will look as follows,

      + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
      class/cluster123
      1210
      2012
      3000
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      • +
    +
    +

    Note

    +

    As clear from the table above, the alignment could be directional in nature i.e. the metrics using the contingency table will generate different score if we compare (class, cluster) and (cluster, class).

    +
    +
      +
    • +

      With this context, let's understand the metrics in detail, [5]

      +
        +
      • Homogeneity: it describes the "purity" of the cluster i.e. it measures whether or not the same class is assigned to all points within a cluster. The formulation is as follows,
        • +
      +
      \[ +h = 1 - \frac {H(C|K)}{H(C)}, where +\]
      +
      \[ +H (C | K) = - \sum_{k=1}^{|K|} \sum_{c=1}^{|C|} \frac{a_{ck}}{N} \log_{}{\frac{a_{ck}}{\sum_{c=1}^{|C|} a_{ck}}} +\]
      +
      \[ +H (C) = - \sum_{c=1}^{|C|} \frac{\sum_{k=1}^{|K|} a_{ck}}{n} \log_{}{\frac{\sum_{k=1}^{|K|} a_{ck}}{n}} +\]
      +
        +
      • Completeness: it describes the "purity" of the class i.e. it measures if all points belonging to a given class are assigned to the same cluster. The formulation is as follows,
        • +
      +
      \[ +c = 1 - \frac {H(K|C)}{H(K)}, where +\]
      +
      \[ +H (K | C) = - \sum_{c=1}^{|C|} \sum_{k=1}^{|K|} \frac{a_{ck}}{N} \log_{}{\frac{a_{ck}}{\sum_{k=1}^{|K|} a_{ck}}} +\]
      +
      \[ +H (K) = - \sum_{k=1}^{|K|} \frac{\sum_{c=1}^{|C|} a_{ck}}{n} \log_{}{\frac{\sum_{c=1}^{|C|} a_{ck}}{n}} +\]
      +
        +
      • V measure: it is a combination of the above scores and is used to measure the overall performance of a clustering algorithm. To be exact, it is the harmoic mean of the Homogeneity and Completeness scores. The formulation is given below, where beta is the ratio of weight attributed to homogeneity vs completeness. If beta is greater than 1, completeness is weighted more strongly in the calculation. If beta is less than 1, homogeneity is weighted more strongly. By default, beta is 1.
        • +
      +
      \[ +v = \frac{(1 + beta) * homogeneity * completeness}{(beta * homogeneity + completeness)} +\]
      +
      • +
    +
    +

    Hint

    +

    Sklearn provides individual and combined implementations for these metrics. Refer sklearn.metrics.homogeneity_completeness_v_measure

    +
    +

    Clustering Algorithms

    +

    K-Means

    +
      +
    • +

      K-means is the swiss army knife of the clustering algorithms, the forever baseline - the first clustering algorithm anyone tries šŸ˜„. It can be easily understood by considering the steps mentioned below. The step (a) is a one time activity done during the initialization part, while steps (b) and (c) are repeated until the convergence i.e. there is no more change in cluster membership even if we continue the process or there is no more noticable centroid movement.

      +
        +
      1. +

        Centroid Assignment: Assign K centroids. There are three points to remember here,

        +
          +
        1. How to decide the value of K? --> here it is an input parameter. In practice we can analyze the cluster results with different k (2 to N) and pick the one with best metric score like silhouette score.
          2. +
        2. Are centroids choosen from data points? --> during initialization they may be selected from data points but over iterations they become their own special points that are part of the same feature space
          3. +
        3. How are the centroids choosen? --> the assignment strategy can be random (pick any random K data points), or follow 'furthest' heuristic (\(i^{th}\) centroid is choosen such that its minimum distance to the preceding centroids is largest) or follow k-mean++ heuristic (selects a point with probability proportional to the square of its distance to the nearest preceding centroid).
          4. +
        +
        +

        Note

        +

        Random initialization is not preferred, as it is possible to get all centroid assigned to data points from only one true cluster. The 'furthest' heuristic is also not preferred as it select data points at the edges for centroid. K-means++ heuristic is more suitable as the centroid selection is more natural.

        +
        +
        2. +
      2. +

        Cluster Assignment: assign all data points to one of the centroids (forming a cluster) based on the closeness of the points to the centroid. The closeness is computed by a similarity function that could be equilidean distance.

        +
        3. +
      3. Centroid Adjustment: adjust the centroids such that it minimises the intra-cluster distance among the cluster member. This is called inertia and its formulation is \(\sum_{i=0}^n \text{min}(||x_i-\mu_{j}||^2)\), where \(\mu_{j}\) is the mean of the respective cluster. The adjustment is done by moving the centroids to the mean position of the cluster members.
        4. +
      +
      • +
    +
      +
    • Remember, K-means algorithm is guaranteed to converge but the final result may vary based on the centroid initialisation. This is why it is suggested to try multiple runs with different initialization and pick the one with best clustering or use smarter initialization technique like k-means++. Refer ML Interview Questions and [2] for more details.
      • +
    +
      +
    • Many clustering algorithms have improved k-means over time, they are:
        +
      • K-Medians: It is a variation of k-means clustering where instead of calculating the mean for each cluster to determine its centroid, we calculates the median. As it uses Manhattan distance (L1-norm distance), the algorithm becomes more reliable for discrete or binary data sets.
        • +
      • K-Medoids: In contrast to the k-means algorithm, k-medoids chooses actual data points as centers instead. Furthermore, k-medoids can be used with arbitrary dissimilarity measures, whereas k-means generally requires Euclidean distance. Because k-medoids minimizes a sum of pairwise dissimilarities instead of a sum of squared Euclidean distances, it is more robust to noise and outliers than k-means. Refer [3] for an intuitive solved example.
        • +
      • K-means++: It is the standard K-means algorithm but with a smarter initialization of the centroids. We start with choosing one center randomly from the data points. Then for each data point \(x\) not chosen yet, we find the distance \(D(x)\) between \(x\) and the nearest center that has already been chosen. The new center is choosen again at random but this time using a weighted probability distribution where a point \(x\) is chosen with probability proportional to \(D(x)^2\). We repeat these steps until k centers have been chosen.
        • +
      • Mini Batch K-means: It is an optimized version of k-means for faster execution with only slighly worse results. Here, at each iteration a set of random data points are selected to form a mini-batch and they are assigned to the nearest centroids. The centroid adjustment happens for each sample by taking streaming average of the sample and all previous samples assigned to that centroid. Mini Batch K-means converges faster than K-means.
        • +
      +
      • +
    +
    +

    Hint

    +

    K-Means works best in datasets with clusters that are roughly equally-sized and shaped roughly regularly. Also the data points should be in euclidean space as the K-means uses euclidean distance measure. It is not recommended for clustering neural network embeddings as they capture semantic meanings which is more suitably captured by cosine similarity. The best that can be done with K-means is to run multiple iterations on embeddings and pick the one with highest cosine silhouette score.

    +
    +
      +
    • Here is the code to perform Kmeans clustering and find the silhouette score [1],
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    # import
+from sklearn.cluster import KMeans
+from sklearn.metrics import silhouette_score
+
+# load the data
+X = np.load('data/embeddings.npy') # dummy
+
+# create the model
+model = KMeans(n_clusters = 2, init='k-means++', max_iter=100, n_init=1)
+# get the label
+labels = model.fit_predict(X)
+# compute silhouette_score
+sil_score = silhouette_score(X, labels, metric='cosine')
+
    +

    Hierarchical Clustering

    +
      +
    • It is a clustering technique that seeks to build a hierarchy of clusters i.e. we start with the dataset divided into N clusters and then some clusters are either merged or split based on their similarity. There are two major strategies to perform hierarchical clustering,
        +
      • Agglomerative: Bottom-up approach where we start with each sample as a separate cluster and then clusters are merged.
        • +
      • Divisive: Top-down approach where we start with all samples part of a single cluster and then at each level we recursively split existing clusters.
        • +
      +
      • +
    • To understand the split or merge process, we need to understand the following,
        +
      • Distance metric: this is the function that gives the distance between two data points. We can choose from a number of functions like euclidean, cosine, manhattan, etc.
        • +
      • Linkage criteria: this is a function that define the distance between two clusters based on applying distance function on pairs of data from the two clusters. There are following strategies to choose from,
          +
        • Complete Linkage: Pick the most distant pair of points as the representation of cluster distance. Formulation is : \(\max \,\{\,d(a,b):a\in A,\,b\in B\,\}.\)
          • +
        • Single Linkage: Pick the least distant pair of points as the representation of cluster distance. Formulation is : \(\min \,\{\,d(a,b):a\in A,\,b\in B\,\}.\)
          • +
        • Ward: find the pair of clusters that leads to minimum increase in total within-cluster variance after merging. This is only applicable for euclidean distance. [4]
          • +
        • Average: Uses the average of distances between all pairs of data points from the two clusters. Formulation is \({\displaystyle {\frac {1}{|A|\cdot |B|}}\sum _{a\in A}\sum _{b\in B}d(a,b).}\)
          • +
        +
        • +
      +
      • +
    • Now it should be easy to understand the overall process. Taking agglomerative as example, to begin with, all samples are separate clusters. At each iteration, we will compute the linkage score between all pairs of clusters and find the pair with the minimum score. We merge that pair of clusters together and go ahead with next iteration. We keep on repeating this process until we have reached a desired number of clusters (n_clusters) or the linkage distance threshold (distance_threshold) has triggered, above which, clusters will not be merged.
      • +
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    +

    Note

    +

    At a time, we can only use either n_clusters or distance_threshold.

    +
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    • Here is a sample code using sklearn package [1],
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    # import
+from sklearn.cluster import AgglomerativeClustering
+from sklearn.metrics import silhouette_score
+
+# load the data
+X = np.load('data/embeddings.npy') # dummy
+
+# create the model
+model = AgglomerativeClustering(n_clusters = 2, affinity='cosine', linkage='average')
+# get the label
+labels = model.fit_predict(X)
+# compute silhouette_score
+sil_score = silhouette_score(X, labels, metric='cosine')
+
    + + +

    References

    +

    [1] Sklearn - Clustering

    +

    [2] Visualizing K Means Clustering - Naftali Harris

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    [3] K-Medoids clustering with solved example

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    [4] Wikipedia - Hierarchical clustering | Ward's method

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    [5] V-Measure: A Conditional Entropy-Based External Cluster Evaluation Measure

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    Interview Questions

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    • Here are some questions and their answers to make you ready for your next interview. Best of luck šŸ‘‹
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    What is deep learning and how is it different from traditional machine learning?

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    Deep learning is a subfield of machine learning that uses neural networks with many layers, called deep neural networks, to learn and make predictions. It is different from traditional machine learning in that it can automatically learn hierarchical representations of the data and doesn't rely heavily on feature engineering.

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    How does backpropagation work in a neural network?

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    Backpropagation is an algorithm used to train neural networks. It starts by propagating the input forward through the network, calculating the output. Then it compares the output to the desired output and calculates the error. The error is then propagated backwards through the network, adjusting the weights in the network so as to minimize the error. This process is repeated multiple times until the error is minimized.

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    While training deep learning models, why do we prefer training on mini-batch rather than on individual sample?

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    First, the gradient of the loss over a mini-batch is an estimate of the gradient over the training set, whose quality improves as the batch size increases. Second, computation over a batch can be much more efficient than m computations for individual examples, due to the parallelism afforded by the modern computing platforms. Ref

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    What are the benefits of using Batch Normalizattion?

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    Batch Normalization also has a beneficial effect on the gradient flow through the network, by reducing the dependence of gradients on the scale of the parameters or of their initial values. This allows us to use much higher learning rates without the risk of divergence. Furthermore, batch normalization regularizes the model and reduces the need for Dropout (Srivastava et al., 2014). Finally, Batch Normalization makes it possible to use saturating nonlinearities by preventing the network from getting stuck in the saturated modes. Ref

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    What is Entropy (information theory)?

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    Entropy is a measurement of uncertainty of a system. Intuitively, it is the amount of information needed to remove uncertainty from the system. The entropy of a probability distribution p for various states of a system can be computed as: \(-\sum_{i}^{} (p_i \log p_i)\)

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    Even though Sigmoid function is non-linear, why is Logistic regression considered a linear classifier?

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    Logistic regression is often referred to as a linear classifier despite using the sigmoid (logistic) activation function because it models the relationship between the input features and the log-odds (logit) of the binary target variable in a linear manner. The linearity in logistic regression refers to the fact that it creates a linear decision boundary in the feature space, which is a hyperplane. Refer

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    What is the difference between Logits, Soft and Hard targets?

    +
    +
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    • Let us understand each of the terms one by one. For better understanding, let's take a dog vs cat image classification as an example.
        +
      • Logits are the un-normalized output of the model. In our cat vs dog example, logits will be, say, 10.1 for cat and 5.6 for dog for an image with cat. Refer this SE question.
        • +
      • Soft target: are normalized logits by applying a linear function. In our example, if we use softmax to the logits we get 0.99 for cat and 0.1 for dog.
        • +
      • Hard targets: are the encoding of the soft targets. In our example, as the model predicted (here correctly) the image as of cat, the hard targets be 1 for cat and 0 for dog.
        • +
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      • +
    +
    graph LR
+    A[Logits] -- normalization --> B[Soft Targets]
+    B -- encoding --> C[Hard Targets]
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    How do you handle overfitting in deep learning models?

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    • Overfitting occurs when a model becomes too complex and starts to fit the noise in the training data, rather than the underlying pattern. There are several ways to handle overfitting in deep learning models, including:
        +
      • Regularization techniques such as L1 and L2 regularization, which add a penalty term to the loss function to discourage large weights
        • +
      • Early stopping, where training is stopped before the model has a chance to fully fit the noise in the training data
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      • Dropout, which randomly drops out a certain percentage of neurons during training to prevent them from co-adapting and becoming too specialized
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      • Adding more data to the training set
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    Can you explain the concept of convolutional neural networks (CNN)?

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    A convolutional neural network (CNN) is a type of neural network that is primarily used for learning image and video patterns. CNNs are designed to automatically and adaptively learn spatial hierarchies of features from input data. They use a variation of multi-layer perceptrons, designed to require minimal preprocessing. Instead of hand-engineered features, CNNs learn features from data using a process called convolution.

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    How do you handle missing data in deep learning?

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    • Missing data can be handled in several ways, including:
        +
      • Removing the rows or columns with missing data
        • +
      • Interpolation or imputation of missing values
        • +
      • Using a technique called masking, which allows the model to ignore missing values when making predictions
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    Can you explain the concept of transfer learning in deep learning?

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    Transfer learning is a technique where a model trained on one task is used as a starting point for a model on a second, related task. This allows the model to take advantage of the features learned from the first task and apply them to the second task, which can lead to faster training and better performance. This can be done by using a pre-trained model as a feature extractor or fine-tuning the pre-trained model on new data.

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    What is Gradient Descent in deep learning?

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    Gradient Descent is an optimization algorithm used to minimize the loss function of a neural network. It works by updating the weights of the network in the opposite direction of the gradient of the loss function with respect to the weights. The magnitude of the update is determined by the learning rate. There are several variants of gradient descent, such as batch gradient descent, stochastic gradient descent, and mini-batch gradient descent.

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    Please explain what is Dropout in deep learning?

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    Dropout is a regularization technique used in deep learning to prevent overfitting. It works by randomly dropping out a certain percentage of neurons during training, effectively reducing the capacity of the network. This forces the network to learn multiple independent representations of the data, making it less prone to overfitting.

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    What are Autoencoder?

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    An autoencoder is a type of neural network that is trained to reconstruct its input. It has an encoder part that compresses the input into a lower-dimensional representation called the bottleneck or latent code, and a decoder part that reconstructs the input from the latent code. Autoencoders can be used for tasks such as dimensionality reduction, anomaly detection and generative modelling.

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    Can you explain the concept of attention mechanism in deep learning?

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    Attention mechanism is a way to weight different parts of the input in a neural network, giving more importance to certain features than others. It is commonly used in tasks such as machine translation, where the model needs to focus on different parts of the input sentence at different times. Attention mechanisms can be implemented in various ways, such as additive attention, dot-product attention, and multi-head attention.

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    What are Generative Adversarial Networks (GANs)?

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    Generative Adversarial Networks (GANs) are a type of generative model that consists of two parts, a generator and a discriminator. The generator is trained to generate new data that is similar to the data it was trained on, while the discriminator is trained to distinguish the generated data from the real data. The two parts are trained together in a game-theoretic manner, where the generator tries to generate data that can fool the discriminator, and the discriminator tries to correctly identify the generated data.

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    Can you explain the concept of Memory Networks in deep learning?

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    Memory networks are a type of neural network architecture that allow the model to access and manipulate an external memory matrix, which can be used to store information that is relevant to the task. This allows the model to reason about the past and use this information to make predictions about the future. Memory networks have been used in tasks such as language understanding and question answering. Refer this for more details.

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    Explain Capsule Networks in deep learning?

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    Capsule networks are a type of neural network architecture that aims to overcome the limitations of traditional convolutional neural networks (CNNs) by using a new type of layer called a capsule. A capsule contains multiple neurons that work together to represent an object or part of an object, and the activities of the neurons are used to represent the properties of the object such as position, size and orientation. Capsule networks have been used in tasks such as image classification and object detection.

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    Can you explain the concept of generative models in deep learning?

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    Generative models are a type of deep learning model that can generate new data that is similar to the data it was trained on. These models are trained on a dataset and learn the underlying probability distribution of the data, allowing them to generate new, unseen data that fits that distribution. Examples of generative models include Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs).

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    What is the concept of adversarial training in deep learning?

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    Adversarial training is a technique used to improve the robustness of deep learning models by generating adversarial examples and using them to train the model. Adversarial examples are inputs that are slightly perturbed in such a way as to cause the model to make a mistake. By training the model on these examples, it becomes more robust to similar perturbations in the real world.

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    What is weight initialization in deep learning?

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    Weight initialization is the process of setting the initial values for the weights in a neural network. The initial values of the weights can have a big impact on the network's performance and training time. There are several methods to initialize weights, including random initialization, Glorot initialization, and He initialization. Each of these methods have different properties and are more suitable for different types of problems.

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    Explain data augmentation?

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    Data augmentation is a technique used to increase the amount of data available for training a deep learning model. This is done by creating new training examples by applying various random transformations to the original data, such as random cropping, flipping, or rotation. Data augmentation can be a powerful tool to prevent overfitting and improve the generalization performance of a mode.

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    What is the different between Standardization and Normalization?

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    Normalization is the process of scaling the data to a common scale. It is also known as Min-Max Scaling where the final range could be [0, 1] or [-1,1] or something else. \(X_{new} = (X - X_{min})/(X_{max} - X_{min})\) Standardization is the process of scaling the data to have zero mean and unit variance. \(X_{new} = (X - mean)/Std\)

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    Is it possible that during ML training, both validation (or test) loss and accuracy, are increasing?

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    Accuracy and loss are not necessarily exactly (inversely) correlated, as loss measures a difference between raw prediction (float) and class (0 or 1), while accuracy measures the difference between thresholded prediction (0 or 1) and class. So if raw predictions change, loss changes but accuracy is more "resilient" as predictions need to go over/under a threshold to actually change accuracy. Soltius's answer on SE

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    Is K-means clustering algorithm guaranteed to converge with unique result?

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    K-means clustering algorithm is guaranteed to converge but the final result may vary based on the centroid initialisation. This is why it is suggested to try multiple initialization strategies and pick the one with best clustering. The convergence is guaranteed as the sum of squared distances between each point and its centroid strictly decreases over each iteration. Also the practical run time of k-means is basically linear. Refer Visualizing K Means Clustering - Naftali Harris

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    In K-means clustering, is it possible that a centroid has no data points assigned to it?

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    Yes it is possible, imagine a centroid placed in middle of ring of other centroids. Several implementations either removes that centroid or random;y replace it somewhere else in the data space. Refer Visualizing K Means Clustering - Naftali Harris

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    What is entropy in information theory?

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    Entropy is a measure of the amount of uncertainty or randomness in a system. It is often used in information theory and statistical mechanics to describe the unpredictability of a system or the amount of information required to describe it. It's formula is, \(\mathrm {H} (X):=-\sum _{x\in {\mathcal {X}}}p(x)\log p(x)=\mathbb {E} [-\log p(X)]\)

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    Here is an excellent video from Aurelien Geron, explaining the topic.

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    What is the difference between supervised and unsupervised learning?

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    Supervised learning uses labeled data to train a model to make predictions, while unsupervised learning uses unlabeled data to find patterns or structure in the data.

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    How do you evaluate the performance of a machine learning model?

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    One common method is to split the data into a training set and a test set, and use metrics such as accuracy, precision, recall, and F1 score to evaluate the model's performance on the test set.

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    What is overfitting in machine learning and how can it be prevented?

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    Overfitting occurs when a model is too complex and is able to fit the noise in the training data, leading to poor performance on new, unseen data. To prevent overfitting, methods such as cross-validation, regularization, and early stopping can be used.

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    What is the difference between a decision tree and random forest?

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    A decision tree is a single tree model that makes a prediction by traversing the tree from the root to a leaf node, while a random forest is an ensemble of decision trees, where the final prediction is made by averaging the predictions of all the trees in the forest.

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    What is the Bias-Variance trade-off in machine learning?

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    The Bias-Variance trade-off refers to the trade-off between a model's ability to fit the training data well (low bias) and its ability to generalize well to new, unseen data (low variance). A model with high bias will underfit the training data, while a model with high variance will overfit the training data.

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    What is the difference between batch and online learning?

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    Batch learning is a type of machine learning where the model is trained on a fixed dataset and the parameters are updated after processing the entire dataset. In contrast, online learning is a type of machine learning where the model is trained on a continuous stream of data and the parameters are updated incrementally after processing each example.

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    What is the difference between a decision boundary and a decision surface in machine learning?

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    A decision boundary is a boundary that separates different classes in a dataset, it can be represented by a line or a hyperplane in a two-dimensional or multi-dimensional space respectively. A decision surface is a generalization of decision boundary, it's a surface that separates different classes in a dataset, it can be represented by a surface in a multi-dimensional space. In simple words, a decision boundary is a one-dimensional representation of a decision surface.

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    What is the use of principal component analysis (PCA) in machine learning?

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    Principal component analysis (PCA) is a technique used to reduce the dimensionality of a dataset by identifying the most important features, called principal components. PCA finds a new set of uncorrelated features, called principal components, that can explain most of the variance in the original data. This can be useful for visualizing high-dimensional data, reducing noise, and improving the performance of machine learning models.

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    What is the use of the Random Forest algorithm in machine learning?

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    Random Forest is an ensemble learning technique that combines multiple decision trees to improve the performance and stability of the model. It works by creating multiple decision trees using a random subset of the features and training data, and then averaging the predictions of all the trees to make a final prediction. Random Forest algorithm is often used for classification and regression problems, it's robust to outliers, missing values, and irrelevant features, and it can also be used for feature selection and feature importance analysis.

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    What is the difference between a generative model and a discriminative model?

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    A generative model learns the probability distribution of the data and can generate new samples from it, while a discriminative model learns the boundary between different classes and make predictions based on it. Generative models are used for tasks such as density estimation, anomaly detection, and data generation, while discriminative models are used for tasks such as classification and regression.

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    What is the difference between an autoencoder and a variational autoencoder?

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    An autoencoder is a type of neural network that learns to encode and decode input data, it can be used to reduce the dimensionality of the data and learn a compact representation of it. A variational autoencoder (VAE) is a type of autoencoder that learns a probabilistic encoding of the input data, it generates new samples from the learned distribution. VAE can be used for tasks such as image generation and anomaly detection.

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    What is Expectation-Maximization (EM) algorithm?

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    The Expectation-Maximization (EM) algorithm is a method for finding maximum likelihood estimates in incomplete data problems, where some of the data is missing or hidden. EM works by iteratively refining estimates of the missing data and the parameters of the model, until it converges to a local maximum of the likelihood function. It can be used for tasks such as clustering, image segmentation, and missing data imputation.

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    What is the difference between L1 and L2 regularization in machine learning?

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    L1 and L2 regularization are methods used to prevent overfitting in machine learning models by adding a penalty term to the loss function. L1 regularization adds the absolute value of the weights to the loss function, while L2 regularization adds the square of the weights. L1 regularization leads to sparse models where some weights will be zero, while L2 regularization leads to models where all weights are small.

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    Explain Support Vector Machine (SVM).

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    Support Vector Machine (SVM) is a supervised learning algorithm that can be used for classification and regression tasks. SVM works by finding the hyperplane that maximally separates the different classes in a dataset and then uses this hyperplane to make predictions. SVM is particularly useful when the data is linearly separable, it's also robust to high-dimensional data and it can be used with kernel functions to solve non-linearly separable problems.

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    What is the use of the k-nearest neighbors (k-NN) algorithm?

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    k-nearest neighbors (k-NN) is a type of instance-based learning algorithm that can be used for classification and regression tasks. The algorithm works by finding the k training examples that are closest to a new input and using the majority class or average value of those examples to make a prediction. k-NN is a simple and efficient algorithm that can be used for tasks such as image classification, anomaly detection, and recommendation systems.

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    What is the use of the Random Sampling method for feature selection in machine learning?

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    Random Sampling is a method for feature selection that involves randomly selecting a subset of features from the dataset and evaluating the performance of a model trained on that subset. The subset of features that result in the best performance are then chosen for further analysis or use in a final model. This method can be useful when the number of features is large and there is no prior knowledge of which features are most relevant.

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    Explain Bagging method in ensemble learning?

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    Bagging (Bootstrap Aggregating) is a method for ensemble learning that involves training multiple models on different subsets of the data and then combining the predictions of those models. The subsets of data are created by randomly sampling the original data with replacement, this method helps to reduce the variance of the model and increase the robustness of the predictions. Bagging is commonly used with decision trees and can be implemented using Random Forest algorithm.

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    Explain AdaBoost method in ensemble learning?

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    AdaBoost (Adaptive Boosting) is a method for ensemble learning that involves training multiple models on different subsets of the data and then combining the predictions of those models. The subsets of data are created by giving more weight to the examples that are misclassified by the previous models, this method helps to increase the accuracy of the model. AdaBoost is commonly used with decision trees and can be used with any type of base classifier.

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    Explain Gradient Boosting method in ensemble learning?

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    Gradient Boosting is a method for ensemble learning that involves training multiple models in a sequential manner, where each model tries to correct the mistakes of the previous model. The method uses gradient descent to minimize the loss function, this method is commonly used with decision trees and it can be used with any type of base classifier. Gradient Boosting is a powerful method that can achieve state-of-the-art performance in many machine learning tasks.

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    Explain XGBoost method in ensemble learning?

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    XGBoost (Extreme Gradient Boosting) is a specific implementation of the Gradient Boosting method that uses a more efficient tree-based model and a number of techniques to speed up the training process and reduce overfitting. XGBoost is commonly used in machine learning competitions and it's one of the most popular libraries used for gradient boosting. It's used for classification and regression problems.

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    Introduction

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    • The first question to ask is "What is Machine Learning (ML)?". And to answer this question we should understand the relation between Artificial Intelligence (AI), Machine Learning (ML) and even Deep Learning (DL). Let's go through them ony by one,
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      • Artificial Intelligence (AI) is the process of creating intelligent machines that can perform tasks that humans can do.
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      • Machine Learning (ML) is subset of AI where we create systems that is able to learn from data and perform specific tasks with near or above human capability.
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      • Deep Learning (DL) is subset of ML where we create neural network based system that is able to capable of identifying patterns from data and perform specific tasks.
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    The hierarchy of Artificial Intelligence

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    Different types of Learning

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    The paradigms of Machine Learning

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    Supervised Learning

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    • It is a machine learning approach wherein we learn a function that transforms an input into an output based on example input-output pairs. Basically, it uses a labeled dataset as a training dataset to learn a generic function that can be later used to predict unseens data.
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    • Classification is one of the most common problems for which supervised learning is utilized. The idea is to learn a generic function that takes an itemā€™s features as input and provides the itemā€™s class as output. To solve this, several classification algorithms try to create boundaries for each class based on the features of labeled data. Later for any new item, the boundaries help decide which class the item belongs to.
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    Unsupervised Learning

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    • It is a machine learning approach wherein we learn patterns from unlabeled data. It is more of a descriptive analysis that can be used to generate insights from the data, which could be later used for downstream predictions.
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    • Clustering is a common example of unsupervised learning. The idea is to make groups of items based on the itemā€™s features. Note, as the data is not labeled, the grouping could be completely different from the userā€™s expectations. Each clustering algorithm has its own internal similarity function and grouping strategy by which the clusters are formed.
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    Semi-Supervised Learning

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    • It is a machine learning approach wherein we use labeled and unlabeled data to train a model. The intention is that the resulting model will be better than one learned over the labeled (supervised) or unlabeled data (unsupervised) alone. Hence it falls between the two methods. We can try semi-supervised learning when we have very little labeled data but a lot of unlabeled data, and if the cost or time of labeling is too high.
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    • We start with training a model on the labeled data. Then the model is used to make predictions on the unlabeled data. Specific unlabeled data are picked and their prediction is considered true. The selection criteria could be some threshold on the prediction probability or top K selection. These selected unlabeled data with the prediction are added to the labeled data set and the next iteration of training begins. This goes on till n-iterations.
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    Note

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    This process is also called Pseudo-labelling, as we are creating pseudo labels on unlabeled dataset using the model trained on only labeled data.

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    Reinforcement Learning

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    • It is a machine learning approach wherein we train agent(s) to interact with an environment to achieve certain goal. The goal is quantified by providing the agent with some positive reward on successful completion or negative reward incase of failure.
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    • The main components in RL are agents, environment and actions. Agents are the intelligent model we want to improve over time. Environment is the simulation where the agent performs some actions. Once an agent takes an action, the state of the agent changes. Based on the environment, the agent could get instant reward for each action or delayed reward on completion of an episode (sequence of actions).
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    Self-supervised Learning

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    • It is a machine learning approach wherein we create supervisory signals from the unlabeled data itself, often leveraging the underlying structure in the data. The idea is to take unlabeled data and create generic tasks, that could be different from the intended downstream task but will help model learn the fundamentals. Then the model could be fine-tuned for the specific downstream task easily with very less labeled data. It is closely connected to how humans learn ā€” as human normally first develop common sense (a general understanding of the world) and then learn specific tasks quite easily (when comparing to machines).
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    • It is becoming a norm in the AI field to train large models using self-supervised learning, as the resulting models are generalist ie. could be used for multiple downstream tasks. The method of training vary wrt the datatype. For example, in NLP, we can hide part of a sentence and predict the hidden words from the remaining words. In CV, we can predict past or future frames in a video (hidden data) from current ones (observed data). Same could be done for Audio.
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    Additional Techniques

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    Active Learning

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    • Active learning is all about selecting the minimal set of training data that will lead to the maximum performance gain. Active learning could have following use cases,
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      • Lots of labeled data: If you have lots of annotated data but you are contraint by time or computation power, you can try to select the smallest set of labeled data to train the model on. This is easy to understand as not all samples have equal contribution in training the model. Thinking intuitively, say for a linear classifier with straight line as the decision boundary, keeping data near the decision line will lead to better overall accuracy than the ones near the end or in middle of cluster of datapoints.
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      • Lots of unlabeled data: In this case, active learning is very similar to Semi-supervised learning, with only one twist - we ask humans to label again, instead of labeling using the model. The process starts similar to pseudo labelling and we train the model on the available labeled dataset. Then the trained model is used to make prediction on the unlabeled data and using preferred logic, we suggest the next batch of datasets that should be labeled by human expert. We keep iterating unless the desired accuracy is reached.
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    Transfer learning

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    • Transfer learning is all about using the knowledge gained while solving one problem to solve another problem. From a practical perspective, we reuse (either as it is or by finetining) an existing base model that was trained for say Task A, for another tasks like B, C, etc.
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    • This approach could be prefered is we don't want to train a model from scratch. For example, if we have a pretrained model that can classify cat vs dogs, it will be easier, in terms of training iterations and even number of required samples, to train for other classes like lion vs elephant. Here, the intuition is that the exsiting image classification model should have gained some knowledge about the important features of animal images. And even though the classes were completely different, it should be mich better and easier than training a new model with new weight initialization.
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    Note

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    There are several ways on how we can use transfer learning to make an existing model work for another task. These could be, (1) Finetune the complete model, (2) Freeze certains layers (preferably the starting one) and then finetune, (3) Replace certains layers (preferably the ending one -- like classification head) and then finetune, or (4) Freeze the complete model and just train additional heads for different downstream tasks.

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    Additional materials

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    Loss functions

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    Note

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    This page is still not complete and new sections might get added later. That said, the existing content is ready to be consumed. šŸ‘

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    Introduction

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    • Loss functions are the "ideal objectives" that neural networks (NN) tries to optimize. In fact, they are the mathematical personification of what we want to achieve with the NN. As the name suggests, it is a function that takes input and compute a loss value that determines how further away the current model is from the ideal model for that example. In an ideal world, we would expect the loss value to be 0, but in reality it could get very close to 0 and sometimes even be high enough so that we terminate training to handle overfitting.
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    • We also have cost functions that is nothing but aggrgation of the loss functions over a batch or complete dataset. The cost function is the function that we use in practice to optimize the model.
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    Hint

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    Loss functions --> loss on one example

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    Cost functions --> loss on entire dataset or a batch

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    Types of Loss functions

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    • Selecting a loss function for your NN depends a lot on your use case and even the type of data you are working with. For example, in case of regression, you can use MSE loss function. In case of classification, you can use Cross Entropy loss function.
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    • Here, we will go through some examples of loss functions.
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    MAE (L1 loss)

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    • Mean Absolute Error (MAE) loss function is used to calculate regression loss. Due to the presence of absolute term instead of square, it is more robust to outliers. It is differentiable at all points expect when predicted target value equals true target value. The actual formula is shown below,
      • +
    +
    \[{\displaystyle \mathrm {MAE_{loss}}(i) ={\left|y_{i}-\hat {y_{i}}\right|}}\]
    +
    \[{\displaystyle \mathrm {MAE_{cost}} ={\frac {1}{n}{\sum _{i=1}^{n} \mathrm{MAE_loss}(i)}}}\]
    +

    MSE (L2 loss)

    +
      +
    • Mean Squared Error (MSE) loss function is used to measure the difference between the predicted value and the actual value. Basically it is the mean squared euclidean distance. It is most widely used loss function for regression tasks and representation (embedding) similarity task. The actual formuale is shown below,
      • +
    +
    \[{\displaystyle \operatorname {MSE_loss}(i) = (y_{i}-{\hat {y_{i}}})^{2}}\]
    +
    \[{\displaystyle \operatorname {MSE_cost} ={\frac {1}{n}}\sum _{i=1}^{n}\operatorname {MSE_loss}(i)}\]
    +
      +
    • The MSE cost function is less resistant to outliers since the loss function squares big mistakes in order to punish the model. As a result, if the data is prone to many outliers, you shouldn't utilise L2 loss.
      • +
    +

    Cross entropy loss

    +
      +
    • Cross entropy loss is used for classification tasks. It is a simplication of Kullbackā€“Leibler divergence that is used to compute the difference between two probability distributions (here the model's prediction and true one). For binary classification the formula is shown below, (\(y\) is the actual class and \(\hat{y}\) is the predicted class)
      • +
    +
    \[{\displaystyle \operatorname {CrossEntropy_loss}(i) = -(y_i \log(\hat{y_i})+(1-y_i) \log(1-\hat{y_i}))}\]
    +
    \[{\displaystyle \operatorname {CrossEntropy_cost} ={\frac {1}{n}}\sum _{i=1}^{n}\operatorname {CrossEntropy_loss}(i)}\]
    +
      +
    • Let's go through the different possibilities,
        +
      • if \(y_i=1\),
          +
        • the loss function reduces to only the left part i.e. \(-y_i \log(\hat{y_i})\)
          • +
        • now to have a small loss, model would want the \(\log(\hat{y_i})\) to be large (bcoz of negative sign).
          • +
        • for this, model will make \(\hat{y_i}\) large (ideally 1)
          • +
        +
        • +
      • if \(y_i=0\),
          +
        • the loss function reduces to only the right part i.e. \(-(1-y_i) \log(1-\hat{y_i})\)
          • +
        • now to have a small loss, model would want the \(\log(1 - \hat{y_i})\) to be large (bcoz of negative sign).
          • +
        • for this, model will make \(\hat{y_i}\) small (ideally 0)
          • +
        +
        • +
      +
      • +
    +
    +

    Note

    +

    For both the cases we want to make sure that \(\hat{y_i}\) is bounded between 0 and 1. For this, usually the output of model is passed through some activation function like sigmoid.

    +

    Additionally, some people may argue why not use a simple \((\hat{y}-y)^2\) as the loss function for classification task. The major problem is that with this loss function the optimization becomes non convex hence the model will not converge.

    +
    + + +
      +
    • Above we can see loss function graph plotted for both the cases,
        +
      • Red line is for case when \(y_i=1\). The line is plotted for \(x=-y_i \log(\hat{y_i})\). As we want the loss to be zero, it is only possible for \(y_i=1\).
        • +
      • Blue line is for case when \(y_i=0\). The line is plotted for \(x=-(1-y_i) \log(1-\hat{y_i})\). As we want the loss to be zero, it is only possible for \(y_i=0\).
        • +
      +
      • +
    +
    +

    Hint

    +

    Refer this excellent video from AndrewNg on CrossEntropyLoss for more details.

    +
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    References

    +

    [1] Triplet Loss and Online Triplet Mining in TensorFlow

    +

    [2] The 7 Most Common Machine Learning Loss Functions

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    Ranking algorithms

    + +

    Introduction

    +
      +
    • Suppose you have a decision to make ā€” like buying a house, or a car, or even a guitar. You donā€™t want to choose randomly or get biased by someoneā€™s suggestion, but want to make an educated decision. For this, you gathered some information about the entity you want to buy (letā€™s say itā€™s a car).
      • +
    • So you have a list of N cars with their price information. As usual, we wonā€™t want to spend more, we can just sort the cars by their price (in ascending order) and pick the top one (with the smallest price), and we are done! This was decision making with a single criterion.
      • +
    • But alas if life is so easy šŸ˜„ We would also like the car to have good mileage, better engine, faster acceleration (if you want to race), and some more. Here, you want to choose a car with the smallest price, but the highest mileage and acceleration, and so on. This problem canā€™t be so easily solved by simple sorting. Enter multi-criteria decision-making algorithms! šŸ˜Ž
      • +
    +
    +

    +

    an Egyptian painting of a man dissecting a car to understand it better (Created by DallE)

    +
    +

    Dataset

    +
      +
    • Letā€™s choose one dataset so it becomes easier to visualize the result, to understand whatā€™s really happening behind the scenes and finally build intuition.
      • +
    • For this, I am picking cars dataset. For each car, we will focus on a subset of attributes and only pick 10 rows (unique cars) to make our life easier. Look at the selected data,
      • +
    +
    +

    +

    10 rows from the cars dataset

    +
    +
      +
    • Explaining some attributes,
        +
      • mpg: a measure of how far a car can travel if you put just one gallon of petrol or diesel in its tank (mileage).
        • +
      • displacement: engine displacement is the measure of the cylinder volume swept by all of the pistons of a piston engine. More displacement means more power.
        • +
      • acceleration: a measure of how long it takes the car to reach a speed from 0. Higher the acceleration, better the car for drag racing šŸŽļø
        • +
      +
      • +
    +
      +
    • +

      Here please notice some points,

      +
        +
      • +

        The unit and distribution of the attributes are not the same. Price plays in thousands of $, acceleration in tens of seconds and so on.

        +

        + +
        describing each of the numerical columns (the attributes) of the selected data
        +

        +
        • +
      +
        +
      • The logic of best for each attribute vary as well. Here, we want to find a car with high values in mpg, displacement and acceleration. At the same time, low values in weight and price. This notion of high and low can be inferred as maximizing and minimizing the attributes, respectively.
        • +
      +
        +
      • There could be an additional requirement where we donā€™t consider each attribute equal. For example, If I want a car for racing and say I am sponsored by a billionaire, then I wonā€™t care about mpg and price so much. I want the faster and lightest car possible. But what if I am a student (hence most probably on a strict budget) and travel a lot, then suddenly mpg and price become the most important attribute and I donā€™t give a damn about displacement. These notions of important of attributes can be inferred as weights assigned to each attribute. Say, price is 30% important, while displacement is only 10% and so on.
        • +
      +
      • +
    +
      +
    • With the requirements clear, letā€™s try to see how we can solve these kinds of problems.
      • +
    +

    Generic methodology

    +
      +
    • +

      Most of the basic multi-criteria decision solvers have a common methodology which tries to,

      +
        +
      • Consider one attribute at a time and try to maximize or minimize it (as per the requirement) to generate optimized score.
        • +
      • Introduce weights to each attributes to get optimized weighted scores.
        • +
      • Combine the weighted scores (of each attribute) to create a final score for an entity (here car).
        • +
      +
      • +
    +
      +
    • After this, we have transformed the requirements into a single numerical attribute (final score), and as done previously we can sort on this to get the best car (this time we sort by descending as we want to pick one with maximum score). Letā€™s explore each step with examples.
      • +
    +

    Maximize and Minimize

    +
      +
    • Remember the first point from the dataset section, attributes have very different units and distributions, which we need to handle. One possible solution is to normalize each attribute between the same range. And we also want the direction of goodness to be similar (irrespective of the logic). Hence after normalization, values near maximum of range (say 1) should mean that car is good in that attribute and lower values (say near 0) means they are bad. We do this with the following formula,
      • +
    +
    +

    +

    normalization logic for maximizing and minimizing an attribute values

    +
    +
      +
    • Look at the first equation for maximizing, one example is update mpg of each car by dividing it by sum of mpg of all cars (sum normalization). We can modify the logic by just considering the max of mpg or other formulae itself. The intention is, after applying this to each attribute, the range of each attribute will be the same as well as we can infer that value close to 1 means good.
      • +
    +
      +
    • The formulae for minimizing is nearly the same as the maximizing one, we just inverse it (1 divided by maximize) or mirror it (by subtracting it from 1) to actually reverse the goodness direction (otherwise 1 will mean bad and 0 will mean good). Letā€™s see how it looks after in practice,
      • +
    +
    +

    +

    Example for sum normalization heatmap of the original data. Check the ā€˜mpgā€™ value of ā€˜ford torinoā€™. Originally its 17 but after sum normalization, it should be 17/156=0.109. Similarly, the ā€˜priceā€™ is 20k, after inverse it will be 1/(20k/287872) = 14.4

    +
    +

    Apply weights

    +
      +
    • +

      We just need to superimpose the weight over the optimized scores, which can be easily done by multiplying the weights to the optimized score. Here as well we can introduce different types of normalization,

      +
        +
      • as it is: directly multiple the weights to optimized score
        • +
      • sum: normalize the weights by sum logic (discussed above) then multiply.
        • +
      • max: normalize by max logic, then multiply.
        • +
      +
      • +
    +
    +

    +

    weight modification logic

    +
    +

    Combine the scores

    +
      +
    • +

      Finally, we combine the score to make it one. This can be done by two different logic,

      +
        +
      • sum: add all individual scores together
        • +
      • product: multiply all individual scores together. In fact, many implementations add the logarithm of the value instead of taking products, this is done to handle very smaller result when multiplying small values.
        • +
      +
      • +
    +

    Implementation

    +
    +

    Note

    +

    Update - March 2022: Due to code breaking changes in the latest version of scikit-criteria, it is recommended to use v0.2.11 of the package for the code discussed in the article. Code repository is here.

    +
    +
      +
    • +

      We have a python package named skcriteria that provides many algorithms for multi criteria decision-making problem. Actually two algorithms inside the skcriteria.madm.simple module are,

      +
        +
      • WeightedSum ā€” individual score combine logic is sum
        • +
      • WeightedProduct ā€” individual score combine logic is product (sum of log)
        • +
      +
      • +
    +
      +
    • +

      And both of these methods take two parameters as input,

      +
        +
      • mnorm ā€” define value maximize normalization logic (minimization is always the inverse of the same maximize logic).
        • +
      • wnorm ā€” define weight normalization logic
        • +
      +
      • +
    +
      +
    • To perform ranking on our data, first, we need to load it as their skcriteria.Data object by,
      • +
    +
    1
+2
+3
+4
+5
+6
+7
    criteria_data = Data(
+    cars_data.iloc[:, 1:],          # the pandas dataframe
+    [MAX, MAX, MIN, MAX, MIN],      # direction of goodness for each column
+    anames = cars_data['car_name'], # each entity's name, here car name
+    cnames = cars_data.columns[1:], # attribute/column name
+    # weights=[1,1,1,1,1]           # weights for each attribute (optional)
+    )
+
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    ALT./CRIT.mpg (max)displacement (max)weight (min)acceleration (max)price (min)
    chevrolet chevelle malibu1830735041225561.6
    buick skylark 32015350369311.524221.4
    plymouth satellite1831834361127240.8
    amc rebel sst1630434331233685
    ford torino17302344910.520000
    ford galaxie 5001542943411030000
    chevrolet impala144544354935764.3
    plymouth fury iii1444043128.525899.5
    pontiac catalina1445544251032882.5
    amc ambassador dpl1539038508.532617.1
    +
      +
    • With the data loaded, all we need to do is call the appropriate decision maker function with data object and parameter settings. The output has one additional rank column to show the final ranking by considering all of the mentioned criteria.
      • +
    +
    1
+2
+3
+4
    from skcriteria.madm import simple
+# weighted sum
+dm = simple.WeightedSum(mnorm="sum")
+dec = dm.decide(criteria_data)
+
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    ALT./CRIT.mpg (max)displacement (max)weight (min)acceleration (max)price (min)Rank
    chevrolet chevelle malibu1830735041225561.63
    buick skylark 32015350369311.524221.42
    plymouth satellite1831834361127240.84
    amc rebel sst16304343312336856
    ford torino17302344910.5200001
    ford galaxie 50015429434110300008
    chevrolet impala144544354935764.310
    plymouth fury iii1444043128.525899.55
    pontiac catalina1445544251032882.59
    amc ambassador dpl1539038508.532617.17
    +
      +
    • We can even export the final score by dec.e_.points and the ranks by dec.rank_.
      • +
    +

    Comparison

    +
      +
    • Letā€™s compare the result of different decision making algorithms (with different parameters) on our dataset. To do so, I use the weightedSum and weightedProduct implementations (once with max and then with sum value normalization). I also implemented a normalize_data function which by default performs minmax and subtract normalization. Then I apply a sum combine on the output.
      • +
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    # import
+from skcriteria.madm import simple
+
+# make a copy of original dataset
+cars_data_copy = cars_data.copy()
+
+# weighted sum, sumNorm
+dm = simple.WeightedSum(mnorm="sum")
+dec = dm.decide(criteria_data)
+cars_data_copy.loc[:, 'rank_weightedSum_sumNorm_inverse'] = dec.rank_
+
+# weighted sum, maxNorm
+dm = simple.WeightedSum(mnorm="max")
+dec = dm.decide(criteria_data)
+cars_data_copy.loc[:, 'rank_weightedSum_maxNorm_inverse'] = dec.rank_
+
+# weighted product, sumNorm
+dm = simple.WeightedProduct(mnorm="sum")
+dec = dm.decide(criteria_data)
+cars_data_copy.loc[:, 'rank_weightedProduct_sumNorm_inverse'] = dec.rank_
+
+# weighted product, sumNorm
+dm = simple.WeightedProduct(mnorm="max")
+dec = dm.decide(criteria_data)
+cars_data_copy.loc[:, 'rank_weightedProduct_maxNorm_inverse'] = dec.rank_
+
+# min max scale + mirror
+cars_data_copy.loc[:, 'rank_weightedSum_minmaxScale_subtract'] =\
+     pd.Series(normalize_data().sum(axis=1)).rank(ascending=False).astype(int)
+
    +
      +
    • Finally, I plot a parallel coordinate graphs, where each axis(vertical line) denotes one solver type and the values denote the rank of a car by that solver. Each line is for one car and going from left to right, it shows the journey ā€” how the rank of a car changes as you switch among different solvers.
      • +
    +
    +

    +

    Journey of a car as we switch decision solver

    +
    +
      +
    • +

      Some points to consider,

      +
        +
      • Ford Torino is rank 1 (car with the highest score) for 4/5 solvers. Minmax favors Chevrolet Malibu.
        • +
      • Impala is the universal low ranker :(
        • +
      • Both implementations of weightedProduct is giving the same ranking to all cars. Nothing interesting here.
        • +
      • High variance in the rankings of both the weightedSum implementations.
        • +
      • MinMax gives the most diverse rankings for top 4 guys.
        • +
      +
      • +
    +
      +
    • The main reason behind the variance of result when changing the normalization (from sum to max) is due to the translation done on the original data. This translation changes the range of data (like scales everything between x and y ) and in case of inverse modifies the linearity as well (say, equal steps of 1 in original data is not consistent in transformed data). This will become more clear by following result,
      • +
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    import numpy as np
+x = np.array(range(1, 10))
+print("X: ", x)
+print("MinMax: ", minmax_scale(x))
+print("MinMax_subtract: ", 1 - minmax_scale(x))
+print("sumNorm: ", x/sum(x))
+print("sumNorm_inverse: ", (1/(x/sum(x))))
+print("maxNorm: ", x/max(x))
+print("maxNorm_inverse: ", 1/(x/max(x)))
+
+## Output
+X:  [1 2 3 4 5 6 7 8 9]
+
+MinMax:  [0.    0.125 0.25  0.375 0.5   0.625 0.75  0.875 1.   ]
+MinMax_subtract:  [1.    0.875 0.75  0.625 0.5   0.375 0.25  0.125 0.   ]
+
+sumNorm:  [0.02222222 0.04444444 0.06666667 0.08888889 0.11111111 0.13333333
+ 0.15555556 0.17777778 0.2       ]
+sumNorm_inverse:  [45.         22.5        15.         11.25        9.          7.5
+  6.42857143  5.625       5.        ]
+
+maxNorm:  [0.11111111 0.22222222 0.33333333 0.44444444 0.55555556 0.66666667
+ 0.77777778 0.88888889 1.        ]
+maxNorm_inverse:  [9.         4.5        3.         2.25       1.8        1.5
+ 1.28571429 1.125      1.        ]
+
    +
      +
    • Here, input data consist numbers 1 to 9 (notice, the difference between any two consecutive number is 1 i.e. step is same). Approach one (minmax) translates the data between 0 and 1 and the step is still the same. Now look at minimization logic ( _inverse ) of approach 2 and 3. Here at the start (low original values) the step is nearly the half of the last element, but near the end (high original value) the step is very small, even though in the original data we are moving with same step of 1.
      • +
    +
      +
    • Because of this, in case of minimization, a very high score is given to ā€œgoodā€ cars (with low values) and even a small impurity matter (when minimized, high value = low score) and results in a drastic decrease in score. Itā€™s like we are being very picky, either you are the best or you get half the score :) On the other hand, for higher values, small impurities doesnā€™t matter. If the car is already bad by that attribute, then we donā€™t care if its value is 7 or 8 or 9 and the reduction in the score is much less! We can use this understanding to pick the right decision solver with the right parameter as per our need.
      • +
    +

    Conclusion

    +
      +
    • This article has just touched the surface of the multi-criteria decision making domain. Even in skcriteria package there are many more algorithms like TOPSIS and MOORA which have quite a different intuition to solve these problems. But even then the notion of goodness and the idea to handle individual attributes to finally combine them all together is used in many of them. So maybe we will explore more algorithms in another article.
      • +
    +
      +
    • But the major takeaways from this article should be to understand the why and what of decision makers. That each such decision can be manipulated by multiple criteria. And also that we may have a different notion of goodness and importance assigned to each criterion. Finally, we have different varieties of solvers that can be build by taking permutation of logic and parameters, and nearly all of them give different and interesting results based on our need!
      • +
    +

    Cheers šŸ‘‹

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    BERT

    +

    Introduction

    +
      +
    • BERT stands for Bidirectional Encoder Representations from Transformers. (devlin2019bert)
      • +
    • Basically, it is a modification of Transformers (vaswani2017attention), where we just keep the encoder part and discard the decoder part.
      • +
    +
    +

    +

    Transformer architecture. BERT is the left part i.e. encoder part. (vaswani2017attention)

    +
    +
      +
    • At the time of release, it obtained state-of-the-art results on eleven natural language processing tasks. To quote the paper, "[paper pushed] the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement)."
      • +
    • The major motivation behind BERT is to handle the limitation of the existing language models which are unidirectional in nature. This means that they only consider text left to right for sentence level inference. BERT on the other hand, allows tokens to attend to both sides in self-attention layer. This is one of the major reason for it high performance.
      • +
    +
    +

    +

    Differences in pre-training model architectures. BERT uses a bidirectional Transformer. OpenAI GPT uses a left-to-right Transformer. ELMo uses the concatenation of independently trained left-to-right and right-toleft LSTMs to generate features for downstream tasks. Among the three, only BERT representations are jointly conditioned on both left and right context in all layers. In addition to the architecture differences, BERT and OpenAI GPT are fine-tuning approaches, while ELMo is a feature-based approach.. (devlin2019bert)
    +

    +
    +
      +
    • The most fascinating feature of BERT is that it is super easy to use it for a large number of NLP tasks. The idea is to take the pretrained BERT model and later fine tune it for the specific task. The pre-trained model is trained on a large corpus in a unsupervised manner, hence the model learns the generic representations of the tokens from large corpus of text. This makes it easy to later fine tune it for any other NLP task, as the model comes pretrained with large context about the language, grammar and semantic representations.
      • +
    +
    +

    +

    Illustrations of Fine-tuning BERT on Different Tasks. (devlin2019bert)
    +

    +
    +
      +
    • Training BERT is an interesting paradigm in itself. The original paper proposed two unsupervised methods for training,
        +
      1. Masked LM (MLM): Where some percentage (15%) of the input tokens are masked at random, and then the model tries to predict those masked tokens. They created a special token [MASK] for this purpose.
        2. +
      2. Next Sentence Prediction (NSP): Where two sentences A and B are chosen such that, 50% of the time B is the actual next sentence that follows A (labelled as IsNext), and 50% of the time it is a random sentence from the corpus (labelled as NotNext). The model is trained to predict if the second sentences follow the first or not.
        3. +
      +
      • +
    +

    Analysis

    +

    BERT output and finetuning (unsupervised)

    +
      +
    • An analysis on the selection of suitable BERT output and the advantage of fine-tuning (unsupervised learning on unlabeled data on tasks like MLM) the model was done. The report provides following performance table comparing different experiments. Complete article here.
      • +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    Exp noModel nameF1 macro scoreAccuracy
    1Pooler output64.6%68.4%
    2Last hidden state86.7%87.5%
    3Fine-tuned and Pooler output87.5%88.1%
    4Fine-tuned and last hidden state79.8%81.3%
    +
      +
    • It also answers following questions,
        +
      • Should I only use CLS token or all token's output for sentence representation? Well, it depends. From the experiments, it seems if you are fine-tuning the model, using the pooler output will be better. But if there is no fine-tuning, the last hidden state output is much better. Personally, I will prefer the last hidden state output, as it provides comparative result without any additional compute expensive fine-tuning.Ā 
        • +
      • Will fine-tuning the model beforehand increase the accuracy? A definite yes! Exp 3 and 4 reports higher score than Exp 1 and 2. So if you have the time and resource (which ironically is not usually the case), go for fine-tuning!
        • +
      +
      • +
    +

    Is BERT a Text Generation model?

    +
      +
    • Short answer is no. BERT is not a text generation model or a language model because the probability of the predicting a token in masked input is dependent on the context of the token. This context is bidirectional, hence the model is not able to predict the next token in the sequence accurately with only one directional context (as expected for language model).
      • +
    • Several analysis were done on the text generation prowess of BERT model. One such analysis is presented in this paper. Here the authors presents BERT as markov random field language model. Then after some errors were pointed out wrt paper, the authors corrected the claim and suggested BERT is a non-equilibrium language model (here)
      • +
    +
    +

    Tip

    +

    Do you know that during mask prediction, BERT model predicts some tokens for [PAD] tokens as well. This is true for sentences that are smaller than the max length of the model and hence require padding. In a sense, this is kind of text generation, where you just provide the sentence and the model predicts the next token till the max length. But as expected the prediction is not that accurate.

    +
    +

    BERT for sentence representation?

    +
      +
    • One question usually asked is that - "Can we use BERT to generate meaningful sentence representations?" The answer is "No". Don't get me wrong, while it is possible to use BERT to generate sentence representations, but the key word here is "meaningful". One of the way to do this is to pass one sentence to the model and get the representation for fixed [CLS] token as sentence representation. But as shown in [2], this common practice yields bad sentence embedding, often even worse than Glove embeddings (which was introduced in 2014)!
      • +
    • The major problem here is the pre-training strategy used to train BERT. While it is good for downstream tasks like classification, it's not that good for generating generic representations. This is because for correct sentence representation, we want the embeddings of similar sentences closer to each other and dissimilar sentences to be further apart. And this is not what happens during BERT pretraining. To cater to this issue, we will have to further finetune the model. And in fact, this is where BERT shines again, as with minimal training (sometimes even for 20 mins with <1000 samples) we can expect good results.
      • +
    • One of the ways to finetune for sentence represenration is to use triplet loss. For this, we prepare a dataset with a combination of (anchor, positive, negative) sentences. Here anchor is the base sentence, positive is the sentence that is similar to the anchor sentence, and negative is the sentence that is dissimilar to the anchor sentence. The model is trained to "bring" the representation of (anchor, positive) closer and (anchor, negative) apart. The loss is defined below, where \(s_*\) is the respective sentence representation and \(\epsilon\) is the margin.
      • +
    +
    \[triplet loss = max( \parallel s_a - s_p \parallel - \parallel s_a - s_n \parallel + \epsilon , 0)\]
    +

    Code

    +

    Pretrained BERT for Sentiment Classification

    +
      +
    • The code contains the Dataset and Dataloader as well, which can be referred for any fine tuning task.
      • +
    • Download dataset from IMDB 50k review
      • +
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    # helper
+import pandas as pd
+import numpy as np
+from tqdm import tqdm
+from sklearn.model_selection import train_test_split
+
+# for BERT model
+from transformers import BertTokenizer, BertModel
+from transformers import BertForSequenceClassification
+
+# for DL stuff
+import torch
+import pytorch_lightning as pl
+from torch.nn import Softmax, Linear
+from torchmetrics import Accuracy, F1
+from torch.utils.data import Dataset, DataLoader
+from pytorch_lightning.loggers import WandbLogger
+
+
+model_name = 'bert-base-uncased'
+
+# load dataset and sample 10k reviews.
+df = pd.read_csv("../input/imdb-dataset-of-50k-movie-reviews/IMDB Dataset.csv")
+df = df.sample(10000, random_state=1)
+
+# divide into test and train
+X_train, X_test, y_train, y_test = \
+          train_test_split(df['review'].tolist(), df['sentiment'].tolist(),
+          shuffle=True, test_size=0.33, random_state=1, stratify=df['sentiment'])
+
+# define dataset with load and prep functions. Pass all the data at a time.
+def squz(x, dim=0):
+    return torch.squeeze(x, dim)
+
+class IMDBDataset(Dataset):
+    def __init__(self, sentences, labels, max_length=512, model_name='bert-base-uncased'):
+        # var
+        self.sentences = sentences
+        self.labels = [['positive', 'negative'].index(x) for x in labels]
+        self.max_length = max_length
+        # tokenizer
+        self.tokenizer = BertTokenizer.from_pretrained(model_name)
+
+    def __len__(self):
+        return len(self.sentences)
+
+    def __getitem__(self, index):
+        # Select sample
+        sentence = self.sentences[index]
+        label = self.labels[index]
+        # Load data and get label
+        X = self.tokenizer(sentence, padding="max_length", truncation=True,
+                        max_length=self.max_length, return_tensors="pt")
+        X = {key: squz(value) for key, value in X.items()}
+        y = label
+        # return
+        return X, y
+
+# init the train and test dataset
+train_dataset = IMDBDataset(X_train, y_train)
+test_dataset = IMDBDataset(X_test, y_test)
+# create the dataloader
+train_dataloader = DataLoader(train_dataset, batch_size=16, shuffle=True)
+test_dataloader = DataLoader(test_dataset, batch_size=16, shuffle=True)
+
+# define BERT model
+class BERT_pooler_output(pl.LightningModule):
+    def __init__(self, model_name='bert-base-uncased'):
+        super().__init__()
+        # model and layers
+        self.BERTModel = BertModel.from_pretrained(model_name)
+        self.linear1 = Linear(768, 128)    
+        self.linear2 = Linear(128, 2)
+        self.softmax = Softmax(dim=1)
+        self.relu = torch.nn.ReLU()
+        # loss
+        self.criterion = torch.nn.CrossEntropyLoss()
+        # performance
+        self.accuracy = Accuracy()
+        self.f1 = F1(num_classes=2, average='macro')
+
+    def forward(self, x):
+        # pass input to BERTmodel
+        input_ids, attention_mask = x['input_ids'], x['attention_mask']
+        bert_output = self.BERTModel(input_ids, attention_mask=attention_mask)
+        output = bert_output.pooler_output     
+        output = self.relu(self.linear1(output))
+        output = self.linear2(output)
+        return output
+
+    def training_step(self, batch, batch_idx):
+        x, y = batch
+        x_hat = self.forward(x)
+        loss = self.criterion(x_hat, y)
+        acc = self.accuracy(x_hat.argmax(dim=1), y)
+        f1 = self.f1(x_hat.argmax(dim=1), y)
+        self.log('loss', loss, on_step=False, on_epoch=True, prog_bar=True, logger=True)
+        self.log('acc', acc, on_step=False, on_epoch=True, prog_bar=True, logger=True)
+        self.log('f1', f1, on_step=False, on_epoch=True, prog_bar=True, logger=True)
+        return {'loss': loss}
+
+    def validation_step(self, batch, batch_idx):
+        x, y = batch
+        x_hat = self.forward(x)
+        acc = self.accuracy(x_hat.argmax(dim=1), y)
+        f1 = self.f1(x_hat.argmax(dim=1), y)
+        self.log('val_acc', acc, on_step=False, on_epoch=True, prog_bar=True, logger=True)
+        self.log('val_f1', f1, on_step=False, on_epoch=True, prog_bar=True, logger=True)
+
+    def configure_optimizers(self):
+        # freezing the params of BERT model
+        for name, param in self.named_parameters():
+            if 'BERTModel' in name:
+                param.requires_grad = False
+        # define the optimizer
+        optimizer = torch.optim.Adam(filter(lambda p: p.requires_grad, self.parameters()),
+                                                                    lr=1e-3)
+        return optimizer
+
+
+# init model
+model = BERT_pooler_output()
+# init trainer
+trainer = pl.Trainer(gpus=1, max_epochs=3)
+# train the model
+trainer.fit(model, train_dataloader, test_dataloader)
+
    +

    Fine tuning the BERT model

    +
      +
    • Fine tuning could include training BERT on one or many of the proposed unsupervised tasks.
      • +
    • Here, we will train the BERT on MLM (Masked language modeling) task.
      • +
    • This includes masking some tokens of input and BERT predicting the token based on the context tokens.
      • +
    • Referenced from this video of James Briggs.
      • +
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    # IMPORT =========
+import pandas as pd
+from tqdm import tqdm
+import numpy as np
+import numpy as np
+
+# for deep learning
+import torch
+import torch.nn as nn
+import torch.optim as optim
+
+# load BERT model
+from transformers import AdamW
+from transformers import BertTokenizer, BertForMaskedLM
+
+# MODEL LOAD =========
+model_path = "bert-base-uncased" # if local copy is not present
+tokenizer = BertTokenizer.from_pretrained(model_path)
+model = BertForMaskedLM.from_pretrained(model_path)
+
+
+# DATA PREP 1 =========
+df = pd.read_csv("file_with_text.csv")
+
+# tokenize
+inputs = tokenizer(df['review'].tolist(), return_tensors='pt', max_length=512,
+                   truncation=True, padding='max_length')
+inputs['labels'] = inputs.input_ids.detach().clone()
+
+# create random array of floats with equal dimensions to input_ids tensor
+rand = torch.rand(inputs.input_ids.shape)
+# create mask array - mask tokens except special tokens like CLS and SEP
+mask_ratio = 0.3
+mask_arr = (rand < mask_ratio) * (inputs.input_ids != 101) * \
+           (inputs.input_ids != 102) * (inputs.input_ids != 0)
+
+# get the indices where to add mask
+selection = []
+for i in range(inputs.input_ids.shape[0]):
+    selection.append(
+        torch.flatten(mask_arr[i].nonzero()).tolist()
+    )
+
+# add the mask
+for i in range(inputs.input_ids.shape[0]):
+    inputs.input_ids[i, selection[i]] = 103
+
+# DATA PREP 2 - DATALOADER =========
+# define dataset class
+class IMDBDataset(torch.utils.data.Dataset):
+    def __init__(self, encodings):
+        self.encodings = encodings
+    def __getitem__(self, idx):
+        return {key: torch.tensor(val[idx]) for key, val in self.encodings.items()}
+    def __len__(self):
+        return len(self.encodings.input_ids)
+
+# create instance
+dataset = IMDBDataset(inputs)
+loader = torch.utils.data.DataLoader(dataset, batch_size=8, shuffle=True)
+
+# PRE_TRAIN =============
+device = torch.device('cuda') if torch.cuda.is_available() else torch.device('cpu')
+# and move our model over to the selected device
+model.to(device)
+# activate training mode
+model.train()
+
+# initialize optimizer
+optimizer = AdamW(model.parameters(), lr=5e-5)
+
+# TRAIN =====================
+epochs = 20
+for epoch in range(epochs):
+    # setup loop with TQDM and dataloader
+    loop = tqdm(loader, leave=True)
+    for batch in loop:
+        # initialize calculated gradients (from prev step)
+        optimizer.zero_grad()
+        # pull all tensor batches required for training
+        input_ids = batch['input_ids'].to(device)
+        attention_mask = batch['attention_mask'].to(device)
+        labels = batch['labels'].to(device)
+        # process
+        outputs = model(input_ids, attention_mask=attention_mask, labels=labels)
+        # extract loss
+        loss = outputs.loss
+        # calculate loss for every parameter that needs grad update
+        loss.backward()
+        # update parameters
+        optimizer.step()
+        # print relevant info to progress bar
+        loop.set_description(f'Epoch {epoch}')
+        loop.set_postfix(loss=loss.item())
+
+# SAVE MODEL =====================
+model.save_pretrained("bert_finetuned_on_text/")
+tokenizer.save_pretrained("bert_finetuned_on_text/")
+
    +

    BERT output for sentence level inference

    +
      +
    • BERT provides pooler_output and last_hidden_state as two potential "representations" for sentence level inference.
      • +
    • pooler_output is the embedding of the [CLS] special token. In many cases it is considered as a valid representation of the complete sentence.
      • +
    +
    1
+2
+3
    BERTModel = BertModel.from_pretrained('bert-base-uncased')
+bert_output = BERTModel(input_ids, attention_mask=attention_mask)
+output = bert_output.pooler_output
+
    +
      +
    • last_hidden_state contains the embeddings of all tokens in the sentence from the last hidden state. We can apply permutation invariant methods (like max, mean or sum) to aggregate the embeddings into a single sentence representation.
      • +
    +
    1
+2
+3
    BERTModel = BertModel.from_pretrained('bert-base-uncased')
+bert_output = BERTModel(input_ids, attention_mask=attention_mask)
+output = squeeze(torch.matmul(attention_mask.type(torch.float32).view(-1, 1, 512), bert_output['last_hidden_state']), 1)
+
    +
    +

    Tip

    +

    Consider finetuning the BERT model (triplet loss) further to generate meaningful sentence representation, as pretrained BERT model is even worse than Glove embeddings [2]. For more details look at this analysis or use S-BERT package to finetune.

    +
    +

    References

    +

    [1] Jay Alammar's blog "The Illustrated BERT, ELMo, and co. (How NLP Cracked Transfer Learning)"

    +

    [2] Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks

    + + +
    + +
    +
    + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + \ No newline at end of file diff --git a/natural_language_processing/FlanModels/index.html b/natural_language_processing/FlanModels/index.html new file mode 100644 index 00000000..b459f122 --- /dev/null +++ b/natural_language_processing/FlanModels/index.html @@ -0,0 +1,2218 @@ + + + + + + + + + + + + + + + + + + FlanModels - A Lazy Data Science Guide + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
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    + + + +
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    +
    +
    + + +
    +
    + + + + + + + + +

    Flan Models

    +

    Introduction

    +
      +
    • In ā€œScaling Instruction-Finetuned Language Modelsā€, researchers propose an LLM fine-tuning strategy that focus on using dataset in form of instructions with other tricks like (1) scaling the number of tasks, (2) scaling the model size, and (3) finetuning on chain-of-thought data.
      • +
    • The resulting models were called Flan-{source_model} like Flan-T5 and Flan-PaLM which showcase enhanced performance when compared with their namesakes.
      • +
    +
    +

    +
    Flan models are finetuned on ~1.8k tasks w & w/o CoT and exemplars (zero shots and few shots) and evaluated on unseen tasks.
    +
    +
    +

    Note

    +

    All Flan models like Flan-T5 are only finetuned and not trained from scratch.

    +
    +

    Dataset

    +
      +
    • The Flan models were finetuned on 1836 tasks. Here are some definitions,
        +
      • ā€œDatasetā€ is an data source (ex: SQuAD),
        • +
      • ā€œTask categoryā€ is a unique task setup like query generation or question answering.
        • +
      • ā€œTaskā€ is unique combinations of <dataset, task category>. This is possible because a single dataset can be used for different task categrory (ex: SQuAD for query generation or QA or context generation).
        • +
      +
      • +
    • Combining 473 datasets and 146 task category we end up with 1836 different tasks.
      • +
    +
    +

    +
    Dataset, Task Category and Task details of finetuning and held out data.
    +
    +

    Finetuning process

    +
      +
    • Instruction finetuning was performed for T5, PaLM and U-PaLM, where the model sizes span from Flan-T5-small (80M parameters), to PaLM and U-PaLM (540B parameters).
      • +
    • Same training procedure was used for each model except for a few hyperparameters like learning rate, batch size, dropout, and finetuning steps. Constant learning rate schedule was used to finetune using the Adafactor optimizer.
      • +
    • Notably, the amount of compute used for finetuning is only a small fraction relative to the training compute, as shown in the table below.
      • +
    +
    +

    +
    All Flan models only use a fraction of compute for finetuning that was used for model training, with max usage going only upto 1.6%.
    +
    +

    Results

    +
    +

    +
    Instruction finetuning (Flan) showcased improved performance across multiple models and dataset, sometimes going as far as double digit improvements! +
    +
    +

    Code

    +
      +
    • The code of inference and finetuning Flan models are very similar to the original models. Below we will show inference code for Flan-T5, which is similar to the T5 inference.
      • +
    • Similarly, if you want to further finetune Flan-T5 for your use case, you can refer T5 finetuning code. If you are using HuggingFace, then all you need to do is replace the model name to google/flan-t5-{size}, where size could be small, base, large, xl and xxl.
      • +
    +

    Flan-T5 Inference

    +
      +
    • The following code is referenced from the HuggingFace Flan-T5 page [2]
      • +
    +
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    # import
+from transformers import AutoModelForSeq2SeqLM, AutoTokenizer
+
+# load the model and tokenizer
+model = AutoModelForSeq2SeqLM.from_pretrained("google/flan-t5-small")
+tokenizer = AutoTokenizer.from_pretrained("google/flan-t5-small")
+
+# run on sample input
+inputs = tokenizer("A step by step recipe to make bolognese pasta:", return_tensors="pt")
+outputs = model.generate(**inputs)
+print(tokenizer.batch_decode(outputs, skip_special_tokens=True))
+
    +

    References

    +

    [1] Scaling Instruction-Finetuned Language Models - Paper

    +

    [2] FlanT5 - HuggingFace

    + + +
    + +
    +
    + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + \ No newline at end of file diff --git a/natural_language_processing/GPTs/index.html b/natural_language_processing/GPTs/index.html new file mode 100644 index 00000000..4f065e7e --- /dev/null +++ b/natural_language_processing/GPTs/index.html @@ -0,0 +1,2775 @@ + + + + + + + + + + + + + + + + + + GPTs - A Lazy Data Science Guide + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + + + + + + + + +

    GPT models

    +

    Introduction

    +
      +
    • GPT stands for "Generative Pre-trained Transformer". It is an autoregressive language model which is based on the decoder block of the Transformer architecture.
      • +
    +
    +

    +

    Transformer architecture. Left part is the encoder, right part is the decoder. GPT is made up of the right part i.e. decoder part. (vaswani2017attention)
    +

    +
    +
      +
    • The idea for the model is similar to any text generation model i.e. it takes some prompt as input and generates text as output. But the caveat is that, GPT model's tunable parameter ranges from 100 million to 175 billion, which leads to the model learning much more than basic language syntax information or word related contextual information. In fact, it has been shown that GPT models store additional real-world information as well, and there has been interesting recent research about how much knowledge you can pack into the parameters (roberts2020knowledge).
      • +
    • GPT models are also famous because they can be easily applied to many downstream NLP tasks. This is because they have been shown to be very good in few-shot leaning, i.e. they are able to perform tasks for which they are not even trained by only providing a few examples!
      • +
    • This lead to a new interesting paradigm of prompt engineering, where creating crisp prompt could lead to good results. This means that, instead of playing around with training, just by modifying the input prompt to the model, we can improve the accuracy. Ofcourse, for better accuracy, it is always preferred to fine-tune the model. Example of a sample prompt is provided below
      • +
    +
    ## prompt input - this can be passed to a GPT based model
+Below are some examples for sentiment detection of movie reviews.
+
+Review: I am sad that the hero died.
+Sentiment: Negative
+
+Review: The ending was perfect.
+Sentiment: Positive
+
+Review: The plot was not so good!
+Sentiment: 
+
+## The model should predict the sentiment for the last review.
+
    +
    +

    Tip

    +

    Copy the prompt from above and try it @ GPT-Neo 2.7B model. You should get "Negative" as output! We just created a Sentiment detection module without a single training epoch!

    +
    +

    Analysis

    +

    Comparing GPT models (basic details)

    +
      +
    • There are two famous series of GPT models,
        +
      • GPT-{1,2,3}: the original series released by OpenAI, a San Francisco-based artificial intelligence research laboratory. It includes GPT-1 (radford2018improving, GPT-2 radford2019language, GPT-3 brown2020language)
        • +
      • GPT-{Neo, J}: the open source series released by EleutherAI. For GPT-Neo, the architecture is quite similar to GPT-3, but training was done on The Pile, an 825 GB sized text dataset.
        • +
      +
      • +
    • Details of the models are as follows, (details)
      • +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    modelsreleased byyearopen-sourcemodel size
    GPT-1OpenAI2018yes110M
    GPT-2OpenAI2019yes117M, 345M, 774M, 1.5B
    GPT-3OpenAI2020no175B
    GPT-NeoEleutherAI2021yes125M, 1.3B, 2.7B
    GPT-JEleutherAI2021yes6B
    +

    Code

    +
      +
    • The most recent open-source models from OpenAI and EleutherAI are GPT-2 and GPT-Neo, respectively. And as they share nearly the same architecture, the majority of the code for inference or training, or fine-tuning remains the same. Hence for brevity's sake, code for GPT-2 will be shared, but I will point out changes required to make it work for GPT-Neo model as well.
      • +
    +

    Inference of GPT-2 pre-trained model

    +
      +
    • For a simple inference, we will load the pre-trained GPT-2 model and use it for a dummy sentiment detection task (using the prompt shared above).
      • +
    • To make this code work for GPT-Neo, + - import GPTNeoForCausalLM at line 2 + - replace line 5 with model_name = "EleutherAI/gpt-neo-2.7B" (choose from any of the available sized models) + - use GPTNeoForCausalLM in place of GPT2LMHeadModel at line 9
      • +
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    # import
+from transformers import GPT2Tokenizer, GPT2LMHeadModel
+
+# model name
+model_name = "gpt2"
+
+# load tokenizer and model
+tokenizer = GPT2Tokenizer.from_pretrained(model_name)
+model = GPT2LMHeadModel.from_pretrained(model_name).cuda()
+
+# create prompt
+prompt = """
+Below are some examples for sentiment detection of movie reviews.
+
+Review: I am sad that the hero died.
+Sentiment: Negative
+
+Review: The ending was perfect.
+Sentiment: Positive
+
+Review: The plot was not so good!
+Sentiment:"""
+
+# generate tokens
+generated = tokenizer(prompt, return_tensors="pt").input_ids.cuda()
+
+# perform prediction 
+sample_outputs = model.generate(generated, do_sample=False, top_k=50, max_length=512, top_p=0.90, 
+        temperature=0, num_return_sequences=0)
+
+# decode the predicted tokens into texts
+predicted_text = tokenizer.decode(sample_outputs[0], skip_special_tokens=True)
+print(predicted_text)
+"""Output --> 
+Below are some examples for sentiment detection of movie reviews.
+
+Review: I am sad that the hero died.
+Sentiment: Negative
+
+Review: The ending was perfect.
+Sentiment: Positive
+
+Review: The plot was not so good!
+Sentiment: Negative
+"""
+
    +
    +

    Note

    +

    As GPT2 is a language model style decoder with no special encoder block, the output contains the input plus additional generations. This can be observed from the above example. On the other hand, output of T5 model is pure new generations (without the repetition of input) as it has encoder-decoder architecture.

    +
    +

    Finetuning GPT-2 (for sentiment classification)

    +
      +
    • Tweet sentiment data can be downloaded from here
      • +
    • We add the special tokens at line 72, so that the model learns the start and end of the prompt. This will be helpful later on during the testing phase, as we don't want the model to keep on writing the next word, but it should know when to stop the process. This can be done by setting the eos_token and training the model to predict the same.
      • +
    +
    +

    Note

    +

    Original GPT-2 paper and implementation uses <|endoftext|> as the eos_token and bos_token (beginning of sentence). We can do the same, but for clarity in the implementation, we can also use <|startoftext|> as bos_token special token.

    +
    +
      +
    • We also define how to process the training data inside data_collator on line 91. The first two elements within the collator are input_ids-ā€Šthe tokenized prompt and attention_mask-ā€Ša simple 1/0 vector which denote which part of the tokenized vector is prompt and which part is the padding. The last part is quite interesting, where we pass the input data as the label instead of just the sentiment labels. This is because we are training a language model, hence we want the model to learn the pattern of the prompt and not just sentiment class. In a sense, the model learns to predict the words of the input tweet + sentiment structured in the prompt, and in the process learn the sentiment detection task.
      • +
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    # download packages
+#!pip install transformers==4.8.2
+
+# import packages
+import re
+import torch
+import random
+import pandas as pd
+from tqdm import tqdm
+from torch.utils.data import Dataset
+from sklearn.metrics import f1_score
+from sklearn.model_selection import train_test_split
+from transformers import GPT2Tokenizer, TrainingArguments, Trainer, GPT2LMHeadModel
+
+## Define class and functions
+#--------
+
+# Dataset class
+class SentimentDataset(Dataset):
+    def __init__(self, txt_list, label_list, tokenizer, max_length):
+        # define variables    
+        self.input_ids = []
+        self.attn_masks = []
+        self.labels = []
+        map_label = {0:'negative', 4: 'positive'}
+        # iterate through the dataset
+        for txt, label in zip(txt_list, label_list):
+            # prepare the text
+            prep_txt = f'<|startoftext|>Tweet: {txt}<|pad|>Sentiment: {map_label[label]}<|endoftext|>'
+            # tokenize
+            encodings_dict = tokenizer(prep_txt, truncation=True,
+                                       max_length=max_length, padding="max_length")
+            # append to list
+            self.input_ids.append(torch.tensor(encodings_dict['input_ids']))
+            self.attn_masks.append(torch.tensor(encodings_dict['attention_mask']))
+            self.labels.append(map_label[label])
+
+    def __len__(self):
+        return len(self.input_ids)
+
+    def __getitem__(self, idx):
+        return self.input_ids[idx], self.attn_masks[idx], self.labels[idx]
+
+# Data load function
+def load_sentiment_dataset(tokenizer):
+    # load dataset and sample 10k reviews.
+    file_path = "../input/sentiment140/training.1600000.processed.noemoticon.csv"
+    df = pd.read_csv(file_path, encoding='ISO-8859-1', header=None)
+    df = df[[0, 5]]
+    df.columns = ['label', 'text']
+    df = df.sample(10000, random_state=1)
+
+    # divide into test and train
+    X_train, X_test, y_train, y_test = \
+              train_test_split(df['text'].tolist(), df['label'].tolist(),
+              shuffle=True, test_size=0.05, random_state=1, stratify=df['label'])
+
+    # format into SentimentDataset class
+    train_dataset = SentimentDataset(X_train, y_train, tokenizer, max_length=512)
+
+    # return
+    return train_dataset, (X_test, y_test)
+
+## Load model and data
+#--------
+
+# set model name
+model_name = "gpt2"
+# seed
+torch.manual_seed(42)
+
+# load tokenizer and model
+tokenizer = GPT2Tokenizer.from_pretrained(model_name, bos_token='<|startoftext|>',
+                                          eos_token='<|endoftext|>', pad_token='<|pad|>')
+model = GPT2LMHeadModel .from_pretrained(model_name).cuda()
+model.resize_token_embeddings(len(tokenizer))
+
+# prepare and load dataset
+train_dataset, test_dataset = load_sentiment_dataset(tokenizer)
+
+## Train
+#--------
+# creating training arguments
+training_args = TrainingArguments(output_dir='results', num_train_epochs=2, logging_steps=10,
+                                 load_best_model_at_end=True, save_strategy="epoch", evaluation_strategy="epoch",
+                                 per_device_train_batch_size=2, per_device_eval_batch_size=2,
+                                 warmup_steps=100, weight_decay=0.01, logging_dir='logs')
+
+# start training
+Trainer(model=model, args=training_args, train_dataset=train_dataset, eval_dataset=test_dataset,
+        data_collator=lambda data: {'input_ids': torch.stack([f[0] for f in data]),
+                                    'attention_mask': torch.stack([f[1] for f in data]),
+                                    'labels': torch.stack([f[0] for f in data])}).train()
+
+## Test
+----------
+
+# set the model to eval mode
+_ = model.eval()
+
+# run model inference on all test data
+original_label, predicted_label, original_text, predicted_text = [], [], [], []
+map_label = {0:'negative', 4: 'positive'}
+# iter over all of the test data
+for text, label in tqdm(zip(test_dataset[0], test_dataset[1])):
+    # create prompt (in compliance with the one used during training)
+    prompt = f'<|startoftext|>Tweet: {text}\nSentiment:'
+    # generate tokens
+    generated = tokenizer(f"{prompt}", return_tensors="pt").input_ids.cuda()
+    # perform prediction
+    sample_outputs = model.generate(generated, do_sample=False, top_k=50, max_length=512, top_p=0.90, 
+            temperature=0, num_return_sequences=0)
+    # decode the predicted tokens into texts
+    predicted_text  = tokenizer.decode(sample_outputs[0], skip_special_tokens=True)
+    # extract the predicted sentiment
+    try:
+        pred_sentiment = re.findall("\nSentiment: (.*)", predicted_text)[-1]
+    except:
+        pred_sentiment = "None"
+    # append results
+    original_label.append(map_label[label])
+    predicted_label.append(pred_sentiment)
+    original_text.append(text)
+    predicted_text.append(pred_text)
+
+# transform result into dataframe
+df = pd.DataFrame({'original_text': original_text, 'predicted_label': predicted_label, 
+                    'original_label': original_label, 'predicted_text': predicted_text})
+
+# predict the accuracy
+print(f1_score(original_label, predicted_label, average='macro'))
+
    +

    Inference of GPT-3

    + +
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    # install 
+# pip install --upgrade openai
+
+# import
+import openai
+
+# set API org and key
+openai.organization = "OPENAI_API_ORG" # replace or comment
+openai.api_key = "OPENAI_API_KEY" # replace
+
+# inference
+response = openai.Completion.create(
+            model = "text-davinci-002", # set as per your need
+            prompt = 'Tell me a Joke:',
+            max_tokens = 256, # set as per your need
+            top_p = 1, # set as per your need
+            temperature = 0.7, # set as per your need
+        )
+
+# Output:
+# "choices": [
+#     {
+#       "finish_reason": "stop",
+#       "index": 0,
+#       "logprobs": null,
+#       "text": "\n\nWhy did the chicken cross the road?\n\nTo get to the other side."
+#     }
+#   ],
+#   "created": 1667925044,
+#   "id": "cmpl-6ALmmOOkVWE03AmO9Cur8XM3jpEXk",
+#   "model": "text-davinci-002",
+#   "object": "text_completion",
+#   "usage": {
+#     "completion_tokens": 19,
+#     "prompt_tokens": 6,
+#     "total_tokens": 25
+#   }
+
    +

    Finetuning GPT-3

    +
      +
    • While GPT-3 is not open source, OpenAI has provided the paid option to finetune the model. At the time of writing, free credits were provided to new users -- so another reason to go and register now šŸ˜‰
      • +
    • They expose several APIs to perform finetuning. In a sense they are doing most of the heavy lifting by making the finetuning process super easy.
      • +
    • To begin with, make sure you have the OpenAI python library installed. Do it by pip install openai.
      • +
    • Then make sure the data is in correct format. Basically you require a .csv file with atleast two columns - prompt and completion with the respective data. Ideally the documentation suggest to have atleast 100 examples.
      • +
    • Next, we will prepare a jsonl file using the csv file. OpenAI expects the data in this format. And they also expose an API to do so, just run the following in CLI.
      • +
    +
    1
    openai tools fine_tunes.prepare_data -f data_to_fine_tune.csv
+
    +
      +
    • Now we will upload the prepared data to the OpenAI Server. Do this by running following code in Python.
      • +
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    # set the organization and api key (you can get them from Manage Account page)
+openai.organization = "my-organization-key"
+openai.api_key = "my-api-key"
+
+# upload the data to OpenAI Server
+file_meta_data = openai.File.create(file = open(f"data_to_fine_tune_prepared.jsonl",
+                                    encoding='utf-8'),
+                                    purpose='fine-tune')
+
    +
      +
    • Now we will train the model. Do this by running following code in Python.
      • +
    +
    1
    print(openai.FineTune.create(model="curie", training_file=file_meta_data["id"]))
+
    +
      +
    • And there we go, the finetuning has started! You can monitor the progress by running openai.FineTune.list(). The last entry will contain a status key that will change to succeeded when the finetuning is complete. Also, note down the model name from the fine_tuned_model key, you will need it later to access the trained model. Btw if you only want to print the last entry try this instead openai.FineTune.list()['data'][-1].
      • +
    • After the finetuning is done, you can use the model as usual from the playground or API!
      • +
    +
    +

    Tip

    +

    OpenAI provides multiple models (engines) with different accuracy and speed. These are Davinci, Curie, Babbadge and Ada - in the descending order of accuracy but increasing speed.

    +
    +

    Additional materials

    +
      +
    • The Illustrated GPT-2 (Visualizing Transformer Language Models) - Link
      • +
    + + +
    + +
    +
    + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + \ No newline at end of file diff --git a/natural_language_processing/T5/index.html b/natural_language_processing/T5/index.html new file mode 100644 index 00000000..24224a1a --- /dev/null +++ b/natural_language_processing/T5/index.html @@ -0,0 +1,2492 @@ + + + + + + + + + + + + + + + + + + T5 - A Lazy Data Science Guide + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
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    + + + +
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    + + + +
    +
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    + + +
    +
    + + + + + + + + +

    T5

    +

    Introduction

    +
      +
    • T5 stands for "Text-to-Text Transfer Transformer". It was released by Google on 2020. As the name suggests, it's a tranformer based encoder-decoder model used for text generation. For more details about other text generation models, refer the Text Generation chapter.
      • +
    • In contrast to other famous Transformer based models like BERT or GPT, which is made up of either the encoder or decoder part of the Transformer, T5 paper showcase that using the complete encoder-decoder architecture is better than only using decoder. Apart from this, the paper also curated and released Colossal Clean Crawled Corpus (C4) - a huge crawled and cleaned dataset for pre-training language model using self-supervised learning. (raffel2020exploring)
      • +
    +
    +

    +

    Transformer architecture. Left part is the encoder, right part is the decoder. T5's architecture is very similar to this one. (vaswani2017attention raffel2020exploring)
    +

    +
    +
      +
    • Due to this nature of T5, for training or finetuning, the model requires a pair of input and output sequences/text. Some example tasks that can be performed using T5 is shown below,
      • +
    +
    +

    +

    T5 text-to-text framework examples. See: Google Blog
    +

    +
    +
      +
    • +

      Based on the original T5, there has been several variations explored such as, (refer T5 @ HuggingFace )

      +
        +
      • T5v1.1: T5v1.1 is an improved version of T5 with some architectural tweaks, and is pre-trained on C4 only without mixing in the supervised tasks.
        • +
      +
        +
      • mT5: mT5 is a multilingual T5 model. It is pre-trained on the mC4 corpus, which includes 101 languages.
        • +
      +
        +
      • byT5: byT5 is a T5 model pre-trained on byte sequences rather than SentencePiece subword token sequences.
        • +
      +
        +
      • Long-T5: For use case where we need to process longer input (Refer)
        • +
      +
      • +
    +
    +

    Note

    +

    As mentioned above, for multi-lingual purpose refer mT5 which was trained on >100 languages. That said, original T5 was also trained on translation task from English to German, French and Romanian. So the output can sometimes contains tokens from these languages!

    +
    +

    Paper details

    +

    Transformer Architecture

    +
      +
    • To compare different architectures suitable for languague models, T5 authors considered basically three varieties,
        +
      • Encoder-Decoder: A standard encoder-decoder architecture uses fully visible masking in the encoder and the encoder-decoder attention, with causal masking (attention mask) in the decoder. Masking is done to make sure the output at a position doesn't attend to future output for prediction.
        • +
      • Language model: A language model consists of a single Transformer layer stack and is fed the concatenation of the input and target, using a causal mask throughout. As usual with LMs, the output only attends to the past input or output.
        • +
      • Prefix LM: Adding a prefix to a language model corresponds to allowing fully-visible masking over a portion of the input. It is very similar to LM, just that any output will attend to a certain portion of the input that contains prefix could could contain task specific information like translate English to German:.
        • +
      +
      • +
    +
    +

    +

    Schematics of the Transformer architecture variants considered by T5 paper. (raffel2020exploring)
    +

    +
    +
      +
    • T5 found the transformer based architecture to perform better than others.
      • +
    +

    Pre-training Strategy

    +
      +
    • T5 is trained with multi-task learning methodology, where the idea is to club multiple tasks while pre-training the model. These multiple tasks are further clubbed into two groups based on how they are trained,
        +
      • Unsupervised training:
          +
        • this includes training on the C4 dataset using the classic language model training tasks with maximum likelihood objective.
          • +
        • For unsupervised tasks like MLM, T5 has 100 special tokens <extra_id_0> to <extra_id_99> which can be used to format the input and output text. For the sentence "My name is Mohit Mayank" where I want to mask "name is", the input is My <extra_id_0> Mohit Mayank and required output will be <extra_id_0> name is <extra_id_1>.
          • +
        +
        • +
      • Supervised training:
          +
        • this includes adding several NLP based tasks like question-answering, summarization, classification, etc. The model is trained with the curated training data in a supervised fashion, but all these tasks are transformed to work with text-in text-out format as suitable for encoder-decoder models.
          • +
        • The data for supervised training is created separately for input and output text. For input it looks like {task_prefix}: {input_sequences}</s> . Similarly for the output text it looks like <pad> {output_sequence}</s>. One example could be: translate English to German: The house is wonderful.</s> for input and <pad> Das Haus ist wunderbar.</s> for output. This is then passed to the model to compute loss on the output part and then backpropagated to decrease the loss.
          • +
        +
        • +
      +
      • +
    +
    +

    Note

    +

    T5 authors also released checkpoint models which are only unsupervised trained on the C4 dataset. More details and model is available here.

    +
    +
      +
    • T5 also compared different unsupervised objectives i.e. different training stratigies for unsupervised training which could lead to better performance. A visual guide to the search space is shown below.
      • +
    +
    +

    +

    A flow chart of the exploration of unsupervised objectives by the T5 paper. (raffel2020exploring)
    +

    +
    +
      +
    • To begin with, there were three High-level approaches, (1) Language modeling: where you take predict the next word based on historical words, (2) BERT-style: where you mask certain words and model predict those masked words, or (3) Deshuffling: where you shuffle the sentence and model predicts the unshuffled correct sentence as output. After experimenting with these BERT-style approach gave the best result and hence was selecte for next level of analysis.
      • +
    • Next, different corruption startegies were tried. Originally BERT proposed MASD based approach but for language model setting, T5 authors observed Replace span works better. In replace span you mask consecutive tokens and language model predicts the masked spans.
      • +
    +
    +

    +

    Span corruption used for unsupervised training in T5. (raffel2020exploring)
    +

    +
    +
      +
    • Finally, different rate of corruption rate and corruption span length were experimentally selected.
      • +
    +

    Performance score

    +
      +
    • T5 paper reports following performance score on different datasets,
      • +
    +
    +

    +

    T5 performance score on different datasets. (raffel2020exploring)
    +

    +
    +

    Code

    +

    T5 Inference

    +
      +
    • Running T5 is super easy using HuggingFace. Let's do it,
      • +
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    # install packages
+!pip install transformers
+
+# import
+from transformers import T5Tokenizer, T5ForConditionalGeneration
+
+# load the tokenizers and model
+tokenizer = T5Tokenizer.from_pretrained("t5-small") # vocab size is 32100.
+model = T5ForConditionalGeneration.from_pretrained("t5-small")
+
+# for a phrase get the tokenised input ids
+input_ids = tokenizer("translate English to German: I am going to the party.", return_tensors="pt").input_ids
+# use the input ids to generte output
+outputs = model.generate(input_ids)
+# decode the output token ids to text
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+## Output --> 
+## Ich werde zur Partei gehen.
+
    +

    T5 finetuning

    +
      +
    • Before we dive into finetuning, here are some tips if you are going to use PyTorch or Keras.
        +
      • We can use high learning rate for AdamW optimizer in range of 1e-4 and 3e-4. Btw T5 was originally pre-trained with AdaFactor optimizer.
        • +
      • We can add task specific prefix like translate English to German: or summarize: in the input sequence if your task is similar to the ones T5 was originally pre-trained with.
        • +
      • We should replace the PAD token ids 0 with -100 so that it is ignored from loss computation. Btw PAD token is used as start sequence token for the labels (text that is to be generated).
        • +
      +
      • +
    • To keep things simpler, we can use SimpleT5, an excellent package that abstract a lot of technicalities. For dataset, we can go with Tweet sentiment data, that can be downloaded from here
      • +
    • Some differences from training other text generation models (due to the SimpleT5 package),
        +
      • We don't need the Dataset class, as SimpleT5 works directly on pandas dataframe. Hence we load the data, do some initial pre-processing, split the data and return the pandas dataframe. (no need to tokenize, create Dataset class, isn't this great!?)
        • +
      • One more point to note is that we do not need to create prompt formats for this package. This way we can separate out the input tweet and sentiment label into different columns, here source_text and target_text, respectively (Line 29 and 30).
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    # import
+import pandas as pd
+from simplet5 import SimpleT5
+from sklearn.metrics import f1_score
+from sklearn.model_selection import train_test_split
+
+# Data loading
+# -------
+
+# Data load function
+def load_sentiment_dataset(random_seed = 1):
+    # load dataset and sample 10k reviews.
+    file_path="../input/sentiment140/training.1600000.processed.noemoticon.csv"
+    df = pd.read_csv(file_path, encoding='ISO-8859-1', header=None)
+    df = df[[0, 5]]
+    df.columns = ['label', 'text']
+    df = df.sample(10000, random_state=1)
+
+    # modify the label  
+    map_label = {0:'negative', 4: 'positive'}
+    df['label'] = df['label'].apply(lambda x: map_label[x])
+
+    # divide into test and train
+    X_train, X_test, y_train, y_test = \
+              train_test_split(df['text'].tolist(), df['label'].tolist(),
+              shuffle=True, test_size=0.05, random_state=random_seed, stratify=df['label'])
+
+    # transform train and test data into pandas dataframe
+    train_data = pd.DataFrame({'source_text': X_train, 'target_text': y_train})    
+    test_data = pd.DataFrame({'source_text': X_test, 'target_text': y_test})    
+
+    # return
+    return train_data, test_data
+
+# load
+train_df, test_df = load_sentiment_dataset()  
+
+# Train
+# -------
+
+# load model
+model = SimpleT5()
+model.from_pretrained(model_type="t5", model_name="t5-base")
+
+# train model
+model.train(train_df=train_df,
+            eval_df=test_df, 
+            source_max_token_len=300, 
+            target_max_token_len=200, 
+            batch_size=8, 
+            max_epochs=2, 
+            outputdir = "outputs",
+            use_gpu=True
+            )
+
+# Test
+# -------
+
+# load the best model
+last_epoch_model = "..." # put the name here
+model.load_model("t5", last_epoch_model, use_gpu=True)
+
+# for each test data perform prediction
+predictions = []
+for index, row in test_df.iterrows():
+    prediction = model.predict(row['source_text'])[0]
+    predictions.append(prediction)
+
+# computer performance
+df = test_df.copy()
+df['predicted_label'] = predictions
+df['original_label'] = df['target_text']
+print(f1_score(df['original_label'], df['predicted_label'], average='macro'))
+
    +

    References

    +

    [1] Exploring Transfer Learning with T5: the Text-To-Text Transfer Transformer - Link

    +

    [2] T5 finetuning tips - HF forum

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    + + + + + + + + + + + + + + \ No newline at end of file diff --git a/natural_language_processing/data_to_text_generation/index.html b/natural_language_processing/data_to_text_generation/index.html new file mode 100644 index 00000000..8f99abff --- /dev/null +++ b/natural_language_processing/data_to_text_generation/index.html @@ -0,0 +1,2076 @@ + + + + + + + + + + + + + + + + + + Data-to-Text Generation - A Lazy Data Science Guide + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    Data-to-Text Generation

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    Warning

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    This page is still ongoing modifications. Please check back after some time or contact me if it has been a while! Sorry for the inconvenience šŸ™

    +
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    Introduction

    +
      +
    • Data to Text generation is a task with structured data as input and unstructured text as output. As an example, consider the use case of trying to summarize a table data, where table is the input and text as output.
      • +
    +
      +
    • KELM [3] is an interesting example. In the paper, the authors try to train a language model using Knowledge graph triplets. But as a KG stores structured data, the first task done by authors was to create subgraphs of KG and verbalize it (as shown below)
      • +
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    +

    An example illustration of converting an entity subgraph (in bubbles) into synthetic natural sentences (far right). [3]

    +
    +
      +
    • For this they "developed a verbalization pipeline named ā€œText from KG Generatorā€ (TEKGEN), which is made up of the following components: a large training corpus of heuristically aligned Wikipedia text and Wikidata KG triples, a text-to-text generator (T5) to convert the KG triples to text, an entity subgraph creator for generating groups of triples to be verbalized together, and finally, a post-processing filter to remove low quality outputs. The result is a corpus containing the entire Wikidata KG as natural text, which we call the Knowledge-Enhanced Language Model (KELM) corpus. It consists of ~18M sentences spanning ~45M triples and ~1500 relations." [3]
      • +
    +

    References

    +

    [1] NLP Progress - Data-to-Text Generation

    +

    [2] The 2020 Bilingual, Bi-Directional WebNLG+ Shared Task: Overview and Evaluation Results (WebNLG+ 2020)

    +

    [3] KELM: Integrating Knowledge Graphs with Language Model Pre-training Corpora

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    + + + + + + + + + + + + + + \ No newline at end of file diff --git a/natural_language_processing/explainable_ai_llm/index.html b/natural_language_processing/explainable_ai_llm/index.html new file mode 100644 index 00000000..941b01be --- /dev/null +++ b/natural_language_processing/explainable_ai_llm/index.html @@ -0,0 +1,2222 @@ + + + + + + + + + + + + + + + + + + Explainable AI: Language Models - A Lazy Data Science Guide + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    Explainable AI: LanguageĀ Models

    +
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    Hold your large language models accountable by learning how to "explain" the predictions.

    +
    + + +

    Introduction

    +

    Just like a coin, explainability in AI has two facesā€Š-ā€Šone it shows to the developers (who actually build the models) and the other to the users (the end customers). The former face (IE i.e. intrinsic explainability) is a technical indicator to the builder that explains the working of the model. Whereas the latter (EE i.e. extrinsic explainability) is proof to the customers about the model's predictions. While IE is required for any reasonable model improvement, we need EE for factual confirmation. A simple layman who ends up using the model's prediction needs to know why is the model suggesting something. This article provides a brief introduction and famous techniques used to infer both types of explainabilities, with a focus on large language models.

    +

    Large LanguageĀ models

    +

    As the name suggests, language models (LMs) try to model a language by creating probability distribution over the sequence of tokens (that can be words). This can be applied to the text generation task, as once the model is good enough with the probability, it can start predicting the next words provided some contextual words (prompts). This idea forms the basis of the research in deep learning-based (DL) Natural Language Processing (NLP), where newer and SotA models are becoming better with the probability distribution. And recently several powerful models like GPT-3 are able to generate text which is very hard to distinguish from human-written text!

    +
    +

    +

    GPT-3 is used to predict the next words for the prompt "To be or not to be". The output is quite impressive! [ByĀ Author]

    +
    +

    LLMs are leading the scoreboard for several tasks in NLP. But one existing problem with them (or any DL model in fact) is that they are inherently black boxes i.e. it becomes extremely difficult to explain the predictions. Let us try to shine some grey lights on these black boxes!

    +

    Intrinsic explainability

    +

    Intrinsic explainability tries to provide technical clarity on why the model made some predictions. Large LMs (LLMs) are mostly DL-based models and while they become better with predictions given more data or network parameters, it comes with a cost of explainability. They are not straight forward explainable and we need to derive ways to infer why a model made the prediction. Below let's briefly go through some of those ways.

    +

    Attention-based

    +

    The recent LLMs utilize Transformer-based architecture for enhanced performance. What makes Transformers unique is the presence of multiple attention layers and heads to capture bidirectional and context-aware token representation. Each attention head learns certain patterns that could help in language modeling. These patterns could be positional (attend to the previous word) or lexical (attend to acronyms, synonyms, etc) in nature. And when we combine multiple such heads in multiple layers, the overall model is able to capture sophisticated patterns and provide accurate predictions.

    +

    These kinds of internal and low-level patterns can be observed by using attention score visualization tools like bertviz [1]. There could be two major types of visualizations, (1) Attention head visualizationā€Š-ā€Šwhich could be helpful to see how the model attends to certain words with another word in context, and (2) Neuron visualizationā€Š-ā€Šwhich could be used to connect the model behaviors to neuron values.

    +

    For the Attention head visualization, you can select the model, the layer, and the head to explore the different attention patterns. One example is shown below.Ā 

    +
    +

    +

    Example of Attention head view to detect association of words in the model. Also useful to detect gender biases.Ā [1]

    +
    +

    Here the layer 5 and 11th head of the model (2nd last blue color box from the color palette) seems to incorporate the gender-specific patterns (lexical) in the language. That is why, the attention score for gender-related words (She, girl), (He, boy), (He, Bob), etc is high. As such, this head could be used to identify gender bias as well, which is evident from the associationsā€Š-ā€Š (nurse, She) and (doctor, He). Similar biases can be identified from other heads.

    +

    The neuron view is useful to drill down even deeper into the attentions scores to identify the specific neuron leading to certain behaviors in the model. One such example is shown below,

    +
    +

    +

    Neuron view of GPT-2 for layer 1, head 10Ā [1]

    +
    +

    Neuron view of BERT for layer 0, head 0 (same one depicted above). Positive and negative values are colored blue and orange, respectively, with color saturation based on the magnitude of the value. As with the attention-head view, connecting lines are weighted based on attention between the words.

    +

    Saliency methods

    +

    Attention presents a very myopic view as they are limited to a certain part of language models. As such they just provide the behavior for that part of the model. As an analogy, it's like explaining just one line of the complete code. This ideation has led to a debate on whether attention really presents the true explainability of the complete model?! To mitigate these concerns, there has been renewed interest in old, tried, and tested saliency methods [2]. These are,

    +
      +
    • Gradient-based: where we compute the gradient of the neural network's outcome with respect to the input. The idea is quite simpleā€Š-ā€Š as we compute gradients of the output wrt to the different parameters to find the importance and magnitude of correction needed for the parameters, why not do the same but for the inputs? This computation provides us a holistic view of the importance of each input wrt to the output.
      • +
    • Propagation-based: Gradient-based approach is a plain vanilla approach where backpropagation happens without additional computation at any layer i.e. it's a simple inverse (not mathematically) of the forward pass. In the propagation-based approach, we perform relevance-based modifications at each layer. It can be thought of as an enhanced version of the gradient-based approach.Ā 
      • +
    • Occlusion based: where we measure the importance of input on the output by erasing the input and estimating the effect on the output. It can be done by either recomputing output with the particular input missing or a mask in its place. Intuitively, the variation in the output will be high in case the input is important, and close to zero otherwise.
      • +
    +

    There are several open-source packages like Ecco, that provide the option to visualize the saliency measure. One example is shown below,

    +
    +

    +

    Gradient-based method to visualize the importance of the input words for the next word prediction (India) [ByĀ Author]

    +
    +

    Here, for the input "New Delhi is the capital of", LLM returned "India" which is factually correct. But intuitively, we would like to know which certain part of the input motivated the model to make such a prediction. The important measure is highlighted with color in the image, and as visible it gave a high score to "New Delhi" and "capital"!

    +

    Extrinsic explainability

    +

    The intrinsic measures fall short of covering the "needs" of explainability due to two reasons. Firstly, while it may help the model developer with access to internal metrics, it becomes quite difficult for the stakeholders on the other end of the spectrum to make sense of these complicated numbers. We are talking about the end-user of the model who could be a doctor in healthcare, engineers in mechanical, sales executive in the sales domain, and so on. They care more about factual correctness than the reason behind a model's prediction. Secondly, even the model developers will be concerned if the internal metrics make sense but the output doesn't (output is incorrect).

    +

    The problem with the existing language models is that they contain all their knowledge within the parameters. GPT-3 is made up of 175 Billion parameters and it has been shown to work on a large number of downstream tasks. It generates the text based on the information it learned and stored in its parameters during the training and fine-tuning phase. Take one task of question answering as an example, where the question is "Who is the Prime Minister of India?". While the model may answer it correctly now, how about after the next election? The answer is obvious, it will fail. This is because the information gained is constant and would need re-training to calibrate constantly with the latest events in the world (which is a costly affair [3]).

    +

    Because of all these reasons, there has been a research interest in enhancing the language models by complimenting them with a connection to a dynamic knowledge baseā€Š-ā€Šthat is kept up to date just like any other database. +Some examples by the leading AI teams in the world are as follows,

    +
      +
    • GopherCite [4] from DeepMind is an enhancement of their language model named Gopher. It was created to solve the existing problem of "hallucinations" in the Gopher model. In their terms, hallucination of the model leads to incorrect, sometimes nonsensical, text generations. Because of this, humans are required to not take the output for granted and perform proof checks. GopherCite removes that human dependency to some extent by using Google search to find relevant quotes as proof for its prediction. In case no such high probable proofs are found, it even admits defeat instead of providing a wrong answer!
      • +
    +
    +

    aaa +

    Gradient-based method to visualize the importance of the input words for the next word prediction (India) [ByĀ Author]

    +
    +
      +
    • WebGPT [5] from OpenAI is an attempt by the creators of GPT-3 to solve the hallucination problem. They complimented GPT with an additional model that interacts with text-based web browsers to find the most relevant web pages for the asked question. For this, the model learned basic commands like "SearchĀ ā€¦", "Find in page:Ā ā€¦", or "Quote:Ā ā€¦". The model goes through web pages to find passages and then uses these to compose an answer. The final output contains references to the source web pages as an attempt to legitimize the results.
      • +
    +
    +

    +

    Final output generated by WebGPT for the question "How do neural networks work?". Note the presence of References at the bottom. Taken from theĀ blog.

    +
    +
      +
    • LaMDA [6] from Google is a language model for dialog-based interactions. A lot of times, the conversation could become complicated (involving non-fictional characters or real entities), and then it becomes imperative that the model knows the correct context and background knowledge. Based on the conversation, LaMDA can call an external information retrieval system to improve the factual groundedness of its responses.
      • +
    +
    +

    +

    Example of a conversation with Mt. Everest using LaMDA. Taken fromĀ blog.

    +
    +

    References

    +

    [1] A Multiscale Visualization of Attention in the Transformer Model

    +

    [2] Why use attention as explanation when we have saliency methods?

    +

    [3] Cost of training GPT-3 | Reddit

    +

    [4] Teaching language models to support answers with verified quotes

    +

    [5] WebGPT: Browser-assisted question-answering with human feedback

    +

    [6] LaMDA: Language Models for Dialog Applications

    +

    Cheers.

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    + + + + + + + + + + + + + + \ No newline at end of file diff --git a/natural_language_processing/interview_questions/index.html b/natural_language_processing/interview_questions/index.html new file mode 100644 index 00000000..be2a11df --- /dev/null +++ b/natural_language_processing/interview_questions/index.html @@ -0,0 +1,2702 @@ + + + + + + + + + + + + + + + + + + Interview Questions - A Lazy Data Science Guide + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    Interview Questions

    + +
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    • Here are some questions and their answers to make you ready for your next interview. Best of luck šŸ‘‹
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    What are the different types of reasoning tasks in NLP?

    +
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    +
      +
    • Arithmetic Reasoning: Arithmetic reasoning is the ability of an NLP system to perform mathematical operations on numerical data. This can include basic arithmetic operations such as addition, subtraction, multiplication, and division as well as more complex operations such as algebraic equations and calculus.
      • +
    • Commonsense Reasoning: Commonsense reasoning refers to the ability of an NLP system to make deductions based on the knowledge and information that is commonly understood by humans. This includes understanding social norms, cultural contexts, and everyday life experiences. (StrategyQA is a sample dataset that contains True/False questions like "Did Aristotle use a laptop?")
      • +
    • Symbolic Reasoning: Symbolic reasoning involves the ability of an NLP system to manipulate and reason about symbolic representations of information, such as words, phrases, and sentences. This includes tasks such as parsing, string operations, semantic role labeling and entity recognition. (Last Letter Concatenation is a sample dataset with questions like "Take the last letters of the words in 'Lady Gaga' and concatenate them")
      • +
    • Logic Reasoning: Logic reasoning refers to the ability of an NLP system to perform logical deductions based on formal rules of inference. This can include tasks such as identifying logical fallacies, determining the validity of arguments, and drawing conclusions based on deductive reasoning. (Date understanding is a sample dataset with questions like "Today is Christmas Eve 1937, what is the date tomorrow in MM/DD/YYYY?")
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    What are word embeddings in NLP?

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    Word embeddings are a type of representation for words in NLP. They are a dense vector representation of a word, learned from the data using techniques such as word2vec or GloVe. The embeddings capture the semantics of the words, meaning that words with similar meanings will have similar vectors. Word embeddings are used as input in many NLP tasks such as language translation, text classification, and text generation.

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    What is Sentence Encoding?

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    Sentence encoding is the process of converting a sentence into a fixed-length vector representation, also known as sentence embeddings. This is done by using techniques such as bag-of-words, TF-IDF, or BERT-based models. Sentence encodings can be used as input in various NLP tasks such as text classification, text generation, and text similarity. Several algorithms first tokenize the sentence in words or tokens, compute thir embedding and then aggregate them (min, max, mean, etc) to get the sentence embedding.

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    Explain the concept of attention mechanism in NLP?

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    Attention mechanism is a way to weight different parts of the input in a neural network, giving more importance to certain parts of the input than others. It is commonly used in NLP tasks such as machine translation, where the model needs to focus on different parts of the input sentence at different times. Attention mechanisms can be implemented in various ways, such as additive attention (\(š‘„+š¾\)) and dot-product attention (\(š‘„š¾^{š‘‡}\))

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    What are transformer models in NLP?

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    Transformer models are a type of neural network architecture that have been successful in various NLP tasks such as language translation and language understanding. They were introduced in the transformer paper and use self-attention mechanism to weigh the different parts of the input. This allows the model to efficiently process long input sequences and handle the dependencies between the words. Refer for more details.

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    Can you explain the concept of Named Entity Recognition (NER) in NLP?

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    Named Entity Recognition (NER) is a subtask of information extraction that seeks to locate and classify named entities in text into predefined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc. NER systems can be rule-based or based on machine learning, and are used in a wide range of applications such as information retrieval, question answering and text summarization.

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    Explain Part-of-Speech (POS) tagging in NLP?

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    Part-of-Speech (POS) tagging is the process of marking each word in a text with its corresponding POS tag. This is a fundamental step in many NLP tasks such as parsing, text summarization, and information extraction. POS tagging can be rule-based or based on machine learning, and is typically done using algorithms such as Hidden Markov Models (HMMs) or Conditional Random Fields (CRFs).

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    Can you explain the concept of Language Modeling in NLP?

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    Language modeling is the task of predicting the next word in a sentence, given the previous words. This is done by training a model on a large dataset of text, which learns the probability distribution of the words in the language. Language models are used in a wide range of NLP tasks such as machine translation, text generation, and speech recognition.

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    Can you explain the concept of Text Summarization?

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    Text summarization is the task of generating a shorter version of a text that retains the most important information. There are two main types of text summarization: extractive and abstractive. Extractive summarization selects important sentences or phrases from the text to form the summary, while abstractive summarization generates new text that captures the meaning of the original text.

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    What is Sentiment Analysis?

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    Sentiment analysis is the task of determining the sentiment or emotion of a piece of text. This is typically done by classifying the text as positive, negative, or neutral. Sentiment analysis can be done using a variety of techniques such as rule-based systems, machine learning, and deep learning. It is used in a wide range of applications such as customer feedback analysis and social media analysis.

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    Can you explain the concept of Dependency Parsing?

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    Dependency parsing is the task of analyzing the grammatical structure of a sentence, identifying the dependencies between the words. This is done by creating a dependency parse tree, which represents the grammatical relationships between the words in a sentence. Dependency parsing is a fundamental step in many NLP tasks such as machine translation, text summarization, and information extraction.

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    Explain the Coreference Resolution task in NLP?

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    Coreference resolution is the task of identifying when different expressions in a text refer to the same entity. This is done by analyzing the text and identifying when two or more expressions have the same referent. Coreference resolution is a fundamental step in many NLP tasks such as machine translation, text summarization, and information extraction. In this example text, "Mohit lives in Pune and he works as a Data Scientist", the co-reference resolution will identify "Mohit" and "he" as belonging to the same entity.

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    Explain Stemming and Lemmatization in NLP?

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    • Stemming is the process of reducing inflected (or sometimes derived) words to their word stem, base or root form. This is done by using a stemmer algorithm which removes the suffixes or prefixes from the word. The goal of stemming is to reduce the dimensionality of the text data, grouping together the inflected forms of a word so they can be analyzed as a single term, which can be useful for tasks such as text classification and information retrieval.
      • +
    • Lemmatization is the process of reducing a word to its base form, which is called the lemma. This is done by using a lemmatizer algorithm which takes into consideration the context and the part of speech of the word. The goal of lemmatization is to reduce the dimensionality of the text data and group together the different forms of a word so they can be analyzed as a single term, which can be useful for tasks such as text classification and information retrieval.
      • +
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    Note

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    An obvious difference is that Lemmatization consider the grammar of the sentence while Stemming only consider the word.

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    What is Text Classification?

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      Text classification is the task of assigning predefined categories or labels to a given text. This is done by training a model on a labeled dataset of text, which learns to predict the label of new text. Text classification is used in a wide range of applications such as sentiment analysis, spam detection, and topic classification. There are multiple types of text classification such as,

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      • Binary classification: where there only two classes (ex: positive vs negative)
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      • Multi-class classification: where there are more than 2 classes (ex: positive, negative and neutral)
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      • Multi-label classification: when there are two or more classes and each text can have more than 1 class/label assigned to them (ex: single text can have some positive phrase and negative phrase)
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    What are Dialogue Systems in NLP?

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    A Dialogue system, also known as a conversational agent or chatbot, is a computer program that can interact with human users through natural language. It can understand the user's input, generate appropriate responses, and carry out tasks such as answering questions, booking a flight, or making a reservation. Dialogue systems can be rule-based, or based on machine learning and deep learning, and can be integrated into various platforms such as smartphones, websites, and messaging apps.

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    Please explain the concept of Text Generation?

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    Text generation is the task of generating new text that is similar to the text it was trained on. This is done by training a model on a large dataset of text, which learns the probability distribution of the words in the language. Text generation can be used for various applications such as chatbot, text completion and summarization. Refer for more details.

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    Can you explain the concept of Text Similarity in NLP?

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    Text Similarity is the task of determining how similar two pieces of text are to each other. This is done by comparing the text using various similarity measures such as cosine similarity, Jaccard similarity, or Levenshtein distance. Text similarity can be used in a wide range of applications such as plagiarism detection and text retrieval. Refer for more details.

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    Please explain Text Clustering?

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    Text Clustering is the process of grouping similar texts together. This is done by using clustering algorithms such as K-means, Hierarchical Clustering, or DBSCAN. Text clustering can be used in a wide range of applications such as topic modeling, sentiment analysis, and text summarization. This is usually a two step process, first the text is converted to a representation (usually by text embedding algorithms) and then a clustering algorithm is used to create clusters.

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    What is Named Entity Disambiguation (NED)?

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    Named Entity Disambiguation (NED) is the task of determining which entity (from a database) a mention (from text or doc) refers to, from a set of candidate entities. This is done by using techniques such as string matching, co-reference resolution, or graph-based methods. NED is important for tasks such as information extraction and knowledge base population. For example, NED will process a wikipedia page and map "Mohit M.", "M. Mayank", "Mohit" and similar named entities with "Mohit Mayank" present in the database.

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    What is the difference between a feedforward neural network and a recurrent neural network?

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    A feedforward neural network is a type of neural network in which the data flows in one direction, from input to output. There are no loops or connections that allow information to flow in a cyclical manner. On the other hand, a recurrent neural network (RNN) is a type of neural network that allows information to flow in a cyclical manner, with connections between neurons allowing information to be passed from one step of the process to the next. RNNs are useful for tasks such as language modeling and speech recognition, where the current input is dependent on the previous inputs.

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    Is BERT a Text Generation model?

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    Short answer is no. BERT is not a text generation model or a language model because the probability of the predicting a token in masked input is dependent on the context of the token. This context is bidirectional, hence the model is not able to predict the next token in the sequence accurately with only one directional context (as expected for language model).

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    What is weight tying in language model?

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    Weight-tying is where you have a language model and use the same weight matrix for the input-to-embedding layer (the input embedding) and the hidden-to-softmax layer (the output embedding). The idea is that these two matrices contain essentially the same information, each having a row per word in the vocabulary. Ref

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    What is so special about the special tokens used in different LM tokenizers?

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    Special tokens are called special because they are added for a certain purpose and are independent of the input. For example, in BERT we have [CLS] token that is added at the start of every input sentence and [SEP] is a special separator token. Similarly in GPT2, <|endoftext|> is special token to denote end of sentence. Users can create their own special token based on their specific use case and train them during finetuning. Refer cronoik's answer in SO

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    What are Attention Masks?

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    Attention masks are the token level boolean identifiers used to differentiate between important and not important tokens in the input. One use case is during batch training, where a batch with text of different lengths can be created by adding paddings to shorter texts. The padding tokens can be identified using 0 in attention mask and the original input tokens can be marked as 1. Refer blog @ lukesalamone.com

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    Note

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    We can use a special token for padding. For example in BERT it can be [PAD] token and in GPT-2 we can use <|endoftext|> token.

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    What is the difference between Attention and Self-Attention?

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    Self-attention (SA) is applied within one component, so the input is from one component only. One example is the encoder block in Transformers, where SA is used, and it takes the tokens from only the sentence as the input. Attention on the other hand can be used to connect two components or modality of data. Decoder in Transformer has Attention layer that connects the encoder and decoder data together. Refer StackExchange QA

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    + + + + + + + + + + + + + + \ No newline at end of file diff --git a/natural_language_processing/llama/index.html b/natural_language_processing/llama/index.html new file mode 100644 index 00000000..01b6fc0d --- /dev/null +++ b/natural_language_processing/llama/index.html @@ -0,0 +1,2294 @@ + + + + + + + + + + + + + + + + + + LLaMA - A Lazy Data Science Guide + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    LLaMA

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    Introduction

    +
      +
    • In Feb 2023, Meta introduced a collection of foundation language models ranging from 7B to 65B parameters under the name of LLaMA.
      • +
    • What makes LLaMA different from other LLMs is,
        +
      • It was trained on 1.4 trillion tokens created using publicly available datasets without resorting to proprietary and inaccessible datasets as done by the likes of Chinchilla, PaLM, or GPT-3.
        • +
      • With added improvements, the resulting models are highly competitive against more powerful models. For instance, LLaMA-13B outperforms GPT-3 (175B) on most benchmarks, and LLaMA- 65B is competitive with the best models, Chinchilla-70B and PaLM-540B.
        • +
      • Finally, LLaMA was open-sourced!
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    Tip

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    ā€œOfficialā€ weights were only released to the research community and even then you need to fill out a form to request access.

    +

    That said, there has been ā€œpiratingā€ of the weights that allow anyone to play around with the model. It was quite interesting, more details in this LinkedIn Post šŸ˜„

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    Architecture Modifications

    +

    To achieve the enhancements, several modifications were made to the original Transformer architecture. They are, [1]

    +
      +
    • Pre-normalization [from GPT3] To improve the training stability, RMSNorm was used to normalize the input of each transformer sub-layer instead of the output.
      • +
    • SwiGLU activation function [from PaLM]. ReLU non-linearity was replaced by the SwiGLU activation function.
      • +
    • Rotary Embeddings [from GPTNeo]. Absolute positional embeddings were replaced with rotary positional embeddings (RoPE) at each layer of the network.
      • +
    +

    Training Optimizations

    +

    On top of architecture modifications, several optimizations were made to improve the training speed of the models. They are, [1]

    +
      +
    • First, an efficient implementation of causal multi-head attention was used to reduce memory usage and runtime. (refer xformers library)
      • +
    • To further improve training efficiency, the number of activations that are recomputed was reduced during the backward pass with checkpointing.
      • +
    • Additional GPU optimizations were done like overlapping the computation of activations and the communication between GPUs over the network.
      • +
    +

    Dataset

    +
      +
    • The dataset used to train LLaMA was created using only open-source data and is a mixture of several sources, as shown below. This led to the creation of 1.4 tokens of the total dataset. [1]
      • +
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    +
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    Models

    +
      +
    • Below is the list of models trained as part of the project with additional details like dimensions, attention layers and head as well as the training metrics of the learning rate, batch size and the number of tokens used for training. [1]
      • +
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    +
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    Alpaca

    +
      +
    • LLaMA authors observed that a very small amount of instruction-based finetuning improves the performance of the model on Massive Multitask Language Understanding Tasks (MMLU). It also further improves the ability of the model to follow instructions. That said, they didnā€™t explore this thread further. Below you can see 5-shot MMLU performance of LLaMA-Instruct model (LLaMA-I) -- it is better than LLaMA model of the same size. [1]
      • +
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    +

    +
    +
      +
    • Enter Stanford Alpaca [2], an instruction-based finetuned LLaMA that further improves the performance of LLaMA models so much so that even 7B Alpaca model is comparable with OpenAIā€™s text-davinci-003.
      • +
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    +

    Warning

    +

    Alpaca team suggested that the model is better than LLaMA. There were no comparitive numbers or tables shared.

    +
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      +
    • The process starts with first generating 52K instruction-following samples using OpenAI's text-davinci-003. Then LLaMA model was finetuned on these data using supervised learning, basically taking inspiration from self-instruct paper. This process reduces the cost of preparing a GPT level model to under ~ $600 ( $500 to generate the data + $100 to finetune). The process is summarised below,
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    Note

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    The code to generate the 52k dataset along with finetuning recipe was open-sourced [2]

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    Code

    +
      +
    • There are many ways to access LLaMA. Sharing some of the most popular ones below,
      • +
    +

    Dalai

    +
      +
    • Dalai is the simplest way to run the LLaMA or Alpaca models on your machine. It also provides an intuitive UI to use the model. All you need to do is,
      • +
    +
    # install model
+npx dalai llama install 7B # or 13B, 30B, 65B
+npx dalai alpaca install 7B # or 13B, 30B, 65B
+
+# launch web UI + socket.io API
+npx dalai serve
+
    +
      +
    • And it looks good! šŸ‘‡
      • +
    +
    +

    +
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    Note

    +

    Internally it uses C/C++ port of LLaMA and Alpaca. You can access them separately for faster execution. The respective packages have also applied quantization to provide much faster execution with some compromise on accuracy.

    +
    +

    HuggingFace

    +
      +
    • HuggingFace has created the port to use the LLaMA model. You can also access the crowd-uploaded model at the hub here. The code to load and use the model like any other LLM is shown below,
      • +
    +
    # install (currently LLamA is in the main branch of HF)
+!pip install git+https://github.com/huggingface/transformers.git
+!pip install sentencepiece
+
+# import
+from transformers import LlamaForCausalLM, LlamaTokenizer
+
+# load the tokenizer and model
+tokenizer = LlamaTokenizer.from_pretrained("decapoda-research/llama-7b-hf")
+model = LlamaForCausalLM.from_pretrained("decapoda-research/llama-7b-hf")
+
    +

    References

    +

    [1] LLaMA - Official Model Card | HuggingFace | Release Blog | Paper

    +

    [2] Alpaca - Release Notes | HuggingFace Model | Github

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    + + + + + + + + + + + + + + \ No newline at end of file diff --git a/natural_language_processing/lstm_gru_rnn/index.html b/natural_language_processing/lstm_gru_rnn/index.html new file mode 100644 index 00000000..689eb3d3 --- /dev/null +++ b/natural_language_processing/lstm_gru_rnn/index.html @@ -0,0 +1,2269 @@ + + + + + + + + + + + + + + + + + + LSTM, GRU & RNN - A Lazy Data Science Guide + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    LSTM, GRU and RNN

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    Introduction

    +
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    • LSTM, GRU or RNN are a type of recurrent layers. They were the state-of-the-art neural network models for text related applications before the transformers based models. They can be used to process any sequential data like timeseries, text, audio, etc. RNN is the simpler of the lot, where as LSTM and GRU are the "gated" (and so a little complex) version of RNN. These gates helps LSTM and GRU to mitigate some of the problems of RNN like exploding gradient.
      • +
    • The most basic unit in these layers is a "cell", which is repeated in a layer - equal to the number of token size. For example, if we want to process a text with 150 words (with word level tokenization), we need to perceptually attach 150 cells one after the another. Hence, each cell process one word and passes on the representation of the word (hidden state value) to the next cell in line.
      • +
    • Starting with RNN, the flow of data and hidden state inside the RNN cell is shown below.
      • +
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    Cell of RNN layer. (practical_guide_to_rnn_and_lstm)
    +

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    • As shown in the figure, x^t is the input token at time t, h^{t-1} is the hidden state output from the last step, y^t and h^t are two notations for the hidden state output of time t. The formulation of an RNN cell is as follow,
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    \[ +h^{t}=g\left(W_{h h} h^{t-1}+W_{h x} x^{t}+b_{h}\right) \\ +y^{t}=h^{t} +\]
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    • LSTM cells on the other hand is more complicated and is shown below.
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    LSTM cell. (lstm_understanding)
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    Note

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    Python libraries take liberty in modifying the architecture of the RNN and LSTM cells. For more details about how these cells are implemented in Keras, check out (practical_guide_to_rnn_and_lstm).

    +
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    • Finally, a GRU cell looks as follow,
      • +
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    GRU cell with formulae. (lstm_understanding)
    +

    +
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      +
    • While RNN is the most basic recurrent layer, LSTM and GRU are the de facto baseline for any text related application. There are lots of debate over which one is better, but the answer is usually fuzzy and it all comes down to the data and use case. In terms of tunable parameter size the order is as follow - RNN < GRU < LSTM. That said, in terms of learing power the order is - RNN < GRU = LSTM.
      • +
    • Go for GRU if you want to reduce the tunable parameters but keep the learning power relatively similar. That said, do not forget to experiment wth LSTM, as it may suprise you once in a while.
      • +
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    Code

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    Using BiLSTM (for regression)

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    """Sample code for recurrent layer based models
+"""
+
+# ---- Imports ----
+import keras
+
+from keras.preprocessing.text import Tokenizer
+from keras.preprocessing.sequence import pad_sequences
+
+from keras.models import Sequential
+from keras.layers import Dense, Dropout, LSTM, Bidirectional, Embedding
+
+# ---- Data loading ----
+# contains a list or series of sentences
+# train = ...
+# test =  ...
+# combined = train['text'] + test['text']
+
+# ---- Data processing ----
+# set max vocab size
+vocab = 10000
+# create tokenizer instances  
+tokenizer = Tokenizer(num_words=vocab, oov_token=0)
+# fit the tokenizer on text sequences
+tokenizer.fit_on_texts(combined)
+# tokenize the complete data
+sequence_combined = tokenizer.texts_to_sequences(combined)
+# get the max len
+max_len = max([len(x) for x in sequence_combined])
+# tokenize the train data
+train_sequences = tokenizer.texts_to_sequences(train['text'])
+# add padding to the data
+padded_train_sequence = pad_sequences(train_sequences, maxlen=max_len, dtype='int32', padding='pre', truncating='pre', value=0)
+
+# ---- Model ----
+model = Sequential()
+# encoder
+model.add(keras.Input(shape=(padded_train_sequence.shape[1], ))) # input layer
+model.add(Embedding(vocab, 256)) # embedding layer
+model.add(Bidirectional(LSTM(256))) # biLSTM layer
+# decoder
+model.add(Dense(256, kernel_initializer='normal', activation='relu'))
+model.add(Dense(1, activation='linear'))
+# summary
+model.summary()
+
+# ---- Compile and Train ----
+# callbacks
+earlystopping = keras.callbacks.EarlyStopping(monitor='loss', patience=3)
+# compile
+model.compile(loss='mse', optimizer='adam', metrics=['mse','mae'])
+# fit
+history = model.fit(padded_train_sequence, train['y'], epochs=100, batch_size=16, verbose=2, callbacks=[earlystopping])
+
+# ---- Prediction ----
+# prepare testing data
+test_sequences = tokenizer.texts_to_sequences(test['text'])
+test_padded_sequences = pad_sequences(test_sequences, maxlen=max_len, dtype='int32', padding='pre',truncating='pre', value=0)
+# perform prediction
+y_pred = model.predict(test_padded_sequences)
+
    +

    Additional materials

    +
      +
    • A practical guide to RNN and LSTM in Keras (practical_guide_to_rnn_and_lstm)
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    • Guide to Custom Recurrent Modeling in Keras (Guide_to_custom_recurrent_modeling)
      • +
    • Understanding LSTM Networks (lstm_understanding)
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    + + + + + + + + + + + + + + \ No newline at end of file diff --git a/natural_language_processing/minilm/index.html b/natural_language_processing/minilm/index.html new file mode 100644 index 00000000..31422aec --- /dev/null +++ b/natural_language_processing/minilm/index.html @@ -0,0 +1,2350 @@ + + + + + + + + + + + + + + + + + + MiniLM - A Lazy Data Science Guide + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    MiniLM

    + +

    Introduction

    +
      +
    • Knowledge distillation is the process of compressing the knowledge of a large model (teacher) into a smaller one (student). MiniLM [1,2,3] propose novel approaches to perform distillation of large models like BERT and RoBERTa into smaller models that could be 99% accurate on certain tasks while being more than 2 times faster in inference!
      • +
    • Apart from sharing details on the distillation process, authors also open-sourced the distilled models at [3]. While the teacher models were encoder models, the author proposes MiniLM can be used for NLU (ex: extractive QA) as well as NLG tasks (ex: abstractive summarization). For NLG task, authors followed UniLM paper and used masked attention layers.
      • +
    +

    MiniLM

    +
      +
    • +

      The first MiniLM paper [1] was published in 2019, and the laid the foundation of the efficient distillation of large NLP models. Distillation of BERT model has been done before in DistillBERT, TinyBERT and MobileBERT. Here is how MiniLM was different,

      +
        +
      • They proposed dual approach of distillation, (1) Use the original teacher for distillation if student has same number of layers but different hidden size, (2) use an intermediate teacher assistant if student has even reduced number of layers. Shown below is the example where L and M are the number of layers, d and di is the hidden size in teacher and student respectively. This teacher assistant method is suggested if, \(M \le \frac{1}{2} L\) and \(di \le \frac{1}{2} d\).
        • +
      +
        graph LR
+  A("Teacher (L, d)") -- distil --> B("Teacher Assistant (L, di)")
+  B -- distil --> C("Student (M, di)")
      +
        +
      • Additionally, relation based distillation of only the last Transformer layer was performed rather than other intermediate layers. This lead to flexible architecture creation for student models, where the number of layers could be less than teacher's.
        • +
      • Finally, they also proposed dual attention transfer i.e Q-K and V-V. For distillation they compared the KL loss between the last transformer layer of teacher and student. Below is the overview diagram,
        • +
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      • +
    +
    +

    +

    Overview of Deep Self-Attention Distillation [1]

    +
    +
      +
    • Here is the comparison of the existing distillation methods with MiniLM wrt the approach and performance on different datasets.
      • +
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    +

    +

    Comparison with previous task-agnostic Transformer based LM distillation approaches. [1]

    +
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    +

    +

    MiniLM performance comparision with existing distilled models [1]

    +
    +
      +
    • Here is a comparison of different distilled MiniLM model against the BERT-Base model [3]. As visible, even with more than 3x reduction in parameters, the accuracy is quite good, sometimes even better!
      • +
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    Model#ParamSQuAD 2.0MNLI-mSST-2QNLICoLARTEMRPCQQP
    BERT-Base109M76.884.593.291.758.968.687.391.3
    MiniLM-L12xH38433M81.785.793.091.558.573.389.591.3
    MiniLM-L6xH38422M75.683.391.590.547.568.888.990.6
    +

    MiniLMv2

    +
      +
    • MiniLMv2 [2] was published in 2021, and here is how it was an enhancement wrt MiniLMv1,
        +
      • It generalize deep self-attention distillation in MiniLMv1 [1] by using self-attention relation distillation for task-agnostic compression of pre-trained Transformers. The proposed method eliminates the restriction on the number of studentā€™s attention heads.
        • +
      • Authors performed teacher layer selection strategy. In MiniLMv1, knowledge from teacher's last layer was transfered to student's last layer. In MiniLMv2, while the transfer still happened to student's last layer, teacher's layer changes,
          +
        • For BERT-large, 21st layer in teacher model was used for transfer
          • +
        • For RoBERTa-large and XML-R-large, 19th layer in teacher model was used for transfer
          • +
        • For base sized models, last layer in teacher model was used for transfer
          • +
        +
        • +
      • Authors experimented with multiple self-attention (Q-K, K- Q, Q-V, V-Q, K-V and V-K relations). However, introducing more self-attention relations also brings a higher computational cost. Hence to achieve a balance between performance and computational cost, author choose to transfer Q-Q, K-K and V-V self-attention relations instead of all self-attention relations.
        • +
      +
      • +
    +
    +

    +

    Overview of multi-head self-attention relation distillation [1]

    +
    +
      +
    • Shown below are the MiniLMv2 models with details on the speedup and performance [3],
      • +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    ModelTeacher ModelSpeedup#ParamMNLI-m (Acc)SQuAD 2.0 (F1)
    L6xH768 MiniLMv2RoBERTa-Large2.0x81M87.081.6
    L12xH384 MiniLMv2RoBERTa-Large2.7x41M86.982.3
    L6xH384 MiniLMv2RoBERTa-Large5.3x30M84.476.4
    L6xH768 MiniLMv2BERT-Large Uncased2.0x66M85.077.7
    L6xH384 MiniLMv2BERT-Large Uncased5.3x22M83.074.3
    L6xH768 MiniLMv2BERT-Base Uncased2.0x66M84.276.3
    L6xH384 MiniLMv2BERT-Base Uncased5.3x22M82.872.9
    +

    Code

    +

    Inference of MiniLM

    +
      +
    • As the MiniLM models are based on BERT and RoBERTa, we can use their code for MiniLM. Here, let's make it much simpler by using the AutoModel function if you are loading the models from Huggingface. You can also download models from [3].
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    # install packages
+!pip install -q transformers
+!pip install -q sentencepiece
+
+# import
+from transformers import AutoTokenizer, AutoModel
+
+# load tokenizer and model
+tokenizer = AutoTokenizer.from_pretrained("microsoft/Multilingual-MiniLM-L12-H384")
+model = AutoModel.from_pretrained("microsoft/Multilingual-MiniLM-L12-H384")
+
+# inference
+inputs = tokenizer("Hello world!", return_tensors="pt")
+# dict with input_ids ---> torch.Size([1, 5]) and attention_mask ---> torch.Size([1, 5])
+outputs = model(**inputs)
+# dict with 'last_hidden_state' --> torch.Size([1, 5, 384]) and pooler_output --> torch.Size([1, 384])
+
    +
      +
    • The tokenization vocabulary is 250002 strong (quite big!), and for input Hello world!. the tokenized output is <s>_Hello_world!</s> with corresponding input ids is tensor([[ 0, 35378, 8999, 38, 2]])
      • +
    +

    References

    +

    [1] MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers

    +

    [2] MiniLMv2: Multi-Head Self-Attention Relation Distillation for Compressing Pretrained Transformers

    +

    [3] MiniLM Official Microsoft Github

    + + +
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    + + + +
    +
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    +
    + + + + + + + + + + + + + + \ No newline at end of file diff --git a/natural_language_processing/named_entity_recognition/index.html b/natural_language_processing/named_entity_recognition/index.html new file mode 100644 index 00000000..d9095741 --- /dev/null +++ b/natural_language_processing/named_entity_recognition/index.html @@ -0,0 +1,2560 @@ + + + + + + + + + + + + + + + + + + Named Entity Recognition - A Lazy Data Science Guide + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
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    + + +
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    + + + + + + + + +

    Named entity recognition

    +

    Introduction

    +
      +
    • Named entity recognition (NER) is the process of identifying entities in the unstructured text, where entities could be objects, people, locations, organizations, etc.
      • +
    • NER's most basic building block consists of pair of entity_type and entity_value. Consider the following example,
      • +
    +
    ## Statement
+My name is Mohit, and I am from India. 
+I am a Data Scientist and I will be leaving for my office around 9 AM.
+
+## Entities
+[{
+    'entity_type': 'PERSON',
+    'entity_value': 'Mohit',
+},
+{
+    'entity_type': 'LOCATION',
+    'entity_value': 'India',
+}, 
+{
+    'entity_type': 'TIME',
+    'entity_value': '9 AM',
+}]
+
    +
      +
    • The process of extracting entities could be done in two ways.
        +
      • Heuristic: by identifying the entities based on the rules.
        • +
      • Semantic: by identifying the entities based on the semantics and context.
        • +
      +
      • +
    +
      +
    • Heuristic based approach is suited only for simple entities for which approximate rules can be created. Take for example EMAILID, PHONE_NUMBER, WEBSITE, etc. It should be easy enough to create regular expressions for such cases and hence heuristic approach could be applied. We can also apply part of speech based rules to extract certain entities.
      • +
    • On the other hand, the Semantic approach is required where the cardinality of the entities is high and the context is required to extract the entities. For example, NAME, LOCATION, DESIGNATION, etc. For these cases, we usually train neural network based models that learn to consider the context and extract the entities.
      • +
    +
    +

    Note

    +

    A good approach to creating your NER solution would be to segregate your entities into simple and complex, and then create either a heuristic or a semantic based solution or a combination of both. In short, it is not always suitable to directly go to fancy NN based semantic approaches - it could be unnecessary overkill.

    +
    +
      +
    • Remember the entity types are not set in stone and we can even train new models or finetune existing models on our own custom entities.
      • +
    • For this, in the Semantic-based approach, it's a good idea to finetune the existing model rather than to train a new one as it will require far fewer data.
      • +
    • The amount of data required to finetune model depends on how similar the custom entities are with the existing entities. Consider the following cases,
        +
      • The model is pretrained to detect PERSON and now you want to finetune it to detect MALE_NAME and FEMALE_NAME. As this is just a lower granularity on the existing PERSON entity, a mere ~200 examples (for each new entity type) could give you good results.
        • +
      • On the other hand, if you now want to finetune a completely new entity like OBJECTIONS_TYPE, you may need ~500 examples.
        • +
      +
      • +
    +
    +

    Note

    +

    Another thing to consider is the length of entity_value. With an increase in entity_value you may require more examples to get good accuracy results.

    +
    +

    Code

    +
      +
    • There are lots of Python-based packages that provide open source NER models. Some of these packages are Spacy, NLTK, Flair, etc. While packages provide an easy interface to the NER models or rules, we can even load and use external open-source NER models.
      • +
    +

    Using Spacy NER model for Inference

    +
      +
    • Spacy comes with several pre-trained models that can be selected based on the use case. For this example, we will use the Transformer model available with Spacy Transformers.
      • +
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    # install spacy-transformers and transformers model
+!pip install spacy-transformers
+!python3 -m spacy download en_core_web_trf
+
+# import the spacy library
+import spacy
+
+# load the model
+nlp = spacy.load("en_core_web_trf")
+# set the text
+text = "My name is Mohit, and I am from India. I am a Data Scientist and I will be leaving for my office around 9 AM."
+# create spacy doc by passing the text
+doc = nlp(text)
+# print all of the identified entities
+for ent in doc.ents:
+  print(f"Type: {ent.label_} -- Value: {ent.text}")
+
+# Output:
+# Type: PERSON -- Value: Mohit
+# Type: GPE -- Value: India
+# Type: TIME -- Value: around 9 AM
+
    +
      +
    • We can even display the results in a much more intuitive and fancy way by,
      • +
    +
    1
+2
    # use displacy to render the result
+spacy.displacy.render(doc, style='ent', jupyter=True, options={'distance': 90})
+
    +
    +

    +

    NER result for the above example

    +
    +

    Training custom NER model using Spacy v3

    +
      +
    • All NER use-cases are not the same, and you may want to train a custom model with new entity types. Spacy provides an option to train custom NER models as well. To be frank the complete process is quite complicated, but nothing to worry, strap on and let's cover them steo by step.
      • +
    +

    Config Creation

    +
      +
    • To begin with, we will define the settings that will be used throughout the training process. Starting with Spacy v3, all parameter settings need to be configured using a .cfg file. We can create a .cfg file following the guide here. Basically, it requires to
        +
      • Firstly, create a base config using the quick widget provided at the page. Do remember to check the correct options. (Refer below image for one example)
        • +
      • Secondly, run CLI command to update the base config python -m spacy init fill-config base_config.cfg config.cfg
        • +
      +
      • +
    +
    +

    +

    Example of options to mark to generate base_config.cfg from Spacy website for NER training.

    +
    +

    Data Preparation

    +
      +
    • Next we need to prepare the dataset. At high level, you need to prepare pairs of text and entities in the text. Consider the following dummy dataset where we want to extract video game's names, (in CSV format)
      • +
    + + + + + + + + + + + + + + + + + +
    textlabel
    I was playing Call of Duty{'entities': [[14, 26, 'Game']]}
    I did not like COD1 and COD2{'entities': [[15, 19, 'Game'], [24, 28, 'Game']]}
    +
      +
    • As obvious, text contains the base text and label contains all the entities that ideally should be extracted from text. This is our golden dataset. Here, inside label we have a dict with entities key and the value is list of different entities. Each entity has [start_index, end_index, entity_type] data. Note, we follow the Python indexing i.e. the indexing starts with 0, start_index is the index of start character and end_index is the index of end character + 1. In the first example, "I was playing Call of Duty"[14:26] will return "Call of Duty" which is a very famous video game šŸŽ®
      • +
    • Now we will convert the CSV file into Spacy format. It is the recommended format supported by the package. To do this, run the following code,
      • +
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    # import 
+from tqdm import tqdm
+from spacy.tokens import DocBin
+
+# function
+
+def convert_to_spacy(data_df, output_path):
+    """
+    Convert the data to spacy format
+
+    Parameters
+    ------------
+    :param data_df: pandas.DataFrame
+        dataframe containing the data to be converted
+    :param output_path: string
+        path to save the converted data
+    """
+    nlp = spacy.blank("en") # load a new blank spacy model
+    db = DocBin() # create a DocBin object
+    # iterate over the dataframe
+    for id_, row in tqdm(data_df.iterrows()): 
+        text = row['text'] # extract the text
+        doc = nlp.make_doc(text) # create doc object from text
+        ents = [] # var to hold entities
+        for entity in row['label']['entities']: # add character indexes
+            start, end, label = entity # extract the entity details
+            span = doc.char_span(start, end, label=label, alignment_mode="contract")
+            if span is None:
+                print("Skipping entity")
+            else:
+                ents.append(span)
+        doc.ents = ents # label the text with the ents
+        db.add(doc)
+    # save to disk
+    db.to_disk(output_path) # save the docbin object
+
+# run the code
+convert_to_spacy(data_df, "train_data.spacy")
+
    +
    +

    Note

    +

    Remember to split the CSV file into train and test data. Then you can run the above code twice to generate two spacy files, one for train and one for test.

    +

    Also, we can use random split, as stratified split is quite difficult to do. This is because each text may heve multiple instances of same or different entities and we want to split the text based on entities! Because of this, a stratified split is equivalent to solving a optimizing problem - How to split the text samples such that the underlying entities are equally distributed! Hence we will use a random split for rough estimation and the result may surprise you šŸ˜‰

    +
    +

    Data Validation

    +
      +
    • The next step is to perform a validation to check if the data is correctly converted or not. Spacy provides readymade CLI command for this purpose,
      • +
    +
    spacy debug data -V /content/config.cfg --paths.train /content/train_data.spacy --paths.dev /content/test_data.spacy
+
    +
      +
    • This should print output similar to,
      • +
    +
    ============================ Data file validation ============================
+āœ” Pipeline can be initialized with data
+āœ” Corpus is loadable
+
+=============================== Training stats ===============================
+Language: en
+Training pipeline: tok2vec, ner
+7916 training docs
+235 evaluation docs
+āœ” No overlap between training and evaluation data
+
+============================== Vocab & Vectors ==============================
+ā„¹ 876405 total word(s) in the data (33656 unique)
+10 most common words: ',' (35938), '.' (24253), '  ' (19522), ':' (17316), 'to'
+(16328), 'you' (15474), 'the' (14626), ' ' (14051), 'Ā  ' (13869), 'Ā ' (12003)
+ā„¹ No word vectors present in the package
+
+========================== Named Entity Recognition ==========================
+ā„¹ 6 label(s)
+0 missing value(s) (tokens with '-' label)
+Labels in train data: 'Game'
+āœ” Good amount of examples for all labels
+āœ” Examples without occurrences available for all labels
+āœ” No entities consisting of or starting/ending with whitespace
+āœ” No entities crossing sentence boundaries
+
+================================== Summary ==================================
+āœ” 7 checks passed
+
    +
      +
    • Based on the quality of annotations or the tool used, you may encounter error like Whitespaces present in the data validation step. This is because the annotations has whitespaces and it becomes difficult to train the model with such examples. In such case, we can fix the data by removing the whitespaces as shown below,
      • +
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    # var to hold the count of faulty annotations
+count = 0
+# remove instances from the dataframe where annotations contains whitespaces
+for index, row in data_df.iterrows():
+    row_label = row['label']['entities']
+    for entity_index, entity in enumerate(row_label):
+        text = row['text'][entity[0]:entity[1]]
+        if len(text) != len(text.strip()):
+            count += 1
+            new_text = text.strip()
+            start_index = row['text'].find(new_text)
+            end_index = start_index + len(new_text)
+            row_label[entity_index] = [start_index, end_index, entity[2]]
+# print the count of faulty annotations that were fixed
+print(count)
+
    +

    Training the Model

    +
      +
    • Now we are ready to train the model. Spacy CLI command can be used,
      • +
    +
    spacy train --output models/ config/config.cfg --paths.train data/train_data.spacy --paths.dev data/test_data.spacy --gpu-id 0
+
    +
    +

    Note

    +

    Make sure to remove --gpu-id 0 if you do not have a GPU.

    +
    +
      +
    • This should print something like,
      • +
    +
    ā„¹ Saving to output directory: models
+ā„¹ Using CPU
+
+=========================== Initializing pipeline ===========================
+[2022-05-31 23:29:00,409] [INFO] Set up nlp object from config
+[2022-05-31 23:29:00,413] [INFO] Pipeline: ['tok2vec', 'ner']
+[2022-05-31 23:29:00,415] [INFO] Created vocabulary
+[2022-05-31 23:29:00,415] [INFO] Finished initializing nlp object
+[2022-05-31 23:29:08,553] [INFO] Initialized pipeline components: ['tok2vec', 'ner']
+āœ” Initialized pipeline
+
+============================= Training pipeline =============================
+ā„¹ Pipeline: ['tok2vec', 'ner']
+ā„¹ Initial learn rate: 0.001
+E    #       LOSS TOK2VEC  LOSS NER  ENTS_F  ENTS_P  ENTS_R  SCORE 
+---  ------  ------------  --------  ------  ------  ------  ------
+  0       0          0.00     14.42    0.39    0.43    0.36    0.00
+  0    1000      75263.20   7992.36    9.34    9.29    9.39    0.09                                        
+  0    2000     473275.24   6660.36   20.33   29.45   15.52    0.20                                        
+  1    3000     203618.32  12177.86   27.76   29.32   26.35    0.28                                        
+  2    4000     394085.98  14795.70   35.14   44.02   29.24    0.35                                        
+  3    5000     280698.47  13595.65   34.71   40.58   30.32    0.35                                        
+  4    6000     332890.64  13044.08   37.39   44.72   32.13    0.37                                        
+  5    7000     645988.19  12552.55   40.72   45.54   36.82    0.41                                        
+  6    8000     155963.67  12083.43   34.97   33.01   37.18    0.35                                        
+  7    9000     802471.84  11443.62   38.64   40.64   36.82    0.39                                        
+  8   10000      44495.21  10276.14   38.79   40.55   37.18    0.39                                        
+  9   11000      86229.51  10011.45   40.08   46.86   35.02    0.40                                        
+ 10   12000     198516.08   9752.18   37.38   40.08   35.02    0.37                                        
+Epoch 11:  66%|ā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–ˆā–                   | 656/1000 [02:34<01:28,  3.89it/s]
+
    +
    +

    Note

    +

    Modify the config.cfg file to specify the model to use, the epochs to train for, the learning rate to use and other settings.

    +
    +

    Evaluation of the Model

    +
      +
    • Finally, once the trainig is done, we can evaluate the model using the Spacy CLI command,
      • +
    +
    spacy evaluate models/model-best data/test_data.spacy --gpu-id 0
+
    +

    Additional materials

    + + + +
    + +
    +
    + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + \ No newline at end of file diff --git a/natural_language_processing/nlq/index.html b/natural_language_processing/nlq/index.html new file mode 100644 index 00000000..86d32078 --- /dev/null +++ b/natural_language_processing/nlq/index.html @@ -0,0 +1,2402 @@ + + + + + + + + + + + + + + + + + + Natural Language Querying - A Lazy Data Science Guide + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
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    +
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    + + + +
    +
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    + + + +
    +
    +
    + + +
    +
    + + + + + + + + +

    Natural Language Querying

    + +

    Introduction

    +
      +
    • Natural Language Querying (NLQ) is the process of querying DBs not in their official querying language but using natural language text. One example could be to fetch results from a SQL table for question - "Who is the Prime Minister of India?" by just using the text and not some technical query like select name from pm_table where country = "India".
      • +
    • There are two main reasons that makes this task important for any IaaS (Information as a service) product or SaaS,
        +
      • Each DB has its own specific querying language. This is a nightmare even for developers, as they will have to gain expertise in mulitple DB languages.
        • +
      • Looking at a product from users perspective, it makes sense to let the user query in the language they prefer and not the technical query languages suitable for each DBs.
        • +
      +
      • +
    +

    Different Approaches

    +
      +
    • Usually there are two approaches to create a NLQ system,
        +
      • Query creator: this is a multi-step process where we first convert the natural text to DB specific language query. This step in itself could have multiple sub steps where we identify entities and intent from the query and then match them with the available data in table. Later we execute the query on the DB and get the data.
        • +
      • Answer extractor: this is a single step process, usually built completely of neural networks, where the data and question are passed to the network and output is returned. Think of it like closed-book QA systems in NLP, where we pass the context (here the DB table) and the question (query) and the model returns us the answer. We could add wrapper on the output to handle complex queries with COUNT, AVERAGE, etc.
        • +
      +
      • +
    +
    graph LR
+  A("What is the size of the data?") --> B(NLQ system)
+  B -- Query creator --> C("Select count(*) from table")
+  C -- querying DB --> E("5")
+  B -- Answer extractor --> D("5")
+  style B stroke:#f66,stroke-width:2px,stroke-dasharray: 5 5
    +
    +

    Note

    +

    NLQ, as a topic, is DB agnostic. But in reality different NLQ systems are built for specific DBs for example SQL, SPARQL, MongoDB QL, etc.

    +
    +

    Code

    +
      +
    • Let us explore the different ready made solutions for NLQ.
      • +
    +

    Large Language Models (LLMs)

    +
      +
    • While LLMs have been proven to work well for a lot of NLP related downstream tasks, will it work for NLQ? Let's think about it, due to huge training data LLMs might have already seen a lot of SQL queries and their respective descriptions. So in fact they have "some idea" on the relationship between a SQL query (or other query language for that matter) and its respective natural language query. Some might even say that it understands the fundamentals of Text-to-SQL task. But what LLM doesn't know is your Database/Table's schema and how you are storing the data. So hypothetically, if we provide these details it should work, right? The answer is yes!
      • +
    • In paper [4], authors took this idea further and evaluated multiple LLMs to answer two questions,
        +
      • Which LLM is best for Text-to-SQL task? Considering only inference, Codex based models like code-davinci-001 were the top perfomers. If we can finetune the models, T5-3B + PICARD was better.
        • +
      • Which prompt is best for Text-to-SQL task? Apart from the instructions, the prompt should also contain the schema of the table (with CREATE TABLE command containing column name, type, column reference and keys) along with a couple of rows as example. Below is an example of just the additional data [3] +
        # schema
+CREATE TABLE "Track" (
+  "TrackId" INTEGER NOT NULL,
+  "Name" NVARCHAR(200) NOT NULL,
+  "AlbumId" INTEGER,
+  "MediaTypeId" INTEGER NOT NULL,
+  "GenreId" INTEGER,
+  "Composer" NVARCHAR(220),
+  "Milliseconds" INTEGER NOT NULL,
+  "Bytes" INTEGER,
+  "UnitPrice" NUMERIC(10, 2) NOT NULL,
+  PRIMARY KEY ("TrackId"),
+  FOREIGN KEY("MediaTypeId") REFERENCES "MediaType" ("MediaTypeId"),
+  FOREIGN KEY("GenreId") REFERENCES "Genre" ("GenreId"),
+  FOREIGN KEY("AlbumId") REFERENCES "Album" ("AlbumId")
+  )
+# examples
+SELECT * FROM 'Track' LIMIT 3;
+  TrackId Name    AlbumId MediaTypeId GenreId Composer    Milliseconds    Bytes   UnitPrice
+  1   For Those About To Rock (We Salute You) 1   1   1   Angus Young, Malcolm Young, Brian Johnson   343719  11170334    0.99
+  2   Balls to the Wall   2   2   1   None    342562  5510424 0.99
+  3   Fast As a Shark 3   2   1   F. Baltes, S. Kaufman, U. Dirkscneider & W. Hoffman 230619  3990994 0.99
+
        • +
      +
      • +
    • If all of this seems too tedius, we can use LangChain that does all of the heavy lifting for us so that we can just do the fun stuff i.e. ask questions šŸ˜‰. Here, we will connect SQLite database with LLM model. (Script inspired from SQLite example)
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      # import
+from langchain import OpenAI, SQLDatabase, SQLDatabaseChain
+
+# connect to SQLite DB
+db = SQLDatabase.from_uri("sqlite://all_employees.db")
+
+# connect to the OpenAI Davinci GPT-3 model
+llm = OpenAI(temperature=0)
+
+# create SQLDatabaseChain
+db_chain = SQLDatabaseChain(llm=llm, database=db, verbose=True)
+
+# run the code
+db_chain.run("How many employees are there?")
+# output
+>> SELECT COUNT(*) FROM Employee;
+>> SQLResult: [(8,)]
+>> Answer: There are 8 employees.
+
      +
      • +
    +
      +
    • +

      Let's talk about what happened with the code above. First, LangChain create a prompt template and fills the variables automatically using the DB we plugin with the chain. The variables are {dialect} (here SQL), {table_info} (the additional data we talked about above) and {input} (the question). The template looks as follow,

      +
      Given an input question, first create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer. Unless the user specifies in his question a specific number of examples he wishes to obtain, always limit your query to at most {top_k} results. You can order the results by a relevant column to return the most interesting examples in the database.
+
+Never query for all the columns from a specific table, only ask for a the few relevant columns given the question.
+
+Pay attention to use only the column names that you can see in the schema description. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table.
+
+Use the following format:
+
+Question: "Question here"
+SQLQuery: "SQL Query to run"
+SQLResult: "Result of the SQLQuery"
+Answer: "Final answer here"
+
+Only use the tables listed below.
+
+{table_info}
+
+Question: {input}
+
      +
      • +
    +
      +
    • Once done, it runs the LLM on the formatted prompt to get the SQL output. Then it execute the query on the connected DB to fetch the result. Finally, it also formats the results into a proper natural language output. All of this with just some prompt engineering! šŸ”„
      • +
    +
    +

    Note

    +

    While the results are quite impressive, do remember that we need to use powerful (read costly) LLMs for it work with respectable accuracy. As we are formatting the prompt with DB schema, the prompt size might become huge if your DB or Table is big. It is hence recommended to create custom prompts when possible. Be also aware of the respective LLM costs if you are using 3rd party LLMs like GPT-4 or Cohere.

    +
    +

    TaPaS

    +
      +
    • TaPas follows Answer extractor based approach to perform NLQ that utilizes specially trained BERT like transformer. Tapas takes the question and table in the format inspired from BERT like [CLS] Question [SEP] Flattened table [SEP]. The answer is selected from the table based on the question.
      • +
    • The model was first pre-trained using unlabeled data on tasks like Masked Language modeling and Sentence-Table support/refute prediction. Later, it was finetuned on datasets like WikiSQL, WTQ and other to perform NLQ.
      • +
    +
    +

    +

    Illustration of the TaPas model for one example (TaPas paper)

    +
    +
      +
    • One interesting differentiator of TaPas is the unique formatting and encoding of the query and the table. As a table contains values spread across columns and rows, special column, rows and segment embeddings are added to the input to make the model learn the proper context. One example is shown below,
      • +
    +
    +

    +

    Encoding process of sample query and table in TaPas (TaPas paper)

    +
    +
    +

    Note

    +

    As TaPas was pre-trained using self-supervised learning on unlabled data, it learned the concept of relationship between text and table. Hence, it can be used (finetuned) for other table-text related downstream tasks as well like refute or support the text based on content in table, etc.

    +
    +
      +
    • Let's get started with the code part. For TAPAS to work, we need to install torch-scatter. For this, we first install pytorch using pip install torch and then get the version of torch using torch.__version__. Next we install torch-scatter by replacing the version of torch in pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.12.0+cu102.html
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    # install required packages
+!pip install -q transformers==4.4.2 torch pandas
+!pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.12.0+cu102.html
+
+# import
+import pandas as pd
+from transformers import pipeline
+
+# load pipeline
+nlq_tapas = pipeline(task="table-question-answering", 
+                     model="google/tapas-base-finetuned-wtq")
+
+# load the data
+data = pd.read_csv("../data/pm_table.csv") # use your table here
+data = data.astype(str)
+
+# query the table
+query = "Who is the Prime Minister of India?"
+answer = nlq_tapas(table=data, query=query)['answer']
+print(answer)
+# Output: "Narendra Modi" (at the time of writing)
+
    +
    +

    Tip

    +

    Personal opinion - TAPAS's accuracy is quite good wrt TableQA, but the major drawback is that it only works for small tables. Hence, forget about using it for industry use case with larger tables.

    +
    +

    TableQA

    +
      +
    • TableQA follows Query creator approach to build an AI tool for querying natural language on tabular data. While the approach was released rather recently (Jan 2022), it's performance is comparable or worse than TaPas. As per the authors, TableQA shines when it comes to NLQ on large tables and complex queries.
      • +
    • It is more of a framework consisting of mulitple modules. The complete process consists of components like table selector, known fields extractor, unknown fields extractor, and agreegator function classifer and SQL generator.
      • +
    +
    +

    +

    System architecture of TableQA (TableQA paper)

    +
    +
      +
    • Let's get TableQA running with following code,
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    # install required packages
+!pip install -q tableqa nltk
+
+# import packages
+import nltk
+import pandas as pd
+from tableqa.agent import Agent
+
+# download nltk data
+nltk.download('omw-1.4')
+
+# load a CSV
+df = pd.read_csv("my_csv_file.csv")
+
+# create an agent
+agent = Agent(df)
+
+# create query
+query = "how many data points in 2011?"
+# get the SQL query
+agent.get_query(query)
+# get the answer
+agent.query_db(query)
+
    +

    Additional materials

    + +

    Cheers šŸ‘‹

    + + +
    + +
    +
    + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + \ No newline at end of file diff --git a/natural_language_processing/paraphraser/index.html b/natural_language_processing/paraphraser/index.html new file mode 100644 index 00000000..e1f6998a --- /dev/null +++ b/natural_language_processing/paraphraser/index.html @@ -0,0 +1,2383 @@ + + + + + + + + + + + + + + + + + + Paraphraser - A Lazy Data Science Guide + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + + + + + + + + +

    Paraphraser

    + +

    Introduction

    +
      +
    • Paraphrasing is a NLP task of reformatting the input text considering a set of objectives. The objectives could be,
        +
      • Adequecy: is the meaning of sentence preserved? It can be measured by using a NLI model that could determine if the paraphrase is entailment of the original sentence or not.
        • +
      • Fluency: is the paraphrase fluent? It can be measured by using fluency classification models.
        • +
      • Diversity: how much different paraphrase is from original sentence? It can be measured by computing text similarity between the original sentence and paraphrase. Lower the text similarity score, higher the diversity. We can use edit based algorithms like Levenshtein.
        • +
      • Tonality: has the tone of the parapharse changed? It can be measured with tone detection models.
        • +
      • Formality: has the writing style of the parapharse changed? It can be measured with formality detection models.
        • +
      • Length: has the paraphrase become more concise or detailed? It can be measured by simple word or token based tokenizers.
        • +
      +
      • +
    +
    +

    Note

    +

    The objectives could be one or multiple. Also, they could be applied while training or inference. Once way to combine existing models with objectives it was not trained on, is to perform multiple generations and pick the one with highest score in terms of objective metrics.

    +
    + +

    Datasets

    +
      +
    • There are mulitple open-source datasets that can be used to train or fine-tune our own paraphrasing model. Below is a list with some useful details, [3]
      • +
    +
    +

    +

    Highlights of primarily used paraphrase generation datasets [3]

    +
    +
      +
    • Thats not all, PAWS and MSRP are also widely used. A more detailed list of dataset is presented here.
      • +
    +

    Code

    +

    Parrot Paraphraser

    +
      +
    • Usually a Seq2Seq or specifically large language models (LLMs) are either directly used or finetuned to perform Paraphrasing. This is because LLM are good with text generation and Paraphrasing can be easily converted to text generation task.
      • +
    • Parrot [2] is a Python package that use finetuned T5 model to perform Paraphrasing. Let's first see how to use the package,
      • +
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    # taken from Parrot Readme -- https://github.com/PrithivirajDamodaran/Parrot_Paraphraser
+# import
+from parrot import Parrot
+import torch
+import warnings
+warnings.filterwarnings("ignore")
+
+#Init models (make sure you init ONLY once if you integrate this to your code)
+parrot = Parrot(model_tag="prithivida/parrot_paraphraser_on_T5")
+
+phrases = ["Can you recommend some upscale restaurants in Newyork?",
+           "What are the famous places we should not miss in Russia?"]
+
+for phrase in phrases:
+    para_phrases = parrot.augment(input_phrase=phrase, use_gpu=False)
+    for para_phrase in para_phrases:
+        print(para_phrase)
+
    +
      +
    • Btw they also provide advanced set of options to tune the objective we discussed before. For this you only need to modify the parameters for the augment function. Example is shown below,
      • +
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    para_phrases = parrot.augment(input_phrase=phrase,
+                               use_gpu=False,
+                               diversity_ranker="levenshtein",
+                               do_diverse=False, 
+                               max_return_phrases = 10, 
+                               max_length=32, 
+                               adequacy_threshold = 0.99, 
+                               fluency_threshold = 0.90)
+
    +
      +
    • As Parrot package internally uses multiple models to detect adequacy, fluency and diversity, the execution time could be slower. We can compromise good generation with execution time by directly using the finetuned model as shown below,
      • +
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    # install packages
+!pip install transformers
+!pip install -q sentencepiece
+
+# import
+from transformers import AutoTokenizer, AutoModelForSeq2SeqLM
+
+# load the tokenizers and model
+tokenizer = AutoTokenizer.from_pretrained("prithivida/parrot_paraphraser_on_T5")
+model = AutoModelForSeq2SeqLM.from_pretrained("prithivida/parrot_paraphraser_on_T5")
+
+# for a phrase get the tokenised input ids
+input_ids = tokenizer("paraphrase: Can I call you after I am done with this thing I am working on?", 
+                     return_tensors="pt").input_ids
+# use the input ids to generte output
+outputs = model.generate(input_ids, max_new_tokens=10, do_sample=False, num_beams=1, length_penalty=5)
+# decode the output token ids to text
+print(tokenizer.decode(outputs[0], skip_special_tokens=True))
+## Output --> 
+## Can I call you after I've finished this
+
    +

    Finetuning T5 as Paraphraser

    +
      +
    • Any LLM can be used for Paraphrase generation by zero-shot for comparative accuracy. If you want to better result, finetune it on your own datasets. Here we will try to finetune T5,
      • +
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    # install
+!pip install -q simplet5
+!pip install -q datasets
+
+# import
+import pandas as pd
+from simplet5 import SimpleT5
+from datasets import load_dataset
+
+# load datasets
+msrp = load_dataset("HHousen/msrp")
+paws = load_dataset("paws", 'labeled_final')
+
+# prepare dataset
+def clean_msrp_paws_dataset(data):
+    df = pd.DataFrame(data)
+    df = df[df['label']==1]
+    df['source_text'] = f'Paraphrase: ' + df['sentence1']
+    return df
+
+# clean both train and test data
+train_msrp_data = clean_msrp_paws_dataset(msrp['train'])
+test_msrp_data = clean_msrp_paws_dataset(msrp['test'])
+
+# clean_msrp_paws_dataset
+train_paws_data = clean_msrp_paws_dataset(paws['train'])
+test_paws_data = clean_msrp_paws_dataset(paws['test'])
+validation_paws_data = clean_msrp_paws_dataset(paws['validation'])
+
+# combine the individual splits of datasets
+msrp_dataset = pd.concat([train_msrp_data, test_msrp_data])
+paws_dataset = pd.concat([train_paws_data, test_paws_data, validation_paws_data])
+
+# combine the datasets
+df1 = msrp_dataset[['source_text', 'sentence2']]
+df1 = df1.rename(columns={'source_text':'source_text', 'sentence2': 'target_text'})
+df2 = paws_dataset[['source_text', 'sentence2']]
+df2 = df2.rename(columns={'source_text':'source_text', 'sentence2': 'target_text'})
+train_data = pd.concat([df1, df2])
+
+# Train
+# load model
+model = SimpleT5()
+model.from_pretrained(model_type="t5", model_name="t5-small")
+
+# train model
+model.train(train_df=train_data,
+            eval_df=train_data.head(100),  # dummy eval, in reality keep some held-out samples as validation/test
+            source_max_token_len=300, 
+            target_max_token_len=200, 
+            batch_size=4, 
+            max_epochs=20, 
+            outputdir = "outputs",
+            use_gpu=True
+            )
+
+# Inference
+# last_epoch_model = "/content/outputs/simplet5-epoch-1-train-loss-1.5314-val-loss-1.2911" # put the name here
+# model.load_model("t5", last_epoch_model, use_gpu=True)
+# model.predict("Paraphrase: He is  going to USA to visit his friend")
+
    +

    References

    +

    [1] Paraphrase Generation: A Survey of the State of the Art

    +

    [2] Parrot Paraphraser

    +

    [3] Paraphrase Generation: A Survey of the State of the Art

    +

    Cheers šŸ‘‹

    + + +
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    + +
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    + + + + + + + + + + + + + + \ No newline at end of file diff --git a/natural_language_processing/prompt_engineering/index.html b/natural_language_processing/prompt_engineering/index.html new file mode 100644 index 00000000..d740da78 --- /dev/null +++ b/natural_language_processing/prompt_engineering/index.html @@ -0,0 +1,2417 @@ + + + + + + + + + + + + + + + + + + Prompt Engineering - A Lazy Data Science Guide + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    + + + + + + + + +

    Prompt Engineering

    +

    Introduction

    +

    Prompt engineering involves crafting well-defined and strategically designed input queries to elicit desired responses from AI systems. It serves as a bridge between human intention and machine understanding, enabling AI models to provide more accurate and contextually relevant outputs. As AI applications continue to proliferate across various domains, mastering the art of prompt engineering has become essential for both developers and users. What makes prompt engineering more tempting is that it does not require any finetuning of the model but nevertheless, it can enhance the model accuracy substantially! In this article, we will explore different key strategies for crafting effective prompts that can enhance AI model capabilities.

    +

    Types of Prompts

    +

    Before getting started with prompt techniques, letā€™s discuss the main types of prompts,

    +

    System Prompts

    +

    System prompts are like global settings that are applied once to set the mood and intention of the AI modelā€™s subsequent generations in the same chat. These prompts are carefully crafted by developers to guide the AI system toward specific outputs that align with the intended use case. ChatGPT UIā€™s custom instruction is a good example of a system prompt, as whatever you mention there is applicable to every chat. Users can provide details to format output in a certain style (like JSON), provide details about themselves so that the responses are personalized, set the tone of the generation, define privacy and ethics details, and much more! An example is shown below

    +
    System Prompt:
+You are a helpful AI Assistant. Help users by replying to their queries and make 
+sure the responses are polite. Do not hallucinate and say "I don't know" if required.
+
    +

    User Prompts

    +

    User prompts are generated on the fly by users and are designed to elicit specific responses to their queries. Unlike system prompts, user prompts are not pre-defined and can vary widely in structure and content. These are more transactional in nature, and are usally present after system prompt and could be multiple in count.

    +
    System Prompt:
+You are a helpful AI Assistant. Help users in replying to their queries and make 
+sure the responses are polite. Do not hallucinate and say "I don't know" if required.
+
+User Prompt:
+What is your name?
+
    +

    Assistant Output

    +

    These are AIā€™s generated output to the System and previous user prompts. In complex use cases, developers can modify this as an example to the AI model to highlight the kind of result expected from the model.

    +
    System Prompt:
+You are a helpful AI Assistant. Help users in replying to their queries and make 
+sure the responses are polite. Do not hallucinate and say "I don't know" if required.
+
+User Prompt:
+What is your name?
+
+Assistant:
+I am an AI Assistant that can help you with your queries. Please let me know your questions!
+
    +

    Prompt Strategies

    +

    Zero-Shot Prompts

    +

    Zero-shot prompts are a fundamental technique in prompt engineering, allowing AI models to provide meaningful responses without any specific training examples. With zero-shot prompts, developers and users can harness the model's innate knowledge and reasoning capabilities to answer questions or complete tasks, even if the model has never seen similar queries before. When using a zero-shot prompt, formulate your question or request as clearly and concisely as possible. You can even provide some context if that helps, overall avoid ambiguity to help the model understand your intention accurately.

    +
    Example 1 - Clear instructions
+################
+User Prompt:
+Translate the following English text to French: "Hello, how are you?"
+
+Example 2 - Provide context
+################
+User Prompt:
+Calculate the total revenue for our company in the last quarter, given the following financial data: [insert data].
+
    +

    Note, deciding which data should goes where (system or user prompt) depends on experimenting how it works for a specific model but a general thumb rule is to keep the constant details in system prompt and dynamic details in user prompt. In the first example above, we can also have following prompts,

    +
    Example 1 - Clear instructions with System prompt
+################
+System prompt: 
+You are a Translator GPT. Given a sentence in English, translate it into French.
+
+User prompt:
+"Hello, how are you?"
+
    +

    Few-Shot Prompts

    +

    While zero-shot prompts are fundamental, there are situations where you may need to provide a bit more guidance to the AI model. In such cases, you can use few-shot prompts that involve providing a small number of examples or demonstrations to help the model understand the desired task or context. Developers can use this approach to further guide the AI model's responses. One example of 2-shot prompt is shown below,

    +
    System prompt: 
+You are a Translator GPT. Given a sentence in English, translate it into French. 
+Examples are shared below,
+
+English: "Hello, how are you?"
+French: "Bonjour, comment Ƨa va ?"
+
+English: "I am learning French."
+French: "J'apprends le franƧais."
+
+User Prompt:
+English: "Please pass the salt."
+French: 
+
    +

    Note, the number of examples to be included (n-shot) is highly experimental. The objective should be to keep the example count as less as possible (otherwise the token size and cost will increase) while making sure the accuracy is not impacted. So the prompt design should be done incrementally, i.e. keep adding more examples if the accuracy is below expectations. Also, make sure to add diverse examples and do not add exact or even semantically similar examples as latest LLMs are quite ā€œsmartā€ enough to learn from few examples.

    +

    Few-shot Chain-of-Thought Prompt

    +

    Few shot CoT Prompting was introduced in [1] and the idea is that generating a chain of thought, i.e. a series of intermediate reasoning steps, can significantly improves the ability of large language models to perform complex reasoning. Experiments shows that chain-of-thought prompting improves performance on a range of arithmetic, commonsense, and symbolic reasoning tasks. Basically it is clubbed with few shot prompt where the examples are provided in CoT format. Example is shown in the below image,

    +
    +

    +

    Example inputs and outputs for Standard 1-shot prompt and CoT prompts

    +
    +

    Zero-shot Chain-of-Thought Prompt

    +

    Zero shot variant of CoT was introduced in [2] and it can significantly increase the accuracy of Zero shot prompts, and all you need to do is to add ā€œLetā€™s think step by step.ā€ šŸ˜œ. Btw additional post-processing is required on the output to extract the correct result, which can either be done by creating regex scripts or calling LLMs again to extract the answer.

    +
    +

    Note

    +

    Few-shot prompts should always give better result than Zero-shot, but the former requires additional token consumption which will increase the cost. To mitigate this, developers can experiment with Zero-shot CoT technique and if the result accuracy is acceptable, it might end up reducing the overall cost.

    +
    +
    +

    +

    Example inputs and outputs of GPT-3 with (a) standard Few-shot, (b) Few-shot-CoT, (c) standard Zero-shot, and (d) Zero-shot-CoT

    +
    +

    Self-consistency

    +

    Self-consistency [3] is based on the idea that there are multiple ways to solve a complex problem i.e. if multiple reasoning paths are leading to the same output, it is highly probable that it is a correct answer. In their own words, "...we hypothesize that correct reasoning processes, even if they are diverse, tend to +have greater agreement in their final answer than incorrect processes.". The self-consistency method consists of three steps:

    +
      +
    1. prompt a language model using chain-of-thought (CoT) prompting;
      2. +
    2. replace the ā€œgreedy decodeā€ in CoT prompting by sampling from the language modelā€™s decoder to generate a diverse set of reasoning paths; and
      3. +
    3. marginalize out the reasoning paths and aggregate by choosing the most consistent answer in the final answer set.
      4. +
    +

    The authors of the paper performed extensive empirical evaluations to shows that self-consistency boosts the performance of chain-of-thought prompting on a range of popular arithmetic and commonsense reasoning +benchmarks, including GSM8K (+17.9%), SVAMP (+11.0%), AQuA (+12.2%), StrategyQA (+6.4%) and ARC-challenge (+3.9%).

    +
    +

    +

    CoT vs Self-consistency prompting example

    +
    +

    Tree-of-Thoughts

    +

    Tree-of-Thoughts (ToT) [4] is based on the idea that to solve any complex problem we need to (a) explore multiple reasoning paths (branches in a graph), and (b) perform planning i.e. lookahead or even backtrack on the paths if required. ToT frames any problem as a search over a tree, where each node is a state s = [x, z1Ā·Ā·Ā·i] representing a partial solution with the input and the sequence of thoughts so far. A specific instantiation of ToT involves answering four questions:

    +
      +
    1. How to decompose the intermediate process into thought steps -- depending on different problems, a thought could be a couple of words (Crosswords), a line of equation (Game of 24), or a whole paragraph of writing plan (Creative Writing). In general, a thought should be ā€œsmallā€ enough so that LMs can generate promising and diverse samples.
      2. +
    2. How to generate potential thoughts from each state -- again it depends on the problem, so for Creative writing we can sample thoughts from a CoT prompt and for Game of 24 and Crosswords we can propose thoughts sequentially using propose prompt.
      3. +
    3. How to heuristically evaluate states -- this can be done automatically by either asking the model to generate a value (score between 1 to 10 or classification of sure/likely/impossible) or voting on different results.
      4. +
    4. What search algorithm to use -- authors propose Breadth-first search (BFS) and Depth-first Search (DFS) and left more complex search algorithms like A* for future works.
      5. +
    +
    +

    +

    Schematic illustrating various approaches to problem solving with LLMs. Each rectangle box represents a thought, which is a coherent language sequence that serves as an intermediate step toward problem solving

    +
    +

    Retrieval Augmented Generation (RAG)

    +

    In all of the previous approaches, the result was generated entirely by the LLMs without any external intervention. This leverages the knowledge stored within the neural networks of the LLMs (read, weights in the network). This poses issues like hallucinations (this happens when model is not sure what to say, especially due to lack of knowledge) and factual inaccuracies (lack of knowledge leads to model lying). To mitigate these issues, we can "connect" LLMs with external data source (vector database, wikipedia, google, etc) so that true, diverse and dynamic data can be fetched from these sources and LLM can do what they are best suited for - reasoning on the provided data to format the final result. This is the fundamental idea behind Retrieval Augmented Generation (RAG).

    +

    One example of such system is Sankshep (by yours truly šŸ˜Ž) that provides ChatGPT-powered assistant to summarize and talk to Arxiv research papers. Here, if you ask a question regarding the paper, Sankshep refer the content of the paper to be better aware and provide factually correct results.

    +
    +

    +

    Sankshep.co.in built considering Retrieval Augmented Generation (RAG)

    +
    +

    ReAct

    +

    ReAct [5] combines the external knowledge of RAG with the planning and reasoning notion of ToT. As per the paper,

    +
    +

    A unique feature of human intelligence is the ability to seamlessly combine task-oriented actions with verbal reasoning. Consider the example of cooking up a dish in the kitchen. Between any two specific actions, we may reason in language in order to track progress (ā€œnow that everything is cut, I should heat up the pot of waterā€), to handle exceptions or adjust the plan according to the situation (ā€œI donā€™t have salt, so let me use soy sauce and pepper insteadā€), and to realize when external information is needed (ā€œhow do I prepare dough? Let me search on the Internetā€).

    +
    +

    The important point of the above quote (and in fact the paper) is the intention to combine two powerful abilities of LLMs ā€” reasoning (e.g. chain-of-thought prompting) and acting (e.g. action plan generation). While the former helps with improving the accuracy of an individual task, the latter provides the LLM power to perform multiple tasks. The idea is quite simple ā€” ask LLM a question (input) and let it ā€œplanā€ what to do next (action) by reasoning on the input (thoughts). It can even propose values to the action (action input). Once we perform the action and get an output (observation) the LLM can again reason (thought) to propose the next ā€œplanā€ (action). In a way, we keep the LLM busy in this loop, until it terminates and we get the result we wanted. To summarize we iterate over ā€œinput ā€”> thoughts ā€”> action ā€”> action input ā€”> observation ā€”> thoughtsā€. For practical details, please refer Creating GPT-driven Applications using LangChain

    +
    +

    +

    LLM reasoning and planning using ReAct technique

    +
    +

    Conclusion

    +

    Prompt engineering is a crucial skill for leveraging the capabilities of LLMs effectively. By understanding the different types of prompts and employing strategies such as zero-shot prompts, few-shot prompts, etc, developers and users can harness the power of AI to achieve more accurate and contextually relevant responses. As AI technologies continue to evolve, mastering prompt engineering will remain an essential tool for unlocking the full potential of AI systems across various domains.

    +

    References

    +

    [1] Chain-of-Thought Prompting Elicits Reasoning in Large Language Models

    +

    [2] Large Language Models are Zero-Shot Reasoners

    +

    [3] Self-consistency improves chain of thought reasoning in language models

    +

    [4] Tree of Thoughts: Deliberate Problem Solving with Large Language Models

    +

    [5] ReAct: Synergizing Reasoning and Acting in Language Models

    +

    [6] Prompting Guide

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    + + + + + + + + + + + + + + \ No newline at end of file diff --git a/natural_language_processing/qa/index.html b/natural_language_processing/qa/index.html new file mode 100644 index 00000000..9bacf0eb --- /dev/null +++ b/natural_language_processing/qa/index.html @@ -0,0 +1,2379 @@ + + + + + + + + + + + + + + + + + + Question Answering - A Lazy Data Science Guide + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    Question Answering

    + +

    Introduction

    +
      +
    • +

      As the name suggests, Question Answering (QA) is a NLP task of finding answer given the question and context (optional). QA could be of two types based on the input,

      +
        +
      • +

        Open-domain QA: where context is not provided. The expectation is that the model has internalised knowledge within its parameters and should be able to answer the question with additional context.

        +
        graph LR
+A("Who is the author of Lazy Data Scientist?") -- Question --> B(QA model)
+B -- Answer --> C("Mohit")
+style B stroke:#f66,stroke-width:2px,stroke-dasharray: 5 5
        +
        • +
      +
        +
      • +

        Closed-domain QA: where context is provided. The expectation is that the model has learned to find answers from context.

        +
        graph LR
+A("Who is the author of Lazy Data Scientist?") -- Question --> B(QA model)
+D("Hi, my name is Mohit. I am the author of Lazy Data Scientist!") -- Context --> B(QA model)
+B -- Answer --> C("Mohit")
+style B stroke:#f66,stroke-width:2px,stroke-dasharray: 5 5
        +
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    +
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    • +

      Answers could be also be of two types,

      +
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      • +

        Short form Answers: where the answer is brief and to the point.

        +
          +
        • In the above examples, the answer (Mohit) is in short form. Majority of the closed domain QA models generate short form answers as they follow extractive approach of finding answer.
          • +
        • For this, encoder based architectures (like BERT) can be used. The input can be provided as [CLS] question [SEP] context [SEP]
          • +
        • As output, we compute probabilities of two special logits -- answer start and answer end. This gives the exact position of the answer. In reality, we apply softmax on the output logits values to get probabilistic estimation for each token in the input.
          • +
        +

        + +
        Behavior of Short form closed QA system. [Top] In case the answer is present in the context, answer start and end token's probabilties can be utilised to get the answer. [Bottom] In case answer is missing, [CLS] is predicted.
        +

        +
          +
        • We pick the pair (answer start and end logit) that has the highest probability (product of their individual probability) and it's a valid answer (answer with positive or zero length and answer with tokens only from context). If we go greedy i.e. pick the top_n = 1 for both logits, we will get the pair with the highest probability but it is not guaranteed to be a valid answer.
          • +
        • To mitigate this, we pick top_n (~10-20) highest probability tokens for both start and end answer values. This gives us \(n^2\) possibilities of answers that can be explored to find the valid answer.
          • +
        +
        +

        Note

        +

        To solve similar tasks, BERT utilises different task specific heads on top of the BERT encoder. In the case of QA, a head could be simple feed forward layer with two classes - answer start and answer end. The same head is applied to each token in the BERT output, and probability of the both classes are computed.

        +
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        • +
      +
        +
      • +

        Long form Answers: where the answer is descriptive, standalone and may also contain additional details.

        +
          +
        • For the above example, long form answer could be Mohit is the author of Lazy Data Scientist.
          • +
        • We can use additional models like LLM (GPTs, T5, etc) on top of QA system to convert short form answers to long form. Existing model will require finetuning with the Question and Short form answer as input and Long form answer as output.
          • +
        • We can even try to n-shot the process using LLMs as shown in the following prompt: +
          Question: What is the author's name?
+Context: Author is Mohit Mayank.
+Short answer: Mohit Mayank.
+Long answer: Author's name is Mohit Mayank.
+
+## provide 2-3 examples as above
+
+Question: What is the captial of India?
+Context: India's new captial New Delhi was setup with ....
+Short answer: New Delhi
+Long answer: # let the model predict long form answer!!
+
          • +
        +
        +

        Note

        +

        Perplexity AI is a good example that uses GPT 3.5 to generate long form answers and even provide evidences for the answer. First, it performs web search using Microsoft Bing to identify relevant websites and the contents are summarized. The summaries are then passed to GPT along with the original question to answer the question with the reference details for the evidence.

        +
        +
        • +
      +
      • +
    +

    Datasets

    +

    SQuAD

    +
      +
    • Stanford Question Answering Dataset (SQuAD) [2] is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. There are two verisons of the dataset,
        +
      • SQuAD 1.1 contains 100,000+ question-answer pairs on 500+ articles.
        • +
      • SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones. To do well on SQuAD2.0, systems must not only answer questions when possible, but also determine when no answer is supported by the paragraph and abstain from answering.
        • +
      +
      • +
    +

    Metrics

    +

    Exact Match

    +
      +
    • As the name suggests, for each question we compare the golden answer with the predicted answer. If the two answers are exactly similar (y_pred == y_true) then exact_match = 1 else exact_match = 0.
      • +
    +

    F1 score

    +
      +
    • There is a possibility that the predicted answer includes the important parts of the golden answer, but due to the nature of exact match, the score is still 0. Let's understand it with an example, here ideally the score should be high (if not 1), but exact match will give 0.
        +
      • Question: When can we meet?
        • +
      • Golden answer: We can meet around 5 PM.
        • +
      • Predicted answer: 5 PM.
        • +
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      • +
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    • For such cases we can perform partial match. Below is the example, +
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      # vars to begin with
+predicted_answer = 'Hi, My name is Mohit'
+golden_answer = 'My name is Mohit'
+
+# tokenize
+predicted_words = set(predicted_answer.lower().split())
+golden_words = set(golden_answer.lower().split())
+
+# find common words
+common_words = predicted_words & golden_words
+
+# compute metrics
+recall = common_words / predicted_words
+precision = common_words / golden_words
+F1 = 2 * precision * recall / (precision + recall)
+
      • +
    +
    +

    Note

    +

    In case one question has multiple independent answers, we can compare each golden answer for the example against the prediction and pick the one with highest score. The overall accuracy is then the average of the individual example level score. This logic can be applied to both the metrics discussed above.

    +
    +

    Code

    +

    Using Transformers (HF hub)

    +
      +
    • Huggingface hosts multiple models for the QA task. Most of these models are fined tuned on SQuAD dataset. Let's pick one and see how to use it.
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    # install packages 
+!pip install -q transformers
+!pip install -q sentencepiece
+
+# import
+from transformers.pipelines import pipeline
+from transformers import AutoModelForQuestionAnswering
+from transformers import AutoTokenizer
+
+# var
+model_name = "deepset/xlm-roberta-base-squad2"
+
+# generate pipeline
+nlp = pipeline('question-answering', model=model_name, tokenizer=model_name)
+
+input = {
+    'question': 'Who is visiting his grandmother?',
+    'context': 'My name is Mohit. I am going to visit my grandmother. She is old.'
+}
+print(nlp(input))
+## Output --> {'score': 0.30, 'start': 10, 'end': 17, 'answer': ' Mohit.'}
+
    +

    References

    +

    [1] Evaluating QA: Metrics, Predictions, and the Null Response

    +

    [2] SQuAD Explorer

    +

    [3] How to Build an Open-Domain Question Answering System?

    +

    [4] Question Answering - Huggingface

    +

    Cheers šŸ‘‹

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    + + + + + + + + + + + + + + \ No newline at end of file diff --git a/natural_language_processing/relation_extraction/index.html b/natural_language_processing/relation_extraction/index.html new file mode 100644 index 00000000..ca1fff71 --- /dev/null +++ b/natural_language_processing/relation_extraction/index.html @@ -0,0 +1,2143 @@ + + + + + + + + + + + + + + + + + + Relation extraction - A Lazy Data Science Guide + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    Relation Extraction

    +

    Introduction

    +
      +
    • Relation extraction (RE) is the process of identifying the relationships between entities in a text. Entities could be of multiple types such as person, location, organization, etc and they can be identified using Named Enitity Recognition (NER).
      • +
    • Let's understand RE with an example: "Ram is the son of Shyam and Shyam is the son of Radhe". Here the entities are identified as: "Ram", "Shyam" and "Radhe". The relations could be (Ram, son of, Shyam), (Shyam, son of, Radhe) and (Ram, grandson of, Radhe).
      • +
    +

    Code

    +

    Using OpenNRE

    +
      +
    • OpenNRE is an open source tool for relationship extraction. OpenNRE makes it easy to extract relationships from text. It is as simple as writing a few lines of code.
      • +
    • One example from their github repository is as follows,
      • +
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    # import opennre
+import opennre
+# load the model
+model = opennre.get_model('wiki80_cnn_softmax')
+# infer for a text
+model.infer({'text': 'He was the son of MĆ”el DĆŗin mac MĆ”ele Fithrich, and grandson of the high king Ɓed Uaridnach (died 612).', 'h': {'pos': (18, 46)}, 't': {'pos': (78, 91)}})
+# Output: ('father', 0.5108704566955566)
+
    +
      +
    • At the time of writing they had following models available:
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    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    model_namedescription
    wiki80_cnn_softmaxtrained on wiki80 dataset with a CNN encoder.
    wiki80_bert_softmaxtrained on wiki80 dataset with a BERT encoder.
    wiki80_bertentity_softmaxtrained on wiki80 dataset with a BERT encoder (using entity representation concatenation).
    tacred_bert_softmaxtrained on TACRED dataset with a BERT encoder.
    tacred_bertentity_softmaxtrained on TACRED dataset with a BERT encoder (using entity representation concatenation).
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    + + + + + + + + + + + + + + \ No newline at end of file diff --git a/natural_language_processing/streaming_chatgpt_gen/index.html b/natural_language_processing/streaming_chatgpt_gen/index.html new file mode 100644 index 00000000..78206f81 --- /dev/null +++ b/natural_language_processing/streaming_chatgpt_gen/index.html @@ -0,0 +1,2372 @@ + + + + + + + + + + + + + + + + + + Streaming ChatGPT Generations - A Lazy Data Science Guide + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    Streaming ChatGPT Generations

    +

    Introduction

    +
      +
    • ChatGPT is an auto-regressive Large Language Model. That means its output is generated token by token in a sequential fashion, where each token could be a combination of characters. Under normal circumstances (and popular coding practices), we access the ChatGPT model via an API which takes an input prompt, generate the output and then returns it. While it may sound okay, there is one problem -- model returns the output when the complete generation is done! This means if you want the model to generate long outputs (or even if your prompt is big due to few-shots examples or lengthy system prompts), you can expect a delay of several seconds before you receive the output.
      • +
    • This is not okay for user-facing applications where your users are patiently waiting for the output. Thats why ChatGPT UI gives the output in streaming fashion. Here, you see characters or words printing on your screen one after the another, rather than showing the complete output at once. This creates a perception of model writing your output as human does, and even though the delay in generating the complete output will be the same, the flow becomes more enduring.
      • +
    • Behind the scene, the ChatGPT API is using Server Side Events (SSE) i.e. media stream events to return each token as and when they are generated. SSE is like an intermediate approach between normal HTTP request (server returns one result per request) and websocket (server and client can send multiple requests and results). In SSE, while client sends one request, server can return multiple results.
      • +
    • In this article, we will try to replicate the ChatGPT streaming output behavior by creating a python application (FastAPI server) that could acts as a middleman between OpenAI and Frontend. In this case, OpenAI returns the outputs to our Python server at token level and the server passes it along to its client in the same manner. Let's get started!
      • +
    +

    Code

    +

    OpenAI Streaming

    +
      +
    • If you know how to use OpenAI package to generate ChatGPT output (which in itself is quite easy), then getting the streaming output nothing but adding one more param (stream=True) in openai.ChatCompletion.create. Below is the code that you can easily copy paste and start using right now,
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    # import 
+import openai
+
+# set the keys
+openai.api_key = ".." # provide the key here
+openai.organization = ".." # provide the key here
+
+# create the ChatCompletion.create obj
+completion = openai.ChatCompletion.create(
+  stream=True, # the magic happens here
+  model="gpt-3.5-turbo-0301",
+  messages=[
+    {"role": "system", "content": "You are a mails assistant. Given an email, write a proper reply."},
+    {"role": "user", "content": """Mail: "We are all set. Thanks -Cam"
+    """}
+  ]
+)
+
+# print in streaming fashion
+for chunk in completion:
+    print(chunk.choices[0].delta.get("content", ""), end="", flush=True)
+
    +
      +
    • In the above code, just by adding the stream=True OpenAI package is able to take care of all the hardwork and we get a completion generator. In Python, you just need to iterate over it to get the result at token level and as and when they are available. For example, if you time it with and without the stream=True param, you will notice the difference in output and as well as in time. While without the param the output could be available after a couple of seconds, with the param the first token will be available within a second, and the subsequent tokens after the previous one with short gap.
      • +
    • To simulate the streaming output, we use print statement with end="" instead of default end="\n" so that to ignore the newline after each iteration. We also use flush=True so that print statment does not wait for its buffer to get full before printing on terminal.
      • +
    +

    OpenAI Streaming App (using FastAPI)

    +
      +
    • Now that we have the OpenAI related part done, let's focus on creating FastAPI App and expose the OpenAI wrapper as an event stream service. Below is the code,
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    # Filename: api.py
+# import
+import openai
+from pydantic import BaseModel
+from fastapi import FastAPI, Request
+from fastapi.responses import StreamingResponse
+from fastapi.middleware.cors import CORSMiddleware
+
+# define app 
+app = FastAPI()
+
+# add CORS policy
+origins = [
+    "*",
+]
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=origins,
+    allow_credentials=True,
+    allow_methods=["*"],
+    allow_headers=["*"],
+)
+
+# Set your OpenAI API key here
+openai.api_key = "..."
+openai.organization = "..."
+
+# Create BaseModel class
+class Message(BaseModel):
+    prompt: str
+
+@app.post("/chat")
+def chat_socket(msg: Message):
+    """
+    Generates a response using the OpenAI Chat API in streaming fashion.
+
+    Returns:
+        A string representing the generated response.
+    """
+    # ChatGPT streaming response function
+    def generate_openai_response():
+        """
+        Generates a response using the OpenAI Chat API in streaming fashion.
+
+        Returns:
+            A string representing the generated response.
+        """
+        completion = openai.ChatCompletion.create(
+            stream=True,
+            model="gpt-3.5-turbo-0301",
+            messages=[
+                {"role": "system", "content": "You are a bot, given the user's input, reply appropriately"},
+                {"role": "user", "content": msg.prompt}]
+        )
+        # print in streaming fashion
+        for chunk in completion:
+            yield chunk.choices[0].delta.get("content", "")
+
+    # return 
+    return StreamingResponse(generate_openai_response(), media_type='text/event-stream')
+
+# welcome page
+@app.get("/")
+async def home():
+    return {"message": "Welcome to the ChatGPT FastAPI App!"}
+
+
+# Run the application
+if __name__ == "__main__":
+    import uvicorn
+    uvicorn.run(app, host="0.0.0.0", port=8000)
+
    +
      +
    • To run the code, you need to just hit python api.py and our endpoint is available at http://0.0.0.0:8000/chat endpoint!
      • +
    +

    Client for FastAPI OpenAI Streaming App

    +
      +
    • Once you have the server running, let's see how we can hit it from any other service. Here I am showing the example of a Python Client.
      • +
    +
     1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13
+14
+15
    # import 
+import json
+import requests
+
+def query_api(url):
+    # query the url
+    response = requests.post(url, json={"prompt": "Tell me a joke!"}, stream=True)
+
+    # parse the response
+    for chunk in response.iter_content(chunk_size=None):
+        if chunk:  # filter out keep-alive new chunks
+            print(chunk.decode('utf-8'), end="", flush=True)
+
+# Example usage
+query_api("http://0.0.0.0:8000/chat")
+
    +

    References

    + + + +
    + +
    +
    + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + \ No newline at end of file diff --git a/natural_language_processing/text_generation/index.html b/natural_language_processing/text_generation/index.html new file mode 100644 index 00000000..f1b7aa3c --- /dev/null +++ b/natural_language_processing/text_generation/index.html @@ -0,0 +1,2275 @@ + + + + + + + + + + + + + + + + + + Text generation - A Lazy Data Science Guide + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
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    + + + +
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    + + + +
    +
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    + + +
    +
    + + + + + + + + +

    Text generation

    +

    Introduction

    +
      +
    • Text generation is an interesting task in NLP, where the intention is to generate text when provided with some prompt as input. Usually, we apply some form of Sequence-to-Sequence model (Seq2Seq) for this task. They are called language models, as they can be used to predict the next word based on the previous sentences. The recent surge in interest in this field is due to two main reasons, (1) the availability of several high performance pre-trained models, and (2) it's very easy to transform a large variety of NLP based tasks into the text-in text-out type of problem. Beacuse of this, it becomes easy to solve several such problems using a single language model.
      • +
    +
      +
    • The Seq2Seq model consists of two main modules -
        +
      • Encoder: which takes text as input and encodes it to a compact vector representation,
        • +
      • Decoder: which takes the compact vector representation as input and generates text as output.
        • +
      +
      • +
    +
      +
    • Conventionally, the models were trained from scratch for a specific task, which requires a lot of training data. Recently, as NLP has deviated more towards fine-tuning methodology, we have a number of pre-trained models which either out-of-the-box work very well or can be fine-tuned for the specific task.
      • +
    +
      +
    • Some of the conventional models were RNN, GRU and, LSTM. Whereas recent pre-trained models include Transformers, GPT-{1, 2, 3}, GPT-{Neo, J}, T5, etc.
      • +
    +
      +
    • Text generation task is a very specific but also a very generic task because we can formulate a lot of NLP tasks in the form of text generation. For example, (not a complete list)
        +
      • Language translation English to Hindi translation, or any language for that matter, is just a text in and text out task
        • +
      • Summarization takes the complete text dump as input and generates crisp informative sentences.
        • +
      • Question answering question is taken as input and answer is the output. We can even include some context as input on closed-domain QA tasks.
        • +
      • Sentiment analysis is a classification task where we can provide the text as input and train the model to generate sentiment tag as output
        • +
      • Rating prediction where we have to rate the writing style, kind of regression problem, but still can be formulated as text in number out
        • +
      +
      • +
    +
      +
    • As obvious from the examples mentioned above, it is possible to formulate a lot of problems as a text-in-text-out task, and it could even expand to classification and regression types of tasks. Some example tasks that can be performed using T5 is shown below,
      • +
    +
    +

    +

    T5 text-to-text framework examples. See: Google Blog (raffel2020exploring)
    +

    +
    + +

    Analysis

    +

    Comparing models (basic details)

    +
      +
    • A brief comparison table of the different models mentioned above is as follows,
      • +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    modelstypeyearpre-trained?parameters
    RNNconventional-no17k (one layer)
    LSTMconventional1997no71k (one layer)
    GRUconventional2014no30-40k (one layer)
    GPT-2recent2019yes117M, 345M, 774M, 1.5B
    GPT-Neorecent2021yes125M, 1.2B, 2.7B
    T5recent2020yes60M, 220M, 770M, 2.8B, 11B
    +

    Comparing models (fine-tuning performance)

    +
      +
    • A more detailed fine-tuning performance of the recent TG models for sentiment detection was performed here. While the analysis is for a specific task, the process remains the same for any NLP problem that can be transformed in the form of text generation.
      • +
    • The following recent language models were discussed in the article,
        +
      • GPT-2: It is the second iteration of the original series of language models released by OpenAI. In fact, this series of GPT models made the language model famous! GPT stands for "Generative Pre-trained Transformer", and currently we have 3 versions of the model (v1, v2 and v3). Out of these only GPT-1 and GPT-2 are open-sourced, and hence we will pick the latest version for our experiment. On the technical side, the architecture of GPT-2 is made up of the decoder part of the Transformer architecture.
        • +
      • GPT-Neo: This model was released by EleutherAI to counter the GPT-3 model which was not open-sourced. The architecture is quite similar to GPT-3, but training was done on The Pile, an 825 GB sized text dataset.
        • +
      • T5: stands for "Text-to-Text Transfer Transformer" and was Google's answer to the world for open source language models. T5 paper showcase that using the complete encoder-decoder architecture (of the transformer) is better than only using the decoder (as done by the GPT series), hence they stay true to the original transformer architecture.
        • +
      +
      • +
    +
      +
    • The results from the analysis are as follows,
      • +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    modeltrial 0trial 1trial 2average
    GPT-Neo0.8240.78930.8080.8071
    GPT-20.83980.8080.8060.8179
    T50.82140.79760.8040.8077
    +
      +
    • Conclusion + - "While GPT-2 may have won this round, the result table does show the prowess of text generation models on whole. All of them performed very well on the sentiment detection task, and all it took was a few epochs of training. Even if this experiment was done for a single task, I hope this helps to show how easy it is to use the TG models for completely new tasks. In a way, if we can transform the NLP problem into that of text generation, rest assured the pre-trained model will not fail, well at least not drasticallyĀ :) This makes them the perfect baseline if not the state-of-the-art for many tasks."
      • +
    +

    Additional materials

    +
      +
    • How to generate text: using different decoding methods for language generation with Transformers - Link
      • +
    • Guide to fine-tuning Text Generation models: GPT-2, GPT-Neo and T5 - Link
      • +
    + + +
    + +
    +
    + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + \ No newline at end of file diff --git a/natural_language_processing/text_similarity/index.html b/natural_language_processing/text_similarity/index.html new file mode 100644 index 00000000..ba5eef72 --- /dev/null +++ b/natural_language_processing/text_similarity/index.html @@ -0,0 +1,2873 @@ + + + + + + + + + + + + + + + + + + Text similarity - A Lazy Data Science Guide + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
    + + + + +
    + + +
    + +
    + + + + + + + + + +
    +
    + + + +
    +
    +
    + + + + + + +
    +
    +
    + + + +
    +
    +
    + + + +
    +
    +
    + + +
    +
    + + + + + + + + +

    Text Similarity

    +

    Introduction

    +
      +
    • Similarity between two words, sentences or documents is a function of commonality shared by them. This commonality can be measured by different metrics.
      • +
    • Recently there has been a trend of using semantic based approaches, but historically, many similarity based non-neural algorthms were built.
      • +
    • But which is the best string similarity algorithm? Well, itā€™s quite hard to answer this question, at least without knowing anything else, like what you require it for i.e. your use case.
      • +
    • And even after having a basic idea, itā€™s quite hard to pinpoint a good algorithm without first trying them out on different datasets. Itā€™s a trial and error process.
      • +
    • To make this journey simpler, I have tried to list down and explain the workings of the most basic string similarity algorithms out there. Give them a try, it may be what you needed all along ā˜®
      • +
    +

    Types of algorithms

    +
      +
    • Based on the properties of operations, string similarity algorithms can be classified into a bunch of domains. Letā€™s discuss a few of them,
        +
      • Edit distance based: Algorithms falling under this category try to compute the number of operations needed to transforms one string to another. More the number of operations, less is the similarity between the two strings. One point to note, in this case, every index character of the string is given equal importance.
        • +
      • Token-based: In this category, the expected input is a set of tokens, rather than complete strings. The idea is to find the similar tokens in both sets. More the number of common tokens, more is the similarity between the sets. A string can be transformed into sets by splitting using a delimiter. This way, we can transform a sentence into tokens of words or n-grams characters. Note, here tokens of different length have equal importance.
        • +
      • Sequence-based: Here, the similarity is a factor of common sub-strings between the two strings. The algorithms, try to find the longest sequence which is present in both strings, the more of these sequences found, higher is the similarity score. Note, here combination of characters of same length have equal importance.
        • +
      • Semantic-based: Here, the similarity is not based on pure presence of common sub-strings, but on the semantic meaning of the sub-strings. Semantic based approaches considers the contextual and linguistic meaning of the sub-strings. For example, in semantic based approaches, the similarity between ā€œtoadā€ and ā€œfrogā€ will be high, which is not possible in other approaches.
        • +
      +
      • +
    +
    +

    Note

    +

    Semantic based algorithms are quite difficult to use as semnatic is a subjective matter. For example, how will you compare these two sentences -- "How is the weather today?" and "How is the weather tomorrow?"? If we go by non-semantic approach, we will get a very high score. But for semantic models, two things are possible and correct at the same time -- get a high score as we are talking about the weather, or get a low score as we are talking about different days. This is why, it is important to finetune the semantic models for your use case. (for example here, what is more important - the topic or the context. Based on the answer, prepare a dataset and finetune).

    +
    +

    Edit distance based algorithms

    +
      +
    • Letā€™s try to understand most widely used algorithms within this type,
      • +
    +

    Hamming distance

    +
      +
    • This distance is computed by overlaying one string over another and finding the places where the strings vary. Note, classical implementation was meant to handle strings of same length. Some implementations may bypass this by adding a padding at prefix or suffix. Nevertheless, the logic is to find the total number of places one string is different from the other. To showcase an examples,
      • +
    +
    1
+2
+3
+4
+5
+6
+7
+8
+9
    >> import textdistance
+>> textdistance.hamming('text', 'test')
+1
+>> textdistance.hamming.normalized_similarity('text', 'test')
+0.75
+>> textdistance.hamming('arrow', 'arow')
+3
+>> textdistance.hamming.normalized_similarity('arrow', 'arow')
+0.4
+
    +
      +
    • As evident, in first example, the two strings vary only at the 3rd position, hence the edit distance is 1. In second example, even though we are only missing one ā€˜rā€™, the ā€˜rowā€™ part is offset by 1, making the edit distance 3 (3rd, 4th and 5th position are dissimilar). One thing to note is the normalized similarity, this is nothing but a function to bound the edit distance between 0 and 1. This signifies, if the score is 0-two strings cannot be more dissimilar, on the other hand, a score of 1 is for a perfect match. So the strings in first example are 75% similar (expected) but in strings in second example are only 40% similar (can we do better?).
      • +
    +

    Levenshtein distance

    +
      +
    • This distance is computed by finding the number of edits which will transform one string to another. The transformations allowed are insertion ā€” adding a new character, deletion ā€” deleting a character and substitution ā€” replace one character by another. By performing these three operations, the algorithm tries to modify first string to match the second one. In the end we get a edit distance. Examples,
      • +
    +
    1
+2
+3
+4
    >> textdistance.levenshtein('arrow', 'arow')
+1
+>> textdistance.levenshtein.normalized_similarity('arrow', 'arow')
+0.8
+
    +
      +
    • As evident, if we insert one ā€˜rā€™ in string 2 i.e. ā€˜arowā€™, it becomes same as the string 1. Hence, the edit distance is 1. Similar with hamming distance, we can generate a bounded similarity score between 0 and 1. The similarity score is 80%, huge improvement over the last algorithm.
      • +
    +

    Jaro-Winkler

    +
      +
    • This algorithms gives high scores to two strings if, (1) they contain same characters, but within a certain distance from one another, and (2) the order of the matching characters is same. To be exact, the distance of finding similar character is 1 less than half of length of longest string. So if longest strings has length of 5, a character at the start of the string 1 must be found before or on ((5/2)ā€“1) ~ 2nd position in the string 2 to be considered valid match. Because of this, the algorithm is directional and gives high score if matching is from the beginning of the strings. Some examples,
      • +
    +
    1
+2
+3
+4
+5
+6
    >> textdistance.jaro_winkler("mes", "messi")
+0.86
+>> textdistance.jaro_winkler("crate", "crat")
+0.96
+>> textdistance.jaro_winkler("crate", "atcr")
+0.0
+
    +
      +
    • In first case, as the strings were matching from the beginning, high score was provided. Similarly, in the second case, only one character was missing and that too at the end of the string 2, hence a very high score was given. Imagine the previous algorithms, the similarity would have been less, 80% to be exact. In third case, we re-arranged the last two character of string 2, by bringing them at front, which resulted in 0% similarity.
      • +
    +

    Token based algorithms

    +
      +
    • Algorithms falling under this category are more or less, set similarity algorithms, modified to work for the case of string tokens. Some of them are,
      • +
    +

    Jaccard index

    +
      +
    • Falling under the set similarity domain, the formulae is to find the number of common tokens and divide it by the total number of unique tokens. Its expressed in the mathematical terms by,
      • +
    +
    \[ +J(A,B) = {{|A \cap B|}\over{|A \cup B|}} = {{|A \cap B|}\over{|A| + |B| - |A \cap B|}}. +\]
    +
      +
    • where, the numerator is the intersection (common tokens) and denominator is union (unique tokens). The second case is for when there is some overlap, for which we must remove the common terms as they would add up twice by combining all tokens of both strings. As the required input is tokens instead of complete strings, it falls to user to efficiently and intelligently tokenize his string, depending on the use case. Examples,
      • +
    +
    1
+2
+3
+4
+5
+6
+7
+8
    >> tokens_1 = "hello world".split()
+>> tokens_2 = "world hello".split()
+>> textdistance.jaccard(tokens_1 , tokens_2)
+1.0
+>> tokens_1 = "hello new world".split()
+>> tokens_2 = "hello world".split()
+>> textdistance.jaccard(tokens_1 , tokens_2)
+0.666
+
    +
      +
    • We first tokenize the string by default space delimiter, to make words in the strings as tokens. Then we compute the similarity score. In first example, as both words are present in both the strings, the score is 1. Just imagine running an edit based algorithm in this case, the score will be very less if not 0.
      • +
    +

    Sorensen-Dice

    +
      +
    • Falling under set similarity, the logic is to find the common tokens, and divide it by the total number of tokens present by combining both sets. The formulae is,
      • +
    +
    \[ +{\displaystyle DSC={\frac {2|X\cap Y|}{|X|+|Y|}}} +\]
    +
      +
    • where, the numerator is twice the intersection of two sets/strings. The idea behind this is if a token is present in both strings, its total count is obviously twice the intersection (which removes duplicates). The denominator is simple combination of all tokens in both strings. Note, its quite different from the jaccardā€™s denominator, which was union of two strings. As the case with intersection, union too removes duplicates and this is avoided in dice algorithm. Because of this, dice will always overestimate the similarity between two strings. Some example,
      • +
    +
    1
+2
+3
+4
+5
+6
+7
+8
    >> tokens_1 = "hello world".split()
+>> tokens_2 = "world hello".split()
+>> textdistance.sorensen(tokens_1 , tokens_2)
+1.0
+>> tokens_1 = "hello new world".split()
+>> tokens_2 = "hello world".split()
+>> textdistance.sorensen(tokens_1 , tokens_2)
+0.8
+
    +

    Sequence based algorithm

    +
      +
    • Lets understand one of the sequence based algorithms,
      • +
    +

    Ratcliff-Obershelp similarity

    +
      +
    • The idea is quite simple yet intuitive. Find the longest common substring from the two strings. Remove that part from both strings, and split at the same location. This breaks the strings into two parts, one left and another to the right of the found common substring. Now take the left part of both strings and call the function again to find the longest common substring. Do this too for the right part. This process is repeated recursively until the size of any broken part is less than a default value. Finally, a formulation similar to the above-mentioned dice is followed to compute the similarity score. The score is twice the number of characters found in common divided by the total number of characters in the two strings. Some examples,
      • +
    +
     1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13
+14
+15
+16
+17
+18
    >> string1, string2 = "i am going home", "gone home"
+>> textdistance.ratcliff_obershelp(string1, string2)
+0.66
+>> string1, string2 = "helloworld", "worldhello"
+>> textdistance.ratcliff_obershelp(string1, string2)
+0.5
+>> string1, string2 = "test", "text"
+>> textdistance.ratcliff_obershelp(string1, string2)
+0.75
+>> string1, string2 = "mes", "simes"
+>> textdistance.ratcliff_obershelp(string1, string2)
+0.75
+>> string1, string2 = "mes", "simes"
+>> textdistance.ratcliff_obershelp(string1, string2)
+0.75
+>> string1, string2 = "arrow", "arow"
+>> textdistance.ratcliff_obershelp(string1, string2)
+0.88
+
    +
      +
    • In first example, it found ā€˜ homeā€™ as the longest substring, then considered ā€˜i am goingā€™ and ā€˜goneā€™ for further processing (left of common substring), where again it found ā€˜goā€™ as longest substring. Later on right of ā€˜goā€™ it also found ā€™nā€™ as the only common and longest substring. Overall the score was 2 * (5 + 2 + 1) / 24 ~ 0.66. In second case, it found ā€˜helloā€™ as the longest longest substring and nothing common on the left and right, hence score is 0.5. The rest of the examples showcase the advantage of using sequence algorithms for cases missed by edit distance based algorithms.
      • +
    +

    Semantic based approaches

    +
      +
    • In semantic search, strings are embedded using some neural network (NN) model. Think of it like a function that takes an input string and returns a vector of numbers. The vector is then used to compare the similarity between two strings.
      • +
    • Usually the NN models work at either token or word level, so to get embedding of a string, we first find embeddings for each token in the string and then aggregate them using mean or similar function.
      • +
    • The expectation is that the embeddings will be able to represent the string such that it capture different aspects of the language. Because of which, the embeddings provides us with much more features to compare strings.
      • +
    • Let's try a couple of ways to compute semantic similarity between strings. Different models can be picked or even fine tuned based on domain and requirement, but we will use the same model for simiplicity's sake. Instead we will just use different packages.
      • +
    +
    +

    Hint

    +

    As embedding is an integral part of semantic search, it is important to check the quality of a embedding method before using it. MTEB is the "Massive Text Embedding Benchmark" python package that lets you test any embedding function on more than 30 tasks. The process is quite simple - usually the text is embedded using the provided function or neural network, and the performance of embedding is computed and checked on downstream tasks like classification, clustering, and more.

    +
    +

    txtai

    +
      +
    • txtai is a python package to perform semantic based tasks on textual data including search, question answering, information extraction, etc.
      • +
    • Today, we will use it for the sole purpose of semantic search. (inspired from txtai readme)
      • +
    +
     1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
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+30
    # import the packages
+import pandas as pd ## for better visualization of result
+from txtai.embeddings import Embeddings
+
+# Create embeddings model, backed by sentence-transformers & transformers
+embeddings = Embeddings({"path": "sentence-transformers/nli-mpnet-base-v2"})
+# the data to search against
+data = ["US tops 5 million confirmed virus cases",
+        "Canada's last fully intact ice shelf has suddenly collapsed, forming a Manhattan-sized iceberg",
+        "Beijing mobilises invasion craft along coast as Taiwan tensions escalate",
+        "The National Park Service warns against sacrificing slower friends in a bear attack",
+        "Maine man wins $1M from $25 lottery ticket",
+        "Make huge profits without work, earn up to $100,000 a day"]
+# var to hold the result
+results = []
+
+# perform search for each of the following query phrase
+for query in ("feel good story", "climate change", "public health story", "war", "wildlife", "asia", "lucky", "dishonest junk"):
+    # Get index of best section that best matches query
+    for id, score in embeddings.similarity(query, data):
+        results.append((query, data[id], score))
+
+# format the result
+results = pd.DataFrame(results)
+results.columns = ["query", "best_match", "score"]
+results['score'] = results['score'].round(2)
+results = results.sort_values(by=['score'], ascending=False)
+
+# print the result
+results.drop_duplicates(subset=['query'])
+
    +
      +
    • Here we are trying to pick the most similar phrase for each of the query from the data. The result will look as follows,
      • +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    querybest_matchscore
    wildlifeThe National Park Service warns against sacrif...0.28
    warBeijing mobilises invasion craft along coast a...0.27
    asiaBeijing mobilises invasion craft along coast a...0.24
    climate changeCanada's last fully intact ice shelf has sudde...0.24
    public health storyUS tops 5 million confirmed virus cases0.17
    feel good storyMaine man wins $1M from $25 lottery ticket0.08
    luckyMaine man wins $1M from $25 lottery ticket0.07
    dishonest junkMake huge profits without work, earn up to $10...0.03
    +
      +
    • As obvious from the result, even though there is hardly any common sub-string between the query and the data, the results make sense in a semantic way. Beijing is connected with asia and war, while ice shelf is connected with climate change, and so on.
      • +
    +
    +

    Note

    +

    The score is the cosine similarity between the embeddings of the two strings (query and data element). It's range is between {-1, 1}, and not {0, 1}. Do not think of it as probability.

    +
    +
      +
    • The above approach could be slow as for each call to similarity, the sentences are embedded again and again. To speed it up, we could use index to precompute the embeddings for the data. This can be done by,
      • +
    +
    1
+2
+3
+4
+5
+6
+7
+8
    # index the data
+embeddings.index([(uid, text, None) for uid, text in enumerate(data)]) 
+# now instead of similarity, use search function
+embeddings.search("feel good story", limit=1)
+# Output:
+# [{'id': '4',
+#   'score': 0.08329004049301147,
+#   'text': 'Maine man wins $1M from $25 lottery ticket'}]
+
    +
      +
    • txtai also provides explaination of the result. For this we can use explain function as follows,
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    # first index the data
+embeddings.index([(uid, text, None) for uid, text in enumerate(data)]) 
+# next call the explain function 
+embeddings.explain("feel good story", limit=1)
+# Output:
+# [{'id': '4',
+#   'score': 0.08329004049301147,
+#   'text': 'Maine man wins $1M from $25 lottery ticket',
+#   'tokens': [('Maine', 0.003297939896583557),
+#    ('man', -0.03039500117301941),
+#    ('wins', 0.03406312316656113),
+#    ('$1M', -0.03121592104434967),
+#    ('from', -0.02270638197660446),
+#    ('$25', 0.012891143560409546),
+#    ('lottery', -0.015372440218925476),
+#    ('ticket', 0.007445111870765686)]}]
+
    +
      +
    • The output contains word level importance score for the top similar sentence in the data (limit=1).
      • +
    • From the output we can see the word win is the most important word wrt to the query. The score computation is based on the occlusion based explaination approach. Here, several permutations of the same sentence is created, each one with one word/token missing. Then cosine similarity is computed between the fixed query and each permutation. Finally, the similairty score of the permutation is subtracted from the score of the original sentence (with all words present). The intuition is as follows,
        +
      • if an important word like win is missing from the sentence, the score of the sentence will be reduced and the difference will be positive
        • +
      • if an unimportant word like man is missing from the sentence, the score of the sentence will increase and the difference will be negative
        • +
      +
      • +
    +

    Sentence Transformer

    +
      +
    • SentenceTransformers is is a Python framework for state-of-the-art sentence, text and image embeddings. The inital work in this framework is detailed in the paper Sentence-BERT
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    # laod packages
+import pandas as pd
+from sentence_transformers import SentenceTransformer, util
+
+# load the model
+model = SentenceTransformer('nli-mpnet-base-v2')
+
+# the data to search against
+data = ["US tops 5 million confirmed virus cases",
+        "Canada's last fully intact ice shelf has suddenly collapsed, forming a Manhattan-sized iceberg",
+        "Beijing mobilises invasion craft along coast as Taiwan tensions escalate",
+        "The National Park Service warns against sacrificing slower friends in a bear attack",
+        "Maine man wins $1M from $25 lottery ticket",
+        "Make huge profits without work, earn up to $100,000 a day"]
+# var to hold the result
+results = []
+
+# embed the data before hand
+embeddings_target = model.encode(data, convert_to_tensor=True)
+# perform search for each of the following query phrase
+for query in ("feel good story", "climate change", "public health story", "war", "wildlife", "asia", "lucky", "dishonest junk"):
+    # embed the query
+    embeddings_query = model.encode([query], convert_to_tensor=True)
+    # compute cosine-similarities for each sentence with each other sentence
+    cosine_scores = util.pytorch_cos_sim(embeddings_query, embeddings_target)
+    # return the index of top 5 values in a list
+    score_list = cosine_scores.tolist()[0]
+    # Get index of best section that best matches query
+    for id, score in enumerate(score_list):
+        results.append((query, data[id], score))
+
+# format the result
+results = pd.DataFrame(results)
+results.columns = ["query", "best_match", "score"]
+results['score'] = results['score'].round(2)
+results = results.sort_values(by=['score'], ascending=False)
+
+# print the result
+results.drop_duplicates(subset=['query'])
+
    +
      +
    • This will return the same table as the previous one. In face if you noticed, we have used the same model. If you look further, in txtai we used sentence-transformers and we have used the same model.
      • +
    • The package provides an extensive variety of pretraied models. A comparitive table is shown below (taken from Sentence-Transformer)
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    Model NamePerformance Sentence Embeddings (14 Datasets)Performance Semantic Search (6 Datasets)Avg. PerformanceSentence Encoding SpeedModel Size
    all-mpnet-base-v269.5757.0263.302800420 MB
    multi-qa-mpnet-base-dot-v166.7657.6062.182800420 MB
    all-distilroberta-v168.7350.9459.844000290 MB
    all-MiniLM-L12-v268.7050.8259.767500120 MB
    multi-qa-distilbert-cos-v165.9852.8359.414000250 MB
    all-MiniLM-L6-v268.0649.5458.801420080 MB
    multi-qa-MiniLM-L6-cos-v164.3351.8358.081420080 MB
    paraphrase-multilingual-mpnet-base-v265.8341.6853.752500970 MB
    paraphrase-albert-small-v264.4640.0452.25500043 MB
    paraphrase-multilingual-MiniLM-L12-v264.2539.1951.727500420 MB
    paraphrase-MiniLM-L3-v262.2939.1950.741900061 MB
    distiluse-base-multilingual-cased-v161.3029.8745.594000480 MB
    distiluse-base-multilingual-cased-v260.1827.3543.774000480 MB
    +
    +

    Note

    +

    Training semantic search model is different from normal classification or regression models. Think of it like this -- for classification each sample has one label, but for semantic search a combination of samples has one label. This is because in search you want to match the query and result, and then provide some score for that combination. You can refer Sbert training page for more details.

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    + + + + + + + + + + + + + + \ No newline at end of file diff --git a/natural_language_processing/transformer/index.html b/natural_language_processing/transformer/index.html new file mode 100644 index 00000000..b33f2121 --- /dev/null +++ b/natural_language_processing/transformer/index.html @@ -0,0 +1,2315 @@ + + + + + + + + + + + + + + + + + + Transformers - A Lazy Data Science Guide + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    Warning

    +

    This page is still ongoing modifications. Please check back after some time or contact me if it has been a while! Sorry for the inconvenience šŸ™

    +
    +

    Transformers

    +

    Introduction

    +
      +
    • "Attention Is All You Need" paper [1] introduced the concept of "Transformers" and it revolutionalized the complete domain of Deep Learning. So much so that in 2022 we have Transformers has become multi-disiplinary and is used across NLP, CV, RL, Graphs, and more! In NLP, Transformers variants are still state of the art! (even after 5 years).
      • +
    • As the paper's name suggests, the authors showcase how only using multiple stacks of attention layers provides enough learning power to the architecture to improve the SotA in multiple takss. Quoting the paper, "Transformer is the first transduction model relying entirely on self-attention to compute representations of its input and output without using sequence-aligned RNNs or convolution."
      • +
    • Let's try to understand the fundamentals of Transformers by going through each components in detail.
      • +
    +
    +

    Hint

    +

    Transduction is a broad term but in nearly all aspect it means converting data, signal or object from one format to another. Transformers transforms input data from one format to another (\(x_i\) in encoder leads to \(y_i\) in decoder), so it is transductive model. Hence the name "Transformers" šŸ˜‰

    +
    +
    +

    +

    Transformer architecture. Left part is the encoder and right part is the decoder part [1]

    +
    +
      +
    • At a high level, the architecture can be divided into two modules,
        +
      • Encoder: the component resonsible to learn representation of the input prompt. It process each token of the input parallely in one iteration and generates attention aware representations for each token. The encoder layer consists of 6 identical blocks with output dimension of \(d_{model} = 512\).
        • +
      • Decoder: the component reposible for generating the output. The execution strategy depends on the scenario. During training as ground truth is also available, complete processing happens in one iteration using teacher forcing and masked attention. During inference multiple iterations are run where one output is generated in one go. Decoder layer also consists of 6 identical blocks.
        • +
      +
      • +
    +

    Components of Transformers

    +

    Self-Attention

    +
      +
    • Probably the most important component of Transformer is the Attention Layer. The intuition here is that the context matters. For example in a sentence, meaning of a word like "bank" could vary based on if it is "river bank" or "financial bank". Similarly, pronouns like He, She, It, etc could also attend to certain other words in the sentence (name of the person or object). With this in mind, a true meaning (representation) of a word can only be identified by attending to all the other words in context. Self-Attention is just one way to do that.
      • +
    +
    +

    +

    Scaled dot product attention [1]

    +
    +
      +
    • Compared to previous approaches like RNN or Word2Vec that were used to create representations of the words, Attention models modifies the representation for each input based on the context. Word2Vec had a fixed context and worked at word level, so "bank" will have 1 representation 9word embedding). RNN on the other hand only conisidered sequential context that lead to forgetting the context of the past (words far on the left).
      • +
    +
    +

    Note

    +

    Transformer works at sub-word level tokens and not words. And context of attention is equal tot he input which could be a phrase, a sentence, a paragraph or a complete article.

    +
    +
      +
    • The process of self-attention is quite simple, and at a high level consists of only two steps. For each token,
        +
      • (1) find the importance score of every token in the context (including the token in question), and
        • +
      • (2) using the score, do a weighted sum of the every token's representations to create the final token representation.
        • +
      +
      • +
    +
      +
    • And thats it šŸ˜„ Well at least at 10k feet āœˆ. Looking at the technicalities, the process drills down to,
        +
      • Every token is not used as-it-is, but first converted to key, value and query format using linear projections. We have key, value and query weights denoted as \(W_k\), \(W_v\) and \(W_q\). Each input token's representation is first multipled with these weights to get \(k_i\), \(v_i\) and \(q_i\).
        • +
      • Next the query of one token is dot product with the keys of all token. On applying softmax to the output, we get a probability score of importance of every token for the the given token.
        • +
      • Finally, we do weighted sum of values of all keys with this score and get the vector representation of the current token.
        • +
      +
      • +
    • It is easy to understand the process while looking at one token at a time, but in reality it is completly vectorized and happens for all the tokens at the same time. The formulation for the self-attention is as follows,
      • +
    +
    \[ +Attention(Q, K, V) = softmax(\frac{QK^T}{\sqrt{d_k}})V +\]
    +
      +
    • where Q, K and V are the matrices you get on multiplication of all input tokens with the query, key and value weights.
      • +
    +
    +

    Note

    +

    Authors introduced the scaling factor to handle potential vanishing gradient problem. In their words, "We suspect that for large values of \(d_k\), the dot products grow large in magnitude, pushing the softmax function into regions where it has extremely small gradients. To counteract this effect, we scale the dot products by \(\frac{1}{\sqrt{d_k}}\)"

    +
    +

    Multi-head Attention

    +
      +
    • Instead of performing the complete attention computation in a single layer, the authors divided the computation into muliple heads or parts. This is done quite intuitively, let's take an example where we want to have say 8 heads in each layer. In this case the projections weights (\(W_k\), \(W_v\) and \(W_q\)) downsize the token embedding into \(\frac{1}{8}\) the size. This can be done by making the dimension of the weights token_dim * token_dim/8 . After this we repeat the complete process as discussed in self-attention.
      • +
    +
    +

    +

    Multi-Head Attention [1]

    +
    +
      +
    • Now at the end we will have 8 outputs instead of one, i.e. 8 different partial representations for each token (which is not strictly correct and not what we want). So to aggregate to get 1 output, authors concatenated the outputs and then apply a linear projection (multiply with \(W^o\)) to get the original sized 1 representation per token. The formulation is shown below,
      • +
    +
    \[ +\text{MultiHead}(Q, K, V) = \text{Concat}(head_1, head_2, ..., head_h) W^o +\]
    +

    Masked Attention

    +
      +
    • As discussed before, during training, decoder use masked attention to prevent model from attending to the future tokens. This is done so that the model does not cheat while predicting the output.
      • +
    +
    +

    Note

    +

    Having bidirectional context on input is okay (as done by encoder with normal attention), but in decoder we should only attend to what we have already seen and not the future.

    +
    +
      +
    • The approach is also quite easy to understand, it is very similar to the normal attention where we compute the query, key and value vectors. The only difference is that while computing attention scores, we explicitly make the score of future tokens zero. This is done by making the score (before softmax) at the future places equal to large negative number. On applying Softmax, those values become zero.
      • +
    +

    Position-wise Feed Forward Layers

    +
      +
    • Encoder and Decoder contains fully connected feed-forward neural network that is applied to each position (token) separately and identically. It consists of two linear transformations with a ReLU activation in between. The formualtion is given below,
      • +
    +
    \[ +\text{FFN}(x)=max(0, xW_1 + b_1)W_2 + b_2 +\]
    +
    +

    Note

    +

    The parameters \(W_1\), \(W_2\), \(b_1\) and \(b_2\) are different in different layers.

    +
    + + +

    Positional Embeddings

    +
      +
    • For the model to consider the order of sequence for representation and prediction, authors injected a sense of relative or absolute position in the input by using positional embeddings. It is injected to each token separately and has the same embedding size as that of the token i.e \(d_{model}\). For each token we have a position vector.
      • +
    • After modification, the token embedding will be a combination of two types of information -- positional (injected by positional embedding) and semantic (learned by the model). While there are many choices for the position embedding, authors wanted to pick the one that does not compromise the semantic information by a lot. In the paper authors alternatively use sine and cosine functions (at indices of vector) of different frequencies,
      • +
    +
    \[ +\text{PE}_{pos, 2i} = sin(\frac{pos}{10000^{2i/d_{model}}}) +\]
    +
    \[ +\text{PE}_{pos, 2i+1} = cos(\frac{pos}{10000^{2i/d_{model}}}) +\]
    +
      +
    • Here pos is the position of the token in sequence and i is the index of the vector for a token.
      • +
    +
    +

    +

    Position Encoding for 100 positions, each of 512 dimension size

    +
    +
      +
    • Let's understand the process using an example sentence - "My name is Mohit" with words as tokens. So we have 4 tokens, and with dimension size of 4, each token could be represented as shown below,
      • +
    +
    ## Token Embedding
+My = [0.1, 0.2, 1, 0.45]
+Name = [0.15, 0.32, 13, 0.51]
+is = [-0.1, 0.21, 0.65, 0.25]
+Mohit = [0.1, -0.12, 0.33, -0.45]
+
    +
      +
    • Now we will compute the positional embeddings using the above formulae,
      • +
    +
    ## Positional embedding
+pos_1 = [ 0.          1.          0.          1.        ]
+pos_2 = [ 0.84147098  0.54030231  0.09983342  0.99500417]
+pos_3 = [ 0.90929743 -0.41614684  0.19866933  0.98006658]
+pos_4 = [ 0.14112001 -0.9899925   0.29552021  0.95533649]
+
    +
      +
    • Finally the modified embedding for the tokens will be addition of the original token embedding and positional embedding.
      • +
    +
    ## Modified embedding
+My = [0.1, 0.2, 1, 0.45] + [ 0.          1.          0.          1.        ]
+Name = [0.15, 0.32, 13, 0.51] + [ 0.84147098  0.54030231  0.09983342  0.99500417]
+is = [-0.1, 0.21, 0.65, 0.25] + [ 0.90929743 -0.41614684  0.19866933  0.98006658]
+Mohit = [0.1, -0.12, 0.33, -0.45] + [ 0.14112001 -0.9899925   0.29552021  0.95533649]
+
    +
    +

    Note

    +

    Some obvious alternatives for position encoding were not considered because of the their disadvantages. For example, using simple numbers like 0, 1, 2...N would have led to unnormalized numbers in vector, which could have degraded the semantic representation of the token. On the other hand if we had used normalized numbers, that would have not made sense for variable length sentences. Similarly using binary numbers could be rejected as it is not continous. [4]

    +
    +

    References

    +

    [1] Attention Is All You Need - Paper

    +

    [2] The Illustrated Transformer - Link

    +

    [3] What exactly are keys, queries, and values in attention mechanisms? - Cross validated

    +

    [4] Master Positional Encoding: Part I - Medium Blog by Jonathan Kernes

    +

    šŸ‘‹ Cheers.

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    Word2Vec

    + +
      +
    • Word2Vec (w2v) [1] is a word embedding technique that represents a word as a vector. Each vector can be thought of as a point in \(R^{D}\) space, where \(D\) is the dimension of each vector.
      • +
    • One thing to note is that these vectors are not randomly spread out in the vector space. They follow certain properties such that, words that are similar like "cat" and "tiger" are relatively closer to each other than a completely unrelated word like "tank".
      • +
    • In the vector space, this means their cosine similarity score is higher. Along with this, we can even observe famous analogies like \(king - man + woman = queen\) which can be replicated by vector addition of these word's representation vector.
      • +
    +
    +

    +

    Vector space representing the position of wordā€™s vector and the relationship between them to showcase the analogy king-man+woman=queen +

    +
    +
      +
    • While such representation is not unique to w2v, its major contribution was to provide a simple and faster neural network based word embedder.
      • +
    • To do so, w2v transformed the training as a classification problem. The neural network tries to answer which word is most probable to be found in the context of a given word. Given a sequence of words that "naturally" appears in some sentence, the input could be any middle word and output could be any of the surrounding words (within some window). The training is done by creating a 1-layer deep neural network where the input word is fed as one-hot encoding and output is softmax applied with intention of getting large value for context word.
      • +
    +
    +

    +

    SkipGram architecture (taken from Lilā€™Log [2]). Its a 1 layer deep NN with input and output as one-hot encoded. The input-to-hidden weight matrix contains the word embeddings.

    +
    +
      +
    • The training data is prepared by sliding a window (of some window size) across the corpus of large text (which could be articles or novels or even complete Wikipedia), and for each such window the middle word is the input word and the remaining words in the context are output words.
      • +
    • For each input word in vector space, we want the context words to be close but the remaining words far. And if two input words will have similar context words, their vector will also be close.
      • +
    • Word2Vec also performs negative sampling, wherein random selection of some negative examples are also added in the training dataset. For these examples, the output probabilities should be \(0\). [4]
      • +
    • After training we can observe something interesting ā€” the weights between the Input-Hidden layer of NN now represent the notions we wanted in our word embeddings, such that words with the same context have similar values across vector dimension. And these weights are used as word embeddings.
      • +
    +
    +

    +

    Heatmap visualization of 5D word embeddings from Wevi [3]. Color denotes the value of cells.

    +
    +
      +
    • The result shown above is from training 5D word embeddings from a cool interactive w2v demo tool called "Wevi" [3].
      • +
    • As visible, words like (juice, milk, water) and (orange, apple) have similar kinds of vectors (some dimensions are equally lit ā€” red or blue).
      • +
    +
    +

    Hint

    +

    One obvious question could be -- why do we only use Input-Hidden layer for embedding and why not Hidden-Last? It seems we can use both based on the type of task we are performing. For more details refer the paper -- Intrinsic analysis for dual word embedding space models

    +
    +

    References

    +

    [1] Efficient Estimation of Word Representations in Vector Space

    +

    [2] Lilā€™Log ā€” Learning word embedding

    +

    [3] Wevi ā€” word embedding visual inspector

    +

    [4] Word2Vec Tutorial Part 2 - Negative Sampling

    +

    Additional Materials

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    DeepWalk

    + +
      +
    • DeepWalk [1] is a technique to create semantic embeddings of the nodes of a graph. For this, DeepWalk employs Skip-gram based technique of Word2Vec (w2v). For context, w2v is a word embedding technique, where we learn to embed words into a vector representation.
      • +
    • In Skip-gram word embedding training procedure, the complete problem is transformed as a classification problem. The neural network tries to answer which word is most probable to be found in the context of a given word. Given a sequence of words that "naturally" appears in some sentence, the input could be any middle word and output could be any of the surrounding words (within some window). The training is done by creating a 1-layer deep neural network where the input word is fed as one-hot encoding and output is softmax applied with intention of getting large value for context word.
      • +
    +
    +

    Hint

    +

    For a brief introduction to Word2Vec, you can refer the respective page in the Lazy Data Science Guide.

    +
    +
      +
    • But how to create training data that captures the notion of context in graphs? While intuitively it seems easy to create training data for w2v by using plethora of text available online, how can we do something similar for a graph?
      • +
    • As it happens, we can follow the same intuition for graphs by applying random walk technique. Here, we start from one node and randomly go to one of it's neighbors. We repeat this process \(L\) time which is the length of the random walk. After this, we restart the process again. If we do this for all nodes (and \(M\) times for each node) we have in some sense transformed the graph structure into a text like corpus used to train w2v where each word is a node and its context defines its neighbor.
      • +
    +

    Code

    +

    Author's Implementation

    +
      +
    • +

      The DeepWalk authors provide a python implementation here. Installation details with other pre-requisite are provided in the readme (windows user be vary of some installation and execution issues). The CLI API exposes several algorithmic and optimization parameters like,

      +
        +
      • input requires the path of the input file which contains graph information. A graph can be stored in several formats, some of the famous (and supported by the code) are ā€” adjacency list (node-all_neighbors list) and edge list (node-node pair which have an edge).
        • +
      • number-walks The number of random walks taken for each node.
        • +
      • representation-size the dimension of final embedding of each node. Also the size of hidden layer in the skipgram model.
        • +
      • walk-length the length of each random walk.
        • +
      • window-size the context window size in the skipgram training.
        • +
      • workers optimization parameter defining number of independent process to spawn for the training.
        • +
      • output the path to output embedding file.
        • +
      +
      • +
    +
      +
    • Authors have also provided example graphs, one of which is Karate club dataset. It's stored in the format of the adjacency list. Now letā€™s read the graph data and create node embeddings by,
      • +
    +
    deepwalk --input example_graphs/karate.adjlist --number-walks 10
+--representation-size 64 --walk-length 40 --window-size 5 
+--workers 8 --output trained_model/karate.embeddings
+
    +
      +
    • This performs start-to-end analysis by taking care of ā€” loading the graph from the file, generating random walks, and finally training skip-gram model on the walk data. We can read the output embedding file which contains vector embedding for each node in the graph.
      • +
    +

    Karate Club Implementation

    +
      +
    • A much simpler API is provided by python package called KarateClub [2]. To do the same set of actions, all we need to do is following.
      • +
    +
     1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
    # import libraries
+import networkx as nx 
+from karateclub import DeepWalk
+# load the karate club dataset
+G = nx.karate_club_graph()
+# load the DeepWalk model and set parameters
+dw = DeepWalk(dimensions=64)
+# fit the model
+dw.fit(G)
+# extract embeddings
+embedding = dw.get_embedding()
+
    +
    +

    Note

    +

    The DeepWalk class also extends the same parameters exposed by the author's code and can be tweaked to do the desired experiment. Refer their docs for more details.

    +
    +

    Analysis

    +

    Seeing DeepWalk in Action

    +
      +
    • To see DeepWalk in action, we will pick one graph and visualize the network as well as the final embeddings. For better understanding, I created a union of 3 complete graphs with some additional edges to connect each graph.
      • +
    +
    +

    +

    Union of 3 complete graphs. We can imagine 3 clusters with nodes 0 to 9 belonging to cluster 1; 10 to 19 to cluster 2 and 20 to 28 in cluster 3.
    +

    +
    +
      +
    • Now, we will create DeepWalk embeddings of the graph. For this, we can use the KarateClub package and by running DeepWalk on default settings we get embeddings of 128 dimensions. To visualize this I use dimensionality reduction technique PCA, which scaled-down embeddings from RĀ¹Ā²āø to RĀ². I will also plot the 128D heatmap of the embedding on the side.
      • +
    +
    +

    +

    Left ā€” The PCA reduced (from 128D to 2D) node embeddings of the graph. Right ā€” The heatmap of the original 128D embeddings.
    +

    +
    +
      +
    • There is a clear segregation of nodes in the left chart which denotes the vector space of the embedding. This showcase how DeepWalk can transform a graph from force layout visualization to vector space visualization while maintaining some of the structural properties. The heatmap plot also hints to a clear segregation of graph into 3 clusters.
      • +
    +
      +
    • Another important thing to note is when the graph is not so complex, we can get by with lower dimension embedding as well. This not only reduces the dimensions but also improves the optimization and convergence as there are fewer parameters in skip-gram to train. To prove this we will create embedding of only size 2. This can be done by setting the parameter in DeepWalk object dw = DeepWalk(dimensions=2) . We will again visualize the same plots.
      • +
    +
    +

    +

    he node embeddings (size=2) of the graph. Right: The heatmap of the embeddings.
    +

    +
    +
      +
    • Both the plots again hint towards the same number of clusters in the graph, and all this by only using 1% of the previous dimensions (from 128 to 2 i.e. ~1%).
      • +
    +

    Conclusion

    +
      +
    • As the answer to this analogy NLP - word2vec + GraphNeuralNetworks = ? can arguably be DeepWalk (is it? šŸ™‚ ), it leads to two interesting points,
        +
      • DeepWalk's impact in GNN can be analogous to Word2Vec's in NLP. And it's true as DeepWalk was one of the first approaches to use NN for node embeddings. It was also a cool example of how some proven SOTA technique from another domain (here, NLP) can be ported to and applied in a completely different domain (here, graphs).
        • +
      • As DeepWalk was published a while ago (in 2014 - less than a decade but a lifetime in AI research), currently, there are lots of other techniques which can be applied to do the job in a better way like Node2Vec or even Graph convolution networks like GraphSAGE, etc.
        • +
      +
      • +
    • That said, as to start learning Deep learning based NLP, word2vec is the best starting point, I think DeepWalk in the same is sense a good beginning for Deep Learning based graph analysis.
      • +
    +

    Cheers šŸ‘‹

    +

    References

    +

    [1] DeepWalk ā€” Paper | Code

    +

    [2] KarateClub ā€” Paper | Code

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    Introduction

    + +
      +
    • Graph Neural Networks are the current hot topic. [1] And this interest is surely justified as GNNs are all about latent representation of the graph in vector space.
      • +
    • Representing an entity as a vector is nothing new. There are many examples like word2vec and Gloves embeddings in NLP which transforms a word into a vector.
      • +
    • What makes such representation powerful are,
        +
      • these vectors incorporate a notion of similarity among them i.e. two words who are similar to each other tend to be closer in the vector space (dot product is large), and
        • +
      • they have application in diverse downstream problems like classification, clustering, etc.
        • +
      +
      • +
    • This is what makes GNN interesting, as while there are many solutions to embed a word or image as a vector, GNN laid the foundation to do so for graphs.
      • +
    +

    References

    +

    [1] EasyAI ā€” GNN may be the future of AI

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    Introduction

    + +
      +
    • Quoting Wikipedia, "Network science is an academic field which studies complex networks such as telecommunication networks, computer networks, biological networks, cognitive and semantic networks, and social networks, considering distinct elements or actors represented by nodes (or vertices) and the connections between the elements or actors as links (or edges)."
      • +
    • To better understand network science, we should understand the underlying building blocks i.e. Graphs!
      • +
    +

    Graphs 101

    +
      +
    • Graph or Networks is used to represent relational data, where the main entities are called nodes. A relationship between nodes is represented by edges. A graph can be made complex by adding multiple types of nodes, edges, direction to edges, or even weights to edges. One example of a graph is shown below.
      • +
    +
    +

    +

    Karate dataset visualization @ Network repository
    +

    +
    +
      +
    • The graph above is the Karate dataset [1] which represents the social information of the members of a university karate club. Each node represents a member of the club, and each edge represents a tie between two members of the club. The left info bar states several graph properties like a number of nodes, edges, density, degree, etc. Network repository contains many such networks from different fields and domains and provides visualization tools and basic stats as shown above.
      • +
    +

    Common Concepts in Graphs

    +
      +
    • Nodes: They are the basic building blocks in the graph that represent certain object, concepts or entities. Nodes (or vertices) can be of one type or of multiple types if the graph is homogenous or heterogenous, respecitively.
      • +
    • Edges: They are the ties that connect two nodes together based on some special relationship criteria. For example, for a graph where each nodes are intersection, an edge between two nodes (or intersection) denotes precense of direct roadways between them. Edges can also be of multiple types if the graph is heterogenous.
      • +
    • Directed or Undirected Graph: Edges may have a direction like A --> B that denotes a directional relationship. Example is Mother-Son relationship, Kavita -- mother-of --> Mohitis true, and not the other way around. If all the edges of a graph are directional, then it is a directed graph. Similarly, if the edges of a graph have no direction like A -- B, it is undirected graph. Example is a neighbor relationship, me and the guy living next door to me, are both neighbors of each other.
      • +
    • Degrees of Node: Degrees denote the number of direct connections of a node to other nodes in the graph. For a directed graph, we have in-degree (edges coming to the node) and out-degree (edges going out from the node). For example in A --> B <-- C, A and C has out-degree = 1 & in-degree = 0 & B has out-degree = 0 & in-degree = 2
      • +
    • Path and Walk: Path or Walk are the route between two nodes that goes through multiple nodes and edges. For example, A --> B --> C is a path of length 2 as there are two edges in the route. The difference between a path and a walk is that walks can repeat edges and nodes. Refer Stack Exchange Answer.
      • +
    • Connected Graph: A graph with a possible path between any two nodes is called a connected graph. On the contrary, the graph will be disconnected and there might be multiple clusters of individual connected sub-graphs in the overall graph.
      • +
    • Clique and Complete graph: A clique of a graph is a set of nodes where every pair of nodes has an edge between them. It is the strongest form of cluster in the graph. Similarly if the graph itself is a clique i.e. there is an edge between all the pairs of nodes, it is called a complete graph. This also means that the graph contains all the possible edges.
      • +
    • Spanning Tree: A tree is an undirected graph where any two nodes are connected by exaclty one path. The spanning tree of a graph is a subgraph that is a tree that contains every node in the graph. In practice, Kruskal Algorithm can be used to find the minimum spanning tree for a graph, where we have multiple possibilities of creating spanning trees but want one with minimum total edge or node weights.
      • +
    • +

      Adjacency matrix: It is a square matrix of size NxN, where N is the number of unique nodes. The matrix contains 1 and 0 value that denotes the presence (1) or absence (0) of an edge between the nodes. unique graph and it's adjacency matrix is shown below, where the column and row represent nodes in alphabetic order. + 

      graph LR
+   A --- B --- C --- D --- E --- A
+   A --- A
+   E --- B
+   D --- F
      + \(M_{Adj} = \begin{bmatrix} 1 & 1 & 0 & 0 & 1 & 0 \\ 1 & 0 & 1 & 0 & 1 & 0 \\ 0 & 1 & 0 & 1 & 0 & 0 \\ 0 & 0 & 1 & 0 & 1 & 1 \\ 1 & 1 & 0 & 1 & 0 & 0 \\ 0 & 0 & 0 & 1 & 0 & 0 \\ \end{bmatrix}\)

      +
      +

      Hint

      +

      \(N^{th}\) power of the Adjacency Matrix (\(M\)) is a new square matrix (\(M^N\)), where \(M_{ij}^N\) represents the number of walks of length \(N\) between the nodes \(i\) and \(j\).

      +
      +
      • +
    +
      +
    • +

      Laplacian matrix: given a graph with \(v_i\) to \(v_n\) nodes, Laplacian matrix \(L_{nxn}\) is

      +
      \[{\displaystyle L_{i,j}:={\begin{cases}\deg(v_{i})&{\mbox{if}}\ i=j\\-1&{\mbox{if}}\ i\neq j\ {\mbox{and}}\ v_{i}{\mbox{ is adjacent to }}v_{j}\\0&{\mbox{otherwise}},\end{cases}}}\]
      +

      Or it can also be computed as \(L = D - A\), where \(D\) is the degree matrix (could be in-degree, out-degree or both) and \(A\) is the adjacency matrix. Laplacian matrix are important objects in the field of Spectral Graph Theory, and we can infer many properties of the graph like connected components by looking at its laplacian matrix. Refer this Maths Stack Exchange Question.

      +
      • +
    +

    References

    +

    [1] Zachary karate club ā€” The KONECT Project

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    KG embedding algorithms

    +
      +
    • Before discussing individual algorithms, we will go through some high-level generalization of the embedding techniques which make each algorithm unique. This will help us differentiate and hence appreciate the individual algorithms.
      • +
    +

    Generalization of embedding methods

    +
      +
    • Embedding is the way of representing an object from its existing environment to another.
      • +
    +
      +
    • Knowledge graph embedding includes representation of relations and entities into continuous space.
      • +
    +
      +
    • Models for KG embedding can be categorised based on their answer for following questions, (Ji_2021)
        +
      • What is the representation space in which the relations and entities are represented?
        • +
      • What is the scoring function for measuring the plausibility of factual triples?
        • +
      +
      • +
    +

    Representation space

    +

    Point-wise Euclidean space

    +
      +
    • The most common representation space.
      • +
    • Embedding space is Euclidean real valued vector or matrix space.
      • +
    • Easy to understand; Not ideal for graphical (tree-like) structure.
      • +
    +
    +

    +

    Point-wise space (Ji_2021)
    +

    +
    +

    Complex vector space

    +
      +
    • Entities and relations are represented in a complex space
      • +
    • Taking head entity as an example, h has a real part Re(h) and an imaginary part Im(h), i.e., \(\textbf{h}=Re(\textbf{h}) + i Im(\textbf{h})\)
      • +
    • Can capture anti-symmetric relations better than operations in Euclidean space.
      • +
    +
    +

    +

    Complex vector space (Ji_2021)
    +

    +
    +

    Gaussian distribution space

    +
      +
    • Entities and relations are represented as probabilistic distribution
      • +
    • Applicable if you want to capture uncertainties.
      • +
    +
    +

    +

    Gaussian distribution (Ji_2021)
    +

    +
    +

    Manifold space

    +
      +
    • Entities and relations are represented in a well defined topological space
      • +
    • Good for graphical (tree-like) structure.
      • +
    +
    +

    +

    Manifold space (Ji_2021)
    +

    +
    +

    Scoring functions

    +

    Distance based

    +
      +
    • Measure plausibility of facts by calculating the distance between the entities.
      • +
    • Additive translation with relation is the most widely used method i.e. \(\textbf{h} + \textbf{r} \approx \textbf{t}\)
      • +
    +
    +

    +

    Translational distancebased scoring of TransE (Ji_2021)
    +

    +
    +

    Similarity based

    +
      +
    • Measure plausibility of the facts by semantic similarity matching
      • +
    • Multiplicative formulation is most widely used method i.e. \(\textbf{h}^T \textbf{M}_r \approx \textbf{t}^T\) , use relation matrix to transform head entity into tail entity.
      • +
    +
    +

    +

    Semantic similarity-based scoring of DistMult (Ji_2021)
    +

    +
    +

    Algorithm Comparison

    +
      +
    • A holistic comparison of different knowledge graph emebdding techniques w.r.t. category and scoring function is provided below,
      • +
    +
    +

    +

    A comprehensive summary of knowledge representation learning models (Ji_2021)
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    Introduction

    +

    What is a Knowledge graph?

    +
      +
    • To better under Knowledge graphs, let's start by understanding its basic unit i.e. "fact".
      • +
    +
      +
    • A fact is the most basic piece of information that can be stored in a KG. Facts can be represented in form of triplets with either of the following ways,
        +
      • HRT: <head, relation, tail>
        • +
      • SPO: <subject, predicate, object>
        • +
      +
      • +
    +
      +
    • Let's follow the HRT representation for this article.
      • +
    +
      +
    • Facts contain 3 elements which can be further represented as a graph,
        +
      • Head or tail + - entities which are real-world objects or abstract concepts + - represented as nodes
        • +
      • Relation
          +
        • the connection between entities
          • +
        • represented as edges
          • +
        +
        • +
      +
      • +
    +
      +
    • A simple KG example is provided below. One example of fact could be <BoB, is_interested_in, The_Mona_Lisa>. You can see the KG is nothing but a collection of multiple such facts.
      • +
    +
    +

    +

    A sample knowledge graph. (rdf_primer)
    +

    +
    +
      +
    • Note, there are no limitations on the data type of the fact stored in KG. As shown in the above example, we have persons (Bob, Alice,Ā ..), paintings (Mona Lisa), dates, etc, represented as nodes in the KG.
      • +
    +
    +

    Why knowledge graphs?

    +

    This is the very first and a valid question anyone will ask when introduced to KG. We will try to go through some points wherein we compare KG with normal graphs and even other ways of storing information. The aim is to highlight the major advantages of using KG.

    +

    Compared to Normal Graph

    +
      +
    • Heterogenous data: supports different types of entities (person, date, painting, etc) and relations (likes, born on, etc).
      • +
    • Model real-world information: closer to our brain's mental model of the world (represents information as a normal human does)
      • +
    • Perform logical reasoning: traverse the graphs in a path to make logical connections (A's father is B and B's father is C, hence C is the grandfather of A)
      • +
    +

    Compared to other storage types

    +
      +
    • Structured representation: far cry from unstructured representations like text data
      • +
    • Removes redundancy: compared to tabular data, there is no need to add mostly empty columns or rows to add new data
      • +
    • Query complex information: better than SQL for data where relationship matters more than individual data points (for example, in case you have to do lots of JOIN statements in a SQL query, which is inherently slow)
      • +
    +
    +

    How to use Knowledge Graph?

    +

    Knowledge graphs can be used for a large number of tasksā€Š-ā€Š be it for logical reasoning, explainable recommendations, complex analysis or just being a better way to store information. There are two very interesting examples which we will discuss briefly.

    +

    Google Knowledge Panel

    +
      +
    • Google search about a famous person, location or concepts return a knowledge panel on the right (as shown in the image below)
      • +
    • The panel contains a wide variety of information (description, education, born, died, quotes, etc) and interestingly in different formats--(text, image, dates, numbers, etc).
      • +
    • All this information can be stored in a KG and one such example is shown below. This showcase how easy it is to store information and also note how intuitive it is to just read and understand the fact from a KG.
      • +
    +
    +

    +

    Example of knowledge graph-based knowledge panel used by Google. [Right] the actual panel shown by google when you search for Einstein. [left] recreation of how we might store similar information in a KG. Source: by Author + Google.
    +

    +
    +

    Movie recommendation

    +
      +
    • Classical algorithms considered user-item interactions to generate recommendations. Over time, newer algorithms are considering additional information about the user as well as items, to improve the recommendations.
      • +
    • Below, we can see one KG which not only contains user-item (here movies) connections but also user-user interactions and item attributes. The idea is that provided all this additional information, we can make much more accurate and informed suggestions.
      • +
    • Without going into the exact algorithm (which we will later in the article), let's rationalize what recommendations could be generated.
      • +
    • "Avatar" could be recommended to, + - Bob: as it belongs to the Sci-fi genre same as Interstellar and Inception (which is already watched by Bob) + - Alice: as it is directed by James Cameron (Titanic)
      • +
    • "Blood Diamond" recommended to, + - Bob: as DiCaprio acted in Inception as well
      • +
    • This simple thought exercise should showcase how a lot of real-world interactions can be easily represented in form of facts using KG. And then we can leverage KG-based algorithms for a downstream use case like generating recommendations.
      • +
    +
    +

    +

    A sample knowledge graph for movie recommendation task. (guo2020survey)
    +

    +
    +
    +

    Open-Source Knowledge graphs

    +

    While there are several small-sized and domain-specific KGs, on the other hand, we also have many huge-sized and domain-agnostic KG that contains facts of all types and forms. Some of the famous open-source knowledge graphs are,

    +
      +
    • DBpedia: is a crowd-sourced community effort to extract structured content from the information created in various Wikimedia projects.
      • +
    • Freebase: a massive, collaboratively edited database of cross-linked data. Touted as ā€œan open shared database of the world's knowledgeā€. It was bought by Google and used to power its own KG. In 2015, it was finally discontinued.
      • +
    • OpenCyc: is a gateway to the full power of Cyc, one of the world's most complete general knowledge base and commonsense reasoning engine.
      • +
    • Wikidata: is a free, collaborative, multilingual, secondary database, collecting structured data to provide support for Wikipedia
      • +
    • YAGO: huge semantic knowledge base, derived from Wikipedia, WordNet and GeoNames.
      • +
    +
    +

    +

    Stats for some of the famous open source knowledge graphs (fƤrber2018knowledge)
    +

    +
    +
    +

    Create custom Knowledge graph

    +

    In spite of having several open-source KGs, we may have a requirement to create domain-specific KG for our use case. There, our base data (from which we want to create the KG), could be of multiple typesā€Š-ā€Štabular, graphical, or text blob. We will cover some steps on how to create KG from unstructured data like text, as it's relatively easier to convert structured data into KG using minimal domain knowledge and scripting. The complete process can be divided into two steps,

    +
      +
    • Facts creation: this is the first step where we parse the text (sentence by sentence) and extract facts in triplet format like <H, R, T>. As we are processing text, we can leverage pre-processing steps like tokenization, stemming, or lemmatization, etc to clean the text. Next, we want to extract the entities and relations (facts) from the text. For entities, we can use Named entity recognition (NER) algorithms. For relation, we can use sentence dependency parsing techniques to find the relationship between any pair of entities. Example article with code.
      • +
    • Facts selection: Once we have extracted several facts, the next obvious steps could be to remove duplicates and identify relevant facts that could be added to a KG. To identify duplicates, we can use entity and relation disambiguation techniques. The idea is to consolidate the same facts or elements of a fact, in case of repetitions. For example, "Albert Einstein" can also be written as "Albert E." or "A. Einstein" in the text, but in reality, they all refer to the same entity. Finally, we can have a comprehensive rule-based system that decides which triplet should be added to the KG or which one could be skipped based on factors like redundant information (A ā†’ sibling of ā†’ B is present, hence B ā†’ sibling of ā†’ A is redundant) or irrelevant information.
      • +
    +
    +

    +

    Steps involved in creating a custom knowledge graph. Source: Author + (Ji_2021)
    +

    +
    +
    +

    Ontology of Knowledge graph

    +
      +
    • An ontology is a model of the world (practically only a subset), listing the types of entities, the relationships that connect them, and constraints on the ways that entities and relationships can be combined. In a way, an ontology defines the rules on how the entities are connected within the world.Ā 
      • +
    • Resource Description Framework (RDF) and Web Ontology Language (OWL) are some of the vocabulary frameworks used to model ontology. They provide a common framework for expressing this information so it can be exchanged between applications without loss of meaning.
      • +
    +
    +

    +

    RDF schema triplets (informal). Source: Author + (rdf_primer)
    +

    +
    +
      +
    • RDF provides languages for creating ontology, which we will use to create a sample KG. Below you can see the KG creating script [on left] in Turtle language for the KG [on right]. Notice, at the top of the script, we are creating references to a lot of predefined ontologies, as there is no need to reinvent the wheel. Next, to create the facts (or triplets) of our KG we can follow the lines below the PREFIX commands.
      • +
    • Notice, each entity and relation has a unique identifier (their unique key or UID). Throughout the code, the same entity or relation should be referenced by the same UID. Next, using the predefined schemas, we can add facts for an entity (in graphical term, add a connecting edge and tail node to the head node). These facts could include another entity (refer by their UID), some text, date (in DateTime format), links, etc.
      • +
    +
    +

    +

    Script in Turtle language to create the sample knowledge graph. Source: Author + (rdf_primer)
    +

    +
    +
      +
    • Finally, once we have prepared the script (with ttl extensionā€Š-ā€Šfor scripts in Turtle language), that script contains the complete schema and definition of our KG. In itself, this may not be interesting, hence the file can be imported into any KG database for beautiful visualization and efficient querying.
      • +
    +
    +

    Hosting Knowledge graphs

    +

    There are two types of databases that can be used to store graphical information. The first is "property graphs" like Neo4j and OrientDB that does not support RDF file (out of the box) and have their own custom query language. On the other hand, we have "RDF triplet stores", that support RDF files and support query language like SPARQL that is universally used to query KG. Some of the most famous ones are (with open source version),Ā  +- GraphDB: a solution by Ontotext, that provides frontend (visualization) and backend (server) services to see and query hosted knowledge graphs.Ā  +- Virtuoso: a solution by OpenLinkSoftware, that provides backend services to query hosted KG. It also supports querying KG using a combination of SQL and SPARQL. On top of it, a lot of open-source KG like DBpedia are hosted on Virtuoso.

    +
    +

    Query a Knowledge graph

    +

    SPARQL

    +
      +
    • Once facts are created as RDF and hosted on an RDF triplet store like Virtuoso, we can query them to extract relevant information. SPARQL is an RDF query language that is able to retrieve and manipulate data stored in RDF format. An interesting read for more detail is Walkthrough Dbpedia And Triplestore.Ā 
      • +
    • Most of the RDF triple stores provide a visual SPARQL query page to fetch the relevant info. For our case, let us use one such visual query helper exposed by Wikidata (shown below). A sample query is shown, where we want to extract all entities that are instances of a house cat (we just want some cats šŸ±). As discussed before, each entity has a UID, hence relation <instance of> is represented as P31, and the entity <house cat> is represented as Q146. The query is quite simple to understand, as from lines 2 to 5, we are just trying to convey that we want any entity that is an instance of a house cat. As Wikidata contains data in multiple languages, line 6 is needed to filter results specific to the English language. The results (entities with their UID and some basic details) are shown below the query.
      • +
    +
    +

    +

    Query Knowledge graph using SPARQL language (wikidata_sparql_query_helper)
    +

    +
    +

    API

    +
      +
    • Open-source KG also exposes several ready-made APIs for frequently used queries. One such API is shown below (for Wikidata), which returns relevant information for a given entity. Below we can see the result of querying wbgetentities API for entity Q9141 which is the UID for the Taj Mahal.
      • +
    +
    +

    +

    Query Knowledge graph using available APIs (wikidata_api_services)
    +

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    + + + + + + + + + + + + + + \ No newline at end of file diff --git a/python/good_practices/index.html b/python/good_practices/index.html new file mode 100644 index 00000000..8077007b --- /dev/null +++ b/python/good_practices/index.html @@ -0,0 +1,2697 @@ + + + + + + + + + + + + + + + + + + Good practices - A Lazy Data Science Guide + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    Python Good Practices

    +

    Introduction

    +
      +
    • Writing code that works now is easy. Writing code that will work tomorrow is hard. Writing code that will work tomorrow and is intuitive enough for anyone to understand and followā€Šā€”ā€Šwell now we have hit the super hard stuff šŸ˜€. Observing several ML engineers and Data scientists working with me, I have noticed nearly all of them have their own unique style of coding. Well, don't get me wrong, subjectively is a good thing and I think it is what leads to innovations. That said while working in a team or even in open source collaboration, it helps to agree to a certain set of rules. And that's the idea behind this article, to provide python practitioners with a set of curated guidelines, from which they can pick and choose.
      • +
    • With that, let's cover some of the good practices, which will not only help us to create a working but also a beautiful piece of code šŸ˜€
      • +
    • To cover this topic, we will go through three parts,
        +
      1. Project structuring: ideas on how to organize your code
        2. +
      2. Code formatting: ideas on how to make your code easy to follow
        3. +
      3. Additional tips: a few things which will help you in the longer run
        4. +
      +
      • +
    +

    Project Structuring

    +
      +
    • In this part, we will basically talk about some good practices on how the complete python project can be structured. For this, we will look at two different possibilities, which anyone can choose based on how simple or complex their project is going to be.
      • +
    +

    Type 1: The Classic

    +
      +
    • This is the most basic format and yet gives the hint of organized structuring. This can be followed when our project consists of only a few scripts. The directory of a sample project could look something like this:
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    my_project             # Root directory of the project
+ā”œā”€ā”€ code               # Source codes
+ā”œā”€ā”€ input              # Input files
+ā”œā”€ā”€ output             # Output files
+ā”œā”€ā”€ config             # Configuration files
+ā”œā”€ā”€ notebooks          # Project related Jupyter notebooks (for experimental code)
+ā”œā”€ā”€ requirements.txt   # List of external package which are project dependency
+ā””ā”€ā”€ README.md          # Project README
+
    +
      +
    • As obvious from the names, folder code contains the individual modules (.py files), input and output contains the input and output files respectively, and notebook contains the .ipynb notebooks files we use for experimentation. Finally, config folder could contain parameters within yaml or json or ini files and can be accessed by the code module files using configparser.
      • +
    • requirements.txt contains a list of all external python packages needed by the project. One advantage of maintaining this file is that all of these packages can be easily installed using pip install -r requirements.txt command. (No need of manually installing each and every external packages!). One example requirements.txt file is shown below (with package_name==package_version format),
      • +
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    BeautifulSoup==3.2.0
+Django==1.3
+Fabric==1.2.0
+Jinja2==2.5.5
+PyYAML==3.09
+Pygments==1.4
+
    +
      +
    • Finally, README.MD contains the what, why and how of the project, with some dummy codes on how to run the project and sample use cases.
      • +
    +

    Type 2: Kedro

    +
      +
    • Kedro is not a project structuring strategy, it's a python tool released by QuantumBlack Labs, which does project structuring for you šŸ˜Ž. On top of it, they provide a plethora of features to make our project organization and even code execution process super-easy, so that we can truly focus on what matters the most -- the experimentations and implementations!
      • +
    • Their project structure is shown below. And btw, we can create a blank project by running kedro new command (don't forget to install kedro first by pip install kedro)
      • +
    +
    get-started         # Parent directory of the template
+ā”œā”€ā”€ conf            # Project configuration files
+ā”œā”€ā”€ data            # Local project data (not committed to version control)
+ā”œā”€ā”€ docs            # Project documentation
+ā”œā”€ā”€ logs            # Project output logs (not committed to version control)
+ā”œā”€ā”€ notebooks       # Project related Jupyter notebooks (can be used for experimental code before moving the code to src)
+ā”œā”€ā”€ README.md       # Project README
+ā”œā”€ā”€ setup.cfg       # Configuration options for `pytest` when doing `kedro test` and for the `isort` utility when doing `kedro lint`
+ā””ā”€ā”€ src             # Project source code
+
    +
      +
    • While most of the directories are similar to other types, a few points should be noted. Kedro's way of grouping different modules is by creating different "pipelines". These pipelines are present within src folder, which in turn contains the module files. Furthermore, they have clear segregation of individual functions which are executed - these are stored within nodes.py file, and these functions are later connected with the input and output within pipeline.py file (all within the individual pipeline folder). Kedro also segregates the code and the parameters, by storing the parameters within conf folder.
      • +
    • Apart from just helping with organizing the project, they also provide options for sequential or parallel executions. We can execute individual functions (within nodes.py), or individual pipelines (which are a combination of functions), or the complete project at one go. We can also create doc of the complete project or compile and package the project as a python .whl file, with just a single command run. For more details, and believe me we have just touched the surface, refer to their official documentation.
      • +
    +

    Code formatting

    +
      +
    • With a top-down approach, let's first have a look at a neat piece of code. We will discuss individual aspects of the code in more detail later. For now, just assume if someone asks you to do some scripting, what an ideal piece of code file should look like.
      • +
    • Following code is take from csv_column_operations.py module file. It was generated for the prompt: "write a function which takes csv file as input and returns the sum of a column".
      • +
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    """Return sum of a column from CSV file
+
+A module with "perform_column_sum" main function that computes and return sum 
+of an user defined numerical column from an csv file passed as pandas dataframe
+
+Author: Mohit Mayank <mohitmayank1@gmail.com>
+
+Created: 4th August, 2021
+"""
+
+# imports
+import sys          # to exit execution
+import pandas as pd # to handle csv files
+
+# modules
+def perform_column_sum(csv_file, column_to_perform_operation_on, operation_to_perform='sum'):
+    """Performs numerical operation over a column of pandas dataframe
+
+    Parameters
+    -------------
+    csv_file: pandas.DataFrame
+        the csv file which contains the column on which operation is to be performed
+
+    column_to_perform_operation_on: string
+        the column name (must be present in the csv file)
+
+    operation_to_perform: string
+        which operation to be performed on the column. Supported: ['sum']
+
+    Returns
+    --------
+    column_operation_result: numeric
+        the result after applying numeric operation on csv column
+    """
+    # Step 1: data check and break
+    # check 1: column should be present in the csv_file
+    check_flag_col_presence = column_to_perform_operation_on in csv_file.columns
+    # break the code if incorrect data is provided
+    if not check_flag_col_presence:
+        print(f"Column {column_to_perform_operation_on} is absent from the csv_file! Breaking code!")
+        sys.exit()
+
+    # check 2: all values in the column should be of type numeric
+    check_flag_data_type = pd.to_numeric(csv_file[column_to_perform_operation_on], errors='coerce').notnull().all()
+    # break the code if incorrect data is provided
+    if not check_flag_data_type:
+        print(f"One or more values in column {column_to_perform_operation_on} is not numeric! Breaking code!")
+        sys.exit()
+
+    # Step 2: extract the column
+    column_data = csv_file[column_to_perform_operation_on]
+
+    # Step 3: Perform the operation
+    column_operation_result = None
+    if operation_to_perform == 'sum':
+        column_operation_result = sum(column_data)
+
+    # Step 4: return the result
+    return column_operation_result
+
+# run when file is directly executed 
+if __name__ == '__main__':
+    # create a dummy dataframe
+    df = pd.DataFrame({'col1': [1, 2, 3], 'col2': ['a', 'a', 'a']})
+    # call the function
+    answer = perform_column_sum(df, 'col1', 'sum')
+    # match if the answer is correct
+    assert(answer==6)
+
    +
    +

    Note

    +

    Some might argue why do such an overkill for a simple piece of code. Note, it's a dummy example. In real life, you will develop more complex pieces of codes and hence it become quite important that we understand the gist.

    +
    +
      +
    • Now let's take a deeper dive into the individual aspect of the above code.
      • +
    +

    Module structure

    +
      +
    • A module is a python file with .py extension that contains the executable code or functions or classes, etc.
      • +
    • Usually, we start the module with module definition, which is an area where we provide some basic details of the module. We can do so using the following template (and it can be easily compared to a real code shown above)
      • +
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    """<Short description>
+
+<Long description>
+
+Author: <Name> <email>
+
+Created: <date>
+"""
+
    +
      +
    • Next, we should clearly segregate the parts of the module such as imports, code area, etc using comment lines.
      • +
    • Finally, at the bottom, we could include some examples on how to run the code. Including these scripts within if __name__ == '__main__': makes sure that they only run when the file is directly executed (like python csv_column_operations.py). So these pieces of code doesn't run when you say import the module in another script.
      • +
    +

    Functions structure

    +
      +
    • Functions are the basic block of code that performs a specific task. A module consists of several functions. To inform the user what a particular block of code does, we start the function with a function definition. A sample template is provided below,
      • +
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    """Description
+
+Paramters
+---------
+<parameter_1>: <data_type>
+    <parameter_1_description>
+
+Returns
+---------
+<output_1>: <data_type>
+    <output_1_description>
+"""
+
    +
      +
    • After this, we can start adding the relevant code lines. Make sure to separate different logical blocks of code within the functions using comments.
      • +
    • One important thing to handle at the start of the coding section is to check the parameters and input data for some data type or data content related basic issues. A majority of code break happens due to silly mistakes like when someone provides wrong input, in which case we should print or log warning message and gracefully exit. The above same code contains two such preliminary but important checks inside the step 1 section.
      • +
    +

    Naming convention

    +
      +
    • There are several formatting conventions that we can follow, like Camel Case, Snake case, etc. It's quite subjective and depends on the developer. Below are some examples of naming different entities of a python code (taken from PIP8 conventions - with some modifications) šŸ˜‡,
        +
      • Module name: Modules should have short, all-lowercase names (ex: csv_column_operations.py)
        • +
      • Function or method name: Function names should be lowercase, with words separated by underscores as necessary to improve readability. Also, don't forget to add your verbs! (ex: perform_column_sum())
        • +
      • Variable name: Similar to function name but without the verbs! (ex: list_of_news)
        • +
      • Class name: Class names should normally use the CapWords convention. (ex: FindMax)
        • +
      • Constant name: Constants are usually defined on a module level and written in all capital letters with underscores separating words. (ex: MAX_OVERFLOW and TOTAL).
        • +
      +
      • +
    +

    Add comments

    +
      +
    • PEP-8 defines three types of comments,
        +
      • Block comments: which is written for a single or a collection of code lines. This can be done either when you want to explain a set of lines or just want to segregate code. In the above example, you can see # Step {1, 2, 3} used as segregation comment and # run when file is directly executed used to explain a set of code lines.
        • +
      • Inline comments: which are added on the same line as the code. For example, see how # to handle csv files is used to justify the pandas package import. PEP-8 suggests using inline comments sparingly.
        • +
      • Documentation Strings: these are used for documentation for module, functions or classes. PEP-257 suggests using multiline comment for docstring (using """). An example of module and function docstrings (short for documentation strings) is provided in the sample code above.
        • +
      +
      • +
    • We should be as descriptive in our comments as possible. Try to separate functional sections of your code, provide explanations for complex code lines, provide details about the input/output of functions, etc. How do you know you have enough comments? - If you think someone with half your expertise can understand the code without calling you middle of the night! šŸ˜¤
      • +
    +

    Indentations - Tabs vs Spaces

    +
      +
    • Frankly, I am only going to touch this topic with a long stick šŸ§¹. There are already several articles, reddit threads and even tv series (Silicon valley šŸ“ŗ) where this topic has been discussed a lot!
      • +
    • Want my 2 cents? Pick any modern IDE (like VSCode, Sublime, etc), set indentations to tabs, and set 1 tab = 4 spaces. Done šŸ˜
      • +
    +

    Additional tips

    +
      +
    • Till now we have discussed how to either structure the project or format the code. Next, we will cover a generic set of good practices which will save you some pain down the line šŸ˜¬
      • +
    +

    Logging

    +
      +
    • Instead of printing statements in the console which is temporary (do a cls and poof it's gonešŸ’Ø), a better idea is to save these statements in a separate file, which you can always go back and refer to. This is logging šŸ“œ
      • +
    • Python provides an inbuilt function for logging. By referring to the official how to, logging to a file is super easy,
      • +
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    # import
+import logging
+# config the logging behavior
+logging.basicConfig(filename='example.log',level=logging.DEBUG)
+# some sample log messages
+logging.debug('This message should go to the log file', exc_info=True)
+logging.info('So should this', exc_info=True)
+logging.warning('And this, too', exc_info=True)
+# we add "exc_info=True" to capture the stack trace
+
    +
      +
    • Note, there is a hierarchical levelling of logs to segregate different severity of logs. In the example shown above, the level parameter denotes the minimal level that is tracked and hence saved to the file. As per the official how to, these are the different logging levels with some details about when to use which (in increasing order of severity),
      • +
    + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    LevelWhen itā€™s used
    DEBUGDetailed information, typically of interest only when diagnosing problems.
    INFOConfirmation that things are working as expected.
    WARNINGAn indication that something unexpected happened, or indicative of some problem in the near future (e.g. ā€˜disk space lowā€™). The software is still working as expected.
    ERRORDue to a more serious problem, the software has not been able to perform some function.
    CRITICALA serious error, indicating that the program itself may be unable to continue running.
    +
      +
    • While the above code is good for normal testing, for production you might want to have more control -- like formatting the output slightly differently (formatter) or have multiple places to publish logs (handlers). One such use case is convered below, where we want to log to console as well as a file in a detailed json format.
      • +
    +
    # import
+import sys
+import logging
+import json_log_formatter
+
+# create formatter (using json_log_formatter)
+formatter = json_log_formatter.VerboseJSONFormatter()
+
+# create two handlers (console and file)
+logger_sys_handler = logging.StreamHandler(sys.stdout)
+logger_file_handler = logging.FileHandler(filename='log.json')
+
+# perform the same formatting for both handler
+logger_sys_handler.setFormatter(formatter)
+logger_file_handler.setFormatter(formatter)
+
+# get the logger and add handlers
+logger = logging.getLogger('my_logger')
+logger.addHandler(logger_sys_handler)
+logger.addHandler(logger_file_handler)
+
+# set level
+logger.setLevel(logging.INFO)
+
    +

    Documentation

    +
      +
    • Documentation of the code is an absolute must, if you are planning to maintain the code or hand it over to someone else in the foreseeable future. Just ask any developer about their excitement on finding a ready-made and well curated documentation for a package they are planning to use! On the other hand, it looks quite difficult to create one yourself, isn't it? I mean, look at the beautiful docs of sklearn or pandas. šŸ˜®
      • +
    • Well, sorry for the scare there, but actually it's quite simple šŸ˜‰. Remember all the function and module docstring and the formatting we followed before? As it turns out, we can leverage many open source tools like pydoc and sphinx to create full-fledged HTML documentations! Going into practical details is out of scope of this article, but it is fairly easy to follow the "how to" steps of both the packages and get your doc ready.
      • +
    • One last thing, if you are using Kedro, this process is even simpler. All you have to do is run one command - kedro build-docs --open to create the documentation and automatically open it in your default browser!
      • +
    +

    Virtual environment

    +
      +
    • Virtual environments (VE) can be thought of as the local copy of python environment, created specifically for a project. This local copy is like a blank slate, as any required package can be installed here separately. It is extremely important to create a new virtual environment for any new project, because,
        +
      • each project has its own dependency tree (one package with a specific version needs another package to run, with its own specific version)
        • +
      • while developing a project we may need to downgrade or upgrade a package, which if done on the base python environment, will affect your system!
        • +
      • hence, a separate copy of python (VE), where you install whatever you want, seems to be the most logical solution.
        • +
      +
      • +
    • Using VE basically requires two steps,
        +
      • Create a VE: this can be done by running command python3 -m venv tutorial-env at the project root directory. (note, tutorial-env is the name of the VE, you can use rename it to anything)
        • +
      • Activate VE: this can be done by running command tutorial-env\Scripts\activate.bat on Windows and source tutorial-env/bin/activate on Unix or MacOS.
        • +
      +
      • +
    • And that's it! Install, uninstall, upgrade or downgrade whatever you want!
      • +
    +
    +

    Note

    +

    Remember to switch to another VE when you start working on another project or/and to deactivate the VE when you want to move to base VE.

    +
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    References

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    Python Snippets

    +

    Add numpy array as Pandas column

    + +
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    import numpy as np
+import pandas as pd
+import scipy.sparse as sparse
+
+df = pd.DataFrame(np.arange(1,10).reshape(3,3))
+arr = sparse.coo_matrix(([1,1,1], ([0,1,2], [1,2,0])), shape=(3,3))
+df['newcol'] = arr.toarray().tolist()
+print(df)
+
    +

    Get members of python object

    +
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    # import
+import networkx as nx
+from inspect import getmembers
+
+# Fetching the name of all drawing related members of NetworkX class.
+for x in getmembers(nx):
+    if 'draw' in x[0]:
+        print(x)
+
    +

    Install packages using code

    +
      +
    • References from here
      • +
    +
     1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
    # import
+import sys
+import subprocess
+
+# install package function
+def install(package):
+    subprocess.check_call([sys.executable, "-m", "pip", "install", package])
+
+# example
+# install("pathfinding")
+
    +

    Python packaging

    +
    1
+2
+3
+4
+5
+6
    # clean the previous build files
+python setup.py clean --all
+# build the new distribution files
+python setup.py sdist bdist_wheel
+# upload the latest version to pypi
+twine upload --skip-existing dist/*
+
    +

    Python virtual environment

    +
    1
+2
+3
+4
+5
+6
+7
    # Create the virtual environment in the current directory
+python -m venv projectnamevenv
+# pick one of the below based on your OS (default: Linux)
+# activate the virtual environment - Linux
+.projectnamevenv\Scripts\activate
+# activate the virtual environment - windows
+# .\\projectnamevenv\\Scripts\\activate.bat
+
    +

    Where is my Python installed?

    +
      +
    • To know the exact location of where the python distribution is installed, follow the steps as suggested here
      • +
    +
    1
+2
+3
    import os
+import sys
+print(os.path.dirname(sys.executable))
+
    +

    Get list of installed Python packages

    +
      +
    • To know exactly which packages are current installed (and their version) in your VE, try
      • +
    +
    1
    pip list --format=freeze > reqirements.txt
+
    +

    Find files or folders

    +
      +
    • glob is a very efficient way to extract relevant files or folders using python.
      • +
    • A few example are shown below.
      • +
    +
     1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
    # import
+from glob import glob
+
+# Ex 1: fetch all files within a directory
+glob("../data/01_raw/CoAID/*")
+
+# Ex 2: fetch all files within a directory with a pattern 'News*COVID-19.csv'
+glob("../data/01_raw/CoAID/folder_1/News*COVID-19.csv")
+
+# Ex 2: fetch all files within multiple directories which
+#       follow a pattern 'News*COVID-19.csv'
+glob("../data/01_raw/CoAID/**/News*COVID-19.csv")
+
    +

    Increase the pandas column width in jupyter lab or notebook

    +
      +
    • Most of the times, we have text in a dataframe column, which while displaying gets truncated.
      • +
    • One way to handle this to increase the max width of all columns in the dataframe (as shown below)
      • +
    +
    1
+2
    import pandas as pd
+pd.set_option('max_colwidth', 100) # increase 100 to add more space for bigger text
+
    +

    Parse date and time from string

    +
      +
    • There are basically 2 ways to do this, (1) Trust machine šŸ¤–: for majority of the 'famous' date writing styles, you can use dateparser package that automatically extracts the date and parse it into datetime format.
      • +
    +
    1
+2
+3
+4
+5
+6
    # import
+from dateparser import parse
+# parse
+text = 'October 05, 2021'
+dateparser.parse(text)
+# output - datetime.datetime(2021, 10, 5, 0, 0)
+
    +

    which will return output ``.

    +
      +
    • Another way is (2) DIY šŸ’Ŗ: if you can create the date time format), you can use datetime package directly.
      • +
    +
    1
+2
+3
+4
+5
+6
+7
    # import
+from datetime import datetime
+# parse
+text = 'October 05, 2021'
+date_format = '%B %d, %Y'
+datetime.strptime(text, date_format)
+# output - datetime.datetime(2021, 10, 5, 0, 0)
+
    +

    Bulk document insert in MongoDB

    +
      +
    • While pymongo provides insert_many function for bulk insert, it breaks in case of duplicate key. We can handle it with following function, which in its worse case is similar to insert_one, but shines otherwise.
      • +
    +
     1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13
+14
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+17
+18
+19
+20
+21
+22
+23
+24
+25
+26
+27
+28
+29
+30
    # import
+import pymongo
+# function
+def insert_many_wrapper(df, col):
+    """bulk insert docs into the MongoDB while handling duplicate docs
+
+    Parameters
+        - df (pandas.dataframe): row as a doc with `_id` col
+        - col (pymongo.db.col): pymongo collection object in which insertion is to be done
+    """
+    # make a copy and reset index
+    df = df.copy().reset_index(drop=True)
+    # vars
+    all_not_inserted = True
+    duplicate_count = 0
+    ttl_docs = df.shape[0]
+    # iterate till all insertions are done (or passed in case of duplicates)
+    while all_not_inserted:
+        # try insertion
+        try:
+            col.insert_many(df.to_dict(orient='records'), ordered=True)
+            all_not_inserted = False
+        except pymongo.errors.BulkWriteError as e:
+            id_till_inserted = e.details['writeErrors'][0]['keyValue']['_id']
+            index_in_df = df[df['_id']==id_till_inserted].index[0]
+            print(f"Duplicate id: {id_till_inserted}, Current index: {index_in_df}")
+            df = df.loc[index_in_df+1:, :]
+            duplicate_count += 1
+    # final status
+    print(f"Total docs: {ttl_docs}, Inserted: {ttl_docs-len(duplicate_count)}, Duplicate found: {len(duplicate_count)}")
+
    +

    Search top StackExchange questions

    +
      +
    • Stack Exchange exposes several API endpoints to process the questions, answers or posts from their website.
      • +
    • A simple implementation to search and download the latest (from yesterday) and top voted questions is shown below. For more such API endpoints, consult their official doc.
      • +
    +
     1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13
+14
+15
+16
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+18
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+22
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+32
+33
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+35
+36
+37
+38
+39
+40
+41
+42
    """
+Request StackExchange API to get the top 10 most voted 
+    questions and their answer from yesterday
+"""
+
+import requests
+import json
+import datetime
+import time
+
+# Get the current date
+today = datetime.date.today()
+yesterday = today - datetime.timedelta(days=1)
+
+# Get the current time
+now = datetime.datetime.now()
+
+# Get the time of yesterday
+yesterday_time = now.replace(day=yesterday.day, month=yesterday.month, year=yesterday.year)
+
+# Convert the time to epoch time
+yesterday_epoch = int(time.mktime(yesterday_time.timetuple()))
+
+# Get the time of today
+today_time = now.replace(day=today.day, month=today.month, year=today.year)
+
+# Convert the time to epoch time
+today_epoch = int(time.mktime(today_time.timetuple()))
+
+# Get the top 10 most voted questions and their answer from yesterday
+url = "https://api.stackexchange.com/2.2/questions?pagesize=10&fromdate=" + \
+    str(yesterday_epoch) + "&todate=" + str(today_epoch) + \
+        "&order=desc&sort=votes&site=stackoverflow"
+
+# Get the response from the API
+response = requests.get(url)
+
+# Convert the response to JSON
+data = response.json()
+
+# Print the data
+print(json.dumps(data, indent=4))
+
    +

    Export complete data from ElasticSearch

    +
      +
    • Due to several memory and efficiency related limitations, it is non-trivial to export complete data from ElasticSearch database.
      • +
    • That said, it is not impossible. PFB an scan based implementation that does the same for a dummy test_news index.
      • +
    +
     1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13
+14
+15
+16
+17
+18
+19
+20
+21
    # import 
+import pandas as pd
+from elasticsearch import Elasticsearch
+from elasticsearch.helpers import scan
+from tqdm import tqdm
+
+# config
+index_name = 'test_news'
+db_ip = 'http://localhost:9200'
+
+# connect to elasticsearch
+es = Elasticsearch([db_ip])
+
+# fetch all data from elasticsearch
+scroll = scan(es, index=index_name, query={"query": {"match_all": {}}})
+data = []
+for res in tqdm(scroll):
+    data.append(res['_source'])
+
+# convert to pandas dataframe and export as csv
+pd.DataFrame(data).to_csv("news_dump.csv", index=False)
+
    +

    Convert python literals from string

    +
      +
    • While I/O from database or config files, we may get some literals (ex list) in form of string, wherein they maintain their structure but the type. We can use ast package to convert them back to their correct type.
      • +
    • Quoting the documentation, "With ast.literal_eval you can safely evaluate an expression node or a string containing a Python literal or container display. The string or node provided may only consist of the following Python literal structures: strings, bytes, numbers, tuples, lists, dicts, booleans, and None."
      • +
    +
    1
+2
+3
+4
+5
+6
    # import
+import ast
+# list literal in string format
+list_as_string = '["a", "b"]'
+# convert
+list_as_list = ast.literal_eval(list_as_string) # Output: ["a", "b"]
+
    +

    Plotly visualization on Pandas dataframe

    +
      +
    • If you want to visualize your pandas dataframe using plotly package, there is no need to use the package explicitly. It can be done right from the pandas dataframe object, with just a couple of lines of code as shown below:
      • +
    +
    1
+2
+3
+4
    # set the backend plotting option
+pd.options.plotting.backend = "plotly"
+# do a normal plot!
+pd.DataFrame(result).plot(x='size', y='mean')
+
    +

    Conda cheat sheet

    +
      +
    • Conda an open-source, cross-platform, language-agnostic package manager and environment management system. Therein again we have multiple varieties,
        +
      • Miniconda: it's a minimilistic package with python, conda and some base packages.
        • +
      • Anaconda: it's a bigger package with all the things in Miniconda plus around 150 high quality packages.
        • +
      +
      • +
    +
      +
    • While the complete documentation can be accessed from here, some important snippets are:
      • +
    +
     1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13
+14
+15
+16
+17
+18
    # list all supported python versions
+conda search python
+
+# create a new conda environment (with new python version)
+# note, py39 is the name of the env
+conda create -n py39 python=3.9
+
+# list all of the environments
+conda info --envs
+
+# activate an environment
+conda activate py39 # where py39 is the name of the env
+
+# deactivate the current environment
+conda deactivate
+
+# delete an environment
+conda env remove -n py39
+
    +

    Requirement files

    +
      +
    • Requirement file is a collection of packages you want to install for a Project. A sample file is shown below,
      • +
    +

    # fine name requirements.txt
+package-one==1.9.4
+git+https://github.com/path/to/package-two@41b95ec#egg=package-two
+package-three==1.0.1
+package-four
+
    +- Note three ways of defining packages, (1) with version number, (2) with github source and (3) without version number (installs the latest). Once done, you can install all these packages at one go by pip install -r requirements.txt

    +

    Pandas Groupby Function

    +
      +
    • Pandas can be utilised for fast analysis of categorical data using groupby. Let's have a look.
      • +
    +
     1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13
+14
+15
+16
+17
+18
    #import
+import numpy as np
+import pandas as pd
+
+# load a dummy df
+df = pd.Dataframe('dummy.csv') # example below
+## Name | Gender   | Salary
+## Ravi | Male     | $20,000
+## Sita | Female   | $40,000
+## Kito | Female   | $11,000
+
+# perform groupby to get average salary per gender
+## Option 1
+df.groupby(['Gender']).agg({'Salary': [np.mean]})
+## Option 2
+df.groupby(['Gender']).mean()
+## Option 3
+df.groupby(['Gender']).apply(lambda x: x['Salary'].mean())
+
    +

    Save and Load from Pickle

    +
      +
    • Pickle can be used to efficiently store and load python objects and data. Refer StackOverflow
      • +
    +
     1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13
+14
+15
+16
    # import
+import pickle
+
+# create data
+a = {'a': 1, 'b': [1,2,3,4]}
+
+# save pickle
+with open('filename.pickle', 'wb') as handle:
+    pickle.dump(a, handle, protocol=pickle.HIGHEST_PROTOCOL)
+
+# load pickle
+with open('filename.pickle', 'rb') as handle:
+    b = pickle.load(handle)
+
+# check
+assert print(a == b)
+
    +

    Download Youtube video

    +
      +
    • Youtube video can be downloaded using the pytube package. Here is an example.
      • +
    +
     1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
    # import
+from pytube import YouTube
+
+## var: link to download
+video_url = "https://www.youtube.com/watch?v=JP41nYZfekE"
+
+# create instance
+yt = YouTube(video_url)
+
+# download
+abs_video_path = yt.streams.filter(progressive=True, file_extension='mp4').order_by('resolution').desc().first().download()
+## print(f"Video downloaded at {abs_video_path}")    
+
    +

    Machine Translation

    +
      +
    • EasyNMT lets you perform state-of-the-art machine translation with just 3 lines of python code!
      • +
    • It supports translation between 150+ languages and automatic language detection for 170+ languages. Pre-trained machine translation models are auto-downloaded and can perform sentence and document translations!
      • +
    +
    1
+2
+3
+4
+5
+6
+7
+8
+9
    # import
+from easynmt import EasyNMT
+
+# load model
+model = EasyNMT('opus-mt')
+
+#Translate a single sentence to German
+print(model.translate('This is a sentence we want to translate to German', target_lang='de'))
+## Output: Dies ist ein Satz, den wir ins Deutsche Ć¼bersetzen wollen
+
    +

    Pandas read excel file

    +
      +
    • While pandas is quite famous for CSV analysis, it can be used to read and process Excel files as well. Here are some snippets,
      • +
    +
     1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13
    # import
+import pandas as pd
+
+# if you just want to read one sheet, by default it reads the first one. 
+df = pd.read_excel("file.xlsx", sheet_name="Page1")
+
+# if you want to get the names of sheet and do more selective reading
+excel_data = pd.ExcelFile("file.xlsx")
+# get the sheet names
+print(excel_data.sheet_names)
+# read one sheet (decide using last print result)
+sheet_name = '..' 
+df = excel_data.parse(sheet_name)
+
    +

    Send Slack Messages

    +
      +
    • One of the easiest way to send Slack message is via unique Incoming Webhook.
      • +
    • Basically, you need to create a Slack App, register an incoming webhook with the app and whenever you want to post a message - just send a payload to the webhook. For more details on setup, you can refer the official page
      • +
    • Once done, you just need to send the message like shown below,
      • +
    +
     1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
    # import requests (needed to connect with webhook)
+import requests
+# func
+def send_message_to_slack(message):
+    # set the webhook
+    webhook_url = "...enter incoming webhook url here..."
+    # modify the message payload
+    payload = '{"text": "%s"}' % message
+    # send the message
+    response = requests.post(webhook_url, payload)
+# test
+send_message_to_slack("test")
+
    + + + +
    + +
    +
    + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + \ No newline at end of file diff --git a/python/scraping_websites/index.html b/python/scraping_websites/index.html new file mode 100644 index 00000000..cfd4cffc --- /dev/null +++ b/python/scraping_websites/index.html @@ -0,0 +1,2293 @@ + + + + + + + + + + + + + + + + + + Scraping websites using Scrapy - A Lazy Data Science Guide + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
    + + + + +
    + + +
    + +
    + + + + + + + + + +
    +
    + + + +
    +
    +
    + + + + + + +
    +
    +
    + + + +
    +
    +
    + + + +
    +
    +
    + + +
    +
    + + + + + + + + +

    Scraping websites using Scrapy

    +

    Introduction

    +
      +
    • Scraping is the process of traversing and collecting data from the web pages. People scrap websites for many reasons -- be it to get information about companies or fetch latest news or get stock prices informations or just create a dataset for the next big AI model šŸ˜Ž
      • +
    • In this article, we will be using Scrapy to scrape data from the Devgan website that hosts details of difference sections in Indian Penal Code (IPC). I think this example provides the perfect blend of basic and advanced scraping techniques. So let's get started!
      • +
    +
    +

    Tip

    +

    The complete code is also available on GitHub

    +
    +

    Understanding the website

    +
      +
    • Before we even start scraping, we need to understand the structure of the website. This is very important, as we want to (1) get an idea of what we want to scrap and (2) where those data are located.
      • +
    +
    +

    +

    The flow of scraping section descriptions from Devgan website

    +
    +
      +
    • Our goal is to scrap the description for each section in IPC. As per the website flow above, the complete process can be divided into two parts,
        +
      • First, we need to traverse to the main page and extract the link of each sections.
        • +
      • Next, we need to traverse to each individual section page and extract the description details present there.
        • +
      +
      • +
    +

    Data extraction methods

    +
      +
    • Now, let's also look into different methods exposed by Scrapy to extract data from the web pages. The basic idea is that scrapy downloads the web page source code in HTML and parse it using different parsers. For this, we can either use XPaths or CSS selectors. The choice is purely up to us.
      • +
    • We can begin with the main page and try to find the link of each section. For this, you can open inspect option from your browser by right clicking on any of the sections and select inspect. This should show you the source code. Next, try to find the position of the tag where each section is defined. Refer the image below, and you can see that each section is within <a> tag inside the <div id="content"> tag. The href component will give you the link of the section and the <span> tag inside gives the section name.
      • +
    +
    +

    +

    Inspecting the source code of the first relevant page in Devgan website

    +
    +
      +
    • To try out the extraction, we can utilize the interpreter functionality of Scapy. For that just activate your Scrapy VM and type scrapy shell '{website-link}', here it will be scrapy shell http://devgan.in/all_sections_ipc.php. This opens a playground where we can play around with response variable to experiment with different extraction queries.
      • +
    • To extract the individual sections we can use CSS query - response.css('div#content').css('a'). Note, here we define the {tag_type}#{id} as the first CSS query and then use another CSS query - a. This will give us a list of all the <a> tags inside the <div id="content"> tag.
      • +
    • Now from within section, to extract the title, we can use CSS query - section.css('span.sectionlink::text').extract(). For this to work, you should save the last query as section variable.
      • +
    • Similar approach can be applied to extract the description from the next page. Just re-run the shell with one of the section's link and try out building CSS query. Once you have all the queries ready, we can move on to the main coding part ā˜®
      • +
    +
    +

    Note

    +

    You can refer the Scrapy official doc for more details on creating CSS or XPath queries.

    +
    +

    Setup Scrapy project

    +
      +
    • First, let us install the Scapy package using pip. It can be easily done by running the following command in the terminal: pip install scrapy. Do make sure to create your own virtual environment (VE), activate it and then install the package in that environment. For confusion regarding VE, refer my snippets on the same topic.
      • +
    • Next, let us setup the Scrapy project. Go to your directory of choice and run the command scrapy startproject tutorial. This will create a project with folder structure as shown below. We can ignore most of the files created here, our main focus will be on the spiders/ directory.
      • +
    +
    tutorial/
+    scrapy.cfg            # deploy configuration file
+    tutorial/             # project's Python module, you'll import your code from here
+        __init__.py
+        items.py          # project items definition file
+        middlewares.py    # project middlewares file
+        pipelines.py      # project pipelines file
+        settings.py       # project settings file
+        spiders/          # a directory where you'll later put your spiders
+            __init__.py
+
    +
    +

    Note

    +

    The above folder structure is taken from the Scrapy Official Tutorial

    +
    +

    Create your Spider

    +
      +
    • Usually we create one spider to scrap one website. For this example we will do exactly the same for Devgan website. So let's create a spider spiders/devgan.py. The code is shown below,
      • +
    +
     1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13
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+30
+31
+32
+33
+34
+35
    # import
+import scrapy
+
+# function
+class DevganSpider(scrapy.Spider):
+    name = "devgan"
+    allowed_domains = ["devgan.in"]
+
+    def start_requests(self):
+        urls = [
+            'http://devgan.in/all_sections_ipc.php',
+        ]
+        for url in urls:
+            yield scrapy.Request(url=url, callback=self.parse_mainpage)
+
+    def parse_mainpage(self, response):
+        # identify the links to the individual section pages
+        sections = response.css('div#content').css('a')#.getall()
+        # for each section
+        for section in sections:
+            # loc var
+            loc = {
+                'title' : section.xpath('@title').extract(),
+                'link' : 'http://devgan.in' + section.xpath('@href').extract()[0],
+                'section': section.css('span.sectionlink::text').extract(),
+            }
+            # traverse again and extract the description
+            yield scrapy.Request(loc['link'], callback=self.parse_section, 
+                    cb_kwargs=dict(meta=loc))
+
+    def parse_section(self, response, meta):
+        # extract the description
+        meta['description'] = " ".join(response.css('tr.mys-desc').css('::text').extract())
+        # return
+        return meta
+
    +
      +
    • Now lets us try to understand the code line by line,
        +
      • Line 2: Importing the scrapy package.
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      • Line 5: Defining the spider class that inherits the scrapy.Spider class.
        • +
      • Line 6-7: We define the name of the spider and allow the spider to crawl the domain devgan.in.
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      • Line 9-14: We define the start_requests method. This method is called when the spider is executed. It calls the scraping function for each url to scrap, it is done using scrapy.Request function call. For each url, the scraping function set within callback parameter is called.
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      • Line 16: We define the scraping function parse_mainpage for the main page. Note, this function receives response as an argument, that contains the source code from the server for the main page url.
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      • Line 18-29: We start with identifying the links to the individual section pages, store them in sections, and call the scraping function parse_section for each section. Before that, we also extract the title, link and section name from the main page using the queries we created before. One point to remember, this particular example is little complex as we want to traverse further inside into the section pages. For this, we again call the scrapy.Request for the individual section links. Finally, we want to pass the data collected form this page to the section page, as we will consolidate all data for individual section there and return it. For this, we use cb_kwargs parameter to pass the meta data to the next function.
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      • Line 31-35: We extract the description from the section page using the CSS query. We add description detail to the metadata and return the complete data that is to be persisted.
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    Executing the spider

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    • To run the spider, traverse to the root directory and execure the following command, scrapy crawl devgan -O sections_details.csv -t csv. Here, devgan is the name of the spider we created earlier, -O is used to set the output file name as sections_details.csv. -t is used to define the output format as csv. This will create the csv file with all details of the sections as separate columns as shown below (only 2 rows)
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    titlelinksectiondescription
    IPC Section 1...http://...Section 1This Act shall be called the Indian Penal Code...
    IPC Section 2...http://...Section 2Every person shall be liable to punishment un....
    +

    And that's it! Cheers! šŸ˜„

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    Interview Questions

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    • Here are some questions and their answers to make you ready for your next interview. Best of luck šŸ‘‹
      • +
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    Explain Reinforcement learning (RL) in deep learning.

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    Reinforcement learning is a type of machine learning where an agent learns to make decisions by interacting with an environment. The agent receives rewards or penalties based on its actions, and the goal is to learn a policy that maximizes the total reward. Deep reinforcement learning combines reinforcement learning with deep neural networks, allowing the agent to make decisions based on complex, high-dimensional input such as images or speech.

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    What is the use of the deep reinforcement learning in machine learning?

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    Deep reinforcement learning (DRL) is a type of reinforcement learning that uses deep neural networks as function approximators to learn policies or value functions. DRL allows for the use of complex, high-dimensional observations such as images or speech and can be used for tasks such as playing games, controlling robots, and optimizing resource allocation.

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    What is the difference between model-free and model-based RL?

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    Model-based RL has an agent that tries to understand (or model) the world by learning state transition function and reward function. So if an agent can predict the next state and the reward before taking the action, it is a model-based RL algorithm. If not then it is model-free algorithm. Refer this AI StackExchange QA

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    What is the difference between value-based and policy-based reinforcement learning?

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    Value-based reinforcement learning methods, such as Q-learning, estimate the value of different actions given a certain state, and then take the action that maximizes this value. Policy-based methods, such as REINFORCE, directly learn a policy that maps states to actions.

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    How does Q-learning work in reinforcement learning?

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    Q-learning is a value-based reinforcement learning algorithm that estimates the value of different actions given a certain state. It uses the Bellman equation to update the Q-value, which is the expected future reward of taking an action in a certain state. Over time, the algorithm converges to the optimal Q-value function that represents the best action to take in each state.

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    What is the difference between on-policy and off-policy reinforcement learning?

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    On-policy reinforcement learning (ex: SARSA) methods learn from the actions taken by the current policy, while off-policy (ex: Q-learning) methods learn from actions taken by a different policy (like greedy approach). On-policy methods are often used when an agent can easily interact with its environment, while off-policy methods are used when it is difficult or expensive to interact with the environment. Refer this StackOverflow QA for more details.

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    What is the use of the SARSA algorithm in reinforcement learning?

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    SARSA (State-Action-Reward-State-Action) is a type of on-policy algorithm that estimates the value of different actions given a certain state. It updates the Q-value based on the action taken in the next state, rather than the optimal action as in Q-learning. This makes SARSA more suitable for problems with non-deterministic environments.

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    Explain actor-critic algorithm.

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    Actor-Critic is a type of algorithm that combines both policy-based and value-based methods in reinforcement learning. The actor network is used to output a probability distribution over actions given a certain state, while the critic network is used to estimate the value of the actions taken by the actor network. The actor network is then updated according to the critic network's evaluation of its actions.

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    Explain A3C algorithm in reinforcement learning.

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    A3C (Asynchronous Advantage Actor-Critic) is an algorithm that uses multiple parallel agents to train a neural network in an asynchronous manner. A3C is an extension of the actor-critic algorithm that allows for faster and more stable training by reducing the correlation between the updates of different agents. It's useful for training agents for tasks that require handling high dimensional and continuous action spaces, such as robotics and gaming.

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    What is the use of the Q-network in reinforcement learning?

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    A Q-network is a neural network used in Q-learning, a value-based reinforcement learning algorithm. The Q-network is trained to estimate the value of taking different actions given a certain state. It's used to approximate the Q-value function, which represents the expected future rewards of taking different actions in different states, and to make decisions based on the current state.

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    What is Monte Carlo Tree Search in reinforcement learning?

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    Monte Carlo Tree Search (MCTS) is a method used to select the next action in a reinforcement learning problem. MCTS works by building a search tree, where each node represents a state and each edge represents an action. The algorithm explores the tree by selecting the best node based on a combination of the current value estimates and the uncertainty of the estimates. MCTS is particularly useful for problems with large and complex action spaces, such as game playing.

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    Introduction

    + +

    Getting started

    +
      +
    • +

      Before we deep-dive into Reinforcement Learning (RL), let's go through some videos that showcase how RL is solving some really complicated real world problems:

      +
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      • +

        Waymo 360 Experience of Autonomous Driving System

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        QT-Opt - Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation

        +

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    +
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    • As obvious from the videos, RL is used across domains - it can control cars, play games and even interact with real world using robot. Isn't it interesting?! šŸ¤–
      • +
    +
      +
    • Now let's answer the first question - What is Reinforcement Learning? Contrary to basic supervised and unsupervised problems, RL is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. [1]
      • +
    • I know this must be too much to unpack, so let's dissect some important keywords in the next section.
      • +
    +

    Important terms

    +
      +
    • Agent: it is the entity that takes some action which is influenced by current scenario and it's past experience. We try to create an algorithm or AI model that can act as an agent.
      • +
    • Environment: this is where agent takes actions. It is the complete world wherein the agent lives and traverse. The environment considers the agent's actions and return rewards (positive or negative based on type of impact) and changes the state of the agent. An environment could be deterministic (taking an action from a state leads to a fixed state always) or non-deteministic (state transition wrt action selection is probabilistic).
      graph LR
+A[Agent] -- state 's' --> B[Environment];
+A -- action 'a' --> B;
+B -- reward 'r' --> A;
+B -- new state 'sn' --> A;
      +
      • +
    +
      +
    • States: An environment consists of multiple states, infact this denotes the different scenarios an agent could be in. States could be (1) engineered (ex: In MountainCar env, combination of calculated position and velocity forms the state), (2) raw (ex: using screen images for video games), or (3) combination of both (ex: autonomus driving system considering raw video feeds and some extracted features). States could be discrete (ex: cell A to cell B) or continous (ex: screenshot of a game, no two images could be same).
      • +
    • Action: These are the possible interactions an agents can make with the environment. Actions could differ wrt to the current state of the agent. It can also be discrete (ex: left turn and right turn) or continous (ex: -120 degree turn).
      • +
    • Reward: Agent performs some actions and enviroment returns rewards. This is usually programmed wrt the behavior we want the agent to learn. If we want the agent to reach a goal -- give +1 point at that point. If we want the agent to move faster -- give -1 for every step it takes (so that it tries ot finish the game asap). Reward designing is a very important part of solving RL problem.
      • +
    • Policy: It is the rulebook that tells the agent what to do for a given state and possible set of actions. Policy based RL agents tries to directly learn the best policy for a given state, whereas value based RL agents learns the value function (estimated reward for each action in a state) and then select action based on their strategy.
      • +
    • Episodes: One complete iteration over the environment by the agent, either till it passed or failed, is called an epsiode. In game terminology, think of a round as one episode.
      • +
    + + + +

    The Paradigms in RL

    +

    Forward Reinforcement Learning

    +
      +
    • It refers to the process of learning a policy or control strategy in an environment by taking actions and receiving feedback or rewards from the environment. The objective of this approach is to maximize the expected cumulative reward obtained from the environment by learning a policy that maps the state of the environment to an action to be taken.
      • +
    +

    Inverse Reinforcement Learning

    +
      +
    • Inverse Reinforcement Learning (IRL) aims to learn the underlying reward function of a given environment from a set of observed trajectories, instead of learning a policy directly. This approach can be used to infer the reward function from human demonstrations or expert behavior, and then learn a policy that maximizes the inferred reward function.
      • +
    +

    Behavior Cloning

    +
      +
    • Behavior cloning refers to a supervised learning technique where a model is trained to mimic the behavior of an expert agent or human in a given task. In this approach, the model is trained on a dataset of state-action pairs, where each action is a direct imitation of the expert's actions in the corresponding state. Behavior cloning can be used as a pre-training step for more complex reinforcement learning algorithms, or as a standalone approach in cases where the expert's behavior is sufficient for solving the task.
      • +
    +

    Apprenticeship Learning

    +
      +
    • Apprenticeship learning, also known as imitation learning, is a machine learning technique where a learner tries to imitate the behavior of an expert agent or human in a given task. Unlike behavior cloning, which only learns to mimic the expert's actions, apprenticeship learning aims to learn the underlying decision-making process or policy of the expert by observing their behavior in a given environment. This approach is useful when the expert's behavior is not easily captured by a simple state-action mapping, and when the reward function of the task is unknown or difficult to specify.
      • +
    +
    +

    Hint

    +

    One easy way to differentiate the above mentioned domains is as follows, [3]

    +
      +
    • In IRL, we recover a Reward function
      • +
    • In Apprenticeship Learning, we find a good policy using the recovered reward function from IRL
      • +
    • In Behavior cloning, we directly learn the teacher's policy using supervised learning
      • +
    +
    +

    Resources

    +

    If you are looking for a deep dive into Reinforcement learning, here are some good materials, šŸ‘Œ

    + +

    References

    +

    [1] Reinforcement Learning - Wikipedia

    +

    [2] Apprenticeship Learning via Inverse Reinforcement Learning - Paper

    +

    [3] Inverse Reinforcement Learning - Lecture PDF

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    + + + + + + + + + + + + + + \ No newline at end of file diff --git a/reinforcement_learning/q_learning/index.html b/reinforcement_learning/q_learning/index.html new file mode 100644 index 00000000..548c167f --- /dev/null +++ b/reinforcement_learning/q_learning/index.html @@ -0,0 +1,2629 @@ + + + + + + + + + + + + + + + + + + Q-Learning - A Lazy Data Science Guide + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
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    Q-Learning

    + +

    Introduction

    +
      +
    • Q-learning is a RL algorithm that computes an expected reward for taking an action from any given state. The expected reward is a composition of the immediate reward and the future rewards possible from the new transitioned state. It is an iteration based algorithm that adjusts the scores over time, and given infinite exploration time it can identify an optimal action-selection policy.
      • +
    +
    +

    Note

    +

    Q-learning does not requires to model the environment, hence it is model-free (check here for more details).

    +
    +
      +
    • To better grasp the intuition, let's understand it through a Grid world example before further complications creeps in. šŸ˜‰
      • +
    +

    The Beer game in Grid world šŸŗ

    +
      +
    • Think of Grid World as a nD world (environment) where an agent will live and traverse. The environment consists of multiple cells, where each cell denotes one state, and it could deliver certain reward (positive or negative) to reach that state. The agent at any given time, will only have the knowledge of the current state and the information it gained in previous episodes.
      • +
    • Now imagine we drop an agent in a 2D grid world that contains a beer (that we want to find) and a pit (that we want to avoid). The agent traverse the world until it either finds the beer or drops into the pit. If we make the agent play this game 1000s of times, how should the agent behave overtime?
      • +
    • The answer seems simple -- at first the agent will have no knowledge of the world, so it will stumble across the env, like a person performing coin toss to decide his fate šŸŖ™. Sometimes, it will find the beer or the pit, and at either points, it starts again.
      • +
    • The interesting part is, after each episode the agent will become more and more aware of the world. And after many episodes it would have developed a detailed map, that guides to the beer with details of the areas you should not go to (as it contains the pit) šŸ˜Ž
      • +
    +
    +

    +

    A technical recreation of The Beer Game with left most cell denoting the pit, the 7th cell contains beer and the rest of the cell denotes the expected reward if we move in the suggested direction. [1] +

    +
    +

    Bellman's Equation

    +
      +
    • The next question is how to capture the details of the map? That's where bellman's equation comes into the picture. Basically it learns Q (quality) scores, that represents expected reward, for taking an action \(a\) from a state \(s\). If we capture this for all the states and actions in the world then viola, we have the map!
      • +
    • The formulation of Q-score is given below.
      • +
    +
    \[Q(s_{t},a_{t}) = r_{t} + \gamma \cdot \max _{a}Q(s_{t+1},a)\]
    +
      +
    • Here, \(Q(s_{t},a_{t})\) is the Q-score for taking action \(a_t\) in state \(s_t\). \(r_t\) is the reward we get to perform the action. \(\gamma\) is the discount factor which is multiplied with the current reward estimation for the best possible action from the next step \(s_{t+1}\). The discount factor (range 0 to \(\inf\)) is used to adjust the importance to be given to immediate reward (low \(\gamma\)) or future reward (high \(\gamma\)).
      • +
    +
      +
    • The formulation to update the Q-score is given below, where \(\alpha\) is the learning rate (yes, like the Neural networks šŸ˜„)
      • +
    +
    \[Q^{new}(s_{t},a_{t}) = Q^{old}(s_{t},a_{t}) + \alpha \cdot (r_{t} + \gamma \cdot \max _{a}Q(s_{t+1},a) - Q^{old}(s_{t},a_{t}))\]
    +
      +
    • Below is an example of a 2D grid world, where we have set the middle cell with a reward of +1 and we perform multiple iterations of the q-learning.
      • +
    +
    +

    +

    Agent performing Q learning over the 2D grid world. [1]

    +
    +
    +

    Hint

    +

    If you want to create and play around with your 2D grid world, try this Interactive Q-Learning playgorund by yours truly. [1] šŸ˜Ž

    +
    +

    Code

    +

    Exploring MountainCar env

    +
      +
    • Now let's get started with some action i.e. coding. We will train a RL agent using Q-learning to beat the game of MountainCar available on OpenAI's Gym package that provides a simple interface to represent and play with general RL problems.
      • +
    • Before that, let's understand some fundamentals of the MountainCar game environment.
        +
      • Aim: To get the car to reach the yellow flag
        • +
      • Observation (states):
          +
        • Position: -1.2 to 0.6
          • +
        • Velocity: -0.07 to 0.07
          • +
        +
        • +
      • Actions:
          +
        • Push left: 0
          • +
        • No push: 1
          • +
        • Right push: 2
          • +
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      • Reward:
          +
        • Reached yellow flag: +1
          • +
        • Other: -0.5
          • +
        +
        • +
      • Why is this difficult? Bcoz a normal continous right push will not work, to win we need to oscillate as we need momentum from left.
        • +
      +
      • +
    +
    +

    +

    Untrained agent in the MountainCar env

    +
    +
      +
    • We can explore the same via code as shown below,
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    """
+Reference: https://blog.paperspace.com/getting-started-with-openai-gym/
+
+Requirements; (don't forget to create new VE)
+    pip install gym
+    pip install "gym[box2d]"
+    pip install "gym[other]"
+"""
+
+# import 
+import gym
+
+# create an env
+env = gym.make("MountainCar-v0", render_mode="human")
+
+# seed 
+env.action_space.seed(42)
+
+# Observation and action space 
+obs_space = env.observation_space
+action_space = env.action_space
+print("The observation space: {}".format(obs_space))
+print("The action space: {}".format(action_space))
+
+# reset the space
+observation, info = env.reset(seed=42)
+print(f"Observation: {observation}")
+print(f"Info: {info}")
+print(f"Sample action; {env.action_space.sample()}")
+
+## OUTPUT
+# The observation space: Box([-1.2  -0.07], [0.6  0.07], (2,), float32)
+# The action space: Discrete(3)
+# Observation: [-0.4452088  0.       ]
+# Info: {}
+# Sample action; 0
+
    +

    Training Agent using Q-learning

    +
    +

    Hint

    +

    Before starting with this section, why not try to code up some basic logic to beat the game? You could try to always push right or oscillate left and right at certain intervals. Give it try, it will be fun šŸ˜„

    +
    +
      +
    • The code shared below (credits to gkhayes) trains a Q-learning agent to play the MountainCar game. It tries to create a Q-table (that contains Q-scores) for all the combination of states and actions. As the states are continous (they are in float), it is first discretized to make it a finite state problem. The final Q-table is of the shape [19, 15, 3], with 19 possible states for position, 15 states for velocity and 3 actions.
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    """
+Reference and Credit: https://gist.github.com/gkhayes/3d154e0505e31d6367be22ed3da2e955
+
+Requirements; (don't forget to create new VE)
+    pip install gym
+    pip install "gym[box2d]"
+    pip install "gym[other]"
+    pip install numpy, matplotlib
+"""
+# import 
+import numpy as np
+import gym
+import matplotlib.pyplot as plt
+
+# Import and initialize Mountain Car Environment
+env = gym.make('MountainCar-v0')
+env.reset()
+
+# Define Q-learning function
+def QLearning(env, learning, discount, epsilon, min_eps, episodes):
+    # Determine size of discretized state space
+    num_states = (env.observation_space.high - env.observation_space.low)*\
+                    np.array([10, 100])
+    num_states = np.round(num_states, 0).astype(int) + 1
+
+    # Initialize Q table
+    Q = np.random.uniform(low = -1, high = 1, 
+                          size = (num_states[0], num_states[1], 
+                                  env.action_space.n))
+    # Initialize variables to track rewards
+    reward_list = []
+    ave_reward_list = []
+
+    # Calculate episodic reduction in epsilon
+    reduction = (epsilon - min_eps)/episodes
+
+    # Run Q learning algorithm
+    for i in range(episodes):
+        # Initialize parameters
+        done = False
+        tot_reward, reward = 0,0
+        state = env.reset()
+
+        # Discretize state
+        state_adj = (state[0] - env.observation_space.low)*np.array([10, 100])
+        state_adj = np.round(state_adj, 0).astype(int)
+
+        while done != True:   
+            # Render environment for last five episodes
+            if i >= (episodes - 20):
+                env.render()
+
+            # Determine next action - epsilon greedy strategy
+            if np.random.random() < 1 - epsilon:
+                action = np.argmax(Q[state_adj[0], state_adj[1]]) 
+            else:
+                action = np.random.randint(0, env.action_space.n)
+
+            # Get next state and reward
+            state2, reward, done, info, _ = env.step(action) 
+
+            # Discretize state2
+            state2_adj = (state2 - env.observation_space.low)*np.array([10, 100])
+            state2_adj = np.round(state2_adj, 0).astype(int)
+
+            #Allow for terminal states
+            if done and state2[0] >= 0.5:
+                Q[state_adj[0], state_adj[1], action] = reward
+
+            # Adjust Q value for current state
+            else:
+                delta = learning*(reward + 
+                                 discount*np.max(Q[state2_adj[0], 
+                                                   state2_adj[1]]) - 
+                                 Q[state_adj[0], state_adj[1],action])
+                Q[state_adj[0], state_adj[1],action] += delta
+
+            # Update variables
+            tot_reward += reward
+            state_adj = state2_adj
+
+        # Decay epsilon
+        if epsilon > min_eps:
+            epsilon -= reduction
+
+        # Track rewards
+        reward_list.append(tot_reward)
+
+        if (i+1) % 100 == 0:
+            ave_reward = np.mean(reward_list)
+            ave_reward_list.append(ave_reward)
+            reward_list = []
+
+        if (i+1) % 100 == 0:    
+            print('Episode {} Average Reward: {}'.format(i+1, ave_reward))
+
+    env.close()
+
+    # save the Q table
+    np.save('MountainCar-v0-q-learning', Q)
+
+    return ave_reward_list
+
+# Run Q-learning algorithm
+rewards = QLearning(env, 0.2, 0.9, 0.8, 0, 5000)
+
+# Plot Rewards
+plt.plot(100*(np.arange(len(rewards)) + 1), rewards)
+plt.xlabel('Episodes')
+plt.ylabel('Average Reward')
+plt.title('Average Reward vs Episodes')
+plt.savefig('rewards.jpg')     
+plt.close()  
+
    +
      +
    • Running the above code trains a RL agent for 5000 episodes and saves the Q-table as numpy array. It also computes the average reward vs episodes graph that is shown below. Do note how the agent is able to get more rewards over more training!
      • +
    +
    +

    +

    Increasing reward over time for the agent in training. The average rewards went up from -4300 to -250 under just 5000 episodes.

    +
    +

    Inference of trained agent

    +
      +
    • To run the trained model, we can use the saved Q-table to select the best action given any state in the game. Below is the code for the same,
      • +
    +
     1
+ 2
+ 3
+ 4
+ 5
+ 6
+ 7
+ 8
+ 9
+10
+11
+12
+13
+14
+15
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+17
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+20
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+34
+35
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+38
+39
+40
+41
+42
+43
    """
+Requirements; (don't forget to create new VE)
+    pip install gym
+    pip install "gym[box2d]"
+    pip install "gym[other]"
+
+    MountainCar-v0-q-table.txt.npy file that contains the modelled environment
+"""
+
+# import 
+import gym
+import numpy as np
+from gym.utils import play
+
+# load trained Q table
+Q = np.load('MountainCar-v0-q-table.txt.npy')
+
+# create an env
+env = gym.make("MountainCar-v0", render_mode="human")
+
+# seed 
+env.action_space.seed(42)
+
+observation, info = env.reset(seed=42)
+
+# total number of steps
+for s in range(200):
+
+    state_adj = (observation - env.observation_space.low)*np.array([10, 100])
+    state_adj = np.round(state_adj, 0).astype(int)
+
+    # define the next step to take
+    next_action = np.argmax(Q[state_adj[0], state_adj[1]]) 
+
+    # perform one step
+    observation, reward, terminated, truncated, info = env.step(next_action)
+    print(s, observation, reward, terminated, truncated, info)
+
+    # if the game ends, restart the game
+    if terminated or truncated:
+        observation, info = env.reset()
+
+env.close()
+
    +
    +

    +

    Trained RL agent performing as expected šŸ˜Ž

    +
    +
      +
    • There you go!! šŸ‘‹
      • +
    +

    References

    + + + +
    + +
    +
    + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + \ No newline at end of file diff --git a/reinforcement_learning/rlhf/index.html b/reinforcement_learning/rlhf/index.html new file mode 100644 index 00000000..764dfdc5 --- /dev/null +++ b/reinforcement_learning/rlhf/index.html @@ -0,0 +1,2285 @@ + + + + + + + + + + + + + + + + + + RLHF - A Lazy Data Science Guide + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + +
    + +
    + + + + +
    + + +
    + +
    + + + + + + + + + +
    +
    + + + +
    +
    +
    + + + + + + +
    +
    +
    + + + +
    +
    +
    + + + +
    +
    +
    + + +
    +
    + + + + + + + + +

    Reinforcement Learning from Human Feedback (RLHF)

    +

    Introduction

    +
      +
    • In Reinforcement learning, an agent learns to perform a task by interacting with an environment and receiving rewards or penalties for its actions. While this approach has shown great success in a wide range of applications, it can be difficult to design reward functions that accurately capture the user's preferences.
      • +
    • Take autonomous driving car as an example, instead of creating a complicated reward function (with metrics like follow the lane, on wet road drive slow, stop at stop sign, don't hit anyone, etc and their weightage) we might want to let AI learn it. One point to note, we also do not want AI to learn to imitate human drivers, but rather learn what humans value in driving behavior and then optimize against those preferences.
      • +
    • Because of this, we not only want trajectories (episodes or examples), but also some form of feedbacks on different trajectories stating which one is better than the other. Once we have this, we can train a model to first learn the reward function (Inverse Reinforcement Learning) and later use the same reward to train an expert model (Apprenticeship learning)
      • +
    +
      +
    • In this article, we will start with understanding the fundamentals of different types of human feedback, how to use them with RL and their pros and cons. Finally, we will discuss application of RLHF in NLP with tasks like Summarization. Let's start šŸ‘
      • +
    +

    Types of human feedback

    +

    There are different types of human feedback that can be used in reinforcement learning. These include:

    +
      +
    • Explicit feedback: This type of feedback is direct and clear. It involves a human providing a specific reward or penalty to reinforce or discourage certain behaviors. For example, a teacher might provide explicit feedback to a student by giving them a grade for their performance.
      • +
    +
      +
    • Implicit feedback: This type of feedback is more subtle and indirect. It involves a human providing information about what they like or dislike without explicitly stating it. For example, a customer might provide implicit feedback to a restaurant by choosing to visit it again or not.
      • +
    +
      +
    • Comparison-based feedback: This type of feedback involves a human comparing the performance of an agent to that of another agent or a benchmark. For example, a manager might provide comparison-based feedback to an employee by comparing their performance to that of other employees in the same position.
      • +
    +

    Incorporating human feedback

    +

    There are several methods for incorporating human feedback in reinforcement learning, including:

    +
      +
    • Reward shaping: This method involves modifying the reward function to incorporate human feedback. The goal is to manually guide the learning process towards behaviors that are more aligned with the user's preferences. For example, if a user wants a robot to clean a room quickly and efficiently, the reward function can be modified to encourage the robot to complete the task as quickly as possible.
      • +
    +
      +
    • Imitation learning: This method involves learning from demonstration. The agent observes a human expert performing a task and learns to mimic their behavior. This method is particularly useful when the task is complex and difficult to learn from scratch. For example, a robot can learn to fold laundry by watching a human expert do it.
      • +
    +
      +
    • Inverse reinforcement learning: This method involves inferring the reward function from human demonstrations. The agent observes a human expert performing a task and tries to learn the underlying reward function that motivated the expert's behavior. This method is particularly useful when the user's preferences are not easily expressed in terms of a reward function. For example, a robot can infer the reward function that motivated a human expert to perform a task and then optimize its behavior accordingly.
      • +
    +

    These methods can be used alone or in combination to incorporate human feedback in reinforcement learning. The choice of method depends on the nature of the task, the type of human feedback available, and the computational resources available for learning.

    +

    Benefits and challenges of using human feedback

    +

    Using human feedback in reinforcement learning has several benefits, but also presents some challenges.

    +

    Benefits

    +
      +
    • Improved efficiency: Incorporating human feedback can accelerate the learning process and reduce the number of trials required to learn a task. By providing feedback that guides the agent towards desirable behaviors, human feedback can help the agent focus on the most promising strategies.
      • +
    +
      +
    • Better performance: Human feedback can improve the quality of the learned policy and increase the success rate of the task. By incorporating the user's preferences, the agent can learn to optimize its behavior for the specific context of the task.
      • +
    +
      +
    • Increased interpretability: Human feedback can make the learned policy more transparent and interpretable. By understanding the user's preferences, the agent can provide explanations for its actions and provide insights into how it works.
      • +
    +

    Challenges

    +
      +
    • Quality of human feedback: The quality of human feedback can vary depending on the user's expertise, knowledge, and ability to articulate their preferences. Some users may have conflicting preferences or provide ambiguous feedback, which can make it difficult for the agent to learn effectively.
      • +
    +
      +
    • Bias and subjectivity: Human feedback can be biased and subjective, depending on the user's cultural background, personal beliefs, and emotional state. These factors can introduce bias into the learning process and make it difficult to generalize the learned policy to different contexts.
      • +
    +
      +
    • Limited scalability: Incorporating human feedback can be resource-intensive and may not be feasible for large-scale applications. Collecting and processing feedback from multiple users can be time-consuming, and the resulting models may not be generalizable to new users.
      • +
    +

    RLHF in NLP

    +
      +
    • Reinforcement learning from human feedback (RLHF) has shown great potential in improving natural language processing (NLP) tasks. In NLP, the use of human feedback can help to capture the nuances of language and better align the agent's behavior with the user's expectations.
      • +
    +

    Summarization

    +
      +
    • Summarization aims to generate summaries that capture the most important information from a longer text. In RLHF, human feedback can be used to evaluate the quality of summaries and guide the agent towards more informative and concise summaries. This is quite difficult to capture using the metrics like ROUGE as they miss the human preferences.
      • +
    • One such approach was proposed in [1], where researchers improved a summarization model using human feedback. The overall process was as follows,
        +
      1. First, an autoregressive model is trained via supervised learning on the dataset (TL;DR dataset with >120k post from reddits and their summaries were taken). The resulting model is termed as initial policy.
        2. +
      2. Then the following steps are performed in iteration,
          +
        1. For each reddit post, samples from initial policy, current policy (for step 0 its same as initial policy), other baselines, and original summaries are taken and send over to human labelers.
          2. +
        2. Based on human labelers's feedback, we now have candidate summary for each post. We use this data to train a reward function (linear layer on initial policy) using supervised learning.
          3. +
        3. The output of reward function is treated as the reward to optimize using the reinforcement learning (authors use PPO algorithm).
          4. +
        +
        3. +
      +
      • +
    • To shed more light on the how policies are trained using rewards, the finetuned model is treated as initial policy. In RL terminology, state is the prompt plus the generations so far, action is token to generate, each step is one token generation and one episode terminates when policy returns <EOS> token. Also, the reward function gives score for the complete summary and not individual generations.
      • +
    • Finally, a conditioning term in added to the final reward that penalizes the KL divergence between the learned RL policy and the original supervised model. Quoting the paper, "This KL term serves two purposes. First, it acts as an entropy bonus, encouraging the policy to explore and deterring it from collapsing to a single mode. Second, it ensures the policy doesnā€™t learn to produce outputs that are too different from those that the reward model has seen during training."
      • +
    +
    +

    +

    Diagram of human feedback, reward model training, and policy training procedure in [1]

    +
    +

    Others

    +
      +
    • RLHF has been utlised for other NLP tasks as well. For example,
        +
      • As Dialogue systems in ChatGPT. Here the aim is to generate responses to user inputs that are coherent, informative, and relevant to the user's goals. In RLHF, human feedback can be used to evaluate the quality of generated responses and guide the agent towards more effective communication strategies. For example, a user can provide explicit feedback on the relevance of a response, or implicit feedback by continuing or ending the conversation.
        • +
      +
      • +
    +
      +
    • While RLHF has shown promise in improving NLP tasks, there are still challenges related to the quality of human feedback and the scalability of the approach. Collecting and processing human feedback can be time-consuming and may not be feasible for large-scale applications. Furthermore, human feedback can be subjective and may not capture the full range of user preferences. However, as RLHF continues to be refined, it has the potential to greatly enhance the quality and effectiveness of NLP systems.
      • +
    +

    References

    +

    [1] Learning to summarize from human feedback

    + + +
    + +
    +
    + +
    + + + +
    +
    +
    +
    + + + + + + + + + + + + + + \ No newline at end of file diff --git a/search/search_index.json b/search/search_index.json new file mode 100644 index 00000000..fc6a3b19 --- /dev/null +++ b/search/search_index.json @@ -0,0 +1 @@ +{"config":{"indexing":"full","lang":["en"],"min_search_length":3,"prebuild_index":false,"separator":"[\\s\\-]+"},"docs":[{"location":"","text":"Hello What is this? A guide book on data science for busy and equally lazy Data Scientists \ud83d\ude04 What does this include? While the book started as a collection of snippets, the plan is to overtime include details about the algorithms, applications, tools, best practices and much more to make this the ultimate guide for a budding or experienced Data Scientists. Why this? While there are lots of good quality articles on several data science topics, the major downside is that they are scattered across the web. This guide tries to consolidate the most relevant topics into one place. How to read? As the name suggests, this is a lazy book for lazy people \ud83d\ude04. On a serious note, there is no specific order. Feel free to search for the topic you are interested in and start reading. How to contribute? Any contribution (typo, error, new topic request, etc) is appreciated. Just create an issue, or start a new discussion on github, and I will be happy to discuss the idea! How to appreciate? Maybe you can do two things? -- (1) Say hello to me on LinkedIn , and/or (2) Click on the \u2b50 @ github ? Thnx! How to cite? Add the following to your bib file, @misc{mohitlazydatascience, title = \"A Lazy Data Science Guide\", author = \"Mohit Mayank\", journal = \"mohitmayank.com\", year = \"2021\", url = \"http://mohitmayank.com/a_lazy_data_science_guide/\" } -- Mohit Mayank","title":"Hello"},{"location":"#hello","text":"What is this? A guide book on data science for busy and equally lazy Data Scientists \ud83d\ude04 What does this include? While the book started as a collection of snippets, the plan is to overtime include details about the algorithms, applications, tools, best practices and much more to make this the ultimate guide for a budding or experienced Data Scientists. Why this? While there are lots of good quality articles on several data science topics, the major downside is that they are scattered across the web. This guide tries to consolidate the most relevant topics into one place. How to read? As the name suggests, this is a lazy book for lazy people \ud83d\ude04. On a serious note, there is no specific order. Feel free to search for the topic you are interested in and start reading. How to contribute? Any contribution (typo, error, new topic request, etc) is appreciated. Just create an issue, or start a new discussion on github, and I will be happy to discuss the idea! How to appreciate? Maybe you can do two things? -- (1) Say hello to me on LinkedIn , and/or (2) Click on the \u2b50 @ github ? Thnx! How to cite? Add the following to your bib file, @misc{mohitlazydatascience, title = \"A Lazy Data Science Guide\", author = \"Mohit Mayank\", journal = \"mohitmayank.com\", year = \"2021\", url = \"http://mohitmayank.com/a_lazy_data_science_guide/\" } -- Mohit Mayank","title":"Hello"},{"location":"audio_intelligence/connectionist_temporal_classification/","text":"Introduction Connectionist Temporal Classification (CTC) is the algorithm to assign probability score to an output Y given any input X. The main advantage is that the size of X and Y do not have to match! This makes CTC an ideal algorithm for use cases like speech recogition and handwriting recoginition where the input and output do not usually match. Take the example of speech recognition. The input is usually a waveform of audio that could contains 16,000 samples per second (if sampling rate is 16kHz). But in a second, we hardly speak a word that could be 5-6 characters. So in ASR we are trying to map a large amount of input to much smaller sized output. This is just one of the use cases where CTC shines. Message collapsing using CTC. Created using the tool available here Understanding CTC To understand the CTC algorithm, we need to understand three aspects. Let's go through them one by one. Collapsing In ASR, while the output is much smaller, we start with normal classification on the input. For this, we first divide the input into multiple equal sized tokens. For example, we can take 400 samples at a time (for 16kHz sampled audio that is 25ms of speech). Now we need to classify each one of these samples into characters available in the vocabulary. For a normal english language we will have all the alphanumeric characters plus some special tokens (not special characters) as the vocabulary. Note In ASR vocab, we do not put any of the special characters for which there is no sound. For example there is no sound for . . But we could identify ' in It's , so it could be there. This will give us a long sequence of characters, and this is where collapsing logic of CTC comes into picture. The idea is to combine consecutive repetitive characters together. For example, hhhhiiiii could be simply written as hi . Now we also want to handle two special cases, (1) there could be spaces between two words, (2) there could be multiple repetitive characters in a valid word like double l in hello . For these cases, we can add two special tokens in the vocab, say [BRK] and [SEP] respectively. So a word like hello could be decoded if we get the classification output as hhelll[SEP]llo . This means for a complicated task like ASR, we can continue with a simple classification task at the output layer and later let CTC decoding logic handle the rest. But the next question is, \"how can we teach model to predict these outputs?\" \ud83e\udd14 Note The overall collapsing algorithm is like this -- (1) First, collapse the consecutive characters, (2) Next remove any [SEP] tokens, and (3) Finally replace [BRK] tokens with space. Relevant Paths For any given sample (time step), the output will be a probability distribution for each character in the vocab (imagine using softmax) . Suppose we only have 3 samples (three time steps) and 3 different characters in the vocab, then we can have \\(3^3=27\\) possible paths to choose from. An example is shown below where you could imagine paths (dashed lines) going from any left circle to every circle in the next time step. One possible path for transcribing hi One interesting property of CTC is that there could be multiple true possible paths. For CTC to transcribe hi any of the following will do -- hhi , hii , h[SEP]i , hi[SEP] or [SEP]hi . Hence the true relevant paths here are 5 out of all 27 available ones. Looking from the perspective of training neural networks, we want to penalize the irrelevant paths and increase the probability of the relevant ones. This is done by two ways, We can train to increase the probability of the characters in the relevant paths at the respective time step. In our case, we can increase the probability of h and [SEP] at the 1st time step as these are the only available choices in set of relevant paths! This can be repeated for the other time steps as well. But this approach has one major con - it is training at time step level and not path level. So even if the probabilities at each step improves, output paths could still not be a relevant one. Another approach is to consider the context of the path by using models like RNN that can compute per step wise probabilities wrt the overall path probability. We take product of probability of all steps in a relevant path ( \\(\\prod_{t=1}^{T}p_t (a_t | X)\\) ) and then sum the path probabilities of the relevant paths ( \\(\\sum_{A\\epsilon A_{X,Y}}^{}\\) ). This gives us the CTC conditional probability, which we want to maximize. \\[ P(Y|X) = \\sum_{A\\epsilon A_{X,Y}}^{}\\prod_{t=1}^{T}p_t (a_t | X) \\] Note This interesting property of CTC helps us to train ASR model without perfect data annotations, where we will have to assign labels to each individual tokens of input. We just need the input audio stream and the expected output transcription and CTC loss takes care of the rest. In fact, famous deep learning frameworks like PyTorch has CTCLoss available for easy use! Inference Now after training the model, we want it to work during the inference. Here, we won't know the correct transcription or the relevant paths, so we will have to find one. For this we can employ a couple of approaches, The easiest approach is to go greedy! At each time step, we pick the token with the highest probability. But this could lead to suboptimal outputs as some time steps could have high probability but incorrect predictions. Also remember we trained the model to improve the summation of probabilty for all relevant paths. Now there could be a scenario where one irrelevant path (say [SEP]ih ) has more probability than all individual paths, but the summation of two relevant paths are higher (say hi[SEP] and hii ). Apart from this, as the predictions are at time step level, they are independent of context and this could lead to other issues like spelling mistakes and wrong grammer. The next approach could be to use Beam search where we keep exploring top N paths, where N is the beam size. To handle the above problem, we can modify the beam search where before selecting the next paths to explore, we consider the summation of the explored paths so far by applying CTC collapsing. More details can be found here and an implementation is here . Another approach could be to utilise a language model for decoding. This will take care of the spelling mistakes and grammer issues. For this we can either use n-gram language model or a neural language model. While neural language model is more powerful, it is more complicated to implement and will be much slower, and the comparitive gain in improvement wrt n-gram is not that much ( Wav2vec2 ). There are several open source packages that can be utilised to create a language model like KenML and then use it for decoding with pyctcdecode . Additional Materials Distill - Sequence Modeling With CTC An Intuitive Explanation of Connectionist Temporal Classification Boosting Wav2Vec2 with n-grams in \ud83e\udd17 Transformers","title":"Connectionist Temporal Classification"},{"location":"audio_intelligence/connectionist_temporal_classification/#introduction","text":"Connectionist Temporal Classification (CTC) is the algorithm to assign probability score to an output Y given any input X. The main advantage is that the size of X and Y do not have to match! This makes CTC an ideal algorithm for use cases like speech recogition and handwriting recoginition where the input and output do not usually match. Take the example of speech recognition. The input is usually a waveform of audio that could contains 16,000 samples per second (if sampling rate is 16kHz). But in a second, we hardly speak a word that could be 5-6 characters. So in ASR we are trying to map a large amount of input to much smaller sized output. This is just one of the use cases where CTC shines. Message collapsing using CTC. Created using the tool available here","title":"Introduction"},{"location":"audio_intelligence/connectionist_temporal_classification/#understanding-ctc","text":"To understand the CTC algorithm, we need to understand three aspects. Let's go through them one by one.","title":"Understanding CTC"},{"location":"audio_intelligence/connectionist_temporal_classification/#collapsing","text":"In ASR, while the output is much smaller, we start with normal classification on the input. For this, we first divide the input into multiple equal sized tokens. For example, we can take 400 samples at a time (for 16kHz sampled audio that is 25ms of speech). Now we need to classify each one of these samples into characters available in the vocabulary. For a normal english language we will have all the alphanumeric characters plus some special tokens (not special characters) as the vocabulary. Note In ASR vocab, we do not put any of the special characters for which there is no sound. For example there is no sound for . . But we could identify ' in It's , so it could be there. This will give us a long sequence of characters, and this is where collapsing logic of CTC comes into picture. The idea is to combine consecutive repetitive characters together. For example, hhhhiiiii could be simply written as hi . Now we also want to handle two special cases, (1) there could be spaces between two words, (2) there could be multiple repetitive characters in a valid word like double l in hello . For these cases, we can add two special tokens in the vocab, say [BRK] and [SEP] respectively. So a word like hello could be decoded if we get the classification output as hhelll[SEP]llo . This means for a complicated task like ASR, we can continue with a simple classification task at the output layer and later let CTC decoding logic handle the rest. But the next question is, \"how can we teach model to predict these outputs?\" \ud83e\udd14 Note The overall collapsing algorithm is like this -- (1) First, collapse the consecutive characters, (2) Next remove any [SEP] tokens, and (3) Finally replace [BRK] tokens with space.","title":"Collapsing"},{"location":"audio_intelligence/connectionist_temporal_classification/#relevant-paths","text":"For any given sample (time step), the output will be a probability distribution for each character in the vocab (imagine using softmax) . Suppose we only have 3 samples (three time steps) and 3 different characters in the vocab, then we can have \\(3^3=27\\) possible paths to choose from. An example is shown below where you could imagine paths (dashed lines) going from any left circle to every circle in the next time step. One possible path for transcribing hi One interesting property of CTC is that there could be multiple true possible paths. For CTC to transcribe hi any of the following will do -- hhi , hii , h[SEP]i , hi[SEP] or [SEP]hi . Hence the true relevant paths here are 5 out of all 27 available ones. Looking from the perspective of training neural networks, we want to penalize the irrelevant paths and increase the probability of the relevant ones. This is done by two ways, We can train to increase the probability of the characters in the relevant paths at the respective time step. In our case, we can increase the probability of h and [SEP] at the 1st time step as these are the only available choices in set of relevant paths! This can be repeated for the other time steps as well. But this approach has one major con - it is training at time step level and not path level. So even if the probabilities at each step improves, output paths could still not be a relevant one. Another approach is to consider the context of the path by using models like RNN that can compute per step wise probabilities wrt the overall path probability. We take product of probability of all steps in a relevant path ( \\(\\prod_{t=1}^{T}p_t (a_t | X)\\) ) and then sum the path probabilities of the relevant paths ( \\(\\sum_{A\\epsilon A_{X,Y}}^{}\\) ). This gives us the CTC conditional probability, which we want to maximize. \\[ P(Y|X) = \\sum_{A\\epsilon A_{X,Y}}^{}\\prod_{t=1}^{T}p_t (a_t | X) \\] Note This interesting property of CTC helps us to train ASR model without perfect data annotations, where we will have to assign labels to each individual tokens of input. We just need the input audio stream and the expected output transcription and CTC loss takes care of the rest. In fact, famous deep learning frameworks like PyTorch has CTCLoss available for easy use!","title":"Relevant Paths"},{"location":"audio_intelligence/connectionist_temporal_classification/#inference","text":"Now after training the model, we want it to work during the inference. Here, we won't know the correct transcription or the relevant paths, so we will have to find one. For this we can employ a couple of approaches, The easiest approach is to go greedy! At each time step, we pick the token with the highest probability. But this could lead to suboptimal outputs as some time steps could have high probability but incorrect predictions. Also remember we trained the model to improve the summation of probabilty for all relevant paths. Now there could be a scenario where one irrelevant path (say [SEP]ih ) has more probability than all individual paths, but the summation of two relevant paths are higher (say hi[SEP] and hii ). Apart from this, as the predictions are at time step level, they are independent of context and this could lead to other issues like spelling mistakes and wrong grammer. The next approach could be to use Beam search where we keep exploring top N paths, where N is the beam size. To handle the above problem, we can modify the beam search where before selecting the next paths to explore, we consider the summation of the explored paths so far by applying CTC collapsing. More details can be found here and an implementation is here . Another approach could be to utilise a language model for decoding. This will take care of the spelling mistakes and grammer issues. For this we can either use n-gram language model or a neural language model. While neural language model is more powerful, it is more complicated to implement and will be much slower, and the comparitive gain in improvement wrt n-gram is not that much ( Wav2vec2 ). There are several open source packages that can be utilised to create a language model like KenML and then use it for decoding with pyctcdecode .","title":"Inference"},{"location":"audio_intelligence/connectionist_temporal_classification/#additional-materials","text":"Distill - Sequence Modeling With CTC An Intuitive Explanation of Connectionist Temporal Classification Boosting Wav2Vec2 with n-grams in \ud83e\udd17 Transformers","title":"Additional Materials"},{"location":"audio_intelligence/interview_questions/","text":"Here are some questions and their answers to make you ready for your next interview. Best of luck Question Answer What is the difference between Sample Rate, Bit Depth and Bit Rate? Sample rate is the number of audio samples recorded per unit of time. For example, an audio with 16kHz sample rate, means that for one second, we have captured 16000 samples. Bit Depth measures how precisely the samples were encoded. Here for a 16kHz sample rate audio, if the bit depth is 8 bits, it means we are using 8 bits of data to store each 16k samples per second. Bit rate is the amount of bits that are recorded per unit of time. For the above example, it means we have 16k * 8 bits of data per second i.e. 128kbps Question Answer What is the difference between Frame, Frame rate, Number of Channels and Sample size? Frame : one sample of the audio data per channel. Frame rate: the number of times per unit time the sound data is sampled. Same as sample rate. Number of channels: indicates if the audio is mono, stereo, or quadro. The sample size: the size of each sample in bytes. Question Answer What is the difference between i-vector, d-vector and x-vector? All of these are vector representation of the audio to capture the speaker information. Let's go through them, i-vector extraction is essentially a dimensionality reduction of the GMM supervector. Refer SO Question d-vector use the Long Short-Term Memory (LSTM) model to the process each individual frame (along with its context) to obtain a frame-level embedding, and average all the frame-level embeddings to obtain the segment-level embedding which can be used as the speaker embedding. Refer paper x-vector take a sliding window of frames as input, and it uses Time Delay Neural Networks (TDNN) to handle the context, to get the frame-level representation. It then has a statistics pooling layer to get the mean and sd of the frame-level embeddings. And then pass the mean and sd to a linear layer to get the segment-level embedding. Refer the original Paper , OxfordWaveResearch Slides and post on r/speechtech","title":"Interview Questions"},{"location":"audio_intelligence/interview_questions/#what-is-the-difference-between-sample-rate-bit-depth-and-bit-rate","text":"Sample rate is the number of audio samples recorded per unit of time. For example, an audio with 16kHz sample rate, means that for one second, we have captured 16000 samples. Bit Depth measures how precisely the samples were encoded. Here for a 16kHz sample rate audio, if the bit depth is 8 bits, it means we are using 8 bits of data to store each 16k samples per second. Bit rate is the amount of bits that are recorded per unit of time. For the above example, it means we have 16k * 8 bits of data per second i.e. 128kbps Question Answer","title":"What is the difference between Sample Rate, Bit Depth and Bit Rate?"},{"location":"audio_intelligence/interview_questions/#what-is-the-difference-between-frame-frame-rate-number-of-channels-and-sample-size","text":"Frame : one sample of the audio data per channel. Frame rate: the number of times per unit time the sound data is sampled. Same as sample rate. Number of channels: indicates if the audio is mono, stereo, or quadro. The sample size: the size of each sample in bytes. Question Answer","title":"What is the difference between Frame, Frame rate, Number of Channels and Sample size?"},{"location":"audio_intelligence/interview_questions/#what-is-the-difference-between-i-vector-d-vector-and-x-vector","text":"All of these are vector representation of the audio to capture the speaker information. Let's go through them, i-vector extraction is essentially a dimensionality reduction of the GMM supervector. Refer SO Question d-vector use the Long Short-Term Memory (LSTM) model to the process each individual frame (along with its context) to obtain a frame-level embedding, and average all the frame-level embeddings to obtain the segment-level embedding which can be used as the speaker embedding. Refer paper x-vector take a sliding window of frames as input, and it uses Time Delay Neural Networks (TDNN) to handle the context, to get the frame-level representation. It then has a statistics pooling layer to get the mean and sd of the frame-level embeddings. And then pass the mean and sd to a linear layer to get the segment-level embedding. Refer the original Paper , OxfordWaveResearch Slides and post on r/speechtech","title":"What is the difference between i-vector, d-vector and x-vector?"},{"location":"audio_intelligence/speaker_diarization/","text":"Introduction Speaker Diarization is the process of segregating different speakers from an audio stream. It is used to answer the question \"who spoke when?\". So if the input is a audio stream with 5 speakers, the output will contain the timestamp in audio when different speakers spoke. A sample output for a conversation between 3 people (differers) could be, start=0.2s stop=1.5s speaker_A start=1.8s stop=3.9s speaker_B start=4.2s stop=5.7s speaker_A start=6.2s stop=7.8s speaker_C ... Note In speaker diarization we separate the speakers (cluster) and not identify them (classify). Hence the output contains anonymous identifiers like speaker_A , speaker_B , etc and not the actual names of the persons. Traditional Diarization Approach The generic approach for Speaker Diarization [1] Traditional Speaker Diarization systems can be generalised into a 5 step process. These are, Feature extraction : here we transform the raw waveform into audio features like mel spectrogram. Voice activity detection : here we identify the chunks in the audio where some voice activity was observed. As we are not interested in silence and noise, we ignore those irrelevant chunks. Speaker change detection : here we identify the speaker changepoints in the conversation present in the audio. It is either capture by heuristic approach, classical algorithms or modern neural blocks. It will further divide the chunks from last step into subchunks. Speech turn representation : here we encode each subchunk by creating feature representations. Recent trends gives preference to neural approach where subchunks are encoded into context aware vector representation. Hint We can use any audio representation algorithm or model for this task. Recently d or x vectors are preferred. One example using speechbrain package is 1 2 3 4 5 6 7 # import import torch from speechbrain.pretrained import EncoderClassifier # load the model encoder = EncoderClassifier . from_hparams ( source = \"speechbrain/spkrec-ecapa-voxceleb\" ) # encode the audio chunk embedding = encoder . encode_batch ( torch . from_numpy ( chunk )) Speech turn clustering : here we cluster the subchunks based on their vector representation. Different clustering algorithms could be applied based on availability of cluster count ( k ) and embedding process of the previous step. Hint While Kmeans is the defacto clustering algorithm, it might create some problems if we use it for speaker diarization. Here are a few problem cases mentioned in [5] Non-gaussian data: speech data are often non-gaussian Imbalanced data: one person may speak more than the other Gender/Age/Racial effects: inter-gender difference is large but intra-gender differences are small. Overlapping speech: overlapping speech creates connections between clusters Kmeans algorithms will not be able to handle these issues of the audio data and will perform incorrect clustering. Hence we can use Spectral Clustering that can overcome all of the mentioned issues! The final output will be the clusters of different subchunks from the audio stream. Each cluster can be given an anonymous identifier (speaker_a, ..) and then it can be mapped with the audio stream to create the speaker aware audio timeline. Metrics Diarization Error Rate (DER) Diarization error rate (DER) provides an estimation (O is good, higher is bad; max may exceed 100%) of diarization performance by calculating the sum of following individual metrics, [2] Speaker error: percentage of scored time for which the wrong speaker id is assigned within a speech region False alarm speech: percentage of scored time for which a nonspeech region is incorrectly marked as containing speech Missed speech: percentage of scored time for which a speech region is incorrectly marked as not containing speech \\[\\text{DER} = \\frac{(\\text{Speaker error} + \\text{False alarm speech} + \\text{Missed speech})}{\\text{Total Duration}}\\] Note Many literature may only report the speaker error in DER. Be aware of such usage. To better understand the individual metrics we can refer an example call timeline that is shown below, Example timeline of an audio with two speakers A and B. We have the original and predicted diarization timeline for the complete duration of call denoted in green for A and pink for B. For each segment, we have mapping of 0, 1, 2, 3 and tick for overlap, speaker error, false alarm, missed speech and correct segment respectively. Note Here we assumed that the mapping of speakers A to A and B to B between the original and predicted transcript is available. But this doesn't happen many times. As diarization is a clustering task, the grouping might be correct but the naming of the groups could be different. One example is where the speaking order in original could be A, B, A, C, D but in predicted it could be X, Y, X, Z, M . While the diarization is correct, the naming is different which should be handeled separately. For this, we can use Hungarian Algorithm to find the optimal map between the speakers in original and predicted before computing the DER. Hint Some packages like [1] and [2] may request the data (both original and predicted timeline) in Rich Transcription Time Marked (RTTM) file format. It is a space-delimited text file that contains one line for each segment with details like start time, duration and speaker name. Refer this for more details. Here is the code to convert RTTM file to CSV file using pandas : 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 def convert_rttm_to_csv ( file ): \"\"\" Inputs: file: str file path of the rttm file to be converted Outputs: df: dataframe Dataframe containing the extracted information from the rttm file \"\"\" # read the file df = pd . read_csv ( file , delimiter = \" \" , header = None ) df = df [[ 3 , 4 , 7 ]] df . columns = [ 'start_time' , 'duration' , 'speaker_name' ] # compute the end time df [ 'end_time' ] = df [ 'start_time' ] + df [ 'duration' ] # convert time to miliseconds df [ 'start_time' ] *= 1000 df [ 'end_time' ] *= 1000 # sort the df based on the start_time df . sort_values ( by = [ 'start_time' ], inplace = True ) # return return df Jaccard Error Rate (JER) While DER is estimated on whole utterance, in JER, per-speaker error rates are computed and then averaged. In this way, JER tries to give equal weightage to each speaker. Below is the formulation of JER, \\[\\text{JER} = \\frac{1}{N} \\sum_{i}^{N_{ref}} \\frac{(\\text{Speaker Error}_i + \\text{False alarm speech}_i + \\text{Missed speech}_i)}{Total_i}\\] Here, \\(Total_i\\) is the union of ith speaker's speaking time in reference and predicted transcript. \\(N_{ref}\\) is the number of speakers in reference script. Note, contrary to DER, JER never exceed 100%. Code PyAnnote Pyannote Audio provides readymade models and neural building blocks for Speaker diarization and other speech related tasks. While the models are also available on HuggingFace , Pyannote is super easy to use. Below is an example from the github repository of the package: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 # Install pyannote ! pip install pyannote . audio # for mac if you get \"OSError: sndfile library not found\" # !conda install -c conda-forge libsndfile # instantiate pretrained speaker diarization pipeline from pyannote.audio import Pipeline pipeline = Pipeline . from_pretrained ( \"pyannote/speaker-diarization\" ) # apply pretrained pipeline diarization = pipeline ( \"audio.wav\" ) # print the result for turn , _ , speaker in diarization . itertracks ( yield_label = True ): print ( f \"start= { turn . start : .1f } s stop= { turn . end : .1f } s speaker_ { speaker } \" ) # start=0.2s stop=1.5s speaker_A # start=1.8s stop=3.9s speaker_B # start=4.2s stop=5.7s speaker_A # ... Hint As per my observation, PyAnnote is quite slow on CPU. Keep this in mind while experimenting with the package. References [1] PyAnnoteAudio - Code | Paper [2] Dscore - Code [3] A Review of Speaker Diarization: Recent Advances with Deep Learning - Paper [4] Low-dimensional speech representation based on Factor Analysis and its applications - Slides [5] Speaker Diarization with LSTM - Paper | Code | Video","title":"Speaker Diarization"},{"location":"audio_intelligence/speaker_diarization/#introduction","text":"Speaker Diarization is the process of segregating different speakers from an audio stream. It is used to answer the question \"who spoke when?\". So if the input is a audio stream with 5 speakers, the output will contain the timestamp in audio when different speakers spoke. A sample output for a conversation between 3 people (differers) could be, start=0.2s stop=1.5s speaker_A start=1.8s stop=3.9s speaker_B start=4.2s stop=5.7s speaker_A start=6.2s stop=7.8s speaker_C ... Note In speaker diarization we separate the speakers (cluster) and not identify them (classify). Hence the output contains anonymous identifiers like speaker_A , speaker_B , etc and not the actual names of the persons.","title":"Introduction"},{"location":"audio_intelligence/speaker_diarization/#traditional-diarization-approach","text":"The generic approach for Speaker Diarization [1] Traditional Speaker Diarization systems can be generalised into a 5 step process. These are, Feature extraction : here we transform the raw waveform into audio features like mel spectrogram. Voice activity detection : here we identify the chunks in the audio where some voice activity was observed. As we are not interested in silence and noise, we ignore those irrelevant chunks. Speaker change detection : here we identify the speaker changepoints in the conversation present in the audio. It is either capture by heuristic approach, classical algorithms or modern neural blocks. It will further divide the chunks from last step into subchunks. Speech turn representation : here we encode each subchunk by creating feature representations. Recent trends gives preference to neural approach where subchunks are encoded into context aware vector representation. Hint We can use any audio representation algorithm or model for this task. Recently d or x vectors are preferred. One example using speechbrain package is 1 2 3 4 5 6 7 # import import torch from speechbrain.pretrained import EncoderClassifier # load the model encoder = EncoderClassifier . from_hparams ( source = \"speechbrain/spkrec-ecapa-voxceleb\" ) # encode the audio chunk embedding = encoder . encode_batch ( torch . from_numpy ( chunk )) Speech turn clustering : here we cluster the subchunks based on their vector representation. Different clustering algorithms could be applied based on availability of cluster count ( k ) and embedding process of the previous step. Hint While Kmeans is the defacto clustering algorithm, it might create some problems if we use it for speaker diarization. Here are a few problem cases mentioned in [5] Non-gaussian data: speech data are often non-gaussian Imbalanced data: one person may speak more than the other Gender/Age/Racial effects: inter-gender difference is large but intra-gender differences are small. Overlapping speech: overlapping speech creates connections between clusters Kmeans algorithms will not be able to handle these issues of the audio data and will perform incorrect clustering. Hence we can use Spectral Clustering that can overcome all of the mentioned issues! The final output will be the clusters of different subchunks from the audio stream. Each cluster can be given an anonymous identifier (speaker_a, ..) and then it can be mapped with the audio stream to create the speaker aware audio timeline.","title":"Traditional Diarization Approach"},{"location":"audio_intelligence/speaker_diarization/#metrics","text":"","title":"Metrics"},{"location":"audio_intelligence/speaker_diarization/#diarization-error-rate-der","text":"Diarization error rate (DER) provides an estimation (O is good, higher is bad; max may exceed 100%) of diarization performance by calculating the sum of following individual metrics, [2] Speaker error: percentage of scored time for which the wrong speaker id is assigned within a speech region False alarm speech: percentage of scored time for which a nonspeech region is incorrectly marked as containing speech Missed speech: percentage of scored time for which a speech region is incorrectly marked as not containing speech \\[\\text{DER} = \\frac{(\\text{Speaker error} + \\text{False alarm speech} + \\text{Missed speech})}{\\text{Total Duration}}\\] Note Many literature may only report the speaker error in DER. Be aware of such usage. To better understand the individual metrics we can refer an example call timeline that is shown below, Example timeline of an audio with two speakers A and B. We have the original and predicted diarization timeline for the complete duration of call denoted in green for A and pink for B. For each segment, we have mapping of 0, 1, 2, 3 and tick for overlap, speaker error, false alarm, missed speech and correct segment respectively. Note Here we assumed that the mapping of speakers A to A and B to B between the original and predicted transcript is available. But this doesn't happen many times. As diarization is a clustering task, the grouping might be correct but the naming of the groups could be different. One example is where the speaking order in original could be A, B, A, C, D but in predicted it could be X, Y, X, Z, M . While the diarization is correct, the naming is different which should be handeled separately. For this, we can use Hungarian Algorithm to find the optimal map between the speakers in original and predicted before computing the DER. Hint Some packages like [1] and [2] may request the data (both original and predicted timeline) in Rich Transcription Time Marked (RTTM) file format. It is a space-delimited text file that contains one line for each segment with details like start time, duration and speaker name. Refer this for more details. Here is the code to convert RTTM file to CSV file using pandas : 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 def convert_rttm_to_csv ( file ): \"\"\" Inputs: file: str file path of the rttm file to be converted Outputs: df: dataframe Dataframe containing the extracted information from the rttm file \"\"\" # read the file df = pd . read_csv ( file , delimiter = \" \" , header = None ) df = df [[ 3 , 4 , 7 ]] df . columns = [ 'start_time' , 'duration' , 'speaker_name' ] # compute the end time df [ 'end_time' ] = df [ 'start_time' ] + df [ 'duration' ] # convert time to miliseconds df [ 'start_time' ] *= 1000 df [ 'end_time' ] *= 1000 # sort the df based on the start_time df . sort_values ( by = [ 'start_time' ], inplace = True ) # return return df","title":"Diarization Error Rate (DER)"},{"location":"audio_intelligence/speaker_diarization/#jaccard-error-rate-jer","text":"While DER is estimated on whole utterance, in JER, per-speaker error rates are computed and then averaged. In this way, JER tries to give equal weightage to each speaker. Below is the formulation of JER, \\[\\text{JER} = \\frac{1}{N} \\sum_{i}^{N_{ref}} \\frac{(\\text{Speaker Error}_i + \\text{False alarm speech}_i + \\text{Missed speech}_i)}{Total_i}\\] Here, \\(Total_i\\) is the union of ith speaker's speaking time in reference and predicted transcript. \\(N_{ref}\\) is the number of speakers in reference script. Note, contrary to DER, JER never exceed 100%.","title":"Jaccard Error Rate (JER)"},{"location":"audio_intelligence/speaker_diarization/#code","text":"","title":"Code"},{"location":"audio_intelligence/speaker_diarization/#pyannote","text":"Pyannote Audio provides readymade models and neural building blocks for Speaker diarization and other speech related tasks. While the models are also available on HuggingFace , Pyannote is super easy to use. Below is an example from the github repository of the package: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 # Install pyannote ! pip install pyannote . audio # for mac if you get \"OSError: sndfile library not found\" # !conda install -c conda-forge libsndfile # instantiate pretrained speaker diarization pipeline from pyannote.audio import Pipeline pipeline = Pipeline . from_pretrained ( \"pyannote/speaker-diarization\" ) # apply pretrained pipeline diarization = pipeline ( \"audio.wav\" ) # print the result for turn , _ , speaker in diarization . itertracks ( yield_label = True ): print ( f \"start= { turn . start : .1f } s stop= { turn . end : .1f } s speaker_ { speaker } \" ) # start=0.2s stop=1.5s speaker_A # start=1.8s stop=3.9s speaker_B # start=4.2s stop=5.7s speaker_A # ... Hint As per my observation, PyAnnote is quite slow on CPU. Keep this in mind while experimenting with the package.","title":"PyAnnote"},{"location":"audio_intelligence/speaker_diarization/#references","text":"[1] PyAnnoteAudio - Code | Paper [2] Dscore - Code [3] A Review of Speaker Diarization: Recent Advances with Deep Learning - Paper [4] Low-dimensional speech representation based on Factor Analysis and its applications - Slides [5] Speaker Diarization with LSTM - Paper | Code | Video","title":"References"},{"location":"audio_intelligence/stt/","text":"Introduction Speech to text task is known by many names, be it Audio Transcription or Automatic Speech Recognition (ASR). Basically, it's the process of generating textual transcription of a conversation, where speech is provided as input and we get text as output. The task is quite complex as it has depedency on several other modules like Voice activity detection and speaker diarization. On top of this, the core transcription module should be able to handle multiple languages, accents and domain specific keywords. We can perform transcriptions in either of the two ways, Online transcription : this is for live use cases where you want to stream ongoing data and perform transcription on the go. For example, an ongoing sales or customer support call or news event, etc. Usually a smaller and faster (hence less accurate) model is used for this. Offline transcription : this is delayed transcription that happens after the call or recording is over. For example, recorded telecast or OTT series's caption, etc. Usually a relatively bigger and more accurate (hence slower) model is used for this. a painting of a woman hand to ear listening to some speech and writing it down in a paper; (Generated by DallE) Available Solutions (Paid) There are a lot of ASR services available online. In fact, there are several startups that only does ASR \ud83e\udd2f. We will try to go through some paid as well as open source options for ASR, so that you are ready to use ASR from day zero using paid option or built an inhouse option using the open source ones. Some of the most popular, widely used but paid ones are as follows, (by any means not the complete list) Amazon Transcribe Google Speech to text Azure Speech to text : AssemblyAI Deepgram Note Please consider accuracy, additional features and cost before selecting any of the above services for your use case. The accuracy of each of the above services are arguably on par, but may vary based on specific domain. For example, one may perform better for healthcare domain, while the other on sales. Be sure to try them out before selecting one. There are several advantages of using paid ASR services, They take care of the major headache in the transcription space like support for multiple languages and accents. Their model is constantly fine-tuned to improve the performance on existing language and even to add support for new ones. They take care of output formatting like adding punctuations, sentence breaks, speaker diarization etc. Otherwise for a inhouse ASR system, we will have to build these as well. They take care of hosting the ASR models. Depending on the use case (for example if you want live transcription which needs to be super fast) , we may end up spending substantially on GPU computes and hardware management. Finally, there has been a trend of providing audio intelligence features on top of core transcription services. For example, AssemblyAI provides add on services like sentiment analysis, entity detection, Auto Chapters, etc. The one and the major con of these services, as you might have guessed, is cost \ud83d\udcb2 Available Solutions (Open Source) Apart from the paid services, there are a lot of open source ASR services available. They are available in form of packages, frameworks or even trained AI models. One major advantage of these models (apart for being free) is that we can further finetune them for our usecase (ex: for our domain and accent) . But this comes at a cost of having to deploy and maintain the complete system model ourself. Some of the widely used open-source ASR systems (\ud83d\udce6 is package and \ud83e\udde0 is model) are, \ud83d\udce6 TensorFlowASR \ud83d\udce6 DeepSpeech \ud83e\udde0 Wav2Vec2 \ud83e\udde0 HuBERT Evaluation Metrics While there are multiple options for ASR, it is important to know how to quantitively compare different ASR systems. To begin, we need the data -- (1) golden transcripts i.e. the source of truth generated by some accurate model or human annotators, and (2) system outputs i.e. output of the model you want to test. Comparing golden transcripts against system output, at word level, we can observe following scenarios -- (1) hit ( \\(H\\) ): word matching in both, (2) insertion ( \\(I\\) ): words only present in system output but not in golden transcripts, (3) deletion ( \\(D\\) ): words only present in golden transcripts but not in system output, (4) substitution ( \\(S\\) ): words substitued in the system output. Word classification of golden transcription (below) \u201cmy computer\u2019s deaf in\u2019he?\u201d against the system output (above) [1] To evaluate the performance, first we need to align the two sentences such that it minimises the errors i.e \\(S+D+I\\) . For this Viterbi alignment is the preferred choice. Once done, we just need to use any metric to quantify the performance. They could be, WER (Word error rate) WER is the proportion of the word errors to the words processed. It's formula is as follows, \\[WER = \\frac{S+D+I}{H+S+D}\\] Note The upper range of WER is not bounded by 1, but by \\(max(N_g, N_s)/N_g\\) where \\(N_g\\) is the number of words in the golden transcription and \\(N_s\\) is the number of words in the system output. So don't be suprised if you get WER > 1, it just means that the system output has a lot more insertions than required (which is bad btw \u274c) MER (Match error rate) MER normalizes the WER by considering the insertions in the denominator. It's formula is as follows, \\[MER = \\frac{S+D+I}{H+S+D+I}\\] Hint If for some transcription, \\(MER = WER\\) , it means there are 0 insertions. Code Recent advances in artificial intelligence and specially transfer learning has lead to release of several pre-trained ASR models that are ready to use from day one. In case you want to improve the accuracy for your domain, we can even fine tune the model further! One of the most famous models right now is Facebook's Wav2Vec2 . We will use this model for coding purposes. More models can be found here . Offline transcription using Wav2Vec2 (CTC) Here is the code to perform offline transcription using Wav2Vec2 model with transformer package. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 # import import torch import librosa from transformers import Wav2Vec2ForCTC , Wav2Vec2Tokenizer # load the tokenizer and model tokenizer = Wav2Vec2Tokenizer . from_pretrained ( \"facebook/wav2vec2-large-960h\" ) model = Wav2Vec2ForCTC . from_pretrained ( \"facebook/wav2vec2-large-960h\" ) # load the audio data (use your own wav file here!) input_audio , sr = librosa . load ( 'my_wav_file.wav' , sr = 16000 ) # tokenize input_values = tokenizer ( input_audio , return_tensors = \"pt\" , padding = \"longest\" ) . input_values # retrieve logits logits = model ( input_values ) . logits # take argmax and decode predicted_ids = torch . argmax ( logits , dim =- 1 ) transcription = tokenizer . batch_decode ( predicted_ids ) # print the output print ( transcription ) Online transcription using Wav2Vec2 (CTC) For live transcription using Wav2Vec2, we can utilize wav2vec2-live package. Once you have cloned the repo and installed the packages from requirements.txt , the live transcription can be started with (taken from the package readme and modified) , 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 # import from live_asr import LiveWav2Vec2 # load model english_model = \"facebook/wav2vec2-large-960h-lv60-self\" asr = LiveWav2Vec2 ( english_model , device_name = \"default\" ) # start the live ASR asr . start () try : while True : text , sample_length , inference_time = asr . get_last_text () print ( f \"Duration: { sample_length : .3f } s \\t Speed: { inference_time : .3f } s \\t { text } \" ) except KeyboardInterrupt : asr . stop () This starts the Live ASR on your terminal. The code listen to the audio in your microphone, identifies the chunks with voice using VAD and then pass the voiced chunks to Wave2Vec2 for transcription. One sample output is shown below, listening to your voice Duration: 0 .780s Speed: 0 .205s hello Duration: 0 .780s Speed: 0 .190s hello Duration: 0 .960s Speed: 0 .223s my name .... References [1] From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition Cheers","title":"Speech to Text"},{"location":"audio_intelligence/stt/#introduction","text":"Speech to text task is known by many names, be it Audio Transcription or Automatic Speech Recognition (ASR). Basically, it's the process of generating textual transcription of a conversation, where speech is provided as input and we get text as output. The task is quite complex as it has depedency on several other modules like Voice activity detection and speaker diarization. On top of this, the core transcription module should be able to handle multiple languages, accents and domain specific keywords. We can perform transcriptions in either of the two ways, Online transcription : this is for live use cases where you want to stream ongoing data and perform transcription on the go. For example, an ongoing sales or customer support call or news event, etc. Usually a smaller and faster (hence less accurate) model is used for this. Offline transcription : this is delayed transcription that happens after the call or recording is over. For example, recorded telecast or OTT series's caption, etc. Usually a relatively bigger and more accurate (hence slower) model is used for this. a painting of a woman hand to ear listening to some speech and writing it down in a paper; (Generated by DallE)","title":"Introduction"},{"location":"audio_intelligence/stt/#available-solutions-paid","text":"There are a lot of ASR services available online. In fact, there are several startups that only does ASR \ud83e\udd2f. We will try to go through some paid as well as open source options for ASR, so that you are ready to use ASR from day zero using paid option or built an inhouse option using the open source ones. Some of the most popular, widely used but paid ones are as follows, (by any means not the complete list) Amazon Transcribe Google Speech to text Azure Speech to text : AssemblyAI Deepgram Note Please consider accuracy, additional features and cost before selecting any of the above services for your use case. The accuracy of each of the above services are arguably on par, but may vary based on specific domain. For example, one may perform better for healthcare domain, while the other on sales. Be sure to try them out before selecting one. There are several advantages of using paid ASR services, They take care of the major headache in the transcription space like support for multiple languages and accents. Their model is constantly fine-tuned to improve the performance on existing language and even to add support for new ones. They take care of output formatting like adding punctuations, sentence breaks, speaker diarization etc. Otherwise for a inhouse ASR system, we will have to build these as well. They take care of hosting the ASR models. Depending on the use case (for example if you want live transcription which needs to be super fast) , we may end up spending substantially on GPU computes and hardware management. Finally, there has been a trend of providing audio intelligence features on top of core transcription services. For example, AssemblyAI provides add on services like sentiment analysis, entity detection, Auto Chapters, etc. The one and the major con of these services, as you might have guessed, is cost \ud83d\udcb2","title":"Available Solutions (Paid)"},{"location":"audio_intelligence/stt/#available-solutions-open-source","text":"Apart from the paid services, there are a lot of open source ASR services available. They are available in form of packages, frameworks or even trained AI models. One major advantage of these models (apart for being free) is that we can further finetune them for our usecase (ex: for our domain and accent) . But this comes at a cost of having to deploy and maintain the complete system model ourself. Some of the widely used open-source ASR systems (\ud83d\udce6 is package and \ud83e\udde0 is model) are, \ud83d\udce6 TensorFlowASR \ud83d\udce6 DeepSpeech \ud83e\udde0 Wav2Vec2 \ud83e\udde0 HuBERT","title":"Available Solutions (Open Source)"},{"location":"audio_intelligence/stt/#evaluation-metrics","text":"While there are multiple options for ASR, it is important to know how to quantitively compare different ASR systems. To begin, we need the data -- (1) golden transcripts i.e. the source of truth generated by some accurate model or human annotators, and (2) system outputs i.e. output of the model you want to test. Comparing golden transcripts against system output, at word level, we can observe following scenarios -- (1) hit ( \\(H\\) ): word matching in both, (2) insertion ( \\(I\\) ): words only present in system output but not in golden transcripts, (3) deletion ( \\(D\\) ): words only present in golden transcripts but not in system output, (4) substitution ( \\(S\\) ): words substitued in the system output. Word classification of golden transcription (below) \u201cmy computer\u2019s deaf in\u2019he?\u201d against the system output (above) [1] To evaluate the performance, first we need to align the two sentences such that it minimises the errors i.e \\(S+D+I\\) . For this Viterbi alignment is the preferred choice. Once done, we just need to use any metric to quantify the performance. They could be,","title":"Evaluation Metrics"},{"location":"audio_intelligence/stt/#wer-word-error-rate","text":"WER is the proportion of the word errors to the words processed. It's formula is as follows, \\[WER = \\frac{S+D+I}{H+S+D}\\] Note The upper range of WER is not bounded by 1, but by \\(max(N_g, N_s)/N_g\\) where \\(N_g\\) is the number of words in the golden transcription and \\(N_s\\) is the number of words in the system output. So don't be suprised if you get WER > 1, it just means that the system output has a lot more insertions than required (which is bad btw \u274c)","title":"WER (Word error rate)"},{"location":"audio_intelligence/stt/#mer-match-error-rate","text":"MER normalizes the WER by considering the insertions in the denominator. It's formula is as follows, \\[MER = \\frac{S+D+I}{H+S+D+I}\\] Hint If for some transcription, \\(MER = WER\\) , it means there are 0 insertions.","title":"MER (Match error rate)"},{"location":"audio_intelligence/stt/#code","text":"Recent advances in artificial intelligence and specially transfer learning has lead to release of several pre-trained ASR models that are ready to use from day one. In case you want to improve the accuracy for your domain, we can even fine tune the model further! One of the most famous models right now is Facebook's Wav2Vec2 . We will use this model for coding purposes. More models can be found here .","title":"Code"},{"location":"audio_intelligence/stt/#offline-transcription-using-wav2vec2-ctc","text":"Here is the code to perform offline transcription using Wav2Vec2 model with transformer package. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 # import import torch import librosa from transformers import Wav2Vec2ForCTC , Wav2Vec2Tokenizer # load the tokenizer and model tokenizer = Wav2Vec2Tokenizer . from_pretrained ( \"facebook/wav2vec2-large-960h\" ) model = Wav2Vec2ForCTC . from_pretrained ( \"facebook/wav2vec2-large-960h\" ) # load the audio data (use your own wav file here!) input_audio , sr = librosa . load ( 'my_wav_file.wav' , sr = 16000 ) # tokenize input_values = tokenizer ( input_audio , return_tensors = \"pt\" , padding = \"longest\" ) . input_values # retrieve logits logits = model ( input_values ) . logits # take argmax and decode predicted_ids = torch . argmax ( logits , dim =- 1 ) transcription = tokenizer . batch_decode ( predicted_ids ) # print the output print ( transcription )","title":"Offline transcription using Wav2Vec2 (CTC)"},{"location":"audio_intelligence/stt/#online-transcription-using-wav2vec2-ctc","text":"For live transcription using Wav2Vec2, we can utilize wav2vec2-live package. Once you have cloned the repo and installed the packages from requirements.txt , the live transcription can be started with (taken from the package readme and modified) , 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 # import from live_asr import LiveWav2Vec2 # load model english_model = \"facebook/wav2vec2-large-960h-lv60-self\" asr = LiveWav2Vec2 ( english_model , device_name = \"default\" ) # start the live ASR asr . start () try : while True : text , sample_length , inference_time = asr . get_last_text () print ( f \"Duration: { sample_length : .3f } s \\t Speed: { inference_time : .3f } s \\t { text } \" ) except KeyboardInterrupt : asr . stop () This starts the Live ASR on your terminal. The code listen to the audio in your microphone, identifies the chunks with voice using VAD and then pass the voiced chunks to Wave2Vec2 for transcription. One sample output is shown below, listening to your voice Duration: 0 .780s Speed: 0 .205s hello Duration: 0 .780s Speed: 0 .190s hello Duration: 0 .960s Speed: 0 .223s my name ....","title":"Online transcription using Wav2Vec2 (CTC)"},{"location":"audio_intelligence/stt/#references","text":"[1] From WER and RIL to MER and WIL: improved evaluation measures for connected speech recognition Cheers","title":"References"},{"location":"audio_intelligence/tts/","text":"Introduction Text to Speech (TTS) is the process of generating synthesized speech for a given text input. This is a complicated task as the generating system has to consider context-based pronunciations, tone, rhythm, language, accent, etc. That said, recent research has achieved significant improvement in overall performance. Speech synthesis is quite old; in fact, Wolfgang von Kempelen, a Hungarian scientist, constructed a speaking machine with a series of bellows, springs, and bagpipes in the second half of the 18th century. Before moving forward, let\u2019s get accustomed to some basic terms related to speech, Phoneme: It is the smallest unit of speech. In English language there are a total of 44 phonemes. Grapheme: Group of letters that represent speech. Count is 250 Syllable: Combination of phonemes to create an intelligent pattern of speech. Count is ~15k Wolfgang von Kempelen ... second half of the 18th century (Created by Stable Diffusion 2.1) Types of TTS Let's start with a quick detour of different important types of TTS systems, [1] Concatenative Synthesis: Concatenative synthesis is based on combining pieces of speech stored in a database. This database usually contains speech units ranging from full sentences to individual syllables, all recorded by voice actors. There are two major problems with this approach, (a) we require a huge database of recordings (which is nearly impossible if you consider all combinations) , and (b) the generated voice is not that smooth. Statistical Parametric Speech Synthesis : SPSS usually consists of three components: a text analysis module, a parameter prediction module (acoustic model), and a vocoder analysis/synthesis module (vocoder). The text analysis module first processes the text, and then extracts the linguistic features, such as phonemes, duration and POS tags from different granularities. The acoustic model process the linguistic features to generate acoustic features which is then processed by the vocoder to generate the waveform. Neural Speech Synthesis: Recent enhancements in Neural Network (NN) based approaches have led to the use of NN in TTS. Usually, these models takes either of the two approaches, (a) replace one or many of the SPSS components with respective NN models, or (b) use an end-to-end NN based model that replaces all SPSS components with one NN. Components of TTS Next, let\u2019s discuss the components of the SPSS in more detail as they are the common denominator in TTS systems. We will go through them one by one. [1] Text analysis: It is used to extract linguistic features from the raw text data. Some common tasks are, Text normalization: We can start with converting non-standard words into spoken forms which can make the words easier to pronounce for the TTS models. Ex: year \u201c1989\u201d into \u201cnineteen eighty-nine\u201d Word Segmentation: Identifying different words in a text seems trivial in alphabet-based languages like English (use spaces) but it is quite tedious in character-based languages like Chinese. POS tagging: Identifying part of speech (like noun, adjective, etc) is important as different words have different pronunciations based on where and how they are used. Prosody prediction: Rhythm, stress, intonation corresponds to variations in syllable duration, loudness and pitch. This plays an important part on how human-like the speech is. Prosody prediction used tagging system to label each kind of prosody and ToBI (tagging and break indices) is a popular tagging system for English. Grapheme to Phoneme Conversion: Converting characters to pronunciation can greatly help with the speech synthesis process. Ex: \u201cspeech\u201d is converted to \u201cs p iy ch\u201d. Acoustic Models: They generate acoustic features from linguistics features or directly from phonemes or characters. While there are several models used in SPSS systems, let\u2019s focus on NN based approaches. RNN-based models: Tacotron leverages an encoder-attention-decoder framework and takes characters as input and outputs linear-spectrograms, and uses Griffin Lim algorithm to generate waveform. Tacotron 2 generates mel-spectrograms and converts them into waveform using an additional WaveNet model. CNN-based models: DeepVoice utilises convolutional neural networks to obtain linguistic features. Then it leverages a WaveNet based vocoder to generate waveform. DeepVoice 2 introduced multi-speaker modeling. DeepVoice 3 leverages a fully-convolutional network structure for speech synthesis, which generates mel-spectrograms from characters and can scale up to real-word multi-speaker datasets. Transformers-based models: TransformerTTS leverage transformer based encoder-attention-decoder architecture to generate mel-spectrogram form phonemes. It tackles two flaws of RNN, (a) RNN based encoder and decoder cannot be trained in parallel due to their recurrent nature, and (b) RNN is not good for long generations. While the voice quality is on par with Tacotron, the generations are not that robust (ex: same word repeating multiple times or missing some words) . FastSpeech mitigated the issues by adopting fast-forward Transformer network and removing the attention mechanism between text and speech. (It is deployed in AzureTTS services) . FastSpeech 2 further improves the overall performance. Vocoder: Early neural vocoders such as WaveNet, Char2Wav, WaveRNN directly take linguistic features as input and generate waveform. Later versions take mel-spectrograms as input and generate waveform. Since speech waveform is very long, autoregressive waveform generation takes much inference time. Thus, generative models such as Flow, GAN, VAE, and DDPM (Denoising Diffusion Probabilistic Model, Diffusion for short) are used in waveform generation. Different process of TTS Systems. Source [1] Code There are lot of open source python package for TTS like Coqui TTS , Mozilla TTS , OpenTTS , ESPNet , PaddleSpeech , etc. Let's go through some of the most famous and easy to use ones, Coqui TTS For this tutorial, let's use Coqui TTS as it is one of the simplest package in terms of usability. In fact you just need to install the package with pip install TTS and then run the server with tts-server , and thats it! It will run a http dashboard on the localhost woth default model and vocoder like shown below, Coqui TTS server dashboard I tried it for \"my name is Mohit\" text and the result is shared below. Btw you can switch to different models or speakers to get different sounding speech. Your browser does not support the audio element. You can check out other models and vocoder available in the package with tts-server --list_models . Note, not all models and vocoder pairs are comparable. On top of this, Coqui TTS also provides the option to train and finetune the models further! OpenTTS Another good package is OpenTTS that unifies access to multiple open source text to speech systems and voices for many languages. One distinctive feature is the partial support to SSML i.e. Speech Synthesis Markup Language. It is a XML-based markup language for assisting the generation of synthetic speech in Web and other applications. One example as shared in their readme is shown below, 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 The 1st thing to remember is that 27 languages are supported in Open TTS as of 10/13/2021 at 3pm. The current voice can be changed, even to a different text to speech system! Breaks are possible between sentences. One language is never enough Eine Sprache ist niemals genug \u8a00\u8a9e\u3092\u4e00\u3064\u306f\u6c7a\u3057\u3066\u8db3\u308a\u306a\u3044 Lugha moja haitoshi SSML support can lead to generation of complex and realistic sound as you can add sentence breaks, pauses, handle spelling out of numbers or date, change model or even languages for a single generation! The package is quite simple to run. First you need to install docker , and then download and run a docker image with docker run -it -p 5500:5500 synesthesiam/opentts: , where could be any of the 20 supported langauge. To begin with you can try en . The downloading will take some time (more than 5GB is downloaded!) but once done, you can access the dashboard on http://localhost:5500 or hit the HTTP APIs on http://localhost:5500/openapi/ . The endpoint details can be found here and the complete list of voices generated by the available models is shared here . Dashboard of OpenTTS I tried it for text \"Hello, how are you? My number is 7350.\" by selecting the coqui-tts: vctk model and ED (0) speaker. The output is quite good and shared below, Your browser does not support the audio element. Additional Materials [1] Paper - A Survey on Neural Speech Synthesis [2] Speech synthesis: A review of the best text to speech architectures with Deep Learning","title":"Text to Speech"},{"location":"audio_intelligence/tts/#introduction","text":"Text to Speech (TTS) is the process of generating synthesized speech for a given text input. This is a complicated task as the generating system has to consider context-based pronunciations, tone, rhythm, language, accent, etc. That said, recent research has achieved significant improvement in overall performance. Speech synthesis is quite old; in fact, Wolfgang von Kempelen, a Hungarian scientist, constructed a speaking machine with a series of bellows, springs, and bagpipes in the second half of the 18th century. Before moving forward, let\u2019s get accustomed to some basic terms related to speech, Phoneme: It is the smallest unit of speech. In English language there are a total of 44 phonemes. Grapheme: Group of letters that represent speech. Count is 250 Syllable: Combination of phonemes to create an intelligent pattern of speech. Count is ~15k Wolfgang von Kempelen ... second half of the 18th century (Created by Stable Diffusion 2.1)","title":"Introduction"},{"location":"audio_intelligence/tts/#types-of-tts","text":"Let's start with a quick detour of different important types of TTS systems, [1] Concatenative Synthesis: Concatenative synthesis is based on combining pieces of speech stored in a database. This database usually contains speech units ranging from full sentences to individual syllables, all recorded by voice actors. There are two major problems with this approach, (a) we require a huge database of recordings (which is nearly impossible if you consider all combinations) , and (b) the generated voice is not that smooth. Statistical Parametric Speech Synthesis : SPSS usually consists of three components: a text analysis module, a parameter prediction module (acoustic model), and a vocoder analysis/synthesis module (vocoder). The text analysis module first processes the text, and then extracts the linguistic features, such as phonemes, duration and POS tags from different granularities. The acoustic model process the linguistic features to generate acoustic features which is then processed by the vocoder to generate the waveform. Neural Speech Synthesis: Recent enhancements in Neural Network (NN) based approaches have led to the use of NN in TTS. Usually, these models takes either of the two approaches, (a) replace one or many of the SPSS components with respective NN models, or (b) use an end-to-end NN based model that replaces all SPSS components with one NN.","title":"Types of TTS"},{"location":"audio_intelligence/tts/#components-of-tts","text":"Next, let\u2019s discuss the components of the SPSS in more detail as they are the common denominator in TTS systems. We will go through them one by one. [1] Text analysis: It is used to extract linguistic features from the raw text data. Some common tasks are, Text normalization: We can start with converting non-standard words into spoken forms which can make the words easier to pronounce for the TTS models. Ex: year \u201c1989\u201d into \u201cnineteen eighty-nine\u201d Word Segmentation: Identifying different words in a text seems trivial in alphabet-based languages like English (use spaces) but it is quite tedious in character-based languages like Chinese. POS tagging: Identifying part of speech (like noun, adjective, etc) is important as different words have different pronunciations based on where and how they are used. Prosody prediction: Rhythm, stress, intonation corresponds to variations in syllable duration, loudness and pitch. This plays an important part on how human-like the speech is. Prosody prediction used tagging system to label each kind of prosody and ToBI (tagging and break indices) is a popular tagging system for English. Grapheme to Phoneme Conversion: Converting characters to pronunciation can greatly help with the speech synthesis process. Ex: \u201cspeech\u201d is converted to \u201cs p iy ch\u201d. Acoustic Models: They generate acoustic features from linguistics features or directly from phonemes or characters. While there are several models used in SPSS systems, let\u2019s focus on NN based approaches. RNN-based models: Tacotron leverages an encoder-attention-decoder framework and takes characters as input and outputs linear-spectrograms, and uses Griffin Lim algorithm to generate waveform. Tacotron 2 generates mel-spectrograms and converts them into waveform using an additional WaveNet model. CNN-based models: DeepVoice utilises convolutional neural networks to obtain linguistic features. Then it leverages a WaveNet based vocoder to generate waveform. DeepVoice 2 introduced multi-speaker modeling. DeepVoice 3 leverages a fully-convolutional network structure for speech synthesis, which generates mel-spectrograms from characters and can scale up to real-word multi-speaker datasets. Transformers-based models: TransformerTTS leverage transformer based encoder-attention-decoder architecture to generate mel-spectrogram form phonemes. It tackles two flaws of RNN, (a) RNN based encoder and decoder cannot be trained in parallel due to their recurrent nature, and (b) RNN is not good for long generations. While the voice quality is on par with Tacotron, the generations are not that robust (ex: same word repeating multiple times or missing some words) . FastSpeech mitigated the issues by adopting fast-forward Transformer network and removing the attention mechanism between text and speech. (It is deployed in AzureTTS services) . FastSpeech 2 further improves the overall performance. Vocoder: Early neural vocoders such as WaveNet, Char2Wav, WaveRNN directly take linguistic features as input and generate waveform. Later versions take mel-spectrograms as input and generate waveform. Since speech waveform is very long, autoregressive waveform generation takes much inference time. Thus, generative models such as Flow, GAN, VAE, and DDPM (Denoising Diffusion Probabilistic Model, Diffusion for short) are used in waveform generation. Different process of TTS Systems. Source [1]","title":"Components of TTS"},{"location":"audio_intelligence/tts/#code","text":"There are lot of open source python package for TTS like Coqui TTS , Mozilla TTS , OpenTTS , ESPNet , PaddleSpeech , etc. Let's go through some of the most famous and easy to use ones,","title":"Code"},{"location":"audio_intelligence/tts/#coqui-tts","text":"For this tutorial, let's use Coqui TTS as it is one of the simplest package in terms of usability. In fact you just need to install the package with pip install TTS and then run the server with tts-server , and thats it! It will run a http dashboard on the localhost woth default model and vocoder like shown below, Coqui TTS server dashboard I tried it for \"my name is Mohit\" text and the result is shared below. Btw you can switch to different models or speakers to get different sounding speech. Your browser does not support the audio element. You can check out other models and vocoder available in the package with tts-server --list_models . Note, not all models and vocoder pairs are comparable. On top of this, Coqui TTS also provides the option to train and finetune the models further!","title":"Coqui TTS"},{"location":"audio_intelligence/tts/#opentts","text":"Another good package is OpenTTS that unifies access to multiple open source text to speech systems and voices for many languages. One distinctive feature is the partial support to SSML i.e. Speech Synthesis Markup Language. It is a XML-based markup language for assisting the generation of synthetic speech in Web and other applications. One example as shared in their readme is shown below, 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 The 1st thing to remember is that 27 languages are supported in Open TTS as of 10/13/2021 at 3pm. The current voice can be changed, even to a different text to speech system! Breaks are possible between sentences. One language is never enough Eine Sprache ist niemals genug \u8a00\u8a9e\u3092\u4e00\u3064\u306f\u6c7a\u3057\u3066\u8db3\u308a\u306a\u3044 Lugha moja haitoshi SSML support can lead to generation of complex and realistic sound as you can add sentence breaks, pauses, handle spelling out of numbers or date, change model or even languages for a single generation! The package is quite simple to run. First you need to install docker , and then download and run a docker image with docker run -it -p 5500:5500 synesthesiam/opentts: , where could be any of the 20 supported langauge. To begin with you can try en . The downloading will take some time (more than 5GB is downloaded!) but once done, you can access the dashboard on http://localhost:5500 or hit the HTTP APIs on http://localhost:5500/openapi/ . The endpoint details can be found here and the complete list of voices generated by the available models is shared here . Dashboard of OpenTTS I tried it for text \"Hello, how are you? My number is 7350.\" by selecting the coqui-tts: vctk model and ED (0) speaker. The output is quite good and shared below, Your browser does not support the audio element.","title":"OpenTTS"},{"location":"audio_intelligence/tts/#additional-materials","text":"[1] Paper - A Survey on Neural Speech Synthesis [2] Speech synthesis: A review of the best text to speech architectures with Deep Learning","title":"Additional Materials"},{"location":"audio_intelligence/voice_activity_detection/","text":"Introduction \ud83d\udde3 Voice activity detection (VAD) is the process of identifying the chunks or parts of an audio stream that contains certain \"voiced activities\". There could be different types of activity detection modules depending on the type of voice we want to identify. It could be human voice (in a conversation) or animal voice (in forest) or something else entirely! a drawing of head of human, dog and cat speaking something Created using DallE Steps in VAD The complete VAD process can be broken down to two simple steps, Step 1: we start with dividing the audio into multiple chunks of small sizes. Usually these chunks are quite small like 10ms , 20ms or 30ms . Step 2: we have a classifier or detector, that takes the chunk as input and predicts if the chunk has voice or not. The classifier could be a simple logic based algorithm or even neural network models. It depends on the acceptable tradeoff between accuracy and speed. Code Py-WebRTC VAD For practice, we will use Py-WebRTC VAD package that is a port to the WebRTC project by Google. It provides sufficiently good accuracy with lightening speed! \u26a1\ufe0f The complete code is shown below, 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 # Python 3.8 on Macbook Pro M1 2020 # import import struct import librosa # librosa==0.9.1 import webrtcvad # webrtcvad==2.0.10 # load data file_path = 'hello_my_name_is_mohit.wav' # load wav file (librosa) y , sr = librosa . load ( file_path , sr = 16000 ) # convert the file to int if it is in float (Py-WebRTC requirement) if y . dtype . kind == 'f' : # convert to int16 y = np . array ([ int ( s * 32768 ) for s in y ]) # bound y [ y > 32767 ] = 32767 y [ y < - 32768 ] = - 32768 # create raw sample in bit raw_samples = struct . pack ( \" %d h\" % len ( y ), * y ) # define webrtcvad VAD vad = webrtcvad . Vad ( 3 ) # set aggressiveness from 0 to 3 window_duration = 0.03 # duration in seconds samples_per_window = int ( window_duration * sr + 0.5 ) bytes_per_sample = 2 # for int16 # Start classifying chunks of samples # var to hold segment wise report segments = [] # iterate over the audio samples for i , start in enumerate ( np . arange ( 0 , len ( y ), samples_per_window )): stop = min ( start + samples_per_window , len ( y )) loc_raw_sample = raw_samples [ start * bytes_per_sample : stop * bytes_per_sample ] try : is_speech = vad . is_speech ( loc_raw_sample , sample_rate = sr ) segments . append ( dict ( start = start , stop = stop , is_speech = is_speech )) except Exception as e : print ( f \"Failed for step { i } , reason: { e } \" ) # import pandas as pd # pd.DataFrame(segments) # result of classification Let's go through the code, Line 3-5 : we import the required packages. Make sure to install them using pip before starting. In case you are facing some issue, please install the specificed python and package versions (mentioned in the code) . Line 7- 18 : we read a sample wav file (use your own ) and then transform the bit depth of the audio into int16 . One point to note here is that webrtcvad only works for sample rate = 16000 and bit depth = int16 . And librosa loads an audio file in float. Because of all this requirement we need to perform the transformations. Line 21 : we transform the numpy array (format in which an audio file is loaded in librosa ) to byte string. This will be required for chunking and VAD analysis. Line 24-27 : we initialize an instance of webrtcvad with aggressiveness parameter. Note, the range is form 0 to 3, and higher the value, the more strict VAD is in classification chunks as voice. This means, you can miss some relevant voice chunks for higher aggressiveness and on the other hand get some false positives with lower aggressiveness. Line 31-45 : the code to first create chunks of the audio and then perform VAD classification at line 37-38 . The final results is stored in segments variable and a sample output is shown below, start stop is_speech 0 480 True 480 960 True 960 1440 False 1440 1920 False 1920 2400 False Here, each row denotes one chunk. The start and stop columns contain the begin and end details of each chunk. Finally the is_speech column contains True or False value depedening on if the chunk was detected as voice chunk or not. Here is the code's output visualized in form of waveform with vice chunks highlighted. Waveform of audio with webrtcvad detected voice chunks highlighted with yellow line on top. The aggressiveness parameter value was 0, hence lot's of false positive (chunks with no voice) are detected as well. Same as above, but with aggressiveness parameter value set to 3. Hence the detection is quite strict (some voice parts are missed) .","title":"Voice Activity Detection"},{"location":"audio_intelligence/voice_activity_detection/#introduction","text":"\ud83d\udde3 Voice activity detection (VAD) is the process of identifying the chunks or parts of an audio stream that contains certain \"voiced activities\". There could be different types of activity detection modules depending on the type of voice we want to identify. It could be human voice (in a conversation) or animal voice (in forest) or something else entirely! a drawing of head of human, dog and cat speaking something Created using DallE","title":"Introduction"},{"location":"audio_intelligence/voice_activity_detection/#steps-in-vad","text":"The complete VAD process can be broken down to two simple steps, Step 1: we start with dividing the audio into multiple chunks of small sizes. Usually these chunks are quite small like 10ms , 20ms or 30ms . Step 2: we have a classifier or detector, that takes the chunk as input and predicts if the chunk has voice or not. The classifier could be a simple logic based algorithm or even neural network models. It depends on the acceptable tradeoff between accuracy and speed.","title":"Steps in VAD"},{"location":"audio_intelligence/voice_activity_detection/#code","text":"","title":"Code"},{"location":"audio_intelligence/voice_activity_detection/#py-webrtc-vad","text":"For practice, we will use Py-WebRTC VAD package that is a port to the WebRTC project by Google. It provides sufficiently good accuracy with lightening speed! \u26a1\ufe0f The complete code is shown below, 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 # Python 3.8 on Macbook Pro M1 2020 # import import struct import librosa # librosa==0.9.1 import webrtcvad # webrtcvad==2.0.10 # load data file_path = 'hello_my_name_is_mohit.wav' # load wav file (librosa) y , sr = librosa . load ( file_path , sr = 16000 ) # convert the file to int if it is in float (Py-WebRTC requirement) if y . dtype . kind == 'f' : # convert to int16 y = np . array ([ int ( s * 32768 ) for s in y ]) # bound y [ y > 32767 ] = 32767 y [ y < - 32768 ] = - 32768 # create raw sample in bit raw_samples = struct . pack ( \" %d h\" % len ( y ), * y ) # define webrtcvad VAD vad = webrtcvad . Vad ( 3 ) # set aggressiveness from 0 to 3 window_duration = 0.03 # duration in seconds samples_per_window = int ( window_duration * sr + 0.5 ) bytes_per_sample = 2 # for int16 # Start classifying chunks of samples # var to hold segment wise report segments = [] # iterate over the audio samples for i , start in enumerate ( np . arange ( 0 , len ( y ), samples_per_window )): stop = min ( start + samples_per_window , len ( y )) loc_raw_sample = raw_samples [ start * bytes_per_sample : stop * bytes_per_sample ] try : is_speech = vad . is_speech ( loc_raw_sample , sample_rate = sr ) segments . append ( dict ( start = start , stop = stop , is_speech = is_speech )) except Exception as e : print ( f \"Failed for step { i } , reason: { e } \" ) # import pandas as pd # pd.DataFrame(segments) # result of classification Let's go through the code, Line 3-5 : we import the required packages. Make sure to install them using pip before starting. In case you are facing some issue, please install the specificed python and package versions (mentioned in the code) . Line 7- 18 : we read a sample wav file (use your own ) and then transform the bit depth of the audio into int16 . One point to note here is that webrtcvad only works for sample rate = 16000 and bit depth = int16 . And librosa loads an audio file in float. Because of all this requirement we need to perform the transformations. Line 21 : we transform the numpy array (format in which an audio file is loaded in librosa ) to byte string. This will be required for chunking and VAD analysis. Line 24-27 : we initialize an instance of webrtcvad with aggressiveness parameter. Note, the range is form 0 to 3, and higher the value, the more strict VAD is in classification chunks as voice. This means, you can miss some relevant voice chunks for higher aggressiveness and on the other hand get some false positives with lower aggressiveness. Line 31-45 : the code to first create chunks of the audio and then perform VAD classification at line 37-38 . The final results is stored in segments variable and a sample output is shown below, start stop is_speech 0 480 True 480 960 True 960 1440 False 1440 1920 False 1920 2400 False Here, each row denotes one chunk. The start and stop columns contain the begin and end details of each chunk. Finally the is_speech column contains True or False value depedening on if the chunk was detected as voice chunk or not. Here is the code's output visualized in form of waveform with vice chunks highlighted. Waveform of audio with webrtcvad detected voice chunks highlighted with yellow line on top. The aggressiveness parameter value was 0, hence lot's of false positive (chunks with no voice) are detected as well. Same as above, but with aggressiveness parameter value set to 3. Hence the detection is quite strict (some voice parts are missed) .","title":"Py-WebRTC VAD"},{"location":"audio_intelligence/wav2vec2/","text":"Introduction Wav2Vec is a framework for self-supervised learning of representations from raw audio data. Basically it learns to efficiently represent the raw audio data as a vector space encoding. Illustration of the Wav2vec2 framework ( Wav2vec2 paper ) A major advantage of this approach is that we end up training a generic audio model that could be used for multiple downtream tasks! And because of the self-supervised learning, we don't need access to huge amount of labeled data. In the paper, after pre-training on unlabeled speech, the model is fine-tuned on small labeled data with a Connectionist Temporal Classification (CTC) loss for speech recognition task . Architecture The complete architecture of the framework can be divided into 3 components, they are Feature encoder : This is the encoder part of the model. It takes the raw audio data as input and outputs feature vectors. Input size is limited to 400 samples which is 20ms for 16kHz sample rate . The raw audio is first standardized to have zero mean and unit variance. Then it is passed to 1D convolutional neural network (temporal convolution) followed by layer normalization and GELU activation function. There could be 7 such convolution blocks with constant channel size (512), decreasing kernel width (10, 3x4, 2x2) and stride (5, 2x6). The output is list of feature vectors each with 512 dimensions. Transformers : The output of the feature encoder is passed on to a transformer layer. One differentiator is use of relative positional embedding by using convolution layers, rather than using fixed positional encoding as done in original Transformers paper. The block size differs, as 12 transformers block with model dimension of 768 is used in BASE model but 24 blocks with 1024 dimension in LARGE version. Quantization module : For self-supervised learning, we need to work with discrete outputs. For this, there is a quantization module that converts the continous vector output to discrete representations, and on top of it, it automatically learns the discete speech units. This is done by maintaining multiple codebooks/groups (320 in size) and the units are sampled from each codebook are later concatenated (320x320=102400 possiblt speech units) . The sampling is done using Gumbel-Softmax which is like argmax but differentiable. Training To pre-train the model, Wav2Vec2 masks certain portions of time steps in the feature encoder which is similar to masked language model. The aim is to teach the model to predict the correct qunatized latent audio representation in a set of distractors for each time step. The overall training objective is to minimize contrastive loss ( \\(L_m\\) ) and diversity loss ( \\(L_d\\) ) in \\(L = L_m + \\alpha L_d\\) . Contrastive loss is the performance on the self-supervised task. Diversity loss is designed to increase the use of the complete quantized codebook representations, and not only a select subset. For pretraining, the datasets used were (1) Librispeech corpus with 960 hours of audio data, (2) LibriVox 60k hours of audio data that was later subset to 53.2k hours. Only unlabeled data was used for pretraining. To make the model more robust to different tasks, we can finetune the model on a different task specific modifications and dataset. Here, the paper finetuned for ASR by adding a randomly initialized classification layer on top on Transformer layer with class size equal to the size of vocab. The model is optimized by minimizing the CTC loss. Adam was used as optimization algorithm and the learning rate is warmed up till 10% of the training duration, then kept constant for next 40% and finally linearly decayed for the remaining duration. Also, for the first 60k updates only output classifier was trained after which Transformer is also updated. The feature encoder is kept frozen (not trained at all). Results There are two interesting points to note from the results of the Wav2Vec2 model, The model is able to learn ASR with as minimum as 10 mins of labeled data! As shown below, \\(LARGE\\) model pre-trained on LV-60k and finetuned on Librispeech with CTC loss is giving 4.6/7.9 WER! This is a very good news incase you want to finetune the model for your domain or accent! The choice of decoder can lead to improvement in performance. As obvious from the results, Transformer decoder is giving best performance, followed by n-gram and then CTC decoding. But also note that the CTC decoding will gives the best inference speed. The suggested decoder could be 4-gram, as it provides huge improvement in performance by fixing the spellling mistakes and grammer issues of CTC and is still faster than Transformer decoders. WER on Librispeech dev/test data ( Wav2vec2 paper ) Code Offline transcription using Wav2Vec2 (CTC) Here is the code to perform offline transcription using Wav2Vec2 model with transformer package. Note the default decoder is CTC. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 # import import torch import librosa from transformers import Wav2Vec2ForCTC , Wav2Vec2Tokenizer # load the tokenizer and model tokenizer = Wav2Vec2Tokenizer . from_pretrained ( \"facebook/wav2vec2-large-960h\" ) model = Wav2Vec2ForCTC . from_pretrained ( \"facebook/wav2vec2-large-960h\" ) # load the audio data (use your own wav file here!) input_audio , sr = librosa . load ( 'my_wav_file.wav' , sr = 16000 ) # tokenize input_values = tokenizer ( input_audio , return_tensors = \"pt\" , padding = \"longest\" ) . input_values # retrieve logits logits = model ( input_values ) . logits # take argmax and decode predicted_ids = torch . argmax ( logits , dim =- 1 ) transcription = tokenizer . batch_decode ( predicted_ids ) # print the output print ( transcription ) Offline transcription using Wav2Vec2 (N-gram) We can also use n-gram language model as decoder using a pre-trained model available in Huggingface. The usage is very similar to the CTC model, we just have to change the model name. Note, this downloads the Wav2Vec2 model plus the N-gram language model which will be around 3.2 GBs! 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 # install dependencies ! pip install pyctcdecode pypi - kenlm # import import librosa from transformers import Wav2Vec2ProcessorWithLM , Wav2Vec2ForCTC # load the processor processor = Wav2Vec2ProcessorWithLM . from_pretrained ( \"patrickvonplaten/wav2vec2-base-100h-with-lm\" ) model = Wav2Vec2ForCTC . from_pretrained ( \"facebook/wav2vec2-large-960h\" ) # load the audio data (use your own wav file here!) input_audio , sr = librosa . load ( 'my_wav_file.wav' , sr = 16000 ) # tokenize input_values = processor ( input_audio , return_tensors = \"pt\" , padding = \"longest\" ) . input_values # retrieve logits logits = model ( input_values ) . logits # decode using n-gram transcription = processor . batch_decode ( logits . detach () . numpy ()) . text # print the output print ( transcription ) Creating your own N-gram language model for Word2Vec2 To use n-gram model we can KenML to create language model and then use pyctcdecode for decoding. This part is referenced from Huggingface blog on Wav2vec2 with n-gram . The steps are as follows, First, we will select one text dataset. This dataset can be the transcript of train data (part of labeled data we used to finetune Wav2Vec2 model) or a related (same domain like medical, telecom, etc) collection of documents. Next we can perform data cleaning like removing special characters and then combine the individual sentences to a free flow text and save that into text file. After this we can run KenML to create a language model. kenlm/build/bin/lmplz -o 3 < \"text.txt\" > \"3-gram.arpa\" The .arpa file contains the n-gram language model that is ready to go with just two minor modifications. As per the Huggingface blog , we need to add end of sentence token as 1 gram as well, so we open the arpa file, duplicate the existing start of sentence token, and just replace the with . Next we also increment the count of 1-gram (present at the top of the .arpa file) by 1, because of what we just did. Then we save the file. Next, we load the a LM-less model and then we can use the pyctcdecode . 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 # Taken from Blog @ https://huggingface.co/blog/wav2vec2-with-ngram # import packages from transformers import AutoProcessor from pyctcdecode import build_ctcdecoder from transformers import Wav2Vec2ProcessorWithLM # load a LM-less model processor = AutoProcessor . from_pretrained ( \"hf-test/xls-r-300m-sv\" ) # get the vocabulary of the tokenizer vocab_dict = processor . tokenizer . get_vocab () sorted_vocab_dict = { k . lower (): v for k , v in sorted ( vocab_dict . items (), key = lambda item : item [ 1 ])} # build the decoder decoder = build_ctcdecoder ( labels = list ( sorted_vocab_dict . keys ()), kenlm_model_path = \"3-gram.arpa\" , ) # create a processor with the decoder processor_with_lm = Wav2Vec2ProcessorWithLM ( feature_extractor = processor . feature_extractor , tokenizer = processor . tokenizer , decoder = decoder ) # now the processor can be used for inference as shown in other above code sections. We can even reduce the size of the LM-model by converting it to a binary file. kenlm/build/bin/build_binary 3 -gram.arpa 3 -gram.bin Online transcription using Wav2Vec2 For live transcription using Wav2Vec2, we can utilize wav2vec2-live package. Once you have cloned the repo and installed the packages from requirements.txt , the live transcription can be started with (taken from the package readme and modified) , 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 # import from live_asr import LiveWav2Vec2 # load model english_model = \"facebook/wav2vec2-large-960h-lv60-self\" asr = LiveWav2Vec2 ( english_model , device_name = \"default\" ) # start the live ASR asr . start () try : while True : text , sample_length , inference_time = asr . get_last_text () print ( f \"Duration: { sample_length : .3f } s \\t Speed: { inference_time : .3f } s \\t { text } \" ) except KeyboardInterrupt : asr . stop () This starts the Live ASR on your terminal. The code listen to the audio in your microphone, identifies the chunks with voice using VAD and then pass the voiced chunks to Wave2Vec2 for transcription. One sample output is shown below, listening to your voice Duration: 0 .780s Speed: 0 .205s hello Duration: 0 .780s Speed: 0 .190s hello Duration: 0 .960s Speed: 0 .223s my name .... Additional Materials An Illustrated Tour of Wav2vec 2.0 by Jonathan Bgn Boosting Wav2Vec2 with n-grams in \ud83e\udd17 Transformers","title":"Wav2Vec2"},{"location":"audio_intelligence/wav2vec2/#introduction","text":"Wav2Vec is a framework for self-supervised learning of representations from raw audio data. Basically it learns to efficiently represent the raw audio data as a vector space encoding. Illustration of the Wav2vec2 framework ( Wav2vec2 paper ) A major advantage of this approach is that we end up training a generic audio model that could be used for multiple downtream tasks! And because of the self-supervised learning, we don't need access to huge amount of labeled data. In the paper, after pre-training on unlabeled speech, the model is fine-tuned on small labeled data with a Connectionist Temporal Classification (CTC) loss for speech recognition task .","title":"Introduction"},{"location":"audio_intelligence/wav2vec2/#architecture","text":"The complete architecture of the framework can be divided into 3 components, they are Feature encoder : This is the encoder part of the model. It takes the raw audio data as input and outputs feature vectors. Input size is limited to 400 samples which is 20ms for 16kHz sample rate . The raw audio is first standardized to have zero mean and unit variance. Then it is passed to 1D convolutional neural network (temporal convolution) followed by layer normalization and GELU activation function. There could be 7 such convolution blocks with constant channel size (512), decreasing kernel width (10, 3x4, 2x2) and stride (5, 2x6). The output is list of feature vectors each with 512 dimensions. Transformers : The output of the feature encoder is passed on to a transformer layer. One differentiator is use of relative positional embedding by using convolution layers, rather than using fixed positional encoding as done in original Transformers paper. The block size differs, as 12 transformers block with model dimension of 768 is used in BASE model but 24 blocks with 1024 dimension in LARGE version. Quantization module : For self-supervised learning, we need to work with discrete outputs. For this, there is a quantization module that converts the continous vector output to discrete representations, and on top of it, it automatically learns the discete speech units. This is done by maintaining multiple codebooks/groups (320 in size) and the units are sampled from each codebook are later concatenated (320x320=102400 possiblt speech units) . The sampling is done using Gumbel-Softmax which is like argmax but differentiable.","title":"Architecture"},{"location":"audio_intelligence/wav2vec2/#training","text":"To pre-train the model, Wav2Vec2 masks certain portions of time steps in the feature encoder which is similar to masked language model. The aim is to teach the model to predict the correct qunatized latent audio representation in a set of distractors for each time step. The overall training objective is to minimize contrastive loss ( \\(L_m\\) ) and diversity loss ( \\(L_d\\) ) in \\(L = L_m + \\alpha L_d\\) . Contrastive loss is the performance on the self-supervised task. Diversity loss is designed to increase the use of the complete quantized codebook representations, and not only a select subset. For pretraining, the datasets used were (1) Librispeech corpus with 960 hours of audio data, (2) LibriVox 60k hours of audio data that was later subset to 53.2k hours. Only unlabeled data was used for pretraining. To make the model more robust to different tasks, we can finetune the model on a different task specific modifications and dataset. Here, the paper finetuned for ASR by adding a randomly initialized classification layer on top on Transformer layer with class size equal to the size of vocab. The model is optimized by minimizing the CTC loss. Adam was used as optimization algorithm and the learning rate is warmed up till 10% of the training duration, then kept constant for next 40% and finally linearly decayed for the remaining duration. Also, for the first 60k updates only output classifier was trained after which Transformer is also updated. The feature encoder is kept frozen (not trained at all).","title":"Training"},{"location":"audio_intelligence/wav2vec2/#results","text":"There are two interesting points to note from the results of the Wav2Vec2 model, The model is able to learn ASR with as minimum as 10 mins of labeled data! As shown below, \\(LARGE\\) model pre-trained on LV-60k and finetuned on Librispeech with CTC loss is giving 4.6/7.9 WER! This is a very good news incase you want to finetune the model for your domain or accent! The choice of decoder can lead to improvement in performance. As obvious from the results, Transformer decoder is giving best performance, followed by n-gram and then CTC decoding. But also note that the CTC decoding will gives the best inference speed. The suggested decoder could be 4-gram, as it provides huge improvement in performance by fixing the spellling mistakes and grammer issues of CTC and is still faster than Transformer decoders. WER on Librispeech dev/test data ( Wav2vec2 paper )","title":"Results"},{"location":"audio_intelligence/wav2vec2/#code","text":"","title":"Code"},{"location":"audio_intelligence/wav2vec2/#offline-transcription-using-wav2vec2-ctc","text":"Here is the code to perform offline transcription using Wav2Vec2 model with transformer package. Note the default decoder is CTC. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 # import import torch import librosa from transformers import Wav2Vec2ForCTC , Wav2Vec2Tokenizer # load the tokenizer and model tokenizer = Wav2Vec2Tokenizer . from_pretrained ( \"facebook/wav2vec2-large-960h\" ) model = Wav2Vec2ForCTC . from_pretrained ( \"facebook/wav2vec2-large-960h\" ) # load the audio data (use your own wav file here!) input_audio , sr = librosa . load ( 'my_wav_file.wav' , sr = 16000 ) # tokenize input_values = tokenizer ( input_audio , return_tensors = \"pt\" , padding = \"longest\" ) . input_values # retrieve logits logits = model ( input_values ) . logits # take argmax and decode predicted_ids = torch . argmax ( logits , dim =- 1 ) transcription = tokenizer . batch_decode ( predicted_ids ) # print the output print ( transcription )","title":"Offline transcription using Wav2Vec2 (CTC)"},{"location":"audio_intelligence/wav2vec2/#offline-transcription-using-wav2vec2-n-gram","text":"We can also use n-gram language model as decoder using a pre-trained model available in Huggingface. The usage is very similar to the CTC model, we just have to change the model name. Note, this downloads the Wav2Vec2 model plus the N-gram language model which will be around 3.2 GBs! 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 # install dependencies ! pip install pyctcdecode pypi - kenlm # import import librosa from transformers import Wav2Vec2ProcessorWithLM , Wav2Vec2ForCTC # load the processor processor = Wav2Vec2ProcessorWithLM . from_pretrained ( \"patrickvonplaten/wav2vec2-base-100h-with-lm\" ) model = Wav2Vec2ForCTC . from_pretrained ( \"facebook/wav2vec2-large-960h\" ) # load the audio data (use your own wav file here!) input_audio , sr = librosa . load ( 'my_wav_file.wav' , sr = 16000 ) # tokenize input_values = processor ( input_audio , return_tensors = \"pt\" , padding = \"longest\" ) . input_values # retrieve logits logits = model ( input_values ) . logits # decode using n-gram transcription = processor . batch_decode ( logits . detach () . numpy ()) . text # print the output print ( transcription )","title":"Offline transcription using Wav2Vec2 (N-gram)"},{"location":"audio_intelligence/wav2vec2/#creating-your-own-n-gram-language-model-for-word2vec2","text":"To use n-gram model we can KenML to create language model and then use pyctcdecode for decoding. This part is referenced from Huggingface blog on Wav2vec2 with n-gram . The steps are as follows, First, we will select one text dataset. This dataset can be the transcript of train data (part of labeled data we used to finetune Wav2Vec2 model) or a related (same domain like medical, telecom, etc) collection of documents. Next we can perform data cleaning like removing special characters and then combine the individual sentences to a free flow text and save that into text file. After this we can run KenML to create a language model. kenlm/build/bin/lmplz -o 3 < \"text.txt\" > \"3-gram.arpa\" The .arpa file contains the n-gram language model that is ready to go with just two minor modifications. As per the Huggingface blog , we need to add end of sentence token as 1 gram as well, so we open the arpa file, duplicate the existing start of sentence token, and just replace the with . Next we also increment the count of 1-gram (present at the top of the .arpa file) by 1, because of what we just did. Then we save the file. Next, we load the a LM-less model and then we can use the pyctcdecode . 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 # Taken from Blog @ https://huggingface.co/blog/wav2vec2-with-ngram # import packages from transformers import AutoProcessor from pyctcdecode import build_ctcdecoder from transformers import Wav2Vec2ProcessorWithLM # load a LM-less model processor = AutoProcessor . from_pretrained ( \"hf-test/xls-r-300m-sv\" ) # get the vocabulary of the tokenizer vocab_dict = processor . tokenizer . get_vocab () sorted_vocab_dict = { k . lower (): v for k , v in sorted ( vocab_dict . items (), key = lambda item : item [ 1 ])} # build the decoder decoder = build_ctcdecoder ( labels = list ( sorted_vocab_dict . keys ()), kenlm_model_path = \"3-gram.arpa\" , ) # create a processor with the decoder processor_with_lm = Wav2Vec2ProcessorWithLM ( feature_extractor = processor . feature_extractor , tokenizer = processor . tokenizer , decoder = decoder ) # now the processor can be used for inference as shown in other above code sections. We can even reduce the size of the LM-model by converting it to a binary file. kenlm/build/bin/build_binary 3 -gram.arpa 3 -gram.bin","title":"Creating your own N-gram language model for Word2Vec2"},{"location":"audio_intelligence/wav2vec2/#online-transcription-using-wav2vec2","text":"For live transcription using Wav2Vec2, we can utilize wav2vec2-live package. Once you have cloned the repo and installed the packages from requirements.txt , the live transcription can be started with (taken from the package readme and modified) , 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 # import from live_asr import LiveWav2Vec2 # load model english_model = \"facebook/wav2vec2-large-960h-lv60-self\" asr = LiveWav2Vec2 ( english_model , device_name = \"default\" ) # start the live ASR asr . start () try : while True : text , sample_length , inference_time = asr . get_last_text () print ( f \"Duration: { sample_length : .3f } s \\t Speed: { inference_time : .3f } s \\t { text } \" ) except KeyboardInterrupt : asr . stop () This starts the Live ASR on your terminal. The code listen to the audio in your microphone, identifies the chunks with voice using VAD and then pass the voiced chunks to Wave2Vec2 for transcription. One sample output is shown below, listening to your voice Duration: 0 .780s Speed: 0 .205s hello Duration: 0 .780s Speed: 0 .190s hello Duration: 0 .960s Speed: 0 .223s my name ....","title":"Online transcription using Wav2Vec2"},{"location":"audio_intelligence/wav2vec2/#additional-materials","text":"An Illustrated Tour of Wav2vec 2.0 by Jonathan Bgn Boosting Wav2Vec2 with n-grams in \ud83e\udd17 Transformers","title":"Additional Materials"},{"location":"audio_intelligence/whisper/","text":"Introduction Whisper is an open source multi-task audio model released by OpenAI. It is an ASR system that works on 97 different languages (including english) and can even perform translation from other languages to english. The model was trained on 680,000 hours of multilingual and multitask data collected from the web. Whisper was trained using large scale weak supervision. Here is an interesting perspective for weak supervision training, There are 3 types of data and training strategies - (1) golden standard data for supervised training, (2) silver standard data for weakly supervised training, and (3) unlabelled data for unsupervised training. Now, it is difficult to get golden dataset due to human involvement which is costly and time consuming. And model trained on unlabelled datatset with unsupervised strategy lead to mediocre decoder part that needs further finetuning for downstream tasks. This gives the silver standard dataset a huge advantage, as it is the middle ground with large size and high accuracy. Note As huge portion of silver standard dataset might have had no humans verfication, there is always a room for faulty data. Hence the name - weak supervision. Dataset Author scraped the internet to collect huge and diverse transcription dataset. As this may also introduce noise, extra care was taken in pre-processing step to clean the data. The intention was to only consider human annotated data, for this any audio-text pair that \"seems\" like machine generated was removed. For this, they removed normalised transcriptions (only upper case, only lower case, lack of punctuations, etc) as they are most likely machine generated (no human writes like that) . They even trained language detection models to make sure that there is no mis-match in the audio and text pair's language. Finally, de-duplication was also done. With this, overall 680,000 hours was dataset was collected. The breakdown is as follows, 117,000 hours of non-english 96 different language data. 125,000 hours of X-->en translation data. 438,000 hours of english transcription data. Every audio files was resampled at 16,000 Hz and broken in 30 secs chunks to be passed to model for training. Transcription were also broken in the same chunk size respectively. Note The training dataset was not released by OpenAI. But they have open sourced the code and the pretrained models. Evaluation dataset details are shared here Architecture Authors picked the Transformer model as it has been widely used since its inception in 2017 and it scales reliably. The audio chunk is first converted into 80-channel log-magnitude Mel spectrogram with 25ms window and stride of 10ms. The features are scaled between -1 and 1 with zero mean across the dataset. Transformer inspired Whisper model [1] The input is first passed to two convolution layers with a filter width of 3 and GELU activation function. Sinusoidal position embeddings are added to the output and it is then passed to the encoder block of Transformer. The decoder block uses learned positional embedding and uses multiple cross-attention layers to apply encoder output. BPE text tokenizer was used like GPT-2. The decoder utilises multiple special tokens to facilitate the multi-task output generation. They are, <|startoftranscript|> to denote start of prediction <|nospeech|> to denote silence or even non-spoken voices (ex: background noise and music) <|transcribe|> to denote 'transcription' task <|translation|> to denote 'translation' task <|notimestamps|> to denote absence of timestamps <|endoftranscript|> to denote end of prediction Overview of Whisper [1] Results The performance of whisper model is very good. On comparing with wav2vec2 large 960h model, whisper large models makes 55% less errors on average. This is huge! In fact in some cases, even the tiny model performs better than older large models! Comparison of Whisper on various datasets [1] Warning Claims on bad performance of Whisper was made in this Twitter thread . Here, Ryan Hileman compared Whisper with NVIDIA and Talon model on several datasets to find Talon performing well against Whisper models. Also, he noted that Whisper models are quite slow in execution (even the tiny model) . Finally, he provided samples for \"catastrophic failures\" of Whisper of following types -- (1) generating complete paragraphs for single word audio input (like dec, car, two, snake, other) , (2) hallucinations and repetition in the output. Anyone thinking of using Whisper for their project should consider these concerns and test them out themselves before deployment. Released Models Authors released 5 variety of models based on size, going from 39M param tiny model to 1550M param large model. For each there are is an english only model {size}.en (ex: tiny.en ) and a multilingual model {size} (ex: tiny ). Size Parameters English-only model Multilingual model Required VRAM Relative speed tiny 39 M tiny.en tiny ~1 GB ~32x base 74 M base.en base ~1 GB ~16x small 244 M small.en small ~2 GB ~6x medium 769 M medium.en medium ~5 GB ~2x large 1550 M N/A large ~10 GB 1x Code We will go through two ways to use Whisper model. Authors have released a Python package called whisper [1] that makes using the pretrained models as easy as writing 3 lines of code. OpenAI recently released the API for Whisper model [2] While it a paid service (~$0.36 for 1 hour of audio transcription), they take care of constantly improving the model and hosting complexities. Python Package Inference Below is the inference code shared in the Readme of official Github repo [1], 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 # Install ! brew install ffmpeg # for mac ! pip install git + https : // github . com / openai / whisper . git # Import import whisper # load the model model = whisper . load_model ( \"base\" ) # get transcription result = model . transcribe ( \"audio.mp3\" , language = \"english\" ) # result contains 3 output, # result['text'] --> complete transcription that with punctuations # result['segments'] --> segment wise transcription with timestamps and other details # result['langauge'] --> detected language of the audio # can be used for translation as well (here, Japanese to English) result = model . transcribe ( \"japanese.wav\" , language = \"Japanese\" , task = \"translate\" , beam_size = 5 , best_of = 5 ) Note Auto language detection only works if you don't specify it explicitly using language param in transcribe function. The package uses only the first 30 secs to detect the language. Also, whisper's translation is not that accurate hence an alternative approach could be to perform transcription using Whisper but use another package to translate the transcription. The package also provides CLI support, here is an example, whisper japanese.wav --language Japanese # generates -- # txt file (transcription), and # vtt file (segment wise transcription with timestamp) Finally for the brave souls, we can even play around with the individual modules and perform the transcription step by step, (from readme [1]) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 # Install ! brew install ffmpeg # for mac ! pip install git + https : // github . com / openai / whisper . git # import import whisper model = whisper . load_model ( \"base\" ) # load audio and pad/trim it to fit 30 seconds audio = whisper . load_audio ( \"audio.mp3\" ) audio = whisper . pad_or_trim ( audio ) # make log-Mel spectrogram and move to the same device as the model mel = whisper . log_mel_spectrogram ( audio ) . to ( model . device ) # detect the spoken language _ , probs = model . detect_language ( mel ) print ( f \"Detected language: { max ( probs , key = probs . get ) } \" ) # decode the audio options = whisper . DecodingOptions () result = whisper . decode ( model , mel , options ) # print the recognized text print ( result . text ) OpenAI API Before we code, here are some important points to be aware of, OpenAI hosted Whisper model is recommended to be used for ~59 languages for which the WER rate is <50%, as for others the accuracy will not be good. Refer Whisper API only supports files that are less than 25 MB. For bigger files, we will have to split or used compressed audio. Finally, we can even use prompt engineering to further improve the accuracy of transcription. It can be used for word boosting, enforcing punctuations, styles and more. Refer Note Use of prompt engineering to enhance the transcription accuracy is a relatively newer approach and needs research for more clarity. With that out of the way, using OpenAI API for audio transcription using Whisper is quite easy as shown below, [1] 1 2 3 4 5 6 7 8 # Install ! pip install openai >= 0.27.0 # import import openai # load the audio file audio_file = open ( \"/path/to/file/audio.mp3\" , \"rb\" ) # pass for transcription transcript = openai . Audio . transcribe ( \"whisper-1\" , audio_file ) Note Apart from file and model , transcribe function supports multiple parameters like, ( Refer API doc ) prompt : for prompt engineering (more details below) response_format : to change the transcription output. We can use verbose_json to get timestamp and logits details. temperature : to introduce randomness in generation (default is 0) language : language of input audio For translation you can directly use 1 2 3 4 5 6 7 8 # Install ! pip install openai >= 0.27.0 # import import openai # load the audio file audio_file = open ( \"/path/to/file/german_audio.mp3\" , \"rb\" ) # pass for translation transcript = openai . Audio . translate ( \"whisper-1\" , audio_file ) Finally, we can use prompt engineering to enhance the accuracy by using prompt param. Below are some examples, 1 2 3 4 5 6 7 8 9 # Word Boosting name - 'Mohit' transcript = openai . Audio . translate ( \"whisper-1\" , audio_file , prompt = \"This transcript may contains people name like Mohit.\" ) # Enforce puntuations and special characters transcript = openai . Audio . translate ( \"whisper-1\" , audio_file , prompt = \"Hello, this transcript may contains people name like 'Mohit'.\" ) # Enforce filler words transcript = openai . Audio . translate ( \"whisper-1\" , audio_file , prompt = \"Hmm...this transcript may contains people name like Mohit.\" ) References [1] Whisper by OpenAI - Blog | Paper | Code | API Doc","title":"Whisper"},{"location":"audio_intelligence/whisper/#introduction","text":"Whisper is an open source multi-task audio model released by OpenAI. It is an ASR system that works on 97 different languages (including english) and can even perform translation from other languages to english. The model was trained on 680,000 hours of multilingual and multitask data collected from the web. Whisper was trained using large scale weak supervision. Here is an interesting perspective for weak supervision training, There are 3 types of data and training strategies - (1) golden standard data for supervised training, (2) silver standard data for weakly supervised training, and (3) unlabelled data for unsupervised training. Now, it is difficult to get golden dataset due to human involvement which is costly and time consuming. And model trained on unlabelled datatset with unsupervised strategy lead to mediocre decoder part that needs further finetuning for downstream tasks. This gives the silver standard dataset a huge advantage, as it is the middle ground with large size and high accuracy. Note As huge portion of silver standard dataset might have had no humans verfication, there is always a room for faulty data. Hence the name - weak supervision.","title":"Introduction"},{"location":"audio_intelligence/whisper/#dataset","text":"Author scraped the internet to collect huge and diverse transcription dataset. As this may also introduce noise, extra care was taken in pre-processing step to clean the data. The intention was to only consider human annotated data, for this any audio-text pair that \"seems\" like machine generated was removed. For this, they removed normalised transcriptions (only upper case, only lower case, lack of punctuations, etc) as they are most likely machine generated (no human writes like that) . They even trained language detection models to make sure that there is no mis-match in the audio and text pair's language. Finally, de-duplication was also done. With this, overall 680,000 hours was dataset was collected. The breakdown is as follows, 117,000 hours of non-english 96 different language data. 125,000 hours of X-->en translation data. 438,000 hours of english transcription data. Every audio files was resampled at 16,000 Hz and broken in 30 secs chunks to be passed to model for training. Transcription were also broken in the same chunk size respectively. Note The training dataset was not released by OpenAI. But they have open sourced the code and the pretrained models. Evaluation dataset details are shared here","title":"Dataset"},{"location":"audio_intelligence/whisper/#architecture","text":"Authors picked the Transformer model as it has been widely used since its inception in 2017 and it scales reliably. The audio chunk is first converted into 80-channel log-magnitude Mel spectrogram with 25ms window and stride of 10ms. The features are scaled between -1 and 1 with zero mean across the dataset. Transformer inspired Whisper model [1] The input is first passed to two convolution layers with a filter width of 3 and GELU activation function. Sinusoidal position embeddings are added to the output and it is then passed to the encoder block of Transformer. The decoder block uses learned positional embedding and uses multiple cross-attention layers to apply encoder output. BPE text tokenizer was used like GPT-2. The decoder utilises multiple special tokens to facilitate the multi-task output generation. They are, <|startoftranscript|> to denote start of prediction <|nospeech|> to denote silence or even non-spoken voices (ex: background noise and music) <|transcribe|> to denote 'transcription' task <|translation|> to denote 'translation' task <|notimestamps|> to denote absence of timestamps <|endoftranscript|> to denote end of prediction Overview of Whisper [1]","title":"Architecture"},{"location":"audio_intelligence/whisper/#results","text":"The performance of whisper model is very good. On comparing with wav2vec2 large 960h model, whisper large models makes 55% less errors on average. This is huge! In fact in some cases, even the tiny model performs better than older large models! Comparison of Whisper on various datasets [1] Warning Claims on bad performance of Whisper was made in this Twitter thread . Here, Ryan Hileman compared Whisper with NVIDIA and Talon model on several datasets to find Talon performing well against Whisper models. Also, he noted that Whisper models are quite slow in execution (even the tiny model) . Finally, he provided samples for \"catastrophic failures\" of Whisper of following types -- (1) generating complete paragraphs for single word audio input (like dec, car, two, snake, other) , (2) hallucinations and repetition in the output. Anyone thinking of using Whisper for their project should consider these concerns and test them out themselves before deployment.","title":"Results"},{"location":"audio_intelligence/whisper/#released-models","text":"Authors released 5 variety of models based on size, going from 39M param tiny model to 1550M param large model. For each there are is an english only model {size}.en (ex: tiny.en ) and a multilingual model {size} (ex: tiny ). Size Parameters English-only model Multilingual model Required VRAM Relative speed tiny 39 M tiny.en tiny ~1 GB ~32x base 74 M base.en base ~1 GB ~16x small 244 M small.en small ~2 GB ~6x medium 769 M medium.en medium ~5 GB ~2x large 1550 M N/A large ~10 GB 1x","title":"Released Models"},{"location":"audio_intelligence/whisper/#code","text":"We will go through two ways to use Whisper model. Authors have released a Python package called whisper [1] that makes using the pretrained models as easy as writing 3 lines of code. OpenAI recently released the API for Whisper model [2] While it a paid service (~$0.36 for 1 hour of audio transcription), they take care of constantly improving the model and hosting complexities.","title":"Code"},{"location":"audio_intelligence/whisper/#python-package-inference","text":"Below is the inference code shared in the Readme of official Github repo [1], 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 # Install ! brew install ffmpeg # for mac ! pip install git + https : // github . com / openai / whisper . git # Import import whisper # load the model model = whisper . load_model ( \"base\" ) # get transcription result = model . transcribe ( \"audio.mp3\" , language = \"english\" ) # result contains 3 output, # result['text'] --> complete transcription that with punctuations # result['segments'] --> segment wise transcription with timestamps and other details # result['langauge'] --> detected language of the audio # can be used for translation as well (here, Japanese to English) result = model . transcribe ( \"japanese.wav\" , language = \"Japanese\" , task = \"translate\" , beam_size = 5 , best_of = 5 ) Note Auto language detection only works if you don't specify it explicitly using language param in transcribe function. The package uses only the first 30 secs to detect the language. Also, whisper's translation is not that accurate hence an alternative approach could be to perform transcription using Whisper but use another package to translate the transcription. The package also provides CLI support, here is an example, whisper japanese.wav --language Japanese # generates -- # txt file (transcription), and # vtt file (segment wise transcription with timestamp) Finally for the brave souls, we can even play around with the individual modules and perform the transcription step by step, (from readme [1]) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 # Install ! brew install ffmpeg # for mac ! pip install git + https : // github . com / openai / whisper . git # import import whisper model = whisper . load_model ( \"base\" ) # load audio and pad/trim it to fit 30 seconds audio = whisper . load_audio ( \"audio.mp3\" ) audio = whisper . pad_or_trim ( audio ) # make log-Mel spectrogram and move to the same device as the model mel = whisper . log_mel_spectrogram ( audio ) . to ( model . device ) # detect the spoken language _ , probs = model . detect_language ( mel ) print ( f \"Detected language: { max ( probs , key = probs . get ) } \" ) # decode the audio options = whisper . DecodingOptions () result = whisper . decode ( model , mel , options ) # print the recognized text print ( result . text )","title":"Python Package Inference"},{"location":"audio_intelligence/whisper/#openai-api","text":"Before we code, here are some important points to be aware of, OpenAI hosted Whisper model is recommended to be used for ~59 languages for which the WER rate is <50%, as for others the accuracy will not be good. Refer Whisper API only supports files that are less than 25 MB. For bigger files, we will have to split or used compressed audio. Finally, we can even use prompt engineering to further improve the accuracy of transcription. It can be used for word boosting, enforcing punctuations, styles and more. Refer Note Use of prompt engineering to enhance the transcription accuracy is a relatively newer approach and needs research for more clarity. With that out of the way, using OpenAI API for audio transcription using Whisper is quite easy as shown below, [1] 1 2 3 4 5 6 7 8 # Install ! pip install openai >= 0.27.0 # import import openai # load the audio file audio_file = open ( \"/path/to/file/audio.mp3\" , \"rb\" ) # pass for transcription transcript = openai . Audio . transcribe ( \"whisper-1\" , audio_file ) Note Apart from file and model , transcribe function supports multiple parameters like, ( Refer API doc ) prompt : for prompt engineering (more details below) response_format : to change the transcription output. We can use verbose_json to get timestamp and logits details. temperature : to introduce randomness in generation (default is 0) language : language of input audio For translation you can directly use 1 2 3 4 5 6 7 8 # Install ! pip install openai >= 0.27.0 # import import openai # load the audio file audio_file = open ( \"/path/to/file/german_audio.mp3\" , \"rb\" ) # pass for translation transcript = openai . Audio . translate ( \"whisper-1\" , audio_file ) Finally, we can use prompt engineering to enhance the accuracy by using prompt param. Below are some examples, 1 2 3 4 5 6 7 8 9 # Word Boosting name - 'Mohit' transcript = openai . Audio . translate ( \"whisper-1\" , audio_file , prompt = \"This transcript may contains people name like Mohit.\" ) # Enforce puntuations and special characters transcript = openai . Audio . translate ( \"whisper-1\" , audio_file , prompt = \"Hello, this transcript may contains people name like 'Mohit'.\" ) # Enforce filler words transcript = openai . Audio . translate ( \"whisper-1\" , audio_file , prompt = \"Hmm...this transcript may contains people name like Mohit.\" )","title":"OpenAI API"},{"location":"audio_intelligence/whisper/#references","text":"[1] Whisper by OpenAI - Blog | Paper | Code | API Doc","title":"References"},{"location":"data_science_tools/compute_and_ai_services/","text":"Compute and AI Services Gone are the days when we needed to buy high end devices to do literally anything. Currently there are plethora of services available online (and many of them are free!) that provide not only compute to use as you feel, but also generic AI services. Let's look into some of the famous and widely used compute and AI services. CaaS: Compute as a Service In this section we will cover some of the famous (and with some hint of free) platforms that provide compute-as-a-service (CaaS). These CaaS sometimes could be plain simple virtual machines, sometime they can be a cluster of nodes, while in other cases they can also be jupyter like coding environment. Let's go through some of the examples. Google Colab Introduction Colaboratory or \"Colab\" in short, is a browser based jupyter notebook environment that is available for free. It requires no installation and even provides access to free GPU and TPU. The main disadvantages of Colab is that you cannot run long-running jobs (limit to max 12 hrs), GPU is subject to availability and in case of consistent usage of Colab, it might take longer to get GPU access. Google provides Pro and Pro+ options which are paid subscriptions to Colab (10$ and 50$ per month, respectively). While it provides longer background execution time and better compute (among others), they do not guarantee GPU and TPU access all the time. Remember, Colab is not an alternative to a full-blown cloud computing environment. It's just a place to test out some ideas quickly. Google Colab Snippets Run tensorboard to visualize embeddings Taken from: how-to-use-tensorboard-embedding-projector 1 2 3 4 5 6 7 8 9 10 11 12 13 14 import numpy as np import tensorflow as tf import tensorboard as tb tf . io . gfile = tb . compat . tensorflow_stub . io . gfile from torch.utils.tensorboard import SummaryWriter vectors = np . array ([[ 0 , 0 , 1 ], [ 0 , 1 , 0 ], [ 1 , 0 , 0 ], [ 1 , 1 , 1 ]]) metadata = [ '001' , '010' , '100' , '111' ] # labels writer = SummaryWriter () writer . add_embedding ( vectors , metadata ) writer . close () % load_ext tensorboard % tensorboard -- logdir = runs Connect with Google Drive and access files This code will prompt you to provide authorization to access your Google Drive. 1 2 from google.colab import drive drive . mount ( '/content/drive' ) Kaggle Apart from being famous for hosting big AI/ML competitions, the next cool thing about the site is that it also provides free GPU/TPU computes! All you have to do is to sign up, create a new notebook and then you can start using it - import their datasets or your own, and start training you AI models! All of this ofcourse has a limit, you get minimum 30 hours of GPU usage per week, and at max 20 hours of TPU per week. Another catch is that you can only use GPU/TPU for 9 hours continuously. That said, Kaggle notebooks are a great place to perform your personal experiments or participate in new competitons to enhance your expertise. For more official work (industry or academics), do remember that you are putting your dataset in 3rd party's hands. Code/Notebook page of Kaggle . DeepNote DeepNote provides a highly customised jupyter like notebook. It's one of the richest service in terms of features. Here goes some examples - you can create projects with multiple notebooks, you can create teams and collaborate with your colleagues live, you can quickly visualize datasets from notebooks, you can schedule notebooks, you can host reports, and best of all - they have free tier There are multiple pricing based tiers. To begin with you can try out the free tier and get upto 750 hours of Standard compute hours per month, that's like keeping one project (that could consist of multiple notebooks) open for the complete month! ( offer subject to change; was valid at the time of writing ) Pricing tiers of Deepnote . Hint Planning to try DeepNote out? Use the refer link to get free 20 Pro compute hours (thats upto 16GB RAM and v4vCPU) MLaaS: Machine Learning as a Service In this section we will cover some of the famous platforms that provide Machine learning-as-a-Service (MLaaS). These MLaaS take care of infrastructure related aspect of data holding, data preparing, model training and model deployment. On top of this, they provide a repository of classical ML algorithms that can be leveraged to create data science solutions. The idea is to make data science as a plug and play solution creation activity, as they take care of most of the engineering aspect. Let's go through some of the examples. AWS Sagemaker (Amazon) AWS Sagemaker is a cloud-based servies that helps data scientists with the complete lifecycle of data science project. They have specialised tools that cover following stages of data science projects, Prepare : It's the pre-processing step of the project. Some of the important services are \" Gound Truth \" that is used for data labeling/annotation and \" Feature Store \" that is used to provide consistence data transformation across teams and services like training and deployment. Build : It's where an Data Scientists spends most of his time coding. \" Studio Notebooks \" provides jupyter notebooks that can be used to perform quick ideation check and build the model. Train & Tune : It's where you can efficiently train and debug your models. \" Automatic Model Training \" can be used for hyper-parameter tuning of the model i.e. finding the best parameters that provides highest accuracy. \" Experiments \" can be used to run and track multiple experiments, its an absolute must if your projects requires multiple runs to find the best architecture or parameters. Deploy & Manage : The final stage, where you deploy your model for the rest of the world to use. \" One-Click Deployment \" can be used to efficiently deploy your model to the cloud. \" Model Monitor \" can be used to manage your model, like deleting, updating, and so on. Services provided by AWS Sagemaker . AWS charges a premium for providing all of these features under a single umbrella. For a more detailed pricing information, you can estimate the cost using this . Hint As AWS Sagemaker is a costly affair, several DS teams try to find workarounds. Some of them are like using spot instances for training as they are cheaper & using AWS Lambda for deploying small models. Kubeflow Introduction Kubeflow is an open-source project that is dedicated to making development, tracking and deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. As per their website, \"Anywhere you are running Kubernetes, you should be able to run Kubeflow.\" While there are many paid MLaaS like Sagemaker , Azure ML Services and Google AI Platform , Kubeflow is an outlier that provides most of the features present in the paid platforms, but for free! We can deploy Kubeflow on Kubernetes by following the guide on their website . Once done, you can boot it up and it should look as shown below, Main page of Kubeflow . Hint Go with Kubeflow if you are setting up a new AI team for your organziation or school, and don't want to commit to costly services like Sagemaker. But beware, it does require DevOps knowledge, as you will need to setup Kubernetes and manage it. While it is completly free, you will be charged for the compute you utilize. To cut down the cost, in case you are connecting Kubeflow with AWS, you can use Spot instances. Components Kubeflow provides several individual components that will help with the ML lifecycle. Note, we can even pick and choose the components you want while installation. Some of them are, Notebook: here we can create jupyter notebook servers and perform quick experimentations. Each server is assigned its own volume (hard memory). On booting up a server, a new compute is procured and you will see Jupyter Lab page where you can create mulitple notebooks, scripts or terminals. The compute could be EC2 instance or Spot instance, incase of AWS connection and based on your configuration. Pipeline: here we define one ML project. Kubeflow supports defining a pipeline in terms of a DAG (Directed Acyclic Graph), where each individual function or module is one node. Pipeline represents a graph of modules, where execution happens in a sequential or parallel manner while considering the inter-module dependencies , ex: module_2 requires output of module_1 . While this leads to modularization of the code, the real intention is to make the pipeline execution traceable and independent from each other. This is achieved by containerizing each module and running them on different instances, making the process truly independent. Experiments: On a single ML project, we may want to run multiple experiments, ex: (1) test_accuracy to try out a couple of parameters and compare accuracy, (2) test_performance to compare latency on different shape and size of data. This is where you define individual experiments. Runs: One execution of an experiment for a pipeline is captured here, ex: for test_accuracy experiment of MNIST pipeline, perform one run with learning_rate = 0.001 . Experiments (AutoML): we cannot try all the parameters for the test_accuracy one by one. The obvious question, why not automate it by doing hyperparameter tuning? AutoML is what you are looking for! Models: after all experimentations and model training, we would like to host/deploy the model. It can done using this component. Creating and running Pipeline Let's start the coding . So for this tutorial, we will create a simple Kubeflow pipeline with two steps, Step 1 - Download data: where we will download data from S3 bucket, pass the downloaded data to the next step for further analysis. Step 2 - Perform analysis: we will perform some rudimentary analysis on the data and log the metrics. We will try to go through some basic and advanced scenarios, so that you can refer the code to create your own pipeline, even if it is completely different. After creating the pipeline, we will register it, create an experiment and then execute a run. Lets start with importing the relevant packages. Make sure to install kfp with the latest version by using pip install kfp --upgrade 1 2 3 4 5 6 # imports import kfp import kfp.dsl as dsl from typing import NamedTuple import kfp.components as comp from kfp.components import InputPath , OutputPath Now we will create the first module that downloads data from S3 bucket. Note, Kubeflow takes care of the logistics of data availability between modules, but we need to share the path where data is downloaded. This is done by typecasing parameter with OutputPath(str) as done on line 2 . The process will be similar for ML models as well. We can download a model in the first module and perform training in another, and perform performance check in the third module. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 ## Step 1 def download_data ( data_path : OutputPath ( str )): # import the functions import os import boto3 # create the path if not exist if not os . path . exists ( data_path ): os . makedirs ( data_path ) # setup boto3 s3 = boto3 . resource ( 's3' ) s3_client = boto3 . client ( 's3' ) bucket_name = 'my-bucket' bucket = s3 . Bucket ( bucket_name ) # get list of all files at the s3 bucket prefix prefix = \"dataset/\" query = s3_client . list_objects ( Bucket = bucket_name , Prefix = prefix , Delimiter = '/' ) files = [] if 'Contents' in query : for obj in query [ 'Contents' ]: files . append ( obj [ 'Key' ]) # download each file into the folder for file_path in files : # get file name file_name = file_path . split ( '/' )[ - 1 ] # download and save the file s3_client . download_file ( bucket_name , file_path , f ' { data_path } / { file_name } ' ) print ( f \"Downloaded: ' { file_path } ' into ' { data_path } / { file_name } '\" ) # done! return print ( \"Done\" ) # download_data() # create kubeflow component download_data_comp = kfp . components . create_component_from_func ( func = download_data , base_image = 'python:3.7' , packages_to_install = [ 'boto3' ]) From line 40 to line 43 , we are converting the function to Kubeflow pipeline component. As the component will run on an independent instance, we need to provide the base_image and packages_to_install information as well. Next, we will create the second module that loads the data from first module and just returns some dummy metrics. In reality, you can do a lot of things like data preprocessing or data transformation or EDA. For now, we will just stick with a dummy example. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 ## Step 2 from typing import NamedTuple def data_analysis ( data_path : InputPath ( str )) -> NamedTuple ( 'Outputs' , [ ( 'mlpipeline_metrics' , 'Metrics' ), ]): # import import json from glob import glob from collections import namedtuple # load each json file for file_path in glob ( f \" { data_path } /*.json\" ): # load the call data file and perform some analysis data = json . load ( open ( file_path )) # print print ( f \"Loaded { file_path } \" ) # --- do something fancy here --- # create metrics that should be logged metrics = { 'metrics' : [ { 'name' : 'accuracy' , 'numberValue' : 89 }, { 'name' : 'f1' , 'numberValue' : 89 }]} return [ json . dumps ( metrics )] # create kubeflow component data_analysis_comp = kfp . components . create_component_from_func ( func = data_analysis , base_image = 'python:3.7' , packages_to_install = []) In the function we are defining the data_path as InputPath(str) and is later used directly on line 14 , without the need of manually sharing the data across instances. We define mlpipeline_metrics as output (by type casing) as this is mandatory if you want to log metrics. This is done on line 21 to line 29 , where we log dummy accuracy and f1 metrics. Next we return the metrics. Finally, we also create Kubeflow component. Next, we will combine all of the components together to create the pipeline. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 ## Create pipeline from kubernetes.client.models import ( V1Affinity , V1NodeAffinity , V1NodeSelector , V1NodeSelectorTerm , V1NodeSelectorRequirement ) # define pipeline @dsl . pipeline ( name = \"my_pipeline\" , description = \"A simple demo of kubeflow pipeline\" ) # Define parameters to be fed into pipeline def my_pipeline ( data_path = 'data/' ): # Step 1 download_data_container = download_data_comp () # Step 2 data_analysis_container = data_analysis_comp ( download_data_container . output ) # add affinity aff = V1Affinity ( node_affinity = V1NodeAffinity ( required_during_scheduling_ignored_during_execution = V1NodeSelector ( node_selector_terms = [ V1NodeSelectorTerm ( match_expressions = [ V1NodeSelectorRequirement ( key = \"xxxx\" , operator = \"In\" , values = [ 'yyy' ])])])) ) download_data_container . add_affinity ( aff ) data_analysis_container . add_affinity ( aff ) # create client that would enable communication with the Pipelines API server client = kfp . Client () experiment_name = 'my_pipeline' # Compile pipeline to generate compressed YAML definition of the pipeline. kfp . compiler . Compiler () . compile ( my_pipeline , ' {} .zip' . format ( experiment_name )) We start with importing relevant modules and creating the pipeline function where we define the name and description of the pipeline. Next we connect the components together. From line 20 to line 30 , we are defining and setting the node wide affinity so that we only use spot instances for the computation. This will keep our cost to the minimum. Finally we create a Kubeflow client and compile the complete pipeline. This will create a zip file of the compiled pipeline that we can upload from the pipeline tab in Kubeflow. Next, we can create an experiment and the perform a run from the respective Kubeflow tabs. The process is quite simple and can be easily done from the UI. Once we have executed a run and the process is completed, we can see the individual modules and the status in the run page as shown below. Run page after successful execution of a run of my_pipeline pipeline And we have done it","title":"Compute and AI Services"},{"location":"data_science_tools/compute_and_ai_services/#compute-and-ai-services","text":"Gone are the days when we needed to buy high end devices to do literally anything. Currently there are plethora of services available online (and many of them are free!) that provide not only compute to use as you feel, but also generic AI services. Let's look into some of the famous and widely used compute and AI services.","title":"Compute and AI Services"},{"location":"data_science_tools/compute_and_ai_services/#caas-compute-as-a-service","text":"In this section we will cover some of the famous (and with some hint of free) platforms that provide compute-as-a-service (CaaS). These CaaS sometimes could be plain simple virtual machines, sometime they can be a cluster of nodes, while in other cases they can also be jupyter like coding environment. Let's go through some of the examples.","title":"CaaS: Compute as a Service"},{"location":"data_science_tools/compute_and_ai_services/#google-colab","text":"","title":"Google Colab"},{"location":"data_science_tools/compute_and_ai_services/#introduction","text":"Colaboratory or \"Colab\" in short, is a browser based jupyter notebook environment that is available for free. It requires no installation and even provides access to free GPU and TPU. The main disadvantages of Colab is that you cannot run long-running jobs (limit to max 12 hrs), GPU is subject to availability and in case of consistent usage of Colab, it might take longer to get GPU access. Google provides Pro and Pro+ options which are paid subscriptions to Colab (10$ and 50$ per month, respectively). While it provides longer background execution time and better compute (among others), they do not guarantee GPU and TPU access all the time. Remember, Colab is not an alternative to a full-blown cloud computing environment. It's just a place to test out some ideas quickly.","title":"Introduction"},{"location":"data_science_tools/compute_and_ai_services/#google-colab-snippets","text":"","title":"Google Colab Snippets"},{"location":"data_science_tools/compute_and_ai_services/#run-tensorboard-to-visualize-embeddings","text":"Taken from: how-to-use-tensorboard-embedding-projector 1 2 3 4 5 6 7 8 9 10 11 12 13 14 import numpy as np import tensorflow as tf import tensorboard as tb tf . io . gfile = tb . compat . tensorflow_stub . io . gfile from torch.utils.tensorboard import SummaryWriter vectors = np . array ([[ 0 , 0 , 1 ], [ 0 , 1 , 0 ], [ 1 , 0 , 0 ], [ 1 , 1 , 1 ]]) metadata = [ '001' , '010' , '100' , '111' ] # labels writer = SummaryWriter () writer . add_embedding ( vectors , metadata ) writer . close () % load_ext tensorboard % tensorboard -- logdir = runs","title":"Run tensorboard to visualize embeddings"},{"location":"data_science_tools/compute_and_ai_services/#connect-with-google-drive-and-access-files","text":"This code will prompt you to provide authorization to access your Google Drive. 1 2 from google.colab import drive drive . mount ( '/content/drive' )","title":"Connect with Google Drive and access files"},{"location":"data_science_tools/compute_and_ai_services/#kaggle","text":"Apart from being famous for hosting big AI/ML competitions, the next cool thing about the site is that it also provides free GPU/TPU computes! All you have to do is to sign up, create a new notebook and then you can start using it - import their datasets or your own, and start training you AI models! All of this ofcourse has a limit, you get minimum 30 hours of GPU usage per week, and at max 20 hours of TPU per week. Another catch is that you can only use GPU/TPU for 9 hours continuously. That said, Kaggle notebooks are a great place to perform your personal experiments or participate in new competitons to enhance your expertise. For more official work (industry or academics), do remember that you are putting your dataset in 3rd party's hands. Code/Notebook page of Kaggle .","title":"Kaggle"},{"location":"data_science_tools/compute_and_ai_services/#deepnote","text":"DeepNote provides a highly customised jupyter like notebook. It's one of the richest service in terms of features. Here goes some examples - you can create projects with multiple notebooks, you can create teams and collaborate with your colleagues live, you can quickly visualize datasets from notebooks, you can schedule notebooks, you can host reports, and best of all - they have free tier There are multiple pricing based tiers. To begin with you can try out the free tier and get upto 750 hours of Standard compute hours per month, that's like keeping one project (that could consist of multiple notebooks) open for the complete month! ( offer subject to change; was valid at the time of writing ) Pricing tiers of Deepnote . Hint Planning to try DeepNote out? Use the refer link to get free 20 Pro compute hours (thats upto 16GB RAM and v4vCPU)","title":"DeepNote"},{"location":"data_science_tools/compute_and_ai_services/#mlaas-machine-learning-as-a-service","text":"In this section we will cover some of the famous platforms that provide Machine learning-as-a-Service (MLaaS). These MLaaS take care of infrastructure related aspect of data holding, data preparing, model training and model deployment. On top of this, they provide a repository of classical ML algorithms that can be leveraged to create data science solutions. The idea is to make data science as a plug and play solution creation activity, as they take care of most of the engineering aspect. Let's go through some of the examples.","title":"MLaaS: Machine Learning as a Service"},{"location":"data_science_tools/compute_and_ai_services/#aws-sagemaker-amazon","text":"AWS Sagemaker is a cloud-based servies that helps data scientists with the complete lifecycle of data science project. They have specialised tools that cover following stages of data science projects, Prepare : It's the pre-processing step of the project. Some of the important services are \" Gound Truth \" that is used for data labeling/annotation and \" Feature Store \" that is used to provide consistence data transformation across teams and services like training and deployment. Build : It's where an Data Scientists spends most of his time coding. \" Studio Notebooks \" provides jupyter notebooks that can be used to perform quick ideation check and build the model. Train & Tune : It's where you can efficiently train and debug your models. \" Automatic Model Training \" can be used for hyper-parameter tuning of the model i.e. finding the best parameters that provides highest accuracy. \" Experiments \" can be used to run and track multiple experiments, its an absolute must if your projects requires multiple runs to find the best architecture or parameters. Deploy & Manage : The final stage, where you deploy your model for the rest of the world to use. \" One-Click Deployment \" can be used to efficiently deploy your model to the cloud. \" Model Monitor \" can be used to manage your model, like deleting, updating, and so on. Services provided by AWS Sagemaker . AWS charges a premium for providing all of these features under a single umbrella. For a more detailed pricing information, you can estimate the cost using this . Hint As AWS Sagemaker is a costly affair, several DS teams try to find workarounds. Some of them are like using spot instances for training as they are cheaper & using AWS Lambda for deploying small models.","title":"AWS Sagemaker (Amazon)"},{"location":"data_science_tools/compute_and_ai_services/#kubeflow","text":"","title":"Kubeflow"},{"location":"data_science_tools/compute_and_ai_services/#introduction_1","text":"Kubeflow is an open-source project that is dedicated to making development, tracking and deployments of machine learning (ML) workflows on Kubernetes simple, portable and scalable. As per their website, \"Anywhere you are running Kubernetes, you should be able to run Kubeflow.\" While there are many paid MLaaS like Sagemaker , Azure ML Services and Google AI Platform , Kubeflow is an outlier that provides most of the features present in the paid platforms, but for free! We can deploy Kubeflow on Kubernetes by following the guide on their website . Once done, you can boot it up and it should look as shown below, Main page of Kubeflow . Hint Go with Kubeflow if you are setting up a new AI team for your organziation or school, and don't want to commit to costly services like Sagemaker. But beware, it does require DevOps knowledge, as you will need to setup Kubernetes and manage it. While it is completly free, you will be charged for the compute you utilize. To cut down the cost, in case you are connecting Kubeflow with AWS, you can use Spot instances.","title":"Introduction"},{"location":"data_science_tools/compute_and_ai_services/#components","text":"Kubeflow provides several individual components that will help with the ML lifecycle. Note, we can even pick and choose the components you want while installation. Some of them are, Notebook: here we can create jupyter notebook servers and perform quick experimentations. Each server is assigned its own volume (hard memory). On booting up a server, a new compute is procured and you will see Jupyter Lab page where you can create mulitple notebooks, scripts or terminals. The compute could be EC2 instance or Spot instance, incase of AWS connection and based on your configuration. Pipeline: here we define one ML project. Kubeflow supports defining a pipeline in terms of a DAG (Directed Acyclic Graph), where each individual function or module is one node. Pipeline represents a graph of modules, where execution happens in a sequential or parallel manner while considering the inter-module dependencies , ex: module_2 requires output of module_1 . While this leads to modularization of the code, the real intention is to make the pipeline execution traceable and independent from each other. This is achieved by containerizing each module and running them on different instances, making the process truly independent. Experiments: On a single ML project, we may want to run multiple experiments, ex: (1) test_accuracy to try out a couple of parameters and compare accuracy, (2) test_performance to compare latency on different shape and size of data. This is where you define individual experiments. Runs: One execution of an experiment for a pipeline is captured here, ex: for test_accuracy experiment of MNIST pipeline, perform one run with learning_rate = 0.001 . Experiments (AutoML): we cannot try all the parameters for the test_accuracy one by one. The obvious question, why not automate it by doing hyperparameter tuning? AutoML is what you are looking for! Models: after all experimentations and model training, we would like to host/deploy the model. It can done using this component.","title":"Components"},{"location":"data_science_tools/compute_and_ai_services/#creating-and-running-pipeline","text":"Let's start the coding . So for this tutorial, we will create a simple Kubeflow pipeline with two steps, Step 1 - Download data: where we will download data from S3 bucket, pass the downloaded data to the next step for further analysis. Step 2 - Perform analysis: we will perform some rudimentary analysis on the data and log the metrics. We will try to go through some basic and advanced scenarios, so that you can refer the code to create your own pipeline, even if it is completely different. After creating the pipeline, we will register it, create an experiment and then execute a run. Lets start with importing the relevant packages. Make sure to install kfp with the latest version by using pip install kfp --upgrade 1 2 3 4 5 6 # imports import kfp import kfp.dsl as dsl from typing import NamedTuple import kfp.components as comp from kfp.components import InputPath , OutputPath Now we will create the first module that downloads data from S3 bucket. Note, Kubeflow takes care of the logistics of data availability between modules, but we need to share the path where data is downloaded. This is done by typecasing parameter with OutputPath(str) as done on line 2 . The process will be similar for ML models as well. We can download a model in the first module and perform training in another, and perform performance check in the third module. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 ## Step 1 def download_data ( data_path : OutputPath ( str )): # import the functions import os import boto3 # create the path if not exist if not os . path . exists ( data_path ): os . makedirs ( data_path ) # setup boto3 s3 = boto3 . resource ( 's3' ) s3_client = boto3 . client ( 's3' ) bucket_name = 'my-bucket' bucket = s3 . Bucket ( bucket_name ) # get list of all files at the s3 bucket prefix prefix = \"dataset/\" query = s3_client . list_objects ( Bucket = bucket_name , Prefix = prefix , Delimiter = '/' ) files = [] if 'Contents' in query : for obj in query [ 'Contents' ]: files . append ( obj [ 'Key' ]) # download each file into the folder for file_path in files : # get file name file_name = file_path . split ( '/' )[ - 1 ] # download and save the file s3_client . download_file ( bucket_name , file_path , f ' { data_path } / { file_name } ' ) print ( f \"Downloaded: ' { file_path } ' into ' { data_path } / { file_name } '\" ) # done! return print ( \"Done\" ) # download_data() # create kubeflow component download_data_comp = kfp . components . create_component_from_func ( func = download_data , base_image = 'python:3.7' , packages_to_install = [ 'boto3' ]) From line 40 to line 43 , we are converting the function to Kubeflow pipeline component. As the component will run on an independent instance, we need to provide the base_image and packages_to_install information as well. Next, we will create the second module that loads the data from first module and just returns some dummy metrics. In reality, you can do a lot of things like data preprocessing or data transformation or EDA. For now, we will just stick with a dummy example. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 ## Step 2 from typing import NamedTuple def data_analysis ( data_path : InputPath ( str )) -> NamedTuple ( 'Outputs' , [ ( 'mlpipeline_metrics' , 'Metrics' ), ]): # import import json from glob import glob from collections import namedtuple # load each json file for file_path in glob ( f \" { data_path } /*.json\" ): # load the call data file and perform some analysis data = json . load ( open ( file_path )) # print print ( f \"Loaded { file_path } \" ) # --- do something fancy here --- # create metrics that should be logged metrics = { 'metrics' : [ { 'name' : 'accuracy' , 'numberValue' : 89 }, { 'name' : 'f1' , 'numberValue' : 89 }]} return [ json . dumps ( metrics )] # create kubeflow component data_analysis_comp = kfp . components . create_component_from_func ( func = data_analysis , base_image = 'python:3.7' , packages_to_install = []) In the function we are defining the data_path as InputPath(str) and is later used directly on line 14 , without the need of manually sharing the data across instances. We define mlpipeline_metrics as output (by type casing) as this is mandatory if you want to log metrics. This is done on line 21 to line 29 , where we log dummy accuracy and f1 metrics. Next we return the metrics. Finally, we also create Kubeflow component. Next, we will combine all of the components together to create the pipeline. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 ## Create pipeline from kubernetes.client.models import ( V1Affinity , V1NodeAffinity , V1NodeSelector , V1NodeSelectorTerm , V1NodeSelectorRequirement ) # define pipeline @dsl . pipeline ( name = \"my_pipeline\" , description = \"A simple demo of kubeflow pipeline\" ) # Define parameters to be fed into pipeline def my_pipeline ( data_path = 'data/' ): # Step 1 download_data_container = download_data_comp () # Step 2 data_analysis_container = data_analysis_comp ( download_data_container . output ) # add affinity aff = V1Affinity ( node_affinity = V1NodeAffinity ( required_during_scheduling_ignored_during_execution = V1NodeSelector ( node_selector_terms = [ V1NodeSelectorTerm ( match_expressions = [ V1NodeSelectorRequirement ( key = \"xxxx\" , operator = \"In\" , values = [ 'yyy' ])])])) ) download_data_container . add_affinity ( aff ) data_analysis_container . add_affinity ( aff ) # create client that would enable communication with the Pipelines API server client = kfp . Client () experiment_name = 'my_pipeline' # Compile pipeline to generate compressed YAML definition of the pipeline. kfp . compiler . Compiler () . compile ( my_pipeline , ' {} .zip' . format ( experiment_name )) We start with importing relevant modules and creating the pipeline function where we define the name and description of the pipeline. Next we connect the components together. From line 20 to line 30 , we are defining and setting the node wide affinity so that we only use spot instances for the computation. This will keep our cost to the minimum. Finally we create a Kubeflow client and compile the complete pipeline. This will create a zip file of the compiled pipeline that we can upload from the pipeline tab in Kubeflow. Next, we can create an experiment and the perform a run from the respective Kubeflow tabs. The process is quite simple and can be easily done from the UI. Once we have executed a run and the process is completed, we can see the individual modules and the status in the run page as shown below. Run page after successful execution of a run of my_pipeline pipeline And we have done it","title":"Creating and running Pipeline"},{"location":"data_science_tools/introduction/","text":"Introduction Several aspiring data scientists think Data science is just about training fancy models. While it's part of the job, what we are missing here is the huge additional effort that is required to make sure that a model is trainable, executable and deployable. Add to it the complexity of working in a team and now we have to make sure the code is well formatted and structured as well. In all, life of a Data scientist is similar to any software engineer, just with a caveat of having the luxury to play with the state-of-the-art AI algorithms once in a while \ud83d\ude04 Now, the industry is trying (or realizing) the capabilities, limitations and responsibilities of professionals in AI or ML field. This is giving rise to increase in requirements for diverse job profiles like ML engineer, Data Engineer, Data Scientist, Research Scientist, etc. That said, Data scientist (DS) are reinventing themselves as well, giving rise to the interesting profile of \"Full stack Data scientists\" - who while researching and experiment with AI models, are not afraid to dabble into the old school engineering aspect of the project. This article is for such aspirants or practitioners. MLOps tools Just like a mechanic should know about all the tools at his disposal, a data scientist should be aware of different ready-made and possibly free services available. You can quote me on this, Quote Never pay for what you can do for free, and never build something which has already been built! With this fortune cookie quote in mind, let's look into different tools and their segregation based on the where and how they are used, Different MLOps tools covering Data, Training, Evaluation and Deployment. ( fullstackdeeplearning ) The rest of this section will cover some of these tools in detail.","title":"Introduction"},{"location":"data_science_tools/introduction/#introduction","text":"Several aspiring data scientists think Data science is just about training fancy models. While it's part of the job, what we are missing here is the huge additional effort that is required to make sure that a model is trainable, executable and deployable. Add to it the complexity of working in a team and now we have to make sure the code is well formatted and structured as well. In all, life of a Data scientist is similar to any software engineer, just with a caveat of having the luxury to play with the state-of-the-art AI algorithms once in a while \ud83d\ude04 Now, the industry is trying (or realizing) the capabilities, limitations and responsibilities of professionals in AI or ML field. This is giving rise to increase in requirements for diverse job profiles like ML engineer, Data Engineer, Data Scientist, Research Scientist, etc. That said, Data scientist (DS) are reinventing themselves as well, giving rise to the interesting profile of \"Full stack Data scientists\" - who while researching and experiment with AI models, are not afraid to dabble into the old school engineering aspect of the project. This article is for such aspirants or practitioners.","title":"Introduction"},{"location":"data_science_tools/introduction/#mlops-tools","text":"Just like a mechanic should know about all the tools at his disposal, a data scientist should be aware of different ready-made and possibly free services available. You can quote me on this, Quote Never pay for what you can do for free, and never build something which has already been built! With this fortune cookie quote in mind, let's look into different tools and their segregation based on the where and how they are used, Different MLOps tools covering Data, Training, Evaluation and Deployment. ( fullstackdeeplearning ) The rest of this section will cover some of these tools in detail.","title":"MLOps tools"},{"location":"data_science_tools/version_control/","text":"Version control (VC) is basically needed for any file(s) that will be maintained for a long time (read, multiple editing process) and/or accessed by multiple people (in collaboration) . If you have such a file or a set of files (as a project) , you will agree the necessity to track the changes. VC tries to help us with the same \ud83c\udd92 Now comes the question of what can be tracked and how? A very simple distinction of different tools available for VC can be introduced efficiently, if we look at them from the lens of what type of data they can \"version control\". Such as, Code: if you want to maintain just the code files, GIT is the defacto answer. There are several GIT based service providers, such as GitHub , whose platform can be used (for free) to maintain a git repository. Data and ML Model: in contrast to GIT, that was developed to maintain relatively small sized files, we need something different if we want to handle big files like datasets or ML/DL models. Enter DVC (Data Version Control), an GIT extension that directly connects the data storages (cloud or local) with the git to maintain data, model and code at the same time! We will now go through some of the tools/services for version control in detail. GIT Introduction In simple words, GIT is a system designed to track changes in your file. True story, it was developed by none other but the creator of Linux, yes, Linus Torvalds in 2005! The story goes something like this -- while he was developing the linux kernel along with other kernel developers, he found it troublesome to maintain, track and handle conflicting (overlapping) pieces of codes. So he ended up coding the GIT system as a side project, just to help him and his fellow developers maintain linux kernel in a more efficient manner! Now, isn't that a cool side project \ud83d\ude0e. You can read more about GIT and the history here . Some of the popular and free services are GitHub , Gitlab and BitBucket . While they differ by UI and add-on functionalities, the core system (Git) used by all of them is the same. Hence if you learn the basic Git commands once, you can use any of the services mentioned above. That is why we will limit ourselves to learn Git the old school way i.e. via GIT bash commands, and leave the fancy UI or tool based operation as an exploration activity for the interested audience. Before we go into the command and syntax, we need to be clear about certain topics to better appreciate Git. These are, Where to download Git? Git is free and available here . Download the latest stable version as per your OS. How do we use Git? After downloading Git (and specifically in Windows) , from any directory in file explorer, on right click, you should get the option to open either \"Git bash\" or \"Git GUI\". For this article, we will use Git bash, as that's how real developers roll \ud83d\ude24 (jk) What is Git Bash? It is something similar to command prompt in windows or terminal in linux, but something specifically designed for Git. To perform any Git related operation, we will use the Git bash. What is the life cycle in Git? Basically any file in a git repository typically goes through three states. These are, (1) working state: the initial stage, where you have created a git repository and git is just looking at the files to note what has changed. (2) staging state: the second state where you mark certain files that should be commited, and (3) commit state: where you finalize the changes made to the files and commit them to the Git's history. Basically, this will create a version of your code and persist it for future reference. What is local vs remote instances? Local instance is a git repository present in your local computer, and on the other hand, remote instance is the common server used to upload the modifications of the local instance. This is done so that there is a centralised repository from where everyone in the team can pull or push the latest code. What are branches in Git? Branches are like parallel universes in Git. We can spawn off new branches anytime and from any other branch. By doing so we create a fork within that branch, where the developer can do whatever they want to do. Finally, after relevant commits/changes, the forked branch is merged back to their original branch using merge request. What is a merge request and merge conflict in Git? Merge request is a request raised by the developer to the maintainer of the Git remote repository, asking them to merge the two branches. The maintainer may want to perform final set of validations before accepting the merge request. It may also happen that the same lines in the same file has been modified in both the branches, this will then lead to a merge conflict. Resolving merge conflict can range from easy to highly complex based on how many files has been affected. To resolve a merge conflict, we will modify the affected lines and then create a new commit. Now, let's take a practical approach of learning Git by role-playing an example to learn the basics. Suppose you are working on a big project that requires mulitple modifications per day. TO efficiently maintain the project, you are looking for a tool that will help to keep track of the changes made in the project, by recording the line wise modification made in the files in the project. For this you want to explore GIT and test if it will make your life easier or not. So, let's get stated with the exploration! \ud83d\ude00 Initializing the Git repository: As we discussed, Git helps in tracking the changes made in a file. On top of it, it's easy to scale it up and track a complete folder that contains hundreds of files! For reference, suppose you have already created a python project and want to track the changes in any of the files present there. To do so, just go to the main directory of the project and initialize git by using command git init . This will mark that directory as a git repository. Next, if you run git status , it will show you an overview of the project and all of files. Note, by default Git keeps a look out at all of the files within the directory and show when any of the files have changed. Staging files: You are going through the project file by file, making modifications as needed. Once you are happy with any file (or think that it is done), you can add that file to the staging area by command git add , or if you want to stage all of the files at one go, do git add . . Now if you run git status again, you can see the files names are in green. This means these files are staged! Example for initializing the git repository to tracking the files. Commit: Now, suppose we just completed one small task. It would be a good idea to take a snapshot of our current code and save it. This can be done by git commit -m \"your message\" , wherein you are asking git to commit all of the changes added in the staging area. This commit can be thought of as a unique snapshot of your code with the commit message as your description. Note, Git also generates hex code that is unique to each commit, that acts as the commit identifier. Your description is just a human readable piece of informance for us mere mortals \ud83d\ude00 Push: Note, all of these modifications has been done on your local instance, and to publish these to the world, we need to push the code to the remote instance. We can push our latest commits to the remote server by git push origin master . Note, here git push signifies we want to push the code, origin denotes the remote server and master denotes the branch of origin on which we want to push. And that's it! We have covered most of the fundamental aspects of using git! One important aspect to remember is that, we should refrain from committing directly to the master branch. Instead whenever we are planning to do some modifications, we should checkout to a new git branch (by using git checkout -b ), do the modifications there, push that particular branch to remote and then create a merge request. This is just a good practice followed when working with a team! Note It may so happens that you only want to certain few files and not all of them. This can be done by creating a .gitignore file and placing it in the root directory. Within the file, add the relative (from root directory) path of all the files or folders you want the Git to ignore. For example, data/input.csv or data/* are respectively the examples to exclude one file or the complete folder from Git's tracking system. GIT Snippets A consolidation of some of the most helper code snippets for GIT. The basic git commands Listing down some of the most basic GIT commands, that you should definitely know about. Most of them are references from the above theory part. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 # list all local branches git branch # list all remote branches git branch - r # create a local copy of a remote branch git checkout -- track origin / branch_name # show the remote links git remote - v # add a new remote git remote add new_remote git @github . com : User / UserRepo . git # pull the latest code from \"master\" branch of \"origin\" remote server git pull origin master # checkout to an existing branch git checkout main # checkout to a new branch git checkout - b use_bert_model # after performing some changes, add files to staging state git add . # commit git commit - m \"added bert model\" # push the branch to remote git push origin use_bert_model Modify config to add email and name 1 2 3 4 5 6 7 8 9 10 11 # Check the value of the config git config -- get user . email git config -- get user . name # Add username git config -- global user . name \"FIRST_NAME LAST_NAME\" # Add email git config -- global user . email \"MY_NAME@example.com\" # For local modification (for a git within a directory), use --local instead of --global # mode details: https://support.atlassian.com/bitbucket-cloud/docs/configure-your-dvcs-username-for-commits/ Publishing to Github using APIs (Python) Publishing (adding files for instance) to Git is quite easy using the local CLI tool and/or Github tools or UI. We have already discussed how to do this using CLI. But what if you are creating your own application and need to do the same using Github APIs? This snippet is a python based function that adds a new file to your git repo, commits and publishes it! Note, this is not as simple as single API call, for detailed information on what happens behind the scene when you commit and push, I will suggest this article . You will need to create a personal token from github using this link . Store that as config['Github']['personal_token'] for this function to work. Note, the function takes config as parameter input. This snippet is inspired from this article and this stackoverflow answer . 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 # function to publish new content to Github def publish_to_github ( config , content , remote_path , commit_message , repo = 'test' , user = 'imohitmayank' , branch = 'main' , name = \"Mohit Mayank\" , email = \"mohitmayank1@gmail.com\" ): \"\"\" Function to publish new content to Github Parameters ----------- config: dict with `config['Github']['personal_token']` containing your Github personal token content: string content you want to push to Github remote_path: path wrt to remote, where you want to save the content to; include the file name commit_message: message for commit \"\"\" # Step 1: Get the SHA of the branch url = f \"https://api.github.com/repos/ { user } / { repo } /branches/ { branch } \" header = { 'Authorization' : f 'token { config [ \"Github\" ][ \"personal_token\" ] } ' } r = requests . get ( url , headers = header ) last_commit_sha = r . json ()[ 'commit' ][ 'sha' ] print ( \"Step 1 Done: SHA fetched.\" ) # Step 2: Create a blob url = f \"https://api.github.com/repos/ { user } / { repo } /git/blobs\" header = { 'Authorization' : f 'token { config [ \"Github\" ][ \"personal_token\" ] } ' } body = { \"content\" : content , \"encoding\" : \"utf-8\" } r = requests . post ( url , json = body , headers = header ) utf8_blob_sha = r . json ()[ 'sha' ] print ( \"Step 2 Done: Blob created.\" ) # Step 3: Create a tree url = f \"https://api.github.com/repos/ { user } / { repo } /git/trees\" header = { 'Authorization' : f 'token { config [ \"Github\" ][ \"personal_token\" ] } ' } body = { \"base_tree\" : last_commit_sha , \"tree\" : [ { \"path\" : remote_path , \"mode\" : \"100644\" , \"type\" : \"blob\" , \"sha\" : utf8_blob_sha } ] } r = requests . post ( url , json = body , headers = header ) tree_sha = r . json ()[ 'sha' ] print ( \"Step 3 Done: Tree created.\" ) ## Step 4: Commit the changes url = f \"https://api.github.com/repos/ { user } / { repo } /git/commits\" header = { 'Authorization' : f 'token { config [ \"Github\" ][ \"personal_token\" ] } ' } body = { \"message\" : commit_message , \"author\" : { \"name\" : name , \"email\" : email }, \"parents\" : [ last_commit_sha ], \"tree\" : tree_sha } r = requests . post ( url , json = body , headers = header ) new_commit_sha = r . json ()[ 'sha' ] print ( \"Step 4 Done: Changes commited\" ) ## Step 5: update the HEAD url = f \"https://api.github.com/repos/ { user } / { repo } /git/refs/heads/ { branch } \" header = { 'Authorization' : f 'token { config [ \"Github\" ][ \"personal_token\" ] } ' } body = { \"ref\" : \"refs/heads/ {branch} \" , \"sha\" : new_commit_sha } r = requests . post ( url , json = body , headers = header ) print ( \"Step 5 Done: HEAD updated\" ) print ( \"------ ALL DONE! ------\" ) Ignore files/folders .gitignore file in the root directory, contains the name of files and folders which should not be tracked by GIT. 1 2 3 4 5 6 7 8 9 # ignore personal.txt personal # ignore everything within \"pic\" folder pic /* # ignore everything except a specific file within a folder ! pic / logo . png pic /* Untrack file/folder and delete them from GIT To untrack the files or folders, we can create .gitignore file and add respective info. To delete the files or folders form GIT (and not from local system), we can delete them from the cache as suggested here , 1 2 3 4 5 # for a single file: git rm -- cached mylogfile . log # for a single directory: git rm -- cached - r mydirectory Stash partial changes Suppose you have made some partial changes and the remote is updated with a new commit. Now you cannot commit your local change (as its partial) and you need to pull the latest code from remote (as its update). git stash comes to the rescue, example below. 1 2 3 4 5 6 7 8 # stash away the current partial changes git stash # pull the latest code (or any other operation) git pull origin master # pop the stashed partial changes git stash pop Reset to the last commit You may want to revert back to the very last commit, discarding every modification from then. This could be because you were playing around with the code or doing some minor experiments. In either cases, you can do this by, 1 git reset -- hard HEAD Otherwise, to just unstage the files which were staged by git add , 1 git reset Refer this stackoverflow QA for more details. Using large files Several VC platform have a maximum file size limit, for example Github has 100MB file size limit. That said, there are ways to bypass such limitations, once of those are Git LFS i.e. Large file system. Using Git LFS we can track large files and it internally takes care of storing them as LFS objects. This way we can store our dataset or models in the git repositories as well. Below is an example track a model in a repository using Git LFS. # install git lfs git lfs install # track a file git lfs track \"model/model.pkl\" # or track a folder git lfs track \"model/**\" # add the git attribute to reflect changes git add .gitattribute # migrate the changes i.e. replace files with pointers (for current branch) git lfs migrate import --include=='model/**' --verbose # or for all branches git lfs migrate import --everything --include=='model/**' --verbose # show the files which are tracked git lfs ls-files Setting up multiple Github accounts on a single machine using SSH It is possible to setup multiple Github account on a single machine using SSH. This is useful when you want to work on multiple projects which are hosted on different Github accounts. For example, you might have one personal and one work account that you want to use on your machine. You can refer this for more details, and I will also summarize the steps below. The process is quite simple, first we will generate two SSH keys (for two accounts) and save them on our machine. Next, we will create a global ssh config file to map the two keys with github. Then, we will add the public keys to the respective github account. Finally, we will create a git config file for each local project and add the user details. Let's get started. Create SSH keys using the command shared below, make sure to replace the email address with your own. You will be prompted to enter a file name, you can enter any name you want. For example, if you want to create a key for your personal account, you can enter id_rsa_personal . Repeat this process twice to generate two keys for two different accounts. ssh-keygen -t rsa -b 4096 -C \"your_email@example.com\" Next, create a config file in .ssh folder to map the two accounts with their keys. If the file is not present, you can create it using the following command. touch ~/.ssh/config After this, open the file and add the following content. # Personal account Host github.com-personal HostName github.com User git IdentityFile ~/.ssh/id_rsa_personal IdentitiesOnly yes # Work account Host github.com-work HostName github.com User git IdentityFile ~/.ssh/id_rsa_work IdentitiesOnly yes Now, we will add the public keys to the respective Github account. You can find the public key in the following location, ~/.ssh/id_rsa_personal.pub and ~/.ssh/id_rsa_work.pub . Copy the content of the file and add it to the respective Github account. You can refer this official doc for more details. Finally go to the local project directory and create a config file in the .git folder. Add the following content to the file. [ user ] name = personal # use work if this is for work account email = { email-address } # add the respective email address [ remote \"origin\" ] url = git@github.com-personal:imohitmayank/my-repo.git # use git@github.com-personal:.. for work fetch = +refs/heads/*:refs/remotes/origin/* And that's it, now for any git related interactions initiated from this directory, personal github account will be used. Note Step a to c is only needed to be done once. After that, you can follow step d for any new project. DVC Coming soon! References GIT DVC","title":"Version control"},{"location":"data_science_tools/version_control/#git","text":"","title":"GIT"},{"location":"data_science_tools/version_control/#introduction","text":"In simple words, GIT is a system designed to track changes in your file. True story, it was developed by none other but the creator of Linux, yes, Linus Torvalds in 2005! The story goes something like this -- while he was developing the linux kernel along with other kernel developers, he found it troublesome to maintain, track and handle conflicting (overlapping) pieces of codes. So he ended up coding the GIT system as a side project, just to help him and his fellow developers maintain linux kernel in a more efficient manner! Now, isn't that a cool side project \ud83d\ude0e. You can read more about GIT and the history here . Some of the popular and free services are GitHub , Gitlab and BitBucket . While they differ by UI and add-on functionalities, the core system (Git) used by all of them is the same. Hence if you learn the basic Git commands once, you can use any of the services mentioned above. That is why we will limit ourselves to learn Git the old school way i.e. via GIT bash commands, and leave the fancy UI or tool based operation as an exploration activity for the interested audience. Before we go into the command and syntax, we need to be clear about certain topics to better appreciate Git. These are, Where to download Git? Git is free and available here . Download the latest stable version as per your OS. How do we use Git? After downloading Git (and specifically in Windows) , from any directory in file explorer, on right click, you should get the option to open either \"Git bash\" or \"Git GUI\". For this article, we will use Git bash, as that's how real developers roll \ud83d\ude24 (jk) What is Git Bash? It is something similar to command prompt in windows or terminal in linux, but something specifically designed for Git. To perform any Git related operation, we will use the Git bash. What is the life cycle in Git? Basically any file in a git repository typically goes through three states. These are, (1) working state: the initial stage, where you have created a git repository and git is just looking at the files to note what has changed. (2) staging state: the second state where you mark certain files that should be commited, and (3) commit state: where you finalize the changes made to the files and commit them to the Git's history. Basically, this will create a version of your code and persist it for future reference. What is local vs remote instances? Local instance is a git repository present in your local computer, and on the other hand, remote instance is the common server used to upload the modifications of the local instance. This is done so that there is a centralised repository from where everyone in the team can pull or push the latest code. What are branches in Git? Branches are like parallel universes in Git. We can spawn off new branches anytime and from any other branch. By doing so we create a fork within that branch, where the developer can do whatever they want to do. Finally, after relevant commits/changes, the forked branch is merged back to their original branch using merge request. What is a merge request and merge conflict in Git? Merge request is a request raised by the developer to the maintainer of the Git remote repository, asking them to merge the two branches. The maintainer may want to perform final set of validations before accepting the merge request. It may also happen that the same lines in the same file has been modified in both the branches, this will then lead to a merge conflict. Resolving merge conflict can range from easy to highly complex based on how many files has been affected. To resolve a merge conflict, we will modify the affected lines and then create a new commit. Now, let's take a practical approach of learning Git by role-playing an example to learn the basics. Suppose you are working on a big project that requires mulitple modifications per day. TO efficiently maintain the project, you are looking for a tool that will help to keep track of the changes made in the project, by recording the line wise modification made in the files in the project. For this you want to explore GIT and test if it will make your life easier or not. So, let's get stated with the exploration! \ud83d\ude00 Initializing the Git repository: As we discussed, Git helps in tracking the changes made in a file. On top of it, it's easy to scale it up and track a complete folder that contains hundreds of files! For reference, suppose you have already created a python project and want to track the changes in any of the files present there. To do so, just go to the main directory of the project and initialize git by using command git init . This will mark that directory as a git repository. Next, if you run git status , it will show you an overview of the project and all of files. Note, by default Git keeps a look out at all of the files within the directory and show when any of the files have changed. Staging files: You are going through the project file by file, making modifications as needed. Once you are happy with any file (or think that it is done), you can add that file to the staging area by command git add , or if you want to stage all of the files at one go, do git add . . Now if you run git status again, you can see the files names are in green. This means these files are staged! Example for initializing the git repository to tracking the files. Commit: Now, suppose we just completed one small task. It would be a good idea to take a snapshot of our current code and save it. This can be done by git commit -m \"your message\" , wherein you are asking git to commit all of the changes added in the staging area. This commit can be thought of as a unique snapshot of your code with the commit message as your description. Note, Git also generates hex code that is unique to each commit, that acts as the commit identifier. Your description is just a human readable piece of informance for us mere mortals \ud83d\ude00 Push: Note, all of these modifications has been done on your local instance, and to publish these to the world, we need to push the code to the remote instance. We can push our latest commits to the remote server by git push origin master . Note, here git push signifies we want to push the code, origin denotes the remote server and master denotes the branch of origin on which we want to push. And that's it! We have covered most of the fundamental aspects of using git! One important aspect to remember is that, we should refrain from committing directly to the master branch. Instead whenever we are planning to do some modifications, we should checkout to a new git branch (by using git checkout -b ), do the modifications there, push that particular branch to remote and then create a merge request. This is just a good practice followed when working with a team! Note It may so happens that you only want to certain few files and not all of them. This can be done by creating a .gitignore file and placing it in the root directory. Within the file, add the relative (from root directory) path of all the files or folders you want the Git to ignore. For example, data/input.csv or data/* are respectively the examples to exclude one file or the complete folder from Git's tracking system.","title":"Introduction"},{"location":"data_science_tools/version_control/#git-snippets","text":"A consolidation of some of the most helper code snippets for GIT.","title":"GIT Snippets"},{"location":"data_science_tools/version_control/#the-basic-git-commands","text":"Listing down some of the most basic GIT commands, that you should definitely know about. Most of them are references from the above theory part. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 # list all local branches git branch # list all remote branches git branch - r # create a local copy of a remote branch git checkout -- track origin / branch_name # show the remote links git remote - v # add a new remote git remote add new_remote git @github . com : User / UserRepo . git # pull the latest code from \"master\" branch of \"origin\" remote server git pull origin master # checkout to an existing branch git checkout main # checkout to a new branch git checkout - b use_bert_model # after performing some changes, add files to staging state git add . # commit git commit - m \"added bert model\" # push the branch to remote git push origin use_bert_model","title":"The basic git commands"},{"location":"data_science_tools/version_control/#modify-config-to-add-email-and-name","text":"1 2 3 4 5 6 7 8 9 10 11 # Check the value of the config git config -- get user . email git config -- get user . name # Add username git config -- global user . name \"FIRST_NAME LAST_NAME\" # Add email git config -- global user . email \"MY_NAME@example.com\" # For local modification (for a git within a directory), use --local instead of --global # mode details: https://support.atlassian.com/bitbucket-cloud/docs/configure-your-dvcs-username-for-commits/","title":"Modify config to add email and name"},{"location":"data_science_tools/version_control/#publishing-to-github-using-apis-python","text":"Publishing (adding files for instance) to Git is quite easy using the local CLI tool and/or Github tools or UI. We have already discussed how to do this using CLI. But what if you are creating your own application and need to do the same using Github APIs? This snippet is a python based function that adds a new file to your git repo, commits and publishes it! Note, this is not as simple as single API call, for detailed information on what happens behind the scene when you commit and push, I will suggest this article . You will need to create a personal token from github using this link . Store that as config['Github']['personal_token'] for this function to work. Note, the function takes config as parameter input. This snippet is inspired from this article and this stackoverflow answer . 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 # function to publish new content to Github def publish_to_github ( config , content , remote_path , commit_message , repo = 'test' , user = 'imohitmayank' , branch = 'main' , name = \"Mohit Mayank\" , email = \"mohitmayank1@gmail.com\" ): \"\"\" Function to publish new content to Github Parameters ----------- config: dict with `config['Github']['personal_token']` containing your Github personal token content: string content you want to push to Github remote_path: path wrt to remote, where you want to save the content to; include the file name commit_message: message for commit \"\"\" # Step 1: Get the SHA of the branch url = f \"https://api.github.com/repos/ { user } / { repo } /branches/ { branch } \" header = { 'Authorization' : f 'token { config [ \"Github\" ][ \"personal_token\" ] } ' } r = requests . get ( url , headers = header ) last_commit_sha = r . json ()[ 'commit' ][ 'sha' ] print ( \"Step 1 Done: SHA fetched.\" ) # Step 2: Create a blob url = f \"https://api.github.com/repos/ { user } / { repo } /git/blobs\" header = { 'Authorization' : f 'token { config [ \"Github\" ][ \"personal_token\" ] } ' } body = { \"content\" : content , \"encoding\" : \"utf-8\" } r = requests . post ( url , json = body , headers = header ) utf8_blob_sha = r . json ()[ 'sha' ] print ( \"Step 2 Done: Blob created.\" ) # Step 3: Create a tree url = f \"https://api.github.com/repos/ { user } / { repo } /git/trees\" header = { 'Authorization' : f 'token { config [ \"Github\" ][ \"personal_token\" ] } ' } body = { \"base_tree\" : last_commit_sha , \"tree\" : [ { \"path\" : remote_path , \"mode\" : \"100644\" , \"type\" : \"blob\" , \"sha\" : utf8_blob_sha } ] } r = requests . post ( url , json = body , headers = header ) tree_sha = r . json ()[ 'sha' ] print ( \"Step 3 Done: Tree created.\" ) ## Step 4: Commit the changes url = f \"https://api.github.com/repos/ { user } / { repo } /git/commits\" header = { 'Authorization' : f 'token { config [ \"Github\" ][ \"personal_token\" ] } ' } body = { \"message\" : commit_message , \"author\" : { \"name\" : name , \"email\" : email }, \"parents\" : [ last_commit_sha ], \"tree\" : tree_sha } r = requests . post ( url , json = body , headers = header ) new_commit_sha = r . json ()[ 'sha' ] print ( \"Step 4 Done: Changes commited\" ) ## Step 5: update the HEAD url = f \"https://api.github.com/repos/ { user } / { repo } /git/refs/heads/ { branch } \" header = { 'Authorization' : f 'token { config [ \"Github\" ][ \"personal_token\" ] } ' } body = { \"ref\" : \"refs/heads/ {branch} \" , \"sha\" : new_commit_sha } r = requests . post ( url , json = body , headers = header ) print ( \"Step 5 Done: HEAD updated\" ) print ( \"------ ALL DONE! ------\" )","title":"Publishing to Github using APIs (Python)"},{"location":"data_science_tools/version_control/#ignore-filesfolders","text":".gitignore file in the root directory, contains the name of files and folders which should not be tracked by GIT. 1 2 3 4 5 6 7 8 9 # ignore personal.txt personal # ignore everything within \"pic\" folder pic /* # ignore everything except a specific file within a folder ! pic / logo . png pic /*","title":"Ignore files/folders"},{"location":"data_science_tools/version_control/#untrack-filefolder-and-delete-them-from-git","text":"To untrack the files or folders, we can create .gitignore file and add respective info. To delete the files or folders form GIT (and not from local system), we can delete them from the cache as suggested here , 1 2 3 4 5 # for a single file: git rm -- cached mylogfile . log # for a single directory: git rm -- cached - r mydirectory","title":"Untrack file/folder and delete them from GIT"},{"location":"data_science_tools/version_control/#stash-partial-changes","text":"Suppose you have made some partial changes and the remote is updated with a new commit. Now you cannot commit your local change (as its partial) and you need to pull the latest code from remote (as its update). git stash comes to the rescue, example below. 1 2 3 4 5 6 7 8 # stash away the current partial changes git stash # pull the latest code (or any other operation) git pull origin master # pop the stashed partial changes git stash pop","title":"Stash partial changes"},{"location":"data_science_tools/version_control/#reset-to-the-last-commit","text":"You may want to revert back to the very last commit, discarding every modification from then. This could be because you were playing around with the code or doing some minor experiments. In either cases, you can do this by, 1 git reset -- hard HEAD Otherwise, to just unstage the files which were staged by git add , 1 git reset Refer this stackoverflow QA for more details.","title":"Reset to the last commit"},{"location":"data_science_tools/version_control/#using-large-files","text":"Several VC platform have a maximum file size limit, for example Github has 100MB file size limit. That said, there are ways to bypass such limitations, once of those are Git LFS i.e. Large file system. Using Git LFS we can track large files and it internally takes care of storing them as LFS objects. This way we can store our dataset or models in the git repositories as well. Below is an example track a model in a repository using Git LFS. # install git lfs git lfs install # track a file git lfs track \"model/model.pkl\" # or track a folder git lfs track \"model/**\" # add the git attribute to reflect changes git add .gitattribute # migrate the changes i.e. replace files with pointers (for current branch) git lfs migrate import --include=='model/**' --verbose # or for all branches git lfs migrate import --everything --include=='model/**' --verbose # show the files which are tracked git lfs ls-files","title":"Using large files"},{"location":"data_science_tools/version_control/#setting-up-multiple-github-accounts-on-a-single-machine-using-ssh","text":"It is possible to setup multiple Github account on a single machine using SSH. This is useful when you want to work on multiple projects which are hosted on different Github accounts. For example, you might have one personal and one work account that you want to use on your machine. You can refer this for more details, and I will also summarize the steps below. The process is quite simple, first we will generate two SSH keys (for two accounts) and save them on our machine. Next, we will create a global ssh config file to map the two keys with github. Then, we will add the public keys to the respective github account. Finally, we will create a git config file for each local project and add the user details. Let's get started. Create SSH keys using the command shared below, make sure to replace the email address with your own. You will be prompted to enter a file name, you can enter any name you want. For example, if you want to create a key for your personal account, you can enter id_rsa_personal . Repeat this process twice to generate two keys for two different accounts. ssh-keygen -t rsa -b 4096 -C \"your_email@example.com\" Next, create a config file in .ssh folder to map the two accounts with their keys. If the file is not present, you can create it using the following command. touch ~/.ssh/config After this, open the file and add the following content. # Personal account Host github.com-personal HostName github.com User git IdentityFile ~/.ssh/id_rsa_personal IdentitiesOnly yes # Work account Host github.com-work HostName github.com User git IdentityFile ~/.ssh/id_rsa_work IdentitiesOnly yes Now, we will add the public keys to the respective Github account. You can find the public key in the following location, ~/.ssh/id_rsa_personal.pub and ~/.ssh/id_rsa_work.pub . Copy the content of the file and add it to the respective Github account. You can refer this official doc for more details. Finally go to the local project directory and create a config file in the .git folder. Add the following content to the file. [ user ] name = personal # use work if this is for work account email = { email-address } # add the respective email address [ remote \"origin\" ] url = git@github.com-personal:imohitmayank/my-repo.git # use git@github.com-personal:.. for work fetch = +refs/heads/*:refs/remotes/origin/* And that's it, now for any git related interactions initiated from this directory, personal github account will be used. Note Step a to c is only needed to be done once. After that, you can follow step d for any new project.","title":"Setting up multiple Github accounts on a single machine using SSH"},{"location":"data_science_tools/version_control/#dvc","text":"Coming soon!","title":"DVC"},{"location":"data_science_tools/version_control/#references","text":"GIT DVC","title":"References"},{"location":"introduction/getting_started/","text":"Introduction Did you recently decide to become a Data Scientist? But you don't know what to do next or how can you get started? -- Well it's common to be confused about these things and specially on how to begin your journey in this amazing world of Data Science \ud83c\udf0e Just like learning any other technique, it's better to approach learning Data Science from scratch and in multiple phases. Remember, this is by no means a magic formula to make you a Data Science expert overnight - we also don't provide any certifications \ud83d\udcc4 . The intention of this guide is to provide the essential knowledge and skills to get you started in the Data Science field. And most importantly, to help you plan your journey in the right direction. One more thing, the field of Data Science is quite vast. The learning approach could vary based on what you want to become -- Data Engineer, Data Scientist, Research Scientist, ML Engineer, etc. In this guide, we will focus on Data Scientist designation while also touching some peripheral topics. But before we get started, let's have a look at the different designations in the Data Science field and what they are all about. Data Engineer: Data Engineer handles data i.e. they are responsible for the data processing and data analysis. Data Scientist: Data Scientist handles model training i.e. they are responsible to leverage the data to generate predictive and actionable models. ML Engineer: ML Engineer deploys models i.e. they are responsible to take the model and perform accessible and scalable deployment. Note Research Scientist is another Data Science related designation that is usually more research and academics oriented, when compared to Data Scientist , which is more practical and industry oriented. Now, let's get started by dividing the herculean task of masting Data Science into three levels, namely L1, L2, and L3. L1 is the most basic level, and L3 is the most advanced level. Note Advancing from one level to another is subject to your own understanding and progress. There is no time related or checkpoint related explicit grading system that I will recommend. I believe, that if it seems you are not learning anything new by following the steps of a certain level, you are qualified enough to move on to the next level. \"To learn is to read, understand and apply.\" -- with this in mind, each level is further divided into two subtasks of theory and practice . The idea is to have at least one action item for each subtask in each level. More details are below, Levels Theory Practice L1 Beginner Online Courses Course Assignments and Code replication L2 Domain Specialization Courses Projects and Competitions L3 Research Papers and Books Create Products and Publish Papers L1: the absolute beginners \ud83d\udc66 This is the starting point of our journey. Here we want to get a basic understanding of the Data Science field and start getting our hands dirty with the basics. Let's try to go through the individual subtasks in detail. Theory: We will limit our scope to going through online courses that provide high level understanding. Here is a list of some free online courses or materials for you to go through. Python for Everybody Specialization and Python docs Machine Learning by Andrew Ng Intro to Deep Learning by MIT Data Engineering Foundations Specialization Practice: it can start with any assignments mentioned in the theory courses or materials. On top of it, we can also perform code replication i.e. to look into basic code snippets and try to replicate them. Pick any existing EDA script or ML code or any other code snippet and try to replicate it on your own. This provides the opportunity to perform guided practice as you can always refer back to the original code if you are stuck! L2: the intermediate level \ud83d\udc68 Now we have moved on to the intermediate level. Data Science in itself is a very vast field and includes multiple sub-domain like NLP, CV, RL, etc. By now, you should have some inclination towards one or more of these fields. It's time to get some domain-specific specializations. Let's go through the individual subtasks in detail. Theory: Based on different fields and generic topics, here is a list of introductory materials that you can refer to, Natural Language Processing Specialization CS224U: Natural Language Understanding CS231n: Deep Learning for Computer Vision Introduction to Reinforcement Learning with David Silver Full Stack Deeplearning Awesome MLOps Github Practice: in terms of practices, code assignment is mandatory. But another piece of advice is to participate in competitions and hackathons published on platforms like Kaggle . The plan could be to first participate in old competitions and get a feel of coding and solving problems. And then to participate in live competitions and compete with some of the best Data Science enthusiasts out there! Another thing that you can do is to create a simple AI/ML project from scratch, this will give you a much-needed practical experience. Here is an article I wrote on how to ideate and publish your projects. You can also refer to this Github repo to get some inspirations or AI project ideas. Note Please be aware that the intention of participation should be to learn. If you enter any competition without proper preparation and practice, you might not be able to get the best results. But that should never demotivate you. Your intention should be to learn, if it's that, I am sure you will come out of any competition with learning something new. Winning or Lossing is just the by-product. L3: the to be experts \ud83d\ude0e We have reached the final and an never-ending level . By now you have already mastered the basics of Data Science and even some advanced topics. But learning is a never-ending process, as the knowledge is not constant. Every day there is new research that takes the cumulative knowledge one step forward. This progress is quite fast in AI/ML field, so while learning all the existing stuff is quite important, it is necessary that you keep learning the new things and not get left behind! With this in mind, let's look into the individual subtasks in detail. Theory: here you should read about the latest trends in the field. The intention is to be up-to-date and even to be ahead of the curve. For this you can refer cited papers, trending blogs, and famous books. Lots of researchers publish their work on Arxiv which is free for all and with sites like Arxiv Sanity you can find the trending papers. Several top AI labs have their own website where they frequently publish their latest research like - Google AI , Meta AI , DeepMind AI , OpenAI , Uber AI , etc. Apart from this, people also follow influencers and groups to get the latest news. People even subscribe to AI newsletters for the same. Note If this sounds like a challenge, then you are not alone. It's quite a task to follow a lot of these sites and people to get the latest news in the field of AI and ML. That is why I created The ML Dojo . ML Dojo is a daily report on the latest and upcoming research, experiments, topics, articles, papers, \u2026 (you name it), in the field of Artificial Intelligence and Machine learning. Give it a try, it's completly free Practice: instead of just reading research papers, the intention should be to find your topic of interest, research, and publish your own work. For this, you can even collaborate with your colleagues or friends. Writing a paper of your own is a challenging but educating process, as you get to ideate, experiment, and write (to defend) the work. It gives you the ability to look at a problem from multiple perspectives and then solve it! Another thing to do is to take the project to the next level -- by creating (and maybe open-sourcing) your own products. While projects were created for the purpose of learning and your own usage, products are created for the sake of other people's usage. This will teach you to look at the problem from someone else's perspective.","title":"Getting started with Data Science"},{"location":"introduction/getting_started/#introduction","text":"Did you recently decide to become a Data Scientist? But you don't know what to do next or how can you get started? -- Well it's common to be confused about these things and specially on how to begin your journey in this amazing world of Data Science \ud83c\udf0e Just like learning any other technique, it's better to approach learning Data Science from scratch and in multiple phases. Remember, this is by no means a magic formula to make you a Data Science expert overnight - we also don't provide any certifications \ud83d\udcc4 . The intention of this guide is to provide the essential knowledge and skills to get you started in the Data Science field. And most importantly, to help you plan your journey in the right direction. One more thing, the field of Data Science is quite vast. The learning approach could vary based on what you want to become -- Data Engineer, Data Scientist, Research Scientist, ML Engineer, etc. In this guide, we will focus on Data Scientist designation while also touching some peripheral topics. But before we get started, let's have a look at the different designations in the Data Science field and what they are all about. Data Engineer: Data Engineer handles data i.e. they are responsible for the data processing and data analysis. Data Scientist: Data Scientist handles model training i.e. they are responsible to leverage the data to generate predictive and actionable models. ML Engineer: ML Engineer deploys models i.e. they are responsible to take the model and perform accessible and scalable deployment. Note Research Scientist is another Data Science related designation that is usually more research and academics oriented, when compared to Data Scientist , which is more practical and industry oriented. Now, let's get started by dividing the herculean task of masting Data Science into three levels, namely L1, L2, and L3. L1 is the most basic level, and L3 is the most advanced level. Note Advancing from one level to another is subject to your own understanding and progress. There is no time related or checkpoint related explicit grading system that I will recommend. I believe, that if it seems you are not learning anything new by following the steps of a certain level, you are qualified enough to move on to the next level. \"To learn is to read, understand and apply.\" -- with this in mind, each level is further divided into two subtasks of theory and practice . The idea is to have at least one action item for each subtask in each level. More details are below, Levels Theory Practice L1 Beginner Online Courses Course Assignments and Code replication L2 Domain Specialization Courses Projects and Competitions L3 Research Papers and Books Create Products and Publish Papers","title":"Introduction"},{"location":"introduction/getting_started/#l1-the-absolute-beginners","text":"This is the starting point of our journey. Here we want to get a basic understanding of the Data Science field and start getting our hands dirty with the basics. Let's try to go through the individual subtasks in detail. Theory: We will limit our scope to going through online courses that provide high level understanding. Here is a list of some free online courses or materials for you to go through. Python for Everybody Specialization and Python docs Machine Learning by Andrew Ng Intro to Deep Learning by MIT Data Engineering Foundations Specialization Practice: it can start with any assignments mentioned in the theory courses or materials. On top of it, we can also perform code replication i.e. to look into basic code snippets and try to replicate them. Pick any existing EDA script or ML code or any other code snippet and try to replicate it on your own. This provides the opportunity to perform guided practice as you can always refer back to the original code if you are stuck!","title":"L1: the absolute beginners \ud83d\udc66"},{"location":"introduction/getting_started/#l2-the-intermediate-level","text":"Now we have moved on to the intermediate level. Data Science in itself is a very vast field and includes multiple sub-domain like NLP, CV, RL, etc. By now, you should have some inclination towards one or more of these fields. It's time to get some domain-specific specializations. Let's go through the individual subtasks in detail. Theory: Based on different fields and generic topics, here is a list of introductory materials that you can refer to, Natural Language Processing Specialization CS224U: Natural Language Understanding CS231n: Deep Learning for Computer Vision Introduction to Reinforcement Learning with David Silver Full Stack Deeplearning Awesome MLOps Github Practice: in terms of practices, code assignment is mandatory. But another piece of advice is to participate in competitions and hackathons published on platforms like Kaggle . The plan could be to first participate in old competitions and get a feel of coding and solving problems. And then to participate in live competitions and compete with some of the best Data Science enthusiasts out there! Another thing that you can do is to create a simple AI/ML project from scratch, this will give you a much-needed practical experience. Here is an article I wrote on how to ideate and publish your projects. You can also refer to this Github repo to get some inspirations or AI project ideas. Note Please be aware that the intention of participation should be to learn. If you enter any competition without proper preparation and practice, you might not be able to get the best results. But that should never demotivate you. Your intention should be to learn, if it's that, I am sure you will come out of any competition with learning something new. Winning or Lossing is just the by-product.","title":"L2: the intermediate level \ud83d\udc68"},{"location":"introduction/getting_started/#l3-the-to-be-experts","text":"We have reached the final and an never-ending level . By now you have already mastered the basics of Data Science and even some advanced topics. But learning is a never-ending process, as the knowledge is not constant. Every day there is new research that takes the cumulative knowledge one step forward. This progress is quite fast in AI/ML field, so while learning all the existing stuff is quite important, it is necessary that you keep learning the new things and not get left behind! With this in mind, let's look into the individual subtasks in detail. Theory: here you should read about the latest trends in the field. The intention is to be up-to-date and even to be ahead of the curve. For this you can refer cited papers, trending blogs, and famous books. Lots of researchers publish their work on Arxiv which is free for all and with sites like Arxiv Sanity you can find the trending papers. Several top AI labs have their own website where they frequently publish their latest research like - Google AI , Meta AI , DeepMind AI , OpenAI , Uber AI , etc. Apart from this, people also follow influencers and groups to get the latest news. People even subscribe to AI newsletters for the same. Note If this sounds like a challenge, then you are not alone. It's quite a task to follow a lot of these sites and people to get the latest news in the field of AI and ML. That is why I created The ML Dojo . ML Dojo is a daily report on the latest and upcoming research, experiments, topics, articles, papers, \u2026 (you name it), in the field of Artificial Intelligence and Machine learning. Give it a try, it's completly free Practice: instead of just reading research papers, the intention should be to find your topic of interest, research, and publish your own work. For this, you can even collaborate with your colleagues or friends. Writing a paper of your own is a challenging but educating process, as you get to ideate, experiment, and write (to defend) the work. It gives you the ability to look at a problem from multiple perspectives and then solve it! Another thing to do is to take the project to the next level -- by creating (and maybe open-sourcing) your own products. While projects were created for the purpose of learning and your own usage, products are created for the sake of other people's usage. This will teach you to look at the problem from someone else's perspective.","title":"L3: the to be experts \ud83d\ude0e"},{"location":"machine_learning/ML_snippets/","text":"Machine Learning / Deep Learning Snippets Sharing some of the most widely used and arguably not so famous Machine Learning snippets Feature importance Feature importance calculation is an important technique to identify the features which \"helps\" with the downstream classification or regression tasks. Sklearn provides several options to infer the importance of a feature. Most importantly, many model automatically computed the importane and store it in model.feature_importances_ , after you call .fit() As an example, lets take the text based classification task and try to infer the following, Part 1: First use CountVectorizer for feature engineering and ExtraTreesClassifier for classification. Part 2: Show the top N features. Part 3: Show evidence of a feature (by value count over different class labels) Following dataset based assumtions have been made, We assume x_train and y_train contains the a list of sentences and labels repectively. We assume a pandas dataframe of name train_df is present which contains x_train and y_train as columns with name title and label respectively. 1 2 3 index = 2282 # the feature's index # the label's distribution if this word is present in sentence train_df . iloc [ np . where ( features [:, index ] . todense () >= 1 )[ 0 ]][ 'label' ] . value_counts () Cross validation Cross validation is a technique in which at each iteration you create different split of train and dev data. At each such iteration, we train he model on the train split and validate on the remaining split. This way, event with small training data, we can perform multiple fold of validation. If you repeat this operation (for \\(N\\) iterations) over the complete data such that (1) each data point belonged to the dev split at most once, (2) each data point belonged to train split \\(N-1\\) times - its cross-validation. I have used Stratified K-Folds cross-validator, you can use any function from the complete list mentioned here - Sklearn Model selection 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 # import ======= from sklearn.model_selection import StratifiedKFold # code ============= # split the dataset into K fold test def split_dataset ( dataset , return_fold = 0 , n_splits = 3 , shuffle = True , random_state = 1 ): \"\"\" dataset: pandas dataframe return_fold: the fold out of `n_split` fold, which is to be returned n_splits: # cross fold \"\"\" # defined the KFOld function skf = StratifiedKFold ( n_splits = n_splits , shuffle = shuffle , random_state = random_state ) # defined the dataset X = dataset y = dataset [ 'class' ] # label/class for i , ( train_index , test_index ) in enumerate ( skf . split ( X , y )): if return_fold == i : return dataset . loc [ train_index ], dataset . loc [ test_index ] # example call if __name__ == '__main__' : # read the dataset df = pd . read_csv ( \"....\" ) # get one specific fold out of train , test = split_dataset ( dataset = df , return_fold = 0 , n_splits = 3 ) # run for all folds for fold in range ( n_splits ): train , test = split_dataset ( dataset = df , return_fold = fold , n_splits = n_splits ) # Hyper-parameter tuning Below is an example of hyperparameter tuning for SVR regression algorithm. There we specify the search space i.e. the list of algorithm parameters to try, and for each parameter combination perform a 5 fold CV test. More details: Sklearn Hyperparameter tuning More details: Sklearn SVR Algorithm 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 # import ======= from sklearn.model_selection import GridSearchCV from sklearn.metrics import make_scorer from sklearn.metrics import mean_squared_error # DATA LOAD ============ train_data = ... # load the features and target on which to train # SEARCH SPACE ============ search_space = [{ 'kernel' : [ 'poly' , 'rbf' , 'sigmoid' ], 'C' : [ 1 , 10 , 100 ], 'epsilon' : [ 10 , 1 , 0.1 , 0.2 , 0.01 ]}] # TUNING ============ scorer = make_scorer ( mean_squared_error , greater_is_better = False ) svr_gs = GridSearchCV ( SVR (), search_space , cv = 5 , scoring = scorer , verbose = 10 , n_jobs = None ) svr_gs . fit ( train_data [ 'features' ], train_data [ 'target' ]) # PRINT RESULT ============ parameter_result = [] print ( \"Grid scores on training set:\" ) means = svr_gs . cv_results_ [ 'mean_test_score' ] stds = svr_gs . cv_results_ [ 'std_test_score' ] for mean , std , params in zip ( means , stds , svr_gs . cv_results_ [ 'params' ]): print ( \" %0.3f (+/- %0.03f ) for %r \" % ( mean , std * 2 , params )) parameter_result . append ({ 'mean' : abs ( mean ), 'std' : std , ** params }) # SELECT BEST PARAMETERS ============ # select the settings with smallest loss parameter_result = pd . DataFrame ( parameter_result ) parameter_result = parameter_result . sort_values ( by = [ 'mean' ]) best_settings = parameter_result . head ( 1 ) . to_dict ( orient = 'records' )[ 0 ] # FIT WITH BEST PARAMETERS ============ SVRModel = SVR ( C = best_settings [ 'C' ], epsilon = best_settings [ 'epsilon' ], kernel = best_settings [ 'kernel' ]) SVRModel . fit ( train_data [ 'features' ], train_data [ 'target' ]) Callbacks Callbacks are the hooks that you can attach to your deep learning training or validation process. It can be used to affect the training process from simple logging metric to even terminating the training in case special conditions are met. Below is an example of EarlyStopping and ModelCheckpoint callbacks. Keras 1 2 3 4 5 # fit the model history = model . fit ( train_data_gen , # training data generator # .... # put usual code here callbacks = [ checkpoint , earlystopping ] ) Mean pooling References this stackoverflow answer . Keras 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 # import import numpy as np import keras # create sample data A = np . array ([[ 1 , 2 , 3 ],[ 4 , 5 , 6 ],[ 0 , 0 , 0 ],[ 0 , 0 , 0 ],[ 0 , 0 , 0 ]]) B = np . array ([[ 1 , 3 , 0 ],[ 4 , 0 , 0 ],[ 0 , 0 , 1 ],[ 0 , 0 , 0 ],[ 0 , 0 , 0 ]]) C = np . array ([ A , B ]) . astype ( \"float32\" ) # expected answer (for temporal mean) np . mean ( C , axis = 1 ) \"\"\" The output is array([[1. , 1.4, 1.8], [1. , 0.6, 0.2]], dtype=float32) Now using AveragePooling1D, \"\"\" model = keras . models . Sequential ( tf . keras . layers . AveragePooling1D ( pool_size = 5 ) ) model . predict ( C ) \"\"\" The output is, array([[[1. , 1.4, 1.8]], [[1. , 0.6, 0.2]]], dtype=float32) \"\"\" Some points to consider, The pool_size should be equal to the step/timesteps size of the recurrent layer. The shape of the output is ( batch_size , downsampled_steps , features ), which contains one additional downsampled_steps dimension. This will be always 1 if you set the pool_size equal to timestep size in recurrent layer. Dataset and Dataloader Dataset can be downloaded from Kaggle . PyTorch 1 2 3 4 5 6 # init the train and test dataset train_dataset = IMDBDataset ( X_train , y_train ) test_dataset = IMDBDataset ( X_test , y_test ) # create the dataloader train_dataloader = DataLoader ( train_dataset , batch_size = 64 , shuffle = True ) test_dataloader = DataLoader ( test_dataset , batch_size = 64 , shuffle = True ) Freeze Layers Example on how to freeze certain layers while training PyTorch lightning 1 2 3 4 5 6 7 8 9 10 11 12 13 { code - block } python # Before defining the optimizer, we need to freeze the layers # In pytorch lightning, as optimizer is defined in configure_optimizers, we freeze layers there. def configure_optimizers ( self ): # iterate through the layers and freeze the one with certain name (here all BERT models) # note: the name of layer depends on the varibale name for name , param in self . named_parameters (): if 'BERTModel' in name : param . requires_grad = False # only pass the non-frozen paramters to optimizer optimizer = torch . optim . Adam ( filter ( lambda p : p . requires_grad , self . parameters ()), lr = 1e-3 ) # return optimizer return optimizer Check for GPU availability We need GPUs for deep learning, and before we start training or inference it's a good idea to check if GPU is available on the system or not. The most basic way to check for GPUs (if it's a NVIDIA one) is to run nvidia-smi command. It will return a detailed output with driver's version, cuda version and the processes using GPU. Refer this for more details on individual components. +-----------------------------------------------------------------------------+ | NVIDIA-SMI 435 .21 Driver Version: 435 .21 CUDA Version: 10 .1 | | -------------------------------+----------------------+----------------------+ | GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. | | =============================== + ====================== + ====================== | | 0 GeForce MX110 Off | 00000000 :01:00.0 Off | N/A | | N/A 43C P0 N/A / N/A | 164MiB / 2004MiB | 0 % Default | +-------------------------------+----------------------+----------------------+ +-----------------------------------------------------------------------------+ | Processes: GPU Memory | | GPU PID Type Process name Usage | | ============================================================================= | | 0 6348 G /usr/lib/xorg 53MiB | | 0 13360 G ...BBBBBaxsxsuxbssxsxs --shared-files 28MiB | +-----------------------------------------------------------------------------+ You can even use deep learning frameworks like Pytorch to check for the GPU availability. In fact, this is where you will most probably use them. 1 2 3 4 5 6 7 8 9 10 11 12 13 # import import torch # checks torch . cuda . is_available () ## Output: True torch . cuda . device_count () ## Output: 1 torch . cuda . current_device () ## Output: 0 torch . cuda . device ( 0 ) ## Output: torch . cuda . get_device_name ( 0 ) ## Output: 'GeForce MX110' HuggingFace Tokenizer Tokenizer is a pre-processing step that converts the text into a sequence of tokens. HuggingFace tokenizer is a wrapper around the tokenizers library , that contains multiple base algorithms for fast tokenization. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 # import from transformers import AutoTokenizer # load a tokenizer (use the model of your choice) tokenizer = AutoTokenizer . from_pretrained ( 'sentence-transformers/all-MiniLM-L6-v2' ) # create an dummy text text = \"Hello my Name is Mohit\" # this will tokenize the text and return a list of tokens tokenizer . tokenize ( text ) # Output: ['hello', 'my', 'name', 'is', 'mo', '##hit'] # this will tokenize the text and return a list of token ids tokenizer . encode ( text ) # Output: [101, 7592, 2026, 2171, 2003, 9587, 16584, 102] # this will return the decoded text (from token ids) tokenizer . decode ( tokenizer . encode ( text )) # Output: [CLS] hello my name is mohit [SEP] # get the token and id details in key value pair vocabulary = tokenizer . get_vocab () # length of vocab here is 30522 # vocabulary['hello'] returns 7592","title":"ML snippets"},{"location":"machine_learning/ML_snippets/#machine-learning-deep-learning-snippets","text":"Sharing some of the most widely used and arguably not so famous Machine Learning snippets","title":"Machine Learning / Deep Learning Snippets"},{"location":"machine_learning/ML_snippets/#feature-importance","text":"Feature importance calculation is an important technique to identify the features which \"helps\" with the downstream classification or regression tasks. Sklearn provides several options to infer the importance of a feature. Most importantly, many model automatically computed the importane and store it in model.feature_importances_ , after you call .fit() As an example, lets take the text based classification task and try to infer the following, Part 1: First use CountVectorizer for feature engineering and ExtraTreesClassifier for classification. Part 2: Show the top N features. Part 3: Show evidence of a feature (by value count over different class labels) Following dataset based assumtions have been made, We assume x_train and y_train contains the a list of sentences and labels repectively. We assume a pandas dataframe of name train_df is present which contains x_train and y_train as columns with name title and label respectively. 1 2 3 index = 2282 # the feature's index # the label's distribution if this word is present in sentence train_df . iloc [ np . where ( features [:, index ] . todense () >= 1 )[ 0 ]][ 'label' ] . value_counts ()","title":"Feature importance"},{"location":"machine_learning/ML_snippets/#cross-validation","text":"Cross validation is a technique in which at each iteration you create different split of train and dev data. At each such iteration, we train he model on the train split and validate on the remaining split. This way, event with small training data, we can perform multiple fold of validation. If you repeat this operation (for \\(N\\) iterations) over the complete data such that (1) each data point belonged to the dev split at most once, (2) each data point belonged to train split \\(N-1\\) times - its cross-validation. I have used Stratified K-Folds cross-validator, you can use any function from the complete list mentioned here - Sklearn Model selection 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 # import ======= from sklearn.model_selection import StratifiedKFold # code ============= # split the dataset into K fold test def split_dataset ( dataset , return_fold = 0 , n_splits = 3 , shuffle = True , random_state = 1 ): \"\"\" dataset: pandas dataframe return_fold: the fold out of `n_split` fold, which is to be returned n_splits: # cross fold \"\"\" # defined the KFOld function skf = StratifiedKFold ( n_splits = n_splits , shuffle = shuffle , random_state = random_state ) # defined the dataset X = dataset y = dataset [ 'class' ] # label/class for i , ( train_index , test_index ) in enumerate ( skf . split ( X , y )): if return_fold == i : return dataset . loc [ train_index ], dataset . loc [ test_index ] # example call if __name__ == '__main__' : # read the dataset df = pd . read_csv ( \"....\" ) # get one specific fold out of train , test = split_dataset ( dataset = df , return_fold = 0 , n_splits = 3 ) # run for all folds for fold in range ( n_splits ): train , test = split_dataset ( dataset = df , return_fold = fold , n_splits = n_splits ) # ","title":"Cross validation"},{"location":"machine_learning/ML_snippets/#hyper-parameter-tuning","text":"Below is an example of hyperparameter tuning for SVR regression algorithm. There we specify the search space i.e. the list of algorithm parameters to try, and for each parameter combination perform a 5 fold CV test. More details: Sklearn Hyperparameter tuning More details: Sklearn SVR Algorithm 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 # import ======= from sklearn.model_selection import GridSearchCV from sklearn.metrics import make_scorer from sklearn.metrics import mean_squared_error # DATA LOAD ============ train_data = ... # load the features and target on which to train # SEARCH SPACE ============ search_space = [{ 'kernel' : [ 'poly' , 'rbf' , 'sigmoid' ], 'C' : [ 1 , 10 , 100 ], 'epsilon' : [ 10 , 1 , 0.1 , 0.2 , 0.01 ]}] # TUNING ============ scorer = make_scorer ( mean_squared_error , greater_is_better = False ) svr_gs = GridSearchCV ( SVR (), search_space , cv = 5 , scoring = scorer , verbose = 10 , n_jobs = None ) svr_gs . fit ( train_data [ 'features' ], train_data [ 'target' ]) # PRINT RESULT ============ parameter_result = [] print ( \"Grid scores on training set:\" ) means = svr_gs . cv_results_ [ 'mean_test_score' ] stds = svr_gs . cv_results_ [ 'std_test_score' ] for mean , std , params in zip ( means , stds , svr_gs . cv_results_ [ 'params' ]): print ( \" %0.3f (+/- %0.03f ) for %r \" % ( mean , std * 2 , params )) parameter_result . append ({ 'mean' : abs ( mean ), 'std' : std , ** params }) # SELECT BEST PARAMETERS ============ # select the settings with smallest loss parameter_result = pd . DataFrame ( parameter_result ) parameter_result = parameter_result . sort_values ( by = [ 'mean' ]) best_settings = parameter_result . head ( 1 ) . to_dict ( orient = 'records' )[ 0 ] # FIT WITH BEST PARAMETERS ============ SVRModel = SVR ( C = best_settings [ 'C' ], epsilon = best_settings [ 'epsilon' ], kernel = best_settings [ 'kernel' ]) SVRModel . fit ( train_data [ 'features' ], train_data [ 'target' ])","title":"Hyper-parameter tuning"},{"location":"machine_learning/ML_snippets/#callbacks","text":"Callbacks are the hooks that you can attach to your deep learning training or validation process. It can be used to affect the training process from simple logging metric to even terminating the training in case special conditions are met. Below is an example of EarlyStopping and ModelCheckpoint callbacks. Keras 1 2 3 4 5 # fit the model history = model . fit ( train_data_gen , # training data generator # .... # put usual code here callbacks = [ checkpoint , earlystopping ] )","title":"Callbacks"},{"location":"machine_learning/ML_snippets/#mean-pooling","text":"References this stackoverflow answer . Keras 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 # import import numpy as np import keras # create sample data A = np . array ([[ 1 , 2 , 3 ],[ 4 , 5 , 6 ],[ 0 , 0 , 0 ],[ 0 , 0 , 0 ],[ 0 , 0 , 0 ]]) B = np . array ([[ 1 , 3 , 0 ],[ 4 , 0 , 0 ],[ 0 , 0 , 1 ],[ 0 , 0 , 0 ],[ 0 , 0 , 0 ]]) C = np . array ([ A , B ]) . astype ( \"float32\" ) # expected answer (for temporal mean) np . mean ( C , axis = 1 ) \"\"\" The output is array([[1. , 1.4, 1.8], [1. , 0.6, 0.2]], dtype=float32) Now using AveragePooling1D, \"\"\" model = keras . models . Sequential ( tf . keras . layers . AveragePooling1D ( pool_size = 5 ) ) model . predict ( C ) \"\"\" The output is, array([[[1. , 1.4, 1.8]], [[1. , 0.6, 0.2]]], dtype=float32) \"\"\" Some points to consider, The pool_size should be equal to the step/timesteps size of the recurrent layer. The shape of the output is ( batch_size , downsampled_steps , features ), which contains one additional downsampled_steps dimension. This will be always 1 if you set the pool_size equal to timestep size in recurrent layer.","title":"Mean pooling"},{"location":"machine_learning/ML_snippets/#dataset-and-dataloader","text":"Dataset can be downloaded from Kaggle . PyTorch 1 2 3 4 5 6 # init the train and test dataset train_dataset = IMDBDataset ( X_train , y_train ) test_dataset = IMDBDataset ( X_test , y_test ) # create the dataloader train_dataloader = DataLoader ( train_dataset , batch_size = 64 , shuffle = True ) test_dataloader = DataLoader ( test_dataset , batch_size = 64 , shuffle = True )","title":"Dataset and Dataloader"},{"location":"machine_learning/ML_snippets/#freeze-layers","text":"Example on how to freeze certain layers while training PyTorch lightning 1 2 3 4 5 6 7 8 9 10 11 12 13 { code - block } python # Before defining the optimizer, we need to freeze the layers # In pytorch lightning, as optimizer is defined in configure_optimizers, we freeze layers there. def configure_optimizers ( self ): # iterate through the layers and freeze the one with certain name (here all BERT models) # note: the name of layer depends on the varibale name for name , param in self . named_parameters (): if 'BERTModel' in name : param . requires_grad = False # only pass the non-frozen paramters to optimizer optimizer = torch . optim . Adam ( filter ( lambda p : p . requires_grad , self . parameters ()), lr = 1e-3 ) # return optimizer return optimizer","title":"Freeze Layers"},{"location":"machine_learning/ML_snippets/#check-for-gpu-availability","text":"We need GPUs for deep learning, and before we start training or inference it's a good idea to check if GPU is available on the system or not. The most basic way to check for GPUs (if it's a NVIDIA one) is to run nvidia-smi command. It will return a detailed output with driver's version, cuda version and the processes using GPU. Refer this for more details on individual components. +-----------------------------------------------------------------------------+ | NVIDIA-SMI 435 .21 Driver Version: 435 .21 CUDA Version: 10 .1 | | -------------------------------+----------------------+----------------------+ | GPU Name Persistence-M | Bus-Id Disp.A | Volatile Uncorr. ECC | | Fan Temp Perf Pwr:Usage/Cap | Memory-Usage | GPU-Util Compute M. | | =============================== + ====================== + ====================== | | 0 GeForce MX110 Off | 00000000 :01:00.0 Off | N/A | | N/A 43C P0 N/A / N/A | 164MiB / 2004MiB | 0 % Default | +-------------------------------+----------------------+----------------------+ +-----------------------------------------------------------------------------+ | Processes: GPU Memory | | GPU PID Type Process name Usage | | ============================================================================= | | 0 6348 G /usr/lib/xorg 53MiB | | 0 13360 G ...BBBBBaxsxsuxbssxsxs --shared-files 28MiB | +-----------------------------------------------------------------------------+ You can even use deep learning frameworks like Pytorch to check for the GPU availability. In fact, this is where you will most probably use them. 1 2 3 4 5 6 7 8 9 10 11 12 13 # import import torch # checks torch . cuda . is_available () ## Output: True torch . cuda . device_count () ## Output: 1 torch . cuda . current_device () ## Output: 0 torch . cuda . device ( 0 ) ## Output: torch . cuda . get_device_name ( 0 ) ## Output: 'GeForce MX110'","title":"Check for GPU availability"},{"location":"machine_learning/ML_snippets/#huggingface-tokenizer","text":"Tokenizer is a pre-processing step that converts the text into a sequence of tokens. HuggingFace tokenizer is a wrapper around the tokenizers library , that contains multiple base algorithms for fast tokenization. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 # import from transformers import AutoTokenizer # load a tokenizer (use the model of your choice) tokenizer = AutoTokenizer . from_pretrained ( 'sentence-transformers/all-MiniLM-L6-v2' ) # create an dummy text text = \"Hello my Name is Mohit\" # this will tokenize the text and return a list of tokens tokenizer . tokenize ( text ) # Output: ['hello', 'my', 'name', 'is', 'mo', '##hit'] # this will tokenize the text and return a list of token ids tokenizer . encode ( text ) # Output: [101, 7592, 2026, 2171, 2003, 9587, 16584, 102] # this will return the decoded text (from token ids) tokenizer . decode ( tokenizer . encode ( text )) # Output: [CLS] hello my name is mohit [SEP] # get the token and id details in key value pair vocabulary = tokenizer . get_vocab () # length of vocab here is 30522 # vocabulary['hello'] returns 7592","title":"HuggingFace Tokenizer"},{"location":"machine_learning/classification/","text":"Note This page is still not complete and new sections might get added later. That said, the existing content is ready to be consumed. \ud83d\udc4d Introduction Classification is a fundamental task in supervised machine learning , where the goal is to assign predefined labels or categories to input data points based on their features. Unlike clustering , which deals with unsupervised learning and aims to discover inherent patterns or groupings in data, classification relies on a labeled dataset to learn a model that can make predictions on new, unseen data. The primary objective of classification is to create a decision boundary or model that can accurately differentiate between different classes or categories within the data. Some use cases of classification could be bank loan defaulter prediction, spam email detection, image recognition, medical diagnosis, sentiment analysis, etc. Metrics Classification metrics are used to evaluate the performance of a classification model by comparing its predictions to the actual ground truth labels. Here are some common classification metrics, An extensive view of Confusion Matrix and different metrics. Source: Wikipedia Accuracy : Accuracy is the most basic classification metric, measuring the ratio of correctly predicted instances to the total number of instances. It provides an overall measure of the model's correctness. However, it may not be suitable for imbalanced datasets, where one class significantly outnumbers the others. Precision : Precision is the ratio of true positive predictions to the total number of positive predictions made by the model. High precision indicates that when the model predicts a positive class, it is likely to be correct. Recall (Sensitivity or True Positive Rate) : Recall is the ratio of true positive predictions to the total number of actual positive instances in the dataset. It measures the model's ability to capture all positive instances. High recall means that the model can find most of the positive cases. F1-Score : The F1-Score is the harmonic mean of precision and recall. It balances both metrics and is particularly useful when you need to consider the trade-off between precision and recall. It's a good overall measure of a model's performance. Please be aware of average params in the Sklearn implementation . Set the param to macro in case of imbalanced dataset, as it will compute the score for each class and then perform unweighted average i.e. giving each class equal importance, no matter their frequency. Setting it to weighted is similar to macro , but now the average will be weighted. Setting to micro will lead to computing the numbers for complete data without considering any class. Specificity (True Negative Rate) : Specificity measures the model's ability to correctly identify negative instances. It is the ratio of true negative predictions to the total number of actual negative instances. It is particularly relevant when false negatives are costly. ROC Curve and AUC : The Receiver Operating Characteristic (ROC) curve is a graphical representation of the model's performance across different thresholds. The Area Under the ROC Curve (AUC) quantifies the overall performance of the model, with a higher AUC indicating better discrimination between classes. Confusion Matrix : A confusion matrix is a table that summarizes the model's predictions compared to the actual labels, breaking down true positives, true negatives, false positives, and false negatives. It provides detailed insights into the model's performance. The choice of which metric to use depends on the specific problem, the nature of the dataset, and the business or application requirements. It's essential to consider the context and goals when selecting the appropriate classification metrics for evaluating a machine learning model. Classification Algorithms While there are many classification algorithms, here are some of the most common and widely used ones, Logistic Regression Logistic Regression is a widely used classification model that is particularly effective for binary classification problems. It works by modeling the relationship between the input features and the probability of belonging to a particular class. It does this by fitting a logistic curve to the data, which allows it to output probabilities that an instance belongs to a specific class. Logistic Regression is a linear model , which means it assumes a linear relationship between the input features and the log-odds of the class probabilities. It's simple, interpretable, and computationally efficient, making it a good choice for problems with a large number of features. Decision Tree A Decision Tree is a versatile and interpretable machine learning model used for both classification and regression tasks. It is a tree-like structure where each internal node represents a feature, each branch represents a decision rule based on that feature, and each leaf node represents the predicted outcome or value. Decision Trees are particularly well-suited for tasks where the decision-making process can be represented as a series of logical if-then-else conditions. One of the significant advantages of Decision Trees is their transparency and interpretability. The model's decision rules can be easily visualized, understood, and explained to non-technical stakeholders, making it a valuable tool in domains where interpretability is crucial, such as healthcare and finance. However, Decision Trees can be prone to overfitting, especially when they are deep and the dataset is small. To mitigate this issue, techniques like pruning, limiting tree depth, and using ensemble methods like Random Forests or Gradient Boosting Trees are often employed. Decision Trees also provide feature importance scores, helping analysts identify the most critical factors driving the model's predictions, which can inform feature selection and data exploration efforts in a broader context.","title":"Classification"},{"location":"machine_learning/classification/#introduction","text":"Classification is a fundamental task in supervised machine learning , where the goal is to assign predefined labels or categories to input data points based on their features. Unlike clustering , which deals with unsupervised learning and aims to discover inherent patterns or groupings in data, classification relies on a labeled dataset to learn a model that can make predictions on new, unseen data. The primary objective of classification is to create a decision boundary or model that can accurately differentiate between different classes or categories within the data. Some use cases of classification could be bank loan defaulter prediction, spam email detection, image recognition, medical diagnosis, sentiment analysis, etc.","title":"Introduction"},{"location":"machine_learning/classification/#metrics","text":"Classification metrics are used to evaluate the performance of a classification model by comparing its predictions to the actual ground truth labels. Here are some common classification metrics, An extensive view of Confusion Matrix and different metrics. Source: Wikipedia Accuracy : Accuracy is the most basic classification metric, measuring the ratio of correctly predicted instances to the total number of instances. It provides an overall measure of the model's correctness. However, it may not be suitable for imbalanced datasets, where one class significantly outnumbers the others. Precision : Precision is the ratio of true positive predictions to the total number of positive predictions made by the model. High precision indicates that when the model predicts a positive class, it is likely to be correct. Recall (Sensitivity or True Positive Rate) : Recall is the ratio of true positive predictions to the total number of actual positive instances in the dataset. It measures the model's ability to capture all positive instances. High recall means that the model can find most of the positive cases. F1-Score : The F1-Score is the harmonic mean of precision and recall. It balances both metrics and is particularly useful when you need to consider the trade-off between precision and recall. It's a good overall measure of a model's performance. Please be aware of average params in the Sklearn implementation . Set the param to macro in case of imbalanced dataset, as it will compute the score for each class and then perform unweighted average i.e. giving each class equal importance, no matter their frequency. Setting it to weighted is similar to macro , but now the average will be weighted. Setting to micro will lead to computing the numbers for complete data without considering any class. Specificity (True Negative Rate) : Specificity measures the model's ability to correctly identify negative instances. It is the ratio of true negative predictions to the total number of actual negative instances. It is particularly relevant when false negatives are costly. ROC Curve and AUC : The Receiver Operating Characteristic (ROC) curve is a graphical representation of the model's performance across different thresholds. The Area Under the ROC Curve (AUC) quantifies the overall performance of the model, with a higher AUC indicating better discrimination between classes. Confusion Matrix : A confusion matrix is a table that summarizes the model's predictions compared to the actual labels, breaking down true positives, true negatives, false positives, and false negatives. It provides detailed insights into the model's performance. The choice of which metric to use depends on the specific problem, the nature of the dataset, and the business or application requirements. It's essential to consider the context and goals when selecting the appropriate classification metrics for evaluating a machine learning model.","title":"Metrics"},{"location":"machine_learning/classification/#classification-algorithms","text":"While there are many classification algorithms, here are some of the most common and widely used ones,","title":"Classification Algorithms"},{"location":"machine_learning/classification/#logistic-regression","text":"Logistic Regression is a widely used classification model that is particularly effective for binary classification problems. It works by modeling the relationship between the input features and the probability of belonging to a particular class. It does this by fitting a logistic curve to the data, which allows it to output probabilities that an instance belongs to a specific class. Logistic Regression is a linear model , which means it assumes a linear relationship between the input features and the log-odds of the class probabilities. It's simple, interpretable, and computationally efficient, making it a good choice for problems with a large number of features.","title":"Logistic Regression"},{"location":"machine_learning/classification/#decision-tree","text":"A Decision Tree is a versatile and interpretable machine learning model used for both classification and regression tasks. It is a tree-like structure where each internal node represents a feature, each branch represents a decision rule based on that feature, and each leaf node represents the predicted outcome or value. Decision Trees are particularly well-suited for tasks where the decision-making process can be represented as a series of logical if-then-else conditions. One of the significant advantages of Decision Trees is their transparency and interpretability. The model's decision rules can be easily visualized, understood, and explained to non-technical stakeholders, making it a valuable tool in domains where interpretability is crucial, such as healthcare and finance. However, Decision Trees can be prone to overfitting, especially when they are deep and the dataset is small. To mitigate this issue, techniques like pruning, limiting tree depth, and using ensemble methods like Random Forests or Gradient Boosting Trees are often employed. Decision Trees also provide feature importance scores, helping analysts identify the most critical factors driving the model's predictions, which can inform feature selection and data exploration efforts in a broader context.","title":"Decision Tree"},{"location":"machine_learning/clustering/","text":"Note This page is still not complete and new sections might get added later. That said, the existing content is ready to be consumed. \ud83d\udc4d Introduction Clustering is an unsupervised task of grouping a set of items based on item\u2019s features where the final grouping should minimize certain cost function. While finding optimum grouping is very hard, there are several algorithms that can help us find sub-optimal solutions. Also note that as the data is not labeled, the grouping could be completely different from the user\u2019s expectation. Each clustering algorithm has its own internal similarity function and grouping strategy by which the clusters are formed. Clustering algorithms can be categorised based on different perspectives. Below are some examples (not a complete list) , Considering time of application, we can have online (streaming data) vs offline (all data present) clustering algorithms. Considering input data type, we have algorithms that consider item's features while others that considers item-item similarity matrix. Considering input parameter, there are algorithms that require no of clusters as input while others do not. Metrics Before starting with Clustering algorithms, let's understand the metrics that can be used to compare the clustering performance. Silhouette Score Silhouette score considers the intra-cluster \\(a(i)\\) and inter-cluster \\(b(i)\\) distances to generate a performance metric for the clustering of a dataset. The score can range between -1 (bad clustering) and 1 (good clustering) , with higher number denoting better clustering. We can choose different distance functions (euclidean, manhattan, cosine, etc) based on the data. The formulation wrt each data point is shown below, where we can average the value to get dataset level scores. \\[ {\\displaystyle a(i)={\\frac {1}{|C_{I}|-1}}\\sum _{j\\in C_{I},i\\neq j}d(i,j)} \\] \\[ {\\displaystyle b(i)=\\min _{J\\neq I}{\\frac {1}{|C_{J}|}}\\sum _{j\\in C_{J}}d(i,j)} \\] \\[ {\\displaystyle s(i)={\\frac {b(i)-a(i)}{\\max\\{a(i),b(i)\\}}}} \\] In \\(a(i)\\) formulation, we compare each data point against all the other points in the same cluster. We use \\(\\frac {1}{|C_{I}|-1}\\) as we ignore the \\(d(i, i)\\) computation. In \\(b(i)\\) formulation, we compare one data point against all data points from other clusters one at a time, and pick the value for the cluster with members having minimum average distance from the data point (think of it like the next best cluster for the data point) . Finally, the silhouette score for that data point is computed in \\(s(i)\\) . It is further simplified below, \\[ s(i) = \\begin{cases} 1-a(i)/b(i), & \\mbox{if } a(i) < b(i) \\\\ 0, & \\mbox{if } a(i) = b(i) \\\\ b(i)/a(i)-1, & \\mbox{if } a(i) > b(i) \\\\ \\end{cases} \\] Sklearn package provides a function to compute silhouette score. The inputs are data point features, the cluster labels and the distance metric. Example call is shown below, 1 2 3 4 5 # import from sklearn.metrics import silhouette_score # compute the score score = silhouette_score ( X , labels , metric = 'cosine' ) Homogeneity, Completeness and V measure For a given set of datapoints, the original labels are called \"Class\" and the output of clustering algorithms is called \"Clusters\". To measure performance of the algorithm, we can check how well the cluster and class align. This alignment can be captured by creating a contingency table that contains distribution of data points falling under different combinations of class and clusters. For example, consider 6 datapoints for which we have the class label [1, 1, 1, 2, 2, 2] and cluster label [1, 1, 2, 2, 3, 3] . The contingency table will contains \\(a_{ck}\\) elements where \\(c\\) denotes the class and \\(k\\) the clusters. It will look as follows, class/cluster 1 2 3 1 2 1 0 2 0 1 2 3 0 0 0 Note As clear from the table above, the alignment could be directional in nature i.e. the metrics using the contingency table will generate different score if we compare (class, cluster) and (cluster, class) . With this context, let's understand the metrics in detail, [5] Homogeneity: it describes the \"purity\" of the cluster i.e. it measures whether or not the same class is assigned to all points within a cluster. The formulation is as follows, \\[ h = 1 - \\frac {H(C|K)}{H(C)}, where \\] \\[ H (C | K) = - \\sum_{k=1}^{|K|} \\sum_{c=1}^{|C|} \\frac{a_{ck}}{N} \\log_{}{\\frac{a_{ck}}{\\sum_{c=1}^{|C|} a_{ck}}} \\] \\[ H (C) = - \\sum_{c=1}^{|C|} \\frac{\\sum_{k=1}^{|K|} a_{ck}}{n} \\log_{}{\\frac{\\sum_{k=1}^{|K|} a_{ck}}{n}} \\] Completeness: it describes the \"purity\" of the class i.e. it measures if all points belonging to a given class are assigned to the same cluster. The formulation is as follows, \\[ c = 1 - \\frac {H(K|C)}{H(K)}, where \\] \\[ H (K | C) = - \\sum_{c=1}^{|C|} \\sum_{k=1}^{|K|} \\frac{a_{ck}}{N} \\log_{}{\\frac{a_{ck}}{\\sum_{k=1}^{|K|} a_{ck}}} \\] \\[ H (K) = - \\sum_{k=1}^{|K|} \\frac{\\sum_{c=1}^{|C|} a_{ck}}{n} \\log_{}{\\frac{\\sum_{c=1}^{|C|} a_{ck}}{n}} \\] V measure: it is a combination of the above scores and is used to measure the overall performance of a clustering algorithm. To be exact, it is the harmoic mean of the Homogeneity and Completeness scores. The formulation is given below, where beta is the ratio of weight attributed to homogeneity vs completeness. If beta is greater than 1, completeness is weighted more strongly in the calculation. If beta is less than 1, homogeneity is weighted more strongly. By default, beta is 1. \\[ v = \\frac{(1 + beta) * homogeneity * completeness}{(beta * homogeneity + completeness)} \\] Hint Sklearn provides individual and combined implementations for these metrics. Refer sklearn.metrics.homogeneity_completeness_v_measure Clustering Algorithms K-Means K-means is the swiss army knife of the clustering algorithms, the forever baseline - the first clustering algorithm anyone tries . It can be easily understood by considering the steps mentioned below. The step (a) is a one time activity done during the initialization part, while steps (b) and (c) are repeated until the convergence i.e. there is no more change in cluster membership even if we continue the process or there is no more noticable centroid movement. Centroid Assignment: Assign K centroids. There are three points to remember here, How to decide the value of K? --> here it is an input parameter. In practice we can analyze the cluster results with different k (2 to N) and pick the one with best metric score like silhouette score. Are centroids choosen from data points? --> during initialization they may be selected from data points but over iterations they become their own special points that are part of the same feature space How are the centroids choosen? --> the assignment strategy can be random (pick any random K data points) , or follow 'furthest' heuristic ( \\(i^{th}\\) centroid is choosen such that its minimum distance to the preceding centroids is largest) or follow k-mean++ heuristic (selects a point with probability proportional to the square of its distance to the nearest preceding centroid) . Note Random initialization is not preferred, as it is possible to get all centroid assigned to data points from only one true cluster. The 'furthest' heuristic is also not preferred as it select data points at the edges for centroid. K-means++ heuristic is more suitable as the centroid selection is more natural. Cluster Assignment: assign all data points to one of the centroids (forming a cluster) based on the closeness of the points to the centroid. The closeness is computed by a similarity function that could be equilidean distance. Centroid Adjustment: adjust the centroids such that it minimises the intra-cluster distance among the cluster member. This is called inertia and its formulation is \\(\\sum_{i=0}^n \\text{min}(||x_i-\\mu_{j}||^2)\\) , where \\(\\mu_{j}\\) is the mean of the respective cluster. The adjustment is done by moving the centroids to the mean position of the cluster members. Remember, K-means algorithm is guaranteed to converge but the final result may vary based on the centroid initialisation. This is why it is suggested to try multiple runs with different initialization and pick the one with best clustering or use smarter initialization technique like k-means++. Refer ML Interview Questions and [2] for more details. Many clustering algorithms have improved k-means over time, they are: K-Medians: It is a variation of k-means clustering where instead of calculating the mean for each cluster to determine its centroid, we calculates the median. As it uses Manhattan distance (L1-norm distance) , the algorithm becomes more reliable for discrete or binary data sets. K-Medoids: In contrast to the k-means algorithm, k-medoids chooses actual data points as centers instead. Furthermore, k-medoids can be used with arbitrary dissimilarity measures, whereas k-means generally requires Euclidean distance. Because k-medoids minimizes a sum of pairwise dissimilarities instead of a sum of squared Euclidean distances, it is more robust to noise and outliers than k-means. Refer [3] for an intuitive solved example. K-means++: It is the standard K-means algorithm but with a smarter initialization of the centroids. We start with choosing one center randomly from the data points. Then for each data point \\(x\\) not chosen yet, we find the distance \\(D(x)\\) between \\(x\\) and the nearest center that has already been chosen. The new center is choosen again at random but this time using a weighted probability distribution where a point \\(x\\) is chosen with probability proportional to \\(D(x)^2\\) . We repeat these steps until k centers have been chosen. Mini Batch K-means: It is an optimized version of k-means for faster execution with only slighly worse results. Here, at each iteration a set of random data points are selected to form a mini-batch and they are assigned to the nearest centroids. The centroid adjustment happens for each sample by taking streaming average of the sample and all previous samples assigned to that centroid. Mini Batch K-means converges faster than K-means. Hint K-Means works best in datasets with clusters that are roughly equally-sized and shaped roughly regularly. Also the data points should be in euclidean space as the K-means uses euclidean distance measure. It is not recommended for clustering neural network embeddings as they capture semantic meanings which is more suitably captured by cosine similarity. The best that can be done with K-means is to run multiple iterations on embeddings and pick the one with highest cosine silhouette score. Here is the code to perform Kmeans clustering and find the silhouette score [1], 1 2 3 4 5 6 7 8 9 10 11 12 13 # import from sklearn.cluster import KMeans from sklearn.metrics import silhouette_score # load the data X = np . load ( 'data/embeddings.npy' ) # dummy # create the model model = KMeans ( n_clusters = 2 , init = 'k-means++' , max_iter = 100 , n_init = 1 ) # get the label labels = model . fit_predict ( X ) # compute silhouette_score sil_score = silhouette_score ( X , labels , metric = 'cosine' ) Hierarchical Clustering It is a clustering technique that seeks to build a hierarchy of clusters i.e. we start with the dataset divided into N clusters and then some clusters are either merged or split based on their similarity. There are two major strategies to perform hierarchical clustering, Agglomerative: Bottom-up approach where we start with each sample as a separate cluster and then clusters are merged. Divisive: Top-down approach where we start with all samples part of a single cluster and then at each level we recursively split existing clusters. To understand the split or merge process, we need to understand the following, Distance metric: this is the function that gives the distance between two data points. We can choose from a number of functions like euclidean, cosine, manhattan, etc. Linkage criteria: this is a function that define the distance between two clusters based on applying distance function on pairs of data from the two clusters. There are following strategies to choose from, Complete Linkage: Pick the most distant pair of points as the representation of cluster distance. Formulation is : \\(\\max \\,\\{\\,d(a,b):a\\in A,\\,b\\in B\\,\\}.\\) Single Linkage: Pick the least distant pair of points as the representation of cluster distance. Formulation is : \\(\\min \\,\\{\\,d(a,b):a\\in A,\\,b\\in B\\,\\}.\\) Ward: find the pair of clusters that leads to minimum increase in total within-cluster variance after merging. This is only applicable for euclidean distance. [4] Average: Uses the average of distances between all pairs of data points from the two clusters. Formulation is \\({\\displaystyle {\\frac {1}{|A|\\cdot |B|}}\\sum _{a\\in A}\\sum _{b\\in B}d(a,b).}\\) Now it should be easy to understand the overall process. Taking agglomerative as example, to begin with, all samples are separate clusters. At each iteration, we will compute the linkage score between all pairs of clusters and find the pair with the minimum score. We merge that pair of clusters together and go ahead with next iteration. We keep on repeating this process until we have reached a desired number of clusters ( n_clusters ) or the linkage distance threshold ( distance_threshold ) has triggered, above which, clusters will not be merged. Note At a time, we can only use either n_clusters or distance_threshold . Here is a sample code using sklearn package [1], 1 2 3 4 5 6 7 8 9 10 11 12 13 # import from sklearn.cluster import AgglomerativeClustering from sklearn.metrics import silhouette_score # load the data X = np . load ( 'data/embeddings.npy' ) # dummy # create the model model = AgglomerativeClustering ( n_clusters = 2 , affinity = 'cosine' , linkage = 'average' ) # get the label labels = model . fit_predict ( X ) # compute silhouette_score sil_score = silhouette_score ( X , labels , metric = 'cosine' ) References [1] Sklearn - Clustering [2] Visualizing K Means Clustering - Naftali Harris [3] K-Medoids clustering with solved example [4] Wikipedia - Hierarchical clustering | Ward's method [5] V-Measure: A Conditional Entropy-Based External Cluster Evaluation Measure","title":"Clustering"},{"location":"machine_learning/clustering/#introduction","text":"Clustering is an unsupervised task of grouping a set of items based on item\u2019s features where the final grouping should minimize certain cost function. While finding optimum grouping is very hard, there are several algorithms that can help us find sub-optimal solutions. Also note that as the data is not labeled, the grouping could be completely different from the user\u2019s expectation. Each clustering algorithm has its own internal similarity function and grouping strategy by which the clusters are formed. Clustering algorithms can be categorised based on different perspectives. Below are some examples (not a complete list) , Considering time of application, we can have online (streaming data) vs offline (all data present) clustering algorithms. Considering input data type, we have algorithms that consider item's features while others that considers item-item similarity matrix. Considering input parameter, there are algorithms that require no of clusters as input while others do not.","title":"Introduction"},{"location":"machine_learning/clustering/#metrics","text":"Before starting with Clustering algorithms, let's understand the metrics that can be used to compare the clustering performance.","title":"Metrics"},{"location":"machine_learning/clustering/#silhouette-score","text":"Silhouette score considers the intra-cluster \\(a(i)\\) and inter-cluster \\(b(i)\\) distances to generate a performance metric for the clustering of a dataset. The score can range between -1 (bad clustering) and 1 (good clustering) , with higher number denoting better clustering. We can choose different distance functions (euclidean, manhattan, cosine, etc) based on the data. The formulation wrt each data point is shown below, where we can average the value to get dataset level scores. \\[ {\\displaystyle a(i)={\\frac {1}{|C_{I}|-1}}\\sum _{j\\in C_{I},i\\neq j}d(i,j)} \\] \\[ {\\displaystyle b(i)=\\min _{J\\neq I}{\\frac {1}{|C_{J}|}}\\sum _{j\\in C_{J}}d(i,j)} \\] \\[ {\\displaystyle s(i)={\\frac {b(i)-a(i)}{\\max\\{a(i),b(i)\\}}}} \\] In \\(a(i)\\) formulation, we compare each data point against all the other points in the same cluster. We use \\(\\frac {1}{|C_{I}|-1}\\) as we ignore the \\(d(i, i)\\) computation. In \\(b(i)\\) formulation, we compare one data point against all data points from other clusters one at a time, and pick the value for the cluster with members having minimum average distance from the data point (think of it like the next best cluster for the data point) . Finally, the silhouette score for that data point is computed in \\(s(i)\\) . It is further simplified below, \\[ s(i) = \\begin{cases} 1-a(i)/b(i), & \\mbox{if } a(i) < b(i) \\\\ 0, & \\mbox{if } a(i) = b(i) \\\\ b(i)/a(i)-1, & \\mbox{if } a(i) > b(i) \\\\ \\end{cases} \\] Sklearn package provides a function to compute silhouette score. The inputs are data point features, the cluster labels and the distance metric. Example call is shown below, 1 2 3 4 5 # import from sklearn.metrics import silhouette_score # compute the score score = silhouette_score ( X , labels , metric = 'cosine' )","title":"Silhouette Score"},{"location":"machine_learning/clustering/#homogeneity-completeness-and-v-measure","text":"For a given set of datapoints, the original labels are called \"Class\" and the output of clustering algorithms is called \"Clusters\". To measure performance of the algorithm, we can check how well the cluster and class align. This alignment can be captured by creating a contingency table that contains distribution of data points falling under different combinations of class and clusters. For example, consider 6 datapoints for which we have the class label [1, 1, 1, 2, 2, 2] and cluster label [1, 1, 2, 2, 3, 3] . The contingency table will contains \\(a_{ck}\\) elements where \\(c\\) denotes the class and \\(k\\) the clusters. It will look as follows, class/cluster 1 2 3 1 2 1 0 2 0 1 2 3 0 0 0 Note As clear from the table above, the alignment could be directional in nature i.e. the metrics using the contingency table will generate different score if we compare (class, cluster) and (cluster, class) . With this context, let's understand the metrics in detail, [5] Homogeneity: it describes the \"purity\" of the cluster i.e. it measures whether or not the same class is assigned to all points within a cluster. The formulation is as follows, \\[ h = 1 - \\frac {H(C|K)}{H(C)}, where \\] \\[ H (C | K) = - \\sum_{k=1}^{|K|} \\sum_{c=1}^{|C|} \\frac{a_{ck}}{N} \\log_{}{\\frac{a_{ck}}{\\sum_{c=1}^{|C|} a_{ck}}} \\] \\[ H (C) = - \\sum_{c=1}^{|C|} \\frac{\\sum_{k=1}^{|K|} a_{ck}}{n} \\log_{}{\\frac{\\sum_{k=1}^{|K|} a_{ck}}{n}} \\] Completeness: it describes the \"purity\" of the class i.e. it measures if all points belonging to a given class are assigned to the same cluster. The formulation is as follows, \\[ c = 1 - \\frac {H(K|C)}{H(K)}, where \\] \\[ H (K | C) = - \\sum_{c=1}^{|C|} \\sum_{k=1}^{|K|} \\frac{a_{ck}}{N} \\log_{}{\\frac{a_{ck}}{\\sum_{k=1}^{|K|} a_{ck}}} \\] \\[ H (K) = - \\sum_{k=1}^{|K|} \\frac{\\sum_{c=1}^{|C|} a_{ck}}{n} \\log_{}{\\frac{\\sum_{c=1}^{|C|} a_{ck}}{n}} \\] V measure: it is a combination of the above scores and is used to measure the overall performance of a clustering algorithm. To be exact, it is the harmoic mean of the Homogeneity and Completeness scores. The formulation is given below, where beta is the ratio of weight attributed to homogeneity vs completeness. If beta is greater than 1, completeness is weighted more strongly in the calculation. If beta is less than 1, homogeneity is weighted more strongly. By default, beta is 1. \\[ v = \\frac{(1 + beta) * homogeneity * completeness}{(beta * homogeneity + completeness)} \\] Hint Sklearn provides individual and combined implementations for these metrics. Refer sklearn.metrics.homogeneity_completeness_v_measure","title":"Homogeneity, Completeness and V measure"},{"location":"machine_learning/clustering/#clustering-algorithms","text":"","title":"Clustering Algorithms"},{"location":"machine_learning/clustering/#k-means","text":"K-means is the swiss army knife of the clustering algorithms, the forever baseline - the first clustering algorithm anyone tries . It can be easily understood by considering the steps mentioned below. The step (a) is a one time activity done during the initialization part, while steps (b) and (c) are repeated until the convergence i.e. there is no more change in cluster membership even if we continue the process or there is no more noticable centroid movement. Centroid Assignment: Assign K centroids. There are three points to remember here, How to decide the value of K? --> here it is an input parameter. In practice we can analyze the cluster results with different k (2 to N) and pick the one with best metric score like silhouette score. Are centroids choosen from data points? --> during initialization they may be selected from data points but over iterations they become their own special points that are part of the same feature space How are the centroids choosen? --> the assignment strategy can be random (pick any random K data points) , or follow 'furthest' heuristic ( \\(i^{th}\\) centroid is choosen such that its minimum distance to the preceding centroids is largest) or follow k-mean++ heuristic (selects a point with probability proportional to the square of its distance to the nearest preceding centroid) . Note Random initialization is not preferred, as it is possible to get all centroid assigned to data points from only one true cluster. The 'furthest' heuristic is also not preferred as it select data points at the edges for centroid. K-means++ heuristic is more suitable as the centroid selection is more natural. Cluster Assignment: assign all data points to one of the centroids (forming a cluster) based on the closeness of the points to the centroid. The closeness is computed by a similarity function that could be equilidean distance. Centroid Adjustment: adjust the centroids such that it minimises the intra-cluster distance among the cluster member. This is called inertia and its formulation is \\(\\sum_{i=0}^n \\text{min}(||x_i-\\mu_{j}||^2)\\) , where \\(\\mu_{j}\\) is the mean of the respective cluster. The adjustment is done by moving the centroids to the mean position of the cluster members. Remember, K-means algorithm is guaranteed to converge but the final result may vary based on the centroid initialisation. This is why it is suggested to try multiple runs with different initialization and pick the one with best clustering or use smarter initialization technique like k-means++. Refer ML Interview Questions and [2] for more details. Many clustering algorithms have improved k-means over time, they are: K-Medians: It is a variation of k-means clustering where instead of calculating the mean for each cluster to determine its centroid, we calculates the median. As it uses Manhattan distance (L1-norm distance) , the algorithm becomes more reliable for discrete or binary data sets. K-Medoids: In contrast to the k-means algorithm, k-medoids chooses actual data points as centers instead. Furthermore, k-medoids can be used with arbitrary dissimilarity measures, whereas k-means generally requires Euclidean distance. Because k-medoids minimizes a sum of pairwise dissimilarities instead of a sum of squared Euclidean distances, it is more robust to noise and outliers than k-means. Refer [3] for an intuitive solved example. K-means++: It is the standard K-means algorithm but with a smarter initialization of the centroids. We start with choosing one center randomly from the data points. Then for each data point \\(x\\) not chosen yet, we find the distance \\(D(x)\\) between \\(x\\) and the nearest center that has already been chosen. The new center is choosen again at random but this time using a weighted probability distribution where a point \\(x\\) is chosen with probability proportional to \\(D(x)^2\\) . We repeat these steps until k centers have been chosen. Mini Batch K-means: It is an optimized version of k-means for faster execution with only slighly worse results. Here, at each iteration a set of random data points are selected to form a mini-batch and they are assigned to the nearest centroids. The centroid adjustment happens for each sample by taking streaming average of the sample and all previous samples assigned to that centroid. Mini Batch K-means converges faster than K-means. Hint K-Means works best in datasets with clusters that are roughly equally-sized and shaped roughly regularly. Also the data points should be in euclidean space as the K-means uses euclidean distance measure. It is not recommended for clustering neural network embeddings as they capture semantic meanings which is more suitably captured by cosine similarity. The best that can be done with K-means is to run multiple iterations on embeddings and pick the one with highest cosine silhouette score. Here is the code to perform Kmeans clustering and find the silhouette score [1], 1 2 3 4 5 6 7 8 9 10 11 12 13 # import from sklearn.cluster import KMeans from sklearn.metrics import silhouette_score # load the data X = np . load ( 'data/embeddings.npy' ) # dummy # create the model model = KMeans ( n_clusters = 2 , init = 'k-means++' , max_iter = 100 , n_init = 1 ) # get the label labels = model . fit_predict ( X ) # compute silhouette_score sil_score = silhouette_score ( X , labels , metric = 'cosine' )","title":"K-Means"},{"location":"machine_learning/clustering/#hierarchical-clustering","text":"It is a clustering technique that seeks to build a hierarchy of clusters i.e. we start with the dataset divided into N clusters and then some clusters are either merged or split based on their similarity. There are two major strategies to perform hierarchical clustering, Agglomerative: Bottom-up approach where we start with each sample as a separate cluster and then clusters are merged. Divisive: Top-down approach where we start with all samples part of a single cluster and then at each level we recursively split existing clusters. To understand the split or merge process, we need to understand the following, Distance metric: this is the function that gives the distance between two data points. We can choose from a number of functions like euclidean, cosine, manhattan, etc. Linkage criteria: this is a function that define the distance between two clusters based on applying distance function on pairs of data from the two clusters. There are following strategies to choose from, Complete Linkage: Pick the most distant pair of points as the representation of cluster distance. Formulation is : \\(\\max \\,\\{\\,d(a,b):a\\in A,\\,b\\in B\\,\\}.\\) Single Linkage: Pick the least distant pair of points as the representation of cluster distance. Formulation is : \\(\\min \\,\\{\\,d(a,b):a\\in A,\\,b\\in B\\,\\}.\\) Ward: find the pair of clusters that leads to minimum increase in total within-cluster variance after merging. This is only applicable for euclidean distance. [4] Average: Uses the average of distances between all pairs of data points from the two clusters. Formulation is \\({\\displaystyle {\\frac {1}{|A|\\cdot |B|}}\\sum _{a\\in A}\\sum _{b\\in B}d(a,b).}\\) Now it should be easy to understand the overall process. Taking agglomerative as example, to begin with, all samples are separate clusters. At each iteration, we will compute the linkage score between all pairs of clusters and find the pair with the minimum score. We merge that pair of clusters together and go ahead with next iteration. We keep on repeating this process until we have reached a desired number of clusters ( n_clusters ) or the linkage distance threshold ( distance_threshold ) has triggered, above which, clusters will not be merged. Note At a time, we can only use either n_clusters or distance_threshold . Here is a sample code using sklearn package [1], 1 2 3 4 5 6 7 8 9 10 11 12 13 # import from sklearn.cluster import AgglomerativeClustering from sklearn.metrics import silhouette_score # load the data X = np . load ( 'data/embeddings.npy' ) # dummy # create the model model = AgglomerativeClustering ( n_clusters = 2 , affinity = 'cosine' , linkage = 'average' ) # get the label labels = model . fit_predict ( X ) # compute silhouette_score sil_score = silhouette_score ( X , labels , metric = 'cosine' )","title":"Hierarchical Clustering"},{"location":"machine_learning/clustering/#references","text":"[1] Sklearn - Clustering [2] Visualizing K Means Clustering - Naftali Harris [3] K-Medoids clustering with solved example [4] Wikipedia - Hierarchical clustering | Ward's method [5] V-Measure: A Conditional Entropy-Based External Cluster Evaluation Measure","title":"References"},{"location":"machine_learning/interview_questions/","text":"Here are some questions and their answers to make you ready for your next interview. Best of luck Question Answer What is deep learning and how is it different from traditional machine learning? Deep learning is a subfield of machine learning that uses neural networks with many layers, called deep neural networks, to learn and make predictions. It is different from traditional machine learning in that it can automatically learn hierarchical representations of the data and doesn't rely heavily on feature engineering. Question Answer How does backpropagation work in a neural network? Backpropagation is an algorithm used to train neural networks. It starts by propagating the input forward through the network, calculating the output. Then it compares the output to the desired output and calculates the error. The error is then propagated backwards through the network, adjusting the weights in the network so as to minimize the error. This process is repeated multiple times until the error is minimized. Question Answer While training deep learning models, why do we prefer training on mini-batch rather than on individual sample? First, the gradient of the loss over a mini-batch is an estimate of the gradient over the training set, whose quality improves as the batch size increases. Second, computation over a batch can be much more efficient than m computations for individual examples, due to the parallelism afforded by the modern computing platforms. Ref Question Answer What are the benefits of using Batch Normalizattion? Batch Normalization also has a beneficial effect on the gradient flow through the network, by reducing the dependence of gradients on the scale of the parameters or of their initial values. This allows us to use much higher learning rates without the risk of divergence. Furthermore, batch normalization regularizes the model and reduces the need for Dropout (Srivastava et al., 2014). Finally, Batch Normalization makes it possible to use saturating nonlinearities by preventing the network from getting stuck in the saturated modes. Ref Question Answer What is Entropy (information theory) ? Entropy is a measurement of uncertainty of a system. Intuitively, it is the amount of information needed to remove uncertainty from the system. The entropy of a probability distribution p for various states of a system can be computed as: \\(-\\sum_{i}^{} (p_i \\log p_i)\\) Question Answer Even though Sigmoid function is non-linear, why is Logistic regression considered a linear classifier? Logistic regression is often referred to as a linear classifier despite using the sigmoid (logistic) activation function because it models the relationship between the input features and the log-odds (logit) of the binary target variable in a linear manner. The linearity in logistic regression refers to the fact that it creates a linear decision boundary in the feature space, which is a hyperplane. Refer Question Answer What is the difference between Logits, Soft and Hard targets? Let us understand each of the terms one by one. For better understanding, let's take a dog vs cat image classification as an example. Logits are the un-normalized output of the model. In our cat vs dog example, logits will be, say, 10.1 for cat and 5.6 for dog for an image with cat. Refer this SE question . Soft target : are normalized logits by applying a linear function . In our example, if we use softmax to the logits we get 0.99 for cat and 0.1 for dog. Hard targets : are the encoding of the soft targets. In our example, as the model predicted (here correctly) the image as of cat, the hard targets be 1 for cat and 0 for dog. graph LR A[Logits] -- normalization --> B[Soft Targets] B -- encoding --> C[Hard Targets] Question Answer How do you handle overfitting in deep learning models? Overfitting occurs when a model becomes too complex and starts to fit the noise in the training data, rather than the underlying pattern. There are several ways to handle overfitting in deep learning models, including: Regularization techniques such as L1 and L2 regularization, which add a penalty term to the loss function to discourage large weights Early stopping , where training is stopped before the model has a chance to fully fit the noise in the training data Dropout , which randomly drops out a certain percentage of neurons during training to prevent them from co-adapting and becoming too specialized Adding more data to the training set Question Answer Can you explain the concept of convolutional neural networks (CNN)? A convolutional neural network (CNN) is a type of neural network that is primarily used for learning image and video patterns. CNNs are designed to automatically and adaptively learn spatial hierarchies of features from input data. They use a variation of multi-layer perceptrons, designed to require minimal preprocessing. Instead of hand-engineered features, CNNs learn features from data using a process called convolution. Question Answer How do you handle missing data in deep learning? Missing data can be handled in several ways, including: Removing the rows or columns with missing data Interpolation or imputation of missing values Using a technique called masking, which allows the model to ignore missing values when making predictions Question Answer Can you explain the concept of transfer learning in deep learning? Transfer learning is a technique where a model trained on one task is used as a starting point for a model on a second, related task. This allows the model to take advantage of the features learned from the first task and apply them to the second task, which can lead to faster training and better performance. This can be done by using a pre-trained model as a feature extractor or fine-tuning the pre-trained model on new data. Question Answer What is Gradient Descent in deep learning? Gradient Descent is an optimization algorithm used to minimize the loss function of a neural network. It works by updating the weights of the network in the opposite direction of the gradient of the loss function with respect to the weights. The magnitude of the update is determined by the learning rate. There are several variants of gradient descent, such as batch gradient descent, stochastic gradient descent, and mini-batch gradient descent. Question Answer Please explain what is Dropout in deep learning? Dropout is a regularization technique used in deep learning to prevent overfitting. It works by randomly dropping out a certain percentage of neurons during training, effectively reducing the capacity of the network. This forces the network to learn multiple independent representations of the data, making it less prone to overfitting. Question Answer What are Autoencoder? An autoencoder is a type of neural network that is trained to reconstruct its input. It has an encoder part that compresses the input into a lower-dimensional representation called the bottleneck or latent code, and a decoder part that reconstructs the input from the latent code. Autoencoders can be used for tasks such as dimensionality reduction, anomaly detection and generative modelling. Question Answer Can you explain the concept of attention mechanism in deep learning? Attention mechanism is a way to weight different parts of the input in a neural network, giving more importance to certain features than others. It is commonly used in tasks such as machine translation, where the model needs to focus on different parts of the input sentence at different times. Attention mechanisms can be implemented in various ways, such as additive attention, dot-product attention, and multi-head attention. Question Answer What are Generative Adversarial Networks (GANs)? Generative Adversarial Networks (GANs) are a type of generative model that consists of two parts, a generator and a discriminator. The generator is trained to generate new data that is similar to the data it was trained on, while the discriminator is trained to distinguish the generated data from the real data. The two parts are trained together in a game-theoretic manner, where the generator tries to generate data that can fool the discriminator, and the discriminator tries to correctly identify the generated data. Question Answer Can you explain the concept of Memory Networks in deep learning? Memory networks are a type of neural network architecture that allow the model to access and manipulate an external memory matrix, which can be used to store information that is relevant to the task. This allows the model to reason about the past and use this information to make predictions about the future. Memory networks have been used in tasks such as language understanding and question answering. Refer this for more details. Question Answer Explain Capsule Networks in deep learning? Capsule networks are a type of neural network architecture that aims to overcome the limitations of traditional convolutional neural networks (CNNs) by using a new type of layer called a capsule. A capsule contains multiple neurons that work together to represent an object or part of an object, and the activities of the neurons are used to represent the properties of the object such as position, size and orientation. Capsule networks have been used in tasks such as image classification and object detection. Question Answer Can you explain the concept of generative models in deep learning? Generative models are a type of deep learning model that can generate new data that is similar to the data it was trained on. These models are trained on a dataset and learn the underlying probability distribution of the data, allowing them to generate new, unseen data that fits that distribution. Examples of generative models include Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). Question Answer What is the concept of adversarial training in deep learning? Adversarial training is a technique used to improve the robustness of deep learning models by generating adversarial examples and using them to train the model. Adversarial examples are inputs that are slightly perturbed in such a way as to cause the model to make a mistake. By training the model on these examples, it becomes more robust to similar perturbations in the real world. Question Answer What is weight initialization in deep learning? Weight initialization is the process of setting the initial values for the weights in a neural network. The initial values of the weights can have a big impact on the network's performance and training time. There are several methods to initialize weights, including random initialization, Glorot initialization, and He initialization. Each of these methods have different properties and are more suitable for different types of problems. Question Answer Explain data augmentation? Data augmentation is a technique used to increase the amount of data available for training a deep learning model. This is done by creating new training examples by applying various random transformations to the original data, such as random cropping, flipping, or rotation. Data augmentation can be a powerful tool to prevent overfitting and improve the generalization performance of a mode. Question Answer What is the different between Standardization and Normalization? Normalization is the process of scaling the data to a common scale. It is also known as Min-Max Scaling where the final range could be [0, 1] or [-1,1] or something else. \\(X_{new} = (X - X_{min})/(X_{max} - X_{min})\\) Standardization is the process of scaling the data to have zero mean and unit variance. \\(X_{new} = (X - mean)/Std\\) Question Answer Is it possible that during ML training, both validation (or test) loss and accuracy, are increasing? Accuracy and loss are not necessarily exactly (inversely) correlated, as loss measures a difference between raw prediction (float) and class (0 or 1), while accuracy measures the difference between thresholded prediction (0 or 1) and class. So if raw predictions change, loss changes but accuracy is more \"resilient\" as predictions need to go over/under a threshold to actually change accuracy. Soltius's answer on SE Question Answer Is K-means clustering algorithm guaranteed to converge with unique result? K-means clustering algorithm is guaranteed to converge but the final result may vary based on the centroid initialisation. This is why it is suggested to try multiple initialization strategies and pick the one with best clustering. The convergence is guaranteed as the sum of squared distances between each point and its centroid strictly decreases over each iteration. Also the practical run time of k-means is basically linear. Refer Visualizing K Means Clustering - Naftali Harris Question Answer In K-means clustering, is it possible that a centroid has no data points assigned to it? Yes it is possible, imagine a centroid placed in middle of ring of other centroids. Several implementations either removes that centroid or random;y replace it somewhere else in the data space. Refer Visualizing K Means Clustering - Naftali Harris Question Answer What is entropy in information theory? Entropy is a measure of the amount of uncertainty or randomness in a system. It is often used in information theory and statistical mechanics to describe the unpredictability of a system or the amount of information required to describe it. It's formula is, \\(\\mathrm {H} (X):=-\\sum _{x\\in {\\mathcal {X}}}p(x)\\log p(x)=\\mathbb {E} [-\\log p(X)]\\) Here is an excellent video from Aurelien Geron, explaining the topic. Question Answer What is the difference between supervised and unsupervised learning? Supervised learning uses labeled data to train a model to make predictions, while unsupervised learning uses unlabeled data to find patterns or structure in the data. Question Answer How do you evaluate the performance of a machine learning model? One common method is to split the data into a training set and a test set, and use metrics such as accuracy, precision, recall, and F1 score to evaluate the model's performance on the test set. Question Answer What is overfitting in machine learning and how can it be prevented? Overfitting occurs when a model is too complex and is able to fit the noise in the training data, leading to poor performance on new, unseen data. To prevent overfitting, methods such as cross-validation, regularization, and early stopping can be used. Question Answer What is the difference between a decision tree and random forest? A decision tree is a single tree model that makes a prediction by traversing the tree from the root to a leaf node, while a random forest is an ensemble of decision trees, where the final prediction is made by averaging the predictions of all the trees in the forest. Question Answer What is the Bias-Variance trade-off in machine learning? The Bias-Variance trade-off refers to the trade-off between a model's ability to fit the training data well (low bias) and its ability to generalize well to new, unseen data (low variance). A model with high bias will underfit the training data, while a model with high variance will overfit the training data. Question Answer What is the difference between batch and online learning? Batch learning is a type of machine learning where the model is trained on a fixed dataset and the parameters are updated after processing the entire dataset. In contrast, online learning is a type of machine learning where the model is trained on a continuous stream of data and the parameters are updated incrementally after processing each example. Question Answer What is the difference between a decision boundary and a decision surface in machine learning? A decision boundary is a boundary that separates different classes in a dataset, it can be represented by a line or a hyperplane in a two-dimensional or multi-dimensional space respectively. A decision surface is a generalization of decision boundary, it's a surface that separates different classes in a dataset, it can be represented by a surface in a multi-dimensional space. In simple words, a decision boundary is a one-dimensional representation of a decision surface. Question Answer What is the use of principal component analysis (PCA) in machine learning? Principal component analysis (PCA) is a technique used to reduce the dimensionality of a dataset by identifying the most important features, called principal components. PCA finds a new set of uncorrelated features, called principal components, that can explain most of the variance in the original data. This can be useful for visualizing high-dimensional data, reducing noise, and improving the performance of machine learning models. Question Answer What is the use of the Random Forest algorithm in machine learning? Random Forest is an ensemble learning technique that combines multiple decision trees to improve the performance and stability of the model. It works by creating multiple decision trees using a random subset of the features and training data, and then averaging the predictions of all the trees to make a final prediction. Random Forest algorithm is often used for classification and regression problems, it's robust to outliers, missing values, and irrelevant features, and it can also be used for feature selection and feature importance analysis. Question Answer What is the difference between a generative model and a discriminative model? A generative model learns the probability distribution of the data and can generate new samples from it, while a discriminative model learns the boundary between different classes and make predictions based on it. Generative models are used for tasks such as density estimation, anomaly detection, and data generation, while discriminative models are used for tasks such as classification and regression. Question Answer What is the difference between an autoencoder and a variational autoencoder? An autoencoder is a type of neural network that learns to encode and decode input data, it can be used to reduce the dimensionality of the data and learn a compact representation of it. A variational autoencoder (VAE) is a type of autoencoder that learns a probabilistic encoding of the input data, it generates new samples from the learned distribution. VAE can be used for tasks such as image generation and anomaly detection. Question Answer What is Expectation-Maximization (EM) algorithm? The Expectation-Maximization (EM) algorithm is a method for finding maximum likelihood estimates in incomplete data problems, where some of the data is missing or hidden. EM works by iteratively refining estimates of the missing data and the parameters of the model, until it converges to a local maximum of the likelihood function. It can be used for tasks such as clustering, image segmentation, and missing data imputation. Question Answer What is the difference between L1 and L2 regularization in machine learning? L1 and L2 regularization are methods used to prevent overfitting in machine learning models by adding a penalty term to the loss function. L1 regularization adds the absolute value of the weights to the loss function, while L2 regularization adds the square of the weights. L1 regularization leads to sparse models where some weights will be zero, while L2 regularization leads to models where all weights are small. Question Answer Explain Support Vector Machine (SVM). Support Vector Machine (SVM) is a supervised learning algorithm that can be used for classification and regression tasks. SVM works by finding the hyperplane that maximally separates the different classes in a dataset and then uses this hyperplane to make predictions. SVM is particularly useful when the data is linearly separable, it's also robust to high-dimensional data and it can be used with kernel functions to solve non-linearly separable problems. Question Answer What is the use of the k-nearest neighbors (k-NN) algorithm? k-nearest neighbors (k-NN) is a type of instance-based learning algorithm that can be used for classification and regression tasks. The algorithm works by finding the k training examples that are closest to a new input and using the majority class or average value of those examples to make a prediction. k-NN is a simple and efficient algorithm that can be used for tasks such as image classification, anomaly detection, and recommendation systems. Question Answer What is the use of the Random Sampling method for feature selection in machine learning? Random Sampling is a method for feature selection that involves randomly selecting a subset of features from the dataset and evaluating the performance of a model trained on that subset. The subset of features that result in the best performance are then chosen for further analysis or use in a final model. This method can be useful when the number of features is large and there is no prior knowledge of which features are most relevant. Question Answer Explain Bagging method in ensemble learning? Bagging (Bootstrap Aggregating) is a method for ensemble learning that involves training multiple models on different subsets of the data and then combining the predictions of those models. The subsets of data are created by randomly sampling the original data with replacement, this method helps to reduce the variance of the model and increase the robustness of the predictions. Bagging is commonly used with decision trees and can be implemented using Random Forest algorithm. Question Answer Explain AdaBoost method in ensemble learning? AdaBoost (Adaptive Boosting) is a method for ensemble learning that involves training multiple models on different subsets of the data and then combining the predictions of those models. The subsets of data are created by giving more weight to the examples that are misclassified by the previous models, this method helps to increase the accuracy of the model. AdaBoost is commonly used with decision trees and can be used with any type of base classifier. Question Answer Explain Gradient Boosting method in ensemble learning? Gradient Boosting is a method for ensemble learning that involves training multiple models in a sequential manner, where each model tries to correct the mistakes of the previous model. The method uses gradient descent to minimize the loss function, this method is commonly used with decision trees and it can be used with any type of base classifier. Gradient Boosting is a powerful method that can achieve state-of-the-art performance in many machine learning tasks. Question Answer Explain XGBoost method in ensemble learning? XGBoost (Extreme Gradient Boosting) is a specific implementation of the Gradient Boosting method that uses a more efficient tree-based model and a number of techniques to speed up the training process and reduce overfitting. XGBoost is commonly used in machine learning competitions and it's one of the most popular libraries used for gradient boosting. It's used for classification and regression problems.","title":"Interview Questions"},{"location":"machine_learning/interview_questions/#what-is-deep-learning-and-how-is-it-different-from-traditional-machine-learning","text":"Deep learning is a subfield of machine learning that uses neural networks with many layers, called deep neural networks, to learn and make predictions. It is different from traditional machine learning in that it can automatically learn hierarchical representations of the data and doesn't rely heavily on feature engineering. Question Answer","title":"What is deep learning and how is it different from traditional machine learning?"},{"location":"machine_learning/interview_questions/#how-does-backpropagation-work-in-a-neural-network","text":"Backpropagation is an algorithm used to train neural networks. It starts by propagating the input forward through the network, calculating the output. Then it compares the output to the desired output and calculates the error. The error is then propagated backwards through the network, adjusting the weights in the network so as to minimize the error. This process is repeated multiple times until the error is minimized. Question Answer","title":"How does backpropagation work in a neural network?"},{"location":"machine_learning/interview_questions/#while-training-deep-learning-models-why-do-we-prefer-training-on-mini-batch-rather-than-on-individual-sample","text":"First, the gradient of the loss over a mini-batch is an estimate of the gradient over the training set, whose quality improves as the batch size increases. Second, computation over a batch can be much more efficient than m computations for individual examples, due to the parallelism afforded by the modern computing platforms. Ref Question Answer","title":"While training deep learning models, why do we prefer training on mini-batch rather than on individual sample?"},{"location":"machine_learning/interview_questions/#what-are-the-benefits-of-using-batch-normalizattion","text":"Batch Normalization also has a beneficial effect on the gradient flow through the network, by reducing the dependence of gradients on the scale of the parameters or of their initial values. This allows us to use much higher learning rates without the risk of divergence. Furthermore, batch normalization regularizes the model and reduces the need for Dropout (Srivastava et al., 2014). Finally, Batch Normalization makes it possible to use saturating nonlinearities by preventing the network from getting stuck in the saturated modes. Ref Question Answer","title":"What are the benefits of using Batch Normalizattion?"},{"location":"machine_learning/interview_questions/#what-is-entropy-information-theory","text":"Entropy is a measurement of uncertainty of a system. Intuitively, it is the amount of information needed to remove uncertainty from the system. The entropy of a probability distribution p for various states of a system can be computed as: \\(-\\sum_{i}^{} (p_i \\log p_i)\\) Question Answer","title":"What is Entropy (information theory)?"},{"location":"machine_learning/interview_questions/#even-though-sigmoid-function-is-non-linear-why-is-logistic-regression-considered-a-linear-classifier","text":"Logistic regression is often referred to as a linear classifier despite using the sigmoid (logistic) activation function because it models the relationship between the input features and the log-odds (logit) of the binary target variable in a linear manner. The linearity in logistic regression refers to the fact that it creates a linear decision boundary in the feature space, which is a hyperplane. Refer Question Answer","title":"Even though Sigmoid function is non-linear, why is Logistic regression considered a linear classifier?"},{"location":"machine_learning/interview_questions/#what-is-the-difference-between-logits-soft-and-hard-targets","text":"Let us understand each of the terms one by one. For better understanding, let's take a dog vs cat image classification as an example. Logits are the un-normalized output of the model. In our cat vs dog example, logits will be, say, 10.1 for cat and 5.6 for dog for an image with cat. Refer this SE question . Soft target : are normalized logits by applying a linear function . In our example, if we use softmax to the logits we get 0.99 for cat and 0.1 for dog. Hard targets : are the encoding of the soft targets. In our example, as the model predicted (here correctly) the image as of cat, the hard targets be 1 for cat and 0 for dog. graph LR A[Logits] -- normalization --> B[Soft Targets] B -- encoding --> C[Hard Targets] Question Answer","title":"What is the difference between Logits, Soft and Hard targets?"},{"location":"machine_learning/interview_questions/#how-do-you-handle-overfitting-in-deep-learning-models","text":"Overfitting occurs when a model becomes too complex and starts to fit the noise in the training data, rather than the underlying pattern. There are several ways to handle overfitting in deep learning models, including: Regularization techniques such as L1 and L2 regularization, which add a penalty term to the loss function to discourage large weights Early stopping , where training is stopped before the model has a chance to fully fit the noise in the training data Dropout , which randomly drops out a certain percentage of neurons during training to prevent them from co-adapting and becoming too specialized Adding more data to the training set Question Answer","title":"How do you handle overfitting in deep learning models?"},{"location":"machine_learning/interview_questions/#can-you-explain-the-concept-of-convolutional-neural-networks-cnn","text":"A convolutional neural network (CNN) is a type of neural network that is primarily used for learning image and video patterns. CNNs are designed to automatically and adaptively learn spatial hierarchies of features from input data. They use a variation of multi-layer perceptrons, designed to require minimal preprocessing. Instead of hand-engineered features, CNNs learn features from data using a process called convolution. Question Answer","title":"Can you explain the concept of convolutional neural networks (CNN)?"},{"location":"machine_learning/interview_questions/#how-do-you-handle-missing-data-in-deep-learning","text":"Missing data can be handled in several ways, including: Removing the rows or columns with missing data Interpolation or imputation of missing values Using a technique called masking, which allows the model to ignore missing values when making predictions Question Answer","title":"How do you handle missing data in deep learning?"},{"location":"machine_learning/interview_questions/#can-you-explain-the-concept-of-transfer-learning-in-deep-learning","text":"Transfer learning is a technique where a model trained on one task is used as a starting point for a model on a second, related task. This allows the model to take advantage of the features learned from the first task and apply them to the second task, which can lead to faster training and better performance. This can be done by using a pre-trained model as a feature extractor or fine-tuning the pre-trained model on new data. Question Answer","title":"Can you explain the concept of transfer learning in deep learning?"},{"location":"machine_learning/interview_questions/#what-is-gradient-descent-in-deep-learning","text":"Gradient Descent is an optimization algorithm used to minimize the loss function of a neural network. It works by updating the weights of the network in the opposite direction of the gradient of the loss function with respect to the weights. The magnitude of the update is determined by the learning rate. There are several variants of gradient descent, such as batch gradient descent, stochastic gradient descent, and mini-batch gradient descent. Question Answer","title":"What is Gradient Descent in deep learning?"},{"location":"machine_learning/interview_questions/#please-explain-what-is-dropout-in-deep-learning","text":"Dropout is a regularization technique used in deep learning to prevent overfitting. It works by randomly dropping out a certain percentage of neurons during training, effectively reducing the capacity of the network. This forces the network to learn multiple independent representations of the data, making it less prone to overfitting. Question Answer","title":"Please explain what is Dropout in deep learning?"},{"location":"machine_learning/interview_questions/#what-are-autoencoder","text":"An autoencoder is a type of neural network that is trained to reconstruct its input. It has an encoder part that compresses the input into a lower-dimensional representation called the bottleneck or latent code, and a decoder part that reconstructs the input from the latent code. Autoencoders can be used for tasks such as dimensionality reduction, anomaly detection and generative modelling. Question Answer","title":"What are Autoencoder?"},{"location":"machine_learning/interview_questions/#can-you-explain-the-concept-of-attention-mechanism-in-deep-learning","text":"Attention mechanism is a way to weight different parts of the input in a neural network, giving more importance to certain features than others. It is commonly used in tasks such as machine translation, where the model needs to focus on different parts of the input sentence at different times. Attention mechanisms can be implemented in various ways, such as additive attention, dot-product attention, and multi-head attention. Question Answer","title":"Can you explain the concept of attention mechanism in deep learning?"},{"location":"machine_learning/interview_questions/#what-are-generative-adversarial-networks-gans","text":"Generative Adversarial Networks (GANs) are a type of generative model that consists of two parts, a generator and a discriminator. The generator is trained to generate new data that is similar to the data it was trained on, while the discriminator is trained to distinguish the generated data from the real data. The two parts are trained together in a game-theoretic manner, where the generator tries to generate data that can fool the discriminator, and the discriminator tries to correctly identify the generated data. Question Answer","title":"What are Generative Adversarial Networks (GANs)?"},{"location":"machine_learning/interview_questions/#can-you-explain-the-concept-of-memory-networks-in-deep-learning","text":"Memory networks are a type of neural network architecture that allow the model to access and manipulate an external memory matrix, which can be used to store information that is relevant to the task. This allows the model to reason about the past and use this information to make predictions about the future. Memory networks have been used in tasks such as language understanding and question answering. Refer this for more details. Question Answer","title":"Can you explain the concept of Memory Networks in deep learning?"},{"location":"machine_learning/interview_questions/#explain-capsule-networks-in-deep-learning","text":"Capsule networks are a type of neural network architecture that aims to overcome the limitations of traditional convolutional neural networks (CNNs) by using a new type of layer called a capsule. A capsule contains multiple neurons that work together to represent an object or part of an object, and the activities of the neurons are used to represent the properties of the object such as position, size and orientation. Capsule networks have been used in tasks such as image classification and object detection. Question Answer","title":"Explain Capsule Networks in deep learning?"},{"location":"machine_learning/interview_questions/#can-you-explain-the-concept-of-generative-models-in-deep-learning","text":"Generative models are a type of deep learning model that can generate new data that is similar to the data it was trained on. These models are trained on a dataset and learn the underlying probability distribution of the data, allowing them to generate new, unseen data that fits that distribution. Examples of generative models include Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). Question Answer","title":"Can you explain the concept of generative models in deep learning?"},{"location":"machine_learning/interview_questions/#what-is-the-concept-of-adversarial-training-in-deep-learning","text":"Adversarial training is a technique used to improve the robustness of deep learning models by generating adversarial examples and using them to train the model. Adversarial examples are inputs that are slightly perturbed in such a way as to cause the model to make a mistake. By training the model on these examples, it becomes more robust to similar perturbations in the real world. Question Answer","title":"What is the concept of adversarial training in deep learning?"},{"location":"machine_learning/interview_questions/#what-is-weight-initialization-in-deep-learning","text":"Weight initialization is the process of setting the initial values for the weights in a neural network. The initial values of the weights can have a big impact on the network's performance and training time. There are several methods to initialize weights, including random initialization, Glorot initialization, and He initialization. Each of these methods have different properties and are more suitable for different types of problems. Question Answer","title":"What is weight initialization in deep learning?"},{"location":"machine_learning/interview_questions/#explain-data-augmentation","text":"Data augmentation is a technique used to increase the amount of data available for training a deep learning model. This is done by creating new training examples by applying various random transformations to the original data, such as random cropping, flipping, or rotation. Data augmentation can be a powerful tool to prevent overfitting and improve the generalization performance of a mode. Question Answer","title":"Explain data augmentation?"},{"location":"machine_learning/interview_questions/#what-is-the-different-between-standardization-and-normalization","text":"Normalization is the process of scaling the data to a common scale. It is also known as Min-Max Scaling where the final range could be [0, 1] or [-1,1] or something else. \\(X_{new} = (X - X_{min})/(X_{max} - X_{min})\\) Standardization is the process of scaling the data to have zero mean and unit variance. \\(X_{new} = (X - mean)/Std\\) Question Answer","title":"What is the different between Standardization and Normalization?"},{"location":"machine_learning/interview_questions/#is-it-possible-that-during-ml-training-both-validation-or-test-loss-and-accuracy-are-increasing","text":"Accuracy and loss are not necessarily exactly (inversely) correlated, as loss measures a difference between raw prediction (float) and class (0 or 1), while accuracy measures the difference between thresholded prediction (0 or 1) and class. So if raw predictions change, loss changes but accuracy is more \"resilient\" as predictions need to go over/under a threshold to actually change accuracy. Soltius's answer on SE Question Answer","title":"Is it possible that during ML training, both validation (or test) loss and accuracy, are increasing?"},{"location":"machine_learning/interview_questions/#is-k-means-clustering-algorithm-guaranteed-to-converge-with-unique-result","text":"K-means clustering algorithm is guaranteed to converge but the final result may vary based on the centroid initialisation. This is why it is suggested to try multiple initialization strategies and pick the one with best clustering. The convergence is guaranteed as the sum of squared distances between each point and its centroid strictly decreases over each iteration. Also the practical run time of k-means is basically linear. Refer Visualizing K Means Clustering - Naftali Harris Question Answer","title":"Is K-means clustering algorithm guaranteed to converge with unique result?"},{"location":"machine_learning/interview_questions/#in-k-means-clustering-is-it-possible-that-a-centroid-has-no-data-points-assigned-to-it","text":"Yes it is possible, imagine a centroid placed in middle of ring of other centroids. Several implementations either removes that centroid or random;y replace it somewhere else in the data space. Refer Visualizing K Means Clustering - Naftali Harris Question Answer","title":"In K-means clustering, is it possible that a centroid has no data points assigned to it?"},{"location":"machine_learning/interview_questions/#what-is-entropy-in-information-theory","text":"Entropy is a measure of the amount of uncertainty or randomness in a system. It is often used in information theory and statistical mechanics to describe the unpredictability of a system or the amount of information required to describe it. It's formula is, \\(\\mathrm {H} (X):=-\\sum _{x\\in {\\mathcal {X}}}p(x)\\log p(x)=\\mathbb {E} [-\\log p(X)]\\) Here is an excellent video from Aurelien Geron, explaining the topic. Question Answer","title":"What is entropy in information theory?"},{"location":"machine_learning/interview_questions/#what-is-the-difference-between-supervised-and-unsupervised-learning","text":"Supervised learning uses labeled data to train a model to make predictions, while unsupervised learning uses unlabeled data to find patterns or structure in the data. Question Answer","title":"What is the difference between supervised and unsupervised learning?"},{"location":"machine_learning/interview_questions/#how-do-you-evaluate-the-performance-of-a-machine-learning-model","text":"One common method is to split the data into a training set and a test set, and use metrics such as accuracy, precision, recall, and F1 score to evaluate the model's performance on the test set. Question Answer","title":"How do you evaluate the performance of a machine learning model?"},{"location":"machine_learning/interview_questions/#what-is-overfitting-in-machine-learning-and-how-can-it-be-prevented","text":"Overfitting occurs when a model is too complex and is able to fit the noise in the training data, leading to poor performance on new, unseen data. To prevent overfitting, methods such as cross-validation, regularization, and early stopping can be used. Question Answer","title":"What is overfitting in machine learning and how can it be prevented?"},{"location":"machine_learning/interview_questions/#what-is-the-difference-between-a-decision-tree-and-random-forest","text":"A decision tree is a single tree model that makes a prediction by traversing the tree from the root to a leaf node, while a random forest is an ensemble of decision trees, where the final prediction is made by averaging the predictions of all the trees in the forest. Question Answer","title":"What is the difference between a decision tree and random forest?"},{"location":"machine_learning/interview_questions/#what-is-the-bias-variance-trade-off-in-machine-learning","text":"The Bias-Variance trade-off refers to the trade-off between a model's ability to fit the training data well (low bias) and its ability to generalize well to new, unseen data (low variance). A model with high bias will underfit the training data, while a model with high variance will overfit the training data. Question Answer","title":"What is the Bias-Variance trade-off in machine learning?"},{"location":"machine_learning/interview_questions/#what-is-the-difference-between-batch-and-online-learning","text":"Batch learning is a type of machine learning where the model is trained on a fixed dataset and the parameters are updated after processing the entire dataset. In contrast, online learning is a type of machine learning where the model is trained on a continuous stream of data and the parameters are updated incrementally after processing each example. Question Answer","title":"What is the difference between batch and online learning?"},{"location":"machine_learning/interview_questions/#what-is-the-difference-between-a-decision-boundary-and-a-decision-surface-in-machine-learning","text":"A decision boundary is a boundary that separates different classes in a dataset, it can be represented by a line or a hyperplane in a two-dimensional or multi-dimensional space respectively. A decision surface is a generalization of decision boundary, it's a surface that separates different classes in a dataset, it can be represented by a surface in a multi-dimensional space. In simple words, a decision boundary is a one-dimensional representation of a decision surface. Question Answer","title":"What is the difference between a decision boundary and a decision surface in machine learning?"},{"location":"machine_learning/interview_questions/#what-is-the-use-of-principal-component-analysis-pca-in-machine-learning","text":"Principal component analysis (PCA) is a technique used to reduce the dimensionality of a dataset by identifying the most important features, called principal components. PCA finds a new set of uncorrelated features, called principal components, that can explain most of the variance in the original data. This can be useful for visualizing high-dimensional data, reducing noise, and improving the performance of machine learning models. Question Answer","title":"What is the use of principal component analysis (PCA) in machine learning?"},{"location":"machine_learning/interview_questions/#what-is-the-use-of-the-random-forest-algorithm-in-machine-learning","text":"Random Forest is an ensemble learning technique that combines multiple decision trees to improve the performance and stability of the model. It works by creating multiple decision trees using a random subset of the features and training data, and then averaging the predictions of all the trees to make a final prediction. Random Forest algorithm is often used for classification and regression problems, it's robust to outliers, missing values, and irrelevant features, and it can also be used for feature selection and feature importance analysis. Question Answer","title":"What is the use of the Random Forest algorithm in machine learning?"},{"location":"machine_learning/interview_questions/#what-is-the-difference-between-a-generative-model-and-a-discriminative-model","text":"A generative model learns the probability distribution of the data and can generate new samples from it, while a discriminative model learns the boundary between different classes and make predictions based on it. Generative models are used for tasks such as density estimation, anomaly detection, and data generation, while discriminative models are used for tasks such as classification and regression. Question Answer","title":"What is the difference between a generative model and a discriminative model?"},{"location":"machine_learning/interview_questions/#what-is-the-difference-between-an-autoencoder-and-a-variational-autoencoder","text":"An autoencoder is a type of neural network that learns to encode and decode input data, it can be used to reduce the dimensionality of the data and learn a compact representation of it. A variational autoencoder (VAE) is a type of autoencoder that learns a probabilistic encoding of the input data, it generates new samples from the learned distribution. VAE can be used for tasks such as image generation and anomaly detection. Question Answer","title":"What is the difference between an autoencoder and a variational autoencoder?"},{"location":"machine_learning/interview_questions/#what-is-expectation-maximization-em-algorithm","text":"The Expectation-Maximization (EM) algorithm is a method for finding maximum likelihood estimates in incomplete data problems, where some of the data is missing or hidden. EM works by iteratively refining estimates of the missing data and the parameters of the model, until it converges to a local maximum of the likelihood function. It can be used for tasks such as clustering, image segmentation, and missing data imputation. Question Answer","title":"What is Expectation-Maximization (EM) algorithm?"},{"location":"machine_learning/interview_questions/#what-is-the-difference-between-l1-and-l2-regularization-in-machine-learning","text":"L1 and L2 regularization are methods used to prevent overfitting in machine learning models by adding a penalty term to the loss function. L1 regularization adds the absolute value of the weights to the loss function, while L2 regularization adds the square of the weights. L1 regularization leads to sparse models where some weights will be zero, while L2 regularization leads to models where all weights are small. Question Answer","title":"What is the difference between L1 and L2 regularization in machine learning?"},{"location":"machine_learning/interview_questions/#explain-support-vector-machine-svm","text":"Support Vector Machine (SVM) is a supervised learning algorithm that can be used for classification and regression tasks. SVM works by finding the hyperplane that maximally separates the different classes in a dataset and then uses this hyperplane to make predictions. SVM is particularly useful when the data is linearly separable, it's also robust to high-dimensional data and it can be used with kernel functions to solve non-linearly separable problems. Question Answer","title":"Explain Support Vector Machine (SVM)."},{"location":"machine_learning/interview_questions/#what-is-the-use-of-the-k-nearest-neighbors-k-nn-algorithm","text":"k-nearest neighbors (k-NN) is a type of instance-based learning algorithm that can be used for classification and regression tasks. The algorithm works by finding the k training examples that are closest to a new input and using the majority class or average value of those examples to make a prediction. k-NN is a simple and efficient algorithm that can be used for tasks such as image classification, anomaly detection, and recommendation systems. Question Answer","title":"What is the use of the k-nearest neighbors (k-NN) algorithm?"},{"location":"machine_learning/interview_questions/#what-is-the-use-of-the-random-sampling-method-for-feature-selection-in-machine-learning","text":"Random Sampling is a method for feature selection that involves randomly selecting a subset of features from the dataset and evaluating the performance of a model trained on that subset. The subset of features that result in the best performance are then chosen for further analysis or use in a final model. This method can be useful when the number of features is large and there is no prior knowledge of which features are most relevant. Question Answer","title":"What is the use of the Random Sampling method for feature selection in machine learning?"},{"location":"machine_learning/interview_questions/#explain-bagging-method-in-ensemble-learning","text":"Bagging (Bootstrap Aggregating) is a method for ensemble learning that involves training multiple models on different subsets of the data and then combining the predictions of those models. The subsets of data are created by randomly sampling the original data with replacement, this method helps to reduce the variance of the model and increase the robustness of the predictions. Bagging is commonly used with decision trees and can be implemented using Random Forest algorithm. Question Answer","title":"Explain Bagging method in ensemble learning?"},{"location":"machine_learning/interview_questions/#explain-adaboost-method-in-ensemble-learning","text":"AdaBoost (Adaptive Boosting) is a method for ensemble learning that involves training multiple models on different subsets of the data and then combining the predictions of those models. The subsets of data are created by giving more weight to the examples that are misclassified by the previous models, this method helps to increase the accuracy of the model. AdaBoost is commonly used with decision trees and can be used with any type of base classifier. Question Answer","title":"Explain AdaBoost method in ensemble learning?"},{"location":"machine_learning/interview_questions/#explain-gradient-boosting-method-in-ensemble-learning","text":"Gradient Boosting is a method for ensemble learning that involves training multiple models in a sequential manner, where each model tries to correct the mistakes of the previous model. The method uses gradient descent to minimize the loss function, this method is commonly used with decision trees and it can be used with any type of base classifier. Gradient Boosting is a powerful method that can achieve state-of-the-art performance in many machine learning tasks. Question Answer","title":"Explain Gradient Boosting method in ensemble learning?"},{"location":"machine_learning/interview_questions/#explain-xgboost-method-in-ensemble-learning","text":"XGBoost (Extreme Gradient Boosting) is a specific implementation of the Gradient Boosting method that uses a more efficient tree-based model and a number of techniques to speed up the training process and reduce overfitting. XGBoost is commonly used in machine learning competitions and it's one of the most popular libraries used for gradient boosting. It's used for classification and regression problems.","title":"Explain XGBoost method in ensemble learning?"},{"location":"machine_learning/introduction/","text":"The first question to ask is \"What is Machine Learning (ML)?\" . And to answer this question we should understand the relation between Artificial Intelligence (AI), Machine Learning (ML) and even Deep Learning (DL). Let's go through them ony by one, Artificial Intelligence (AI) is the process of creating intelligent machines that can perform tasks that humans can do. Machine Learning (ML) is subset of AI where we create systems that is able to learn from data and perform specific tasks with near or above human capability. Deep Learning (DL) is subset of ML where we create neural network based system that is able to capable of identifying patterns from data and perform specific tasks. The hierarchy of Artificial Intelligence Different types of Learning The paradigms of Machine Learning Supervised Learning It is a machine learning approach wherein we learn a function that transforms an input into an output based on example input-output pairs. Basically, it uses a labeled dataset as a training dataset to learn a generic function that can be later used to predict unseens data. Classification is one of the most common problems for which supervised learning is utilized. The idea is to learn a generic function that takes an item\u2019s features as input and provides the item\u2019s class as output. To solve this, several classification algorithms try to create boundaries for each class based on the features of labeled data. Later for any new item, the boundaries help decide which class the item belongs to. Unsupervised Learning It is a machine learning approach wherein we learn patterns from unlabeled data. It is more of a descriptive analysis that can be used to generate insights from the data, which could be later used for downstream predictions. Clustering is a common example of unsupervised learning. The idea is to make groups of items based on the item\u2019s features. Note, as the data is not labeled, the grouping could be completely different from the user\u2019s expectations. Each clustering algorithm has its own internal similarity function and grouping strategy by which the clusters are formed. Semi-Supervised Learning It is a machine learning approach wherein we use labeled and unlabeled data to train a model. The intention is that the resulting model will be better than one learned over the labeled (supervised) or unlabeled data (unsupervised) alone. Hence it falls between the two methods. We can try semi-supervised learning when we have very little labeled data but a lot of unlabeled data, and if the cost or time of labeling is too high. We start with training a model on the labeled data. Then the model is used to make predictions on the unlabeled data. Specific unlabeled data are picked and their prediction is considered true. The selection criteria could be some threshold on the prediction probability or top K selection. These selected unlabeled data with the prediction are added to the labeled data set and the next iteration of training begins. This goes on till n-iterations . Note This process is also called Pseudo-labelling, as we are creating pseudo labels on unlabeled dataset using the model trained on only labeled data. Reinforcement Learning It is a machine learning approach wherein we train agent(s) to interact with an environment to achieve certain goal. The goal is quantified by providing the agent with some positive reward on successful completion or negative reward incase of failure. The main components in RL are agents, environment and actions. Agents are the intelligent model we want to improve over time. Environment is the simulation where the agent performs some actions. Once an agent takes an action, the state of the agent changes. Based on the environment, the agent could get instant reward for each action or delayed reward on completion of an episode (sequence of actions). Self-supervised Learning It is a machine learning approach wherein we create supervisory signals from the unlabeled data itself, often leveraging the underlying structure in the data. The idea is to take unlabeled data and create generic tasks, that could be different from the intended downstream task but will help model learn the fundamentals. Then the model could be fine-tuned for the specific downstream task easily with very less labeled data. It is closely connected to how humans learn \u2014 as human normally first develop common sense (a general understanding of the world) and then learn specific tasks quite easily (when comparing to machines). It is becoming a norm in the AI field to train large models using self-supervised learning, as the resulting models are generalist ie. could be used for multiple downstream tasks. The method of training vary wrt the datatype. For example, in NLP, we can hide part of a sentence and predict the hidden words from the remaining words. In CV, we can predict past or future frames in a video (hidden data) from current ones (observed data). Same could be done for Audio. Additional Techniques Active Learning Active learning is all about selecting the minimal set of training data that will lead to the maximum performance gain. Active learning could have following use cases, Lots of labeled data: If you have lots of annotated data but you are contraint by time or computation power, you can try to select the smallest set of labeled data to train the model on. This is easy to understand as not all samples have equal contribution in training the model. Thinking intuitively, say for a linear classifier with straight line as the decision boundary, keeping data near the decision line will lead to better overall accuracy than the ones near the end or in middle of cluster of datapoints. Lots of unlabeled data: In this case, active learning is very similar to Semi-supervised learning, with only one twist - we ask humans to label again, instead of labeling using the model. The process starts similar to pseudo labelling and we train the model on the available labeled dataset. Then the trained model is used to make prediction on the unlabeled data and using preferred logic, we suggest the next batch of datasets that should be labeled by human expert. We keep iterating unless the desired accuracy is reached. Transfer learning Transfer learning is all about using the knowledge gained while solving one problem to solve another problem. From a practical perspective, we reuse (either as it is or by finetining) an existing base model that was trained for say Task A, for another tasks like B, C, etc. This approach could be prefered is we don't want to train a model from scratch. For example, if we have a pretrained model that can classify cat vs dogs, it will be easier, in terms of training iterations and even number of required samples, to train for other classes like lion vs elephant. Here, the intuition is that the exsiting image classification model should have gained some knowledge about the important features of animal images. And even though the classes were completely different, it should be mich better and easier than training a new model with new weight initialization. Note There are several ways on how we can use transfer learning to make an existing model work for another task. These could be, (1) Finetune the complete model, (2) Freeze certains layers (preferably the starting one) and then finetune, (3) Replace certains layers (preferably the ending one -- like classification head) and then finetune, or (4) Freeze the complete model and just train additional heads for different downstream tasks. Additional materials 14 Different Types of Learning in Machine Learning Semi-Supervised Wikipedia and Semi-Supervised Sklearn Self-supervised learning: The dark matter of intelligence Transfer Learning - Machine Learning Mastery","title":"Introduction"},{"location":"machine_learning/introduction/#different-types-of-learning","text":"The paradigms of Machine Learning","title":"Different types of Learning"},{"location":"machine_learning/introduction/#supervised-learning","text":"It is a machine learning approach wherein we learn a function that transforms an input into an output based on example input-output pairs. Basically, it uses a labeled dataset as a training dataset to learn a generic function that can be later used to predict unseens data. Classification is one of the most common problems for which supervised learning is utilized. The idea is to learn a generic function that takes an item\u2019s features as input and provides the item\u2019s class as output. To solve this, several classification algorithms try to create boundaries for each class based on the features of labeled data. Later for any new item, the boundaries help decide which class the item belongs to.","title":"Supervised Learning"},{"location":"machine_learning/introduction/#unsupervised-learning","text":"It is a machine learning approach wherein we learn patterns from unlabeled data. It is more of a descriptive analysis that can be used to generate insights from the data, which could be later used for downstream predictions. Clustering is a common example of unsupervised learning. The idea is to make groups of items based on the item\u2019s features. Note, as the data is not labeled, the grouping could be completely different from the user\u2019s expectations. Each clustering algorithm has its own internal similarity function and grouping strategy by which the clusters are formed.","title":"Unsupervised Learning"},{"location":"machine_learning/introduction/#semi-supervised-learning","text":"It is a machine learning approach wherein we use labeled and unlabeled data to train a model. The intention is that the resulting model will be better than one learned over the labeled (supervised) or unlabeled data (unsupervised) alone. Hence it falls between the two methods. We can try semi-supervised learning when we have very little labeled data but a lot of unlabeled data, and if the cost or time of labeling is too high. We start with training a model on the labeled data. Then the model is used to make predictions on the unlabeled data. Specific unlabeled data are picked and their prediction is considered true. The selection criteria could be some threshold on the prediction probability or top K selection. These selected unlabeled data with the prediction are added to the labeled data set and the next iteration of training begins. This goes on till n-iterations . Note This process is also called Pseudo-labelling, as we are creating pseudo labels on unlabeled dataset using the model trained on only labeled data.","title":"Semi-Supervised Learning"},{"location":"machine_learning/introduction/#reinforcement-learning","text":"It is a machine learning approach wherein we train agent(s) to interact with an environment to achieve certain goal. The goal is quantified by providing the agent with some positive reward on successful completion or negative reward incase of failure. The main components in RL are agents, environment and actions. Agents are the intelligent model we want to improve over time. Environment is the simulation where the agent performs some actions. Once an agent takes an action, the state of the agent changes. Based on the environment, the agent could get instant reward for each action or delayed reward on completion of an episode (sequence of actions).","title":"Reinforcement Learning"},{"location":"machine_learning/introduction/#self-supervised-learning","text":"It is a machine learning approach wherein we create supervisory signals from the unlabeled data itself, often leveraging the underlying structure in the data. The idea is to take unlabeled data and create generic tasks, that could be different from the intended downstream task but will help model learn the fundamentals. Then the model could be fine-tuned for the specific downstream task easily with very less labeled data. It is closely connected to how humans learn \u2014 as human normally first develop common sense (a general understanding of the world) and then learn specific tasks quite easily (when comparing to machines). It is becoming a norm in the AI field to train large models using self-supervised learning, as the resulting models are generalist ie. could be used for multiple downstream tasks. The method of training vary wrt the datatype. For example, in NLP, we can hide part of a sentence and predict the hidden words from the remaining words. In CV, we can predict past or future frames in a video (hidden data) from current ones (observed data). Same could be done for Audio.","title":"Self-supervised Learning"},{"location":"machine_learning/introduction/#additional-techniques","text":"","title":"Additional Techniques"},{"location":"machine_learning/introduction/#active-learning","text":"Active learning is all about selecting the minimal set of training data that will lead to the maximum performance gain. Active learning could have following use cases, Lots of labeled data: If you have lots of annotated data but you are contraint by time or computation power, you can try to select the smallest set of labeled data to train the model on. This is easy to understand as not all samples have equal contribution in training the model. Thinking intuitively, say for a linear classifier with straight line as the decision boundary, keeping data near the decision line will lead to better overall accuracy than the ones near the end or in middle of cluster of datapoints. Lots of unlabeled data: In this case, active learning is very similar to Semi-supervised learning, with only one twist - we ask humans to label again, instead of labeling using the model. The process starts similar to pseudo labelling and we train the model on the available labeled dataset. Then the trained model is used to make prediction on the unlabeled data and using preferred logic, we suggest the next batch of datasets that should be labeled by human expert. We keep iterating unless the desired accuracy is reached.","title":"Active Learning"},{"location":"machine_learning/introduction/#transfer-learning","text":"Transfer learning is all about using the knowledge gained while solving one problem to solve another problem. From a practical perspective, we reuse (either as it is or by finetining) an existing base model that was trained for say Task A, for another tasks like B, C, etc. This approach could be prefered is we don't want to train a model from scratch. For example, if we have a pretrained model that can classify cat vs dogs, it will be easier, in terms of training iterations and even number of required samples, to train for other classes like lion vs elephant. Here, the intuition is that the exsiting image classification model should have gained some knowledge about the important features of animal images. And even though the classes were completely different, it should be mich better and easier than training a new model with new weight initialization. Note There are several ways on how we can use transfer learning to make an existing model work for another task. These could be, (1) Finetune the complete model, (2) Freeze certains layers (preferably the starting one) and then finetune, (3) Replace certains layers (preferably the ending one -- like classification head) and then finetune, or (4) Freeze the complete model and just train additional heads for different downstream tasks.","title":"Transfer learning"},{"location":"machine_learning/introduction/#additional-materials","text":"14 Different Types of Learning in Machine Learning Semi-Supervised Wikipedia and Semi-Supervised Sklearn Self-supervised learning: The dark matter of intelligence Transfer Learning - Machine Learning Mastery","title":"Additional materials"},{"location":"machine_learning/loss_functions/","text":"Note This page is still not complete and new sections might get added later. That said, the existing content is ready to be consumed. \ud83d\udc4d Introduction Loss functions are the \"ideal objectives\" that neural networks (NN) tries to optimize. In fact, they are the mathematical personification of what we want to achieve with the NN. As the name suggests, it is a function that takes input and compute a loss value that determines how further away the current model is from the ideal model for that example. In an ideal world, we would expect the loss value to be 0, but in reality it could get very close to 0 and sometimes even be high enough so that we terminate training to handle overfitting. We also have cost functions that is nothing but aggrgation of the loss functions over a batch or complete dataset. The cost function is the function that we use in practice to optimize the model. Hint Loss functions --> loss on one example Cost functions --> loss on entire dataset or a batch Types of Loss functions Selecting a loss function for your NN depends a lot on your use case and even the type of data you are working with. For example, in case of regression, you can use MSE loss function. In case of classification, you can use Cross Entropy loss function. Here, we will go through some examples of loss functions. MAE (L1 loss) Mean Absolute Error (MAE) loss function is used to calculate regression loss. Due to the presence of absolute term instead of square, it is more robust to outliers. It is differentiable at all points expect when predicted target value equals true target value. The actual formula is shown below, \\[{\\displaystyle \\mathrm {MAE_{loss}}(i) ={\\left|y_{i}-\\hat {y_{i}}\\right|}}\\] \\[{\\displaystyle \\mathrm {MAE_{cost}} ={\\frac {1}{n}{\\sum _{i=1}^{n} \\mathrm{MAE_loss}(i)}}}\\] MSE (L2 loss) Mean Squared Error (MSE) loss function is used to measure the difference between the predicted value and the actual value. Basically it is the mean squared euclidean distance. It is most widely used loss function for regression tasks and representation (embedding) similarity task. The actual formuale is shown below, \\[{\\displaystyle \\operatorname {MSE_loss}(i) = (y_{i}-{\\hat {y_{i}}})^{2}}\\] \\[{\\displaystyle \\operatorname {MSE_cost} ={\\frac {1}{n}}\\sum _{i=1}^{n}\\operatorname {MSE_loss}(i)}\\] The MSE cost function is less resistant to outliers since the loss function squares big mistakes in order to punish the model. As a result, if the data is prone to many outliers, you shouldn't utilise L2 loss. Cross entropy loss Cross entropy loss is used for classification tasks. It is a simplication of Kullback\u2013Leibler divergence that is used to compute the difference between two probability distributions (here the model's prediction and true one) . For binary classification the formula is shown below, ( \\(y\\) is the actual class and \\(\\hat{y}\\) is the predicted class) \\[{\\displaystyle \\operatorname {CrossEntropy_loss}(i) = -(y_i \\log(\\hat{y_i})+(1-y_i) \\log(1-\\hat{y_i}))}\\] \\[{\\displaystyle \\operatorname {CrossEntropy_cost} ={\\frac {1}{n}}\\sum _{i=1}^{n}\\operatorname {CrossEntropy_loss}(i)}\\] Let's go through the different possibilities, if \\(y_i=1\\) , the loss function reduces to only the left part i.e. \\(-y_i \\log(\\hat{y_i})\\) now to have a small loss, model would want the \\(\\log(\\hat{y_i})\\) to be large (bcoz of negative sign) . for this, model will make \\(\\hat{y_i}\\) large (ideally 1) if \\(y_i=0\\) , the loss function reduces to only the right part i.e. \\(-(1-y_i) \\log(1-\\hat{y_i})\\) now to have a small loss, model would want the \\(\\log(1 - \\hat{y_i})\\) to be large (bcoz of negative sign) . for this, model will make \\(\\hat{y_i}\\) small (ideally 0) Note For both the cases we want to make sure that \\(\\hat{y_i}\\) is bounded between 0 and 1. For this, usually the output of model is passed through some activation function like sigmoid. Additionally, some people may argue why not use a simple \\((\\hat{y}-y)^2\\) as the loss function for classification task. The major problem is that with this loss function the optimization becomes non convex hence the model will not converge. Above we can see loss function graph plotted for both the cases, Red line is for case when \\(y_i=1\\) . The line is plotted for \\(x=-y_i \\log(\\hat{y_i})\\) . As we want the loss to be zero, it is only possible for \\(y_i=1\\) . Blue line is for case when \\(y_i=0\\) . The line is plotted for \\(x=-(1-y_i) \\log(1-\\hat{y_i})\\) . As we want the loss to be zero, it is only possible for \\(y_i=0\\) . Hint Refer this excellent video from AndrewNg on CrossEntropyLoss for more details. References [1] Triplet Loss and Online Triplet Mining in TensorFlow [2] The 7 Most Common Machine Learning Loss Functions","title":"Loss functions"},{"location":"machine_learning/loss_functions/#introduction","text":"Loss functions are the \"ideal objectives\" that neural networks (NN) tries to optimize. In fact, they are the mathematical personification of what we want to achieve with the NN. As the name suggests, it is a function that takes input and compute a loss value that determines how further away the current model is from the ideal model for that example. In an ideal world, we would expect the loss value to be 0, but in reality it could get very close to 0 and sometimes even be high enough so that we terminate training to handle overfitting. We also have cost functions that is nothing but aggrgation of the loss functions over a batch or complete dataset. The cost function is the function that we use in practice to optimize the model. Hint Loss functions --> loss on one example Cost functions --> loss on entire dataset or a batch","title":"Introduction"},{"location":"machine_learning/loss_functions/#types-of-loss-functions","text":"Selecting a loss function for your NN depends a lot on your use case and even the type of data you are working with. For example, in case of regression, you can use MSE loss function. In case of classification, you can use Cross Entropy loss function. Here, we will go through some examples of loss functions.","title":"Types of Loss functions"},{"location":"machine_learning/loss_functions/#mae-l1-loss","text":"Mean Absolute Error (MAE) loss function is used to calculate regression loss. Due to the presence of absolute term instead of square, it is more robust to outliers. It is differentiable at all points expect when predicted target value equals true target value. The actual formula is shown below, \\[{\\displaystyle \\mathrm {MAE_{loss}}(i) ={\\left|y_{i}-\\hat {y_{i}}\\right|}}\\] \\[{\\displaystyle \\mathrm {MAE_{cost}} ={\\frac {1}{n}{\\sum _{i=1}^{n} \\mathrm{MAE_loss}(i)}}}\\]","title":"MAE (L1 loss)"},{"location":"machine_learning/loss_functions/#mse-l2-loss","text":"Mean Squared Error (MSE) loss function is used to measure the difference between the predicted value and the actual value. Basically it is the mean squared euclidean distance. It is most widely used loss function for regression tasks and representation (embedding) similarity task. The actual formuale is shown below, \\[{\\displaystyle \\operatorname {MSE_loss}(i) = (y_{i}-{\\hat {y_{i}}})^{2}}\\] \\[{\\displaystyle \\operatorname {MSE_cost} ={\\frac {1}{n}}\\sum _{i=1}^{n}\\operatorname {MSE_loss}(i)}\\] The MSE cost function is less resistant to outliers since the loss function squares big mistakes in order to punish the model. As a result, if the data is prone to many outliers, you shouldn't utilise L2 loss.","title":"MSE (L2 loss)"},{"location":"machine_learning/loss_functions/#cross-entropy-loss","text":"Cross entropy loss is used for classification tasks. It is a simplication of Kullback\u2013Leibler divergence that is used to compute the difference between two probability distributions (here the model's prediction and true one) . For binary classification the formula is shown below, ( \\(y\\) is the actual class and \\(\\hat{y}\\) is the predicted class) \\[{\\displaystyle \\operatorname {CrossEntropy_loss}(i) = -(y_i \\log(\\hat{y_i})+(1-y_i) \\log(1-\\hat{y_i}))}\\] \\[{\\displaystyle \\operatorname {CrossEntropy_cost} ={\\frac {1}{n}}\\sum _{i=1}^{n}\\operatorname {CrossEntropy_loss}(i)}\\] Let's go through the different possibilities, if \\(y_i=1\\) , the loss function reduces to only the left part i.e. \\(-y_i \\log(\\hat{y_i})\\) now to have a small loss, model would want the \\(\\log(\\hat{y_i})\\) to be large (bcoz of negative sign) . for this, model will make \\(\\hat{y_i}\\) large (ideally 1) if \\(y_i=0\\) , the loss function reduces to only the right part i.e. \\(-(1-y_i) \\log(1-\\hat{y_i})\\) now to have a small loss, model would want the \\(\\log(1 - \\hat{y_i})\\) to be large (bcoz of negative sign) . for this, model will make \\(\\hat{y_i}\\) small (ideally 0) Note For both the cases we want to make sure that \\(\\hat{y_i}\\) is bounded between 0 and 1. For this, usually the output of model is passed through some activation function like sigmoid. Additionally, some people may argue why not use a simple \\((\\hat{y}-y)^2\\) as the loss function for classification task. The major problem is that with this loss function the optimization becomes non convex hence the model will not converge. Above we can see loss function graph plotted for both the cases, Red line is for case when \\(y_i=1\\) . The line is plotted for \\(x=-y_i \\log(\\hat{y_i})\\) . As we want the loss to be zero, it is only possible for \\(y_i=1\\) . Blue line is for case when \\(y_i=0\\) . The line is plotted for \\(x=-(1-y_i) \\log(1-\\hat{y_i})\\) . As we want the loss to be zero, it is only possible for \\(y_i=0\\) . Hint Refer this excellent video from AndrewNg on CrossEntropyLoss for more details.","title":"Cross entropy loss"},{"location":"machine_learning/loss_functions/#references","text":"[1] Triplet Loss and Online Triplet Mining in TensorFlow [2] The 7 Most Common Machine Learning Loss Functions","title":"References"},{"location":"machine_learning/ranking_algorithms/","text":"Introduction Suppose you have a decision to make \u2014 like buying a house, or a car, or even a guitar. You don\u2019t want to choose randomly or get biased by someone\u2019s suggestion, but want to make an educated decision. For this, you gathered some information about the entity you want to buy (let\u2019s say it\u2019s a car). So you have a list of N cars with their price information. As usual, we won\u2019t want to spend more, we can just sort the cars by their price (in ascending order) and pick the top one (with the smallest price), and we are done! This was decision making with a single criterion. But alas if life is so easy We would also like the car to have good mileage, better engine, faster acceleration (if you want to race), and some more. Here, you want to choose a car with the smallest price, but the highest mileage and acceleration, and so on. This problem can\u2019t be so easily solved by simple sorting. Enter multi-criteria decision-making algorithms! an Egyptian painting of a man dissecting a car to understand it better (Created by DallE) Dataset Let\u2019s choose one dataset so it becomes easier to visualize the result, to understand what\u2019s really happening behind the scenes and finally build intuition. For this, I am picking cars dataset. For each car, we will focus on a subset of attributes and only pick 10 rows (unique cars) to make our life easier. Look at the selected data, 10 rows from the cars dataset Explaining some attributes, mpg : a measure of how far a car can travel if you put just one gallon of petrol or diesel in its tank (mileage). displacement : engine displacement is the measure of the cylinder volume swept by all of the pistons of a piston engine. More displacement means more power. acceleration : a measure of how long it takes the car to reach a speed from 0. Higher the acceleration, better the car for drag racing \ud83c\udfce\ufe0f Here please notice some points, The unit and distribution of the attributes are not the same. Price plays in thousands of $, acceleration in tens of seconds and so on. describing each of the numerical columns (the attributes) of the selected data The logic of best for each attribute vary as well. Here, we want to find a car with high values in mpg, displacement and acceleration. At the same time, low values in weight and price. This notion of high and low can be inferred as maximizing and minimizing the attributes, respectively. There could be an additional requirement where we don\u2019t consider each attribute equal. For example, If I want a car for racing and say I am sponsored by a billionaire, then I won\u2019t care about mpg and price so much. I want the faster and lightest car possible. But what if I am a student (hence most probably on a strict budget) and travel a lot, then suddenly mpg and price become the most important attribute and I don\u2019t give a damn about displacement. These notions of important of attributes can be inferred as weights assigned to each attribute. Say, price is 30% important, while displacement is only 10% and so on. With the requirements clear, let\u2019s try to see how we can solve these kinds of problems. Generic methodology Most of the basic multi-criteria decision solvers have a common methodology which tries to, Consider one attribute at a time and try to maximize or minimize it (as per the requirement) to generate optimized score. Introduce weights to each attributes to get optimized weighted scores. Combine the weighted scores (of each attribute) to create a final score for an entity (here car). After this, we have transformed the requirements into a single numerical attribute (final score), and as done previously we can sort on this to get the best car (this time we sort by descending as we want to pick one with maximum score). Let\u2019s explore each step with examples. Maximize and Minimize Remember the first point from the dataset section, attributes have very different units and distributions, which we need to handle. One possible solution is to normalize each attribute between the same range. And we also want the direction of goodness to be similar (irrespective of the logic). Hence after normalization, values near maximum of range (say 1) should mean that car is good in that attribute and lower values (say near 0) means they are bad. We do this with the following formula, normalization logic for maximizing and minimizing an attribute values Look at the first equation for maximizing, one example is update mpg of each car by dividing it by sum of mpg of all cars (sum normalization). We can modify the logic by just considering the max of mpg or other formulae itself. The intention is, after applying this to each attribute, the range of each attribute will be the same as well as we can infer that value close to 1 means good. The formulae for minimizing is nearly the same as the maximizing one, we just inverse it (1 divided by maximize) or mirror it (by subtracting it from 1) to actually reverse the goodness direction (otherwise 1 will mean bad and 0 will mean good). Let\u2019s see how it looks after in practice, Example for sum normalization heatmap of the original data. Check the \u2018mpg\u2019 value of \u2018ford torino\u2019. Originally its 17 but after sum normalization, it should be 17/156=0.109. Similarly, the \u2018price\u2019 is 20k, after inverse it will be 1/(20k/287872) = 14.4 Apply weights We just need to superimpose the weight over the optimized scores, which can be easily done by multiplying the weights to the optimized score. Here as well we can introduce different types of normalization, as it is : directly multiple the weights to optimized score sum : normalize the weights by sum logic (discussed above) then multiply. max : normalize by max logic, then multiply. weight modification logic Combine the scores Finally, we combine the score to make it one. This can be done by two different logic, sum : add all individual scores together product : multiply all individual scores together. In fact, many implementations add the logarithm of the value instead of taking products, this is done to handle very smaller result when multiplying small values. Implementation Note Update - March 2022: Due to code breaking changes in the latest version of scikit-criteria, it is recommended to use v0.2.11 of the package for the code discussed in the article. Code repository is here. We have a python package named skcriteria that provides many algorithms for multi criteria decision-making problem. Actually two algorithms inside the skcriteria.madm.simple module are, WeightedSum \u2014 individual score combine logic is sum WeightedProduct \u2014 individual score combine logic is product (sum of log) And both of these methods take two parameters as input, mnorm \u2014 define value maximize normalization logic (minimization is always the inverse of the same maximize logic). wnorm \u2014 define weight normalization logic To perform ranking on our data, first, we need to load it as their skcriteria.Data object by, 1 2 3 4 5 6 7 criteria_data = Data ( cars_data . iloc [:, 1 :], # the pandas dataframe [ MAX , MAX , MIN , MAX , MIN ], # direction of goodness for each column anames = cars_data [ 'car_name' ], # each entity's name, here car name cnames = cars_data . columns [ 1 :], # attribute/column name # weights=[1,1,1,1,1] # weights for each attribute (optional) ) ALT./CRIT. mpg (max) displacement (max) weight (min) acceleration (max) price (min) chevrolet chevelle malibu 18 307 3504 12 25561.6 buick skylark 320 15 350 3693 11.5 24221.4 plymouth satellite 18 318 3436 11 27240.8 amc rebel sst 16 304 3433 12 33685 ford torino 17 302 3449 10.5 20000 ford galaxie 500 15 429 4341 10 30000 chevrolet impala 14 454 4354 9 35764.3 plymouth fury iii 14 440 4312 8.5 25899.5 pontiac catalina 14 455 4425 10 32882.5 amc ambassador dpl 15 390 3850 8.5 32617.1 With the data loaded, all we need to do is call the appropriate decision maker function with data object and parameter settings. The output has one additional rank column to show the final ranking by considering all of the mentioned criteria. 1 2 3 4 from skcriteria.madm import simple # weighted sum dm = simple . WeightedSum ( mnorm = \"sum\" ) dec = dm . decide ( criteria_data ) ALT./CRIT. mpg (max) displacement (max) weight (min) acceleration (max) price (min) Rank chevrolet chevelle malibu 18 307 3504 12 25561.6 3 buick skylark 320 15 350 3693 11.5 24221.4 2 plymouth satellite 18 318 3436 11 27240.8 4 amc rebel sst 16 304 3433 12 33685 6 ford torino 17 302 3449 10.5 20000 1 ford galaxie 500 15 429 4341 10 30000 8 chevrolet impala 14 454 4354 9 35764.3 10 plymouth fury iii 14 440 4312 8.5 25899.5 5 pontiac catalina 14 455 4425 10 32882.5 9 amc ambassador dpl 15 390 3850 8.5 32617.1 7 We can even export the final score by dec.e_.points and the ranks by dec.rank_ . Comparison Let\u2019s compare the result of different decision making algorithms (with different parameters) on our dataset. To do so, I use the weightedSum and weightedProduct implementations (once with max and then with sum value normalization). I also implemented a normalize_data function which by default performs minmax and subtract normalization. Then I apply a sum combine on the output. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 # import from skcriteria.madm import simple # make a copy of original dataset cars_data_copy = cars_data . copy () # weighted sum, sumNorm dm = simple . WeightedSum ( mnorm = \"sum\" ) dec = dm . decide ( criteria_data ) cars_data_copy . loc [:, 'rank_weightedSum_sumNorm_inverse' ] = dec . rank_ # weighted sum, maxNorm dm = simple . WeightedSum ( mnorm = \"max\" ) dec = dm . decide ( criteria_data ) cars_data_copy . loc [:, 'rank_weightedSum_maxNorm_inverse' ] = dec . rank_ # weighted product, sumNorm dm = simple . WeightedProduct ( mnorm = \"sum\" ) dec = dm . decide ( criteria_data ) cars_data_copy . loc [:, 'rank_weightedProduct_sumNorm_inverse' ] = dec . rank_ # weighted product, sumNorm dm = simple . WeightedProduct ( mnorm = \"max\" ) dec = dm . decide ( criteria_data ) cars_data_copy . loc [:, 'rank_weightedProduct_maxNorm_inverse' ] = dec . rank_ # min max scale + mirror cars_data_copy . loc [:, 'rank_weightedSum_minmaxScale_subtract' ] = \\ pd . Series ( normalize_data () . sum ( axis = 1 )) . rank ( ascending = False ) . astype ( int ) Finally, I plot a parallel coordinate graphs, where each axis(vertical line) denotes one solver type and the values denote the rank of a car by that solver. Each line is for one car and going from left to right, it shows the journey \u2014 how the rank of a car changes as you switch among different solvers. Journey of a car as we switch decision solver Some points to consider, Ford Torino is rank 1 (car with the highest score) for 4/5 solvers. Minmax favors Chevrolet Malibu. Impala is the universal low ranker :( Both implementations of weightedProduct is giving the same ranking to all cars. Nothing interesting here. High variance in the rankings of both the weightedSum implementations. MinMax gives the most diverse rankings for top 4 guys. The main reason behind the variance of result when changing the normalization (from sum to max) is due to the translation done on the original data. This translation changes the range of data (like scales everything between x and y ) and in case of inverse modifies the linearity as well (say, equal steps of 1 in original data is not consistent in transformed data). This will become more clear by following result, 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 import numpy as np x = np . array ( range ( 1 , 10 )) print ( \"X: \" , x ) print ( \"MinMax: \" , minmax_scale ( x )) print ( \"MinMax_subtract: \" , 1 - minmax_scale ( x )) print ( \"sumNorm: \" , x / sum ( x )) print ( \"sumNorm_inverse: \" , ( 1 / ( x / sum ( x )))) print ( \"maxNorm: \" , x / max ( x )) print ( \"maxNorm_inverse: \" , 1 / ( x / max ( x ))) ## Output X : [ 1 2 3 4 5 6 7 8 9 ] MinMax : [ 0. 0.125 0.25 0.375 0.5 0.625 0.75 0.875 1. ] MinMax_subtract : [ 1. 0.875 0.75 0.625 0.5 0.375 0.25 0.125 0. ] sumNorm : [ 0.02222222 0.04444444 0.06666667 0.08888889 0.11111111 0.13333333 0.15555556 0.17777778 0.2 ] sumNorm_inverse : [ 45. 22.5 15. 11.25 9. 7.5 6.42857143 5.625 5. ] maxNorm : [ 0.11111111 0.22222222 0.33333333 0.44444444 0.55555556 0.66666667 0.77777778 0.88888889 1. ] maxNorm_inverse : [ 9. 4.5 3. 2.25 1.8 1.5 1.28571429 1.125 1. ] Here, input data consist numbers 1 to 9 (notice, the difference between any two consecutive number is 1 i.e. step is same). Approach one (minmax) translates the data between 0 and 1 and the step is still the same. Now look at minimization logic ( _inverse ) of approach 2 and 3. Here at the start (low original values) the step is nearly the half of the last element, but near the end (high original value) the step is very small, even though in the original data we are moving with same step of 1. Because of this, in case of minimization, a very high score is given to \u201cgood\u201d cars (with low values) and even a small impurity matter (when minimized, high value = low score) and results in a drastic decrease in score. It\u2019s like we are being very picky, either you are the best or you get half the score :) On the other hand, for higher values, small impurities doesn\u2019t matter. If the car is already bad by that attribute, then we don\u2019t care if its value is 7 or 8 or 9 and the reduction in the score is much less! We can use this understanding to pick the right decision solver with the right parameter as per our need. Conclusion This article has just touched the surface of the multi-criteria decision making domain. Even in skcriteria package there are many more algorithms like TOPSIS and MOORA which have quite a different intuition to solve these problems. But even then the notion of goodness and the idea to handle individual attributes to finally combine them all together is used in many of them. So maybe we will explore more algorithms in another article. But the major takeaways from this article should be to understand the why and what of decision makers. That each such decision can be manipulated by multiple criteria. And also that we may have a different notion of goodness and importance assigned to each criterion. Finally, we have different varieties of solvers that can be build by taking permutation of logic and parameters, and nearly all of them give different and interesting results based on our need! Cheers","title":"Ranking algorithms"},{"location":"machine_learning/ranking_algorithms/#introduction","text":"Suppose you have a decision to make \u2014 like buying a house, or a car, or even a guitar. You don\u2019t want to choose randomly or get biased by someone\u2019s suggestion, but want to make an educated decision. For this, you gathered some information about the entity you want to buy (let\u2019s say it\u2019s a car). So you have a list of N cars with their price information. As usual, we won\u2019t want to spend more, we can just sort the cars by their price (in ascending order) and pick the top one (with the smallest price), and we are done! This was decision making with a single criterion. But alas if life is so easy We would also like the car to have good mileage, better engine, faster acceleration (if you want to race), and some more. Here, you want to choose a car with the smallest price, but the highest mileage and acceleration, and so on. This problem can\u2019t be so easily solved by simple sorting. Enter multi-criteria decision-making algorithms! an Egyptian painting of a man dissecting a car to understand it better (Created by DallE)","title":"Introduction"},{"location":"machine_learning/ranking_algorithms/#dataset","text":"Let\u2019s choose one dataset so it becomes easier to visualize the result, to understand what\u2019s really happening behind the scenes and finally build intuition. For this, I am picking cars dataset. For each car, we will focus on a subset of attributes and only pick 10 rows (unique cars) to make our life easier. Look at the selected data, 10 rows from the cars dataset Explaining some attributes, mpg : a measure of how far a car can travel if you put just one gallon of petrol or diesel in its tank (mileage). displacement : engine displacement is the measure of the cylinder volume swept by all of the pistons of a piston engine. More displacement means more power. acceleration : a measure of how long it takes the car to reach a speed from 0. Higher the acceleration, better the car for drag racing \ud83c\udfce\ufe0f Here please notice some points, The unit and distribution of the attributes are not the same. Price plays in thousands of $, acceleration in tens of seconds and so on. describing each of the numerical columns (the attributes) of the selected data The logic of best for each attribute vary as well. Here, we want to find a car with high values in mpg, displacement and acceleration. At the same time, low values in weight and price. This notion of high and low can be inferred as maximizing and minimizing the attributes, respectively. There could be an additional requirement where we don\u2019t consider each attribute equal. For example, If I want a car for racing and say I am sponsored by a billionaire, then I won\u2019t care about mpg and price so much. I want the faster and lightest car possible. But what if I am a student (hence most probably on a strict budget) and travel a lot, then suddenly mpg and price become the most important attribute and I don\u2019t give a damn about displacement. These notions of important of attributes can be inferred as weights assigned to each attribute. Say, price is 30% important, while displacement is only 10% and so on. With the requirements clear, let\u2019s try to see how we can solve these kinds of problems.","title":"Dataset"},{"location":"machine_learning/ranking_algorithms/#generic-methodology","text":"Most of the basic multi-criteria decision solvers have a common methodology which tries to, Consider one attribute at a time and try to maximize or minimize it (as per the requirement) to generate optimized score. Introduce weights to each attributes to get optimized weighted scores. Combine the weighted scores (of each attribute) to create a final score for an entity (here car). After this, we have transformed the requirements into a single numerical attribute (final score), and as done previously we can sort on this to get the best car (this time we sort by descending as we want to pick one with maximum score). Let\u2019s explore each step with examples.","title":"Generic methodology"},{"location":"machine_learning/ranking_algorithms/#maximize-and-minimize","text":"Remember the first point from the dataset section, attributes have very different units and distributions, which we need to handle. One possible solution is to normalize each attribute between the same range. And we also want the direction of goodness to be similar (irrespective of the logic). Hence after normalization, values near maximum of range (say 1) should mean that car is good in that attribute and lower values (say near 0) means they are bad. We do this with the following formula, normalization logic for maximizing and minimizing an attribute values Look at the first equation for maximizing, one example is update mpg of each car by dividing it by sum of mpg of all cars (sum normalization). We can modify the logic by just considering the max of mpg or other formulae itself. The intention is, after applying this to each attribute, the range of each attribute will be the same as well as we can infer that value close to 1 means good. The formulae for minimizing is nearly the same as the maximizing one, we just inverse it (1 divided by maximize) or mirror it (by subtracting it from 1) to actually reverse the goodness direction (otherwise 1 will mean bad and 0 will mean good). Let\u2019s see how it looks after in practice, Example for sum normalization heatmap of the original data. Check the \u2018mpg\u2019 value of \u2018ford torino\u2019. Originally its 17 but after sum normalization, it should be 17/156=0.109. Similarly, the \u2018price\u2019 is 20k, after inverse it will be 1/(20k/287872) = 14.4","title":"Maximize and Minimize"},{"location":"machine_learning/ranking_algorithms/#apply-weights","text":"We just need to superimpose the weight over the optimized scores, which can be easily done by multiplying the weights to the optimized score. Here as well we can introduce different types of normalization, as it is : directly multiple the weights to optimized score sum : normalize the weights by sum logic (discussed above) then multiply. max : normalize by max logic, then multiply. weight modification logic","title":"Apply weights"},{"location":"machine_learning/ranking_algorithms/#combine-the-scores","text":"Finally, we combine the score to make it one. This can be done by two different logic, sum : add all individual scores together product : multiply all individual scores together. In fact, many implementations add the logarithm of the value instead of taking products, this is done to handle very smaller result when multiplying small values.","title":"Combine the scores"},{"location":"machine_learning/ranking_algorithms/#implementation","text":"Note Update - March 2022: Due to code breaking changes in the latest version of scikit-criteria, it is recommended to use v0.2.11 of the package for the code discussed in the article. Code repository is here. We have a python package named skcriteria that provides many algorithms for multi criteria decision-making problem. Actually two algorithms inside the skcriteria.madm.simple module are, WeightedSum \u2014 individual score combine logic is sum WeightedProduct \u2014 individual score combine logic is product (sum of log) And both of these methods take two parameters as input, mnorm \u2014 define value maximize normalization logic (minimization is always the inverse of the same maximize logic). wnorm \u2014 define weight normalization logic To perform ranking on our data, first, we need to load it as their skcriteria.Data object by, 1 2 3 4 5 6 7 criteria_data = Data ( cars_data . iloc [:, 1 :], # the pandas dataframe [ MAX , MAX , MIN , MAX , MIN ], # direction of goodness for each column anames = cars_data [ 'car_name' ], # each entity's name, here car name cnames = cars_data . columns [ 1 :], # attribute/column name # weights=[1,1,1,1,1] # weights for each attribute (optional) ) ALT./CRIT. mpg (max) displacement (max) weight (min) acceleration (max) price (min) chevrolet chevelle malibu 18 307 3504 12 25561.6 buick skylark 320 15 350 3693 11.5 24221.4 plymouth satellite 18 318 3436 11 27240.8 amc rebel sst 16 304 3433 12 33685 ford torino 17 302 3449 10.5 20000 ford galaxie 500 15 429 4341 10 30000 chevrolet impala 14 454 4354 9 35764.3 plymouth fury iii 14 440 4312 8.5 25899.5 pontiac catalina 14 455 4425 10 32882.5 amc ambassador dpl 15 390 3850 8.5 32617.1 With the data loaded, all we need to do is call the appropriate decision maker function with data object and parameter settings. The output has one additional rank column to show the final ranking by considering all of the mentioned criteria. 1 2 3 4 from skcriteria.madm import simple # weighted sum dm = simple . WeightedSum ( mnorm = \"sum\" ) dec = dm . decide ( criteria_data ) ALT./CRIT. mpg (max) displacement (max) weight (min) acceleration (max) price (min) Rank chevrolet chevelle malibu 18 307 3504 12 25561.6 3 buick skylark 320 15 350 3693 11.5 24221.4 2 plymouth satellite 18 318 3436 11 27240.8 4 amc rebel sst 16 304 3433 12 33685 6 ford torino 17 302 3449 10.5 20000 1 ford galaxie 500 15 429 4341 10 30000 8 chevrolet impala 14 454 4354 9 35764.3 10 plymouth fury iii 14 440 4312 8.5 25899.5 5 pontiac catalina 14 455 4425 10 32882.5 9 amc ambassador dpl 15 390 3850 8.5 32617.1 7 We can even export the final score by dec.e_.points and the ranks by dec.rank_ .","title":"Implementation"},{"location":"machine_learning/ranking_algorithms/#comparison","text":"Let\u2019s compare the result of different decision making algorithms (with different parameters) on our dataset. To do so, I use the weightedSum and weightedProduct implementations (once with max and then with sum value normalization). I also implemented a normalize_data function which by default performs minmax and subtract normalization. Then I apply a sum combine on the output. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 # import from skcriteria.madm import simple # make a copy of original dataset cars_data_copy = cars_data . copy () # weighted sum, sumNorm dm = simple . WeightedSum ( mnorm = \"sum\" ) dec = dm . decide ( criteria_data ) cars_data_copy . loc [:, 'rank_weightedSum_sumNorm_inverse' ] = dec . rank_ # weighted sum, maxNorm dm = simple . WeightedSum ( mnorm = \"max\" ) dec = dm . decide ( criteria_data ) cars_data_copy . loc [:, 'rank_weightedSum_maxNorm_inverse' ] = dec . rank_ # weighted product, sumNorm dm = simple . WeightedProduct ( mnorm = \"sum\" ) dec = dm . decide ( criteria_data ) cars_data_copy . loc [:, 'rank_weightedProduct_sumNorm_inverse' ] = dec . rank_ # weighted product, sumNorm dm = simple . WeightedProduct ( mnorm = \"max\" ) dec = dm . decide ( criteria_data ) cars_data_copy . loc [:, 'rank_weightedProduct_maxNorm_inverse' ] = dec . rank_ # min max scale + mirror cars_data_copy . loc [:, 'rank_weightedSum_minmaxScale_subtract' ] = \\ pd . Series ( normalize_data () . sum ( axis = 1 )) . rank ( ascending = False ) . astype ( int ) Finally, I plot a parallel coordinate graphs, where each axis(vertical line) denotes one solver type and the values denote the rank of a car by that solver. Each line is for one car and going from left to right, it shows the journey \u2014 how the rank of a car changes as you switch among different solvers. Journey of a car as we switch decision solver Some points to consider, Ford Torino is rank 1 (car with the highest score) for 4/5 solvers. Minmax favors Chevrolet Malibu. Impala is the universal low ranker :( Both implementations of weightedProduct is giving the same ranking to all cars. Nothing interesting here. High variance in the rankings of both the weightedSum implementations. MinMax gives the most diverse rankings for top 4 guys. The main reason behind the variance of result when changing the normalization (from sum to max) is due to the translation done on the original data. This translation changes the range of data (like scales everything between x and y ) and in case of inverse modifies the linearity as well (say, equal steps of 1 in original data is not consistent in transformed data). This will become more clear by following result, 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 import numpy as np x = np . array ( range ( 1 , 10 )) print ( \"X: \" , x ) print ( \"MinMax: \" , minmax_scale ( x )) print ( \"MinMax_subtract: \" , 1 - minmax_scale ( x )) print ( \"sumNorm: \" , x / sum ( x )) print ( \"sumNorm_inverse: \" , ( 1 / ( x / sum ( x )))) print ( \"maxNorm: \" , x / max ( x )) print ( \"maxNorm_inverse: \" , 1 / ( x / max ( x ))) ## Output X : [ 1 2 3 4 5 6 7 8 9 ] MinMax : [ 0. 0.125 0.25 0.375 0.5 0.625 0.75 0.875 1. ] MinMax_subtract : [ 1. 0.875 0.75 0.625 0.5 0.375 0.25 0.125 0. ] sumNorm : [ 0.02222222 0.04444444 0.06666667 0.08888889 0.11111111 0.13333333 0.15555556 0.17777778 0.2 ] sumNorm_inverse : [ 45. 22.5 15. 11.25 9. 7.5 6.42857143 5.625 5. ] maxNorm : [ 0.11111111 0.22222222 0.33333333 0.44444444 0.55555556 0.66666667 0.77777778 0.88888889 1. ] maxNorm_inverse : [ 9. 4.5 3. 2.25 1.8 1.5 1.28571429 1.125 1. ] Here, input data consist numbers 1 to 9 (notice, the difference between any two consecutive number is 1 i.e. step is same). Approach one (minmax) translates the data between 0 and 1 and the step is still the same. Now look at minimization logic ( _inverse ) of approach 2 and 3. Here at the start (low original values) the step is nearly the half of the last element, but near the end (high original value) the step is very small, even though in the original data we are moving with same step of 1. Because of this, in case of minimization, a very high score is given to \u201cgood\u201d cars (with low values) and even a small impurity matter (when minimized, high value = low score) and results in a drastic decrease in score. It\u2019s like we are being very picky, either you are the best or you get half the score :) On the other hand, for higher values, small impurities doesn\u2019t matter. If the car is already bad by that attribute, then we don\u2019t care if its value is 7 or 8 or 9 and the reduction in the score is much less! We can use this understanding to pick the right decision solver with the right parameter as per our need.","title":"Comparison"},{"location":"machine_learning/ranking_algorithms/#conclusion","text":"This article has just touched the surface of the multi-criteria decision making domain. Even in skcriteria package there are many more algorithms like TOPSIS and MOORA which have quite a different intuition to solve these problems. But even then the notion of goodness and the idea to handle individual attributes to finally combine them all together is used in many of them. So maybe we will explore more algorithms in another article. But the major takeaways from this article should be to understand the why and what of decision makers. That each such decision can be manipulated by multiple criteria. And also that we may have a different notion of goodness and importance assigned to each criterion. Finally, we have different varieties of solvers that can be build by taking permutation of logic and parameters, and nearly all of them give different and interesting results based on our need! Cheers","title":"Conclusion"},{"location":"natural_language_processing/BERT/","text":"BERT Introduction BERT stands for B idirectional E ncoder R epresentations from T ransformers. ( devlin2019bert ) Basically, it is a modification of Transformers ( vaswani2017attention ), where we just keep the encoder part and discard the decoder part. Transformer architecture. BERT is the left part i.e. encoder part. ( vaswani2017attention ) At the time of release, it obtained state-of-the-art results on eleven natural language processing tasks. To quote the paper, \" [paper pushed] the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement). \" The major motivation behind BERT is to handle the limitation of the existing language models which are unidirectional in nature. This means that they only consider text left to right for sentence level inference. BERT on the other hand, allows tokens to attend to both sides in self-attention layer. This is one of the major reason for it high performance. Differences in pre-training model architectures. BERT uses a bidirectional Transformer. OpenAI GPT uses a left-to-right Transformer. ELMo uses the concatenation of independently trained left-to-right and right-toleft LSTMs to generate features for downstream tasks. Among the three, only BERT representations are jointly conditioned on both left and right context in all layers. In addition to the architecture differences, BERT and OpenAI GPT are fine-tuning approaches, while ELMo is a feature-based approach.. ( devlin2019bert ) The most fascinating feature of BERT is that it is super easy to use it for a large number of NLP tasks. The idea is to take the pretrained BERT model and later fine tune it for the specific task. The pre-trained model is trained on a large corpus in a unsupervised manner, hence the model learns the generic representations of the tokens from large corpus of text. This makes it easy to later fine tune it for any other NLP task, as the model comes pretrained with large context about the language, grammar and semantic representations. Illustrations of Fine-tuning BERT on Different Tasks. ( devlin2019bert ) Training BERT is an interesting paradigm in itself. The original paper proposed two unsupervised methods for training, Masked LM (MLM) : Where some percentage (15%) of the input tokens are masked at random, and then the model tries to predict those masked tokens. They created a special token [MASK] for this purpose. Next Sentence Prediction (NSP) : Where two sentences A and B are chosen such that, 50% of the time B is the actual next sentence that follows A (labelled as IsNext ), and 50% of the time it is a random sentence from the corpus (labelled as NotNext ). The model is trained to predict if the second sentences follow the first or not. Analysis BERT output and finetuning (unsupervised) An analysis on the selection of suitable BERT output and the advantage of fine-tuning (unsupervised learning on unlabeled data on tasks like MLM) the model was done. The report provides following performance table comparing different experiments. Complete article here . Exp no Model name F1 macro score Accuracy 1 Pooler output 64.6% 68.4% 2 Last hidden state 86.7% 87.5% 3 Fine-tuned and Pooler output 87.5% 88.1% 4 Fine-tuned and last hidden state 79.8% 81.3% It also answers following questions, Should I only use CLS token or all token's output for sentence representation? Well, it depends. From the experiments, it seems if you are fine-tuning the model, using the pooler output will be better. But if there is no fine-tuning, the last hidden state output is much better. Personally, I will prefer the last hidden state output, as it provides comparative result without any additional compute expensive fine-tuning. Will fine-tuning the model beforehand increase the accuracy? A definite yes! Exp 3 and 4 reports higher score than Exp 1 and 2. So if you have the time and resource (which ironically is not usually the case), go for fine-tuning! Is BERT a Text Generation model? Short answer is no. BERT is not a text generation model or a language model because the probability of the predicting a token in masked input is dependent on the context of the token. This context is bidirectional, hence the model is not able to predict the next token in the sequence accurately with only one directional context (as expected for language model) . Several analysis were done on the text generation prowess of BERT model. One such analysis is presented in this paper . Here the authors presents BERT as markov random field language model. Then after some errors were pointed out wrt paper, the authors corrected the claim and suggested BERT is a non-equilibrium language model ( here ) Tip Do you know that during mask prediction, BERT model predicts some tokens for [PAD] tokens as well. This is true for sentences that are smaller than the max length of the model and hence require padding. In a sense, this is kind of text generation, where you just provide the sentence and the model predicts the next token till the max length. But as expected the prediction is not that accurate. BERT for sentence representation? One question usually asked is that - \"Can we use BERT to generate meaningful sentence representations?\" The answer is \"No\". Don't get me wrong, while it is possible to use BERT to generate sentence representations, but the key word here is \"meaningful\". One of the way to do this is to pass one sentence to the model and get the representation for fixed [CLS] token as sentence representation. But as shown in [2], this common practice yields bad sentence embedding, often even worse than Glove embeddings (which was introduced in 2014) ! The major problem here is the pre-training strategy used to train BERT. While it is good for downstream tasks like classification, it's not that good for generating generic representations. This is because for correct sentence representation, we want the embeddings of similar sentences closer to each other and dissimilar sentences to be further apart. And this is not what happens during BERT pretraining. To cater to this issue, we will have to further finetune the model. And in fact, this is where BERT shines again, as with minimal training (sometimes even for 20 mins with <1000 samples) we can expect good results. One of the ways to finetune for sentence represenration is to use triplet loss. For this, we prepare a dataset with a combination of (anchor, positive, negative) sentences. Here anchor is the base sentence, positive is the sentence that is similar to the anchor sentence, and negative is the sentence that is dissimilar to the anchor sentence. The model is trained to \"bring\" the representation of (anchor, positive) closer and (anchor, negative) apart. The loss is defined below, where \\(s_*\\) is the respective sentence representation and \\(\\epsilon\\) is the margin. \\[triplet loss = max( \\parallel s_a - s_p \\parallel - \\parallel s_a - s_n \\parallel + \\epsilon , 0)\\] Code Pretrained BERT for Sentiment Classification The code contains the Dataset and Dataloader as well, which can be referred for any fine tuning task. Download dataset from IMDB 50k review 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 # helper import pandas as pd import numpy as np from tqdm import tqdm from sklearn.model_selection import train_test_split # for BERT model from transformers import BertTokenizer , BertModel from transformers import BertForSequenceClassification # for DL stuff import torch import pytorch_lightning as pl from torch.nn import Softmax , Linear from torchmetrics import Accuracy , F1 from torch.utils.data import Dataset , DataLoader from pytorch_lightning.loggers import WandbLogger model_name = 'bert-base-uncased' # load dataset and sample 10k reviews. df = pd . read_csv ( \"../input/imdb-dataset-of-50k-movie-reviews/IMDB Dataset.csv\" ) df = df . sample ( 10000 , random_state = 1 ) # divide into test and train X_train , X_test , y_train , y_test = \\ train_test_split ( df [ 'review' ] . tolist (), df [ 'sentiment' ] . tolist (), shuffle = True , test_size = 0.33 , random_state = 1 , stratify = df [ 'sentiment' ]) # define dataset with load and prep functions. Pass all the data at a time. def squz ( x , dim = 0 ): return torch . squeeze ( x , dim ) class IMDBDataset ( Dataset ): def __init__ ( self , sentences , labels , max_length = 512 , model_name = 'bert-base-uncased' ): # var self . sentences = sentences self . labels = [[ 'positive' , 'negative' ] . index ( x ) for x in labels ] self . max_length = max_length # tokenizer self . tokenizer = BertTokenizer . from_pretrained ( model_name ) def __len__ ( self ): return len ( self . sentences ) def __getitem__ ( self , index ): # Select sample sentence = self . sentences [ index ] label = self . labels [ index ] # Load data and get label X = self . tokenizer ( sentence , padding = \"max_length\" , truncation = True , max_length = self . max_length , return_tensors = \"pt\" ) X = { key : squz ( value ) for key , value in X . items ()} y = label # return return X , y # init the train and test dataset train_dataset = IMDBDataset ( X_train , y_train ) test_dataset = IMDBDataset ( X_test , y_test ) # create the dataloader train_dataloader = DataLoader ( train_dataset , batch_size = 16 , shuffle = True ) test_dataloader = DataLoader ( test_dataset , batch_size = 16 , shuffle = True ) # define BERT model class BERT_pooler_output ( pl . LightningModule ): def __init__ ( self , model_name = 'bert-base-uncased' ): super () . __init__ () # model and layers self . BERTModel = BertModel . from_pretrained ( model_name ) self . linear1 = Linear ( 768 , 128 ) self . linear2 = Linear ( 128 , 2 ) self . softmax = Softmax ( dim = 1 ) self . relu = torch . nn . ReLU () # loss self . criterion = torch . nn . CrossEntropyLoss () # performance self . accuracy = Accuracy () self . f1 = F1 ( num_classes = 2 , average = 'macro' ) def forward ( self , x ): # pass input to BERTmodel input_ids , attention_mask = x [ 'input_ids' ], x [ 'attention_mask' ] bert_output = self . BERTModel ( input_ids , attention_mask = attention_mask ) output = bert_output . pooler_output output = self . relu ( self . linear1 ( output )) output = self . linear2 ( output ) return output def training_step ( self , batch , batch_idx ): x , y = batch x_hat = self . forward ( x ) loss = self . criterion ( x_hat , y ) acc = self . accuracy ( x_hat . argmax ( dim = 1 ), y ) f1 = self . f1 ( x_hat . argmax ( dim = 1 ), y ) self . log ( 'loss' , loss , on_step = False , on_epoch = True , prog_bar = True , logger = True ) self . log ( 'acc' , acc , on_step = False , on_epoch = True , prog_bar = True , logger = True ) self . log ( 'f1' , f1 , on_step = False , on_epoch = True , prog_bar = True , logger = True ) return { 'loss' : loss } def validation_step ( self , batch , batch_idx ): x , y = batch x_hat = self . forward ( x ) acc = self . accuracy ( x_hat . argmax ( dim = 1 ), y ) f1 = self . f1 ( x_hat . argmax ( dim = 1 ), y ) self . log ( 'val_acc' , acc , on_step = False , on_epoch = True , prog_bar = True , logger = True ) self . log ( 'val_f1' , f1 , on_step = False , on_epoch = True , prog_bar = True , logger = True ) def configure_optimizers ( self ): # freezing the params of BERT model for name , param in self . named_parameters (): if 'BERTModel' in name : param . requires_grad = False # define the optimizer optimizer = torch . optim . Adam ( filter ( lambda p : p . requires_grad , self . parameters ()), lr = 1e-3 ) return optimizer # init model model = BERT_pooler_output () # init trainer trainer = pl . Trainer ( gpus = 1 , max_epochs = 3 ) # train the model trainer . fit ( model , train_dataloader , test_dataloader ) Fine tuning the BERT model Fine tuning could include training BERT on one or many of the proposed unsupervised tasks. Here, we will train the BERT on MLM (Masked language modeling) task. This includes masking some tokens of input and BERT predicting the token based on the context tokens. Referenced from this video of James Briggs . 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 # IMPORT ========= import pandas as pd from tqdm import tqdm import numpy as np import numpy as np # for deep learning import torch import torch.nn as nn import torch.optim as optim # load BERT model from transformers import AdamW from transformers import BertTokenizer , BertForMaskedLM # MODEL LOAD ========= model_path = \"bert-base-uncased\" # if local copy is not present tokenizer = BertTokenizer . from_pretrained ( model_path ) model = BertForMaskedLM . from_pretrained ( model_path ) # DATA PREP 1 ========= df = pd . read_csv ( \"file_with_text.csv\" ) # tokenize inputs = tokenizer ( df [ 'review' ] . tolist (), return_tensors = 'pt' , max_length = 512 , truncation = True , padding = 'max_length' ) inputs [ 'labels' ] = inputs . input_ids . detach () . clone () # create random array of floats with equal dimensions to input_ids tensor rand = torch . rand ( inputs . input_ids . shape ) # create mask array - mask tokens except special tokens like CLS and SEP mask_ratio = 0.3 mask_arr = ( rand < mask_ratio ) * ( inputs . input_ids != 101 ) * \\ ( inputs . input_ids != 102 ) * ( inputs . input_ids != 0 ) # get the indices where to add mask selection = [] for i in range ( inputs . input_ids . shape [ 0 ]): selection . append ( torch . flatten ( mask_arr [ i ] . nonzero ()) . tolist () ) # add the mask for i in range ( inputs . input_ids . shape [ 0 ]): inputs . input_ids [ i , selection [ i ]] = 103 # DATA PREP 2 - DATALOADER ========= # define dataset class class IMDBDataset ( torch . utils . data . Dataset ): def __init__ ( self , encodings ): self . encodings = encodings def __getitem__ ( self , idx ): return { key : torch . tensor ( val [ idx ]) for key , val in self . encodings . items ()} def __len__ ( self ): return len ( self . encodings . input_ids ) # create instance dataset = IMDBDataset ( inputs ) loader = torch . utils . data . DataLoader ( dataset , batch_size = 8 , shuffle = True ) # PRE_TRAIN ============= device = torch . device ( 'cuda' ) if torch . cuda . is_available () else torch . device ( 'cpu' ) # and move our model over to the selected device model . to ( device ) # activate training mode model . train () # initialize optimizer optimizer = AdamW ( model . parameters (), lr = 5e-5 ) # TRAIN ===================== epochs = 20 for epoch in range ( epochs ): # setup loop with TQDM and dataloader loop = tqdm ( loader , leave = True ) for batch in loop : # initialize calculated gradients (from prev step) optimizer . zero_grad () # pull all tensor batches required for training input_ids = batch [ 'input_ids' ] . to ( device ) attention_mask = batch [ 'attention_mask' ] . to ( device ) labels = batch [ 'labels' ] . to ( device ) # process outputs = model ( input_ids , attention_mask = attention_mask , labels = labels ) # extract loss loss = outputs . loss # calculate loss for every parameter that needs grad update loss . backward () # update parameters optimizer . step () # print relevant info to progress bar loop . set_description ( f 'Epoch { epoch } ' ) loop . set_postfix ( loss = loss . item ()) # SAVE MODEL ===================== model . save_pretrained ( \"bert_finetuned_on_text/\" ) tokenizer . save_pretrained ( \"bert_finetuned_on_text/\" ) BERT output for sentence level inference BERT provides pooler_output and last_hidden_state as two potential \" representations \" for sentence level inference. pooler_output is the embedding of the [CLS] special token. In many cases it is considered as a valid representation of the complete sentence. 1 2 3 BERTModel = BertModel . from_pretrained ( 'bert-base-uncased' ) bert_output = BERTModel ( input_ids , attention_mask = attention_mask ) output = bert_output . pooler_output last_hidden_state contains the embeddings of all tokens in the sentence from the last hidden state. We can apply permutation invariant methods (like max, mean or sum) to aggregate the embeddings into a single sentence representation. 1 2 3 BERTModel = BertModel . from_pretrained ( 'bert-base-uncased' ) bert_output = BERTModel ( input_ids , attention_mask = attention_mask ) output = squeeze ( torch . matmul ( attention_mask . type ( torch . float32 ) . view ( - 1 , 1 , 512 ), bert_output [ 'last_hidden_state' ]), 1 ) Tip Consider finetuning the BERT model (triplet loss) further to generate meaningful sentence representation, as pretrained BERT model is even worse than Glove embeddings [2]. For more details look at this analysis or use S-BERT package to finetune. References [1] Jay Alammar's blog \"The Illustrated BERT, ELMo, and co. (How NLP Cracked Transfer Learning)\" [2] Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks","title":"BERT"},{"location":"natural_language_processing/BERT/#bert","text":"","title":"BERT"},{"location":"natural_language_processing/BERT/#introduction","text":"BERT stands for B idirectional E ncoder R epresentations from T ransformers. ( devlin2019bert ) Basically, it is a modification of Transformers ( vaswani2017attention ), where we just keep the encoder part and discard the decoder part. Transformer architecture. BERT is the left part i.e. encoder part. ( vaswani2017attention ) At the time of release, it obtained state-of-the-art results on eleven natural language processing tasks. To quote the paper, \" [paper pushed] the GLUE score to 80.5% (7.7% point absolute improvement), MultiNLI accuracy to 86.7% (4.6% absolute improvement), SQuAD v1.1 question answering Test F1 to 93.2 (1.5 point absolute improvement) and SQuAD v2.0 Test F1 to 83.1 (5.1 point absolute improvement). \" The major motivation behind BERT is to handle the limitation of the existing language models which are unidirectional in nature. This means that they only consider text left to right for sentence level inference. BERT on the other hand, allows tokens to attend to both sides in self-attention layer. This is one of the major reason for it high performance. Differences in pre-training model architectures. BERT uses a bidirectional Transformer. OpenAI GPT uses a left-to-right Transformer. ELMo uses the concatenation of independently trained left-to-right and right-toleft LSTMs to generate features for downstream tasks. Among the three, only BERT representations are jointly conditioned on both left and right context in all layers. In addition to the architecture differences, BERT and OpenAI GPT are fine-tuning approaches, while ELMo is a feature-based approach.. ( devlin2019bert ) The most fascinating feature of BERT is that it is super easy to use it for a large number of NLP tasks. The idea is to take the pretrained BERT model and later fine tune it for the specific task. The pre-trained model is trained on a large corpus in a unsupervised manner, hence the model learns the generic representations of the tokens from large corpus of text. This makes it easy to later fine tune it for any other NLP task, as the model comes pretrained with large context about the language, grammar and semantic representations. Illustrations of Fine-tuning BERT on Different Tasks. ( devlin2019bert ) Training BERT is an interesting paradigm in itself. The original paper proposed two unsupervised methods for training, Masked LM (MLM) : Where some percentage (15%) of the input tokens are masked at random, and then the model tries to predict those masked tokens. They created a special token [MASK] for this purpose. Next Sentence Prediction (NSP) : Where two sentences A and B are chosen such that, 50% of the time B is the actual next sentence that follows A (labelled as IsNext ), and 50% of the time it is a random sentence from the corpus (labelled as NotNext ). The model is trained to predict if the second sentences follow the first or not.","title":"Introduction"},{"location":"natural_language_processing/BERT/#analysis","text":"","title":"Analysis"},{"location":"natural_language_processing/BERT/#bert-output-and-finetuning-unsupervised","text":"An analysis on the selection of suitable BERT output and the advantage of fine-tuning (unsupervised learning on unlabeled data on tasks like MLM) the model was done. The report provides following performance table comparing different experiments. Complete article here . Exp no Model name F1 macro score Accuracy 1 Pooler output 64.6% 68.4% 2 Last hidden state 86.7% 87.5% 3 Fine-tuned and Pooler output 87.5% 88.1% 4 Fine-tuned and last hidden state 79.8% 81.3% It also answers following questions, Should I only use CLS token or all token's output for sentence representation? Well, it depends. From the experiments, it seems if you are fine-tuning the model, using the pooler output will be better. But if there is no fine-tuning, the last hidden state output is much better. Personally, I will prefer the last hidden state output, as it provides comparative result without any additional compute expensive fine-tuning. Will fine-tuning the model beforehand increase the accuracy? A definite yes! Exp 3 and 4 reports higher score than Exp 1 and 2. So if you have the time and resource (which ironically is not usually the case), go for fine-tuning!","title":"BERT output and finetuning (unsupervised)"},{"location":"natural_language_processing/BERT/#is-bert-a-text-generation-model","text":"Short answer is no. BERT is not a text generation model or a language model because the probability of the predicting a token in masked input is dependent on the context of the token. This context is bidirectional, hence the model is not able to predict the next token in the sequence accurately with only one directional context (as expected for language model) . Several analysis were done on the text generation prowess of BERT model. One such analysis is presented in this paper . Here the authors presents BERT as markov random field language model. Then after some errors were pointed out wrt paper, the authors corrected the claim and suggested BERT is a non-equilibrium language model ( here ) Tip Do you know that during mask prediction, BERT model predicts some tokens for [PAD] tokens as well. This is true for sentences that are smaller than the max length of the model and hence require padding. In a sense, this is kind of text generation, where you just provide the sentence and the model predicts the next token till the max length. But as expected the prediction is not that accurate.","title":"Is BERT a Text Generation model?"},{"location":"natural_language_processing/BERT/#bert-for-sentence-representation","text":"One question usually asked is that - \"Can we use BERT to generate meaningful sentence representations?\" The answer is \"No\". Don't get me wrong, while it is possible to use BERT to generate sentence representations, but the key word here is \"meaningful\". One of the way to do this is to pass one sentence to the model and get the representation for fixed [CLS] token as sentence representation. But as shown in [2], this common practice yields bad sentence embedding, often even worse than Glove embeddings (which was introduced in 2014) ! The major problem here is the pre-training strategy used to train BERT. While it is good for downstream tasks like classification, it's not that good for generating generic representations. This is because for correct sentence representation, we want the embeddings of similar sentences closer to each other and dissimilar sentences to be further apart. And this is not what happens during BERT pretraining. To cater to this issue, we will have to further finetune the model. And in fact, this is where BERT shines again, as with minimal training (sometimes even for 20 mins with <1000 samples) we can expect good results. One of the ways to finetune for sentence represenration is to use triplet loss. For this, we prepare a dataset with a combination of (anchor, positive, negative) sentences. Here anchor is the base sentence, positive is the sentence that is similar to the anchor sentence, and negative is the sentence that is dissimilar to the anchor sentence. The model is trained to \"bring\" the representation of (anchor, positive) closer and (anchor, negative) apart. The loss is defined below, where \\(s_*\\) is the respective sentence representation and \\(\\epsilon\\) is the margin. \\[triplet loss = max( \\parallel s_a - s_p \\parallel - \\parallel s_a - s_n \\parallel + \\epsilon , 0)\\]","title":"BERT for sentence representation?"},{"location":"natural_language_processing/BERT/#code","text":"","title":"Code"},{"location":"natural_language_processing/BERT/#pretrained-bert-for-sentiment-classification","text":"The code contains the Dataset and Dataloader as well, which can be referred for any fine tuning task. Download dataset from IMDB 50k review 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 # helper import pandas as pd import numpy as np from tqdm import tqdm from sklearn.model_selection import train_test_split # for BERT model from transformers import BertTokenizer , BertModel from transformers import BertForSequenceClassification # for DL stuff import torch import pytorch_lightning as pl from torch.nn import Softmax , Linear from torchmetrics import Accuracy , F1 from torch.utils.data import Dataset , DataLoader from pytorch_lightning.loggers import WandbLogger model_name = 'bert-base-uncased' # load dataset and sample 10k reviews. df = pd . read_csv ( \"../input/imdb-dataset-of-50k-movie-reviews/IMDB Dataset.csv\" ) df = df . sample ( 10000 , random_state = 1 ) # divide into test and train X_train , X_test , y_train , y_test = \\ train_test_split ( df [ 'review' ] . tolist (), df [ 'sentiment' ] . tolist (), shuffle = True , test_size = 0.33 , random_state = 1 , stratify = df [ 'sentiment' ]) # define dataset with load and prep functions. Pass all the data at a time. def squz ( x , dim = 0 ): return torch . squeeze ( x , dim ) class IMDBDataset ( Dataset ): def __init__ ( self , sentences , labels , max_length = 512 , model_name = 'bert-base-uncased' ): # var self . sentences = sentences self . labels = [[ 'positive' , 'negative' ] . index ( x ) for x in labels ] self . max_length = max_length # tokenizer self . tokenizer = BertTokenizer . from_pretrained ( model_name ) def __len__ ( self ): return len ( self . sentences ) def __getitem__ ( self , index ): # Select sample sentence = self . sentences [ index ] label = self . labels [ index ] # Load data and get label X = self . tokenizer ( sentence , padding = \"max_length\" , truncation = True , max_length = self . max_length , return_tensors = \"pt\" ) X = { key : squz ( value ) for key , value in X . items ()} y = label # return return X , y # init the train and test dataset train_dataset = IMDBDataset ( X_train , y_train ) test_dataset = IMDBDataset ( X_test , y_test ) # create the dataloader train_dataloader = DataLoader ( train_dataset , batch_size = 16 , shuffle = True ) test_dataloader = DataLoader ( test_dataset , batch_size = 16 , shuffle = True ) # define BERT model class BERT_pooler_output ( pl . LightningModule ): def __init__ ( self , model_name = 'bert-base-uncased' ): super () . __init__ () # model and layers self . BERTModel = BertModel . from_pretrained ( model_name ) self . linear1 = Linear ( 768 , 128 ) self . linear2 = Linear ( 128 , 2 ) self . softmax = Softmax ( dim = 1 ) self . relu = torch . nn . ReLU () # loss self . criterion = torch . nn . CrossEntropyLoss () # performance self . accuracy = Accuracy () self . f1 = F1 ( num_classes = 2 , average = 'macro' ) def forward ( self , x ): # pass input to BERTmodel input_ids , attention_mask = x [ 'input_ids' ], x [ 'attention_mask' ] bert_output = self . BERTModel ( input_ids , attention_mask = attention_mask ) output = bert_output . pooler_output output = self . relu ( self . linear1 ( output )) output = self . linear2 ( output ) return output def training_step ( self , batch , batch_idx ): x , y = batch x_hat = self . forward ( x ) loss = self . criterion ( x_hat , y ) acc = self . accuracy ( x_hat . argmax ( dim = 1 ), y ) f1 = self . f1 ( x_hat . argmax ( dim = 1 ), y ) self . log ( 'loss' , loss , on_step = False , on_epoch = True , prog_bar = True , logger = True ) self . log ( 'acc' , acc , on_step = False , on_epoch = True , prog_bar = True , logger = True ) self . log ( 'f1' , f1 , on_step = False , on_epoch = True , prog_bar = True , logger = True ) return { 'loss' : loss } def validation_step ( self , batch , batch_idx ): x , y = batch x_hat = self . forward ( x ) acc = self . accuracy ( x_hat . argmax ( dim = 1 ), y ) f1 = self . f1 ( x_hat . argmax ( dim = 1 ), y ) self . log ( 'val_acc' , acc , on_step = False , on_epoch = True , prog_bar = True , logger = True ) self . log ( 'val_f1' , f1 , on_step = False , on_epoch = True , prog_bar = True , logger = True ) def configure_optimizers ( self ): # freezing the params of BERT model for name , param in self . named_parameters (): if 'BERTModel' in name : param . requires_grad = False # define the optimizer optimizer = torch . optim . Adam ( filter ( lambda p : p . requires_grad , self . parameters ()), lr = 1e-3 ) return optimizer # init model model = BERT_pooler_output () # init trainer trainer = pl . Trainer ( gpus = 1 , max_epochs = 3 ) # train the model trainer . fit ( model , train_dataloader , test_dataloader )","title":"Pretrained BERT for Sentiment Classification"},{"location":"natural_language_processing/BERT/#fine-tuning-the-bert-model","text":"Fine tuning could include training BERT on one or many of the proposed unsupervised tasks. Here, we will train the BERT on MLM (Masked language modeling) task. This includes masking some tokens of input and BERT predicting the token based on the context tokens. Referenced from this video of James Briggs . 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 # IMPORT ========= import pandas as pd from tqdm import tqdm import numpy as np import numpy as np # for deep learning import torch import torch.nn as nn import torch.optim as optim # load BERT model from transformers import AdamW from transformers import BertTokenizer , BertForMaskedLM # MODEL LOAD ========= model_path = \"bert-base-uncased\" # if local copy is not present tokenizer = BertTokenizer . from_pretrained ( model_path ) model = BertForMaskedLM . from_pretrained ( model_path ) # DATA PREP 1 ========= df = pd . read_csv ( \"file_with_text.csv\" ) # tokenize inputs = tokenizer ( df [ 'review' ] . tolist (), return_tensors = 'pt' , max_length = 512 , truncation = True , padding = 'max_length' ) inputs [ 'labels' ] = inputs . input_ids . detach () . clone () # create random array of floats with equal dimensions to input_ids tensor rand = torch . rand ( inputs . input_ids . shape ) # create mask array - mask tokens except special tokens like CLS and SEP mask_ratio = 0.3 mask_arr = ( rand < mask_ratio ) * ( inputs . input_ids != 101 ) * \\ ( inputs . input_ids != 102 ) * ( inputs . input_ids != 0 ) # get the indices where to add mask selection = [] for i in range ( inputs . input_ids . shape [ 0 ]): selection . append ( torch . flatten ( mask_arr [ i ] . nonzero ()) . tolist () ) # add the mask for i in range ( inputs . input_ids . shape [ 0 ]): inputs . input_ids [ i , selection [ i ]] = 103 # DATA PREP 2 - DATALOADER ========= # define dataset class class IMDBDataset ( torch . utils . data . Dataset ): def __init__ ( self , encodings ): self . encodings = encodings def __getitem__ ( self , idx ): return { key : torch . tensor ( val [ idx ]) for key , val in self . encodings . items ()} def __len__ ( self ): return len ( self . encodings . input_ids ) # create instance dataset = IMDBDataset ( inputs ) loader = torch . utils . data . DataLoader ( dataset , batch_size = 8 , shuffle = True ) # PRE_TRAIN ============= device = torch . device ( 'cuda' ) if torch . cuda . is_available () else torch . device ( 'cpu' ) # and move our model over to the selected device model . to ( device ) # activate training mode model . train () # initialize optimizer optimizer = AdamW ( model . parameters (), lr = 5e-5 ) # TRAIN ===================== epochs = 20 for epoch in range ( epochs ): # setup loop with TQDM and dataloader loop = tqdm ( loader , leave = True ) for batch in loop : # initialize calculated gradients (from prev step) optimizer . zero_grad () # pull all tensor batches required for training input_ids = batch [ 'input_ids' ] . to ( device ) attention_mask = batch [ 'attention_mask' ] . to ( device ) labels = batch [ 'labels' ] . to ( device ) # process outputs = model ( input_ids , attention_mask = attention_mask , labels = labels ) # extract loss loss = outputs . loss # calculate loss for every parameter that needs grad update loss . backward () # update parameters optimizer . step () # print relevant info to progress bar loop . set_description ( f 'Epoch { epoch } ' ) loop . set_postfix ( loss = loss . item ()) # SAVE MODEL ===================== model . save_pretrained ( \"bert_finetuned_on_text/\" ) tokenizer . save_pretrained ( \"bert_finetuned_on_text/\" )","title":"Fine tuning the BERT model"},{"location":"natural_language_processing/BERT/#bert-output-for-sentence-level-inference","text":"BERT provides pooler_output and last_hidden_state as two potential \" representations \" for sentence level inference. pooler_output is the embedding of the [CLS] special token. In many cases it is considered as a valid representation of the complete sentence. 1 2 3 BERTModel = BertModel . from_pretrained ( 'bert-base-uncased' ) bert_output = BERTModel ( input_ids , attention_mask = attention_mask ) output = bert_output . pooler_output last_hidden_state contains the embeddings of all tokens in the sentence from the last hidden state. We can apply permutation invariant methods (like max, mean or sum) to aggregate the embeddings into a single sentence representation. 1 2 3 BERTModel = BertModel . from_pretrained ( 'bert-base-uncased' ) bert_output = BERTModel ( input_ids , attention_mask = attention_mask ) output = squeeze ( torch . matmul ( attention_mask . type ( torch . float32 ) . view ( - 1 , 1 , 512 ), bert_output [ 'last_hidden_state' ]), 1 ) Tip Consider finetuning the BERT model (triplet loss) further to generate meaningful sentence representation, as pretrained BERT model is even worse than Glove embeddings [2]. For more details look at this analysis or use S-BERT package to finetune.","title":"BERT output for sentence level inference"},{"location":"natural_language_processing/BERT/#references","text":"[1] Jay Alammar's blog \"The Illustrated BERT, ELMo, and co. (How NLP Cracked Transfer Learning)\" [2] Sentence-BERT: Sentence Embeddings using Siamese BERT-Networks","title":"References"},{"location":"natural_language_processing/FlanModels/","text":"Flan Models Introduction In \u201cScaling Instruction-Finetuned Language Models\u201d, researchers propose an LLM fine-tuning strategy that focus on using dataset in form of instructions with other tricks like (1) scaling the number of tasks, (2) scaling the model size, and (3) finetuning on chain-of-thought data. The resulting models were called Flan-{source_model} like Flan-T5 and Flan-PaLM which showcase enhanced performance when compared with their namesakes. Flan models are finetuned on ~1.8k tasks w & w/o CoT and exemplars (zero shots and few shots) and evaluated on unseen tasks. Note All Flan models like Flan-T5 are only finetuned and not trained from scratch. Dataset The Flan models were finetuned on 1836 tasks. Here are some definitions, \u201c Dataset\u201d is an data source (ex: SQuAD) , \u201c Task category \u201d is a unique task setup like query generation or question answering. \u201c Task \u201d is unique combinations of . This is possible because a single dataset can be used for different task categrory (ex: SQuAD for query generation or QA or context generation) . Combining 473 datasets and 146 task category we end up with 1836 different tasks. Dataset, Task Category and Task details of finetuning and held out data. Finetuning process Instruction finetuning was performed for T5, PaLM and U-PaLM, where the model sizes span from Flan-T5-small (80M parameters) , to PaLM and U-PaLM (540B parameters) . Same training procedure was used for each model except for a few hyperparameters like learning rate, batch size, dropout, and finetuning steps. Constant learning rate schedule was used to finetune using the Adafactor optimizer. Notably, the amount of compute used for finetuning is only a small fraction relative to the training compute, as shown in the table below. All Flan models only use a fraction of compute for finetuning that was used for model training, with max usage going only upto 1.6%. Results Instruction finetuning (Flan) showcased improved performance across multiple models and dataset, sometimes going as far as double digit improvements! Code The code of inference and finetuning Flan models are very similar to the original models. Below we will show inference code for Flan-T5, which is similar to the T5 inference . Similarly, if you want to further finetune Flan-T5 for your use case, you can refer T5 finetuning code . If you are using HuggingFace, then all you need to do is replace the model name to google/flan-t5-{size} , where size could be small , base , large , xl and xxl . Flan-T5 Inference The following code is referenced from the HuggingFace Flan-T5 page [2] 1 2 3 4 5 6 7 8 9 10 11 # import from transformers import AutoModelForSeq2SeqLM , AutoTokenizer # load the model and tokenizer model = AutoModelForSeq2SeqLM . from_pretrained ( \"google/flan-t5-small\" ) tokenizer = AutoTokenizer . from_pretrained ( \"google/flan-t5-small\" ) # run on sample input inputs = tokenizer ( \"A step by step recipe to make bolognese pasta:\" , return_tensors = \"pt\" ) outputs = model . generate ( ** inputs ) print ( tokenizer . batch_decode ( outputs , skip_special_tokens = True )) References [1] Scaling Instruction-Finetuned Language Models - Paper [2] FlanT5 - HuggingFace","title":"FlanModels"},{"location":"natural_language_processing/FlanModels/#flan-models","text":"","title":"Flan Models"},{"location":"natural_language_processing/FlanModels/#introduction","text":"In \u201cScaling Instruction-Finetuned Language Models\u201d, researchers propose an LLM fine-tuning strategy that focus on using dataset in form of instructions with other tricks like (1) scaling the number of tasks, (2) scaling the model size, and (3) finetuning on chain-of-thought data. The resulting models were called Flan-{source_model} like Flan-T5 and Flan-PaLM which showcase enhanced performance when compared with their namesakes. Flan models are finetuned on ~1.8k tasks w & w/o CoT and exemplars (zero shots and few shots) and evaluated on unseen tasks. Note All Flan models like Flan-T5 are only finetuned and not trained from scratch.","title":"Introduction"},{"location":"natural_language_processing/FlanModels/#dataset","text":"The Flan models were finetuned on 1836 tasks. Here are some definitions, \u201c Dataset\u201d is an data source (ex: SQuAD) , \u201c Task category \u201d is a unique task setup like query generation or question answering. \u201c Task \u201d is unique combinations of . This is possible because a single dataset can be used for different task categrory (ex: SQuAD for query generation or QA or context generation) . Combining 473 datasets and 146 task category we end up with 1836 different tasks. Dataset, Task Category and Task details of finetuning and held out data.","title":"Dataset"},{"location":"natural_language_processing/FlanModels/#finetuning-process","text":"Instruction finetuning was performed for T5, PaLM and U-PaLM, where the model sizes span from Flan-T5-small (80M parameters) , to PaLM and U-PaLM (540B parameters) . Same training procedure was used for each model except for a few hyperparameters like learning rate, batch size, dropout, and finetuning steps. Constant learning rate schedule was used to finetune using the Adafactor optimizer. Notably, the amount of compute used for finetuning is only a small fraction relative to the training compute, as shown in the table below. All Flan models only use a fraction of compute for finetuning that was used for model training, with max usage going only upto 1.6%.","title":"Finetuning process"},{"location":"natural_language_processing/FlanModels/#results","text":"Instruction finetuning (Flan) showcased improved performance across multiple models and dataset, sometimes going as far as double digit improvements!","title":"Results"},{"location":"natural_language_processing/FlanModels/#code","text":"The code of inference and finetuning Flan models are very similar to the original models. Below we will show inference code for Flan-T5, which is similar to the T5 inference . Similarly, if you want to further finetune Flan-T5 for your use case, you can refer T5 finetuning code . If you are using HuggingFace, then all you need to do is replace the model name to google/flan-t5-{size} , where size could be small , base , large , xl and xxl .","title":"Code"},{"location":"natural_language_processing/FlanModels/#flan-t5-inference","text":"The following code is referenced from the HuggingFace Flan-T5 page [2] 1 2 3 4 5 6 7 8 9 10 11 # import from transformers import AutoModelForSeq2SeqLM , AutoTokenizer # load the model and tokenizer model = AutoModelForSeq2SeqLM . from_pretrained ( \"google/flan-t5-small\" ) tokenizer = AutoTokenizer . from_pretrained ( \"google/flan-t5-small\" ) # run on sample input inputs = tokenizer ( \"A step by step recipe to make bolognese pasta:\" , return_tensors = \"pt\" ) outputs = model . generate ( ** inputs ) print ( tokenizer . batch_decode ( outputs , skip_special_tokens = True ))","title":"Flan-T5 Inference"},{"location":"natural_language_processing/FlanModels/#references","text":"[1] Scaling Instruction-Finetuned Language Models - Paper [2] FlanT5 - HuggingFace","title":"References"},{"location":"natural_language_processing/GPTs/","text":"GPT models Introduction GPT stands for \"Generative Pre-trained Transformer\". It is an autoregressive language model which is based on the decoder block of the Transformer architecture. Transformer architecture. Left part is the encoder, right part is the decoder. GPT is made up of the right part i.e. decoder part. ( vaswani2017attention ) The idea for the model is similar to any text generation model i.e. it takes some prompt as input and generates text as output. But the caveat is that, GPT model's tunable parameter ranges from 100 million to 175 billion, which leads to the model learning much more than basic language syntax information or word related contextual information. In fact, it has been shown that GPT models store additional real-world information as well, and there has been interesting recent research about how much knowledge you can pack into the parameters ( roberts2020knowledge ). GPT models are also famous because they can be easily applied to many downstream NLP tasks. This is because they have been shown to be very good in few-shot leaning, i.e. they are able to perform tasks for which they are not even trained by only providing a few examples! This lead to a new interesting paradigm of prompt engineering, where creating crisp prompt could lead to good results. This means that, instead of playing around with training, just by modifying the input prompt to the model, we can improve the accuracy. Ofcourse, for better accuracy, it is always preferred to fine-tune the model. Example of a sample prompt is provided below ## prompt input - this can be passed to a GPT based model Below are some examples for sentiment detection of movie reviews. Review: I am sad that the hero died. Sentiment: Negative Review: The ending was perfect. Sentiment: Positive Review: The plot was not so good! Sentiment: ## The model should predict the sentiment for the last review. Tip Copy the prompt from above and try it @ GPT-Neo 2.7B model . You should get \"Negative\" as output! We just created a Sentiment detection module without a single training epoch! Analysis Comparing GPT models (basic details) There are two famous series of GPT models, GPT-{1,2,3}: the original series released by OpenAI , a San Francisco-based artificial intelligence research laboratory. It includes GPT-1 ( radford2018improving , GPT-2 radford2019language , GPT-3 brown2020language ) GPT-{Neo, J}: the open source series released by EleutherAI . For GPT-Neo, the architecture is quite similar to GPT-3, but training was done on The Pile , an 825 GB sized text dataset. Details of the models are as follows, ( details ) models released by year open-source model size GPT-1 OpenAI 2018 yes 110M GPT-2 OpenAI 2019 yes 117M, 345M, 774M, 1.5B GPT-3 OpenAI 2020 no 175B GPT-Neo EleutherAI 2021 yes 125M, 1.3B, 2.7B GPT-J EleutherAI 2021 yes 6B Code The most recent open-source models from OpenAI and EleutherAI are GPT-2 and GPT-Neo, respectively. And as they share nearly the same architecture, the majority of the code for inference or training, or fine-tuning remains the same. Hence for brevity's sake, code for GPT-2 will be shared, but I will point out changes required to make it work for GPT-Neo model as well. Inference of GPT-2 pre-trained model For a simple inference, we will load the pre-trained GPT-2 model and use it for a dummy sentiment detection task (using the prompt shared above). To make this code work for GPT-Neo, - import GPTNeoForCausalLM at line 2 - replace line 5 with model_name = \"EleutherAI/gpt-neo-2.7B\" (choose from any of the available sized models) - use GPTNeoForCausalLM in place of GPT2LMHeadModel at line 9 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 # import from transformers import GPT2Tokenizer , GPT2LMHeadModel # model name model_name = \"gpt2\" # load tokenizer and model tokenizer = GPT2Tokenizer . from_pretrained ( model_name ) model = GPT2LMHeadModel . from_pretrained ( model_name ) . cuda () # create prompt prompt = \"\"\" Below are some examples for sentiment detection of movie reviews. Review: I am sad that the hero died. Sentiment: Negative Review: The ending was perfect. Sentiment: Positive Review: The plot was not so good! Sentiment:\"\"\" # generate tokens generated = tokenizer ( prompt , return_tensors = \"pt\" ) . input_ids . cuda () # perform prediction sample_outputs = model . generate ( generated , do_sample = False , top_k = 50 , max_length = 512 , top_p = 0.90 , temperature = 0 , num_return_sequences = 0 ) # decode the predicted tokens into texts predicted_text = tokenizer . decode ( sample_outputs [ 0 ], skip_special_tokens = True ) print ( predicted_text ) \"\"\"Output --> Below are some examples for sentiment detection of movie reviews. Review: I am sad that the hero died. Sentiment: Negative Review: The ending was perfect. Sentiment: Positive Review: The plot was not so good! Sentiment: Negative \"\"\" Note As GPT2 is a language model style decoder with no special encoder block, the output contains the input plus additional generations. This can be observed from the above example. On the other hand, output of T5 model is pure new generations (without the repetition of input) as it has encoder-decoder architecture. Finetuning GPT-2 (for sentiment classification) Tweet sentiment data can be downloaded from here We add the special tokens at line 72, so that the model learns the start and end of the prompt. This will be helpful later on during the testing phase, as we don't want the model to keep on writing the next word, but it should know when to stop the process. This can be done by setting the eos_token and training the model to predict the same. Note Original GPT-2 paper and implementation uses <|endoftext|> as the eos_token and bos_token (beginning of sentence) . We can do the same, but for clarity in the implementation, we can also use <|startoftext|> as bos_token special token. We also define how to process the training data inside data_collator on line 91. The first two elements within the collator are input_ids -\u200athe tokenized prompt and attention_mask -\u200aa simple 1/0 vector which denote which part of the tokenized vector is prompt and which part is the padding. The last part is quite interesting, where we pass the input data as the label instead of just the sentiment labels. This is because we are training a language model, hence we want the model to learn the pattern of the prompt and not just sentiment class. In a sense, the model learns to predict the words of the input tweet + sentiment structured in the prompt, and in the process learn the sentiment detection task. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 # download packages #!pip install transformers==4.8.2 # import packages import re import torch import random import pandas as pd from tqdm import tqdm from torch.utils.data import Dataset from sklearn.metrics import f1_score from sklearn.model_selection import train_test_split from transformers import GPT2Tokenizer , TrainingArguments , Trainer , GPT2LMHeadModel ## Define class and functions #-------- # Dataset class class SentimentDataset ( Dataset ): def __init__ ( self , txt_list , label_list , tokenizer , max_length ): # define variables self . input_ids = [] self . attn_masks = [] self . labels = [] map_label = { 0 : 'negative' , 4 : 'positive' } # iterate through the dataset for txt , label in zip ( txt_list , label_list ): # prepare the text prep_txt = f '<|startoftext|>Tweet: { txt } <|pad|>Sentiment: { map_label [ label ] } <|endoftext|>' # tokenize encodings_dict = tokenizer ( prep_txt , truncation = True , max_length = max_length , padding = \"max_length\" ) # append to list self . input_ids . append ( torch . tensor ( encodings_dict [ 'input_ids' ])) self . attn_masks . append ( torch . tensor ( encodings_dict [ 'attention_mask' ])) self . labels . append ( map_label [ label ]) def __len__ ( self ): return len ( self . input_ids ) def __getitem__ ( self , idx ): return self . input_ids [ idx ], self . attn_masks [ idx ], self . labels [ idx ] # Data load function def load_sentiment_dataset ( tokenizer ): # load dataset and sample 10k reviews. file_path = \"../input/sentiment140/training.1600000.processed.noemoticon.csv\" df = pd . read_csv ( file_path , encoding = 'ISO-8859-1' , header = None ) df = df [[ 0 , 5 ]] df . columns = [ 'label' , 'text' ] df = df . sample ( 10000 , random_state = 1 ) # divide into test and train X_train , X_test , y_train , y_test = \\ train_test_split ( df [ 'text' ] . tolist (), df [ 'label' ] . tolist (), shuffle = True , test_size = 0.05 , random_state = 1 , stratify = df [ 'label' ]) # format into SentimentDataset class train_dataset = SentimentDataset ( X_train , y_train , tokenizer , max_length = 512 ) # return return train_dataset , ( X_test , y_test ) ## Load model and data #-------- # set model name model_name = \"gpt2\" # seed torch . manual_seed ( 42 ) # load tokenizer and model tokenizer = GPT2Tokenizer . from_pretrained ( model_name , bos_token = '<|startoftext|>' , eos_token = '<|endoftext|>' , pad_token = '<|pad|>' ) model = GPT2LMHeadModel . from_pretrained ( model_name ) . cuda () model . resize_token_embeddings ( len ( tokenizer )) # prepare and load dataset train_dataset , test_dataset = load_sentiment_dataset ( tokenizer ) ## Train #-------- # creating training arguments training_args = TrainingArguments ( output_dir = 'results' , num_train_epochs = 2 , logging_steps = 10 , load_best_model_at_end = True , save_strategy = \"epoch\" , evaluation_strategy = \"epoch\" , per_device_train_batch_size = 2 , per_device_eval_batch_size = 2 , warmup_steps = 100 , weight_decay = 0.01 , logging_dir = 'logs' ) # start training Trainer ( model = model , args = training_args , train_dataset = train_dataset , eval_dataset = test_dataset , data_collator = lambda data : { 'input_ids' : torch . stack ([ f [ 0 ] for f in data ]), 'attention_mask' : torch . stack ([ f [ 1 ] for f in data ]), 'labels' : torch . stack ([ f [ 0 ] for f in data ])}) . train () ## Test ---------- # set the model to eval mode _ = model . eval () # run model inference on all test data original_label , predicted_label , original_text , predicted_text = [], [], [], [] map_label = { 0 : 'negative' , 4 : 'positive' } # iter over all of the test data for text , label in tqdm ( zip ( test_dataset [ 0 ], test_dataset [ 1 ])): # create prompt (in compliance with the one used during training) prompt = f '<|startoftext|>Tweet: { text } \\n Sentiment:' # generate tokens generated = tokenizer ( f \" { prompt } \" , return_tensors = \"pt\" ) . input_ids . cuda () # perform prediction sample_outputs = model . generate ( generated , do_sample = False , top_k = 50 , max_length = 512 , top_p = 0.90 , temperature = 0 , num_return_sequences = 0 ) # decode the predicted tokens into texts predicted_text = tokenizer . decode ( sample_outputs [ 0 ], skip_special_tokens = True ) # extract the predicted sentiment try : pred_sentiment = re . findall ( \" \\n Sentiment: (.*)\" , predicted_text )[ - 1 ] except : pred_sentiment = \"None\" # append results original_label . append ( map_label [ label ]) predicted_label . append ( pred_sentiment ) original_text . append ( text ) predicted_text . append ( pred_text ) # transform result into dataframe df = pd . DataFrame ({ 'original_text' : original_text , 'predicted_label' : predicted_label , 'original_label' : original_label , 'predicted_text' : predicted_text }) # predict the accuracy print ( f1_score ( original_label , predicted_label , average = 'macro' )) Inference of GPT-3 Running GPT-3 model is super easy using the OpenAI python package . Here is how to do it, 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 # install # pip install --upgrade openai # import import openai # set API org and key openai . organization = \"OPENAI_API_ORG\" # replace or comment openai . api_key = \"OPENAI_API_KEY\" # replace # inference response = openai . Completion . create ( model = \"text-davinci-002\" , # set as per your need prompt = 'Tell me a Joke:' , max_tokens = 256 , # set as per your need top_p = 1 , # set as per your need temperature = 0.7 , # set as per your need ) # Output: # \"choices\": [ # { # \"finish_reason\": \"stop\", # \"index\": 0, # \"logprobs\": null, # \"text\": \"\\n\\nWhy did the chicken cross the road?\\n\\nTo get to the other side.\" # } # ], # \"created\": 1667925044, # \"id\": \"cmpl-6ALmmOOkVWE03AmO9Cur8XM3jpEXk\", # \"model\": \"text-davinci-002\", # \"object\": \"text_completion\", # \"usage\": { # \"completion_tokens\": 19, # \"prompt_tokens\": 6, # \"total_tokens\": 25 # } Finetuning GPT-3 While GPT-3 is not open source, OpenAI has provided the paid option to finetune the model . At the time of writing, free credits were provided to new users -- so another reason to go and register now They expose several APIs to perform finetuning. In a sense they are doing most of the heavy lifting by making the finetuning process super easy. To begin with, make sure you have the OpenAI python library installed. Do it by pip install openai . Then make sure the data is in correct format. Basically you require a .csv file with atleast two columns - prompt and completion with the respective data. Ideally the documentation suggest to have atleast 100 examples. Next, we will prepare a jsonl file using the csv file. OpenAI expects the data in this format. And they also expose an API to do so, just run the following in CLI. 1 openai tools fine_tunes.prepare_data -f data_to_fine_tune.csv Now we will upload the prepared data to the OpenAI Server. Do this by running following code in Python. 1 2 3 4 5 6 7 8 # set the organization and api key (you can get them from Manage Account page) openai . organization = \"my-organization-key\" openai . api_key = \"my-api-key\" # upload the data to OpenAI Server file_meta_data = openai . File . create ( file = open ( f \"data_to_fine_tune_prepared.jsonl\" , encoding = 'utf-8' ), purpose = 'fine-tune' ) Now we will train the model. Do this by running following code in Python. 1 print ( openai . FineTune . create ( model = \"curie\" , training_file = file_meta_data [ \"id\" ])) And there we go, the finetuning has started! You can monitor the progress by running openai.FineTune.list() . The last entry will contain a status key that will change to succeeded when the finetuning is complete. Also, note down the model name from the fine_tuned_model key, you will need it later to access the trained model. Btw if you only want to print the last entry try this instead openai.FineTune.list()['data'][-1] . After the finetuning is done, you can use the model as usual from the playground or API! Tip OpenAI provides multiple models (engines) with different accuracy and speed. These are Davinci , Curie , Babbadge and Ada - in the descending order of accuracy but increasing speed. Additional materials The Illustrated GPT-2 (Visualizing Transformer Language Models) - Link","title":"GPTs"},{"location":"natural_language_processing/GPTs/#gpt-models","text":"","title":"GPT models"},{"location":"natural_language_processing/GPTs/#introduction","text":"GPT stands for \"Generative Pre-trained Transformer\". It is an autoregressive language model which is based on the decoder block of the Transformer architecture. Transformer architecture. Left part is the encoder, right part is the decoder. GPT is made up of the right part i.e. decoder part. ( vaswani2017attention ) The idea for the model is similar to any text generation model i.e. it takes some prompt as input and generates text as output. But the caveat is that, GPT model's tunable parameter ranges from 100 million to 175 billion, which leads to the model learning much more than basic language syntax information or word related contextual information. In fact, it has been shown that GPT models store additional real-world information as well, and there has been interesting recent research about how much knowledge you can pack into the parameters ( roberts2020knowledge ). GPT models are also famous because they can be easily applied to many downstream NLP tasks. This is because they have been shown to be very good in few-shot leaning, i.e. they are able to perform tasks for which they are not even trained by only providing a few examples! This lead to a new interesting paradigm of prompt engineering, where creating crisp prompt could lead to good results. This means that, instead of playing around with training, just by modifying the input prompt to the model, we can improve the accuracy. Ofcourse, for better accuracy, it is always preferred to fine-tune the model. Example of a sample prompt is provided below ## prompt input - this can be passed to a GPT based model Below are some examples for sentiment detection of movie reviews. Review: I am sad that the hero died. Sentiment: Negative Review: The ending was perfect. Sentiment: Positive Review: The plot was not so good! Sentiment: ## The model should predict the sentiment for the last review. Tip Copy the prompt from above and try it @ GPT-Neo 2.7B model . You should get \"Negative\" as output! We just created a Sentiment detection module without a single training epoch!","title":"Introduction"},{"location":"natural_language_processing/GPTs/#analysis","text":"","title":"Analysis"},{"location":"natural_language_processing/GPTs/#comparing-gpt-models-basic-details","text":"There are two famous series of GPT models, GPT-{1,2,3}: the original series released by OpenAI , a San Francisco-based artificial intelligence research laboratory. It includes GPT-1 ( radford2018improving , GPT-2 radford2019language , GPT-3 brown2020language ) GPT-{Neo, J}: the open source series released by EleutherAI . For GPT-Neo, the architecture is quite similar to GPT-3, but training was done on The Pile , an 825 GB sized text dataset. Details of the models are as follows, ( details ) models released by year open-source model size GPT-1 OpenAI 2018 yes 110M GPT-2 OpenAI 2019 yes 117M, 345M, 774M, 1.5B GPT-3 OpenAI 2020 no 175B GPT-Neo EleutherAI 2021 yes 125M, 1.3B, 2.7B GPT-J EleutherAI 2021 yes 6B","title":"Comparing GPT models (basic details)"},{"location":"natural_language_processing/GPTs/#code","text":"The most recent open-source models from OpenAI and EleutherAI are GPT-2 and GPT-Neo, respectively. And as they share nearly the same architecture, the majority of the code for inference or training, or fine-tuning remains the same. Hence for brevity's sake, code for GPT-2 will be shared, but I will point out changes required to make it work for GPT-Neo model as well.","title":"Code"},{"location":"natural_language_processing/GPTs/#inference-of-gpt-2-pre-trained-model","text":"For a simple inference, we will load the pre-trained GPT-2 model and use it for a dummy sentiment detection task (using the prompt shared above). To make this code work for GPT-Neo, - import GPTNeoForCausalLM at line 2 - replace line 5 with model_name = \"EleutherAI/gpt-neo-2.7B\" (choose from any of the available sized models) - use GPTNeoForCausalLM in place of GPT2LMHeadModel at line 9 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 # import from transformers import GPT2Tokenizer , GPT2LMHeadModel # model name model_name = \"gpt2\" # load tokenizer and model tokenizer = GPT2Tokenizer . from_pretrained ( model_name ) model = GPT2LMHeadModel . from_pretrained ( model_name ) . cuda () # create prompt prompt = \"\"\" Below are some examples for sentiment detection of movie reviews. Review: I am sad that the hero died. Sentiment: Negative Review: The ending was perfect. Sentiment: Positive Review: The plot was not so good! Sentiment:\"\"\" # generate tokens generated = tokenizer ( prompt , return_tensors = \"pt\" ) . input_ids . cuda () # perform prediction sample_outputs = model . generate ( generated , do_sample = False , top_k = 50 , max_length = 512 , top_p = 0.90 , temperature = 0 , num_return_sequences = 0 ) # decode the predicted tokens into texts predicted_text = tokenizer . decode ( sample_outputs [ 0 ], skip_special_tokens = True ) print ( predicted_text ) \"\"\"Output --> Below are some examples for sentiment detection of movie reviews. Review: I am sad that the hero died. Sentiment: Negative Review: The ending was perfect. Sentiment: Positive Review: The plot was not so good! Sentiment: Negative \"\"\" Note As GPT2 is a language model style decoder with no special encoder block, the output contains the input plus additional generations. This can be observed from the above example. On the other hand, output of T5 model is pure new generations (without the repetition of input) as it has encoder-decoder architecture.","title":"Inference of GPT-2 pre-trained model"},{"location":"natural_language_processing/GPTs/#finetuning-gpt-2-for-sentiment-classification","text":"Tweet sentiment data can be downloaded from here We add the special tokens at line 72, so that the model learns the start and end of the prompt. This will be helpful later on during the testing phase, as we don't want the model to keep on writing the next word, but it should know when to stop the process. This can be done by setting the eos_token and training the model to predict the same. Note Original GPT-2 paper and implementation uses <|endoftext|> as the eos_token and bos_token (beginning of sentence) . We can do the same, but for clarity in the implementation, we can also use <|startoftext|> as bos_token special token. We also define how to process the training data inside data_collator on line 91. The first two elements within the collator are input_ids -\u200athe tokenized prompt and attention_mask -\u200aa simple 1/0 vector which denote which part of the tokenized vector is prompt and which part is the padding. The last part is quite interesting, where we pass the input data as the label instead of just the sentiment labels. This is because we are training a language model, hence we want the model to learn the pattern of the prompt and not just sentiment class. In a sense, the model learns to predict the words of the input tweet + sentiment structured in the prompt, and in the process learn the sentiment detection task. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 # download packages #!pip install transformers==4.8.2 # import packages import re import torch import random import pandas as pd from tqdm import tqdm from torch.utils.data import Dataset from sklearn.metrics import f1_score from sklearn.model_selection import train_test_split from transformers import GPT2Tokenizer , TrainingArguments , Trainer , GPT2LMHeadModel ## Define class and functions #-------- # Dataset class class SentimentDataset ( Dataset ): def __init__ ( self , txt_list , label_list , tokenizer , max_length ): # define variables self . input_ids = [] self . attn_masks = [] self . labels = [] map_label = { 0 : 'negative' , 4 : 'positive' } # iterate through the dataset for txt , label in zip ( txt_list , label_list ): # prepare the text prep_txt = f '<|startoftext|>Tweet: { txt } <|pad|>Sentiment: { map_label [ label ] } <|endoftext|>' # tokenize encodings_dict = tokenizer ( prep_txt , truncation = True , max_length = max_length , padding = \"max_length\" ) # append to list self . input_ids . append ( torch . tensor ( encodings_dict [ 'input_ids' ])) self . attn_masks . append ( torch . tensor ( encodings_dict [ 'attention_mask' ])) self . labels . append ( map_label [ label ]) def __len__ ( self ): return len ( self . input_ids ) def __getitem__ ( self , idx ): return self . input_ids [ idx ], self . attn_masks [ idx ], self . labels [ idx ] # Data load function def load_sentiment_dataset ( tokenizer ): # load dataset and sample 10k reviews. file_path = \"../input/sentiment140/training.1600000.processed.noemoticon.csv\" df = pd . read_csv ( file_path , encoding = 'ISO-8859-1' , header = None ) df = df [[ 0 , 5 ]] df . columns = [ 'label' , 'text' ] df = df . sample ( 10000 , random_state = 1 ) # divide into test and train X_train , X_test , y_train , y_test = \\ train_test_split ( df [ 'text' ] . tolist (), df [ 'label' ] . tolist (), shuffle = True , test_size = 0.05 , random_state = 1 , stratify = df [ 'label' ]) # format into SentimentDataset class train_dataset = SentimentDataset ( X_train , y_train , tokenizer , max_length = 512 ) # return return train_dataset , ( X_test , y_test ) ## Load model and data #-------- # set model name model_name = \"gpt2\" # seed torch . manual_seed ( 42 ) # load tokenizer and model tokenizer = GPT2Tokenizer . from_pretrained ( model_name , bos_token = '<|startoftext|>' , eos_token = '<|endoftext|>' , pad_token = '<|pad|>' ) model = GPT2LMHeadModel . from_pretrained ( model_name ) . cuda () model . resize_token_embeddings ( len ( tokenizer )) # prepare and load dataset train_dataset , test_dataset = load_sentiment_dataset ( tokenizer ) ## Train #-------- # creating training arguments training_args = TrainingArguments ( output_dir = 'results' , num_train_epochs = 2 , logging_steps = 10 , load_best_model_at_end = True , save_strategy = \"epoch\" , evaluation_strategy = \"epoch\" , per_device_train_batch_size = 2 , per_device_eval_batch_size = 2 , warmup_steps = 100 , weight_decay = 0.01 , logging_dir = 'logs' ) # start training Trainer ( model = model , args = training_args , train_dataset = train_dataset , eval_dataset = test_dataset , data_collator = lambda data : { 'input_ids' : torch . stack ([ f [ 0 ] for f in data ]), 'attention_mask' : torch . stack ([ f [ 1 ] for f in data ]), 'labels' : torch . stack ([ f [ 0 ] for f in data ])}) . train () ## Test ---------- # set the model to eval mode _ = model . eval () # run model inference on all test data original_label , predicted_label , original_text , predicted_text = [], [], [], [] map_label = { 0 : 'negative' , 4 : 'positive' } # iter over all of the test data for text , label in tqdm ( zip ( test_dataset [ 0 ], test_dataset [ 1 ])): # create prompt (in compliance with the one used during training) prompt = f '<|startoftext|>Tweet: { text } \\n Sentiment:' # generate tokens generated = tokenizer ( f \" { prompt } \" , return_tensors = \"pt\" ) . input_ids . cuda () # perform prediction sample_outputs = model . generate ( generated , do_sample = False , top_k = 50 , max_length = 512 , top_p = 0.90 , temperature = 0 , num_return_sequences = 0 ) # decode the predicted tokens into texts predicted_text = tokenizer . decode ( sample_outputs [ 0 ], skip_special_tokens = True ) # extract the predicted sentiment try : pred_sentiment = re . findall ( \" \\n Sentiment: (.*)\" , predicted_text )[ - 1 ] except : pred_sentiment = \"None\" # append results original_label . append ( map_label [ label ]) predicted_label . append ( pred_sentiment ) original_text . append ( text ) predicted_text . append ( pred_text ) # transform result into dataframe df = pd . DataFrame ({ 'original_text' : original_text , 'predicted_label' : predicted_label , 'original_label' : original_label , 'predicted_text' : predicted_text }) # predict the accuracy print ( f1_score ( original_label , predicted_label , average = 'macro' ))","title":"Finetuning GPT-2 (for sentiment classification)"},{"location":"natural_language_processing/GPTs/#inference-of-gpt-3","text":"Running GPT-3 model is super easy using the OpenAI python package . Here is how to do it, 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 # install # pip install --upgrade openai # import import openai # set API org and key openai . organization = \"OPENAI_API_ORG\" # replace or comment openai . api_key = \"OPENAI_API_KEY\" # replace # inference response = openai . Completion . create ( model = \"text-davinci-002\" , # set as per your need prompt = 'Tell me a Joke:' , max_tokens = 256 , # set as per your need top_p = 1 , # set as per your need temperature = 0.7 , # set as per your need ) # Output: # \"choices\": [ # { # \"finish_reason\": \"stop\", # \"index\": 0, # \"logprobs\": null, # \"text\": \"\\n\\nWhy did the chicken cross the road?\\n\\nTo get to the other side.\" # } # ], # \"created\": 1667925044, # \"id\": \"cmpl-6ALmmOOkVWE03AmO9Cur8XM3jpEXk\", # \"model\": \"text-davinci-002\", # \"object\": \"text_completion\", # \"usage\": { # \"completion_tokens\": 19, # \"prompt_tokens\": 6, # \"total_tokens\": 25 # }","title":"Inference of GPT-3"},{"location":"natural_language_processing/GPTs/#finetuning-gpt-3","text":"While GPT-3 is not open source, OpenAI has provided the paid option to finetune the model . At the time of writing, free credits were provided to new users -- so another reason to go and register now They expose several APIs to perform finetuning. In a sense they are doing most of the heavy lifting by making the finetuning process super easy. To begin with, make sure you have the OpenAI python library installed. Do it by pip install openai . Then make sure the data is in correct format. Basically you require a .csv file with atleast two columns - prompt and completion with the respective data. Ideally the documentation suggest to have atleast 100 examples. Next, we will prepare a jsonl file using the csv file. OpenAI expects the data in this format. And they also expose an API to do so, just run the following in CLI. 1 openai tools fine_tunes.prepare_data -f data_to_fine_tune.csv Now we will upload the prepared data to the OpenAI Server. Do this by running following code in Python. 1 2 3 4 5 6 7 8 # set the organization and api key (you can get them from Manage Account page) openai . organization = \"my-organization-key\" openai . api_key = \"my-api-key\" # upload the data to OpenAI Server file_meta_data = openai . File . create ( file = open ( f \"data_to_fine_tune_prepared.jsonl\" , encoding = 'utf-8' ), purpose = 'fine-tune' ) Now we will train the model. Do this by running following code in Python. 1 print ( openai . FineTune . create ( model = \"curie\" , training_file = file_meta_data [ \"id\" ])) And there we go, the finetuning has started! You can monitor the progress by running openai.FineTune.list() . The last entry will contain a status key that will change to succeeded when the finetuning is complete. Also, note down the model name from the fine_tuned_model key, you will need it later to access the trained model. Btw if you only want to print the last entry try this instead openai.FineTune.list()['data'][-1] . After the finetuning is done, you can use the model as usual from the playground or API! Tip OpenAI provides multiple models (engines) with different accuracy and speed. These are Davinci , Curie , Babbadge and Ada - in the descending order of accuracy but increasing speed.","title":"Finetuning GPT-3"},{"location":"natural_language_processing/GPTs/#additional-materials","text":"The Illustrated GPT-2 (Visualizing Transformer Language Models) - Link","title":"Additional materials"},{"location":"natural_language_processing/T5/","text":"T5 Introduction T5 stands for \"Text-to-Text Transfer Transformer\". It was released by Google on 2020. As the name suggests, it's a tranformer based encoder-decoder model used for text generation. For more details about other text generation models, refer the Text Generation chapter. In contrast to other famous Transformer based models like BERT or GPT, which is made up of either the encoder or decoder part of the Transformer, T5 paper showcase that using the complete encoder-decoder architecture is better than only using decoder. Apart from this, the paper also curated and released Colossal Clean Crawled Corpus (C4) - a huge crawled and cleaned dataset for pre-training language model using self-supervised learning. ( raffel2020exploring ) Transformer architecture. Left part is the encoder, right part is the decoder. T5's architecture is very similar to this one. ( vaswani2017attention raffel2020exploring ) Due to this nature of T5, for training or finetuning, the model requires a pair of input and output sequences/text. Some example tasks that can be performed using T5 is shown below, T5 text-to-text framework examples. See: Google Blog Based on the original T5, there has been several variations explored such as, (refer T5 @ HuggingFace ) T5v1.1: T5v1.1 is an improved version of T5 with some architectural tweaks, and is pre-trained on C4 only without mixing in the supervised tasks. mT5: mT5 is a multilingual T5 model. It is pre-trained on the mC4 corpus, which includes 101 languages. byT5: byT5 is a T5 model pre-trained on byte sequences rather than SentencePiece subword token sequences. Long-T5: For use case where we need to process longer input ( Refer ) Note As mentioned above, for multi-lingual purpose refer mT5 which was trained on >100 languages. That said, original T5 was also trained on translation task from English to German, French and Romanian. So the output can sometimes contains tokens from these languages! Paper details Transformer Architecture To compare different architectures suitable for languague models, T5 authors considered basically three varieties, Encoder-Decoder: A standard encoder-decoder architecture uses fully visible masking in the encoder and the encoder-decoder attention, with causal masking (attention mask) in the decoder. Masking is done to make sure the output at a position doesn't attend to future output for prediction. Language model: A language model consists of a single Transformer layer stack and is fed the concatenation of the input and target, using a causal mask throughout. As usual with LMs, the output only attends to the past input or output. Prefix LM: Adding a prefix to a language model corresponds to allowing fully-visible masking over a portion of the input. It is very similar to LM, just that any output will attend to a certain portion of the input that contains prefix could could contain task specific information like translate English to German: . Schematics of the Transformer architecture variants considered by T5 paper. ( raffel2020exploring ) T5 found the transformer based architecture to perform better than others. Pre-training Strategy T5 is trained with multi-task learning methodology, where the idea is to club multiple tasks while pre-training the model. These multiple tasks are further clubbed into two groups based on how they are trained, Unsupervised training: this includes training on the C4 dataset using the classic language model training tasks with maximum likelihood objective. For unsupervised tasks like MLM, T5 has 100 special tokens to which can be used to format the input and output text. For the sentence \"My name is Mohit Mayank\" where I want to mask \"name is\", the input is My Mohit Mayank and required output will be name is . Supervised training: this includes adding several NLP based tasks like question-answering, summarization, classification, etc. The model is trained with the curated training data in a supervised fashion, but all these tasks are transformed to work with text-in text-out format as suitable for encoder-decoder models. The data for supervised training is created separately for input and output text. For input it looks like {task_prefix}: {input_sequences} . Similarly for the output text it looks like {output_sequence} . One example could be: translate English to German: The house is wonderful. for input and Das Haus ist wunderbar. for output. This is then passed to the model to compute loss on the output part and then backpropagated to decrease the loss. Note T5 authors also released checkpoint models which are only unsupervised trained on the C4 dataset. More details and model is available here . T5 also compared different unsupervised objectives i.e. different training stratigies for unsupervised training which could lead to better performance. A visual guide to the search space is shown below. A flow chart of the exploration of unsupervised objectives by the T5 paper. ( raffel2020exploring ) To begin with, there were three High-level approaches, (1) Language modeling: where you take predict the next word based on historical words, (2) BERT-style: where you mask certain words and model predict those masked words, or (3) Deshuffling: where you shuffle the sentence and model predicts the unshuffled correct sentence as output. After experimenting with these BERT-style approach gave the best result and hence was selecte for next level of analysis. Next, different corruption startegies were tried. Originally BERT proposed MASD based approach but for language model setting, T5 authors observed Replace span works better. In replace span you mask consecutive tokens and language model predicts the masked spans. Span corruption used for unsupervised training in T5. ( raffel2020exploring ) Finally, different rate of corruption rate and corruption span length were experimentally selected. Performance score T5 paper reports following performance score on different datasets, T5 performance score on different datasets. ( raffel2020exploring ) Code T5 Inference Running T5 is super easy using HuggingFace . Let's do it, 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 # install packages ! pip install transformers # import from transformers import T5Tokenizer , T5ForConditionalGeneration # load the tokenizers and model tokenizer = T5Tokenizer . from_pretrained ( \"t5-small\" ) # vocab size is 32100. model = T5ForConditionalGeneration . from_pretrained ( \"t5-small\" ) # for a phrase get the tokenised input ids input_ids = tokenizer ( \"translate English to German: I am going to the party.\" , return_tensors = \"pt\" ) . input_ids # use the input ids to generte output outputs = model . generate ( input_ids ) # decode the output token ids to text print ( tokenizer . decode ( outputs [ 0 ], skip_special_tokens = True )) ## Output --> ## Ich werde zur Partei gehen. T5 finetuning Before we dive into finetuning, here are some tips if you are going to use PyTorch or Keras. We can use high learning rate for AdamW optimizer in range of 1e-4 and 3e-4. Btw T5 was originally pre-trained with AdaFactor optimizer. We can add task specific prefix like translate English to German: or summarize: in the input sequence if your task is similar to the ones T5 was originally pre-trained with. We should replace the PAD token ids 0 with -100 so that it is ignored from loss computation. Btw PAD token is used as start sequence token for the labels (text that is to be generated). To keep things simpler, we can use SimpleT5 , an excellent package that abstract a lot of technicalities. For dataset, we can go with Tweet sentiment data, that can be downloaded from here Some differences from training other text generation models (due to the SimpleT5 package), We don't need the Dataset class, as SimpleT5 works directly on pandas dataframe. Hence we load the data, do some initial pre-processing, split the data and return the pandas dataframe. (no need to tokenize, create Dataset class, isn't this great!?) One more point to note is that we do not need to create prompt formats for this package. This way we can separate out the input tweet and sentiment label into different columns, here source_text and target_text , respectively (Line 29 and 30) . 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 # import import pandas as pd from simplet5 import SimpleT5 from sklearn.metrics import f1_score from sklearn.model_selection import train_test_split # Data loading # ------- # Data load function def load_sentiment_dataset ( random_seed = 1 ): # load dataset and sample 10k reviews. file_path = \"../input/sentiment140/training.1600000.processed.noemoticon.csv\" df = pd . read_csv ( file_path , encoding = 'ISO-8859-1' , header = None ) df = df [[ 0 , 5 ]] df . columns = [ 'label' , 'text' ] df = df . sample ( 10000 , random_state = 1 ) # modify the label map_label = { 0 : 'negative' , 4 : 'positive' } df [ 'label' ] = df [ 'label' ] . apply ( lambda x : map_label [ x ]) # divide into test and train X_train , X_test , y_train , y_test = \\ train_test_split ( df [ 'text' ] . tolist (), df [ 'label' ] . tolist (), shuffle = True , test_size = 0.05 , random_state = random_seed , stratify = df [ 'label' ]) # transform train and test data into pandas dataframe train_data = pd . DataFrame ({ 'source_text' : X_train , 'target_text' : y_train }) test_data = pd . DataFrame ({ 'source_text' : X_test , 'target_text' : y_test }) # return return train_data , test_data # load train_df , test_df = load_sentiment_dataset () # Train # ------- # load model model = SimpleT5 () model . from_pretrained ( model_type = \"t5\" , model_name = \"t5-base\" ) # train model model . train ( train_df = train_df , eval_df = test_df , source_max_token_len = 300 , target_max_token_len = 200 , batch_size = 8 , max_epochs = 2 , outputdir = \"outputs\" , use_gpu = True ) # Test # ------- # load the best model last_epoch_model = \"...\" # put the name here model . load_model ( \"t5\" , last_epoch_model , use_gpu = True ) # for each test data perform prediction predictions = [] for index , row in test_df . iterrows (): prediction = model . predict ( row [ 'source_text' ])[ 0 ] predictions . append ( prediction ) # computer performance df = test_df . copy () df [ 'predicted_label' ] = predictions df [ 'original_label' ] = df [ 'target_text' ] print ( f1_score ( df [ 'original_label' ], df [ 'predicted_label' ], average = 'macro' )) References [1] Exploring Transfer Learning with T5: the Text-To-Text Transfer Transformer - Link [2] T5 finetuning tips - HF forum","title":"T5"},{"location":"natural_language_processing/T5/#t5","text":"","title":"T5"},{"location":"natural_language_processing/T5/#introduction","text":"T5 stands for \"Text-to-Text Transfer Transformer\". It was released by Google on 2020. As the name suggests, it's a tranformer based encoder-decoder model used for text generation. For more details about other text generation models, refer the Text Generation chapter. In contrast to other famous Transformer based models like BERT or GPT, which is made up of either the encoder or decoder part of the Transformer, T5 paper showcase that using the complete encoder-decoder architecture is better than only using decoder. Apart from this, the paper also curated and released Colossal Clean Crawled Corpus (C4) - a huge crawled and cleaned dataset for pre-training language model using self-supervised learning. ( raffel2020exploring ) Transformer architecture. Left part is the encoder, right part is the decoder. T5's architecture is very similar to this one. ( vaswani2017attention raffel2020exploring ) Due to this nature of T5, for training or finetuning, the model requires a pair of input and output sequences/text. Some example tasks that can be performed using T5 is shown below, T5 text-to-text framework examples. See: Google Blog Based on the original T5, there has been several variations explored such as, (refer T5 @ HuggingFace ) T5v1.1: T5v1.1 is an improved version of T5 with some architectural tweaks, and is pre-trained on C4 only without mixing in the supervised tasks. mT5: mT5 is a multilingual T5 model. It is pre-trained on the mC4 corpus, which includes 101 languages. byT5: byT5 is a T5 model pre-trained on byte sequences rather than SentencePiece subword token sequences. Long-T5: For use case where we need to process longer input ( Refer ) Note As mentioned above, for multi-lingual purpose refer mT5 which was trained on >100 languages. That said, original T5 was also trained on translation task from English to German, French and Romanian. So the output can sometimes contains tokens from these languages!","title":"Introduction"},{"location":"natural_language_processing/T5/#paper-details","text":"","title":"Paper details"},{"location":"natural_language_processing/T5/#transformer-architecture","text":"To compare different architectures suitable for languague models, T5 authors considered basically three varieties, Encoder-Decoder: A standard encoder-decoder architecture uses fully visible masking in the encoder and the encoder-decoder attention, with causal masking (attention mask) in the decoder. Masking is done to make sure the output at a position doesn't attend to future output for prediction. Language model: A language model consists of a single Transformer layer stack and is fed the concatenation of the input and target, using a causal mask throughout. As usual with LMs, the output only attends to the past input or output. Prefix LM: Adding a prefix to a language model corresponds to allowing fully-visible masking over a portion of the input. It is very similar to LM, just that any output will attend to a certain portion of the input that contains prefix could could contain task specific information like translate English to German: . Schematics of the Transformer architecture variants considered by T5 paper. ( raffel2020exploring ) T5 found the transformer based architecture to perform better than others.","title":"Transformer Architecture"},{"location":"natural_language_processing/T5/#pre-training-strategy","text":"T5 is trained with multi-task learning methodology, where the idea is to club multiple tasks while pre-training the model. These multiple tasks are further clubbed into two groups based on how they are trained, Unsupervised training: this includes training on the C4 dataset using the classic language model training tasks with maximum likelihood objective. For unsupervised tasks like MLM, T5 has 100 special tokens to which can be used to format the input and output text. For the sentence \"My name is Mohit Mayank\" where I want to mask \"name is\", the input is My Mohit Mayank and required output will be name is . Supervised training: this includes adding several NLP based tasks like question-answering, summarization, classification, etc. The model is trained with the curated training data in a supervised fashion, but all these tasks are transformed to work with text-in text-out format as suitable for encoder-decoder models. The data for supervised training is created separately for input and output text. For input it looks like {task_prefix}: {input_sequences} . Similarly for the output text it looks like {output_sequence} . One example could be: translate English to German: The house is wonderful. for input and Das Haus ist wunderbar. for output. This is then passed to the model to compute loss on the output part and then backpropagated to decrease the loss. Note T5 authors also released checkpoint models which are only unsupervised trained on the C4 dataset. More details and model is available here . T5 also compared different unsupervised objectives i.e. different training stratigies for unsupervised training which could lead to better performance. A visual guide to the search space is shown below. A flow chart of the exploration of unsupervised objectives by the T5 paper. ( raffel2020exploring ) To begin with, there were three High-level approaches, (1) Language modeling: where you take predict the next word based on historical words, (2) BERT-style: where you mask certain words and model predict those masked words, or (3) Deshuffling: where you shuffle the sentence and model predicts the unshuffled correct sentence as output. After experimenting with these BERT-style approach gave the best result and hence was selecte for next level of analysis. Next, different corruption startegies were tried. Originally BERT proposed MASD based approach but for language model setting, T5 authors observed Replace span works better. In replace span you mask consecutive tokens and language model predicts the masked spans. Span corruption used for unsupervised training in T5. ( raffel2020exploring ) Finally, different rate of corruption rate and corruption span length were experimentally selected.","title":"Pre-training Strategy"},{"location":"natural_language_processing/T5/#performance-score","text":"T5 paper reports following performance score on different datasets, T5 performance score on different datasets. ( raffel2020exploring )","title":"Performance score"},{"location":"natural_language_processing/T5/#code","text":"","title":"Code"},{"location":"natural_language_processing/T5/#t5-inference","text":"Running T5 is super easy using HuggingFace . Let's do it, 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 # install packages ! pip install transformers # import from transformers import T5Tokenizer , T5ForConditionalGeneration # load the tokenizers and model tokenizer = T5Tokenizer . from_pretrained ( \"t5-small\" ) # vocab size is 32100. model = T5ForConditionalGeneration . from_pretrained ( \"t5-small\" ) # for a phrase get the tokenised input ids input_ids = tokenizer ( \"translate English to German: I am going to the party.\" , return_tensors = \"pt\" ) . input_ids # use the input ids to generte output outputs = model . generate ( input_ids ) # decode the output token ids to text print ( tokenizer . decode ( outputs [ 0 ], skip_special_tokens = True )) ## Output --> ## Ich werde zur Partei gehen.","title":"T5 Inference"},{"location":"natural_language_processing/T5/#t5-finetuning","text":"Before we dive into finetuning, here are some tips if you are going to use PyTorch or Keras. We can use high learning rate for AdamW optimizer in range of 1e-4 and 3e-4. Btw T5 was originally pre-trained with AdaFactor optimizer. We can add task specific prefix like translate English to German: or summarize: in the input sequence if your task is similar to the ones T5 was originally pre-trained with. We should replace the PAD token ids 0 with -100 so that it is ignored from loss computation. Btw PAD token is used as start sequence token for the labels (text that is to be generated). To keep things simpler, we can use SimpleT5 , an excellent package that abstract a lot of technicalities. For dataset, we can go with Tweet sentiment data, that can be downloaded from here Some differences from training other text generation models (due to the SimpleT5 package), We don't need the Dataset class, as SimpleT5 works directly on pandas dataframe. Hence we load the data, do some initial pre-processing, split the data and return the pandas dataframe. (no need to tokenize, create Dataset class, isn't this great!?) One more point to note is that we do not need to create prompt formats for this package. This way we can separate out the input tweet and sentiment label into different columns, here source_text and target_text , respectively (Line 29 and 30) . 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 # import import pandas as pd from simplet5 import SimpleT5 from sklearn.metrics import f1_score from sklearn.model_selection import train_test_split # Data loading # ------- # Data load function def load_sentiment_dataset ( random_seed = 1 ): # load dataset and sample 10k reviews. file_path = \"../input/sentiment140/training.1600000.processed.noemoticon.csv\" df = pd . read_csv ( file_path , encoding = 'ISO-8859-1' , header = None ) df = df [[ 0 , 5 ]] df . columns = [ 'label' , 'text' ] df = df . sample ( 10000 , random_state = 1 ) # modify the label map_label = { 0 : 'negative' , 4 : 'positive' } df [ 'label' ] = df [ 'label' ] . apply ( lambda x : map_label [ x ]) # divide into test and train X_train , X_test , y_train , y_test = \\ train_test_split ( df [ 'text' ] . tolist (), df [ 'label' ] . tolist (), shuffle = True , test_size = 0.05 , random_state = random_seed , stratify = df [ 'label' ]) # transform train and test data into pandas dataframe train_data = pd . DataFrame ({ 'source_text' : X_train , 'target_text' : y_train }) test_data = pd . DataFrame ({ 'source_text' : X_test , 'target_text' : y_test }) # return return train_data , test_data # load train_df , test_df = load_sentiment_dataset () # Train # ------- # load model model = SimpleT5 () model . from_pretrained ( model_type = \"t5\" , model_name = \"t5-base\" ) # train model model . train ( train_df = train_df , eval_df = test_df , source_max_token_len = 300 , target_max_token_len = 200 , batch_size = 8 , max_epochs = 2 , outputdir = \"outputs\" , use_gpu = True ) # Test # ------- # load the best model last_epoch_model = \"...\" # put the name here model . load_model ( \"t5\" , last_epoch_model , use_gpu = True ) # for each test data perform prediction predictions = [] for index , row in test_df . iterrows (): prediction = model . predict ( row [ 'source_text' ])[ 0 ] predictions . append ( prediction ) # computer performance df = test_df . copy () df [ 'predicted_label' ] = predictions df [ 'original_label' ] = df [ 'target_text' ] print ( f1_score ( df [ 'original_label' ], df [ 'predicted_label' ], average = 'macro' ))","title":"T5 finetuning"},{"location":"natural_language_processing/T5/#references","text":"[1] Exploring Transfer Learning with T5: the Text-To-Text Transfer Transformer - Link [2] T5 finetuning tips - HF forum","title":"References"},{"location":"natural_language_processing/data_to_text_generation/","text":"Warning This page is still ongoing modifications. Please check back after some time or contact me if it has been a while! Sorry for the inconvenience Introduction Data to Text generation is a task with structured data as input and unstructured text as output. As an example, consider the use case of trying to summarize a table data, where table is the input and text as output. KELM [3] is an interesting example. In the paper , the authors try to train a language model using Knowledge graph triplets. But as a KG stores structured data, the first task done by authors was to create subgraphs of KG and verbalize it (as shown below) An example illustration of converting an entity subgraph (in bubbles) into synthetic natural sentences (far right). [3] For this they \"developed a verbalization pipeline named \u201cText from KG Generator\u201d (TEKGEN), which is made up of the following components: a large training corpus of heuristically aligned Wikipedia text and Wikidata KG triples, a text-to-text generator (T5) to convert the KG triples to text, an entity subgraph creator for generating groups of triples to be verbalized together, and finally, a post-processing filter to remove low quality outputs. The result is a corpus containing the entire Wikidata KG as natural text, which we call the Knowledge-Enhanced Language Model (KELM) corpus. It consists of ~18M sentences spanning ~45M triples and ~1500 relations.\" [3] References [1] NLP Progress - Data-to-Text Generation [2] The 2020 Bilingual, Bi-Directional WebNLG+ Shared Task: Overview and Evaluation Results (WebNLG+ 2020) [3] KELM: Integrating Knowledge Graphs with Language Model Pre-training Corpora","title":"Data-to-Text Generation"},{"location":"natural_language_processing/data_to_text_generation/#introduction","text":"Data to Text generation is a task with structured data as input and unstructured text as output. As an example, consider the use case of trying to summarize a table data, where table is the input and text as output. KELM [3] is an interesting example. In the paper , the authors try to train a language model using Knowledge graph triplets. But as a KG stores structured data, the first task done by authors was to create subgraphs of KG and verbalize it (as shown below) An example illustration of converting an entity subgraph (in bubbles) into synthetic natural sentences (far right). [3] For this they \"developed a verbalization pipeline named \u201cText from KG Generator\u201d (TEKGEN), which is made up of the following components: a large training corpus of heuristically aligned Wikipedia text and Wikidata KG triples, a text-to-text generator (T5) to convert the KG triples to text, an entity subgraph creator for generating groups of triples to be verbalized together, and finally, a post-processing filter to remove low quality outputs. The result is a corpus containing the entire Wikidata KG as natural text, which we call the Knowledge-Enhanced Language Model (KELM) corpus. It consists of ~18M sentences spanning ~45M triples and ~1500 relations.\" [3]","title":"Introduction"},{"location":"natural_language_processing/data_to_text_generation/#references","text":"[1] NLP Progress - Data-to-Text Generation [2] The 2020 Bilingual, Bi-Directional WebNLG+ Shared Task: Overview and Evaluation Results (WebNLG+ 2020) [3] KELM: Integrating Knowledge Graphs with Language Model Pre-training Corpora","title":"References"},{"location":"natural_language_processing/explainable_ai_llm/","text":"Explainable AI: Language Models Hold your large language models accountable by learning how to \"explain\" the predictions. Introduction Just like a coin, explainability in AI has two faces\u200a-\u200aone it shows to the developers (who actually build the models) and the other to the users (the end customers). The former face (IE i.e. intrinsic explainability) is a technical indicator to the builder that explains the working of the model. Whereas the latter (EE i.e. extrinsic explainability) is proof to the customers about the model's predictions. While IE is required for any reasonable model improvement, we need EE for factual confirmation. A simple layman who ends up using the model's prediction needs to know why is the model suggesting something. This article provides a brief introduction and famous techniques used to infer both types of explainabilities, with a focus on large language models. Large Language models As the name suggests, language models (LMs) try to model a language by creating probability distribution over the sequence of tokens (that can be words). This can be applied to the text generation task, as once the model is good enough with the probability, it can start predicting the next words provided some contextual words (prompts). This idea forms the basis of the research in deep learning-based (DL) Natural Language Processing (NLP), where newer and SotA models are becoming better with the probability distribution. And recently several powerful models like GPT-3 are able to generate text which is very hard to distinguish from human-written text! GPT-3 is used to predict the next words for the prompt \"To be or not to be\". The output is quite impressive! [By Author] LLMs are leading the scoreboard for several tasks in NLP. But one existing problem with them (or any DL model in fact) is that they are inherently black boxes i.e. it becomes extremely difficult to explain the predictions. Let us try to shine some grey lights on these black boxes! Intrinsic explainability Intrinsic explainability tries to provide technical clarity on why the model made some predictions. Large LMs (LLMs) are mostly DL-based models and while they become better with predictions given more data or network parameters, it comes with a cost of explainability. They are not straight forward explainable and we need to derive ways to infer why a model made the prediction. Below let's briefly go through some of those ways. Attention-based The recent LLMs utilize Transformer-based architecture for enhanced performance. What makes Transformers unique is the presence of multiple attention layers and heads to capture bidirectional and context-aware token representation. Each attention head learns certain patterns that could help in language modeling. These patterns could be positional (attend to the previous word) or lexical (attend to acronyms, synonyms, etc) in nature. And when we combine multiple such heads in multiple layers, the overall model is able to capture sophisticated patterns and provide accurate predictions. These kinds of internal and low-level patterns can be observed by using attention score visualization tools like bertviz [1]. There could be two major types of visualizations, (1) Attention head visualization\u200a-\u200awhich could be helpful to see how the model attends to certain words with another word in context, and (2) Neuron visualization\u200a-\u200awhich could be used to connect the model behaviors to neuron values. For the Attention head visualization, you can select the model, the layer, and the head to explore the different attention patterns. One example is shown below. Example of Attention head view to detect association of words in the model. Also useful to detect gender biases. [1] Here the layer 5 and 11th head of the model (2nd last blue color box from the color palette) seems to incorporate the gender-specific patterns (lexical) in the language. That is why, the attention score for gender-related words (She, girl), (He, boy), (He, Bob), etc is high. As such, this head could be used to identify gender bias as well, which is evident from the associations\u200a-\u200a (nurse, She) and (doctor, He). Similar biases can be identified from other heads. The neuron view is useful to drill down even deeper into the attentions scores to identify the specific neuron leading to certain behaviors in the model. One such example is shown below, Neuron view of GPT-2 for layer 1, head 10 [1] Neuron view of BERT for layer 0, head 0 (same one depicted above). Positive and negative values are colored blue and orange, respectively, with color saturation based on the magnitude of the value. As with the attention-head view, connecting lines are weighted based on attention between the words. Saliency methods Attention presents a very myopic view as they are limited to a certain part of language models. As such they just provide the behavior for that part of the model. As an analogy, it's like explaining just one line of the complete code. This ideation has led to a debate on whether attention really presents the true explainability of the complete model?! To mitigate these concerns, there has been renewed interest in old, tried, and tested saliency methods [2]. These are, Gradient-based: where we compute the gradient of the neural network's outcome with respect to the input. The idea is quite simple\u200a-\u200a as we compute gradients of the output wrt to the different parameters to find the importance and magnitude of correction needed for the parameters, why not do the same but for the inputs? This computation provides us a holistic view of the importance of each input wrt to the output. Propagation-based: Gradient-based approach is a plain vanilla approach where backpropagation happens without additional computation at any layer i.e. it's a simple inverse (not mathematically) of the forward pass. In the propagation-based approach, we perform relevance-based modifications at each layer. It can be thought of as an enhanced version of the gradient-based approach. Occlusion based: where we measure the importance of input on the output by erasing the input and estimating the effect on the output. It can be done by either recomputing output with the particular input missing or a mask in its place. Intuitively, the variation in the output will be high in case the input is important, and close to zero otherwise. There are several open-source packages like Ecco, that provide the option to visualize the saliency measure. One example is shown below, Gradient-based method to visualize the importance of the input words for the next word prediction (India) [By Author] Here, for the input \"New Delhi is the capital of\", LLM returned \"India\" which is factually correct. But intuitively, we would like to know which certain part of the input motivated the model to make such a prediction. The important measure is highlighted with color in the image, and as visible it gave a high score to \"New Delhi\" and \"capital\"! Extrinsic explainability The intrinsic measures fall short of covering the \"needs\" of explainability due to two reasons. Firstly, while it may help the model developer with access to internal metrics, it becomes quite difficult for the stakeholders on the other end of the spectrum to make sense of these complicated numbers. We are talking about the end-user of the model who could be a doctor in healthcare, engineers in mechanical, sales executive in the sales domain, and so on. They care more about factual correctness than the reason behind a model's prediction. Secondly, even the model developers will be concerned if the internal metrics make sense but the output doesn't (output is incorrect). The problem with the existing language models is that they contain all their knowledge within the parameters. GPT-3 is made up of 175 Billion parameters and it has been shown to work on a large number of downstream tasks. It generates the text based on the information it learned and stored in its parameters during the training and fine-tuning phase. Take one task of question answering as an example, where the question is \"Who is the Prime Minister of India?\". While the model may answer it correctly now, how about after the next election? The answer is obvious, it will fail. This is because the information gained is constant and would need re-training to calibrate constantly with the latest events in the world (which is a costly affair [3]). Because of all these reasons, there has been a research interest in enhancing the language models by complimenting them with a connection to a dynamic knowledge base\u200a-\u200athat is kept up to date just like any other database. Some examples by the leading AI teams in the world are as follows, GopherCite [4] from DeepMind is an enhancement of their language model named Gopher. It was created to solve the existing problem of \"hallucinations\" in the Gopher model. In their terms, hallucination of the model leads to incorrect, sometimes nonsensical, text generations. Because of this, humans are required to not take the output for granted and perform proof checks. GopherCite removes that human dependency to some extent by using Google search to find relevant quotes as proof for its prediction. In case no such high probable proofs are found, it even admits defeat instead of providing a wrong answer! Gradient-based method to visualize the importance of the input words for the next word prediction (India) [By Author] WebGPT [5] from OpenAI is an attempt by the creators of GPT-3 to solve the hallucination problem. They complimented GPT with an additional model that interacts with text-based web browsers to find the most relevant web pages for the asked question. For this, the model learned basic commands like \"Search \u2026\", \"Find in page: \u2026\", or \"Quote: \u2026\". The model goes through web pages to find passages and then uses these to compose an answer. The final output contains references to the source web pages as an attempt to legitimize the results. Final output generated by WebGPT for the question \"How do neural networks work?\". Note the presence of References at the bottom. Taken from the blog. LaMDA [6] from Google is a language model for dialog-based interactions. A lot of times, the conversation could become complicated (involving non-fictional characters or real entities), and then it becomes imperative that the model knows the correct context and background knowledge. Based on the conversation, LaMDA can call an external information retrieval system to improve the factual groundedness of its responses. Example of a conversation with Mt. Everest using LaMDA. Taken from blog. References [1] A Multiscale Visualization of Attention in the Transformer Model [2] Why use attention as explanation when we have saliency methods? [3] Cost of training GPT-3 | Reddit [4] Teaching language models to support answers with verified quotes [5] WebGPT: Browser-assisted question-answering with human feedback [6] LaMDA: Language Models for Dialog Applications Cheers.","title":"Explainable AI: Language\u00a0Models"},{"location":"natural_language_processing/explainable_ai_llm/#explainable-ai-language-models","text":"Hold your large language models accountable by learning how to \"explain\" the predictions.","title":"Explainable AI: Language\u00a0Models"},{"location":"natural_language_processing/explainable_ai_llm/#introduction","text":"Just like a coin, explainability in AI has two faces\u200a-\u200aone it shows to the developers (who actually build the models) and the other to the users (the end customers). The former face (IE i.e. intrinsic explainability) is a technical indicator to the builder that explains the working of the model. Whereas the latter (EE i.e. extrinsic explainability) is proof to the customers about the model's predictions. While IE is required for any reasonable model improvement, we need EE for factual confirmation. A simple layman who ends up using the model's prediction needs to know why is the model suggesting something. This article provides a brief introduction and famous techniques used to infer both types of explainabilities, with a focus on large language models.","title":"Introduction"},{"location":"natural_language_processing/explainable_ai_llm/#large-language-models","text":"As the name suggests, language models (LMs) try to model a language by creating probability distribution over the sequence of tokens (that can be words). This can be applied to the text generation task, as once the model is good enough with the probability, it can start predicting the next words provided some contextual words (prompts). This idea forms the basis of the research in deep learning-based (DL) Natural Language Processing (NLP), where newer and SotA models are becoming better with the probability distribution. And recently several powerful models like GPT-3 are able to generate text which is very hard to distinguish from human-written text! GPT-3 is used to predict the next words for the prompt \"To be or not to be\". The output is quite impressive! [By Author] LLMs are leading the scoreboard for several tasks in NLP. But one existing problem with them (or any DL model in fact) is that they are inherently black boxes i.e. it becomes extremely difficult to explain the predictions. Let us try to shine some grey lights on these black boxes!","title":"Large Language\u00a0models"},{"location":"natural_language_processing/explainable_ai_llm/#intrinsic-explainability","text":"Intrinsic explainability tries to provide technical clarity on why the model made some predictions. Large LMs (LLMs) are mostly DL-based models and while they become better with predictions given more data or network parameters, it comes with a cost of explainability. They are not straight forward explainable and we need to derive ways to infer why a model made the prediction. Below let's briefly go through some of those ways.","title":"Intrinsic explainability"},{"location":"natural_language_processing/explainable_ai_llm/#attention-based","text":"The recent LLMs utilize Transformer-based architecture for enhanced performance. What makes Transformers unique is the presence of multiple attention layers and heads to capture bidirectional and context-aware token representation. Each attention head learns certain patterns that could help in language modeling. These patterns could be positional (attend to the previous word) or lexical (attend to acronyms, synonyms, etc) in nature. And when we combine multiple such heads in multiple layers, the overall model is able to capture sophisticated patterns and provide accurate predictions. These kinds of internal and low-level patterns can be observed by using attention score visualization tools like bertviz [1]. There could be two major types of visualizations, (1) Attention head visualization\u200a-\u200awhich could be helpful to see how the model attends to certain words with another word in context, and (2) Neuron visualization\u200a-\u200awhich could be used to connect the model behaviors to neuron values. For the Attention head visualization, you can select the model, the layer, and the head to explore the different attention patterns. One example is shown below. Example of Attention head view to detect association of words in the model. Also useful to detect gender biases. [1] Here the layer 5 and 11th head of the model (2nd last blue color box from the color palette) seems to incorporate the gender-specific patterns (lexical) in the language. That is why, the attention score for gender-related words (She, girl), (He, boy), (He, Bob), etc is high. As such, this head could be used to identify gender bias as well, which is evident from the associations\u200a-\u200a (nurse, She) and (doctor, He). Similar biases can be identified from other heads. The neuron view is useful to drill down even deeper into the attentions scores to identify the specific neuron leading to certain behaviors in the model. One such example is shown below, Neuron view of GPT-2 for layer 1, head 10 [1] Neuron view of BERT for layer 0, head 0 (same one depicted above). Positive and negative values are colored blue and orange, respectively, with color saturation based on the magnitude of the value. As with the attention-head view, connecting lines are weighted based on attention between the words.","title":"Attention-based"},{"location":"natural_language_processing/explainable_ai_llm/#saliency-methods","text":"Attention presents a very myopic view as they are limited to a certain part of language models. As such they just provide the behavior for that part of the model. As an analogy, it's like explaining just one line of the complete code. This ideation has led to a debate on whether attention really presents the true explainability of the complete model?! To mitigate these concerns, there has been renewed interest in old, tried, and tested saliency methods [2]. These are, Gradient-based: where we compute the gradient of the neural network's outcome with respect to the input. The idea is quite simple\u200a-\u200a as we compute gradients of the output wrt to the different parameters to find the importance and magnitude of correction needed for the parameters, why not do the same but for the inputs? This computation provides us a holistic view of the importance of each input wrt to the output. Propagation-based: Gradient-based approach is a plain vanilla approach where backpropagation happens without additional computation at any layer i.e. it's a simple inverse (not mathematically) of the forward pass. In the propagation-based approach, we perform relevance-based modifications at each layer. It can be thought of as an enhanced version of the gradient-based approach. Occlusion based: where we measure the importance of input on the output by erasing the input and estimating the effect on the output. It can be done by either recomputing output with the particular input missing or a mask in its place. Intuitively, the variation in the output will be high in case the input is important, and close to zero otherwise. There are several open-source packages like Ecco, that provide the option to visualize the saliency measure. One example is shown below, Gradient-based method to visualize the importance of the input words for the next word prediction (India) [By Author] Here, for the input \"New Delhi is the capital of\", LLM returned \"India\" which is factually correct. But intuitively, we would like to know which certain part of the input motivated the model to make such a prediction. The important measure is highlighted with color in the image, and as visible it gave a high score to \"New Delhi\" and \"capital\"!","title":"Saliency methods"},{"location":"natural_language_processing/explainable_ai_llm/#extrinsic-explainability","text":"The intrinsic measures fall short of covering the \"needs\" of explainability due to two reasons. Firstly, while it may help the model developer with access to internal metrics, it becomes quite difficult for the stakeholders on the other end of the spectrum to make sense of these complicated numbers. We are talking about the end-user of the model who could be a doctor in healthcare, engineers in mechanical, sales executive in the sales domain, and so on. They care more about factual correctness than the reason behind a model's prediction. Secondly, even the model developers will be concerned if the internal metrics make sense but the output doesn't (output is incorrect). The problem with the existing language models is that they contain all their knowledge within the parameters. GPT-3 is made up of 175 Billion parameters and it has been shown to work on a large number of downstream tasks. It generates the text based on the information it learned and stored in its parameters during the training and fine-tuning phase. Take one task of question answering as an example, where the question is \"Who is the Prime Minister of India?\". While the model may answer it correctly now, how about after the next election? The answer is obvious, it will fail. This is because the information gained is constant and would need re-training to calibrate constantly with the latest events in the world (which is a costly affair [3]). Because of all these reasons, there has been a research interest in enhancing the language models by complimenting them with a connection to a dynamic knowledge base\u200a-\u200athat is kept up to date just like any other database. Some examples by the leading AI teams in the world are as follows, GopherCite [4] from DeepMind is an enhancement of their language model named Gopher. It was created to solve the existing problem of \"hallucinations\" in the Gopher model. In their terms, hallucination of the model leads to incorrect, sometimes nonsensical, text generations. Because of this, humans are required to not take the output for granted and perform proof checks. GopherCite removes that human dependency to some extent by using Google search to find relevant quotes as proof for its prediction. In case no such high probable proofs are found, it even admits defeat instead of providing a wrong answer! Gradient-based method to visualize the importance of the input words for the next word prediction (India) [By Author] WebGPT [5] from OpenAI is an attempt by the creators of GPT-3 to solve the hallucination problem. They complimented GPT with an additional model that interacts with text-based web browsers to find the most relevant web pages for the asked question. For this, the model learned basic commands like \"Search \u2026\", \"Find in page: \u2026\", or \"Quote: \u2026\". The model goes through web pages to find passages and then uses these to compose an answer. The final output contains references to the source web pages as an attempt to legitimize the results. Final output generated by WebGPT for the question \"How do neural networks work?\". Note the presence of References at the bottom. Taken from the blog. LaMDA [6] from Google is a language model for dialog-based interactions. A lot of times, the conversation could become complicated (involving non-fictional characters or real entities), and then it becomes imperative that the model knows the correct context and background knowledge. Based on the conversation, LaMDA can call an external information retrieval system to improve the factual groundedness of its responses. Example of a conversation with Mt. Everest using LaMDA. Taken from blog.","title":"Extrinsic explainability"},{"location":"natural_language_processing/explainable_ai_llm/#references","text":"[1] A Multiscale Visualization of Attention in the Transformer Model [2] Why use attention as explanation when we have saliency methods? [3] Cost of training GPT-3 | Reddit [4] Teaching language models to support answers with verified quotes [5] WebGPT: Browser-assisted question-answering with human feedback [6] LaMDA: Language Models for Dialog Applications Cheers.","title":"References"},{"location":"natural_language_processing/interview_questions/","text":"Here are some questions and their answers to make you ready for your next interview. Best of luck Question Answer What are the different types of reasoning tasks in NLP? Arithmetic Reasoning: Arithmetic reasoning is the ability of an NLP system to perform mathematical operations on numerical data. This can include basic arithmetic operations such as addition, subtraction, multiplication, and division as well as more complex operations such as algebraic equations and calculus. Commonsense Reasoning: Commonsense reasoning refers to the ability of an NLP system to make deductions based on the knowledge and information that is commonly understood by humans. This includes understanding social norms, cultural contexts, and everyday life experiences. ( StrategyQA is a sample dataset that contains True/False questions like \"Did Aristotle use a laptop?\") Symbolic Reasoning: Symbolic reasoning involves the ability of an NLP system to manipulate and reason about symbolic representations of information, such as words, phrases, and sentences. This includes tasks such as parsing, string operations, semantic role labeling and entity recognition. (Last Letter Concatenation is a sample dataset with questions like \"Take the last letters of the words in 'Lady Gaga' and concatenate them\") Logic Reasoning: Logic reasoning refers to the ability of an NLP system to perform logical deductions based on formal rules of inference. This can include tasks such as identifying logical fallacies, determining the validity of arguments, and drawing conclusions based on deductive reasoning. (Date understanding is a sample dataset with questions like \"Today is Christmas Eve 1937, what is the date tomorrow in MM/DD/YYYY?\") Question Answer What are word embeddings in NLP? Word embeddings are a type of representation for words in NLP. They are a dense vector representation of a word, learned from the data using techniques such as word2vec or GloVe. The embeddings capture the semantics of the words, meaning that words with similar meanings will have similar vectors. Word embeddings are used as input in many NLP tasks such as language translation, text classification, and text generation. Question Answer What is Sentence Encoding? Sentence encoding is the process of converting a sentence into a fixed-length vector representation, also known as sentence embeddings. This is done by using techniques such as bag-of-words, TF-IDF, or BERT-based models. Sentence encodings can be used as input in various NLP tasks such as text classification, text generation, and text similarity. Several algorithms first tokenize the sentence in words or tokens, compute thir embedding and then aggregate them (min, max, mean, etc) to get the sentence embedding. Question Answer Explain the concept of attention mechanism in NLP? Attention mechanism is a way to weight different parts of the input in a neural network, giving more importance to certain parts of the input than others. It is commonly used in NLP tasks such as machine translation, where the model needs to focus on different parts of the input sentence at different times. Attention mechanisms can be implemented in various ways, such as additive attention ( \\(\ud835\udc44+\ud835\udc3e\\) ) and dot-product attention ( \\(\ud835\udc44\ud835\udc3e^{\ud835\udc47}\\) ) Question Answer What are transformer models in NLP? Transformer models are a type of neural network architecture that have been successful in various NLP tasks such as language translation and language understanding. They were introduced in the transformer paper and use self-attention mechanism to weigh the different parts of the input. This allows the model to efficiently process long input sequences and handle the dependencies between the words. Refer for more details. Question Answer Can you explain the concept of Named Entity Recognition (NER) in NLP? Named Entity Recognition (NER) is a subtask of information extraction that seeks to locate and classify named entities in text into predefined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc. NER systems can be rule-based or based on machine learning, and are used in a wide range of applications such as information retrieval, question answering and text summarization. Question Answer Explain Part-of-Speech (POS) tagging in NLP? Part-of-Speech (POS) tagging is the process of marking each word in a text with its corresponding POS tag. This is a fundamental step in many NLP tasks such as parsing, text summarization, and information extraction. POS tagging can be rule-based or based on machine learning, and is typically done using algorithms such as Hidden Markov Models (HMMs) or Conditional Random Fields (CRFs). Question Answer Can you explain the concept of Language Modeling in NLP? Language modeling is the task of predicting the next word in a sentence, given the previous words. This is done by training a model on a large dataset of text, which learns the probability distribution of the words in the language. Language models are used in a wide range of NLP tasks such as machine translation, text generation, and speech recognition. Question Answer Can you explain the concept of Text Summarization? Text summarization is the task of generating a shorter version of a text that retains the most important information. There are two main types of text summarization: extractive and abstractive. Extractive summarization selects important sentences or phrases from the text to form the summary, while abstractive summarization generates new text that captures the meaning of the original text. Question Answer What is Sentiment Analysis? Sentiment analysis is the task of determining the sentiment or emotion of a piece of text. This is typically done by classifying the text as positive, negative, or neutral. Sentiment analysis can be done using a variety of techniques such as rule-based systems, machine learning, and deep learning. It is used in a wide range of applications such as customer feedback analysis and social media analysis. Question Answer Can you explain the concept of Dependency Parsing? Dependency parsing is the task of analyzing the grammatical structure of a sentence, identifying the dependencies between the words. This is done by creating a dependency parse tree, which represents the grammatical relationships between the words in a sentence. Dependency parsing is a fundamental step in many NLP tasks such as machine translation, text summarization, and information extraction. Question Answer Explain the Coreference Resolution task in NLP? Coreference resolution is the task of identifying when different expressions in a text refer to the same entity. This is done by analyzing the text and identifying when two or more expressions have the same referent. Coreference resolution is a fundamental step in many NLP tasks such as machine translation, text summarization, and information extraction. In this example text, \"Mohit lives in Pune and he works as a Data Scientist\" , the co-reference resolution will identify \"Mohit\" and \"he\" as belonging to the same entity. Question Answer Explain Stemming and Lemmatization in NLP? Stemming is the process of reducing inflected (or sometimes derived) words to their word stem, base or root form. This is done by using a stemmer algorithm which removes the suffixes or prefixes from the word. The goal of stemming is to reduce the dimensionality of the text data, grouping together the inflected forms of a word so they can be analyzed as a single term, which can be useful for tasks such as text classification and information retrieval. Lemmatization is the process of reducing a word to its base form, which is called the lemma. This is done by using a lemmatizer algorithm which takes into consideration the context and the part of speech of the word. The goal of lemmatization is to reduce the dimensionality of the text data and group together the different forms of a word so they can be analyzed as a single term, which can be useful for tasks such as text classification and information retrieval. Note An obvious difference is that Lemmatization consider the grammar of the sentence while Stemming only consider the word. Question Answer What is Text Classification? Text classification is the task of assigning predefined categories or labels to a given text. This is done by training a model on a labeled dataset of text, which learns to predict the label of new text. Text classification is used in a wide range of applications such as sentiment analysis, spam detection, and topic classification. There are multiple types of text classification such as, Binary classification: where there only two classes (ex: positive vs negative) Multi-class classification : where there are more than 2 classes (ex: positive, negative and neutral) Multi-label classification : when there are two or more classes and each text can have more than 1 class/label assigned to them (ex: single text can have some positive phrase and negative phrase) Question Answer What are Dialogue Systems in NLP? A Dialogue system, also known as a conversational agent or chatbot, is a computer program that can interact with human users through natural language. It can understand the user's input, generate appropriate responses, and carry out tasks such as answering questions, booking a flight, or making a reservation. Dialogue systems can be rule-based, or based on machine learning and deep learning, and can be integrated into various platforms such as smartphones, websites, and messaging apps. Question Answer Please explain the concept of Text Generation? Text generation is the task of generating new text that is similar to the text it was trained on. This is done by training a model on a large dataset of text, which learns the probability distribution of the words in the language. Text generation can be used for various applications such as chatbot, text completion and summarization. Refer for more details. Question Answer Can you explain the concept of Text Similarity in NLP? Text Similarity is the task of determining how similar two pieces of text are to each other. This is done by comparing the text using various similarity measures such as cosine similarity, Jaccard similarity, or Levenshtein distance. Text similarity can be used in a wide range of applications such as plagiarism detection and text retrieval. Refer for more details. Question Answer Please explain Text Clustering? Text Clustering is the process of grouping similar texts together. This is done by using clustering algorithms such as K-means, Hierarchical Clustering, or DBSCAN. Text clustering can be used in a wide range of applications such as topic modeling, sentiment analysis, and text summarization. This is usually a two step process, first the text is converted to a representation (usually by text embedding algorithms) and then a clustering algorithm is used to create clusters. Question Answer What is Named Entity Disambiguation (NED)? Named Entity Disambiguation (NED) is the task of determining which entity (from a database) a mention (from text or doc) refers to, from a set of candidate entities. This is done by using techniques such as string matching, co-reference resolution, or graph-based methods. NED is important for tasks such as information extraction and knowledge base population. For example, NED will process a wikipedia page and map \"Mohit M.\", \"M. Mayank\", \"Mohit\" and similar named entities with \"Mohit Mayank\" present in the database. Question Answer What is the difference between a feedforward neural network and a recurrent neural network? A feedforward neural network is a type of neural network in which the data flows in one direction, from input to output. There are no loops or connections that allow information to flow in a cyclical manner. On the other hand, a recurrent neural network (RNN) is a type of neural network that allows information to flow in a cyclical manner, with connections between neurons allowing information to be passed from one step of the process to the next. RNNs are useful for tasks such as language modeling and speech recognition, where the current input is dependent on the previous inputs. Question Answer Is BERT a Text Generation model? Short answer is no. BERT is not a text generation model or a language model because the probability of the predicting a token in masked input is dependent on the context of the token. This context is bidirectional, hence the model is not able to predict the next token in the sequence accurately with only one directional context (as expected for language model) . Question Answer What is weight tying in language model? Weight-tying is where you have a language model and use the same weight matrix for the input-to-embedding layer (the input embedding) and the hidden-to-softmax layer (the output embedding). The idea is that these two matrices contain essentially the same information, each having a row per word in the vocabulary. Ref Question Answer What is so special about the special tokens used in different LM tokenizers? Special tokens are called special because they are added for a certain purpose and are independent of the input. For example, in BERT we have [CLS] token that is added at the start of every input sentence and [SEP] is a special separator token. Similarly in GPT2, <|endoftext|> is special token to denote end of sentence. Users can create their own special token based on their specific use case and train them during finetuning. Refer cronoik's answer in SO Question Answer What are Attention Masks? Attention masks are the token level boolean identifiers used to differentiate between important and not important tokens in the input. One use case is during batch training, where a batch with text of different lengths can be created by adding paddings to shorter texts. The padding tokens can be identified using 0 in attention mask and the original input tokens can be marked as 1. Refer blog @ lukesalamone.com Note We can use a special token for padding. For example in BERT it can be [PAD] token and in GPT-2 we can use <|endoftext|> token. Question Answer What is the difference between Attention and Self-Attention? Self-attention (SA) is applied within one component, so the input is from one component only. One example is the encoder block in Transformers, where SA is used, and it takes the tokens from only the sentence as the input. Attention on the other hand can be used to connect two components or modality of data. Decoder in Transformer has Attention layer that connects the encoder and decoder data together. Refer StackExchange QA","title":"Interview Questions"},{"location":"natural_language_processing/interview_questions/#what-are-the-different-types-of-reasoning-tasks-in-nlp","text":"Arithmetic Reasoning: Arithmetic reasoning is the ability of an NLP system to perform mathematical operations on numerical data. This can include basic arithmetic operations such as addition, subtraction, multiplication, and division as well as more complex operations such as algebraic equations and calculus. Commonsense Reasoning: Commonsense reasoning refers to the ability of an NLP system to make deductions based on the knowledge and information that is commonly understood by humans. This includes understanding social norms, cultural contexts, and everyday life experiences. ( StrategyQA is a sample dataset that contains True/False questions like \"Did Aristotle use a laptop?\") Symbolic Reasoning: Symbolic reasoning involves the ability of an NLP system to manipulate and reason about symbolic representations of information, such as words, phrases, and sentences. This includes tasks such as parsing, string operations, semantic role labeling and entity recognition. (Last Letter Concatenation is a sample dataset with questions like \"Take the last letters of the words in 'Lady Gaga' and concatenate them\") Logic Reasoning: Logic reasoning refers to the ability of an NLP system to perform logical deductions based on formal rules of inference. This can include tasks such as identifying logical fallacies, determining the validity of arguments, and drawing conclusions based on deductive reasoning. (Date understanding is a sample dataset with questions like \"Today is Christmas Eve 1937, what is the date tomorrow in MM/DD/YYYY?\") Question Answer","title":"What are the different types of reasoning tasks in NLP?"},{"location":"natural_language_processing/interview_questions/#what-are-word-embeddings-in-nlp","text":"Word embeddings are a type of representation for words in NLP. They are a dense vector representation of a word, learned from the data using techniques such as word2vec or GloVe. The embeddings capture the semantics of the words, meaning that words with similar meanings will have similar vectors. Word embeddings are used as input in many NLP tasks such as language translation, text classification, and text generation. Question Answer","title":"What are word embeddings in NLP?"},{"location":"natural_language_processing/interview_questions/#what-is-sentence-encoding","text":"Sentence encoding is the process of converting a sentence into a fixed-length vector representation, also known as sentence embeddings. This is done by using techniques such as bag-of-words, TF-IDF, or BERT-based models. Sentence encodings can be used as input in various NLP tasks such as text classification, text generation, and text similarity. Several algorithms first tokenize the sentence in words or tokens, compute thir embedding and then aggregate them (min, max, mean, etc) to get the sentence embedding. Question Answer","title":"What is Sentence Encoding?"},{"location":"natural_language_processing/interview_questions/#explain-the-concept-of-attention-mechanism-in-nlp","text":"Attention mechanism is a way to weight different parts of the input in a neural network, giving more importance to certain parts of the input than others. It is commonly used in NLP tasks such as machine translation, where the model needs to focus on different parts of the input sentence at different times. Attention mechanisms can be implemented in various ways, such as additive attention ( \\(\ud835\udc44+\ud835\udc3e\\) ) and dot-product attention ( \\(\ud835\udc44\ud835\udc3e^{\ud835\udc47}\\) ) Question Answer","title":"Explain the concept of attention mechanism in NLP?"},{"location":"natural_language_processing/interview_questions/#what-are-transformer-models-in-nlp","text":"Transformer models are a type of neural network architecture that have been successful in various NLP tasks such as language translation and language understanding. They were introduced in the transformer paper and use self-attention mechanism to weigh the different parts of the input. This allows the model to efficiently process long input sequences and handle the dependencies between the words. Refer for more details. Question Answer","title":"What are transformer models in NLP?"},{"location":"natural_language_processing/interview_questions/#can-you-explain-the-concept-of-named-entity-recognition-ner-in-nlp","text":"Named Entity Recognition (NER) is a subtask of information extraction that seeks to locate and classify named entities in text into predefined categories such as person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc. NER systems can be rule-based or based on machine learning, and are used in a wide range of applications such as information retrieval, question answering and text summarization. Question Answer","title":"Can you explain the concept of Named Entity Recognition (NER) in NLP?"},{"location":"natural_language_processing/interview_questions/#explain-part-of-speech-pos-tagging-in-nlp","text":"Part-of-Speech (POS) tagging is the process of marking each word in a text with its corresponding POS tag. This is a fundamental step in many NLP tasks such as parsing, text summarization, and information extraction. POS tagging can be rule-based or based on machine learning, and is typically done using algorithms such as Hidden Markov Models (HMMs) or Conditional Random Fields (CRFs). Question Answer","title":"Explain Part-of-Speech (POS) tagging in NLP?"},{"location":"natural_language_processing/interview_questions/#can-you-explain-the-concept-of-language-modeling-in-nlp","text":"Language modeling is the task of predicting the next word in a sentence, given the previous words. This is done by training a model on a large dataset of text, which learns the probability distribution of the words in the language. Language models are used in a wide range of NLP tasks such as machine translation, text generation, and speech recognition. Question Answer","title":"Can you explain the concept of Language Modeling in NLP?"},{"location":"natural_language_processing/interview_questions/#can-you-explain-the-concept-of-text-summarization","text":"Text summarization is the task of generating a shorter version of a text that retains the most important information. There are two main types of text summarization: extractive and abstractive. Extractive summarization selects important sentences or phrases from the text to form the summary, while abstractive summarization generates new text that captures the meaning of the original text. Question Answer","title":"Can you explain the concept of Text Summarization?"},{"location":"natural_language_processing/interview_questions/#what-is-sentiment-analysis","text":"Sentiment analysis is the task of determining the sentiment or emotion of a piece of text. This is typically done by classifying the text as positive, negative, or neutral. Sentiment analysis can be done using a variety of techniques such as rule-based systems, machine learning, and deep learning. It is used in a wide range of applications such as customer feedback analysis and social media analysis. Question Answer","title":"What is Sentiment Analysis?"},{"location":"natural_language_processing/interview_questions/#can-you-explain-the-concept-of-dependency-parsing","text":"Dependency parsing is the task of analyzing the grammatical structure of a sentence, identifying the dependencies between the words. This is done by creating a dependency parse tree, which represents the grammatical relationships between the words in a sentence. Dependency parsing is a fundamental step in many NLP tasks such as machine translation, text summarization, and information extraction. Question Answer","title":"Can you explain the concept of Dependency Parsing?"},{"location":"natural_language_processing/interview_questions/#explain-the-coreference-resolution-task-in-nlp","text":"Coreference resolution is the task of identifying when different expressions in a text refer to the same entity. This is done by analyzing the text and identifying when two or more expressions have the same referent. Coreference resolution is a fundamental step in many NLP tasks such as machine translation, text summarization, and information extraction. In this example text, \"Mohit lives in Pune and he works as a Data Scientist\" , the co-reference resolution will identify \"Mohit\" and \"he\" as belonging to the same entity. Question Answer","title":"Explain the Coreference Resolution task in NLP?"},{"location":"natural_language_processing/interview_questions/#explain-stemming-and-lemmatization-in-nlp","text":"Stemming is the process of reducing inflected (or sometimes derived) words to their word stem, base or root form. This is done by using a stemmer algorithm which removes the suffixes or prefixes from the word. The goal of stemming is to reduce the dimensionality of the text data, grouping together the inflected forms of a word so they can be analyzed as a single term, which can be useful for tasks such as text classification and information retrieval. Lemmatization is the process of reducing a word to its base form, which is called the lemma. This is done by using a lemmatizer algorithm which takes into consideration the context and the part of speech of the word. The goal of lemmatization is to reduce the dimensionality of the text data and group together the different forms of a word so they can be analyzed as a single term, which can be useful for tasks such as text classification and information retrieval. Note An obvious difference is that Lemmatization consider the grammar of the sentence while Stemming only consider the word. Question Answer","title":"Explain Stemming and Lemmatization in NLP?"},{"location":"natural_language_processing/interview_questions/#what-is-text-classification","text":"Text classification is the task of assigning predefined categories or labels to a given text. This is done by training a model on a labeled dataset of text, which learns to predict the label of new text. Text classification is used in a wide range of applications such as sentiment analysis, spam detection, and topic classification. There are multiple types of text classification such as, Binary classification: where there only two classes (ex: positive vs negative) Multi-class classification : where there are more than 2 classes (ex: positive, negative and neutral) Multi-label classification : when there are two or more classes and each text can have more than 1 class/label assigned to them (ex: single text can have some positive phrase and negative phrase) Question Answer","title":"What is Text Classification?"},{"location":"natural_language_processing/interview_questions/#what-are-dialogue-systems-in-nlp","text":"A Dialogue system, also known as a conversational agent or chatbot, is a computer program that can interact with human users through natural language. It can understand the user's input, generate appropriate responses, and carry out tasks such as answering questions, booking a flight, or making a reservation. Dialogue systems can be rule-based, or based on machine learning and deep learning, and can be integrated into various platforms such as smartphones, websites, and messaging apps. Question Answer","title":"What are Dialogue Systems in NLP?"},{"location":"natural_language_processing/interview_questions/#please-explain-the-concept-of-text-generation","text":"Text generation is the task of generating new text that is similar to the text it was trained on. This is done by training a model on a large dataset of text, which learns the probability distribution of the words in the language. Text generation can be used for various applications such as chatbot, text completion and summarization. Refer for more details. Question Answer","title":"Please explain the concept of Text Generation?"},{"location":"natural_language_processing/interview_questions/#can-you-explain-the-concept-of-text-similarity-in-nlp","text":"Text Similarity is the task of determining how similar two pieces of text are to each other. This is done by comparing the text using various similarity measures such as cosine similarity, Jaccard similarity, or Levenshtein distance. Text similarity can be used in a wide range of applications such as plagiarism detection and text retrieval. Refer for more details. Question Answer","title":"Can you explain the concept of Text Similarity in NLP?"},{"location":"natural_language_processing/interview_questions/#please-explain-text-clustering","text":"Text Clustering is the process of grouping similar texts together. This is done by using clustering algorithms such as K-means, Hierarchical Clustering, or DBSCAN. Text clustering can be used in a wide range of applications such as topic modeling, sentiment analysis, and text summarization. This is usually a two step process, first the text is converted to a representation (usually by text embedding algorithms) and then a clustering algorithm is used to create clusters. Question Answer","title":"Please explain Text Clustering?"},{"location":"natural_language_processing/interview_questions/#what-is-named-entity-disambiguation-ned","text":"Named Entity Disambiguation (NED) is the task of determining which entity (from a database) a mention (from text or doc) refers to, from a set of candidate entities. This is done by using techniques such as string matching, co-reference resolution, or graph-based methods. NED is important for tasks such as information extraction and knowledge base population. For example, NED will process a wikipedia page and map \"Mohit M.\", \"M. Mayank\", \"Mohit\" and similar named entities with \"Mohit Mayank\" present in the database. Question Answer","title":"What is Named Entity Disambiguation (NED)?"},{"location":"natural_language_processing/interview_questions/#what-is-the-difference-between-a-feedforward-neural-network-and-a-recurrent-neural-network","text":"A feedforward neural network is a type of neural network in which the data flows in one direction, from input to output. There are no loops or connections that allow information to flow in a cyclical manner. On the other hand, a recurrent neural network (RNN) is a type of neural network that allows information to flow in a cyclical manner, with connections between neurons allowing information to be passed from one step of the process to the next. RNNs are useful for tasks such as language modeling and speech recognition, where the current input is dependent on the previous inputs. Question Answer","title":"What is the difference between a feedforward neural network and a recurrent neural network?"},{"location":"natural_language_processing/interview_questions/#is-bert-a-text-generation-model","text":"Short answer is no. BERT is not a text generation model or a language model because the probability of the predicting a token in masked input is dependent on the context of the token. This context is bidirectional, hence the model is not able to predict the next token in the sequence accurately with only one directional context (as expected for language model) . Question Answer","title":"Is BERT a Text Generation model?"},{"location":"natural_language_processing/interview_questions/#what-is-weight-tying-in-language-model","text":"Weight-tying is where you have a language model and use the same weight matrix for the input-to-embedding layer (the input embedding) and the hidden-to-softmax layer (the output embedding). The idea is that these two matrices contain essentially the same information, each having a row per word in the vocabulary. Ref Question Answer","title":"What is weight tying in language model?"},{"location":"natural_language_processing/interview_questions/#what-is-so-special-about-the-special-tokens-used-in-different-lm-tokenizers","text":"Special tokens are called special because they are added for a certain purpose and are independent of the input. For example, in BERT we have [CLS] token that is added at the start of every input sentence and [SEP] is a special separator token. Similarly in GPT2, <|endoftext|> is special token to denote end of sentence. Users can create their own special token based on their specific use case and train them during finetuning. Refer cronoik's answer in SO Question Answer","title":"What is so special about the special tokens used in different LM tokenizers?"},{"location":"natural_language_processing/interview_questions/#what-are-attention-masks","text":"Attention masks are the token level boolean identifiers used to differentiate between important and not important tokens in the input. One use case is during batch training, where a batch with text of different lengths can be created by adding paddings to shorter texts. The padding tokens can be identified using 0 in attention mask and the original input tokens can be marked as 1. Refer blog @ lukesalamone.com Note We can use a special token for padding. For example in BERT it can be [PAD] token and in GPT-2 we can use <|endoftext|> token. Question Answer","title":"What are Attention Masks?"},{"location":"natural_language_processing/interview_questions/#what-is-the-difference-between-attention-and-self-attention","text":"Self-attention (SA) is applied within one component, so the input is from one component only. One example is the encoder block in Transformers, where SA is used, and it takes the tokens from only the sentence as the input. Attention on the other hand can be used to connect two components or modality of data. Decoder in Transformer has Attention layer that connects the encoder and decoder data together. Refer StackExchange QA","title":"What is the difference between Attention and Self-Attention?"},{"location":"natural_language_processing/llama/","text":"LLaMA Introduction In Feb 2023, Meta introduced a collection of foundation language models ranging from 7B to 65B parameters under the name of LLaMA. What makes LLaMA different from other LLMs is, It was trained on 1.4 trillion tokens created using publicly available datasets without resorting to proprietary and inaccessible datasets as done by the likes of Chinchilla, PaLM, or GPT-3. With added improvements, the resulting models are highly competitive against more powerful models. For instance, LLaMA-13B outperforms GPT-3 (175B) on most benchmarks, and LLaMA- 65B is competitive with the best models, Chinchilla-70B and PaLM-540B. Finally, LLaMA was open-sourced! Tip \u201cOfficial\u201d weights were only released to the research community and even then you need to fill out a form to request access. That said, there has been \u201cpirating\u201d of the weights that allow anyone to play around with the model. It was quite interesting, more details in this LinkedIn Post Architecture Modifications To achieve the enhancements, several modifications were made to the original Transformer architecture. They are, [1] Pre-normalization [from GPT3] To improve the training stability, RMSNorm was used to normalize the input of each transformer sub-layer instead of the output. SwiGLU activation function [from PaLM]. ReLU non-linearity was replaced by the SwiGLU activation function. Rotary Embeddings [from GPTNeo] . Absolute positional embeddings were replaced with rotary positional embeddings (RoPE) at each layer of the network. Training Optimizations On top of architecture modifications, several optimizations were made to improve the training speed of the models. They are, [1] First, an efficient implementation of causal multi-head attention was used to reduce memory usage and runtime. (refer xformers library) To further improve training efficiency, the number of activations that are recomputed was reduced during the backward pass with checkpointing. Additional GPU optimizations were done like overlapping the computation of activations and the communication between GPUs over the network. Dataset The dataset used to train LLaMA was created using only open-source data and is a mixture of several sources, as shown below. This led to the creation of 1.4 tokens of the total dataset. [1] Models Below is the list of models trained as part of the project with additional details like dimensions, attention layers and head as well as the training metrics of the learning rate, batch size and the number of tokens used for training. [1] Alpaca LLaMA authors observed that a very small amount of instruction-based finetuning improves the performance of the model on Massive Multitask Language Understanding Tasks (MMLU). It also further improves the ability of the model to follow instructions. That said, they didn\u2019t explore this thread further. Below you can see 5-shot MMLU performance of LLaMA-Instruct model (LLaMA-I) -- it is better than LLaMA model of the same size. [1] Enter Stanford Alpaca [2], an instruction-based finetuned LLaMA that further improves the performance of LLaMA models so much so that even 7B Alpaca model is comparable with OpenAI\u2019s text-davinci-003. Warning Alpaca team suggested that the model is better than LLaMA. There were no comparitive numbers or tables shared. The process starts with first generating 52K instruction-following samples using OpenAI's text-davinci-003. Then LLaMA model was finetuned on these data using supervised learning, basically taking inspiration from self-instruct paper. This process reduces the cost of preparing a GPT level model to under ~ $600 ( $500 to generate the data + $100 to finetune). The process is summarised below, Note The code to generate the 52k dataset along with finetuning recipe was open-sourced [2] Code There are many ways to access LLaMA. Sharing some of the most popular ones below, Dalai Dalai is the simplest way to run the LLaMA or Alpaca models on your machine. It also provides an intuitive UI to use the model. All you need to do is, # install model npx dalai llama install 7 B # or 13B, 30B, 65B npx dalai alpaca install 7 B # or 13B, 30B, 65B # launch web UI + socket.io API npx dalai serve And it looks good! \ud83d\udc47 Note Internally it uses C/C++ port of LLaMA and Alpaca . You can access them separately for faster execution. The respective packages have also applied quantization to provide much faster execution with some compromise on accuracy. HuggingFace HuggingFace has created the port to use the LLaMA model. You can also access the crowd-uploaded model at the hub here . The code to load and use the model like any other LLM is shown below, # install (currently LLamA is in the main branch of HF) ! pip install git + https : // github . com / huggingface / transformers . git ! pip install sentencepiece # import from transformers import LlamaForCausalLM , LlamaTokenizer # load the tokenizer and model tokenizer = LlamaTokenizer . from_pretrained ( \"decapoda-research/llama-7b-hf\" ) model = LlamaForCausalLM . from_pretrained ( \"decapoda-research/llama-7b-hf\" ) References [1] LLaMA - Official Model Card | HuggingFace | Release Blog | Paper [2] Alpaca - Release Notes | HuggingFace Model | Github","title":"LLaMA"},{"location":"natural_language_processing/llama/#llama","text":"","title":"LLaMA"},{"location":"natural_language_processing/llama/#introduction","text":"In Feb 2023, Meta introduced a collection of foundation language models ranging from 7B to 65B parameters under the name of LLaMA. What makes LLaMA different from other LLMs is, It was trained on 1.4 trillion tokens created using publicly available datasets without resorting to proprietary and inaccessible datasets as done by the likes of Chinchilla, PaLM, or GPT-3. With added improvements, the resulting models are highly competitive against more powerful models. For instance, LLaMA-13B outperforms GPT-3 (175B) on most benchmarks, and LLaMA- 65B is competitive with the best models, Chinchilla-70B and PaLM-540B. Finally, LLaMA was open-sourced! Tip \u201cOfficial\u201d weights were only released to the research community and even then you need to fill out a form to request access. That said, there has been \u201cpirating\u201d of the weights that allow anyone to play around with the model. It was quite interesting, more details in this LinkedIn Post","title":"Introduction"},{"location":"natural_language_processing/llama/#architecture-modifications","text":"To achieve the enhancements, several modifications were made to the original Transformer architecture. They are, [1] Pre-normalization [from GPT3] To improve the training stability, RMSNorm was used to normalize the input of each transformer sub-layer instead of the output. SwiGLU activation function [from PaLM]. ReLU non-linearity was replaced by the SwiGLU activation function. Rotary Embeddings [from GPTNeo] . Absolute positional embeddings were replaced with rotary positional embeddings (RoPE) at each layer of the network.","title":"Architecture Modifications"},{"location":"natural_language_processing/llama/#training-optimizations","text":"On top of architecture modifications, several optimizations were made to improve the training speed of the models. They are, [1] First, an efficient implementation of causal multi-head attention was used to reduce memory usage and runtime. (refer xformers library) To further improve training efficiency, the number of activations that are recomputed was reduced during the backward pass with checkpointing. Additional GPU optimizations were done like overlapping the computation of activations and the communication between GPUs over the network.","title":"Training Optimizations"},{"location":"natural_language_processing/llama/#dataset","text":"The dataset used to train LLaMA was created using only open-source data and is a mixture of several sources, as shown below. This led to the creation of 1.4 tokens of the total dataset. [1]","title":"Dataset"},{"location":"natural_language_processing/llama/#models","text":"Below is the list of models trained as part of the project with additional details like dimensions, attention layers and head as well as the training metrics of the learning rate, batch size and the number of tokens used for training. [1]","title":"Models"},{"location":"natural_language_processing/llama/#alpaca","text":"LLaMA authors observed that a very small amount of instruction-based finetuning improves the performance of the model on Massive Multitask Language Understanding Tasks (MMLU). It also further improves the ability of the model to follow instructions. That said, they didn\u2019t explore this thread further. Below you can see 5-shot MMLU performance of LLaMA-Instruct model (LLaMA-I) -- it is better than LLaMA model of the same size. [1] Enter Stanford Alpaca [2], an instruction-based finetuned LLaMA that further improves the performance of LLaMA models so much so that even 7B Alpaca model is comparable with OpenAI\u2019s text-davinci-003. Warning Alpaca team suggested that the model is better than LLaMA. There were no comparitive numbers or tables shared. The process starts with first generating 52K instruction-following samples using OpenAI's text-davinci-003. Then LLaMA model was finetuned on these data using supervised learning, basically taking inspiration from self-instruct paper. This process reduces the cost of preparing a GPT level model to under ~ $600 ( $500 to generate the data + $100 to finetune). The process is summarised below, Note The code to generate the 52k dataset along with finetuning recipe was open-sourced [2]","title":"Alpaca"},{"location":"natural_language_processing/llama/#code","text":"There are many ways to access LLaMA. Sharing some of the most popular ones below,","title":"Code"},{"location":"natural_language_processing/llama/#dalai","text":"Dalai is the simplest way to run the LLaMA or Alpaca models on your machine. It also provides an intuitive UI to use the model. All you need to do is, # install model npx dalai llama install 7 B # or 13B, 30B, 65B npx dalai alpaca install 7 B # or 13B, 30B, 65B # launch web UI + socket.io API npx dalai serve And it looks good! \ud83d\udc47 Note Internally it uses C/C++ port of LLaMA and Alpaca . You can access them separately for faster execution. The respective packages have also applied quantization to provide much faster execution with some compromise on accuracy.","title":"Dalai"},{"location":"natural_language_processing/llama/#huggingface","text":"HuggingFace has created the port to use the LLaMA model. You can also access the crowd-uploaded model at the hub here . The code to load and use the model like any other LLM is shown below, # install (currently LLamA is in the main branch of HF) ! pip install git + https : // github . com / huggingface / transformers . git ! pip install sentencepiece # import from transformers import LlamaForCausalLM , LlamaTokenizer # load the tokenizer and model tokenizer = LlamaTokenizer . from_pretrained ( \"decapoda-research/llama-7b-hf\" ) model = LlamaForCausalLM . from_pretrained ( \"decapoda-research/llama-7b-hf\" )","title":"HuggingFace"},{"location":"natural_language_processing/llama/#references","text":"[1] LLaMA - Official Model Card | HuggingFace | Release Blog | Paper [2] Alpaca - Release Notes | HuggingFace Model | Github","title":"References"},{"location":"natural_language_processing/lstm_gru_rnn/","text":"LSTM, GRU and RNN Introduction LSTM, GRU or RNN are a type of recurrent layers. They were the state-of-the-art neural network models for text related applications before the transformers based models. They can be used to process any sequential data like timeseries, text, audio, etc. RNN is the simpler of the lot, where as LSTM and GRU are the \"gated\" (and so a little complex) version of RNN. These gates helps LSTM and GRU to mitigate some of the problems of RNN like exploding gradient. The most basic unit in these layers is a \"cell\", which is repeated in a layer - equal to the number of token size. For example, if we want to process a text with 150 words (with word level tokenization), we need to perceptually attach 150 cells one after the another. Hence, each cell process one word and passes on the representation of the word (hidden state value) to the next cell in line. Starting with RNN, the flow of data and hidden state inside the RNN cell is shown below. Cell of RNN layer. ( practical_guide_to_rnn_and_lstm ) As shown in the figure, x^t is the input token at time t , h^{t-1} is the hidden state output from the last step, y^t and h^t are two notations for the hidden state output of time t . The formulation of an RNN cell is as follow, \\[ h^{t}=g\\left(W_{h h} h^{t-1}+W_{h x} x^{t}+b_{h}\\right) \\\\ y^{t}=h^{t} \\] LSTM cells on the other hand is more complicated and is shown below. LSTM cell. ( lstm_understanding ) Note Python libraries take liberty in modifying the architecture of the RNN and LSTM cells. For more details about how these cells are implemented in Keras, check out ( practical_guide_to_rnn_and_lstm ). Finally, a GRU cell looks as follow, GRU cell with formulae. ( lstm_understanding ) While RNN is the most basic recurrent layer, LSTM and GRU are the de facto baseline for any text related application. There are lots of debate over which one is better, but the answer is usually fuzzy and it all comes down to the data and use case. In terms of tunable parameter size the order is as follow - RNN < GRU < LSTM . That said, in terms of learing power the order is - RNN < GRU = LSTM . Go for GRU if you want to reduce the tunable parameters but keep the learning power relatively similar. That said, do not forget to experiment wth LSTM, as it may suprise you once in a while. Code Using BiLSTM (for regression) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 \"\"\"Sample code for recurrent layer based models \"\"\" # ---- Imports ---- import keras from keras.preprocessing.text import Tokenizer from keras.preprocessing.sequence import pad_sequences from keras.models import Sequential from keras.layers import Dense , Dropout , LSTM , Bidirectional , Embedding # ---- Data loading ---- # contains a list or series of sentences # train = ... # test = ... # combined = train['text'] + test['text'] # ---- Data processing ---- # set max vocab size vocab = 10000 # create tokenizer instances tokenizer = Tokenizer ( num_words = vocab , oov_token = 0 ) # fit the tokenizer on text sequences tokenizer . fit_on_texts ( combined ) # tokenize the complete data sequence_combined = tokenizer . texts_to_sequences ( combined ) # get the max len max_len = max ([ len ( x ) for x in sequence_combined ]) # tokenize the train data train_sequences = tokenizer . texts_to_sequences ( train [ 'text' ]) # add padding to the data padded_train_sequence = pad_sequences ( train_sequences , maxlen = max_len , dtype = 'int32' , padding = 'pre' , truncating = 'pre' , value = 0 ) # ---- Model ---- model = Sequential () # encoder model . add ( keras . Input ( shape = ( padded_train_sequence . shape [ 1 ], ))) # input layer model . add ( Embedding ( vocab , 256 )) # embedding layer model . add ( Bidirectional ( LSTM ( 256 ))) # biLSTM layer # decoder model . add ( Dense ( 256 , kernel_initializer = 'normal' , activation = 'relu' )) model . add ( Dense ( 1 , activation = 'linear' )) # summary model . summary () # ---- Compile and Train ---- # callbacks earlystopping = keras . callbacks . EarlyStopping ( monitor = 'loss' , patience = 3 ) # compile model . compile ( loss = 'mse' , optimizer = 'adam' , metrics = [ 'mse' , 'mae' ]) # fit history = model . fit ( padded_train_sequence , train [ 'y' ], epochs = 100 , batch_size = 16 , verbose = 2 , callbacks = [ earlystopping ]) # ---- Prediction ---- # prepare testing data test_sequences = tokenizer . texts_to_sequences ( test [ 'text' ]) test_padded_sequences = pad_sequences ( test_sequences , maxlen = max_len , dtype = 'int32' , padding = 'pre' , truncating = 'pre' , value = 0 ) # perform prediction y_pred = model . predict ( test_padded_sequences ) Additional materials A practical guide to RNN and LSTM in Keras ( practical_guide_to_rnn_and_lstm ) Guide to Custom Recurrent Modeling in Keras ( Guide_to_custom_recurrent_modeling ) Understanding LSTM Networks ( lstm_understanding )","title":"LSTM, GRU & RNN"},{"location":"natural_language_processing/lstm_gru_rnn/#lstm-gru-and-rnn","text":"","title":"LSTM, GRU and RNN"},{"location":"natural_language_processing/lstm_gru_rnn/#introduction","text":"LSTM, GRU or RNN are a type of recurrent layers. They were the state-of-the-art neural network models for text related applications before the transformers based models. They can be used to process any sequential data like timeseries, text, audio, etc. RNN is the simpler of the lot, where as LSTM and GRU are the \"gated\" (and so a little complex) version of RNN. These gates helps LSTM and GRU to mitigate some of the problems of RNN like exploding gradient. The most basic unit in these layers is a \"cell\", which is repeated in a layer - equal to the number of token size. For example, if we want to process a text with 150 words (with word level tokenization), we need to perceptually attach 150 cells one after the another. Hence, each cell process one word and passes on the representation of the word (hidden state value) to the next cell in line. Starting with RNN, the flow of data and hidden state inside the RNN cell is shown below. Cell of RNN layer. ( practical_guide_to_rnn_and_lstm ) As shown in the figure, x^t is the input token at time t , h^{t-1} is the hidden state output from the last step, y^t and h^t are two notations for the hidden state output of time t . The formulation of an RNN cell is as follow, \\[ h^{t}=g\\left(W_{h h} h^{t-1}+W_{h x} x^{t}+b_{h}\\right) \\\\ y^{t}=h^{t} \\] LSTM cells on the other hand is more complicated and is shown below. LSTM cell. ( lstm_understanding ) Note Python libraries take liberty in modifying the architecture of the RNN and LSTM cells. For more details about how these cells are implemented in Keras, check out ( practical_guide_to_rnn_and_lstm ). Finally, a GRU cell looks as follow, GRU cell with formulae. ( lstm_understanding ) While RNN is the most basic recurrent layer, LSTM and GRU are the de facto baseline for any text related application. There are lots of debate over which one is better, but the answer is usually fuzzy and it all comes down to the data and use case. In terms of tunable parameter size the order is as follow - RNN < GRU < LSTM . That said, in terms of learing power the order is - RNN < GRU = LSTM . Go for GRU if you want to reduce the tunable parameters but keep the learning power relatively similar. That said, do not forget to experiment wth LSTM, as it may suprise you once in a while.","title":"Introduction"},{"location":"natural_language_processing/lstm_gru_rnn/#code","text":"","title":"Code"},{"location":"natural_language_processing/lstm_gru_rnn/#using-bilstm-for-regression","text":"1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 \"\"\"Sample code for recurrent layer based models \"\"\" # ---- Imports ---- import keras from keras.preprocessing.text import Tokenizer from keras.preprocessing.sequence import pad_sequences from keras.models import Sequential from keras.layers import Dense , Dropout , LSTM , Bidirectional , Embedding # ---- Data loading ---- # contains a list or series of sentences # train = ... # test = ... # combined = train['text'] + test['text'] # ---- Data processing ---- # set max vocab size vocab = 10000 # create tokenizer instances tokenizer = Tokenizer ( num_words = vocab , oov_token = 0 ) # fit the tokenizer on text sequences tokenizer . fit_on_texts ( combined ) # tokenize the complete data sequence_combined = tokenizer . texts_to_sequences ( combined ) # get the max len max_len = max ([ len ( x ) for x in sequence_combined ]) # tokenize the train data train_sequences = tokenizer . texts_to_sequences ( train [ 'text' ]) # add padding to the data padded_train_sequence = pad_sequences ( train_sequences , maxlen = max_len , dtype = 'int32' , padding = 'pre' , truncating = 'pre' , value = 0 ) # ---- Model ---- model = Sequential () # encoder model . add ( keras . Input ( shape = ( padded_train_sequence . shape [ 1 ], ))) # input layer model . add ( Embedding ( vocab , 256 )) # embedding layer model . add ( Bidirectional ( LSTM ( 256 ))) # biLSTM layer # decoder model . add ( Dense ( 256 , kernel_initializer = 'normal' , activation = 'relu' )) model . add ( Dense ( 1 , activation = 'linear' )) # summary model . summary () # ---- Compile and Train ---- # callbacks earlystopping = keras . callbacks . EarlyStopping ( monitor = 'loss' , patience = 3 ) # compile model . compile ( loss = 'mse' , optimizer = 'adam' , metrics = [ 'mse' , 'mae' ]) # fit history = model . fit ( padded_train_sequence , train [ 'y' ], epochs = 100 , batch_size = 16 , verbose = 2 , callbacks = [ earlystopping ]) # ---- Prediction ---- # prepare testing data test_sequences = tokenizer . texts_to_sequences ( test [ 'text' ]) test_padded_sequences = pad_sequences ( test_sequences , maxlen = max_len , dtype = 'int32' , padding = 'pre' , truncating = 'pre' , value = 0 ) # perform prediction y_pred = model . predict ( test_padded_sequences )","title":"Using BiLSTM (for regression)"},{"location":"natural_language_processing/lstm_gru_rnn/#additional-materials","text":"A practical guide to RNN and LSTM in Keras ( practical_guide_to_rnn_and_lstm ) Guide to Custom Recurrent Modeling in Keras ( Guide_to_custom_recurrent_modeling ) Understanding LSTM Networks ( lstm_understanding )","title":"Additional materials"},{"location":"natural_language_processing/minilm/","text":"Introduction Knowledge distillation is the process of compressing the knowledge of a large model (teacher) into a smaller one (student). MiniLM [1,2,3] propose novel approaches to perform distillation of large models like BERT and RoBERTa into smaller models that could be 99% accurate on certain tasks while being more than 2 times faster in inference! Apart from sharing details on the distillation process, authors also open-sourced the distilled models at [3]. While the teacher models were encoder models, the author proposes MiniLM can be used for NLU (ex: extractive QA) as well as NLG tasks (ex: abstractive summarization) . For NLG task, authors followed UniLM paper and used masked attention layers. MiniLM The first MiniLM paper [1] was published in 2019, and the laid the foundation of the efficient distillation of large NLP models. Distillation of BERT model has been done before in DistillBERT, TinyBERT and MobileBERT. Here is how MiniLM was different, They proposed dual approach of distillation, (1) Use the original teacher for distillation if student has same number of layers but different hidden size, (2) use an intermediate teacher assistant if student has even reduced number of layers. Shown below is the example where L and M are the number of layers, d and di is the hidden size in teacher and student respectively. This teacher assistant method is suggested if, \\(M \\le \\frac{1}{2} L\\) and \\(di \\le \\frac{1}{2} d\\) . graph LR A(\"Teacher (L, d)\") -- distil --> B(\"Teacher Assistant (L, di)\") B -- distil --> C(\"Student (M, di)\") Additionally, relation based distillation of only the last Transformer layer was performed rather than other intermediate layers. This lead to flexible architecture creation for student models, where the number of layers could be less than teacher's. Finally, they also proposed dual attention transfer i.e Q-K and V-V. For distillation they compared the KL loss between the last transformer layer of teacher and student. Below is the overview diagram, Overview of Deep Self-Attention Distillation [1] Here is the comparison of the existing distillation methods with MiniLM wrt the approach and performance on different datasets. Comparison with previous task-agnostic Transformer based LM distillation approaches. [1] MiniLM performance comparision with existing distilled models [1] Here is a comparison of different distilled MiniLM model against the BERT-Base model [3]. As visible, even with more than 3x reduction in parameters, the accuracy is quite good, sometimes even better! Model #Param SQuAD 2.0 MNLI-m SST-2 QNLI CoLA RTE MRPC QQP BERT-Base 109M 76.8 84.5 93.2 91.7 58.9 68.6 87.3 91.3 MiniLM-L12xH384 33M 81.7 85.7 93.0 91.5 58.5 73.3 89.5 91.3 MiniLM-L6xH384 22M 75.6 83.3 91.5 90.5 47.5 68.8 88.9 90.6 MiniLMv2 MiniLMv2 [2] was published in 2021, and here is how it was an enhancement wrt MiniLMv1, It generalize deep self-attention distillation in MiniLMv1 [1] by using self-attention relation distillation for task-agnostic compression of pre-trained Transformers. The proposed method eliminates the restriction on the number of student\u2019s attention heads . Authors performed teacher layer selection strategy. In MiniLMv1, knowledge from teacher's last layer was transfered to student's last layer. In MiniLMv2, while the transfer still happened to student's last layer, teacher's layer changes, For BERT-large, 21st layer in teacher model was used for transfer For RoBERTa-large and XML-R-large, 19th layer in teacher model was used for transfer For base sized models, last layer in teacher model was used for transfer Authors experimented with multiple self-attention (Q-K, K- Q, Q-V, V-Q, K-V and V-K relations). However, introducing more self-attention relations also brings a higher computational cost. Hence to achieve a balance between performance and computational cost, author choose to transfer Q-Q, K-K and V-V self-attention relations instead of all self-attention relations. Overview of multi-head self-attention relation distillation [1] Shown below are the MiniLMv2 models with details on the speedup and performance [3], Model Teacher Model Speedup #Param MNLI-m (Acc) SQuAD 2.0 (F1) L6xH768 MiniLMv2 RoBERTa-Large 2.0x 81M 87.0 81.6 L12xH384 MiniLMv2 RoBERTa-Large 2.7x 41M 86.9 82.3 L6xH384 MiniLMv2 RoBERTa-Large 5.3x 30M 84.4 76.4 L6xH768 MiniLMv2 BERT-Large Uncased 2.0x 66M 85.0 77.7 L6xH384 MiniLMv2 BERT-Large Uncased 5.3x 22M 83.0 74.3 L6xH768 MiniLMv2 BERT-Base Uncased 2.0x 66M 84.2 76.3 L6xH384 MiniLMv2 BERT-Base Uncased 5.3x 22M 82.8 72.9 Code Inference of MiniLM As the MiniLM models are based on BERT and RoBERTa, we can use their code for MiniLM. Here, let's make it much simpler by using the AutoModel function if you are loading the models from Huggingface. You can also download models from [3]. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 # install packages ! pip install - q transformers ! pip install - q sentencepiece # import from transformers import AutoTokenizer , AutoModel # load tokenizer and model tokenizer = AutoTokenizer . from_pretrained ( \"microsoft/Multilingual-MiniLM-L12-H384\" ) model = AutoModel . from_pretrained ( \"microsoft/Multilingual-MiniLM-L12-H384\" ) # inference inputs = tokenizer ( \"Hello world!\" , return_tensors = \"pt\" ) # dict with input_ids ---> torch.Size([1, 5]) and attention_mask ---> torch.Size([1, 5]) outputs = model ( ** inputs ) # dict with 'last_hidden_state' --> torch.Size([1, 5, 384]) and pooler_output --> torch.Size([1, 384]) The tokenization vocabulary is 250002 strong (quite big!) , and for input Hello world! . the tokenized output is _Hello_world! with corresponding input ids is tensor([[ 0, 35378, 8999, 38, 2]]) References [1] MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers [2] MiniLMv2: Multi-Head Self-Attention Relation Distillation for Compressing Pretrained Transformers [3] MiniLM Official Microsoft Github","title":"MiniLM"},{"location":"natural_language_processing/minilm/#introduction","text":"Knowledge distillation is the process of compressing the knowledge of a large model (teacher) into a smaller one (student). MiniLM [1,2,3] propose novel approaches to perform distillation of large models like BERT and RoBERTa into smaller models that could be 99% accurate on certain tasks while being more than 2 times faster in inference! Apart from sharing details on the distillation process, authors also open-sourced the distilled models at [3]. While the teacher models were encoder models, the author proposes MiniLM can be used for NLU (ex: extractive QA) as well as NLG tasks (ex: abstractive summarization) . For NLG task, authors followed UniLM paper and used masked attention layers.","title":"Introduction"},{"location":"natural_language_processing/minilm/#minilm","text":"The first MiniLM paper [1] was published in 2019, and the laid the foundation of the efficient distillation of large NLP models. Distillation of BERT model has been done before in DistillBERT, TinyBERT and MobileBERT. Here is how MiniLM was different, They proposed dual approach of distillation, (1) Use the original teacher for distillation if student has same number of layers but different hidden size, (2) use an intermediate teacher assistant if student has even reduced number of layers. Shown below is the example where L and M are the number of layers, d and di is the hidden size in teacher and student respectively. This teacher assistant method is suggested if, \\(M \\le \\frac{1}{2} L\\) and \\(di \\le \\frac{1}{2} d\\) . graph LR A(\"Teacher (L, d)\") -- distil --> B(\"Teacher Assistant (L, di)\") B -- distil --> C(\"Student (M, di)\") Additionally, relation based distillation of only the last Transformer layer was performed rather than other intermediate layers. This lead to flexible architecture creation for student models, where the number of layers could be less than teacher's. Finally, they also proposed dual attention transfer i.e Q-K and V-V. For distillation they compared the KL loss between the last transformer layer of teacher and student. Below is the overview diagram, Overview of Deep Self-Attention Distillation [1] Here is the comparison of the existing distillation methods with MiniLM wrt the approach and performance on different datasets. Comparison with previous task-agnostic Transformer based LM distillation approaches. [1] MiniLM performance comparision with existing distilled models [1] Here is a comparison of different distilled MiniLM model against the BERT-Base model [3]. As visible, even with more than 3x reduction in parameters, the accuracy is quite good, sometimes even better! Model #Param SQuAD 2.0 MNLI-m SST-2 QNLI CoLA RTE MRPC QQP BERT-Base 109M 76.8 84.5 93.2 91.7 58.9 68.6 87.3 91.3 MiniLM-L12xH384 33M 81.7 85.7 93.0 91.5 58.5 73.3 89.5 91.3 MiniLM-L6xH384 22M 75.6 83.3 91.5 90.5 47.5 68.8 88.9 90.6","title":"MiniLM"},{"location":"natural_language_processing/minilm/#minilmv2","text":"MiniLMv2 [2] was published in 2021, and here is how it was an enhancement wrt MiniLMv1, It generalize deep self-attention distillation in MiniLMv1 [1] by using self-attention relation distillation for task-agnostic compression of pre-trained Transformers. The proposed method eliminates the restriction on the number of student\u2019s attention heads . Authors performed teacher layer selection strategy. In MiniLMv1, knowledge from teacher's last layer was transfered to student's last layer. In MiniLMv2, while the transfer still happened to student's last layer, teacher's layer changes, For BERT-large, 21st layer in teacher model was used for transfer For RoBERTa-large and XML-R-large, 19th layer in teacher model was used for transfer For base sized models, last layer in teacher model was used for transfer Authors experimented with multiple self-attention (Q-K, K- Q, Q-V, V-Q, K-V and V-K relations). However, introducing more self-attention relations also brings a higher computational cost. Hence to achieve a balance between performance and computational cost, author choose to transfer Q-Q, K-K and V-V self-attention relations instead of all self-attention relations. Overview of multi-head self-attention relation distillation [1] Shown below are the MiniLMv2 models with details on the speedup and performance [3], Model Teacher Model Speedup #Param MNLI-m (Acc) SQuAD 2.0 (F1) L6xH768 MiniLMv2 RoBERTa-Large 2.0x 81M 87.0 81.6 L12xH384 MiniLMv2 RoBERTa-Large 2.7x 41M 86.9 82.3 L6xH384 MiniLMv2 RoBERTa-Large 5.3x 30M 84.4 76.4 L6xH768 MiniLMv2 BERT-Large Uncased 2.0x 66M 85.0 77.7 L6xH384 MiniLMv2 BERT-Large Uncased 5.3x 22M 83.0 74.3 L6xH768 MiniLMv2 BERT-Base Uncased 2.0x 66M 84.2 76.3 L6xH384 MiniLMv2 BERT-Base Uncased 5.3x 22M 82.8 72.9","title":"MiniLMv2"},{"location":"natural_language_processing/minilm/#code","text":"","title":"Code"},{"location":"natural_language_processing/minilm/#inference-of-minilm","text":"As the MiniLM models are based on BERT and RoBERTa, we can use their code for MiniLM. Here, let's make it much simpler by using the AutoModel function if you are loading the models from Huggingface. You can also download models from [3]. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 # install packages ! pip install - q transformers ! pip install - q sentencepiece # import from transformers import AutoTokenizer , AutoModel # load tokenizer and model tokenizer = AutoTokenizer . from_pretrained ( \"microsoft/Multilingual-MiniLM-L12-H384\" ) model = AutoModel . from_pretrained ( \"microsoft/Multilingual-MiniLM-L12-H384\" ) # inference inputs = tokenizer ( \"Hello world!\" , return_tensors = \"pt\" ) # dict with input_ids ---> torch.Size([1, 5]) and attention_mask ---> torch.Size([1, 5]) outputs = model ( ** inputs ) # dict with 'last_hidden_state' --> torch.Size([1, 5, 384]) and pooler_output --> torch.Size([1, 384]) The tokenization vocabulary is 250002 strong (quite big!) , and for input Hello world! . the tokenized output is _Hello_world! with corresponding input ids is tensor([[ 0, 35378, 8999, 38, 2]])","title":"Inference of MiniLM"},{"location":"natural_language_processing/minilm/#references","text":"[1] MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers [2] MiniLMv2: Multi-Head Self-Attention Relation Distillation for Compressing Pretrained Transformers [3] MiniLM Official Microsoft Github","title":"References"},{"location":"natural_language_processing/named_entity_recognition/","text":"Named entity recognition Introduction Named entity recognition (NER) is the process of identifying entities in the unstructured text, where entities could be objects, people, locations, organizations, etc. NER's most basic building block consists of pair of entity_type and entity_value . Consider the following example, ## Statement My name is Mohit, and I am from India. I am a Data Scientist and I will be leaving for my office around 9 AM. ## Entities [{ 'entity_type': 'PERSON', 'entity_value': 'Mohit', }, { 'entity_type': 'LOCATION', 'entity_value': 'India', }, { 'entity_type': 'TIME', 'entity_value': '9 AM', }] The process of extracting entities could be done in two ways. Heuristic : by identifying the entities based on the rules. Semantic : by identifying the entities based on the semantics and context. Heuristic based approach is suited only for simple entities for which approximate rules can be created. Take for example EMAILID, PHONE_NUMBER, WEBSITE, etc. It should be easy enough to create regular expressions for such cases and hence heuristic approach could be applied. We can also apply part of speech based rules to extract certain entities. On the other hand, the Semantic approach is required where the cardinality of the entities is high and the context is required to extract the entities. For example, NAME, LOCATION, DESIGNATION, etc. For these cases, we usually train neural network based models that learn to consider the context and extract the entities. Note A good approach to creating your NER solution would be to segregate your entities into simple and complex, and then create either a heuristic or a semantic based solution or a combination of both. In short, it is not always suitable to directly go to fancy NN based semantic approaches - it could be unnecessary overkill. Remember the entity types are not set in stone and we can even train new models or finetune existing models on our own custom entities. For this, in the Semantic-based approach, it's a good idea to finetune the existing model rather than to train a new one as it will require far fewer data. The amount of data required to finetune model depends on how similar the custom entities are with the existing entities. Consider the following cases, The model is pretrained to detect PERSON and now you want to finetune it to detect MALE_NAME and FEMALE_NAME. As this is just a lower granularity on the existing PERSON entity, a mere ~200 examples (for each new entity type) could give you good results. On the other hand, if you now want to finetune a completely new entity like OBJECTIONS_TYPE, you may need ~500 examples. Note Another thing to consider is the length of entity_value . With an increase in entity_value you may require more examples to get good accuracy results. Code There are lots of Python-based packages that provide open source NER models. Some of these packages are Spacy , NLTK , Flair , etc. While packages provide an easy interface to the NER models or rules, we can even load and use external open-source NER models. Using Spacy NER model for Inference Spacy comes with several pre-trained models that can be selected based on the use case. For this example, we will use the Transformer model available with Spacy Transformers . 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 # install spacy-transformers and transformers model ! pip install spacy - transformers ! python3 - m spacy download en_core_web_trf # import the spacy library import spacy # load the model nlp = spacy . load ( \"en_core_web_trf\" ) # set the text text = \"My name is Mohit, and I am from India. I am a Data Scientist and I will be leaving for my office around 9 AM.\" # create spacy doc by passing the text doc = nlp ( text ) # print all of the identified entities for ent in doc . ents : print ( f \"Type: { ent . label_ } -- Value: { ent . text } \" ) # Output: # Type: PERSON -- Value: Mohit # Type: GPE -- Value: India # Type: TIME -- Value: around 9 AM We can even display the results in a much more intuitive and fancy way by, 1 2 # use displacy to render the result spacy . displacy . render ( doc , style = 'ent' , jupyter = True , options = { 'distance' : 90 }) NER result for the above example Training custom NER model using Spacy v3 All NER use-cases are not the same, and you may want to train a custom model with new entity types. Spacy provides an option to train custom NER models as well. To be frank the complete process is quite complicated, but nothing to worry, strap on and let's cover them steo by step. Config Creation To begin with, we will define the settings that will be used throughout the training process. Starting with Spacy v3, all parameter settings need to be configured using a .cfg file. We can create a .cfg file following the guide here . Basically, it requires to Firstly, create a base config using the quick widget provided at the page . Do remember to check the correct options. (Refer below image for one example) Secondly, run CLI command to update the base config python -m spacy init fill-config base_config.cfg config.cfg Example of options to mark to generate base_config.cfg from Spacy website for NER training. Data Preparation Next we need to prepare the dataset. At high level, you need to prepare pairs of text and entities in the text. Consider the following dummy dataset where we want to extract video game's names, (in CSV format) text label I was playing Call of Duty {'entities': [[14, 26, 'Game']]} I did not like COD1 and COD2 {'entities': [[15, 19, 'Game'], [24, 28, 'Game']]} As obvious, text contains the base text and label contains all the entities that ideally should be extracted from text. This is our golden dataset. Here, inside label we have a dict with entities key and the value is list of different entities. Each entity has [start_index, end_index, entity_type] data. Note, we follow the Python indexing i.e. the indexing starts with 0, start_index is the index of start character and end_index is the index of end character + 1 . In the first example, \"I was playing Call of Duty\"[14:26] will return \"Call of Duty\" which is a very famous video game \ud83c\udfae Now we will convert the CSV file into Spacy format. It is the recommended format supported by the package. To do this, run the following code, 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 # import from tqdm import tqdm from spacy.tokens import DocBin # function def convert_to_spacy ( data_df , output_path ): \"\"\" Convert the data to spacy format Parameters ------------ :param data_df: pandas.DataFrame dataframe containing the data to be converted :param output_path: string path to save the converted data \"\"\" nlp = spacy . blank ( \"en\" ) # load a new blank spacy model db = DocBin () # create a DocBin object # iterate over the dataframe for id_ , row in tqdm ( data_df . iterrows ()): text = row [ 'text' ] # extract the text doc = nlp . make_doc ( text ) # create doc object from text ents = [] # var to hold entities for entity in row [ 'label' ][ 'entities' ]: # add character indexes start , end , label = entity # extract the entity details span = doc . char_span ( start , end , label = label , alignment_mode = \"contract\" ) if span is None : print ( \"Skipping entity\" ) else : ents . append ( span ) doc . ents = ents # label the text with the ents db . add ( doc ) # save to disk db . to_disk ( output_path ) # save the docbin object # run the code convert_to_spacy ( data_df , \"train_data.spacy\" ) Note Remember to split the CSV file into train and test data. Then you can run the above code twice to generate two spacy files, one for train and one for test. Also, we can use random split, as stratified split is quite difficult to do. This is because each text may heve multiple instances of same or different entities and we want to split the text based on entities! Because of this, a stratified split is equivalent to solving a optimizing problem - How to split the text samples such that the underlying entities are equally distributed! Hence we will use a random split for rough estimation and the result may surprise you Data Validation The next step is to perform a validation to check if the data is correctly converted or not. Spacy provides readymade CLI command for this purpose, spacy debug data -V /content/config.cfg --paths.train /content/train_data.spacy --paths.dev /content/test_data.spacy This should print output similar to, ============================ Data file validation ============================ \u2714 Pipeline can be initialized with data \u2714 Corpus is loadable =============================== Training stats =============================== Language: en Training pipeline: tok2vec, ner 7916 training docs 235 evaluation docs \u2714 No overlap between training and evaluation data ============================== Vocab & Vectors ============================== \u2139 876405 total word ( s ) in the data ( 33656 unique ) 10 most common words: ',' ( 35938 ) , '.' ( 24253 ) , ' ' ( 19522 ) , ':' ( 17316 ) , 'to' ( 16328 ) , 'you' ( 15474 ) , 'the' ( 14626 ) , ' ' ( 14051 ) , ' ' ( 13869 ) , ' ' ( 12003 ) \u2139 No word vectors present in the package ========================== Named Entity Recognition ========================== \u2139 6 label ( s ) 0 missing value ( s ) ( tokens with '-' label ) Labels in train data: 'Game' \u2714 Good amount of examples for all labels \u2714 Examples without occurrences available for all labels \u2714 No entities consisting of or starting/ending with whitespace \u2714 No entities crossing sentence boundaries ================================== Summary ================================== \u2714 7 checks passed Based on the quality of annotations or the tool used, you may encounter error like Whitespaces present in the data validation step. This is because the annotations has whitespaces and it becomes difficult to train the model with such examples. In such case, we can fix the data by removing the whitespaces as shown below, 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 # var to hold the count of faulty annotations count = 0 # remove instances from the dataframe where annotations contains whitespaces for index , row in data_df . iterrows (): row_label = row [ 'label' ][ 'entities' ] for entity_index , entity in enumerate ( row_label ): text = row [ 'text' ][ entity [ 0 ]: entity [ 1 ]] if len ( text ) != len ( text . strip ()): count += 1 new_text = text . strip () start_index = row [ 'text' ] . find ( new_text ) end_index = start_index + len ( new_text ) row_label [ entity_index ] = [ start_index , end_index , entity [ 2 ]] # print the count of faulty annotations that were fixed print ( count ) Training the Model Now we are ready to train the model. Spacy CLI command can be used, spacy train --output models/ config/config.cfg --paths.train data/train_data.spacy --paths.dev data/test_data.spacy --gpu-id 0 Note Make sure to remove --gpu-id 0 if you do not have a GPU. This should print something like, \u2139 Saving to output directory: models \u2139 Using CPU =========================== Initializing pipeline =========================== [ 2022 -05-31 23 :29:00,409 ] [ INFO ] Set up nlp object from config [ 2022 -05-31 23 :29:00,413 ] [ INFO ] Pipeline: [ 'tok2vec' , 'ner' ] [ 2022 -05-31 23 :29:00,415 ] [ INFO ] Created vocabulary [ 2022 -05-31 23 :29:00,415 ] [ INFO ] Finished initializing nlp object [ 2022 -05-31 23 :29:08,553 ] [ INFO ] Initialized pipeline components: [ 'tok2vec' , 'ner' ] \u2714 Initialized pipeline ============================= Training pipeline ============================= \u2139 Pipeline: [ 'tok2vec' , 'ner' ] \u2139 Initial learn rate: 0 .001 E # LOSS TOK2VEC LOSS NER ENTS_F ENTS_P ENTS_R SCORE --- ------ ------------ -------- ------ ------ ------ ------ 0 0 0 .00 14 .42 0 .39 0 .43 0 .36 0 .00 0 1000 75263 .20 7992 .36 9 .34 9 .29 9 .39 0 .09 0 2000 473275 .24 6660 .36 20 .33 29 .45 15 .52 0 .20 1 3000 203618 .32 12177 .86 27 .76 29 .32 26 .35 0 .28 2 4000 394085 .98 14795 .70 35 .14 44 .02 29 .24 0 .35 3 5000 280698 .47 13595 .65 34 .71 40 .58 30 .32 0 .35 4 6000 332890 .64 13044 .08 37 .39 44 .72 32 .13 0 .37 5 7000 645988 .19 12552 .55 40 .72 45 .54 36 .82 0 .41 6 8000 155963 .67 12083 .43 34 .97 33 .01 37 .18 0 .35 7 9000 802471 .84 11443 .62 38 .64 40 .64 36 .82 0 .39 8 10000 44495 .21 10276 .14 38 .79 40 .55 37 .18 0 .39 9 11000 86229 .51 10011 .45 40 .08 46 .86 35 .02 0 .40 10 12000 198516 .08 9752 .18 37 .38 40 .08 35 .02 0 .37 Epoch 11 : 66 % | \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d | 656 /1000 [ 02 :34< 01 :28, 3 .89it/s ] Note Modify the config.cfg file to specify the model to use, the epochs to train for, the learning rate to use and other settings. Evaluation of the Model Finally, once the trainig is done, we can evaluate the model using the Spacy CLI command, spacy evaluate models/model-best data/test_data.spacy --gpu-id 0 Additional materials To train Spacy NER model on a custom dataset: Spacy v3 Custom NER Named-Entity evaluation metrics based on entity-level","title":"Named Entity Recognition"},{"location":"natural_language_processing/named_entity_recognition/#named-entity-recognition","text":"","title":"Named entity recognition"},{"location":"natural_language_processing/named_entity_recognition/#introduction","text":"Named entity recognition (NER) is the process of identifying entities in the unstructured text, where entities could be objects, people, locations, organizations, etc. NER's most basic building block consists of pair of entity_type and entity_value . Consider the following example, ## Statement My name is Mohit, and I am from India. I am a Data Scientist and I will be leaving for my office around 9 AM. ## Entities [{ 'entity_type': 'PERSON', 'entity_value': 'Mohit', }, { 'entity_type': 'LOCATION', 'entity_value': 'India', }, { 'entity_type': 'TIME', 'entity_value': '9 AM', }] The process of extracting entities could be done in two ways. Heuristic : by identifying the entities based on the rules. Semantic : by identifying the entities based on the semantics and context. Heuristic based approach is suited only for simple entities for which approximate rules can be created. Take for example EMAILID, PHONE_NUMBER, WEBSITE, etc. It should be easy enough to create regular expressions for such cases and hence heuristic approach could be applied. We can also apply part of speech based rules to extract certain entities. On the other hand, the Semantic approach is required where the cardinality of the entities is high and the context is required to extract the entities. For example, NAME, LOCATION, DESIGNATION, etc. For these cases, we usually train neural network based models that learn to consider the context and extract the entities. Note A good approach to creating your NER solution would be to segregate your entities into simple and complex, and then create either a heuristic or a semantic based solution or a combination of both. In short, it is not always suitable to directly go to fancy NN based semantic approaches - it could be unnecessary overkill. Remember the entity types are not set in stone and we can even train new models or finetune existing models on our own custom entities. For this, in the Semantic-based approach, it's a good idea to finetune the existing model rather than to train a new one as it will require far fewer data. The amount of data required to finetune model depends on how similar the custom entities are with the existing entities. Consider the following cases, The model is pretrained to detect PERSON and now you want to finetune it to detect MALE_NAME and FEMALE_NAME. As this is just a lower granularity on the existing PERSON entity, a mere ~200 examples (for each new entity type) could give you good results. On the other hand, if you now want to finetune a completely new entity like OBJECTIONS_TYPE, you may need ~500 examples. Note Another thing to consider is the length of entity_value . With an increase in entity_value you may require more examples to get good accuracy results.","title":"Introduction"},{"location":"natural_language_processing/named_entity_recognition/#code","text":"There are lots of Python-based packages that provide open source NER models. Some of these packages are Spacy , NLTK , Flair , etc. While packages provide an easy interface to the NER models or rules, we can even load and use external open-source NER models.","title":"Code"},{"location":"natural_language_processing/named_entity_recognition/#using-spacy-ner-model-for-inference","text":"Spacy comes with several pre-trained models that can be selected based on the use case. For this example, we will use the Transformer model available with Spacy Transformers . 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 # install spacy-transformers and transformers model ! pip install spacy - transformers ! python3 - m spacy download en_core_web_trf # import the spacy library import spacy # load the model nlp = spacy . load ( \"en_core_web_trf\" ) # set the text text = \"My name is Mohit, and I am from India. I am a Data Scientist and I will be leaving for my office around 9 AM.\" # create spacy doc by passing the text doc = nlp ( text ) # print all of the identified entities for ent in doc . ents : print ( f \"Type: { ent . label_ } -- Value: { ent . text } \" ) # Output: # Type: PERSON -- Value: Mohit # Type: GPE -- Value: India # Type: TIME -- Value: around 9 AM We can even display the results in a much more intuitive and fancy way by, 1 2 # use displacy to render the result spacy . displacy . render ( doc , style = 'ent' , jupyter = True , options = { 'distance' : 90 }) NER result for the above example","title":"Using Spacy NER model for Inference"},{"location":"natural_language_processing/named_entity_recognition/#training-custom-ner-model-using-spacy-v3","text":"All NER use-cases are not the same, and you may want to train a custom model with new entity types. Spacy provides an option to train custom NER models as well. To be frank the complete process is quite complicated, but nothing to worry, strap on and let's cover them steo by step.","title":"Training custom NER model using Spacy v3"},{"location":"natural_language_processing/named_entity_recognition/#config-creation","text":"To begin with, we will define the settings that will be used throughout the training process. Starting with Spacy v3, all parameter settings need to be configured using a .cfg file. We can create a .cfg file following the guide here . Basically, it requires to Firstly, create a base config using the quick widget provided at the page . Do remember to check the correct options. (Refer below image for one example) Secondly, run CLI command to update the base config python -m spacy init fill-config base_config.cfg config.cfg Example of options to mark to generate base_config.cfg from Spacy website for NER training.","title":"Config Creation"},{"location":"natural_language_processing/named_entity_recognition/#data-preparation","text":"Next we need to prepare the dataset. At high level, you need to prepare pairs of text and entities in the text. Consider the following dummy dataset where we want to extract video game's names, (in CSV format) text label I was playing Call of Duty {'entities': [[14, 26, 'Game']]} I did not like COD1 and COD2 {'entities': [[15, 19, 'Game'], [24, 28, 'Game']]} As obvious, text contains the base text and label contains all the entities that ideally should be extracted from text. This is our golden dataset. Here, inside label we have a dict with entities key and the value is list of different entities. Each entity has [start_index, end_index, entity_type] data. Note, we follow the Python indexing i.e. the indexing starts with 0, start_index is the index of start character and end_index is the index of end character + 1 . In the first example, \"I was playing Call of Duty\"[14:26] will return \"Call of Duty\" which is a very famous video game \ud83c\udfae Now we will convert the CSV file into Spacy format. It is the recommended format supported by the package. To do this, run the following code, 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 # import from tqdm import tqdm from spacy.tokens import DocBin # function def convert_to_spacy ( data_df , output_path ): \"\"\" Convert the data to spacy format Parameters ------------ :param data_df: pandas.DataFrame dataframe containing the data to be converted :param output_path: string path to save the converted data \"\"\" nlp = spacy . blank ( \"en\" ) # load a new blank spacy model db = DocBin () # create a DocBin object # iterate over the dataframe for id_ , row in tqdm ( data_df . iterrows ()): text = row [ 'text' ] # extract the text doc = nlp . make_doc ( text ) # create doc object from text ents = [] # var to hold entities for entity in row [ 'label' ][ 'entities' ]: # add character indexes start , end , label = entity # extract the entity details span = doc . char_span ( start , end , label = label , alignment_mode = \"contract\" ) if span is None : print ( \"Skipping entity\" ) else : ents . append ( span ) doc . ents = ents # label the text with the ents db . add ( doc ) # save to disk db . to_disk ( output_path ) # save the docbin object # run the code convert_to_spacy ( data_df , \"train_data.spacy\" ) Note Remember to split the CSV file into train and test data. Then you can run the above code twice to generate two spacy files, one for train and one for test. Also, we can use random split, as stratified split is quite difficult to do. This is because each text may heve multiple instances of same or different entities and we want to split the text based on entities! Because of this, a stratified split is equivalent to solving a optimizing problem - How to split the text samples such that the underlying entities are equally distributed! Hence we will use a random split for rough estimation and the result may surprise you","title":"Data Preparation"},{"location":"natural_language_processing/named_entity_recognition/#data-validation","text":"The next step is to perform a validation to check if the data is correctly converted or not. Spacy provides readymade CLI command for this purpose, spacy debug data -V /content/config.cfg --paths.train /content/train_data.spacy --paths.dev /content/test_data.spacy This should print output similar to, ============================ Data file validation ============================ \u2714 Pipeline can be initialized with data \u2714 Corpus is loadable =============================== Training stats =============================== Language: en Training pipeline: tok2vec, ner 7916 training docs 235 evaluation docs \u2714 No overlap between training and evaluation data ============================== Vocab & Vectors ============================== \u2139 876405 total word ( s ) in the data ( 33656 unique ) 10 most common words: ',' ( 35938 ) , '.' ( 24253 ) , ' ' ( 19522 ) , ':' ( 17316 ) , 'to' ( 16328 ) , 'you' ( 15474 ) , 'the' ( 14626 ) , ' ' ( 14051 ) , ' ' ( 13869 ) , ' ' ( 12003 ) \u2139 No word vectors present in the package ========================== Named Entity Recognition ========================== \u2139 6 label ( s ) 0 missing value ( s ) ( tokens with '-' label ) Labels in train data: 'Game' \u2714 Good amount of examples for all labels \u2714 Examples without occurrences available for all labels \u2714 No entities consisting of or starting/ending with whitespace \u2714 No entities crossing sentence boundaries ================================== Summary ================================== \u2714 7 checks passed Based on the quality of annotations or the tool used, you may encounter error like Whitespaces present in the data validation step. This is because the annotations has whitespaces and it becomes difficult to train the model with such examples. In such case, we can fix the data by removing the whitespaces as shown below, 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 # var to hold the count of faulty annotations count = 0 # remove instances from the dataframe where annotations contains whitespaces for index , row in data_df . iterrows (): row_label = row [ 'label' ][ 'entities' ] for entity_index , entity in enumerate ( row_label ): text = row [ 'text' ][ entity [ 0 ]: entity [ 1 ]] if len ( text ) != len ( text . strip ()): count += 1 new_text = text . strip () start_index = row [ 'text' ] . find ( new_text ) end_index = start_index + len ( new_text ) row_label [ entity_index ] = [ start_index , end_index , entity [ 2 ]] # print the count of faulty annotations that were fixed print ( count )","title":"Data Validation"},{"location":"natural_language_processing/named_entity_recognition/#training-the-model","text":"Now we are ready to train the model. Spacy CLI command can be used, spacy train --output models/ config/config.cfg --paths.train data/train_data.spacy --paths.dev data/test_data.spacy --gpu-id 0 Note Make sure to remove --gpu-id 0 if you do not have a GPU. This should print something like, \u2139 Saving to output directory: models \u2139 Using CPU =========================== Initializing pipeline =========================== [ 2022 -05-31 23 :29:00,409 ] [ INFO ] Set up nlp object from config [ 2022 -05-31 23 :29:00,413 ] [ INFO ] Pipeline: [ 'tok2vec' , 'ner' ] [ 2022 -05-31 23 :29:00,415 ] [ INFO ] Created vocabulary [ 2022 -05-31 23 :29:00,415 ] [ INFO ] Finished initializing nlp object [ 2022 -05-31 23 :29:08,553 ] [ INFO ] Initialized pipeline components: [ 'tok2vec' , 'ner' ] \u2714 Initialized pipeline ============================= Training pipeline ============================= \u2139 Pipeline: [ 'tok2vec' , 'ner' ] \u2139 Initial learn rate: 0 .001 E # LOSS TOK2VEC LOSS NER ENTS_F ENTS_P ENTS_R SCORE --- ------ ------------ -------- ------ ------ ------ ------ 0 0 0 .00 14 .42 0 .39 0 .43 0 .36 0 .00 0 1000 75263 .20 7992 .36 9 .34 9 .29 9 .39 0 .09 0 2000 473275 .24 6660 .36 20 .33 29 .45 15 .52 0 .20 1 3000 203618 .32 12177 .86 27 .76 29 .32 26 .35 0 .28 2 4000 394085 .98 14795 .70 35 .14 44 .02 29 .24 0 .35 3 5000 280698 .47 13595 .65 34 .71 40 .58 30 .32 0 .35 4 6000 332890 .64 13044 .08 37 .39 44 .72 32 .13 0 .37 5 7000 645988 .19 12552 .55 40 .72 45 .54 36 .82 0 .41 6 8000 155963 .67 12083 .43 34 .97 33 .01 37 .18 0 .35 7 9000 802471 .84 11443 .62 38 .64 40 .64 36 .82 0 .39 8 10000 44495 .21 10276 .14 38 .79 40 .55 37 .18 0 .39 9 11000 86229 .51 10011 .45 40 .08 46 .86 35 .02 0 .40 10 12000 198516 .08 9752 .18 37 .38 40 .08 35 .02 0 .37 Epoch 11 : 66 % | \u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u2588\u258d | 656 /1000 [ 02 :34< 01 :28, 3 .89it/s ] Note Modify the config.cfg file to specify the model to use, the epochs to train for, the learning rate to use and other settings.","title":"Training the Model"},{"location":"natural_language_processing/named_entity_recognition/#evaluation-of-the-model","text":"Finally, once the trainig is done, we can evaluate the model using the Spacy CLI command, spacy evaluate models/model-best data/test_data.spacy --gpu-id 0","title":"Evaluation of the Model"},{"location":"natural_language_processing/named_entity_recognition/#additional-materials","text":"To train Spacy NER model on a custom dataset: Spacy v3 Custom NER Named-Entity evaluation metrics based on entity-level","title":"Additional materials"},{"location":"natural_language_processing/nlq/","text":"Introduction Natural Language Querying (NLQ) is the process of querying DBs not in their official querying language but using natural language text. One example could be to fetch results from a SQL table for question - \"Who is the Prime Minister of India?\" by just using the text and not some technical query like select name from pm_table where country = \"India\" . There are two main reasons that makes this task important for any IaaS (Information as a service) product or SaaS, Each DB has its own specific querying language. This is a nightmare even for developers, as they will have to gain expertise in mulitple DB languages. Looking at a product from users perspective, it makes sense to let the user query in the language they prefer and not the technical query languages suitable for each DBs. Different Approaches Usually there are two approaches to create a NLQ system, Query creator: this is a multi-step process where we first convert the natural text to DB specific language query. This step in itself could have multiple sub steps where we identify entities and intent from the query and then match them with the available data in table. Later we execute the query on the DB and get the data. Answer extractor: this is a single step process, usually built completely of neural networks, where the data and question are passed to the network and output is returned. Think of it like closed-book QA systems in NLP, where we pass the context (here the DB table) and the question (query) and the model returns us the answer. We could add wrapper on the output to handle complex queries with COUNT, AVERAGE, etc. graph LR A(\"What is the size of the data?\") --> B(NLQ system) B -- Query creator --> C(\"Select count(*) from table\") C -- querying DB --> E(\"5\") B -- Answer extractor --> D(\"5\") style B stroke:#f66,stroke-width:2px,stroke-dasharray: 5 5 Note NLQ, as a topic, is DB agnostic. But in reality different NLQ systems are built for specific DBs for example SQL, SPARQL, MongoDB QL, etc. Code Let us explore the different ready made solutions for NLQ. Large Language Models (LLMs) While LLMs have been proven to work well for a lot of NLP related downstream tasks, will it work for NLQ? Let's think about it, due to huge training data LLMs might have already seen a lot of SQL queries and their respective descriptions. So in fact they have \"some idea\" on the relationship between a SQL query (or other query language for that matter) and its respective natural language query. Some might even say that it understands the fundamentals of Text-to-SQL task. But what LLM doesn't know is your Database/Table's schema and how you are storing the data. So hypothetically, if we provide these details it should work, right? The answer is yes! In paper [4], authors took this idea further and evaluated multiple LLMs to answer two questions, Which LLM is best for Text-to-SQL task? Considering only inference, Codex based models like code-davinci-001 were the top perfomers. If we can finetune the models, T5-3B + PICARD was better. Which prompt is best for Text-to-SQL task? Apart from the instructions, the prompt should also contain the schema of the table (with CREATE TABLE command containing column name, type, column reference and keys) along with a couple of rows as example. Below is an example of just the additional data [3] # schema CREATE TABLE \"Track\" ( \"TrackId\" INTEGER NOT NULL , \"Name\" NVARCHAR ( 200 ) NOT NULL , \"AlbumId\" INTEGER , \"MediaTypeId\" INTEGER NOT NULL , \"GenreId\" INTEGER , \"Composer\" NVARCHAR ( 220 ), \"Milliseconds\" INTEGER NOT NULL , \"Bytes\" INTEGER , \"UnitPrice\" NUMERIC ( 10 , 2 ) NOT NULL , PRIMARY KEY ( \"TrackId\" ), FOREIGN KEY ( \"MediaTypeId\" ) REFERENCES \"MediaType\" ( \"MediaTypeId\" ), FOREIGN KEY ( \"GenreId\" ) REFERENCES \"Genre\" ( \"GenreId\" ), FOREIGN KEY ( \"AlbumId\" ) REFERENCES \"Album\" ( \"AlbumId\" ) ) # examples SELECT * FROM 'Track' LIMIT 3 ; TrackId Name AlbumId MediaTypeId GenreId Composer Milliseconds Bytes UnitPrice 1 For Those About To Rock ( We Salute You ) 1 1 1 Angus Young , Malcolm Young , Brian Johnson 343719 11170334 0 . 99 2 Balls to the Wall 2 2 1 None 342562 5510424 0 . 99 3 Fast As a Shark 3 2 1 F . Baltes , S . Kaufman , U . Dirkscneider & W . Hoffman 230619 3990994 0 . 99 If all of this seems too tedius, we can use LangChain that does all of the heavy lifting for us so that we can just do the fun stuff i.e. ask questions . Here, we will connect SQLite database with LLM model. (Script inspired from SQLite example ) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 # import from langchain import OpenAI , SQLDatabase , SQLDatabaseChain # connect to SQLite DB db = SQLDatabase . from_uri ( \"sqlite://all_employees.db\" ) # connect to the OpenAI Davinci GPT-3 model llm = OpenAI ( temperature = 0 ) # create SQLDatabaseChain db_chain = SQLDatabaseChain ( llm = llm , database = db , verbose = True ) # run the code db_chain . run ( \"How many employees are there?\" ) # output >> SELECT COUNT ( * ) FROM Employee ; >> SQLResult : [( 8 ,)] >> Answer : There are 8 employees . Let's talk about what happened with the code above. First, LangChain create a prompt template and fills the variables automatically using the DB we plugin with the chain. The variables are {dialect} (here SQL) , {table_info} (the additional data we talked about above) and {input} (the question) . The template looks as follow, Given an input question, first create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer. Unless the user specifies in his question a specific number of examples he wishes to obtain, always limit your query to at most {top_k} results. You can order the results by a relevant column to return the most interesting examples in the database. Never query for all the columns from a specific table, only ask for a the few relevant columns given the question. Pay attention to use only the column names that you can see in the schema description. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table. Use the following format: Question: \"Question here\" SQLQuery: \"SQL Query to run\" SQLResult: \"Result of the SQLQuery\" Answer: \"Final answer here\" Only use the tables listed below. {table_info} Question: {input} Once done, it runs the LLM on the formatted prompt to get the SQL output. Then it execute the query on the connected DB to fetch the result. Finally, it also formats the results into a proper natural language output. All of this with just some prompt engineering! Note While the results are quite impressive, do remember that we need to use powerful (read costly) LLMs for it work with respectable accuracy. As we are formatting the prompt with DB schema, the prompt size might become huge if your DB or Table is big. It is hence recommended to create custom prompts when possible. Be also aware of the respective LLM costs if you are using 3rd party LLMs like GPT-4 or Cohere. TaPaS TaPas follows Answer extractor based approach to perform NLQ that utilizes specially trained BERT like transformer. Tapas takes the question and table in the format inspired from BERT like [CLS] Question [SEP] Flattened table [SEP] . The answer is selected from the table based on the question. The model was first pre-trained using unlabeled data on tasks like Masked Language modeling and Sentence-Table support/refute prediction. Later, it was finetuned on datasets like WikiSQL, WTQ and other to perform NLQ. Illustration of the TaPas model for one example ( TaPas paper ) One interesting differentiator of TaPas is the unique formatting and encoding of the query and the table. As a table contains values spread across columns and rows, special column, rows and segment embeddings are added to the input to make the model learn the proper context. One example is shown below, Encoding process of sample query and table in TaPas ( TaPas paper ) Note As TaPas was pre-trained using self-supervised learning on unlabled data, it learned the concept of relationship between text and table. Hence, it can be used (finetuned) for other table-text related downstream tasks as well like refute or support the text based on content in table, etc. Let's get started with the code part. For TAPAS to work, we need to install torch-scatter . For this, we first install pytorch using pip install torch and then get the version of torch using torch.__version__ . Next we install torch-scatter by replacing the version of torch in pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.12.0+cu102.html 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 # install required packages ! pip install - q transformers == 4.4.2 torch pandas ! pip install torch - scatter - f https : // pytorch - geometric . com / whl / torch - 1.12.0 + cu102 . html # import import pandas as pd from transformers import pipeline # load pipeline nlq_tapas = pipeline ( task = \"table-question-answering\" , model = \"google/tapas-base-finetuned-wtq\" ) # load the data data = pd . read_csv ( \"../data/pm_table.csv\" ) # use your table here data = data . astype ( str ) # query the table query = \"Who is the Prime Minister of India?\" answer = nlq_tapas ( table = data , query = query )[ 'answer' ] print ( answer ) # Output: \"Narendra Modi\" (at the time of writing) Tip Personal opinion - TAPAS's accuracy is quite good wrt TableQA, but the major drawback is that it only works for small tables. Hence, forget about using it for industry use case with larger tables. TableQA TableQA follows Query creator approach to build an AI tool for querying natural language on tabular data. While the approach was released rather recently (Jan 2022) , it's performance is comparable or worse than TaPas. As per the authors, TableQA shines when it comes to NLQ on large tables and complex queries. It is more of a framework consisting of mulitple modules. The complete process consists of components like table selector, known fields extractor, unknown fields extractor, and agreegator function classifer and SQL generator. System architecture of TableQA ( TableQA paper ) Let's get TableQA running with following code, 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 # install required packages ! pip install - q tableqa nltk # import packages import nltk import pandas as pd from tableqa.agent import Agent # download nltk data nltk . download ( 'omw-1.4' ) # load a CSV df = pd . read_csv ( \"my_csv_file.csv\" ) # create an agent agent = Agent ( df ) # create query query = \"how many data points in 2011?\" # get the SQL query agent . get_query ( query ) # get the answer agent . query_db ( query ) Additional materials How to Talk to Your Database - Salesforce Stack Exchange: Natural Language to SQL query LangChain Blog: LLMs and SQL Evaluating the Text-to-SQL Capabilities of Large Language Models Cheers","title":"Natural Language Querying"},{"location":"natural_language_processing/nlq/#introduction","text":"Natural Language Querying (NLQ) is the process of querying DBs not in their official querying language but using natural language text. One example could be to fetch results from a SQL table for question - \"Who is the Prime Minister of India?\" by just using the text and not some technical query like select name from pm_table where country = \"India\" . There are two main reasons that makes this task important for any IaaS (Information as a service) product or SaaS, Each DB has its own specific querying language. This is a nightmare even for developers, as they will have to gain expertise in mulitple DB languages. Looking at a product from users perspective, it makes sense to let the user query in the language they prefer and not the technical query languages suitable for each DBs.","title":"Introduction"},{"location":"natural_language_processing/nlq/#different-approaches","text":"Usually there are two approaches to create a NLQ system, Query creator: this is a multi-step process where we first convert the natural text to DB specific language query. This step in itself could have multiple sub steps where we identify entities and intent from the query and then match them with the available data in table. Later we execute the query on the DB and get the data. Answer extractor: this is a single step process, usually built completely of neural networks, where the data and question are passed to the network and output is returned. Think of it like closed-book QA systems in NLP, where we pass the context (here the DB table) and the question (query) and the model returns us the answer. We could add wrapper on the output to handle complex queries with COUNT, AVERAGE, etc. graph LR A(\"What is the size of the data?\") --> B(NLQ system) B -- Query creator --> C(\"Select count(*) from table\") C -- querying DB --> E(\"5\") B -- Answer extractor --> D(\"5\") style B stroke:#f66,stroke-width:2px,stroke-dasharray: 5 5 Note NLQ, as a topic, is DB agnostic. But in reality different NLQ systems are built for specific DBs for example SQL, SPARQL, MongoDB QL, etc.","title":"Different Approaches"},{"location":"natural_language_processing/nlq/#code","text":"Let us explore the different ready made solutions for NLQ.","title":"Code"},{"location":"natural_language_processing/nlq/#large-language-models-llms","text":"While LLMs have been proven to work well for a lot of NLP related downstream tasks, will it work for NLQ? Let's think about it, due to huge training data LLMs might have already seen a lot of SQL queries and their respective descriptions. So in fact they have \"some idea\" on the relationship between a SQL query (or other query language for that matter) and its respective natural language query. Some might even say that it understands the fundamentals of Text-to-SQL task. But what LLM doesn't know is your Database/Table's schema and how you are storing the data. So hypothetically, if we provide these details it should work, right? The answer is yes! In paper [4], authors took this idea further and evaluated multiple LLMs to answer two questions, Which LLM is best for Text-to-SQL task? Considering only inference, Codex based models like code-davinci-001 were the top perfomers. If we can finetune the models, T5-3B + PICARD was better. Which prompt is best for Text-to-SQL task? Apart from the instructions, the prompt should also contain the schema of the table (with CREATE TABLE command containing column name, type, column reference and keys) along with a couple of rows as example. Below is an example of just the additional data [3] # schema CREATE TABLE \"Track\" ( \"TrackId\" INTEGER NOT NULL , \"Name\" NVARCHAR ( 200 ) NOT NULL , \"AlbumId\" INTEGER , \"MediaTypeId\" INTEGER NOT NULL , \"GenreId\" INTEGER , \"Composer\" NVARCHAR ( 220 ), \"Milliseconds\" INTEGER NOT NULL , \"Bytes\" INTEGER , \"UnitPrice\" NUMERIC ( 10 , 2 ) NOT NULL , PRIMARY KEY ( \"TrackId\" ), FOREIGN KEY ( \"MediaTypeId\" ) REFERENCES \"MediaType\" ( \"MediaTypeId\" ), FOREIGN KEY ( \"GenreId\" ) REFERENCES \"Genre\" ( \"GenreId\" ), FOREIGN KEY ( \"AlbumId\" ) REFERENCES \"Album\" ( \"AlbumId\" ) ) # examples SELECT * FROM 'Track' LIMIT 3 ; TrackId Name AlbumId MediaTypeId GenreId Composer Milliseconds Bytes UnitPrice 1 For Those About To Rock ( We Salute You ) 1 1 1 Angus Young , Malcolm Young , Brian Johnson 343719 11170334 0 . 99 2 Balls to the Wall 2 2 1 None 342562 5510424 0 . 99 3 Fast As a Shark 3 2 1 F . Baltes , S . Kaufman , U . Dirkscneider & W . Hoffman 230619 3990994 0 . 99 If all of this seems too tedius, we can use LangChain that does all of the heavy lifting for us so that we can just do the fun stuff i.e. ask questions . Here, we will connect SQLite database with LLM model. (Script inspired from SQLite example ) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 # import from langchain import OpenAI , SQLDatabase , SQLDatabaseChain # connect to SQLite DB db = SQLDatabase . from_uri ( \"sqlite://all_employees.db\" ) # connect to the OpenAI Davinci GPT-3 model llm = OpenAI ( temperature = 0 ) # create SQLDatabaseChain db_chain = SQLDatabaseChain ( llm = llm , database = db , verbose = True ) # run the code db_chain . run ( \"How many employees are there?\" ) # output >> SELECT COUNT ( * ) FROM Employee ; >> SQLResult : [( 8 ,)] >> Answer : There are 8 employees . Let's talk about what happened with the code above. First, LangChain create a prompt template and fills the variables automatically using the DB we plugin with the chain. The variables are {dialect} (here SQL) , {table_info} (the additional data we talked about above) and {input} (the question) . The template looks as follow, Given an input question, first create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer. Unless the user specifies in his question a specific number of examples he wishes to obtain, always limit your query to at most {top_k} results. You can order the results by a relevant column to return the most interesting examples in the database. Never query for all the columns from a specific table, only ask for a the few relevant columns given the question. Pay attention to use only the column names that you can see in the schema description. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which table. Use the following format: Question: \"Question here\" SQLQuery: \"SQL Query to run\" SQLResult: \"Result of the SQLQuery\" Answer: \"Final answer here\" Only use the tables listed below. {table_info} Question: {input} Once done, it runs the LLM on the formatted prompt to get the SQL output. Then it execute the query on the connected DB to fetch the result. Finally, it also formats the results into a proper natural language output. All of this with just some prompt engineering! Note While the results are quite impressive, do remember that we need to use powerful (read costly) LLMs for it work with respectable accuracy. As we are formatting the prompt with DB schema, the prompt size might become huge if your DB or Table is big. It is hence recommended to create custom prompts when possible. Be also aware of the respective LLM costs if you are using 3rd party LLMs like GPT-4 or Cohere.","title":"Large Language Models (LLMs)"},{"location":"natural_language_processing/nlq/#tapas","text":"TaPas follows Answer extractor based approach to perform NLQ that utilizes specially trained BERT like transformer. Tapas takes the question and table in the format inspired from BERT like [CLS] Question [SEP] Flattened table [SEP] . The answer is selected from the table based on the question. The model was first pre-trained using unlabeled data on tasks like Masked Language modeling and Sentence-Table support/refute prediction. Later, it was finetuned on datasets like WikiSQL, WTQ and other to perform NLQ. Illustration of the TaPas model for one example ( TaPas paper ) One interesting differentiator of TaPas is the unique formatting and encoding of the query and the table. As a table contains values spread across columns and rows, special column, rows and segment embeddings are added to the input to make the model learn the proper context. One example is shown below, Encoding process of sample query and table in TaPas ( TaPas paper ) Note As TaPas was pre-trained using self-supervised learning on unlabled data, it learned the concept of relationship between text and table. Hence, it can be used (finetuned) for other table-text related downstream tasks as well like refute or support the text based on content in table, etc. Let's get started with the code part. For TAPAS to work, we need to install torch-scatter . For this, we first install pytorch using pip install torch and then get the version of torch using torch.__version__ . Next we install torch-scatter by replacing the version of torch in pip install torch-scatter -f https://pytorch-geometric.com/whl/torch-1.12.0+cu102.html 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 # install required packages ! pip install - q transformers == 4.4.2 torch pandas ! pip install torch - scatter - f https : // pytorch - geometric . com / whl / torch - 1.12.0 + cu102 . html # import import pandas as pd from transformers import pipeline # load pipeline nlq_tapas = pipeline ( task = \"table-question-answering\" , model = \"google/tapas-base-finetuned-wtq\" ) # load the data data = pd . read_csv ( \"../data/pm_table.csv\" ) # use your table here data = data . astype ( str ) # query the table query = \"Who is the Prime Minister of India?\" answer = nlq_tapas ( table = data , query = query )[ 'answer' ] print ( answer ) # Output: \"Narendra Modi\" (at the time of writing) Tip Personal opinion - TAPAS's accuracy is quite good wrt TableQA, but the major drawback is that it only works for small tables. Hence, forget about using it for industry use case with larger tables.","title":"TaPaS"},{"location":"natural_language_processing/nlq/#tableqa","text":"TableQA follows Query creator approach to build an AI tool for querying natural language on tabular data. While the approach was released rather recently (Jan 2022) , it's performance is comparable or worse than TaPas. As per the authors, TableQA shines when it comes to NLQ on large tables and complex queries. It is more of a framework consisting of mulitple modules. The complete process consists of components like table selector, known fields extractor, unknown fields extractor, and agreegator function classifer and SQL generator. System architecture of TableQA ( TableQA paper ) Let's get TableQA running with following code, 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 # install required packages ! pip install - q tableqa nltk # import packages import nltk import pandas as pd from tableqa.agent import Agent # download nltk data nltk . download ( 'omw-1.4' ) # load a CSV df = pd . read_csv ( \"my_csv_file.csv\" ) # create an agent agent = Agent ( df ) # create query query = \"how many data points in 2011?\" # get the SQL query agent . get_query ( query ) # get the answer agent . query_db ( query )","title":"TableQA"},{"location":"natural_language_processing/nlq/#additional-materials","text":"How to Talk to Your Database - Salesforce Stack Exchange: Natural Language to SQL query LangChain Blog: LLMs and SQL Evaluating the Text-to-SQL Capabilities of Large Language Models Cheers","title":"Additional materials"},{"location":"natural_language_processing/paraphraser/","text":"Introduction Paraphrasing is a NLP task of reformatting the input text considering a set of objectives. The objectives could be, Adequecy: is the meaning of sentence preserved? It can be measured by using a NLI model that could determine if the paraphrase is entailment of the original sentence or not. Fluency: is the paraphrase fluent? It can be measured by using fluency classification models. Diversity: how much different paraphrase is from original sentence? It can be measured by computing text similarity between the original sentence and paraphrase. Lower the text similarity score, higher the diversity. We can use edit based algorithms like Levenshtein. Tonality: has the tone of the parapharse changed? It can be measured with tone detection models. Formality: has the writing style of the parapharse changed? It can be measured with formality detection models. Length: has the paraphrase become more concise or detailed? It can be measured by simple word or token based tokenizers. Note The objectives could be one or multiple. Also, they could be applied while training or inference. Once way to combine existing models with objectives it was not trained on, is to perform multiple generations and pick the one with highest score in terms of objective metrics. While we will go through the programmer way of performing Paraphrasing, here are some of the free tools (limited) available online for Paraphrasing -- Quillbot , Paraphraser.io , Rephrase.Info , Outwrite , Grammarly , etc. Datasets There are mulitple open-source datasets that can be used to train or fine-tune our own paraphrasing model. Below is a list with some useful details, [3] Highlights of primarily used paraphrase generation datasets [3] Thats not all, PAWS and MSRP are also widely used. A more detailed list of dataset is presented here . Code Parrot Paraphraser Usually a Seq2Seq or specifically large language models (LLMs) are either directly used or finetuned to perform Paraphrasing. This is because LLM are good with text generation and Paraphrasing can be easily converted to text generation task. Parrot [2] is a Python package that use finetuned T5 model to perform Paraphrasing. Let's first see how to use the package, 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 # taken from Parrot Readme -- https://github.com/PrithivirajDamodaran/Parrot_Paraphraser # import from parrot import Parrot import torch import warnings warnings . filterwarnings ( \"ignore\" ) #Init models (make sure you init ONLY once if you integrate this to your code) parrot = Parrot ( model_tag = \"prithivida/parrot_paraphraser_on_T5\" ) phrases = [ \"Can you recommend some upscale restaurants in Newyork?\" , \"What are the famous places we should not miss in Russia?\" ] for phrase in phrases : para_phrases = parrot . augment ( input_phrase = phrase , use_gpu = False ) for para_phrase in para_phrases : print ( para_phrase ) Btw they also provide advanced set of options to tune the objective we discussed before. For this you only need to modify the parameters for the augment function. Example is shown below, 1 2 3 4 5 6 7 8 para_phrases = parrot . augment ( input_phrase = phrase , use_gpu = False , diversity_ranker = \"levenshtein\" , do_diverse = False , max_return_phrases = 10 , max_length = 32 , adequacy_threshold = 0.99 , fluency_threshold = 0.90 ) As Parrot package internally uses multiple models to detect adequacy, fluency and diversity, the execution time could be slower. We can compromise good generation with execution time by directly using the finetuned model as shown below, 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 # install packages ! pip install transformers ! pip install - q sentencepiece # import from transformers import AutoTokenizer , AutoModelForSeq2SeqLM # load the tokenizers and model tokenizer = AutoTokenizer . from_pretrained ( \"prithivida/parrot_paraphraser_on_T5\" ) model = AutoModelForSeq2SeqLM . from_pretrained ( \"prithivida/parrot_paraphraser_on_T5\" ) # for a phrase get the tokenised input ids input_ids = tokenizer ( \"paraphrase: Can I call you after I am done with this thing I am working on?\" , return_tensors = \"pt\" ) . input_ids # use the input ids to generte output outputs = model . generate ( input_ids , max_new_tokens = 10 , do_sample = False , num_beams = 1 , length_penalty = 5 ) # decode the output token ids to text print ( tokenizer . decode ( outputs [ 0 ], skip_special_tokens = True )) ## Output --> ## Can I call you after I've finished this Finetuning T5 as Paraphraser Any LLM can be used for Paraphrase generation by zero-shot for comparative accuracy. If you want to better result, finetune it on your own datasets. Here we will try to finetune T5 , 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 # install ! pip install - q simplet5 ! pip install - q datasets # import import pandas as pd from simplet5 import SimpleT5 from datasets import load_dataset # load datasets msrp = load_dataset ( \"HHousen/msrp\" ) paws = load_dataset ( \"paws\" , 'labeled_final' ) # prepare dataset def clean_msrp_paws_dataset ( data ): df = pd . DataFrame ( data ) df = df [ df [ 'label' ] == 1 ] df [ 'source_text' ] = f 'Paraphrase: ' + df [ 'sentence1' ] return df # clean both train and test data train_msrp_data = clean_msrp_paws_dataset ( msrp [ 'train' ]) test_msrp_data = clean_msrp_paws_dataset ( msrp [ 'test' ]) # clean_msrp_paws_dataset train_paws_data = clean_msrp_paws_dataset ( paws [ 'train' ]) test_paws_data = clean_msrp_paws_dataset ( paws [ 'test' ]) validation_paws_data = clean_msrp_paws_dataset ( paws [ 'validation' ]) # combine the individual splits of datasets msrp_dataset = pd . concat ([ train_msrp_data , test_msrp_data ]) paws_dataset = pd . concat ([ train_paws_data , test_paws_data , validation_paws_data ]) # combine the datasets df1 = msrp_dataset [[ 'source_text' , 'sentence2' ]] df1 = df1 . rename ( columns = { 'source_text' : 'source_text' , 'sentence2' : 'target_text' }) df2 = paws_dataset [[ 'source_text' , 'sentence2' ]] df2 = df2 . rename ( columns = { 'source_text' : 'source_text' , 'sentence2' : 'target_text' }) train_data = pd . concat ([ df1 , df2 ]) # Train # load model model = SimpleT5 () model . from_pretrained ( model_type = \"t5\" , model_name = \"t5-small\" ) # train model model . train ( train_df = train_data , eval_df = train_data . head ( 100 ), # dummy eval, in reality keep some held-out samples as validation/test source_max_token_len = 300 , target_max_token_len = 200 , batch_size = 4 , max_epochs = 20 , outputdir = \"outputs\" , use_gpu = True ) # Inference # last_epoch_model = \"/content/outputs/simplet5-epoch-1-train-loss-1.5314-val-loss-1.2911\" # put the name here # model.load_model(\"t5\", last_epoch_model, use_gpu=True) # model.predict(\"Paraphrase: He is going to USA to visit his friend\") References [1] Paraphrase Generation: A Survey of the State of the Art [2] Parrot Paraphraser [3] Paraphrase Generation: A Survey of the State of the Art Cheers","title":"Paraphraser"},{"location":"natural_language_processing/paraphraser/#introduction","text":"Paraphrasing is a NLP task of reformatting the input text considering a set of objectives. The objectives could be, Adequecy: is the meaning of sentence preserved? It can be measured by using a NLI model that could determine if the paraphrase is entailment of the original sentence or not. Fluency: is the paraphrase fluent? It can be measured by using fluency classification models. Diversity: how much different paraphrase is from original sentence? It can be measured by computing text similarity between the original sentence and paraphrase. Lower the text similarity score, higher the diversity. We can use edit based algorithms like Levenshtein. Tonality: has the tone of the parapharse changed? It can be measured with tone detection models. Formality: has the writing style of the parapharse changed? It can be measured with formality detection models. Length: has the paraphrase become more concise or detailed? It can be measured by simple word or token based tokenizers. Note The objectives could be one or multiple. Also, they could be applied while training or inference. Once way to combine existing models with objectives it was not trained on, is to perform multiple generations and pick the one with highest score in terms of objective metrics. While we will go through the programmer way of performing Paraphrasing, here are some of the free tools (limited) available online for Paraphrasing -- Quillbot , Paraphraser.io , Rephrase.Info , Outwrite , Grammarly , etc.","title":"Introduction"},{"location":"natural_language_processing/paraphraser/#datasets","text":"There are mulitple open-source datasets that can be used to train or fine-tune our own paraphrasing model. Below is a list with some useful details, [3] Highlights of primarily used paraphrase generation datasets [3] Thats not all, PAWS and MSRP are also widely used. A more detailed list of dataset is presented here .","title":"Datasets"},{"location":"natural_language_processing/paraphraser/#code","text":"","title":"Code"},{"location":"natural_language_processing/paraphraser/#parrot-paraphraser","text":"Usually a Seq2Seq or specifically large language models (LLMs) are either directly used or finetuned to perform Paraphrasing. This is because LLM are good with text generation and Paraphrasing can be easily converted to text generation task. Parrot [2] is a Python package that use finetuned T5 model to perform Paraphrasing. Let's first see how to use the package, 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 # taken from Parrot Readme -- https://github.com/PrithivirajDamodaran/Parrot_Paraphraser # import from parrot import Parrot import torch import warnings warnings . filterwarnings ( \"ignore\" ) #Init models (make sure you init ONLY once if you integrate this to your code) parrot = Parrot ( model_tag = \"prithivida/parrot_paraphraser_on_T5\" ) phrases = [ \"Can you recommend some upscale restaurants in Newyork?\" , \"What are the famous places we should not miss in Russia?\" ] for phrase in phrases : para_phrases = parrot . augment ( input_phrase = phrase , use_gpu = False ) for para_phrase in para_phrases : print ( para_phrase ) Btw they also provide advanced set of options to tune the objective we discussed before. For this you only need to modify the parameters for the augment function. Example is shown below, 1 2 3 4 5 6 7 8 para_phrases = parrot . augment ( input_phrase = phrase , use_gpu = False , diversity_ranker = \"levenshtein\" , do_diverse = False , max_return_phrases = 10 , max_length = 32 , adequacy_threshold = 0.99 , fluency_threshold = 0.90 ) As Parrot package internally uses multiple models to detect adequacy, fluency and diversity, the execution time could be slower. We can compromise good generation with execution time by directly using the finetuned model as shown below, 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 # install packages ! pip install transformers ! pip install - q sentencepiece # import from transformers import AutoTokenizer , AutoModelForSeq2SeqLM # load the tokenizers and model tokenizer = AutoTokenizer . from_pretrained ( \"prithivida/parrot_paraphraser_on_T5\" ) model = AutoModelForSeq2SeqLM . from_pretrained ( \"prithivida/parrot_paraphraser_on_T5\" ) # for a phrase get the tokenised input ids input_ids = tokenizer ( \"paraphrase: Can I call you after I am done with this thing I am working on?\" , return_tensors = \"pt\" ) . input_ids # use the input ids to generte output outputs = model . generate ( input_ids , max_new_tokens = 10 , do_sample = False , num_beams = 1 , length_penalty = 5 ) # decode the output token ids to text print ( tokenizer . decode ( outputs [ 0 ], skip_special_tokens = True )) ## Output --> ## Can I call you after I've finished this","title":"Parrot Paraphraser"},{"location":"natural_language_processing/paraphraser/#finetuning-t5-as-paraphraser","text":"Any LLM can be used for Paraphrase generation by zero-shot for comparative accuracy. If you want to better result, finetune it on your own datasets. Here we will try to finetune T5 , 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 # install ! pip install - q simplet5 ! pip install - q datasets # import import pandas as pd from simplet5 import SimpleT5 from datasets import load_dataset # load datasets msrp = load_dataset ( \"HHousen/msrp\" ) paws = load_dataset ( \"paws\" , 'labeled_final' ) # prepare dataset def clean_msrp_paws_dataset ( data ): df = pd . DataFrame ( data ) df = df [ df [ 'label' ] == 1 ] df [ 'source_text' ] = f 'Paraphrase: ' + df [ 'sentence1' ] return df # clean both train and test data train_msrp_data = clean_msrp_paws_dataset ( msrp [ 'train' ]) test_msrp_data = clean_msrp_paws_dataset ( msrp [ 'test' ]) # clean_msrp_paws_dataset train_paws_data = clean_msrp_paws_dataset ( paws [ 'train' ]) test_paws_data = clean_msrp_paws_dataset ( paws [ 'test' ]) validation_paws_data = clean_msrp_paws_dataset ( paws [ 'validation' ]) # combine the individual splits of datasets msrp_dataset = pd . concat ([ train_msrp_data , test_msrp_data ]) paws_dataset = pd . concat ([ train_paws_data , test_paws_data , validation_paws_data ]) # combine the datasets df1 = msrp_dataset [[ 'source_text' , 'sentence2' ]] df1 = df1 . rename ( columns = { 'source_text' : 'source_text' , 'sentence2' : 'target_text' }) df2 = paws_dataset [[ 'source_text' , 'sentence2' ]] df2 = df2 . rename ( columns = { 'source_text' : 'source_text' , 'sentence2' : 'target_text' }) train_data = pd . concat ([ df1 , df2 ]) # Train # load model model = SimpleT5 () model . from_pretrained ( model_type = \"t5\" , model_name = \"t5-small\" ) # train model model . train ( train_df = train_data , eval_df = train_data . head ( 100 ), # dummy eval, in reality keep some held-out samples as validation/test source_max_token_len = 300 , target_max_token_len = 200 , batch_size = 4 , max_epochs = 20 , outputdir = \"outputs\" , use_gpu = True ) # Inference # last_epoch_model = \"/content/outputs/simplet5-epoch-1-train-loss-1.5314-val-loss-1.2911\" # put the name here # model.load_model(\"t5\", last_epoch_model, use_gpu=True) # model.predict(\"Paraphrase: He is going to USA to visit his friend\")","title":"Finetuning T5 as Paraphraser"},{"location":"natural_language_processing/paraphraser/#references","text":"[1] Paraphrase Generation: A Survey of the State of the Art [2] Parrot Paraphraser [3] Paraphrase Generation: A Survey of the State of the Art Cheers","title":"References"},{"location":"natural_language_processing/prompt_engineering/","text":"Prompt Engineering Introduction Prompt engineering involves crafting well-defined and strategically designed input queries to elicit desired responses from AI systems. It serves as a bridge between human intention and machine understanding, enabling AI models to provide more accurate and contextually relevant outputs. As AI applications continue to proliferate across various domains, mastering the art of prompt engineering has become essential for both developers and users. What makes prompt engineering more tempting is that it does not require any finetuning of the model but nevertheless, it can enhance the model accuracy substantially! In this article, we will explore different key strategies for crafting effective prompts that can enhance AI model capabilities. Types of Prompts Before getting started with prompt techniques, let\u2019s discuss the main types of prompts, System Prompts System prompts are like global settings that are applied once to set the mood and intention of the AI model\u2019s subsequent generations in the same chat. These prompts are carefully crafted by developers to guide the AI system toward specific outputs that align with the intended use case. ChatGPT UI\u2019s custom instruction is a good example of a system prompt, as whatever you mention there is applicable to every chat. Users can provide details to format output in a certain style (like JSON), provide details about themselves so that the responses are personalized, set the tone of the generation, define privacy and ethics details, and much more! An example is shown below System Prompt : You are a helpful AI Assistant . Help users by replying to their queries and make sure the responses are polite . Do not hallucinate and say \"I don't know\" if required . User Prompts User prompts are generated on the fly by users and are designed to elicit specific responses to their queries. Unlike system prompts, user prompts are not pre-defined and can vary widely in structure and content. These are more transactional in nature, and are usally present after system prompt and could be multiple in count. System Prompt : You are a helpful AI Assistant . Help users in replying to their queries and make sure the responses are polite . Do not hallucinate and say \"I don't know\" if required . User Prompt : What is your name ? Assistant Output These are AI\u2019s generated output to the System and previous user prompts. In complex use cases, developers can modify this as an example to the AI model to highlight the kind of result expected from the model. System Prompt : You are a helpful AI Assistant . Help users in replying to their queries and make sure the responses are polite . Do not hallucinate and say \"I don't know\" if required . User Prompt : What is your name ? Assistant : I am an AI Assistant that can help you with your queries . Please let me know your questions ! Prompt Strategies Zero-Shot Prompts Zero-shot prompts are a fundamental technique in prompt engineering, allowing AI models to provide meaningful responses without any specific training examples. With zero-shot prompts, developers and users can harness the model's innate knowledge and reasoning capabilities to answer questions or complete tasks, even if the model has never seen similar queries before. When using a zero-shot prompt, formulate your question or request as clearly and concisely as possible. You can even provide some context if that helps, overall avoid ambiguity to help the model understand your intention accurately. Example 1 - Clear instructions ################ User Prompt : Translate the following English text to French : \"Hello, how are you?\" Example 2 - Provide context ################ User Prompt : Calculate the total revenue for our company in the last quarter , given the following financial data : [ insert data ]. Note, deciding which data should goes where (system or user prompt) depends on experimenting how it works for a specific model but a general thumb rule is to keep the constant details in system prompt and dynamic details in user prompt. In the first example above, we can also have following prompts, Example 1 - Clear instructions with System prompt ################ System prompt : You are a Translator GPT . Given a sentence in English , translate it into French . User prompt : \"Hello, how are you?\" Few-Shot Prompts While zero-shot prompts are fundamental, there are situations where you may need to provide a bit more guidance to the AI model. In such cases, you can use few-shot prompts that involve providing a small number of examples or demonstrations to help the model understand the desired task or context. Developers can use this approach to further guide the AI model's responses. One example of 2-shot prompt is shown below, System prompt : You are a Translator GPT . Given a sentence in English , translate it into French . Examples are shared below , English : \"Hello, how are you?\" French : \"Bonjour, comment \u00e7a va ?\" English : \"I am learning French.\" French : \"J'apprends le fran\u00e7ais.\" User Prompt : English : \"Please pass the salt.\" French : Note, the number of examples to be included (n-shot) is highly experimental. The objective should be to keep the example count as less as possible (otherwise the token size and cost will increase) while making sure the accuracy is not impacted. So the prompt design should be done incrementally, i.e. keep adding more examples if the accuracy is below expectations. Also, make sure to add diverse examples and do not add exact or even semantically similar examples as latest LLMs are quite \u201csmart\u201d enough to learn from few examples. Few-shot Chain-of-Thought Prompt Few shot CoT Prompting was introduced in [1] and the idea is that generating a chain of thought, i.e. a series of intermediate reasoning steps, can significantly improves the ability of large language models to perform complex reasoning. Experiments shows that chain-of-thought prompting improves performance on a range of arithmetic, commonsense, and symbolic reasoning tasks. Basically it is clubbed with few shot prompt where the examples are provided in CoT format. Example is shown in the below image, Example inputs and outputs for Standard 1-shot prompt and CoT prompts Zero-shot Chain-of-Thought Prompt Zero shot variant of CoT was introduced in [2] and it can significantly increase the accuracy of Zero shot prompts, and all you need to do is to add \u201cLet\u2019s think step by step.\u201d \ud83d\ude1c. Btw additional post-processing is required on the output to extract the correct result, which can either be done by creating regex scripts or calling LLMs again to extract the answer. Note Few-shot prompts should always give better result than Zero-shot, but the former requires additional token consumption which will increase the cost. To mitigate this, developers can experiment with Zero-shot CoT technique and if the result accuracy is acceptable, it might end up reducing the overall cost. Example inputs and outputs of GPT-3 with (a) standard Few-shot, (b) Few-shot-CoT, (c) standard Zero-shot, and (d) Zero-shot-CoT Self-consistency Self-consistency [3] is based on the idea that there are multiple ways to solve a complex problem i.e. if multiple reasoning paths are leading to the same output, it is highly probable that it is a correct answer. In their own words, \"...we hypothesize that correct reasoning processes, even if they are diverse, tend to have greater agreement in their final answer than incorrect processes.\" . The self-consistency method consists of three steps: prompt a language model using chain-of-thought (CoT) prompting; replace the \u201cgreedy decode\u201d in CoT prompting by sampling from the language model\u2019s decoder to generate a diverse set of reasoning paths; and marginalize out the reasoning paths and aggregate by choosing the most consistent answer in the final answer set. The authors of the paper performed extensive empirical evaluations to shows that self-consistency boosts the performance of chain-of-thought prompting on a range of popular arithmetic and commonsense reasoning benchmarks, including GSM8K (+17.9%), SVAMP (+11.0%), AQuA (+12.2%), StrategyQA (+6.4%) and ARC-challenge (+3.9%). CoT vs Self-consistency prompting example Tree-of-Thoughts Tree-of-Thoughts (ToT) [4] is based on the idea that to solve any complex problem we need to (a) explore multiple reasoning paths (branches in a graph) , and (b) perform planning i.e. lookahead or even backtrack on the paths if required. ToT frames any problem as a search over a tree, where each node is a state s = [x, z1\u00b7\u00b7\u00b7i] representing a partial solution with the input and the sequence of thoughts so far. A specific instantiation of ToT involves answering four questions: How to decompose the intermediate process into thought steps -- depending on different problems, a thought could be a couple of words (Crosswords), a line of equation (Game of 24), or a whole paragraph of writing plan (Creative Writing). In general, a thought should be \u201csmall\u201d enough so that LMs can generate promising and diverse samples. How to generate potential thoughts from each state -- again it depends on the problem, so for Creative writing we can sample thoughts from a CoT prompt and for Game of 24 and Crosswords we can propose thoughts sequentially using propose prompt. How to heuristically evaluate states -- this can be done automatically by either asking the model to generate a value (score between 1 to 10 or classification of sure/likely/impossible) or voting on different results. What search algorithm to use -- authors propose Breadth-first search (BFS) and Depth-first Search (DFS) and left more complex search algorithms like A* for future works. Schematic illustrating various approaches to problem solving with LLMs. Each rectangle box represents a thought, which is a coherent language sequence that serves as an intermediate step toward problem solving Retrieval Augmented Generation (RAG) In all of the previous approaches, the result was generated entirely by the LLMs without any external intervention. This leverages the knowledge stored within the neural networks of the LLMs (read, weights in the network) . This poses issues like hallucinations (this happens when model is not sure what to say, especially due to lack of knowledge) and factual inaccuracies (lack of knowledge leads to model lying) . To mitigate these issues, we can \"connect\" LLMs with external data source (vector database, wikipedia, google, etc) so that true, diverse and dynamic data can be fetched from these sources and LLM can do what they are best suited for - reasoning on the provided data to format the final result. This is the fundamental idea behind Retrieval Augmented Generation (RAG). One example of such system is Sankshep (by yours truly ) that provides ChatGPT-powered assistant to summarize and talk to Arxiv research papers. Here, if you ask a question regarding the paper, Sankshep refer the content of the paper to be better aware and provide factually correct results. Sankshep.co.in built considering Retrieval Augmented Generation (RAG) ReAct ReAct [5] combines the external knowledge of RAG with the planning and reasoning notion of ToT. As per the paper, A unique feature of human intelligence is the ability to seamlessly combine task-oriented actions with verbal reasoning. Consider the example of cooking up a dish in the kitchen. Between any two specific actions, we may reason in language in order to track progress (\u201cnow that everything is cut, I should heat up the pot of water\u201d), to handle exceptions or adjust the plan according to the situation (\u201cI don\u2019t have salt, so let me use soy sauce and pepper instead\u201d), and to realize when external information is needed (\u201chow do I prepare dough? Let me search on the Internet\u201d). The important point of the above quote (and in fact the paper) is the intention to combine two powerful abilities of LLMs \u2014 reasoning (e.g. chain-of-thought prompting) and acting (e.g. action plan generation). While the former helps with improving the accuracy of an individual task, the latter provides the LLM power to perform multiple tasks. The idea is quite simple \u2014 ask LLM a question (input) and let it \u201cplan\u201d what to do next (action) by reasoning on the input (thoughts). It can even propose values to the action (action input). Once we perform the action and get an output (observation) the LLM can again reason (thought) to propose the next \u201cplan\u201d (action). In a way, we keep the LLM busy in this loop, until it terminates and we get the result we wanted. To summarize we iterate over \u201cinput \u2014> thoughts \u2014> action \u2014> action input \u2014> observation \u2014> thoughts\u201d. For practical details, please refer Creating GPT-driven Applications using LangChain LLM reasoning and planning using ReAct technique Conclusion Prompt engineering is a crucial skill for leveraging the capabilities of LLMs effectively. By understanding the different types of prompts and employing strategies such as zero-shot prompts, few-shot prompts, etc, developers and users can harness the power of AI to achieve more accurate and contextually relevant responses. As AI technologies continue to evolve, mastering prompt engineering will remain an essential tool for unlocking the full potential of AI systems across various domains. References [1] Chain-of-Thought Prompting Elicits Reasoning in Large Language Models [2] Large Language Models are Zero-Shot Reasoners [3] Self-consistency improves chain of thought reasoning in language models [4] Tree of Thoughts: Deliberate Problem Solving with Large Language Models [5] ReAct: Synergizing Reasoning and Acting in Language Models [6] Prompting Guide","title":"Prompt Engineering"},{"location":"natural_language_processing/prompt_engineering/#prompt-engineering","text":"","title":"Prompt Engineering"},{"location":"natural_language_processing/prompt_engineering/#introduction","text":"Prompt engineering involves crafting well-defined and strategically designed input queries to elicit desired responses from AI systems. It serves as a bridge between human intention and machine understanding, enabling AI models to provide more accurate and contextually relevant outputs. As AI applications continue to proliferate across various domains, mastering the art of prompt engineering has become essential for both developers and users. What makes prompt engineering more tempting is that it does not require any finetuning of the model but nevertheless, it can enhance the model accuracy substantially! In this article, we will explore different key strategies for crafting effective prompts that can enhance AI model capabilities.","title":"Introduction"},{"location":"natural_language_processing/prompt_engineering/#types-of-prompts","text":"Before getting started with prompt techniques, let\u2019s discuss the main types of prompts,","title":"Types of Prompts"},{"location":"natural_language_processing/prompt_engineering/#system-prompts","text":"System prompts are like global settings that are applied once to set the mood and intention of the AI model\u2019s subsequent generations in the same chat. These prompts are carefully crafted by developers to guide the AI system toward specific outputs that align with the intended use case. ChatGPT UI\u2019s custom instruction is a good example of a system prompt, as whatever you mention there is applicable to every chat. Users can provide details to format output in a certain style (like JSON), provide details about themselves so that the responses are personalized, set the tone of the generation, define privacy and ethics details, and much more! An example is shown below System Prompt : You are a helpful AI Assistant . Help users by replying to their queries and make sure the responses are polite . Do not hallucinate and say \"I don't know\" if required .","title":"System Prompts"},{"location":"natural_language_processing/prompt_engineering/#user-prompts","text":"User prompts are generated on the fly by users and are designed to elicit specific responses to their queries. Unlike system prompts, user prompts are not pre-defined and can vary widely in structure and content. These are more transactional in nature, and are usally present after system prompt and could be multiple in count. System Prompt : You are a helpful AI Assistant . Help users in replying to their queries and make sure the responses are polite . Do not hallucinate and say \"I don't know\" if required . User Prompt : What is your name ?","title":"User Prompts"},{"location":"natural_language_processing/prompt_engineering/#assistant-output","text":"These are AI\u2019s generated output to the System and previous user prompts. In complex use cases, developers can modify this as an example to the AI model to highlight the kind of result expected from the model. System Prompt : You are a helpful AI Assistant . Help users in replying to their queries and make sure the responses are polite . Do not hallucinate and say \"I don't know\" if required . User Prompt : What is your name ? Assistant : I am an AI Assistant that can help you with your queries . Please let me know your questions !","title":"Assistant Output"},{"location":"natural_language_processing/prompt_engineering/#prompt-strategies","text":"","title":"Prompt Strategies"},{"location":"natural_language_processing/prompt_engineering/#zero-shot-prompts","text":"Zero-shot prompts are a fundamental technique in prompt engineering, allowing AI models to provide meaningful responses without any specific training examples. With zero-shot prompts, developers and users can harness the model's innate knowledge and reasoning capabilities to answer questions or complete tasks, even if the model has never seen similar queries before. When using a zero-shot prompt, formulate your question or request as clearly and concisely as possible. You can even provide some context if that helps, overall avoid ambiguity to help the model understand your intention accurately. Example 1 - Clear instructions ################ User Prompt : Translate the following English text to French : \"Hello, how are you?\" Example 2 - Provide context ################ User Prompt : Calculate the total revenue for our company in the last quarter , given the following financial data : [ insert data ]. Note, deciding which data should goes where (system or user prompt) depends on experimenting how it works for a specific model but a general thumb rule is to keep the constant details in system prompt and dynamic details in user prompt. In the first example above, we can also have following prompts, Example 1 - Clear instructions with System prompt ################ System prompt : You are a Translator GPT . Given a sentence in English , translate it into French . User prompt : \"Hello, how are you?\"","title":"Zero-Shot Prompts"},{"location":"natural_language_processing/prompt_engineering/#few-shot-prompts","text":"While zero-shot prompts are fundamental, there are situations where you may need to provide a bit more guidance to the AI model. In such cases, you can use few-shot prompts that involve providing a small number of examples or demonstrations to help the model understand the desired task or context. Developers can use this approach to further guide the AI model's responses. One example of 2-shot prompt is shown below, System prompt : You are a Translator GPT . Given a sentence in English , translate it into French . Examples are shared below , English : \"Hello, how are you?\" French : \"Bonjour, comment \u00e7a va ?\" English : \"I am learning French.\" French : \"J'apprends le fran\u00e7ais.\" User Prompt : English : \"Please pass the salt.\" French : Note, the number of examples to be included (n-shot) is highly experimental. The objective should be to keep the example count as less as possible (otherwise the token size and cost will increase) while making sure the accuracy is not impacted. So the prompt design should be done incrementally, i.e. keep adding more examples if the accuracy is below expectations. Also, make sure to add diverse examples and do not add exact or even semantically similar examples as latest LLMs are quite \u201csmart\u201d enough to learn from few examples.","title":"Few-Shot Prompts"},{"location":"natural_language_processing/prompt_engineering/#few-shot-chain-of-thought-prompt","text":"Few shot CoT Prompting was introduced in [1] and the idea is that generating a chain of thought, i.e. a series of intermediate reasoning steps, can significantly improves the ability of large language models to perform complex reasoning. Experiments shows that chain-of-thought prompting improves performance on a range of arithmetic, commonsense, and symbolic reasoning tasks. Basically it is clubbed with few shot prompt where the examples are provided in CoT format. Example is shown in the below image, Example inputs and outputs for Standard 1-shot prompt and CoT prompts","title":"Few-shot Chain-of-Thought Prompt"},{"location":"natural_language_processing/prompt_engineering/#zero-shot-chain-of-thought-prompt","text":"Zero shot variant of CoT was introduced in [2] and it can significantly increase the accuracy of Zero shot prompts, and all you need to do is to add \u201cLet\u2019s think step by step.\u201d \ud83d\ude1c. Btw additional post-processing is required on the output to extract the correct result, which can either be done by creating regex scripts or calling LLMs again to extract the answer. Note Few-shot prompts should always give better result than Zero-shot, but the former requires additional token consumption which will increase the cost. To mitigate this, developers can experiment with Zero-shot CoT technique and if the result accuracy is acceptable, it might end up reducing the overall cost. Example inputs and outputs of GPT-3 with (a) standard Few-shot, (b) Few-shot-CoT, (c) standard Zero-shot, and (d) Zero-shot-CoT","title":"Zero-shot Chain-of-Thought Prompt"},{"location":"natural_language_processing/prompt_engineering/#self-consistency","text":"Self-consistency [3] is based on the idea that there are multiple ways to solve a complex problem i.e. if multiple reasoning paths are leading to the same output, it is highly probable that it is a correct answer. In their own words, \"...we hypothesize that correct reasoning processes, even if they are diverse, tend to have greater agreement in their final answer than incorrect processes.\" . The self-consistency method consists of three steps: prompt a language model using chain-of-thought (CoT) prompting; replace the \u201cgreedy decode\u201d in CoT prompting by sampling from the language model\u2019s decoder to generate a diverse set of reasoning paths; and marginalize out the reasoning paths and aggregate by choosing the most consistent answer in the final answer set. The authors of the paper performed extensive empirical evaluations to shows that self-consistency boosts the performance of chain-of-thought prompting on a range of popular arithmetic and commonsense reasoning benchmarks, including GSM8K (+17.9%), SVAMP (+11.0%), AQuA (+12.2%), StrategyQA (+6.4%) and ARC-challenge (+3.9%). CoT vs Self-consistency prompting example","title":"Self-consistency"},{"location":"natural_language_processing/prompt_engineering/#tree-of-thoughts","text":"Tree-of-Thoughts (ToT) [4] is based on the idea that to solve any complex problem we need to (a) explore multiple reasoning paths (branches in a graph) , and (b) perform planning i.e. lookahead or even backtrack on the paths if required. ToT frames any problem as a search over a tree, where each node is a state s = [x, z1\u00b7\u00b7\u00b7i] representing a partial solution with the input and the sequence of thoughts so far. A specific instantiation of ToT involves answering four questions: How to decompose the intermediate process into thought steps -- depending on different problems, a thought could be a couple of words (Crosswords), a line of equation (Game of 24), or a whole paragraph of writing plan (Creative Writing). In general, a thought should be \u201csmall\u201d enough so that LMs can generate promising and diverse samples. How to generate potential thoughts from each state -- again it depends on the problem, so for Creative writing we can sample thoughts from a CoT prompt and for Game of 24 and Crosswords we can propose thoughts sequentially using propose prompt. How to heuristically evaluate states -- this can be done automatically by either asking the model to generate a value (score between 1 to 10 or classification of sure/likely/impossible) or voting on different results. What search algorithm to use -- authors propose Breadth-first search (BFS) and Depth-first Search (DFS) and left more complex search algorithms like A* for future works. Schematic illustrating various approaches to problem solving with LLMs. Each rectangle box represents a thought, which is a coherent language sequence that serves as an intermediate step toward problem solving","title":"Tree-of-Thoughts"},{"location":"natural_language_processing/prompt_engineering/#retrieval-augmented-generation-rag","text":"In all of the previous approaches, the result was generated entirely by the LLMs without any external intervention. This leverages the knowledge stored within the neural networks of the LLMs (read, weights in the network) . This poses issues like hallucinations (this happens when model is not sure what to say, especially due to lack of knowledge) and factual inaccuracies (lack of knowledge leads to model lying) . To mitigate these issues, we can \"connect\" LLMs with external data source (vector database, wikipedia, google, etc) so that true, diverse and dynamic data can be fetched from these sources and LLM can do what they are best suited for - reasoning on the provided data to format the final result. This is the fundamental idea behind Retrieval Augmented Generation (RAG). One example of such system is Sankshep (by yours truly ) that provides ChatGPT-powered assistant to summarize and talk to Arxiv research papers. Here, if you ask a question regarding the paper, Sankshep refer the content of the paper to be better aware and provide factually correct results. Sankshep.co.in built considering Retrieval Augmented Generation (RAG)","title":"Retrieval Augmented Generation (RAG)"},{"location":"natural_language_processing/prompt_engineering/#react","text":"ReAct [5] combines the external knowledge of RAG with the planning and reasoning notion of ToT. As per the paper, A unique feature of human intelligence is the ability to seamlessly combine task-oriented actions with verbal reasoning. Consider the example of cooking up a dish in the kitchen. Between any two specific actions, we may reason in language in order to track progress (\u201cnow that everything is cut, I should heat up the pot of water\u201d), to handle exceptions or adjust the plan according to the situation (\u201cI don\u2019t have salt, so let me use soy sauce and pepper instead\u201d), and to realize when external information is needed (\u201chow do I prepare dough? Let me search on the Internet\u201d). The important point of the above quote (and in fact the paper) is the intention to combine two powerful abilities of LLMs \u2014 reasoning (e.g. chain-of-thought prompting) and acting (e.g. action plan generation). While the former helps with improving the accuracy of an individual task, the latter provides the LLM power to perform multiple tasks. The idea is quite simple \u2014 ask LLM a question (input) and let it \u201cplan\u201d what to do next (action) by reasoning on the input (thoughts). It can even propose values to the action (action input). Once we perform the action and get an output (observation) the LLM can again reason (thought) to propose the next \u201cplan\u201d (action). In a way, we keep the LLM busy in this loop, until it terminates and we get the result we wanted. To summarize we iterate over \u201cinput \u2014> thoughts \u2014> action \u2014> action input \u2014> observation \u2014> thoughts\u201d. For practical details, please refer Creating GPT-driven Applications using LangChain LLM reasoning and planning using ReAct technique","title":"ReAct"},{"location":"natural_language_processing/prompt_engineering/#conclusion","text":"Prompt engineering is a crucial skill for leveraging the capabilities of LLMs effectively. By understanding the different types of prompts and employing strategies such as zero-shot prompts, few-shot prompts, etc, developers and users can harness the power of AI to achieve more accurate and contextually relevant responses. As AI technologies continue to evolve, mastering prompt engineering will remain an essential tool for unlocking the full potential of AI systems across various domains.","title":"Conclusion"},{"location":"natural_language_processing/prompt_engineering/#references","text":"[1] Chain-of-Thought Prompting Elicits Reasoning in Large Language Models [2] Large Language Models are Zero-Shot Reasoners [3] Self-consistency improves chain of thought reasoning in language models [4] Tree of Thoughts: Deliberate Problem Solving with Large Language Models [5] ReAct: Synergizing Reasoning and Acting in Language Models [6] Prompting Guide","title":"References"},{"location":"natural_language_processing/qa/","text":"Introduction As the name suggests, Question Answering (QA) is a NLP task of finding answer given the question and context (optional) . QA could be of two types based on the input, Open-domain QA : where context is not provided . The expectation is that the model has internalised knowledge within its parameters and should be able to answer the question with additional context. graph LR A(\"Who is the author of Lazy Data Scientist?\") -- Question --> B(QA model) B -- Answer --> C(\"Mohit\") style B stroke:#f66,stroke-width:2px,stroke-dasharray: 5 5 Closed-domain QA : where context is provided . The expectation is that the model has learned to find answers from context. graph LR A(\"Who is the author of Lazy Data Scientist?\") -- Question --> B(QA model) D(\"Hi, my name is Mohit. I am the author of Lazy Data Scientist!\") -- Context --> B(QA model) B -- Answer --> C(\"Mohit\") style B stroke:#f66,stroke-width:2px,stroke-dasharray: 5 5 Answers could be also be of two types, Short form Answers : where the answer is brief and to the point. In the above examples, the answer ( Mohit ) is in short form. Majority of the closed domain QA models generate short form answers as they follow extractive approach of finding answer. For this, encoder based architectures (like BERT ) can be used. The input can be provided as [CLS] question [SEP] context [SEP] As output, we compute probabilities of two special logits -- answer start and answer end. This gives the exact position of the answer. In reality, we apply softmax on the output logits values to get probabilistic estimation for each token in the input. Behavior of Short form closed QA system. [Top] In case the answer is present in the context, answer start and end token's probabilties can be utilised to get the answer. [Bottom] In case answer is missing, [CLS] is predicted. We pick the pair (answer start and end logit) that has the highest probability (product of their individual probability) and it's a valid answer (answer with positive or zero length and answer with tokens only from context) . If we go greedy i.e. pick the top_n = 1 for both logits, we will get the pair with the highest probability but it is not guaranteed to be a valid answer. To mitigate this, we pick top_n (~10-20) highest probability tokens for both start and end answer values. This gives us \\(n^2\\) possibilities of answers that can be explored to find the valid answer. Note To solve similar tasks, BERT utilises different task specific heads on top of the BERT encoder. In the case of QA, a head could be simple feed forward layer with two classes - answer start and answer end. The same head is applied to each token in the BERT output, and probability of the both classes are computed. Long form Answers : where the answer is descriptive, standalone and may also contain additional details. For the above example, long form answer could be Mohit is the author of Lazy Data Scientist . We can use additional models like LLM (GPTs, T5, etc) on top of QA system to convert short form answers to long form. Existing model will require finetuning with the Question and Short form answer as input and Long form answer as output. We can even try to n-shot the process using LLMs as shown in the following prompt: Question : What is the author 's name? Context : Author is Mohit Mayank . Short answer : Mohit Mayank . Long answer : Author 's name is Mohit Mayank. ## provide 2-3 examples as above Question : What is the captial of India ? Context : India 's new captial New Delhi was setup with .... Short answer : New Delhi Long answer : # let the model predict long form answer!! Note Perplexity AI is a good example that uses GPT 3.5 to generate long form answers and even provide evidences for the answer. First, it performs web search using Microsoft Bing to identify relevant websites and the contents are summarized. The summaries are then passed to GPT along with the original question to answer the question with the reference details for the evidence. Datasets SQuAD Stanford Question Answering Dataset (SQuAD) [2] is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. There are two verisons of the dataset, SQuAD 1.1 contains 100,000+ question-answer pairs on 500+ articles. SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones. To do well on SQuAD2.0, systems must not only answer questions when possible, but also determine when no answer is supported by the paragraph and abstain from answering. Metrics Exact Match As the name suggests, for each question we compare the golden answer with the predicted answer. If the two answers are exactly similar ( y_pred == y_true ) then exact_match = 1 else exact_match = 0 . F1 score There is a possibility that the predicted answer includes the important parts of the golden answer, but due to the nature of exact match, the score is still 0. Let's understand it with an example, here ideally the score should be high (if not 1) , but exact match will give 0. Question: When can we meet? Golden answer: We can meet around 5 PM. Predicted answer: 5 PM. For such cases we can perform partial match. Below is the example, 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 # vars to begin with predicted_answer = 'Hi, My name is Mohit' golden_answer = 'My name is Mohit' # tokenize predicted_words = set ( predicted_answer . lower () . split ()) golden_words = set ( golden_answer . lower () . split ()) # find common words common_words = predicted_words & golden_words # compute metrics recall = common_words / predicted_words precision = common_words / golden_words F1 = 2 * precision * recall / ( precision + recall ) Note In case one question has multiple independent answers, we can compare each golden answer for the example against the prediction and pick the one with highest score. The overall accuracy is then the average of the individual example level score. This logic can be applied to both the metrics discussed above. Code Using Transformers (HF hub) Huggingface hosts multiple models for the QA task. Most of these models are fined tuned on SQuAD dataset. Let's pick one and see how to use it. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 # install packages ! pip install - q transformers ! pip install - q sentencepiece # import from transformers.pipelines import pipeline from transformers import AutoModelForQuestionAnswering from transformers import AutoTokenizer # var model_name = \"deepset/xlm-roberta-base-squad2\" # generate pipeline nlp = pipeline ( 'question-answering' , model = model_name , tokenizer = model_name ) input = { 'question' : 'Who is visiting his grandmother?' , 'context' : 'My name is Mohit. I am going to visit my grandmother. She is old.' } print ( nlp ( input )) ## Output --> {'score': 0.30, 'start': 10, 'end': 17, 'answer': ' Mohit.'} References [1] Evaluating QA: Metrics, Predictions, and the Null Response [2] SQuAD Explorer [3] How to Build an Open-Domain Question Answering System? [4] Question Answering - Huggingface Cheers","title":"Question Answering"},{"location":"natural_language_processing/qa/#introduction","text":"As the name suggests, Question Answering (QA) is a NLP task of finding answer given the question and context (optional) . QA could be of two types based on the input, Open-domain QA : where context is not provided . The expectation is that the model has internalised knowledge within its parameters and should be able to answer the question with additional context. graph LR A(\"Who is the author of Lazy Data Scientist?\") -- Question --> B(QA model) B -- Answer --> C(\"Mohit\") style B stroke:#f66,stroke-width:2px,stroke-dasharray: 5 5 Closed-domain QA : where context is provided . The expectation is that the model has learned to find answers from context. graph LR A(\"Who is the author of Lazy Data Scientist?\") -- Question --> B(QA model) D(\"Hi, my name is Mohit. I am the author of Lazy Data Scientist!\") -- Context --> B(QA model) B -- Answer --> C(\"Mohit\") style B stroke:#f66,stroke-width:2px,stroke-dasharray: 5 5 Answers could be also be of two types, Short form Answers : where the answer is brief and to the point. In the above examples, the answer ( Mohit ) is in short form. Majority of the closed domain QA models generate short form answers as they follow extractive approach of finding answer. For this, encoder based architectures (like BERT ) can be used. The input can be provided as [CLS] question [SEP] context [SEP] As output, we compute probabilities of two special logits -- answer start and answer end. This gives the exact position of the answer. In reality, we apply softmax on the output logits values to get probabilistic estimation for each token in the input. Behavior of Short form closed QA system. [Top] In case the answer is present in the context, answer start and end token's probabilties can be utilised to get the answer. [Bottom] In case answer is missing, [CLS] is predicted. We pick the pair (answer start and end logit) that has the highest probability (product of their individual probability) and it's a valid answer (answer with positive or zero length and answer with tokens only from context) . If we go greedy i.e. pick the top_n = 1 for both logits, we will get the pair with the highest probability but it is not guaranteed to be a valid answer. To mitigate this, we pick top_n (~10-20) highest probability tokens for both start and end answer values. This gives us \\(n^2\\) possibilities of answers that can be explored to find the valid answer. Note To solve similar tasks, BERT utilises different task specific heads on top of the BERT encoder. In the case of QA, a head could be simple feed forward layer with two classes - answer start and answer end. The same head is applied to each token in the BERT output, and probability of the both classes are computed. Long form Answers : where the answer is descriptive, standalone and may also contain additional details. For the above example, long form answer could be Mohit is the author of Lazy Data Scientist . We can use additional models like LLM (GPTs, T5, etc) on top of QA system to convert short form answers to long form. Existing model will require finetuning with the Question and Short form answer as input and Long form answer as output. We can even try to n-shot the process using LLMs as shown in the following prompt: Question : What is the author 's name? Context : Author is Mohit Mayank . Short answer : Mohit Mayank . Long answer : Author 's name is Mohit Mayank. ## provide 2-3 examples as above Question : What is the captial of India ? Context : India 's new captial New Delhi was setup with .... Short answer : New Delhi Long answer : # let the model predict long form answer!! Note Perplexity AI is a good example that uses GPT 3.5 to generate long form answers and even provide evidences for the answer. First, it performs web search using Microsoft Bing to identify relevant websites and the contents are summarized. The summaries are then passed to GPT along with the original question to answer the question with the reference details for the evidence.","title":"Introduction"},{"location":"natural_language_processing/qa/#datasets","text":"","title":"Datasets"},{"location":"natural_language_processing/qa/#squad","text":"Stanford Question Answering Dataset (SQuAD) [2] is a reading comprehension dataset, consisting of questions posed by crowdworkers on a set of Wikipedia articles, where the answer to every question is a segment of text, or span, from the corresponding reading passage, or the question might be unanswerable. There are two verisons of the dataset, SQuAD 1.1 contains 100,000+ question-answer pairs on 500+ articles. SQuAD2.0 combines the 100,000 questions in SQuAD1.1 with over 50,000 unanswerable questions written adversarially by crowdworkers to look similar to answerable ones. To do well on SQuAD2.0, systems must not only answer questions when possible, but also determine when no answer is supported by the paragraph and abstain from answering.","title":"SQuAD"},{"location":"natural_language_processing/qa/#metrics","text":"","title":"Metrics"},{"location":"natural_language_processing/qa/#exact-match","text":"As the name suggests, for each question we compare the golden answer with the predicted answer. If the two answers are exactly similar ( y_pred == y_true ) then exact_match = 1 else exact_match = 0 .","title":"Exact Match"},{"location":"natural_language_processing/qa/#f1-score","text":"There is a possibility that the predicted answer includes the important parts of the golden answer, but due to the nature of exact match, the score is still 0. Let's understand it with an example, here ideally the score should be high (if not 1) , but exact match will give 0. Question: When can we meet? Golden answer: We can meet around 5 PM. Predicted answer: 5 PM. For such cases we can perform partial match. Below is the example, 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 # vars to begin with predicted_answer = 'Hi, My name is Mohit' golden_answer = 'My name is Mohit' # tokenize predicted_words = set ( predicted_answer . lower () . split ()) golden_words = set ( golden_answer . lower () . split ()) # find common words common_words = predicted_words & golden_words # compute metrics recall = common_words / predicted_words precision = common_words / golden_words F1 = 2 * precision * recall / ( precision + recall ) Note In case one question has multiple independent answers, we can compare each golden answer for the example against the prediction and pick the one with highest score. The overall accuracy is then the average of the individual example level score. This logic can be applied to both the metrics discussed above.","title":"F1 score"},{"location":"natural_language_processing/qa/#code","text":"","title":"Code"},{"location":"natural_language_processing/qa/#using-transformers-hf-hub","text":"Huggingface hosts multiple models for the QA task. Most of these models are fined tuned on SQuAD dataset. Let's pick one and see how to use it. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 # install packages ! pip install - q transformers ! pip install - q sentencepiece # import from transformers.pipelines import pipeline from transformers import AutoModelForQuestionAnswering from transformers import AutoTokenizer # var model_name = \"deepset/xlm-roberta-base-squad2\" # generate pipeline nlp = pipeline ( 'question-answering' , model = model_name , tokenizer = model_name ) input = { 'question' : 'Who is visiting his grandmother?' , 'context' : 'My name is Mohit. I am going to visit my grandmother. She is old.' } print ( nlp ( input )) ## Output --> {'score': 0.30, 'start': 10, 'end': 17, 'answer': ' Mohit.'}","title":"Using Transformers (HF hub)"},{"location":"natural_language_processing/qa/#references","text":"[1] Evaluating QA: Metrics, Predictions, and the Null Response [2] SQuAD Explorer [3] How to Build an Open-Domain Question Answering System? [4] Question Answering - Huggingface Cheers","title":"References"},{"location":"natural_language_processing/relation_extraction/","text":"Relation Extraction Introduction Relation extraction (RE) is the process of identifying the relationships between entities in a text. Entities could be of multiple types such as person, location, organization, etc and they can be identified using Named Enitity Recognition (NER). Let's understand RE with an example: \"Ram is the son of Shyam and Shyam is the son of Radhe\" . Here the entities are identified as: \"Ram\", \"Shyam\" and \"Radhe\". The relations could be (Ram, son of, Shyam) , (Shyam, son of, Radhe) and (Ram, grandson of, Radhe) . Code Using OpenNRE OpenNRE is an open source tool for relationship extraction. OpenNRE makes it easy to extract relationships from text. It is as simple as writing a few lines of code. One example from their github repository is as follows, 1 2 3 4 5 6 7 # import opennre import opennre # load the model model = opennre . get_model ( 'wiki80_cnn_softmax' ) # infer for a text model . infer ({ 'text' : 'He was the son of M\u00e1el D\u00fain mac M\u00e1ele Fithrich, and grandson of the high king \u00c1ed Uaridnach (died 612).' , 'h' : { 'pos' : ( 18 , 46 )}, 't' : { 'pos' : ( 78 , 91 )}}) # Output: ('father', 0.5108704566955566) At the time of writing they had following models available: model_name description wiki80_cnn_softmax trained on wiki80 dataset with a CNN encoder. wiki80_bert_softmax trained on wiki80 dataset with a BERT encoder. wiki80_bertentity_softmax trained on wiki80 dataset with a BERT encoder (using entity representation concatenation). tacred_bert_softmax trained on TACRED dataset with a BERT encoder. tacred_bertentity_softmax trained on TACRED dataset with a BERT encoder (using entity representation concatenation).","title":"Relation extraction"},{"location":"natural_language_processing/relation_extraction/#relation-extraction","text":"","title":"Relation Extraction"},{"location":"natural_language_processing/relation_extraction/#introduction","text":"Relation extraction (RE) is the process of identifying the relationships between entities in a text. Entities could be of multiple types such as person, location, organization, etc and they can be identified using Named Enitity Recognition (NER). Let's understand RE with an example: \"Ram is the son of Shyam and Shyam is the son of Radhe\" . Here the entities are identified as: \"Ram\", \"Shyam\" and \"Radhe\". The relations could be (Ram, son of, Shyam) , (Shyam, son of, Radhe) and (Ram, grandson of, Radhe) .","title":"Introduction"},{"location":"natural_language_processing/relation_extraction/#code","text":"","title":"Code"},{"location":"natural_language_processing/relation_extraction/#using-opennre","text":"OpenNRE is an open source tool for relationship extraction. OpenNRE makes it easy to extract relationships from text. It is as simple as writing a few lines of code. One example from their github repository is as follows, 1 2 3 4 5 6 7 # import opennre import opennre # load the model model = opennre . get_model ( 'wiki80_cnn_softmax' ) # infer for a text model . infer ({ 'text' : 'He was the son of M\u00e1el D\u00fain mac M\u00e1ele Fithrich, and grandson of the high king \u00c1ed Uaridnach (died 612).' , 'h' : { 'pos' : ( 18 , 46 )}, 't' : { 'pos' : ( 78 , 91 )}}) # Output: ('father', 0.5108704566955566) At the time of writing they had following models available: model_name description wiki80_cnn_softmax trained on wiki80 dataset with a CNN encoder. wiki80_bert_softmax trained on wiki80 dataset with a BERT encoder. wiki80_bertentity_softmax trained on wiki80 dataset with a BERT encoder (using entity representation concatenation). tacred_bert_softmax trained on TACRED dataset with a BERT encoder. tacred_bertentity_softmax trained on TACRED dataset with a BERT encoder (using entity representation concatenation).","title":"Using OpenNRE"},{"location":"natural_language_processing/streaming_chatgpt_gen/","text":"Streaming ChatGPT Generations Introduction ChatGPT is an auto-regressive Large Language Model. That means its output is generated token by token in a sequential fashion, where each token could be a combination of characters. Under normal circumstances (and popular coding practices) , we access the ChatGPT model via an API which takes an input prompt, generate the output and then returns it. While it may sound okay, there is one problem -- model returns the output when the complete generation is done! This means if you want the model to generate long outputs (or even if your prompt is big due to few-shots examples or lengthy system prompts) , you can expect a delay of several seconds before you receive the output. This is not okay for user-facing applications where your users are patiently waiting for the output. Thats why ChatGPT UI gives the output in streaming fashion. Here, you see characters or words printing on your screen one after the another, rather than showing the complete output at once. This creates a perception of model writing your output as human does, and even though the delay in generating the complete output will be the same, the flow becomes more enduring. Behind the scene, the ChatGPT API is using Server Side Events (SSE) i.e. media stream events to return each token as and when they are generated. SSE is like an intermediate approach between normal HTTP request (server returns one result per request) and websocket (server and client can send multiple requests and results) . In SSE, while client sends one request, server can return multiple results. In this article, we will try to replicate the ChatGPT streaming output behavior by creating a python application (FastAPI server) that could acts as a middleman between OpenAI and Frontend. In this case, OpenAI returns the outputs to our Python server at token level and the server passes it along to its client in the same manner. Let's get started! Code OpenAI Streaming If you know how to use OpenAI package to generate ChatGPT output (which in itself is quite easy) , then getting the streaming output nothing but adding one more param ( stream=True ) in openai.ChatCompletion.create . Below is the code that you can easily copy paste and start using right now, 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 # import import openai # set the keys openai . api_key = \"..\" # provide the key here openai . organization = \"..\" # provide the key here # create the ChatCompletion.create obj completion = openai . ChatCompletion . create ( stream = True , # the magic happens here model = \"gpt-3.5-turbo-0301\" , messages = [ { \"role\" : \"system\" , \"content\" : \"You are a mails assistant. Given an email, write a proper reply.\" }, { \"role\" : \"user\" , \"content\" : \"\"\"Mail: \"We are all set. Thanks -Cam\" \"\"\" } ] ) # print in streaming fashion for chunk in completion : print ( chunk . choices [ 0 ] . delta . get ( \"content\" , \"\" ), end = \"\" , flush = True ) In the above code, just by adding the stream=True OpenAI package is able to take care of all the hardwork and we get a completion generator. In Python, you just need to iterate over it to get the result at token level and as and when they are available. For example, if you time it with and without the stream=True param, you will notice the difference in output and as well as in time. While without the param the output could be available after a couple of seconds, with the param the first token will be available within a second, and the subsequent tokens after the previous one with short gap. To simulate the streaming output, we use print statement with end=\"\" instead of default end=\"\\n\" so that to ignore the newline after each iteration. We also use flush=True so that print statment does not wait for its buffer to get full before printing on terminal. OpenAI Streaming App (using FastAPI) Now that we have the OpenAI related part done, let's focus on creating FastAPI App and expose the OpenAI wrapper as an event stream service. Below is the code, 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 # Filename: api.py # import import openai from pydantic import BaseModel from fastapi import FastAPI , Request from fastapi.responses import StreamingResponse from fastapi.middleware.cors import CORSMiddleware # define app app = FastAPI () # add CORS policy origins = [ \"*\" , ] app . add_middleware ( CORSMiddleware , allow_origins = origins , allow_credentials = True , allow_methods = [ \"*\" ], allow_headers = [ \"*\" ], ) # Set your OpenAI API key here openai . api_key = \"...\" openai . organization = \"...\" # Create BaseModel class class Message ( BaseModel ): prompt : str @app . post ( \"/chat\" ) def chat_socket ( msg : Message ): \"\"\" Generates a response using the OpenAI Chat API in streaming fashion. Returns: A string representing the generated response. \"\"\" # ChatGPT streaming response function def generate_openai_response (): \"\"\" Generates a response using the OpenAI Chat API in streaming fashion. Returns: A string representing the generated response. \"\"\" completion = openai . ChatCompletion . create ( stream = True , model = \"gpt-3.5-turbo-0301\" , messages = [ { \"role\" : \"system\" , \"content\" : \"You are a bot, given the user's input, reply appropriately\" }, { \"role\" : \"user\" , \"content\" : msg . prompt }] ) # print in streaming fashion for chunk in completion : yield chunk . choices [ 0 ] . delta . get ( \"content\" , \"\" ) # return return StreamingResponse ( generate_openai_response (), media_type = 'text/event-stream' ) # welcome page @app . get ( \"/\" ) async def home (): return { \"message\" : \"Welcome to the ChatGPT FastAPI App!\" } # Run the application if __name__ == \"__main__\" : import uvicorn uvicorn . run ( app , host = \"0.0.0.0\" , port = 8000 ) To run the code, you need to just hit python api.py and our endpoint is available at http://0.0.0.0:8000/chat endpoint! Client for FastAPI OpenAI Streaming App Once you have the server running, let's see how we can hit it from any other service. Here I am showing the example of a Python Client. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 # import import json import requests def query_api ( url ): # query the url response = requests . post ( url , json = { \"prompt\" : \"Tell me a joke!\" }, stream = True ) # parse the response for chunk in response . iter_content ( chunk_size = None ): if chunk : # filter out keep-alive new chunks print ( chunk . decode ( 'utf-8' ), end = \"\" , flush = True ) # Example usage query_api ( \"http://0.0.0.0:8000/chat\" ) References OpenAI Codebook - Examples","title":"Streaming ChatGPT Generations"},{"location":"natural_language_processing/streaming_chatgpt_gen/#streaming-chatgpt-generations","text":"","title":"Streaming ChatGPT Generations"},{"location":"natural_language_processing/streaming_chatgpt_gen/#introduction","text":"ChatGPT is an auto-regressive Large Language Model. That means its output is generated token by token in a sequential fashion, where each token could be a combination of characters. Under normal circumstances (and popular coding practices) , we access the ChatGPT model via an API which takes an input prompt, generate the output and then returns it. While it may sound okay, there is one problem -- model returns the output when the complete generation is done! This means if you want the model to generate long outputs (or even if your prompt is big due to few-shots examples or lengthy system prompts) , you can expect a delay of several seconds before you receive the output. This is not okay for user-facing applications where your users are patiently waiting for the output. Thats why ChatGPT UI gives the output in streaming fashion. Here, you see characters or words printing on your screen one after the another, rather than showing the complete output at once. This creates a perception of model writing your output as human does, and even though the delay in generating the complete output will be the same, the flow becomes more enduring. Behind the scene, the ChatGPT API is using Server Side Events (SSE) i.e. media stream events to return each token as and when they are generated. SSE is like an intermediate approach between normal HTTP request (server returns one result per request) and websocket (server and client can send multiple requests and results) . In SSE, while client sends one request, server can return multiple results. In this article, we will try to replicate the ChatGPT streaming output behavior by creating a python application (FastAPI server) that could acts as a middleman between OpenAI and Frontend. In this case, OpenAI returns the outputs to our Python server at token level and the server passes it along to its client in the same manner. Let's get started!","title":"Introduction"},{"location":"natural_language_processing/streaming_chatgpt_gen/#code","text":"","title":"Code"},{"location":"natural_language_processing/streaming_chatgpt_gen/#openai-streaming","text":"If you know how to use OpenAI package to generate ChatGPT output (which in itself is quite easy) , then getting the streaming output nothing but adding one more param ( stream=True ) in openai.ChatCompletion.create . Below is the code that you can easily copy paste and start using right now, 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 # import import openai # set the keys openai . api_key = \"..\" # provide the key here openai . organization = \"..\" # provide the key here # create the ChatCompletion.create obj completion = openai . ChatCompletion . create ( stream = True , # the magic happens here model = \"gpt-3.5-turbo-0301\" , messages = [ { \"role\" : \"system\" , \"content\" : \"You are a mails assistant. Given an email, write a proper reply.\" }, { \"role\" : \"user\" , \"content\" : \"\"\"Mail: \"We are all set. Thanks -Cam\" \"\"\" } ] ) # print in streaming fashion for chunk in completion : print ( chunk . choices [ 0 ] . delta . get ( \"content\" , \"\" ), end = \"\" , flush = True ) In the above code, just by adding the stream=True OpenAI package is able to take care of all the hardwork and we get a completion generator. In Python, you just need to iterate over it to get the result at token level and as and when they are available. For example, if you time it with and without the stream=True param, you will notice the difference in output and as well as in time. While without the param the output could be available after a couple of seconds, with the param the first token will be available within a second, and the subsequent tokens after the previous one with short gap. To simulate the streaming output, we use print statement with end=\"\" instead of default end=\"\\n\" so that to ignore the newline after each iteration. We also use flush=True so that print statment does not wait for its buffer to get full before printing on terminal.","title":"OpenAI Streaming"},{"location":"natural_language_processing/streaming_chatgpt_gen/#openai-streaming-app-using-fastapi","text":"Now that we have the OpenAI related part done, let's focus on creating FastAPI App and expose the OpenAI wrapper as an event stream service. Below is the code, 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 # Filename: api.py # import import openai from pydantic import BaseModel from fastapi import FastAPI , Request from fastapi.responses import StreamingResponse from fastapi.middleware.cors import CORSMiddleware # define app app = FastAPI () # add CORS policy origins = [ \"*\" , ] app . add_middleware ( CORSMiddleware , allow_origins = origins , allow_credentials = True , allow_methods = [ \"*\" ], allow_headers = [ \"*\" ], ) # Set your OpenAI API key here openai . api_key = \"...\" openai . organization = \"...\" # Create BaseModel class class Message ( BaseModel ): prompt : str @app . post ( \"/chat\" ) def chat_socket ( msg : Message ): \"\"\" Generates a response using the OpenAI Chat API in streaming fashion. Returns: A string representing the generated response. \"\"\" # ChatGPT streaming response function def generate_openai_response (): \"\"\" Generates a response using the OpenAI Chat API in streaming fashion. Returns: A string representing the generated response. \"\"\" completion = openai . ChatCompletion . create ( stream = True , model = \"gpt-3.5-turbo-0301\" , messages = [ { \"role\" : \"system\" , \"content\" : \"You are a bot, given the user's input, reply appropriately\" }, { \"role\" : \"user\" , \"content\" : msg . prompt }] ) # print in streaming fashion for chunk in completion : yield chunk . choices [ 0 ] . delta . get ( \"content\" , \"\" ) # return return StreamingResponse ( generate_openai_response (), media_type = 'text/event-stream' ) # welcome page @app . get ( \"/\" ) async def home (): return { \"message\" : \"Welcome to the ChatGPT FastAPI App!\" } # Run the application if __name__ == \"__main__\" : import uvicorn uvicorn . run ( app , host = \"0.0.0.0\" , port = 8000 ) To run the code, you need to just hit python api.py and our endpoint is available at http://0.0.0.0:8000/chat endpoint!","title":"OpenAI Streaming App (using FastAPI)"},{"location":"natural_language_processing/streaming_chatgpt_gen/#client-for-fastapi-openai-streaming-app","text":"Once you have the server running, let's see how we can hit it from any other service. Here I am showing the example of a Python Client. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 # import import json import requests def query_api ( url ): # query the url response = requests . post ( url , json = { \"prompt\" : \"Tell me a joke!\" }, stream = True ) # parse the response for chunk in response . iter_content ( chunk_size = None ): if chunk : # filter out keep-alive new chunks print ( chunk . decode ( 'utf-8' ), end = \"\" , flush = True ) # Example usage query_api ( \"http://0.0.0.0:8000/chat\" )","title":"Client for FastAPI OpenAI Streaming App"},{"location":"natural_language_processing/streaming_chatgpt_gen/#references","text":"OpenAI Codebook - Examples","title":"References"},{"location":"natural_language_processing/text_generation/","text":"Text generation Introduction Text generation is an interesting task in NLP, where the intention is to generate text when provided with some prompt as input. Usually, we apply some form of Sequence-to-Sequence model (Seq2Seq) for this task. They are called language models, as they can be used to predict the next word based on the previous sentences. The recent surge in interest in this field is due to two main reasons, (1) the availability of several high performance pre-trained models, and (2) it's very easy to transform a large variety of NLP based tasks into the text-in text-out type of problem. Beacuse of this, it becomes easy to solve several such problems using a single language model. The Seq2Seq model consists of two main modules - Encoder : which takes text as input and encodes it to a compact vector representation, Decoder : which takes the compact vector representation as input and generates text as output. Conventionally, the models were trained from scratch for a specific task, which requires a lot of training data. Recently, as NLP has deviated more towards fine-tuning methodology, we have a number of pre-trained models which either out-of-the-box work very well or can be fine-tuned for the specific task. Some of the conventional models were RNN, GRU and, LSTM. Whereas recent pre-trained models include Transformers, GPT-{1, 2, 3}, GPT-{Neo, J}, T5, etc. Text generation task is a very specific but also a very generic task because we can formulate a lot of NLP tasks in the form of text generation. For example, (not a complete list) Language translation English to Hindi translation, or any language for that matter, is just a text in and text out task Summarization takes the complete text dump as input and generates crisp informative sentences. Question answering question is taken as input and answer is the output. We can even include some context as input on closed-domain QA tasks. Sentiment analysis is a classification task where we can provide the text as input and train the model to generate sentiment tag as output Rating prediction where we have to rate the writing style, kind of regression problem, but still can be formulated as text in number out As obvious from the examples mentioned above, it is possible to formulate a lot of problems as a text-in-text-out task, and it could even expand to classification and regression types of tasks. Some example tasks that can be performed using T5 is shown below, T5 text-to-text framework examples. See: Google Blog ( raffel2020exploring ) Analysis Comparing models (basic details) A brief comparison table of the different models mentioned above is as follows, models type year pre-trained? parameters RNN conventional - no 17k (one layer) LSTM conventional 1997 no 71k (one layer) GRU conventional 2014 no 30-40k (one layer) GPT-2 recent 2019 yes 117M, 345M, 774M, 1.5B GPT-Neo recent 2021 yes 125M, 1.2B, 2.7B T5 recent 2020 yes 60M, 220M, 770M, 2.8B, 11B Comparing models (fine-tuning performance) A more detailed fine-tuning performance of the recent TG models for sentiment detection was performed here . While the analysis is for a specific task, the process remains the same for any NLP problem that can be transformed in the form of text generation. The following recent language models were discussed in the article, GPT-2 : It is the second iteration of the original series of language models released by OpenAI. In fact, this series of GPT models made the language model famous! GPT stands for \"Generative Pre-trained Transformer\", and currently we have 3 versions of the model (v1, v2 and v3). Out of these only GPT-1 and GPT-2 are open-sourced, and hence we will pick the latest version for our experiment. On the technical side, the architecture of GPT-2 is made up of the decoder part of the Transformer architecture. GPT-Neo : This model was released by EleutherAI to counter the GPT-3 model which was not open-sourced. The architecture is quite similar to GPT-3, but training was done on The Pile , an 825 GB sized text dataset. T5 : stands for \"Text-to-Text Transfer Transformer\" and was Google's answer to the world for open source language models. T5 paper showcase that using the complete encoder-decoder architecture (of the transformer) is better than only using the decoder (as done by the GPT series), hence they stay true to the original transformer architecture. The results from the analysis are as follows, model trial 0 trial 1 trial 2 average GPT-Neo 0.824 0.7893 0.808 0.8071 GPT-2 0.8398 0.808 0.806 0.8179 T5 0.8214 0.7976 0.804 0.8077 Conclusion - \"While GPT-2 may have won this round, the result table does show the prowess of text generation models on whole. All of them performed very well on the sentiment detection task, and all it took was a few epochs of training. Even if this experiment was done for a single task, I hope this helps to show how easy it is to use the TG models for completely new tasks. In a way, if we can transform the NLP problem into that of text generation, rest assured the pre-trained model will not fail, well at least not drastically :) This makes them the perfect baseline if not the state-of-the-art for many tasks.\" Additional materials How to generate text: using different decoding methods for language generation with Transformers - Link Guide to fine-tuning Text Generation models: GPT-2, GPT-Neo and T5 - Link","title":"Text generation"},{"location":"natural_language_processing/text_generation/#text-generation","text":"","title":"Text generation"},{"location":"natural_language_processing/text_generation/#introduction","text":"Text generation is an interesting task in NLP, where the intention is to generate text when provided with some prompt as input. Usually, we apply some form of Sequence-to-Sequence model (Seq2Seq) for this task. They are called language models, as they can be used to predict the next word based on the previous sentences. The recent surge in interest in this field is due to two main reasons, (1) the availability of several high performance pre-trained models, and (2) it's very easy to transform a large variety of NLP based tasks into the text-in text-out type of problem. Beacuse of this, it becomes easy to solve several such problems using a single language model. The Seq2Seq model consists of two main modules - Encoder : which takes text as input and encodes it to a compact vector representation, Decoder : which takes the compact vector representation as input and generates text as output. Conventionally, the models were trained from scratch for a specific task, which requires a lot of training data. Recently, as NLP has deviated more towards fine-tuning methodology, we have a number of pre-trained models which either out-of-the-box work very well or can be fine-tuned for the specific task. Some of the conventional models were RNN, GRU and, LSTM. Whereas recent pre-trained models include Transformers, GPT-{1, 2, 3}, GPT-{Neo, J}, T5, etc. Text generation task is a very specific but also a very generic task because we can formulate a lot of NLP tasks in the form of text generation. For example, (not a complete list) Language translation English to Hindi translation, or any language for that matter, is just a text in and text out task Summarization takes the complete text dump as input and generates crisp informative sentences. Question answering question is taken as input and answer is the output. We can even include some context as input on closed-domain QA tasks. Sentiment analysis is a classification task where we can provide the text as input and train the model to generate sentiment tag as output Rating prediction where we have to rate the writing style, kind of regression problem, but still can be formulated as text in number out As obvious from the examples mentioned above, it is possible to formulate a lot of problems as a text-in-text-out task, and it could even expand to classification and regression types of tasks. Some example tasks that can be performed using T5 is shown below, T5 text-to-text framework examples. See: Google Blog ( raffel2020exploring )","title":"Introduction"},{"location":"natural_language_processing/text_generation/#analysis","text":"","title":"Analysis"},{"location":"natural_language_processing/text_generation/#comparing-models-basic-details","text":"A brief comparison table of the different models mentioned above is as follows, models type year pre-trained? parameters RNN conventional - no 17k (one layer) LSTM conventional 1997 no 71k (one layer) GRU conventional 2014 no 30-40k (one layer) GPT-2 recent 2019 yes 117M, 345M, 774M, 1.5B GPT-Neo recent 2021 yes 125M, 1.2B, 2.7B T5 recent 2020 yes 60M, 220M, 770M, 2.8B, 11B","title":"Comparing models (basic details)"},{"location":"natural_language_processing/text_generation/#comparing-models-fine-tuning-performance","text":"A more detailed fine-tuning performance of the recent TG models for sentiment detection was performed here . While the analysis is for a specific task, the process remains the same for any NLP problem that can be transformed in the form of text generation. The following recent language models were discussed in the article, GPT-2 : It is the second iteration of the original series of language models released by OpenAI. In fact, this series of GPT models made the language model famous! GPT stands for \"Generative Pre-trained Transformer\", and currently we have 3 versions of the model (v1, v2 and v3). Out of these only GPT-1 and GPT-2 are open-sourced, and hence we will pick the latest version for our experiment. On the technical side, the architecture of GPT-2 is made up of the decoder part of the Transformer architecture. GPT-Neo : This model was released by EleutherAI to counter the GPT-3 model which was not open-sourced. The architecture is quite similar to GPT-3, but training was done on The Pile , an 825 GB sized text dataset. T5 : stands for \"Text-to-Text Transfer Transformer\" and was Google's answer to the world for open source language models. T5 paper showcase that using the complete encoder-decoder architecture (of the transformer) is better than only using the decoder (as done by the GPT series), hence they stay true to the original transformer architecture. The results from the analysis are as follows, model trial 0 trial 1 trial 2 average GPT-Neo 0.824 0.7893 0.808 0.8071 GPT-2 0.8398 0.808 0.806 0.8179 T5 0.8214 0.7976 0.804 0.8077 Conclusion - \"While GPT-2 may have won this round, the result table does show the prowess of text generation models on whole. All of them performed very well on the sentiment detection task, and all it took was a few epochs of training. Even if this experiment was done for a single task, I hope this helps to show how easy it is to use the TG models for completely new tasks. In a way, if we can transform the NLP problem into that of text generation, rest assured the pre-trained model will not fail, well at least not drastically :) This makes them the perfect baseline if not the state-of-the-art for many tasks.\"","title":"Comparing models (fine-tuning performance)"},{"location":"natural_language_processing/text_generation/#additional-materials","text":"How to generate text: using different decoding methods for language generation with Transformers - Link Guide to fine-tuning Text Generation models: GPT-2, GPT-Neo and T5 - Link","title":"Additional materials"},{"location":"natural_language_processing/text_similarity/","text":"Text Similarity Introduction Similarity between two words, sentences or documents is a function of commonality shared by them. This commonality can be measured by different metrics. Recently there has been a trend of using semantic based approaches, but historically, many similarity based non-neural algorthms were built. But which is the best string similarity algorithm? Well, it\u2019s quite hard to answer this question, at least without knowing anything else, like what you require it for i.e. your use case. And even after having a basic idea, it\u2019s quite hard to pinpoint a good algorithm without first trying them out on different datasets. It\u2019s a trial and error process. To make this journey simpler, I have tried to list down and explain the workings of the most basic string similarity algorithms out there. Give them a try, it may be what you needed all along Types of algorithms Based on the properties of operations, string similarity algorithms can be classified into a bunch of domains. Let\u2019s discuss a few of them, Edit distance based: Algorithms falling under this category try to compute the number of operations needed to transforms one string to another. More the number of operations, less is the similarity between the two strings. One point to note, in this case, every index character of the string is given equal importance. Token-based: In this category, the expected input is a set of tokens, rather than complete strings. The idea is to find the similar tokens in both sets. More the number of common tokens, more is the similarity between the sets. A string can be transformed into sets by splitting using a delimiter. This way, we can transform a sentence into tokens of words or n-grams characters. Note, here tokens of different length have equal importance. Sequence-based: Here, the similarity is a factor of common sub-strings between the two strings. The algorithms, try to find the longest sequence which is present in both strings, the more of these sequences found, higher is the similarity score. Note, here combination of characters of same length have equal importance. Semantic-based: Here, the similarity is not based on pure presence of common sub-strings, but on the semantic meaning of the sub-strings. Semantic based approaches considers the contextual and linguistic meaning of the sub-strings. For example, in semantic based approaches, the similarity between \u201ctoad\u201d and \u201cfrog\u201d will be high, which is not possible in other approaches. Note Semantic based algorithms are quite difficult to use as semnatic is a subjective matter. For example, how will you compare these two sentences -- \"How is the weather today?\" and \"How is the weather tomorrow?\"? If we go by non-semantic approach, we will get a very high score. But for semantic models, two things are possible and correct at the same time -- get a high score as we are talking about the weather, or get a low score as we are talking about different days. This is why, it is important to finetune the semantic models for your use case. (for example here, what is more important - the topic or the context. Based on the answer, prepare a dataset and finetune). Edit distance based algorithms Let\u2019s try to understand most widely used algorithms within this type, Hamming distance This distance is computed by overlaying one string over another and finding the places where the strings vary. Note, classical implementation was meant to handle strings of same length. Some implementations may bypass this by adding a padding at prefix or suffix. Nevertheless, the logic is to find the total number of places one string is different from the other. To showcase an examples, 1 2 3 4 5 6 7 8 9 >> import textdistance >> textdistance . hamming ( 'text' , 'test' ) 1 >> textdistance . hamming . normalized_similarity ( 'text' , 'test' ) 0.75 >> textdistance . hamming ( 'arrow' , 'arow' ) 3 >> textdistance . hamming . normalized_similarity ( 'arrow' , 'arow' ) 0.4 As evident, in first example, the two strings vary only at the 3rd position, hence the edit distance is 1. In second example, even though we are only missing one \u2018r\u2019, the \u2018row\u2019 part is offset by 1, making the edit distance 3 (3rd, 4th and 5th position are dissimilar). One thing to note is the normalized similarity, this is nothing but a function to bound the edit distance between 0 and 1. This signifies, if the score is 0-two strings cannot be more dissimilar, on the other hand, a score of 1 is for a perfect match. So the strings in first example are 75% similar (expected) but in strings in second example are only 40% similar (can we do better?). Levenshtein distance This distance is computed by finding the number of edits which will transform one string to another. The transformations allowed are insertion \u2014 adding a new character, deletion \u2014 deleting a character and substitution \u2014 replace one character by another. By performing these three operations, the algorithm tries to modify first string to match the second one. In the end we get a edit distance. Examples, 1 2 3 4 >> textdistance . levenshtein ( 'arrow' , 'arow' ) 1 >> textdistance . levenshtein . normalized_similarity ( 'arrow' , 'arow' ) 0.8 As evident, if we insert one \u2018r\u2019 in string 2 i.e. \u2018arow\u2019, it becomes same as the string 1. Hence, the edit distance is 1. Similar with hamming distance, we can generate a bounded similarity score between 0 and 1. The similarity score is 80%, huge improvement over the last algorithm. Jaro-Winkler This algorithms gives high scores to two strings if, (1) they contain same characters, but within a certain distance from one another, and (2) the order of the matching characters is same. To be exact, the distance of finding similar character is 1 less than half of length of longest string. So if longest strings has length of 5, a character at the start of the string 1 must be found before or on ((5/2)\u20131) ~ 2nd position in the string 2 to be considered valid match. Because of this, the algorithm is directional and gives high score if matching is from the beginning of the strings. Some examples, 1 2 3 4 5 6 >> textdistance . jaro_winkler ( \"mes\" , \"messi\" ) 0.86 >> textdistance . jaro_winkler ( \"crate\" , \"crat\" ) 0.96 >> textdistance . jaro_winkler ( \"crate\" , \"atcr\" ) 0.0 In first case, as the strings were matching from the beginning, high score was provided. Similarly, in the second case, only one character was missing and that too at the end of the string 2, hence a very high score was given. Imagine the previous algorithms, the similarity would have been less, 80% to be exact. In third case, we re-arranged the last two character of string 2, by bringing them at front, which resulted in 0% similarity. Token based algorithms Algorithms falling under this category are more or less, set similarity algorithms, modified to work for the case of string tokens. Some of them are, Jaccard index Falling under the set similarity domain, the formulae is to find the number of common tokens and divide it by the total number of unique tokens. Its expressed in the mathematical terms by, \\[ J(A,B) = {{|A \\cap B|}\\over{|A \\cup B|}} = {{|A \\cap B|}\\over{|A| + |B| - |A \\cap B|}}. \\] where, the numerator is the intersection (common tokens) and denominator is union (unique tokens). The second case is for when there is some overlap, for which we must remove the common terms as they would add up twice by combining all tokens of both strings. As the required input is tokens instead of complete strings, it falls to user to efficiently and intelligently tokenize his string, depending on the use case. Examples, 1 2 3 4 5 6 7 8 >> tokens_1 = \"hello world\" . split () >> tokens_2 = \"world hello\" . split () >> textdistance . jaccard ( tokens_1 , tokens_2 ) 1.0 >> tokens_1 = \"hello new world\" . split () >> tokens_2 = \"hello world\" . split () >> textdistance . jaccard ( tokens_1 , tokens_2 ) 0.666 We first tokenize the string by default space delimiter, to make words in the strings as tokens. Then we compute the similarity score. In first example, as both words are present in both the strings, the score is 1. Just imagine running an edit based algorithm in this case, the score will be very less if not 0. Sorensen-Dice Falling under set similarity, the logic is to find the common tokens, and divide it by the total number of tokens present by combining both sets. The formulae is, \\[ {\\displaystyle DSC={\\frac {2|X\\cap Y|}{|X|+|Y|}}} \\] where, the numerator is twice the intersection of two sets/strings. The idea behind this is if a token is present in both strings, its total count is obviously twice the intersection (which removes duplicates). The denominator is simple combination of all tokens in both strings. Note, its quite different from the jaccard\u2019s denominator, which was union of two strings. As the case with intersection, union too removes duplicates and this is avoided in dice algorithm. Because of this, dice will always overestimate the similarity between two strings. Some example, 1 2 3 4 5 6 7 8 >> tokens_1 = \"hello world\" . split () >> tokens_2 = \"world hello\" . split () >> textdistance . sorensen ( tokens_1 , tokens_2 ) 1.0 >> tokens_1 = \"hello new world\" . split () >> tokens_2 = \"hello world\" . split () >> textdistance . sorensen ( tokens_1 , tokens_2 ) 0.8 Sequence based algorithm Lets understand one of the sequence based algorithms, Ratcliff-Obershelp similarity The idea is quite simple yet intuitive. Find the longest common substring from the two strings. Remove that part from both strings, and split at the same location. This breaks the strings into two parts, one left and another to the right of the found common substring. Now take the left part of both strings and call the function again to find the longest common substring. Do this too for the right part. This process is repeated recursively until the size of any broken part is less than a default value. Finally, a formulation similar to the above-mentioned dice is followed to compute the similarity score. The score is twice the number of characters found in common divided by the total number of characters in the two strings. Some examples, 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 >> string1 , string2 = \"i am going home\" , \"gone home\" >> textdistance . ratcliff_obershelp ( string1 , string2 ) 0.66 >> string1 , string2 = \"helloworld\" , \"worldhello\" >> textdistance . ratcliff_obershelp ( string1 , string2 ) 0.5 >> string1 , string2 = \"test\" , \"text\" >> textdistance . ratcliff_obershelp ( string1 , string2 ) 0.75 >> string1 , string2 = \"mes\" , \"simes\" >> textdistance . ratcliff_obershelp ( string1 , string2 ) 0.75 >> string1 , string2 = \"mes\" , \"simes\" >> textdistance . ratcliff_obershelp ( string1 , string2 ) 0.75 >> string1 , string2 = \"arrow\" , \"arow\" >> textdistance . ratcliff_obershelp ( string1 , string2 ) 0.88 In first example, it found \u2018 home\u2019 as the longest substring, then considered \u2018i am going\u2019 and \u2018gone\u2019 for further processing (left of common substring), where again it found \u2018go\u2019 as longest substring. Later on right of \u2018go\u2019 it also found \u2019n\u2019 as the only common and longest substring. Overall the score was 2 * (5 + 2 + 1) / 24 ~ 0.66. In second case, it found \u2018hello\u2019 as the longest longest substring and nothing common on the left and right, hence score is 0.5. The rest of the examples showcase the advantage of using sequence algorithms for cases missed by edit distance based algorithms. Semantic based approaches In semantic search, strings are embedded using some neural network (NN) model. Think of it like a function that takes an input string and returns a vector of numbers. The vector is then used to compare the similarity between two strings. Usually the NN models work at either token or word level, so to get embedding of a string, we first find embeddings for each token in the string and then aggregate them using mean or similar function. The expectation is that the embeddings will be able to represent the string such that it capture different aspects of the language. Because of which, the embeddings provides us with much more features to compare strings. Let's try a couple of ways to compute semantic similarity between strings. Different models can be picked or even fine tuned based on domain and requirement, but we will use the same model for simiplicity's sake. Instead we will just use different packages. Hint As embedding is an integral part of semantic search, it is important to check the quality of a embedding method before using it. MTEB is the \"Massive Text Embedding Benchmark\" python package that lets you test any embedding function on more than 30 tasks. The process is quite simple - usually the text is embedded using the provided function or neural network, and the performance of embedding is computed and checked on downstream tasks like classification, clustering, and more. txtai txtai is a python package to perform semantic based tasks on textual data including search, question answering, information extraction, etc. Today, we will use it for the sole purpose of semantic search. (inspired from txtai readme ) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 # import the packages import pandas as pd ## for better visualization of result from txtai.embeddings import Embeddings # Create embeddings model, backed by sentence-transformers & transformers embeddings = Embeddings ({ \"path\" : \"sentence-transformers/nli-mpnet-base-v2\" }) # the data to search against data = [ \"US tops 5 million confirmed virus cases\" , \"Canada's last fully intact ice shelf has suddenly collapsed, forming a Manhattan-sized iceberg\" , \"Beijing mobilises invasion craft along coast as Taiwan tensions escalate\" , \"The National Park Service warns against sacrificing slower friends in a bear attack\" , \"Maine man wins $1M from $25 lottery ticket\" , \"Make huge profits without work, earn up to $100,000 a day\" ] # var to hold the result results = [] # perform search for each of the following query phrase for query in ( \"feel good story\" , \"climate change\" , \"public health story\" , \"war\" , \"wildlife\" , \"asia\" , \"lucky\" , \"dishonest junk\" ): # Get index of best section that best matches query for id , score in embeddings . similarity ( query , data ): results . append (( query , data [ id ], score )) # format the result results = pd . DataFrame ( results ) results . columns = [ \"query\" , \"best_match\" , \"score\" ] results [ 'score' ] = results [ 'score' ] . round ( 2 ) results = results . sort_values ( by = [ 'score' ], ascending = False ) # print the result results . drop_duplicates ( subset = [ 'query' ]) Here we are trying to pick the most similar phrase for each of the query from the data. The result will look as follows, query best_match score wildlife The National Park Service warns against sacrif... 0.28 war Beijing mobilises invasion craft along coast a... 0.27 asia Beijing mobilises invasion craft along coast a... 0.24 climate change Canada's last fully intact ice shelf has sudde... 0.24 public health story US tops 5 million confirmed virus cases 0.17 feel good story Maine man wins $1M from $25 lottery ticket 0.08 lucky Maine man wins $1M from $25 lottery ticket 0.07 dishonest junk Make huge profits without work, earn up to $10... 0.03 As obvious from the result, even though there is hardly any common sub-string between the query and the data, the results make sense in a semantic way. Beijing is connected with asia and war , while ice shelf is connected with climate change , and so on. Note The score is the cosine similarity between the embeddings of the two strings (query and data element). It's range is between {-1, 1}, and not {0, 1}. Do not think of it as probability. The above approach could be slow as for each call to similarity , the sentences are embedded again and again. To speed it up, we could use index to precompute the embeddings for the data. This can be done by, 1 2 3 4 5 6 7 8 # index the data embeddings . index ([( uid , text , None ) for uid , text in enumerate ( data )]) # now instead of similarity, use search function embeddings . search ( \"feel good story\" , limit = 1 ) # Output: # [{'id': '4', # 'score': 0.08329004049301147, # 'text': 'Maine man wins $1M from $25 lottery ticket'}] txtai also provides explaination of the result. For this we can use explain function as follows, 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 # first index the data embeddings . index ([( uid , text , None ) for uid , text in enumerate ( data )]) # next call the explain function embeddings . explain ( \"feel good story\" , limit = 1 ) # Output: # [{'id': '4', # 'score': 0.08329004049301147, # 'text': 'Maine man wins $1M from $25 lottery ticket', # 'tokens': [('Maine', 0.003297939896583557), # ('man', -0.03039500117301941), # ('wins', 0.03406312316656113), # ('$1M', -0.03121592104434967), # ('from', -0.02270638197660446), # ('$25', 0.012891143560409546), # ('lottery', -0.015372440218925476), # ('ticket', 0.007445111870765686)]}] The output contains word level importance score for the top similar sentence in the data ( limit=1 ). From the output we can see the word win is the most important word wrt to the query. The score computation is based on the occlusion based explaination approach. Here, several permutations of the same sentence is created, each one with one word/token missing. Then cosine similarity is computed between the fixed query and each permutation. Finally, the similairty score of the permutation is subtracted from the score of the original sentence (with all words present). The intuition is as follows, if an important word like win is missing from the sentence, the score of the sentence will be reduced and the difference will be positive if an unimportant word like man is missing from the sentence, the score of the sentence will increase and the difference will be negative Sentence Transformer SentenceTransformers is is a Python framework for state-of-the-art sentence, text and image embeddings. The inital work in this framework is detailed in the paper Sentence-BERT 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 # laod packages import pandas as pd from sentence_transformers import SentenceTransformer , util # load the model model = SentenceTransformer ( 'nli-mpnet-base-v2' ) # the data to search against data = [ \"US tops 5 million confirmed virus cases\" , \"Canada's last fully intact ice shelf has suddenly collapsed, forming a Manhattan-sized iceberg\" , \"Beijing mobilises invasion craft along coast as Taiwan tensions escalate\" , \"The National Park Service warns against sacrificing slower friends in a bear attack\" , \"Maine man wins $1M from $25 lottery ticket\" , \"Make huge profits without work, earn up to $100,000 a day\" ] # var to hold the result results = [] # embed the data before hand embeddings_target = model . encode ( data , convert_to_tensor = True ) # perform search for each of the following query phrase for query in ( \"feel good story\" , \"climate change\" , \"public health story\" , \"war\" , \"wildlife\" , \"asia\" , \"lucky\" , \"dishonest junk\" ): # embed the query embeddings_query = model . encode ([ query ], convert_to_tensor = True ) # compute cosine-similarities for each sentence with each other sentence cosine_scores = util . pytorch_cos_sim ( embeddings_query , embeddings_target ) # return the index of top 5 values in a list score_list = cosine_scores . tolist ()[ 0 ] # Get index of best section that best matches query for id , score in enumerate ( score_list ): results . append (( query , data [ id ], score )) # format the result results = pd . DataFrame ( results ) results . columns = [ \"query\" , \"best_match\" , \"score\" ] results [ 'score' ] = results [ 'score' ] . round ( 2 ) results = results . sort_values ( by = [ 'score' ], ascending = False ) # print the result results . drop_duplicates ( subset = [ 'query' ]) This will return the same table as the previous one. In face if you noticed, we have used the same model. If you look further, in txtai we used sentence-transformers and we have used the same model. The package provides an extensive variety of pretraied models. A comparitive table is shown below (taken from Sentence-Transformer ) Model Name Performance Sentence Embeddings (14 Datasets) Performance Semantic Search (6 Datasets) Avg. Performance Sentence Encoding Speed Model Size all-mpnet-base-v2 69.57 57.02 63.30 2800 420 MB multi-qa-mpnet-base-dot-v1 66.76 57.60 62.18 2800 420 MB all-distilroberta-v1 68.73 50.94 59.84 4000 290 MB all-MiniLM-L12-v2 68.70 50.82 59.76 7500 120 MB multi-qa-distilbert-cos-v1 65.98 52.83 59.41 4000 250 MB all-MiniLM-L6-v2 68.06 49.54 58.80 14200 80 MB multi-qa-MiniLM-L6-cos-v1 64.33 51.83 58.08 14200 80 MB paraphrase-multilingual-mpnet-base-v2 65.83 41.68 53.75 2500 970 MB paraphrase-albert-small-v2 64.46 40.04 52.25 5000 43 MB paraphrase-multilingual-MiniLM-L12-v2 64.25 39.19 51.72 7500 420 MB paraphrase-MiniLM-L3-v2 62.29 39.19 50.74 19000 61 MB distiluse-base-multilingual-cased-v1 61.30 29.87 45.59 4000 480 MB distiluse-base-multilingual-cased-v2 60.18 27.35 43.77 4000 480 MB Note Training semantic search model is different from normal classification or regression models. Think of it like this -- for classification each sample has one label, but for semantic search a combination of samples has one label. This is because in search you want to match the query and result, and then provide some score for that combination. You can refer Sbert training page for more details.","title":"Text similarity"},{"location":"natural_language_processing/text_similarity/#text-similarity","text":"","title":"Text Similarity"},{"location":"natural_language_processing/text_similarity/#introduction","text":"Similarity between two words, sentences or documents is a function of commonality shared by them. This commonality can be measured by different metrics. Recently there has been a trend of using semantic based approaches, but historically, many similarity based non-neural algorthms were built. But which is the best string similarity algorithm? Well, it\u2019s quite hard to answer this question, at least without knowing anything else, like what you require it for i.e. your use case. And even after having a basic idea, it\u2019s quite hard to pinpoint a good algorithm without first trying them out on different datasets. It\u2019s a trial and error process. To make this journey simpler, I have tried to list down and explain the workings of the most basic string similarity algorithms out there. Give them a try, it may be what you needed all along","title":"Introduction"},{"location":"natural_language_processing/text_similarity/#types-of-algorithms","text":"Based on the properties of operations, string similarity algorithms can be classified into a bunch of domains. Let\u2019s discuss a few of them, Edit distance based: Algorithms falling under this category try to compute the number of operations needed to transforms one string to another. More the number of operations, less is the similarity between the two strings. One point to note, in this case, every index character of the string is given equal importance. Token-based: In this category, the expected input is a set of tokens, rather than complete strings. The idea is to find the similar tokens in both sets. More the number of common tokens, more is the similarity between the sets. A string can be transformed into sets by splitting using a delimiter. This way, we can transform a sentence into tokens of words or n-grams characters. Note, here tokens of different length have equal importance. Sequence-based: Here, the similarity is a factor of common sub-strings between the two strings. The algorithms, try to find the longest sequence which is present in both strings, the more of these sequences found, higher is the similarity score. Note, here combination of characters of same length have equal importance. Semantic-based: Here, the similarity is not based on pure presence of common sub-strings, but on the semantic meaning of the sub-strings. Semantic based approaches considers the contextual and linguistic meaning of the sub-strings. For example, in semantic based approaches, the similarity between \u201ctoad\u201d and \u201cfrog\u201d will be high, which is not possible in other approaches. Note Semantic based algorithms are quite difficult to use as semnatic is a subjective matter. For example, how will you compare these two sentences -- \"How is the weather today?\" and \"How is the weather tomorrow?\"? If we go by non-semantic approach, we will get a very high score. But for semantic models, two things are possible and correct at the same time -- get a high score as we are talking about the weather, or get a low score as we are talking about different days. This is why, it is important to finetune the semantic models for your use case. (for example here, what is more important - the topic or the context. Based on the answer, prepare a dataset and finetune).","title":"Types of algorithms"},{"location":"natural_language_processing/text_similarity/#edit-distance-based-algorithms","text":"Let\u2019s try to understand most widely used algorithms within this type,","title":"Edit distance based algorithms"},{"location":"natural_language_processing/text_similarity/#hamming-distance","text":"This distance is computed by overlaying one string over another and finding the places where the strings vary. Note, classical implementation was meant to handle strings of same length. Some implementations may bypass this by adding a padding at prefix or suffix. Nevertheless, the logic is to find the total number of places one string is different from the other. To showcase an examples, 1 2 3 4 5 6 7 8 9 >> import textdistance >> textdistance . hamming ( 'text' , 'test' ) 1 >> textdistance . hamming . normalized_similarity ( 'text' , 'test' ) 0.75 >> textdistance . hamming ( 'arrow' , 'arow' ) 3 >> textdistance . hamming . normalized_similarity ( 'arrow' , 'arow' ) 0.4 As evident, in first example, the two strings vary only at the 3rd position, hence the edit distance is 1. In second example, even though we are only missing one \u2018r\u2019, the \u2018row\u2019 part is offset by 1, making the edit distance 3 (3rd, 4th and 5th position are dissimilar). One thing to note is the normalized similarity, this is nothing but a function to bound the edit distance between 0 and 1. This signifies, if the score is 0-two strings cannot be more dissimilar, on the other hand, a score of 1 is for a perfect match. So the strings in first example are 75% similar (expected) but in strings in second example are only 40% similar (can we do better?).","title":"Hamming distance"},{"location":"natural_language_processing/text_similarity/#levenshtein-distance","text":"This distance is computed by finding the number of edits which will transform one string to another. The transformations allowed are insertion \u2014 adding a new character, deletion \u2014 deleting a character and substitution \u2014 replace one character by another. By performing these three operations, the algorithm tries to modify first string to match the second one. In the end we get a edit distance. Examples, 1 2 3 4 >> textdistance . levenshtein ( 'arrow' , 'arow' ) 1 >> textdistance . levenshtein . normalized_similarity ( 'arrow' , 'arow' ) 0.8 As evident, if we insert one \u2018r\u2019 in string 2 i.e. \u2018arow\u2019, it becomes same as the string 1. Hence, the edit distance is 1. Similar with hamming distance, we can generate a bounded similarity score between 0 and 1. The similarity score is 80%, huge improvement over the last algorithm.","title":"Levenshtein distance"},{"location":"natural_language_processing/text_similarity/#jaro-winkler","text":"This algorithms gives high scores to two strings if, (1) they contain same characters, but within a certain distance from one another, and (2) the order of the matching characters is same. To be exact, the distance of finding similar character is 1 less than half of length of longest string. So if longest strings has length of 5, a character at the start of the string 1 must be found before or on ((5/2)\u20131) ~ 2nd position in the string 2 to be considered valid match. Because of this, the algorithm is directional and gives high score if matching is from the beginning of the strings. Some examples, 1 2 3 4 5 6 >> textdistance . jaro_winkler ( \"mes\" , \"messi\" ) 0.86 >> textdistance . jaro_winkler ( \"crate\" , \"crat\" ) 0.96 >> textdistance . jaro_winkler ( \"crate\" , \"atcr\" ) 0.0 In first case, as the strings were matching from the beginning, high score was provided. Similarly, in the second case, only one character was missing and that too at the end of the string 2, hence a very high score was given. Imagine the previous algorithms, the similarity would have been less, 80% to be exact. In third case, we re-arranged the last two character of string 2, by bringing them at front, which resulted in 0% similarity.","title":"Jaro-Winkler"},{"location":"natural_language_processing/text_similarity/#token-based-algorithms","text":"Algorithms falling under this category are more or less, set similarity algorithms, modified to work for the case of string tokens. Some of them are,","title":"Token based algorithms"},{"location":"natural_language_processing/text_similarity/#jaccard-index","text":"Falling under the set similarity domain, the formulae is to find the number of common tokens and divide it by the total number of unique tokens. Its expressed in the mathematical terms by, \\[ J(A,B) = {{|A \\cap B|}\\over{|A \\cup B|}} = {{|A \\cap B|}\\over{|A| + |B| - |A \\cap B|}}. \\] where, the numerator is the intersection (common tokens) and denominator is union (unique tokens). The second case is for when there is some overlap, for which we must remove the common terms as they would add up twice by combining all tokens of both strings. As the required input is tokens instead of complete strings, it falls to user to efficiently and intelligently tokenize his string, depending on the use case. Examples, 1 2 3 4 5 6 7 8 >> tokens_1 = \"hello world\" . split () >> tokens_2 = \"world hello\" . split () >> textdistance . jaccard ( tokens_1 , tokens_2 ) 1.0 >> tokens_1 = \"hello new world\" . split () >> tokens_2 = \"hello world\" . split () >> textdistance . jaccard ( tokens_1 , tokens_2 ) 0.666 We first tokenize the string by default space delimiter, to make words in the strings as tokens. Then we compute the similarity score. In first example, as both words are present in both the strings, the score is 1. Just imagine running an edit based algorithm in this case, the score will be very less if not 0.","title":"Jaccard index"},{"location":"natural_language_processing/text_similarity/#sorensen-dice","text":"Falling under set similarity, the logic is to find the common tokens, and divide it by the total number of tokens present by combining both sets. The formulae is, \\[ {\\displaystyle DSC={\\frac {2|X\\cap Y|}{|X|+|Y|}}} \\] where, the numerator is twice the intersection of two sets/strings. The idea behind this is if a token is present in both strings, its total count is obviously twice the intersection (which removes duplicates). The denominator is simple combination of all tokens in both strings. Note, its quite different from the jaccard\u2019s denominator, which was union of two strings. As the case with intersection, union too removes duplicates and this is avoided in dice algorithm. Because of this, dice will always overestimate the similarity between two strings. Some example, 1 2 3 4 5 6 7 8 >> tokens_1 = \"hello world\" . split () >> tokens_2 = \"world hello\" . split () >> textdistance . sorensen ( tokens_1 , tokens_2 ) 1.0 >> tokens_1 = \"hello new world\" . split () >> tokens_2 = \"hello world\" . split () >> textdistance . sorensen ( tokens_1 , tokens_2 ) 0.8","title":"Sorensen-Dice"},{"location":"natural_language_processing/text_similarity/#sequence-based-algorithm","text":"Lets understand one of the sequence based algorithms,","title":"Sequence based algorithm"},{"location":"natural_language_processing/text_similarity/#ratcliff-obershelp-similarity","text":"The idea is quite simple yet intuitive. Find the longest common substring from the two strings. Remove that part from both strings, and split at the same location. This breaks the strings into two parts, one left and another to the right of the found common substring. Now take the left part of both strings and call the function again to find the longest common substring. Do this too for the right part. This process is repeated recursively until the size of any broken part is less than a default value. Finally, a formulation similar to the above-mentioned dice is followed to compute the similarity score. The score is twice the number of characters found in common divided by the total number of characters in the two strings. Some examples, 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 >> string1 , string2 = \"i am going home\" , \"gone home\" >> textdistance . ratcliff_obershelp ( string1 , string2 ) 0.66 >> string1 , string2 = \"helloworld\" , \"worldhello\" >> textdistance . ratcliff_obershelp ( string1 , string2 ) 0.5 >> string1 , string2 = \"test\" , \"text\" >> textdistance . ratcliff_obershelp ( string1 , string2 ) 0.75 >> string1 , string2 = \"mes\" , \"simes\" >> textdistance . ratcliff_obershelp ( string1 , string2 ) 0.75 >> string1 , string2 = \"mes\" , \"simes\" >> textdistance . ratcliff_obershelp ( string1 , string2 ) 0.75 >> string1 , string2 = \"arrow\" , \"arow\" >> textdistance . ratcliff_obershelp ( string1 , string2 ) 0.88 In first example, it found \u2018 home\u2019 as the longest substring, then considered \u2018i am going\u2019 and \u2018gone\u2019 for further processing (left of common substring), where again it found \u2018go\u2019 as longest substring. Later on right of \u2018go\u2019 it also found \u2019n\u2019 as the only common and longest substring. Overall the score was 2 * (5 + 2 + 1) / 24 ~ 0.66. In second case, it found \u2018hello\u2019 as the longest longest substring and nothing common on the left and right, hence score is 0.5. The rest of the examples showcase the advantage of using sequence algorithms for cases missed by edit distance based algorithms.","title":"Ratcliff-Obershelp similarity"},{"location":"natural_language_processing/text_similarity/#semantic-based-approaches","text":"In semantic search, strings are embedded using some neural network (NN) model. Think of it like a function that takes an input string and returns a vector of numbers. The vector is then used to compare the similarity between two strings. Usually the NN models work at either token or word level, so to get embedding of a string, we first find embeddings for each token in the string and then aggregate them using mean or similar function. The expectation is that the embeddings will be able to represent the string such that it capture different aspects of the language. Because of which, the embeddings provides us with much more features to compare strings. Let's try a couple of ways to compute semantic similarity between strings. Different models can be picked or even fine tuned based on domain and requirement, but we will use the same model for simiplicity's sake. Instead we will just use different packages. Hint As embedding is an integral part of semantic search, it is important to check the quality of a embedding method before using it. MTEB is the \"Massive Text Embedding Benchmark\" python package that lets you test any embedding function on more than 30 tasks. The process is quite simple - usually the text is embedded using the provided function or neural network, and the performance of embedding is computed and checked on downstream tasks like classification, clustering, and more.","title":"Semantic based approaches"},{"location":"natural_language_processing/text_similarity/#txtai","text":"txtai is a python package to perform semantic based tasks on textual data including search, question answering, information extraction, etc. Today, we will use it for the sole purpose of semantic search. (inspired from txtai readme ) 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 # import the packages import pandas as pd ## for better visualization of result from txtai.embeddings import Embeddings # Create embeddings model, backed by sentence-transformers & transformers embeddings = Embeddings ({ \"path\" : \"sentence-transformers/nli-mpnet-base-v2\" }) # the data to search against data = [ \"US tops 5 million confirmed virus cases\" , \"Canada's last fully intact ice shelf has suddenly collapsed, forming a Manhattan-sized iceberg\" , \"Beijing mobilises invasion craft along coast as Taiwan tensions escalate\" , \"The National Park Service warns against sacrificing slower friends in a bear attack\" , \"Maine man wins $1M from $25 lottery ticket\" , \"Make huge profits without work, earn up to $100,000 a day\" ] # var to hold the result results = [] # perform search for each of the following query phrase for query in ( \"feel good story\" , \"climate change\" , \"public health story\" , \"war\" , \"wildlife\" , \"asia\" , \"lucky\" , \"dishonest junk\" ): # Get index of best section that best matches query for id , score in embeddings . similarity ( query , data ): results . append (( query , data [ id ], score )) # format the result results = pd . DataFrame ( results ) results . columns = [ \"query\" , \"best_match\" , \"score\" ] results [ 'score' ] = results [ 'score' ] . round ( 2 ) results = results . sort_values ( by = [ 'score' ], ascending = False ) # print the result results . drop_duplicates ( subset = [ 'query' ]) Here we are trying to pick the most similar phrase for each of the query from the data. The result will look as follows, query best_match score wildlife The National Park Service warns against sacrif... 0.28 war Beijing mobilises invasion craft along coast a... 0.27 asia Beijing mobilises invasion craft along coast a... 0.24 climate change Canada's last fully intact ice shelf has sudde... 0.24 public health story US tops 5 million confirmed virus cases 0.17 feel good story Maine man wins $1M from $25 lottery ticket 0.08 lucky Maine man wins $1M from $25 lottery ticket 0.07 dishonest junk Make huge profits without work, earn up to $10... 0.03 As obvious from the result, even though there is hardly any common sub-string between the query and the data, the results make sense in a semantic way. Beijing is connected with asia and war , while ice shelf is connected with climate change , and so on. Note The score is the cosine similarity between the embeddings of the two strings (query and data element). It's range is between {-1, 1}, and not {0, 1}. Do not think of it as probability. The above approach could be slow as for each call to similarity , the sentences are embedded again and again. To speed it up, we could use index to precompute the embeddings for the data. This can be done by, 1 2 3 4 5 6 7 8 # index the data embeddings . index ([( uid , text , None ) for uid , text in enumerate ( data )]) # now instead of similarity, use search function embeddings . search ( \"feel good story\" , limit = 1 ) # Output: # [{'id': '4', # 'score': 0.08329004049301147, # 'text': 'Maine man wins $1M from $25 lottery ticket'}] txtai also provides explaination of the result. For this we can use explain function as follows, 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 # first index the data embeddings . index ([( uid , text , None ) for uid , text in enumerate ( data )]) # next call the explain function embeddings . explain ( \"feel good story\" , limit = 1 ) # Output: # [{'id': '4', # 'score': 0.08329004049301147, # 'text': 'Maine man wins $1M from $25 lottery ticket', # 'tokens': [('Maine', 0.003297939896583557), # ('man', -0.03039500117301941), # ('wins', 0.03406312316656113), # ('$1M', -0.03121592104434967), # ('from', -0.02270638197660446), # ('$25', 0.012891143560409546), # ('lottery', -0.015372440218925476), # ('ticket', 0.007445111870765686)]}] The output contains word level importance score for the top similar sentence in the data ( limit=1 ). From the output we can see the word win is the most important word wrt to the query. The score computation is based on the occlusion based explaination approach. Here, several permutations of the same sentence is created, each one with one word/token missing. Then cosine similarity is computed between the fixed query and each permutation. Finally, the similairty score of the permutation is subtracted from the score of the original sentence (with all words present). The intuition is as follows, if an important word like win is missing from the sentence, the score of the sentence will be reduced and the difference will be positive if an unimportant word like man is missing from the sentence, the score of the sentence will increase and the difference will be negative","title":"txtai"},{"location":"natural_language_processing/text_similarity/#sentence-transformer","text":"SentenceTransformers is is a Python framework for state-of-the-art sentence, text and image embeddings. The inital work in this framework is detailed in the paper Sentence-BERT 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 # laod packages import pandas as pd from sentence_transformers import SentenceTransformer , util # load the model model = SentenceTransformer ( 'nli-mpnet-base-v2' ) # the data to search against data = [ \"US tops 5 million confirmed virus cases\" , \"Canada's last fully intact ice shelf has suddenly collapsed, forming a Manhattan-sized iceberg\" , \"Beijing mobilises invasion craft along coast as Taiwan tensions escalate\" , \"The National Park Service warns against sacrificing slower friends in a bear attack\" , \"Maine man wins $1M from $25 lottery ticket\" , \"Make huge profits without work, earn up to $100,000 a day\" ] # var to hold the result results = [] # embed the data before hand embeddings_target = model . encode ( data , convert_to_tensor = True ) # perform search for each of the following query phrase for query in ( \"feel good story\" , \"climate change\" , \"public health story\" , \"war\" , \"wildlife\" , \"asia\" , \"lucky\" , \"dishonest junk\" ): # embed the query embeddings_query = model . encode ([ query ], convert_to_tensor = True ) # compute cosine-similarities for each sentence with each other sentence cosine_scores = util . pytorch_cos_sim ( embeddings_query , embeddings_target ) # return the index of top 5 values in a list score_list = cosine_scores . tolist ()[ 0 ] # Get index of best section that best matches query for id , score in enumerate ( score_list ): results . append (( query , data [ id ], score )) # format the result results = pd . DataFrame ( results ) results . columns = [ \"query\" , \"best_match\" , \"score\" ] results [ 'score' ] = results [ 'score' ] . round ( 2 ) results = results . sort_values ( by = [ 'score' ], ascending = False ) # print the result results . drop_duplicates ( subset = [ 'query' ]) This will return the same table as the previous one. In face if you noticed, we have used the same model. If you look further, in txtai we used sentence-transformers and we have used the same model. The package provides an extensive variety of pretraied models. A comparitive table is shown below (taken from Sentence-Transformer ) Model Name Performance Sentence Embeddings (14 Datasets) Performance Semantic Search (6 Datasets) Avg. Performance Sentence Encoding Speed Model Size all-mpnet-base-v2 69.57 57.02 63.30 2800 420 MB multi-qa-mpnet-base-dot-v1 66.76 57.60 62.18 2800 420 MB all-distilroberta-v1 68.73 50.94 59.84 4000 290 MB all-MiniLM-L12-v2 68.70 50.82 59.76 7500 120 MB multi-qa-distilbert-cos-v1 65.98 52.83 59.41 4000 250 MB all-MiniLM-L6-v2 68.06 49.54 58.80 14200 80 MB multi-qa-MiniLM-L6-cos-v1 64.33 51.83 58.08 14200 80 MB paraphrase-multilingual-mpnet-base-v2 65.83 41.68 53.75 2500 970 MB paraphrase-albert-small-v2 64.46 40.04 52.25 5000 43 MB paraphrase-multilingual-MiniLM-L12-v2 64.25 39.19 51.72 7500 420 MB paraphrase-MiniLM-L3-v2 62.29 39.19 50.74 19000 61 MB distiluse-base-multilingual-cased-v1 61.30 29.87 45.59 4000 480 MB distiluse-base-multilingual-cased-v2 60.18 27.35 43.77 4000 480 MB Note Training semantic search model is different from normal classification or regression models. Think of it like this -- for classification each sample has one label, but for semantic search a combination of samples has one label. This is because in search you want to match the query and result, and then provide some score for that combination. You can refer Sbert training page for more details.","title":"Sentence Transformer"},{"location":"natural_language_processing/transformer/","text":"Warning This page is still ongoing modifications. Please check back after some time or contact me if it has been a while! Sorry for the inconvenience Transformers Introduction \"Attention Is All You Need\" paper [1] introduced the concept of \"Transformers\" and it revolutionalized the complete domain of Deep Learning. So much so that in 2022 we have Transformers has become multi-disiplinary and is used across NLP, CV, RL, Graphs, and more! In NLP, Transformers variants are still state of the art! (even after 5 years) . As the paper's name suggests, the authors showcase how only using multiple stacks of attention layers provides enough learning power to the architecture to improve the SotA in multiple takss. Quoting the paper, \"Transformer is the first transduction model relying entirely on self-attention to compute representations of its input and output without using sequence-aligned RNNs or convolution.\" Let's try to understand the fundamentals of Transformers by going through each components in detail. Hint Transduction is a broad term but in nearly all aspect it means converting data, signal or object from one format to another. Transformers transforms input data from one format to another ( \\(x_i\\) in encoder leads to \\(y_i\\) in decoder) , so it is transductive model. Hence the name \"Transformers\" Transformer architecture. Left part is the encoder and right part is the decoder part [1] At a high level, the architecture can be divided into two modules, Encoder: the component resonsible to learn representation of the input prompt. It process each token of the input parallely in one iteration and generates attention aware representations for each token. The encoder layer consists of 6 identical blocks with output dimension of \\(d_{model} = 512\\) . Decoder: the component reposible for generating the output. The execution strategy depends on the scenario. During training as ground truth is also available, complete processing happens in one iteration using teacher forcing and masked attention. During inference multiple iterations are run where one output is generated in one go. Decoder layer also consists of 6 identical blocks. Components of Transformers Self-Attention Probably the most important component of Transformer is the Attention Layer. The intuition here is that the context matters. For example in a sentence, meaning of a word like \"bank\" could vary based on if it is \"river bank\" or \"financial bank\". Similarly, pronouns like He, She, It, etc could also attend to certain other words in the sentence (name of the person or object) . With this in mind, a true meaning (representation) of a word can only be identified by attending to all the other words in context. Self-Attention is just one way to do that. Scaled dot product attention [1] Compared to previous approaches like RNN or Word2Vec that were used to create representations of the words, Attention models modifies the representation for each input based on the context. Word2Vec had a fixed context and worked at word level, so \"bank\" will have 1 representation 9word embedding). RNN on the other hand only conisidered sequential context that lead to forgetting the context of the past (words far on the left) . Note Transformer works at sub-word level tokens and not words. And context of attention is equal tot he input which could be a phrase, a sentence, a paragraph or a complete article. The process of self-attention is quite simple, and at a high level consists of only two steps. For each token, (1) find the importance score of every token in the context (including the token in question) , and (2) using the score, do a weighted sum of the every token's representations to create the final token representation. And thats it Well at least at 10k feet . Looking at the technicalities, the process drills down to, Every token is not used as-it-is, but first converted to key, value and query format using linear projections. We have key, value and query weights denoted as \\(W_k\\) , \\(W_v\\) and \\(W_q\\) . Each input token's representation is first multipled with these weights to get \\(k_i\\) , \\(v_i\\) and \\(q_i\\) . Next the query of one token is dot product with the keys of all token. On applying softmax to the output, we get a probability score of importance of every token for the the given token. Finally, we do weighted sum of values of all keys with this score and get the vector representation of the current token. It is easy to understand the process while looking at one token at a time, but in reality it is completly vectorized and happens for all the tokens at the same time. The formulation for the self-attention is as follows, \\[ Attention(Q, K, V) = softmax(\\frac{QK^T}{\\sqrt{d_k}})V \\] where Q, K and V are the matrices you get on multiplication of all input tokens with the query, key and value weights. Note Authors introduced the scaling factor to handle potential vanishing gradient problem. In their words, \"We suspect that for large values of \\(d_k\\) , the dot products grow large in magnitude, pushing the softmax function into regions where it has extremely small gradients. To counteract this effect, we scale the dot products by \\(\\frac{1}{\\sqrt{d_k}}\\) \" Multi-head Attention Instead of performing the complete attention computation in a single layer, the authors divided the computation into muliple heads or parts. This is done quite intuitively, let's take an example where we want to have say 8 heads in each layer. In this case the projections weights ( \\(W_k\\) , \\(W_v\\) and \\(W_q\\) ) downsize the token embedding into \\(\\frac{1}{8}\\) the size. This can be done by making the dimension of the weights token_dim * token_dim/8 . After this we repeat the complete process as discussed in self-attention. Multi-Head Attention [1] Now at the end we will have 8 outputs instead of one, i.e. 8 different partial representations for each token (which is not strictly correct and not what we want) . So to aggregate to get 1 output, authors concatenated the outputs and then apply a linear projection (multiply with \\(W^o\\) ) to get the original sized 1 representation per token. The formulation is shown below, \\[ \\text{MultiHead}(Q, K, V) = \\text{Concat}(head_1, head_2, ..., head_h) W^o \\] Masked Attention As discussed before, during training, decoder use masked attention to prevent model from attending to the future tokens. This is done so that the model does not cheat while predicting the output. Note Having bidirectional context on input is okay (as done by encoder with normal attention) , but in decoder we should only attend to what we have already seen and not the future. The approach is also quite easy to understand, it is very similar to the normal attention where we compute the query, key and value vectors. The only difference is that while computing attention scores, we explicitly make the score of future tokens zero. This is done by making the score (before softmax) at the future places equal to large negative number. On applying Softmax, those values become zero. Position-wise Feed Forward Layers Encoder and Decoder contains fully connected feed-forward neural network that is applied to each position (token) separately and identically. It consists of two linear transformations with a ReLU activation in between. The formualtion is given below, \\[ \\text{FFN}(x)=max(0, xW_1 + b_1)W_2 + b_2 \\] Note The parameters \\(W_1\\) , \\(W_2\\) , \\(b_1\\) and \\(b_2\\) are different in different layers. Positional Embeddings For the model to consider the order of sequence for representation and prediction, authors injected a sense of relative or absolute position in the input by using positional embeddings. It is injected to each token separately and has the same embedding size as that of the token i.e \\(d_{model}\\) . For each token we have a position vector. After modification, the token embedding will be a combination of two types of information -- positional (injected by positional embedding) and semantic (learned by the model) . While there are many choices for the position embedding, authors wanted to pick the one that does not compromise the semantic information by a lot. In the paper authors alternatively use sine and cosine functions (at indices of vector) of different frequencies, \\[ \\text{PE}_{pos, 2i} = sin(\\frac{pos}{10000^{2i/d_{model}}}) \\] \\[ \\text{PE}_{pos, 2i+1} = cos(\\frac{pos}{10000^{2i/d_{model}}}) \\] Here pos is the position of the token in sequence and i is the index of the vector for a token. Position Encoding for 100 positions, each of 512 dimension size Let's understand the process using an example sentence - \"My name is Mohit\" with words as tokens. So we have 4 tokens, and with dimension size of 4, each token could be represented as shown below, ## Token Embedding My = [ 0.1 , 0.2 , 1 , 0.45 ] Name = [ 0.15 , 0.32 , 13 , 0.51 ] is = [ - 0.1 , 0.21 , 0.65 , 0.25 ] Mohit = [ 0.1 , - 0.12 , 0.33 , - 0.45 ] Now we will compute the positional embeddings using the above formulae, ## Positional embedding pos_1 = [ 0. 1. 0. 1. ] pos_2 = [ 0.84147098 0.54030231 0.09983342 0.99500417 ] pos_3 = [ 0.90929743 - 0.41614684 0.19866933 0.98006658 ] pos_4 = [ 0.14112001 - 0.9899925 0.29552021 0.95533649 ] Finally the modified embedding for the tokens will be addition of the original token embedding and positional embedding. ## Modified embedding My = [ 0.1 , 0.2 , 1 , 0.45 ] + [ 0. 1. 0. 1. ] Name = [ 0.15 , 0.32 , 13 , 0.51 ] + [ 0.84147098 0.54030231 0.09983342 0.99500417 ] is = [ - 0.1 , 0.21 , 0.65 , 0.25 ] + [ 0.90929743 - 0.41614684 0.19866933 0.98006658 ] Mohit = [ 0.1 , - 0.12 , 0.33 , - 0.45 ] + [ 0.14112001 - 0.9899925 0.29552021 0.95533649 ] Note Some obvious alternatives for position encoding were not considered because of the their disadvantages. For example, using simple numbers like 0, 1, 2...N would have led to unnormalized numbers in vector, which could have degraded the semantic representation of the token. On the other hand if we had used normalized numbers, that would have not made sense for variable length sentences. Similarly using binary numbers could be rejected as it is not continous. [4] References [1] Attention Is All You Need - Paper [2] The Illustrated Transformer - Link [3] What exactly are keys, queries, and values in attention mechanisms? - Cross validated [4] Master Positional Encoding: Part I - Medium Blog by Jonathan Kernes Cheers.","title":"Transformers"},{"location":"natural_language_processing/transformer/#transformers","text":"","title":"Transformers"},{"location":"natural_language_processing/transformer/#introduction","text":"\"Attention Is All You Need\" paper [1] introduced the concept of \"Transformers\" and it revolutionalized the complete domain of Deep Learning. So much so that in 2022 we have Transformers has become multi-disiplinary and is used across NLP, CV, RL, Graphs, and more! In NLP, Transformers variants are still state of the art! (even after 5 years) . As the paper's name suggests, the authors showcase how only using multiple stacks of attention layers provides enough learning power to the architecture to improve the SotA in multiple takss. Quoting the paper, \"Transformer is the first transduction model relying entirely on self-attention to compute representations of its input and output without using sequence-aligned RNNs or convolution.\" Let's try to understand the fundamentals of Transformers by going through each components in detail. Hint Transduction is a broad term but in nearly all aspect it means converting data, signal or object from one format to another. Transformers transforms input data from one format to another ( \\(x_i\\) in encoder leads to \\(y_i\\) in decoder) , so it is transductive model. Hence the name \"Transformers\" Transformer architecture. Left part is the encoder and right part is the decoder part [1] At a high level, the architecture can be divided into two modules, Encoder: the component resonsible to learn representation of the input prompt. It process each token of the input parallely in one iteration and generates attention aware representations for each token. The encoder layer consists of 6 identical blocks with output dimension of \\(d_{model} = 512\\) . Decoder: the component reposible for generating the output. The execution strategy depends on the scenario. During training as ground truth is also available, complete processing happens in one iteration using teacher forcing and masked attention. During inference multiple iterations are run where one output is generated in one go. Decoder layer also consists of 6 identical blocks.","title":"Introduction"},{"location":"natural_language_processing/transformer/#components-of-transformers","text":"","title":"Components of Transformers"},{"location":"natural_language_processing/transformer/#self-attention","text":"Probably the most important component of Transformer is the Attention Layer. The intuition here is that the context matters. For example in a sentence, meaning of a word like \"bank\" could vary based on if it is \"river bank\" or \"financial bank\". Similarly, pronouns like He, She, It, etc could also attend to certain other words in the sentence (name of the person or object) . With this in mind, a true meaning (representation) of a word can only be identified by attending to all the other words in context. Self-Attention is just one way to do that. Scaled dot product attention [1] Compared to previous approaches like RNN or Word2Vec that were used to create representations of the words, Attention models modifies the representation for each input based on the context. Word2Vec had a fixed context and worked at word level, so \"bank\" will have 1 representation 9word embedding). RNN on the other hand only conisidered sequential context that lead to forgetting the context of the past (words far on the left) . Note Transformer works at sub-word level tokens and not words. And context of attention is equal tot he input which could be a phrase, a sentence, a paragraph or a complete article. The process of self-attention is quite simple, and at a high level consists of only two steps. For each token, (1) find the importance score of every token in the context (including the token in question) , and (2) using the score, do a weighted sum of the every token's representations to create the final token representation. And thats it Well at least at 10k feet . Looking at the technicalities, the process drills down to, Every token is not used as-it-is, but first converted to key, value and query format using linear projections. We have key, value and query weights denoted as \\(W_k\\) , \\(W_v\\) and \\(W_q\\) . Each input token's representation is first multipled with these weights to get \\(k_i\\) , \\(v_i\\) and \\(q_i\\) . Next the query of one token is dot product with the keys of all token. On applying softmax to the output, we get a probability score of importance of every token for the the given token. Finally, we do weighted sum of values of all keys with this score and get the vector representation of the current token. It is easy to understand the process while looking at one token at a time, but in reality it is completly vectorized and happens for all the tokens at the same time. The formulation for the self-attention is as follows, \\[ Attention(Q, K, V) = softmax(\\frac{QK^T}{\\sqrt{d_k}})V \\] where Q, K and V are the matrices you get on multiplication of all input tokens with the query, key and value weights. Note Authors introduced the scaling factor to handle potential vanishing gradient problem. In their words, \"We suspect that for large values of \\(d_k\\) , the dot products grow large in magnitude, pushing the softmax function into regions where it has extremely small gradients. To counteract this effect, we scale the dot products by \\(\\frac{1}{\\sqrt{d_k}}\\) \"","title":"Self-Attention"},{"location":"natural_language_processing/transformer/#multi-head-attention","text":"Instead of performing the complete attention computation in a single layer, the authors divided the computation into muliple heads or parts. This is done quite intuitively, let's take an example where we want to have say 8 heads in each layer. In this case the projections weights ( \\(W_k\\) , \\(W_v\\) and \\(W_q\\) ) downsize the token embedding into \\(\\frac{1}{8}\\) the size. This can be done by making the dimension of the weights token_dim * token_dim/8 . After this we repeat the complete process as discussed in self-attention. Multi-Head Attention [1] Now at the end we will have 8 outputs instead of one, i.e. 8 different partial representations for each token (which is not strictly correct and not what we want) . So to aggregate to get 1 output, authors concatenated the outputs and then apply a linear projection (multiply with \\(W^o\\) ) to get the original sized 1 representation per token. The formulation is shown below, \\[ \\text{MultiHead}(Q, K, V) = \\text{Concat}(head_1, head_2, ..., head_h) W^o \\]","title":"Multi-head Attention"},{"location":"natural_language_processing/transformer/#masked-attention","text":"As discussed before, during training, decoder use masked attention to prevent model from attending to the future tokens. This is done so that the model does not cheat while predicting the output. Note Having bidirectional context on input is okay (as done by encoder with normal attention) , but in decoder we should only attend to what we have already seen and not the future. The approach is also quite easy to understand, it is very similar to the normal attention where we compute the query, key and value vectors. The only difference is that while computing attention scores, we explicitly make the score of future tokens zero. This is done by making the score (before softmax) at the future places equal to large negative number. On applying Softmax, those values become zero.","title":"Masked Attention"},{"location":"natural_language_processing/transformer/#position-wise-feed-forward-layers","text":"Encoder and Decoder contains fully connected feed-forward neural network that is applied to each position (token) separately and identically. It consists of two linear transformations with a ReLU activation in between. The formualtion is given below, \\[ \\text{FFN}(x)=max(0, xW_1 + b_1)W_2 + b_2 \\] Note The parameters \\(W_1\\) , \\(W_2\\) , \\(b_1\\) and \\(b_2\\) are different in different layers.","title":"Position-wise Feed Forward Layers"},{"location":"natural_language_processing/transformer/#positional-embeddings","text":"For the model to consider the order of sequence for representation and prediction, authors injected a sense of relative or absolute position in the input by using positional embeddings. It is injected to each token separately and has the same embedding size as that of the token i.e \\(d_{model}\\) . For each token we have a position vector. After modification, the token embedding will be a combination of two types of information -- positional (injected by positional embedding) and semantic (learned by the model) . While there are many choices for the position embedding, authors wanted to pick the one that does not compromise the semantic information by a lot. In the paper authors alternatively use sine and cosine functions (at indices of vector) of different frequencies, \\[ \\text{PE}_{pos, 2i} = sin(\\frac{pos}{10000^{2i/d_{model}}}) \\] \\[ \\text{PE}_{pos, 2i+1} = cos(\\frac{pos}{10000^{2i/d_{model}}}) \\] Here pos is the position of the token in sequence and i is the index of the vector for a token. Position Encoding for 100 positions, each of 512 dimension size Let's understand the process using an example sentence - \"My name is Mohit\" with words as tokens. So we have 4 tokens, and with dimension size of 4, each token could be represented as shown below, ## Token Embedding My = [ 0.1 , 0.2 , 1 , 0.45 ] Name = [ 0.15 , 0.32 , 13 , 0.51 ] is = [ - 0.1 , 0.21 , 0.65 , 0.25 ] Mohit = [ 0.1 , - 0.12 , 0.33 , - 0.45 ] Now we will compute the positional embeddings using the above formulae, ## Positional embedding pos_1 = [ 0. 1. 0. 1. ] pos_2 = [ 0.84147098 0.54030231 0.09983342 0.99500417 ] pos_3 = [ 0.90929743 - 0.41614684 0.19866933 0.98006658 ] pos_4 = [ 0.14112001 - 0.9899925 0.29552021 0.95533649 ] Finally the modified embedding for the tokens will be addition of the original token embedding and positional embedding. ## Modified embedding My = [ 0.1 , 0.2 , 1 , 0.45 ] + [ 0. 1. 0. 1. ] Name = [ 0.15 , 0.32 , 13 , 0.51 ] + [ 0.84147098 0.54030231 0.09983342 0.99500417 ] is = [ - 0.1 , 0.21 , 0.65 , 0.25 ] + [ 0.90929743 - 0.41614684 0.19866933 0.98006658 ] Mohit = [ 0.1 , - 0.12 , 0.33 , - 0.45 ] + [ 0.14112001 - 0.9899925 0.29552021 0.95533649 ] Note Some obvious alternatives for position encoding were not considered because of the their disadvantages. For example, using simple numbers like 0, 1, 2...N would have led to unnormalized numbers in vector, which could have degraded the semantic representation of the token. On the other hand if we had used normalized numbers, that would have not made sense for variable length sentences. Similarly using binary numbers could be rejected as it is not continous. [4]","title":"Positional Embeddings"},{"location":"natural_language_processing/transformer/#references","text":"[1] Attention Is All You Need - Paper [2] The Illustrated Transformer - Link [3] What exactly are keys, queries, and values in attention mechanisms? - Cross validated [4] Master Positional Encoding: Part I - Medium Blog by Jonathan Kernes Cheers.","title":"References"},{"location":"natural_language_processing/word2vec/","text":"Word2Vec (w2v) [1] is a word embedding technique that represents a word as a vector. Each vector can be thought of as a point in \\(R^{D}\\) space, where \\(D\\) is the dimension of each vector. One thing to note is that these vectors are not randomly spread out in the vector space. They follow certain properties such that, words that are similar like \"cat\" and \"tiger\" are relatively closer to each other than a completely unrelated word like \"tank\". In the vector space, this means their cosine similarity score is higher. Along with this, we can even observe famous analogies like \\(king - man + woman = queen\\) which can be replicated by vector addition of these word's representation vector. Vector space representing the position of word\u2019s vector and the relationship between them to showcase the analogy king-man+woman=queen While such representation is not unique to w2v, its major contribution was to provide a simple and faster neural network based word embedder. To do so, w2v transformed the training as a classification problem. The neural network tries to answer which word is most probable to be found in the context of a given word. Given a sequence of words that \"naturally\" appears in some sentence, the input could be any middle word and output could be any of the surrounding words (within some window). The training is done by creating a 1-layer deep neural network where the input word is fed as one-hot encoding and output is softmax applied with intention of getting large value for context word. SkipGram architecture (taken from Lil\u2019Log [2]). Its a 1 layer deep NN with input and output as one-hot encoded. The input-to-hidden weight matrix contains the word embeddings. The training data is prepared by sliding a window (of some window size) across the corpus of large text (which could be articles or novels or even complete Wikipedia), and for each such window the middle word is the input word and the remaining words in the context are output words. For each input word in vector space, we want the context words to be close but the remaining words far. And if two input words will have similar context words, their vector will also be close. Word2Vec also performs negative sampling, wherein random selection of some negative examples are also added in the training dataset. For these examples, the output probabilities should be \\(0\\) . [4] After training we can observe something interesting \u2014 the weights between the Input-Hidden layer of NN now represent the notions we wanted in our word embeddings, such that words with the same context have similar values across vector dimension. And these weights are used as word embeddings. Heatmap visualization of 5D word embeddings from Wevi [3]. Color denotes the value of cells. The result shown above is from training 5D word embeddings from a cool interactive w2v demo tool called \"Wevi\" [3]. As visible, words like (juice, milk, water) and (orange, apple) have similar kinds of vectors (some dimensions are equally lit \u2014 red or blue). Hint One obvious question could be -- why do we only use Input-Hidden layer for embedding and why not Hidden-Last? It seems we can use both based on the type of task we are performing. For more details refer the paper -- Intrinsic analysis for dual word embedding space models References [1] Efficient Estimation of Word Representations in Vector Space [2] Lil\u2019Log \u2014 Learning word embedding [3] Wevi \u2014 word embedding visual inspector [4] Word2Vec Tutorial Part 2 - Negative Sampling Additional Materials Intrinsic analysis for dual word embedding space models","title":"Word2Vec"},{"location":"natural_language_processing/word2vec/#references","text":"[1] Efficient Estimation of Word Representations in Vector Space [2] Lil\u2019Log \u2014 Learning word embedding [3] Wevi \u2014 word embedding visual inspector [4] Word2Vec Tutorial Part 2 - Negative Sampling","title":"References"},{"location":"natural_language_processing/word2vec/#additional-materials","text":"Intrinsic analysis for dual word embedding space models","title":"Additional Materials"},{"location":"network_science/gnn_deepwalk/","text":"DeepWalk [1] is a technique to create semantic embeddings of the nodes of a graph. For this, DeepWalk employs Skip-gram based technique of Word2Vec (w2v). For context, w2v is a word embedding technique, where we learn to embed words into a vector representation. In Skip-gram word embedding training procedure, the complete problem is transformed as a classification problem. The neural network tries to answer which word is most probable to be found in the context of a given word. Given a sequence of words that \"naturally\" appears in some sentence, the input could be any middle word and output could be any of the surrounding words (within some window). The training is done by creating a 1-layer deep neural network where the input word is fed as one-hot encoding and output is softmax applied with intention of getting large value for context word. Hint For a brief introduction to Word2Vec, you can refer the respective page in the Lazy Data Science Guide . But how to create training data that captures the notion of context in graphs? While intuitively it seems easy to create training data for w2v by using plethora of text available online, how can we do something similar for a graph? As it happens, we can follow the same intuition for graphs by applying random walk technique. Here, we start from one node and randomly go to one of it's neighbors. We repeat this process \\(L\\) time which is the length of the random walk. After this, we restart the process again. If we do this for all nodes (and \\(M\\) times for each node) we have in some sense transformed the graph structure into a text like corpus used to train w2v where each word is a node and its context defines its neighbor. Code Author's Implementation The DeepWalk authors provide a python implementation here . Installation details with other pre-requisite are provided in the readme (windows user be vary of some installation and execution issues). The CLI API exposes several algorithmic and optimization parameters like, input requires the path of the input file which contains graph information. A graph can be stored in several formats, some of the famous (and supported by the code) are \u2014 adjacency list (node-all_neighbors list) and edge list (node-node pair which have an edge). number-walks The number of random walks taken for each node. representation-size the dimension of final embedding of each node. Also the size of hidden layer in the skipgram model. walk-length the length of each random walk. window-size the context window size in the skipgram training. workers optimization parameter defining number of independent process to spawn for the training. output the path to output embedding file. Authors have also provided example graphs, one of which is Karate club dataset. It's stored in the format of the adjacency list. Now let\u2019s read the graph data and create node embeddings by, deepwalk --input example_graphs/karate.adjlist --number-walks 10 --representation-size 64 --walk-length 40 --window-size 5 --workers 8 --output trained_model/karate.embeddings This performs start-to-end analysis by taking care of \u2014 loading the graph from the file, generating random walks, and finally training skip-gram model on the walk data. We can read the output embedding file which contains vector embedding for each node in the graph. Karate Club Implementation A much simpler API is provided by python package called KarateClub [2]. To do the same set of actions, all we need to do is following. 1 2 3 4 5 6 7 8 9 10 11 # import libraries import networkx as nx from karateclub import DeepWalk # load the karate club dataset G = nx . karate_club_graph () # load the DeepWalk model and set parameters dw = DeepWalk ( dimensions = 64 ) # fit the model dw . fit ( G ) # extract embeddings embedding = dw . get_embedding () Note The DeepWalk class also extends the same parameters exposed by the author's code and can be tweaked to do the desired experiment. Refer their docs for more details. Analysis Seeing DeepWalk in Action To see DeepWalk in action, we will pick one graph and visualize the network as well as the final embeddings. For better understanding, I created a union of 3 complete graphs with some additional edges to connect each graph. Union of 3 complete graphs. We can imagine 3 clusters with nodes 0 to 9 belonging to cluster 1; 10 to 19 to cluster 2 and 20 to 28 in cluster 3. Now, we will create DeepWalk embeddings of the graph. For this, we can use the KarateClub package and by running DeepWalk on default settings we get embeddings of 128 dimensions. To visualize this I use dimensionality reduction technique PCA, which scaled-down embeddings from R\u00b9\u00b2\u2078 to R\u00b2. I will also plot the 128D heatmap of the embedding on the side. Left \u2014 The PCA reduced (from 128D to 2D) node embeddings of the graph. Right \u2014 The heatmap of the original 128D embeddings. There is a clear segregation of nodes in the left chart which denotes the vector space of the embedding. This showcase how DeepWalk can transform a graph from force layout visualization to vector space visualization while maintaining some of the structural properties. The heatmap plot also hints to a clear segregation of graph into 3 clusters. Another important thing to note is when the graph is not so complex, we can get by with lower dimension embedding as well. This not only reduces the dimensions but also improves the optimization and convergence as there are fewer parameters in skip-gram to train. To prove this we will create embedding of only size 2. This can be done by setting the parameter in DeepWalk object dw = DeepWalk(dimensions=2) . We will again visualize the same plots. he node embeddings (size=2) of the graph. Right: The heatmap of the embeddings. Both the plots again hint towards the same number of clusters in the graph, and all this by only using 1% of the previous dimensions (from 128 to 2 i.e. ~1%). Conclusion As the answer to this analogy NLP - word2vec + GraphNeuralNetworks = ? can arguably be DeepWalk (is it? \ud83d\ude42 ), it leads to two interesting points, DeepWalk's impact in GNN can be analogous to Word2Vec's in NLP. And it's true as DeepWalk was one of the first approaches to use NN for node embeddings. It was also a cool example of how some proven SOTA technique from another domain (here, NLP) can be ported to and applied in a completely different domain (here, graphs). As DeepWalk was published a while ago (in 2014 - less than a decade but a lifetime in AI research), currently, there are lots of other techniques which can be applied to do the job in a better way like Node2Vec or even Graph convolution networks like GraphSAGE, etc. That said, as to start learning Deep learning based NLP, word2vec is the best starting point, I think DeepWalk in the same is sense a good beginning for Deep Learning based graph analysis. Cheers References [1] DeepWalk \u2014 Paper | Code [2] KarateClub \u2014 Paper | Code","title":"DeepWalk"},{"location":"network_science/gnn_deepwalk/#code","text":"","title":"Code"},{"location":"network_science/gnn_deepwalk/#authors-implementation","text":"The DeepWalk authors provide a python implementation here . Installation details with other pre-requisite are provided in the readme (windows user be vary of some installation and execution issues). The CLI API exposes several algorithmic and optimization parameters like, input requires the path of the input file which contains graph information. A graph can be stored in several formats, some of the famous (and supported by the code) are \u2014 adjacency list (node-all_neighbors list) and edge list (node-node pair which have an edge). number-walks The number of random walks taken for each node. representation-size the dimension of final embedding of each node. Also the size of hidden layer in the skipgram model. walk-length the length of each random walk. window-size the context window size in the skipgram training. workers optimization parameter defining number of independent process to spawn for the training. output the path to output embedding file. Authors have also provided example graphs, one of which is Karate club dataset. It's stored in the format of the adjacency list. Now let\u2019s read the graph data and create node embeddings by, deepwalk --input example_graphs/karate.adjlist --number-walks 10 --representation-size 64 --walk-length 40 --window-size 5 --workers 8 --output trained_model/karate.embeddings This performs start-to-end analysis by taking care of \u2014 loading the graph from the file, generating random walks, and finally training skip-gram model on the walk data. We can read the output embedding file which contains vector embedding for each node in the graph.","title":"Author's Implementation"},{"location":"network_science/gnn_deepwalk/#karate-club-implementation","text":"A much simpler API is provided by python package called KarateClub [2]. To do the same set of actions, all we need to do is following. 1 2 3 4 5 6 7 8 9 10 11 # import libraries import networkx as nx from karateclub import DeepWalk # load the karate club dataset G = nx . karate_club_graph () # load the DeepWalk model and set parameters dw = DeepWalk ( dimensions = 64 ) # fit the model dw . fit ( G ) # extract embeddings embedding = dw . get_embedding () Note The DeepWalk class also extends the same parameters exposed by the author's code and can be tweaked to do the desired experiment. Refer their docs for more details.","title":"Karate Club Implementation"},{"location":"network_science/gnn_deepwalk/#analysis","text":"","title":"Analysis"},{"location":"network_science/gnn_deepwalk/#seeing-deepwalk-in-action","text":"To see DeepWalk in action, we will pick one graph and visualize the network as well as the final embeddings. For better understanding, I created a union of 3 complete graphs with some additional edges to connect each graph. Union of 3 complete graphs. We can imagine 3 clusters with nodes 0 to 9 belonging to cluster 1; 10 to 19 to cluster 2 and 20 to 28 in cluster 3. Now, we will create DeepWalk embeddings of the graph. For this, we can use the KarateClub package and by running DeepWalk on default settings we get embeddings of 128 dimensions. To visualize this I use dimensionality reduction technique PCA, which scaled-down embeddings from R\u00b9\u00b2\u2078 to R\u00b2. I will also plot the 128D heatmap of the embedding on the side. Left \u2014 The PCA reduced (from 128D to 2D) node embeddings of the graph. Right \u2014 The heatmap of the original 128D embeddings. There is a clear segregation of nodes in the left chart which denotes the vector space of the embedding. This showcase how DeepWalk can transform a graph from force layout visualization to vector space visualization while maintaining some of the structural properties. The heatmap plot also hints to a clear segregation of graph into 3 clusters. Another important thing to note is when the graph is not so complex, we can get by with lower dimension embedding as well. This not only reduces the dimensions but also improves the optimization and convergence as there are fewer parameters in skip-gram to train. To prove this we will create embedding of only size 2. This can be done by setting the parameter in DeepWalk object dw = DeepWalk(dimensions=2) . We will again visualize the same plots. he node embeddings (size=2) of the graph. Right: The heatmap of the embeddings. Both the plots again hint towards the same number of clusters in the graph, and all this by only using 1% of the previous dimensions (from 128 to 2 i.e. ~1%).","title":"Seeing DeepWalk in Action"},{"location":"network_science/gnn_deepwalk/#conclusion","text":"As the answer to this analogy NLP - word2vec + GraphNeuralNetworks = ? can arguably be DeepWalk (is it? \ud83d\ude42 ), it leads to two interesting points, DeepWalk's impact in GNN can be analogous to Word2Vec's in NLP. And it's true as DeepWalk was one of the first approaches to use NN for node embeddings. It was also a cool example of how some proven SOTA technique from another domain (here, NLP) can be ported to and applied in a completely different domain (here, graphs). As DeepWalk was published a while ago (in 2014 - less than a decade but a lifetime in AI research), currently, there are lots of other techniques which can be applied to do the job in a better way like Node2Vec or even Graph convolution networks like GraphSAGE, etc. That said, as to start learning Deep learning based NLP, word2vec is the best starting point, I think DeepWalk in the same is sense a good beginning for Deep Learning based graph analysis. Cheers","title":"Conclusion"},{"location":"network_science/gnn_deepwalk/#references","text":"[1] DeepWalk \u2014 Paper | Code [2] KarateClub \u2014 Paper | Code","title":"References"},{"location":"network_science/gnn_introduction/","text":"Graph Neural Networks are the current hot topic. [1] And this interest is surely justified as GNNs are all about latent representation of the graph in vector space. Representing an entity as a vector is nothing new. There are many examples like word2vec and Gloves embeddings in NLP which transforms a word into a vector. What makes such representation powerful are, these vectors incorporate a notion of similarity among them i.e. two words who are similar to each other tend to be closer in the vector space (dot product is large), and they have application in diverse downstream problems like classification, clustering, etc. This is what makes GNN interesting, as while there are many solutions to embed a word or image as a vector, GNN laid the foundation to do so for graphs. References [1] EasyAI \u2014 GNN may be the future of AI","title":"Introduction"},{"location":"network_science/gnn_introduction/#references","text":"[1] EasyAI \u2014 GNN may be the future of AI","title":"References"},{"location":"network_science/introduction/","text":"Quoting Wikipedia , \"Network science is an academic field which studies complex networks such as telecommunication networks, computer networks, biological networks, cognitive and semantic networks, and social networks, considering distinct elements or actors represented by nodes (or vertices) and the connections between the elements or actors as links (or edges).\" To better understand network science, we should understand the underlying building blocks i.e. Graphs! Graphs 101 Graph or Networks is used to represent relational data, where the main entities are called nodes. A relationship between nodes is represented by edges. A graph can be made complex by adding multiple types of nodes, edges, direction to edges, or even weights to edges. One example of a graph is shown below. Karate dataset visualization @ Network repository The graph above is the Karate dataset [1] which represents the social information of the members of a university karate club. Each node represents a member of the club, and each edge represents a tie between two members of the club. The left info bar states several graph properties like a number of nodes, edges, density, degree, etc. Network repository contains many such networks from different fields and domains and provides visualization tools and basic stats as shown above. Common Concepts in Graphs Nodes: They are the basic building blocks in the graph that represent certain object, concepts or entities. Nodes (or vertices) can be of one type or of multiple types if the graph is homogenous or heterogenous, respecitively. Edges: They are the ties that connect two nodes together based on some special relationship criteria. For example, for a graph where each nodes are intersection, an edge between two nodes (or intersection) denotes precense of direct roadways between them. Edges can also be of multiple types if the graph is heterogenous. Directed or Undirected Graph: Edges may have a direction like A --> B that denotes a directional relationship. Example is Mother-Son relationship, Kavita -- mother-of --> Mohit is true, and not the other way around. If all the edges of a graph are directional, then it is a directed graph. Similarly, if the edges of a graph have no direction like A -- B , it is undirected graph. Example is a neighbor relationship, me and the guy living next door to me, are both neighbors of each other. Degrees of Node: Degrees denote the number of direct connections of a node to other nodes in the graph. For a directed graph, we have in-degree (edges coming to the node) and out-degree (edges going out from the node) . For example in A --> B <-- C , A and C has out-degree = 1 & in-degree = 0 & B has out-degree = 0 & in-degree = 2 Path and Walk : Path or Walk are the route between two nodes that goes through multiple nodes and edges. For example, A --> B --> C is a path of length 2 as there are two edges in the route. The difference between a path and a walk is that walks can repeat edges and nodes. Refer Stack Exchange Answer . Connected Graph: A graph with a possible path between any two nodes is called a connected graph. On the contrary, the graph will be disconnected and there might be multiple clusters of individual connected sub-graphs in the overall graph. Clique and Complete graph: A clique of a graph is a set of nodes where every pair of nodes has an edge between them. It is the strongest form of cluster in the graph. Similarly if the graph itself is a clique i.e. there is an edge between all the pairs of nodes, it is called a complete graph. This also means that the graph contains all the possible edges. Spanning Tree: A tree is an undirected graph where any two nodes are connected by exaclty one path. The spanning tree of a graph is a subgraph that is a tree that contains every node in the graph. In practice, Kruskal Algorithm can be used to find the minimum spanning tree for a graph, where we have multiple possibilities of creating spanning trees but want one with minimum total edge or node weights. Adjacency matrix: It is a square matrix of size NxN , where N is the number of unique nodes. The matrix contains 1 and 0 value that denotes the presence (1) or absence (0) of an edge between the nodes. unique graph and it's adjacency matrix is shown below, where the column and row represent nodes in alphabetic order. graph LR A --- B --- C --- D --- E --- A A --- A E --- B D --- F \\(M_{Adj} = \\begin{bmatrix} 1 & 1 & 0 & 0 & 1 & 0 \\\\ 1 & 0 & 1 & 0 & 1 & 0 \\\\ 0 & 1 & 0 & 1 & 0 & 0 \\\\ 0 & 0 & 1 & 0 & 1 & 1 \\\\ 1 & 1 & 0 & 1 & 0 & 0 \\\\ 0 & 0 & 0 & 1 & 0 & 0 \\\\ \\end{bmatrix}\\) Hint \\(N^{th}\\) power of the Adjacency Matrix ( \\(M\\) ) is a new square matrix ( \\(M^N\\) ), where \\(M_{ij}^N\\) represents the number of walks of length \\(N\\) between the nodes \\(i\\) and \\(j\\) . Laplacian matrix: given a graph with \\(v_i\\) to \\(v_n\\) nodes, Laplacian matrix \\(L_{nxn}\\) is \\[{\\displaystyle L_{i,j}:={\\begin{cases}\\deg(v_{i})&{\\mbox{if}}\\ i=j\\\\-1&{\\mbox{if}}\\ i\\neq j\\ {\\mbox{and}}\\ v_{i}{\\mbox{ is adjacent to }}v_{j}\\\\0&{\\mbox{otherwise}},\\end{cases}}}\\] Or it can also be computed as \\(L = D - A\\) , where \\(D\\) is the degree matrix (could be in-degree, out-degree or both) and \\(A\\) is the adjacency matrix. Laplacian matrix are important objects in the field of Spectral Graph Theory , and we can infer many properties of the graph like connected components by looking at its laplacian matrix. Refer this Maths Stack Exchange Question . References [1] Zachary karate club \u2014 The KONECT Project","title":"Introduction"},{"location":"network_science/introduction/#graphs-101","text":"Graph or Networks is used to represent relational data, where the main entities are called nodes. A relationship between nodes is represented by edges. A graph can be made complex by adding multiple types of nodes, edges, direction to edges, or even weights to edges. One example of a graph is shown below. Karate dataset visualization @ Network repository The graph above is the Karate dataset [1] which represents the social information of the members of a university karate club. Each node represents a member of the club, and each edge represents a tie between two members of the club. The left info bar states several graph properties like a number of nodes, edges, density, degree, etc. Network repository contains many such networks from different fields and domains and provides visualization tools and basic stats as shown above.","title":"Graphs 101"},{"location":"network_science/introduction/#common-concepts-in-graphs","text":"Nodes: They are the basic building blocks in the graph that represent certain object, concepts or entities. Nodes (or vertices) can be of one type or of multiple types if the graph is homogenous or heterogenous, respecitively. Edges: They are the ties that connect two nodes together based on some special relationship criteria. For example, for a graph where each nodes are intersection, an edge between two nodes (or intersection) denotes precense of direct roadways between them. Edges can also be of multiple types if the graph is heterogenous. Directed or Undirected Graph: Edges may have a direction like A --> B that denotes a directional relationship. Example is Mother-Son relationship, Kavita -- mother-of --> Mohit is true, and not the other way around. If all the edges of a graph are directional, then it is a directed graph. Similarly, if the edges of a graph have no direction like A -- B , it is undirected graph. Example is a neighbor relationship, me and the guy living next door to me, are both neighbors of each other. Degrees of Node: Degrees denote the number of direct connections of a node to other nodes in the graph. For a directed graph, we have in-degree (edges coming to the node) and out-degree (edges going out from the node) . For example in A --> B <-- C , A and C has out-degree = 1 & in-degree = 0 & B has out-degree = 0 & in-degree = 2 Path and Walk : Path or Walk are the route between two nodes that goes through multiple nodes and edges. For example, A --> B --> C is a path of length 2 as there are two edges in the route. The difference between a path and a walk is that walks can repeat edges and nodes. Refer Stack Exchange Answer . Connected Graph: A graph with a possible path between any two nodes is called a connected graph. On the contrary, the graph will be disconnected and there might be multiple clusters of individual connected sub-graphs in the overall graph. Clique and Complete graph: A clique of a graph is a set of nodes where every pair of nodes has an edge between them. It is the strongest form of cluster in the graph. Similarly if the graph itself is a clique i.e. there is an edge between all the pairs of nodes, it is called a complete graph. This also means that the graph contains all the possible edges. Spanning Tree: A tree is an undirected graph where any two nodes are connected by exaclty one path. The spanning tree of a graph is a subgraph that is a tree that contains every node in the graph. In practice, Kruskal Algorithm can be used to find the minimum spanning tree for a graph, where we have multiple possibilities of creating spanning trees but want one with minimum total edge or node weights. Adjacency matrix: It is a square matrix of size NxN , where N is the number of unique nodes. The matrix contains 1 and 0 value that denotes the presence (1) or absence (0) of an edge between the nodes. unique graph and it's adjacency matrix is shown below, where the column and row represent nodes in alphabetic order. graph LR A --- B --- C --- D --- E --- A A --- A E --- B D --- F \\(M_{Adj} = \\begin{bmatrix} 1 & 1 & 0 & 0 & 1 & 0 \\\\ 1 & 0 & 1 & 0 & 1 & 0 \\\\ 0 & 1 & 0 & 1 & 0 & 0 \\\\ 0 & 0 & 1 & 0 & 1 & 1 \\\\ 1 & 1 & 0 & 1 & 0 & 0 \\\\ 0 & 0 & 0 & 1 & 0 & 0 \\\\ \\end{bmatrix}\\) Hint \\(N^{th}\\) power of the Adjacency Matrix ( \\(M\\) ) is a new square matrix ( \\(M^N\\) ), where \\(M_{ij}^N\\) represents the number of walks of length \\(N\\) between the nodes \\(i\\) and \\(j\\) . Laplacian matrix: given a graph with \\(v_i\\) to \\(v_n\\) nodes, Laplacian matrix \\(L_{nxn}\\) is \\[{\\displaystyle L_{i,j}:={\\begin{cases}\\deg(v_{i})&{\\mbox{if}}\\ i=j\\\\-1&{\\mbox{if}}\\ i\\neq j\\ {\\mbox{and}}\\ v_{i}{\\mbox{ is adjacent to }}v_{j}\\\\0&{\\mbox{otherwise}},\\end{cases}}}\\] Or it can also be computed as \\(L = D - A\\) , where \\(D\\) is the degree matrix (could be in-degree, out-degree or both) and \\(A\\) is the adjacency matrix. Laplacian matrix are important objects in the field of Spectral Graph Theory , and we can infer many properties of the graph like connected components by looking at its laplacian matrix. Refer this Maths Stack Exchange Question .","title":"Common Concepts in Graphs"},{"location":"network_science/introduction/#references","text":"[1] Zachary karate club \u2014 The KONECT Project","title":"References"},{"location":"network_science/kg_embedding_algorithms/","text":"KG embedding algorithms Before discussing individual algorithms, we will go through some high-level generalization of the embedding techniques which make each algorithm unique. This will help us differentiate and hence appreciate the individual algorithms. Generalization of embedding methods Embedding is the way of representing an object from its existing environment to another. Knowledge graph embedding includes representation of relations and entities into continuous space. Models for KG embedding can be categorised based on their answer for following questions, ( Ji_2021 ) What is the representation space in which the relations and entities are represented? What is the scoring function for measuring the plausibility of factual triples? Representation space Point-wise Euclidean space The most common representation space. Embedding space is Euclidean real valued vector or matrix space. Easy to understand; Not ideal for graphical (tree-like) structure. Point-wise space ( Ji_2021 ) Complex vector space Entities and relations are represented in a complex space Taking head entity as an example, h has a real part Re(h) and an imaginary part Im(h), i.e., \\(\\textbf{h}=Re(\\textbf{h}) + i Im(\\textbf{h})\\) Can capture anti-symmetric relations better than operations in Euclidean space. Complex vector space ( Ji_2021 ) Gaussian distribution space Entities and relations are represented as probabilistic distribution Applicable if you want to capture uncertainties. Gaussian distribution ( Ji_2021 ) Manifold space Entities and relations are represented in a well defined topological space Good for graphical (tree-like) structure. Manifold space ( Ji_2021 ) Scoring functions Distance based Measure plausibility of facts by calculating the distance between the entities. Additive translation with relation is the most widely used method i.e. \\(\\textbf{h} + \\textbf{r} \\approx \\textbf{t}\\) Translational distancebased scoring of TransE ( Ji_2021 ) Similarity based Measure plausibility of the facts by semantic similarity matching Multiplicative formulation is most widely used method i.e. \\(\\textbf{h}^T \\textbf{M}_r \\approx \\textbf{t}^T\\) , use relation matrix to transform head entity into tail entity. Semantic similarity-based scoring of DistMult ( Ji_2021 ) Algorithm Comparison A holistic comparison of different knowledge graph emebdding techniques w.r.t. category and scoring function is provided below, A comprehensive summary of knowledge representation learning models ( Ji_2021 )","title":"KG Embedding Algorithms"},{"location":"network_science/kg_embedding_algorithms/#kg-embedding-algorithms","text":"Before discussing individual algorithms, we will go through some high-level generalization of the embedding techniques which make each algorithm unique. This will help us differentiate and hence appreciate the individual algorithms.","title":"KG embedding algorithms"},{"location":"network_science/kg_embedding_algorithms/#generalization-of-embedding-methods","text":"Embedding is the way of representing an object from its existing environment to another. Knowledge graph embedding includes representation of relations and entities into continuous space. Models for KG embedding can be categorised based on their answer for following questions, ( Ji_2021 ) What is the representation space in which the relations and entities are represented? What is the scoring function for measuring the plausibility of factual triples?","title":"Generalization of embedding methods"},{"location":"network_science/kg_embedding_algorithms/#representation-space","text":"","title":"Representation space"},{"location":"network_science/kg_embedding_algorithms/#point-wise-euclidean-space","text":"The most common representation space. Embedding space is Euclidean real valued vector or matrix space. Easy to understand; Not ideal for graphical (tree-like) structure. Point-wise space ( Ji_2021 )","title":"Point-wise Euclidean space"},{"location":"network_science/kg_embedding_algorithms/#complex-vector-space","text":"Entities and relations are represented in a complex space Taking head entity as an example, h has a real part Re(h) and an imaginary part Im(h), i.e., \\(\\textbf{h}=Re(\\textbf{h}) + i Im(\\textbf{h})\\) Can capture anti-symmetric relations better than operations in Euclidean space. Complex vector space ( Ji_2021 )","title":"Complex vector space"},{"location":"network_science/kg_embedding_algorithms/#gaussian-distribution-space","text":"Entities and relations are represented as probabilistic distribution Applicable if you want to capture uncertainties. Gaussian distribution ( Ji_2021 )","title":"Gaussian distribution space"},{"location":"network_science/kg_embedding_algorithms/#manifold-space","text":"Entities and relations are represented in a well defined topological space Good for graphical (tree-like) structure. Manifold space ( Ji_2021 )","title":"Manifold space"},{"location":"network_science/kg_embedding_algorithms/#scoring-functions","text":"","title":"Scoring functions"},{"location":"network_science/kg_embedding_algorithms/#distance-based","text":"Measure plausibility of facts by calculating the distance between the entities. Additive translation with relation is the most widely used method i.e. \\(\\textbf{h} + \\textbf{r} \\approx \\textbf{t}\\) Translational distancebased scoring of TransE ( Ji_2021 )","title":"Distance based"},{"location":"network_science/kg_embedding_algorithms/#similarity-based","text":"Measure plausibility of the facts by semantic similarity matching Multiplicative formulation is most widely used method i.e. \\(\\textbf{h}^T \\textbf{M}_r \\approx \\textbf{t}^T\\) , use relation matrix to transform head entity into tail entity. Semantic similarity-based scoring of DistMult ( Ji_2021 )","title":"Similarity based"},{"location":"network_science/kg_embedding_algorithms/#algorithm-comparison","text":"A holistic comparison of different knowledge graph emebdding techniques w.r.t. category and scoring function is provided below, A comprehensive summary of knowledge representation learning models ( Ji_2021 )","title":"Algorithm Comparison"},{"location":"network_science/kg_introduction/","text":"Introduction What is a Knowledge graph? To better under Knowledge graphs, let's start by understanding its basic unit i.e. \"fact\". A fact is the most basic piece of information that can be stored in a KG. Facts can be represented in form of triplets with either of the following ways, HRT : SPO : Let's follow the HRT representation for this article. Facts contain 3 elements which can be further represented as a graph, Head or tail - entities which are real-world objects or abstract concepts - represented as nodes Relation the connection between entities represented as edges A simple KG example is provided below. One example of fact could be . You can see the KG is nothing but a collection of multiple such facts. A sample knowledge graph. ( rdf_primer ) Note, there are no limitations on the data type of the fact stored in KG. As shown in the above example, we have persons (Bob, Alice, ..), paintings (Mona Lisa), dates, etc, represented as nodes in the KG. Why knowledge graphs? This is the very first and a valid question anyone will ask when introduced to KG. We will try to go through some points wherein we compare KG with normal graphs and even other ways of storing information. The aim is to highlight the major advantages of using KG. Compared to Normal Graph Heterogenous data: supports different types of entities (person, date, painting, etc) and relations (likes, born on, etc). Model real-world information: closer to our brain's mental model of the world (represents information as a normal human does) Perform logical reasoning: traverse the graphs in a path to make logical connections (A's father is B and B's father is C, hence C is the grandfather of A) Compared to other storage types Structured representation: far cry from unstructured representations like text data Removes redundancy: compared to tabular data, there is no need to add mostly empty columns or rows to add new data Query complex information: better than SQL for data where relationship matters more than individual data points (for example, in case you have to do lots of JOIN statements in a SQL query, which is inherently slow) How to use Knowledge Graph? Knowledge graphs can be used for a large number of tasks\u200a-\u200a be it for logical reasoning, explainable recommendations, complex analysis or just being a better way to store information. There are two very interesting examples which we will discuss briefly. Google Knowledge Panel Google search about a famous person, location or concepts return a knowledge panel on the right (as shown in the image below) The panel contains a wide variety of information (description, education, born, died, quotes, etc) and interestingly in different formats--(text, image, dates, numbers, etc). All this information can be stored in a KG and one such example is shown below. This showcase how easy it is to store information and also note how intuitive it is to just read and understand the fact from a KG. Example of knowledge graph-based knowledge panel used by Google. [Right] the actual panel shown by google when you search for Einstein. [left] recreation of how we might store similar information in a KG. Source: by Author + Google. Movie recommendation Classical algorithms considered user-item interactions to generate recommendations. Over time, newer algorithms are considering additional information about the user as well as items, to improve the recommendations. Below, we can see one KG which not only contains user-item (here movies) connections but also user-user interactions and item attributes. The idea is that provided all this additional information, we can make much more accurate and informed suggestions. Without going into the exact algorithm (which we will later in the article), let's rationalize what recommendations could be generated. \"Avatar\" could be recommended to, - Bob: as it belongs to the Sci-fi genre same as Interstellar and Inception (which is already watched by Bob) - Alice: as it is directed by James Cameron (Titanic) \"Blood Diamond\" recommended to, - Bob: as DiCaprio acted in Inception as well This simple thought exercise should showcase how a lot of real-world interactions can be easily represented in form of facts using KG. And then we can leverage KG-based algorithms for a downstream use case like generating recommendations. A sample knowledge graph for movie recommendation task. ( guo2020survey ) Open-Source Knowledge graphs While there are several small-sized and domain-specific KGs, on the other hand, we also have many huge-sized and domain-agnostic KG that contains facts of all types and forms. Some of the famous open-source knowledge graphs are, DBpedia : is a crowd-sourced community effort to extract structured content from the information created in various Wikimedia projects. Freebase : a massive, collaboratively edited database of cross-linked data. Touted as \u201can open shared database of the world's knowledge\u201d. It was bought by Google and used to power its own KG. In 2015, it was finally discontinued. OpenCyc : is a gateway to the full power of Cyc, one of the world's most complete general knowledge base and commonsense reasoning engine. Wikidata : is a free, collaborative, multilingual, secondary database, collecting structured data to provide support for Wikipedia YAGO : huge semantic knowledge base, derived from Wikipedia, WordNet and GeoNames. Stats for some of the famous open source knowledge graphs ( f\u00e4rber2018knowledge ) Create custom Knowledge graph In spite of having several open-source KGs, we may have a requirement to create domain-specific KG for our use case. There, our base data (from which we want to create the KG), could be of multiple types\u200a-\u200atabular, graphical, or text blob. We will cover some steps on how to create KG from unstructured data like text, as it's relatively easier to convert structured data into KG using minimal domain knowledge and scripting. The complete process can be divided into two steps, Facts creation: this is the first step where we parse the text (sentence by sentence) and extract facts in triplet format like . As we are processing text, we can leverage pre-processing steps like tokenization, stemming, or lemmatization, etc to clean the text. Next, we want to extract the entities and relations (facts) from the text. For entities, we can use Named entity recognition (NER) algorithms. For relation, we can use sentence dependency parsing techniques to find the relationship between any pair of entities. Example article with code . Facts selection: Once we have extracted several facts, the next obvious steps could be to remove duplicates and identify relevant facts that could be added to a KG. To identify duplicates, we can use entity and relation disambiguation techniques. The idea is to consolidate the same facts or elements of a fact, in case of repetitions. For example, \"Albert Einstein\" can also be written as \"Albert E.\" or \"A. Einstein\" in the text, but in reality, they all refer to the same entity. Finally, we can have a comprehensive rule-based system that decides which triplet should be added to the KG or which one could be skipped based on factors like redundant information ( A \u2192 sibling of \u2192 B is present, hence B \u2192 sibling of \u2192 A is redundant) or irrelevant information. Steps involved in creating a custom knowledge graph. Source: Author + ( Ji_2021 ) Ontology of Knowledge graph An ontology is a model of the world (practically only a subset), listing the types of entities, the relationships that connect them, and constraints on the ways that entities and relationships can be combined. In a way, an ontology defines the rules on how the entities are connected within the world. Resource Description Framework (RDF) and Web Ontology Language (OWL) are some of the vocabulary frameworks used to model ontology. They provide a common framework for expressing this information so it can be exchanged between applications without loss of meaning. RDF schema triplets (informal). Source: Author + ( rdf_primer ) RDF provides languages for creating ontology, which we will use to create a sample KG. Below you can see the KG creating script [on left] in Turtle language for the KG [on right]. Notice, at the top of the script, we are creating references to a lot of predefined ontologies, as there is no need to reinvent the wheel. Next, to create the facts (or triplets) of our KG we can follow the lines below the PREFIX commands. Notice, each entity and relation has a unique identifier (their unique key or UID). Throughout the code, the same entity or relation should be referenced by the same UID. Next, using the predefined schemas, we can add facts for an entity (in graphical term, add a connecting edge and tail node to the head node). These facts could include another entity (refer by their UID), some text, date (in DateTime format), links, etc. Script in Turtle language to create the sample knowledge graph. Source: Author + ( rdf_primer ) Finally, once we have prepared the script (with ttl extension\u200a-\u200afor scripts in Turtle language), that script contains the complete schema and definition of our KG. In itself, this may not be interesting, hence the file can be imported into any KG database for beautiful visualization and efficient querying. Hosting Knowledge graphs There are two types of databases that can be used to store graphical information. The first is \"property graphs\" like Neo4j and OrientDB that does not support RDF file (out of the box) and have their own custom query language. On the other hand, we have \"RDF triplet stores\", that support RDF files and support query language like SPARQL that is universally used to query KG. Some of the most famous ones are (with open source version), - GraphDB : a solution by Ontotext, that provides frontend (visualization) and backend (server) services to see and query hosted knowledge graphs. - Virtuoso : a solution by OpenLinkSoftware, that provides backend services to query hosted KG. It also supports querying KG using a combination of SQL and SPARQL. On top of it, a lot of open-source KG like DBpedia are hosted on Virtuoso. Query a Knowledge graph SPARQL Once facts are created as RDF and hosted on an RDF triplet store like Virtuoso, we can query them to extract relevant information. SPARQL is an RDF query language that is able to retrieve and manipulate data stored in RDF format. An interesting read for more detail is Walkthrough Dbpedia And Triplestore . Most of the RDF triple stores provide a visual SPARQL query page to fetch the relevant info. For our case, let us use one such visual query helper exposed by Wikidata (shown below). A sample query is shown, where we want to extract all entities that are instances of a house cat (we just want some cats \ud83d\udc31). As discussed before, each entity has a UID, hence relation is represented as P31 , and the entity is represented as Q146 . The query is quite simple to understand, as from lines 2 to 5, we are just trying to convey that we want any entity that is an instance of a house cat. As Wikidata contains data in multiple languages, line 6 is needed to filter results specific to the English language. The results (entities with their UID and some basic details) are shown below the query. Query Knowledge graph using SPARQL language ( wikidata_sparql_query_helper ) API Open-source KG also exposes several ready-made APIs for frequently used queries. One such API is shown below (for Wikidata), which returns relevant information for a given entity. Below we can see the result of querying wbgetentities API for entity Q9141 which is the UID for the Taj Mahal. Query Knowledge graph using available APIs ( wikidata_api_services )","title":"Introduction"},{"location":"network_science/kg_introduction/#introduction","text":"","title":"Introduction"},{"location":"network_science/kg_introduction/#what-is-a-knowledge-graph","text":"To better under Knowledge graphs, let's start by understanding its basic unit i.e. \"fact\". A fact is the most basic piece of information that can be stored in a KG. Facts can be represented in form of triplets with either of the following ways, HRT : SPO : Let's follow the HRT representation for this article. Facts contain 3 elements which can be further represented as a graph, Head or tail - entities which are real-world objects or abstract concepts - represented as nodes Relation the connection between entities represented as edges A simple KG example is provided below. One example of fact could be . You can see the KG is nothing but a collection of multiple such facts. A sample knowledge graph. ( rdf_primer ) Note, there are no limitations on the data type of the fact stored in KG. As shown in the above example, we have persons (Bob, Alice, ..), paintings (Mona Lisa), dates, etc, represented as nodes in the KG.","title":"What is a Knowledge graph?"},{"location":"network_science/kg_introduction/#why-knowledge-graphs","text":"This is the very first and a valid question anyone will ask when introduced to KG. We will try to go through some points wherein we compare KG with normal graphs and even other ways of storing information. The aim is to highlight the major advantages of using KG.","title":"Why knowledge graphs?"},{"location":"network_science/kg_introduction/#compared-to-normal-graph","text":"Heterogenous data: supports different types of entities (person, date, painting, etc) and relations (likes, born on, etc). Model real-world information: closer to our brain's mental model of the world (represents information as a normal human does) Perform logical reasoning: traverse the graphs in a path to make logical connections (A's father is B and B's father is C, hence C is the grandfather of A)","title":"Compared to Normal Graph"},{"location":"network_science/kg_introduction/#compared-to-other-storage-types","text":"Structured representation: far cry from unstructured representations like text data Removes redundancy: compared to tabular data, there is no need to add mostly empty columns or rows to add new data Query complex information: better than SQL for data where relationship matters more than individual data points (for example, in case you have to do lots of JOIN statements in a SQL query, which is inherently slow)","title":"Compared to other storage types"},{"location":"network_science/kg_introduction/#how-to-use-knowledge-graph","text":"Knowledge graphs can be used for a large number of tasks\u200a-\u200a be it for logical reasoning, explainable recommendations, complex analysis or just being a better way to store information. There are two very interesting examples which we will discuss briefly.","title":"How to use Knowledge Graph?"},{"location":"network_science/kg_introduction/#google-knowledge-panel","text":"Google search about a famous person, location or concepts return a knowledge panel on the right (as shown in the image below) The panel contains a wide variety of information (description, education, born, died, quotes, etc) and interestingly in different formats--(text, image, dates, numbers, etc). All this information can be stored in a KG and one such example is shown below. This showcase how easy it is to store information and also note how intuitive it is to just read and understand the fact from a KG. Example of knowledge graph-based knowledge panel used by Google. [Right] the actual panel shown by google when you search for Einstein. [left] recreation of how we might store similar information in a KG. Source: by Author + Google.","title":"Google Knowledge Panel"},{"location":"network_science/kg_introduction/#movie-recommendation","text":"Classical algorithms considered user-item interactions to generate recommendations. Over time, newer algorithms are considering additional information about the user as well as items, to improve the recommendations. Below, we can see one KG which not only contains user-item (here movies) connections but also user-user interactions and item attributes. The idea is that provided all this additional information, we can make much more accurate and informed suggestions. Without going into the exact algorithm (which we will later in the article), let's rationalize what recommendations could be generated. \"Avatar\" could be recommended to, - Bob: as it belongs to the Sci-fi genre same as Interstellar and Inception (which is already watched by Bob) - Alice: as it is directed by James Cameron (Titanic) \"Blood Diamond\" recommended to, - Bob: as DiCaprio acted in Inception as well This simple thought exercise should showcase how a lot of real-world interactions can be easily represented in form of facts using KG. And then we can leverage KG-based algorithms for a downstream use case like generating recommendations. A sample knowledge graph for movie recommendation task. ( guo2020survey )","title":"Movie recommendation"},{"location":"network_science/kg_introduction/#open-source-knowledge-graphs","text":"While there are several small-sized and domain-specific KGs, on the other hand, we also have many huge-sized and domain-agnostic KG that contains facts of all types and forms. Some of the famous open-source knowledge graphs are, DBpedia : is a crowd-sourced community effort to extract structured content from the information created in various Wikimedia projects. Freebase : a massive, collaboratively edited database of cross-linked data. Touted as \u201can open shared database of the world's knowledge\u201d. It was bought by Google and used to power its own KG. In 2015, it was finally discontinued. OpenCyc : is a gateway to the full power of Cyc, one of the world's most complete general knowledge base and commonsense reasoning engine. Wikidata : is a free, collaborative, multilingual, secondary database, collecting structured data to provide support for Wikipedia YAGO : huge semantic knowledge base, derived from Wikipedia, WordNet and GeoNames. Stats for some of the famous open source knowledge graphs ( f\u00e4rber2018knowledge )","title":"Open-Source Knowledge graphs"},{"location":"network_science/kg_introduction/#create-custom-knowledge-graph","text":"In spite of having several open-source KGs, we may have a requirement to create domain-specific KG for our use case. There, our base data (from which we want to create the KG), could be of multiple types\u200a-\u200atabular, graphical, or text blob. We will cover some steps on how to create KG from unstructured data like text, as it's relatively easier to convert structured data into KG using minimal domain knowledge and scripting. The complete process can be divided into two steps, Facts creation: this is the first step where we parse the text (sentence by sentence) and extract facts in triplet format like . As we are processing text, we can leverage pre-processing steps like tokenization, stemming, or lemmatization, etc to clean the text. Next, we want to extract the entities and relations (facts) from the text. For entities, we can use Named entity recognition (NER) algorithms. For relation, we can use sentence dependency parsing techniques to find the relationship between any pair of entities. Example article with code . Facts selection: Once we have extracted several facts, the next obvious steps could be to remove duplicates and identify relevant facts that could be added to a KG. To identify duplicates, we can use entity and relation disambiguation techniques. The idea is to consolidate the same facts or elements of a fact, in case of repetitions. For example, \"Albert Einstein\" can also be written as \"Albert E.\" or \"A. Einstein\" in the text, but in reality, they all refer to the same entity. Finally, we can have a comprehensive rule-based system that decides which triplet should be added to the KG or which one could be skipped based on factors like redundant information ( A \u2192 sibling of \u2192 B is present, hence B \u2192 sibling of \u2192 A is redundant) or irrelevant information. Steps involved in creating a custom knowledge graph. Source: Author + ( Ji_2021 )","title":"Create custom Knowledge graph"},{"location":"network_science/kg_introduction/#ontology-of-knowledge-graph","text":"An ontology is a model of the world (practically only a subset), listing the types of entities, the relationships that connect them, and constraints on the ways that entities and relationships can be combined. In a way, an ontology defines the rules on how the entities are connected within the world. Resource Description Framework (RDF) and Web Ontology Language (OWL) are some of the vocabulary frameworks used to model ontology. They provide a common framework for expressing this information so it can be exchanged between applications without loss of meaning. RDF schema triplets (informal). Source: Author + ( rdf_primer ) RDF provides languages for creating ontology, which we will use to create a sample KG. Below you can see the KG creating script [on left] in Turtle language for the KG [on right]. Notice, at the top of the script, we are creating references to a lot of predefined ontologies, as there is no need to reinvent the wheel. Next, to create the facts (or triplets) of our KG we can follow the lines below the PREFIX commands. Notice, each entity and relation has a unique identifier (their unique key or UID). Throughout the code, the same entity or relation should be referenced by the same UID. Next, using the predefined schemas, we can add facts for an entity (in graphical term, add a connecting edge and tail node to the head node). These facts could include another entity (refer by their UID), some text, date (in DateTime format), links, etc. Script in Turtle language to create the sample knowledge graph. Source: Author + ( rdf_primer ) Finally, once we have prepared the script (with ttl extension\u200a-\u200afor scripts in Turtle language), that script contains the complete schema and definition of our KG. In itself, this may not be interesting, hence the file can be imported into any KG database for beautiful visualization and efficient querying.","title":"Ontology of Knowledge graph"},{"location":"network_science/kg_introduction/#hosting-knowledge-graphs","text":"There are two types of databases that can be used to store graphical information. The first is \"property graphs\" like Neo4j and OrientDB that does not support RDF file (out of the box) and have their own custom query language. On the other hand, we have \"RDF triplet stores\", that support RDF files and support query language like SPARQL that is universally used to query KG. Some of the most famous ones are (with open source version), - GraphDB : a solution by Ontotext, that provides frontend (visualization) and backend (server) services to see and query hosted knowledge graphs. - Virtuoso : a solution by OpenLinkSoftware, that provides backend services to query hosted KG. It also supports querying KG using a combination of SQL and SPARQL. On top of it, a lot of open-source KG like DBpedia are hosted on Virtuoso.","title":"Hosting Knowledge graphs"},{"location":"network_science/kg_introduction/#query-a-knowledge-graph","text":"","title":"Query a Knowledge graph"},{"location":"network_science/kg_introduction/#sparql","text":"Once facts are created as RDF and hosted on an RDF triplet store like Virtuoso, we can query them to extract relevant information. SPARQL is an RDF query language that is able to retrieve and manipulate data stored in RDF format. An interesting read for more detail is Walkthrough Dbpedia And Triplestore . Most of the RDF triple stores provide a visual SPARQL query page to fetch the relevant info. For our case, let us use one such visual query helper exposed by Wikidata (shown below). A sample query is shown, where we want to extract all entities that are instances of a house cat (we just want some cats \ud83d\udc31). As discussed before, each entity has a UID, hence relation is represented as P31 , and the entity is represented as Q146 . The query is quite simple to understand, as from lines 2 to 5, we are just trying to convey that we want any entity that is an instance of a house cat. As Wikidata contains data in multiple languages, line 6 is needed to filter results specific to the English language. The results (entities with their UID and some basic details) are shown below the query. Query Knowledge graph using SPARQL language ( wikidata_sparql_query_helper )","title":"SPARQL"},{"location":"network_science/kg_introduction/#api","text":"Open-source KG also exposes several ready-made APIs for frequently used queries. One such API is shown below (for Wikidata), which returns relevant information for a given entity. Below we can see the result of querying wbgetentities API for entity Q9141 which is the UID for the Taj Mahal. Query Knowledge graph using available APIs ( wikidata_api_services )","title":"API"},{"location":"python/good_practices/","text":"Python Good Practices Introduction Writing code that works now is easy. Writing code that will work tomorrow is hard. Writing code that will work tomorrow and is intuitive enough for anyone to understand and follow\u200a\u2014\u200awell now we have hit the super hard stuff \ud83d\ude00. Observing several ML engineers and Data scientists working with me, I have noticed nearly all of them have their own unique style of coding. Well, don't get me wrong, subjectively is a good thing and I think it is what leads to innovations. That said while working in a team or even in open source collaboration, it helps to agree to a certain set of rules. And that's the idea behind this article, to provide python practitioners with a set of curated guidelines, from which they can pick and choose. With that, let's cover some of the good practices, which will not only help us to create a working but also a beautiful piece of code \ud83d\ude00 To cover this topic, we will go through three parts, Project structuring: ideas on how to organize your code Code formatting: ideas on how to make your code easy to follow Additional tips: a few things which will help you in the longer run Project Structuring In this part, we will basically talk about some good practices on how the complete python project can be structured. For this, we will look at two different possibilities, which anyone can choose based on how simple or complex their project is going to be. Type 1: The Classic This is the most basic format and yet gives the hint of organized structuring. This can be followed when our project consists of only a few scripts. The directory of a sample project could look something like this: 1 2 3 4 5 6 7 8 my_project # Root directory of the project \u251c\u2500\u2500 code # Source codes \u251c\u2500\u2500 input # Input files \u251c\u2500\u2500 output # Output files \u251c\u2500\u2500 config # Configuration files \u251c\u2500\u2500 notebooks # Project related Jupyter notebooks (for experimental code) \u251c\u2500\u2500 requirements . txt # List of external package which are project dependency \u2514\u2500\u2500 README . md # Project README As obvious from the names, folder code contains the individual modules ( .py files), input and output contains the input and output files respectively, and notebook contains the .ipynb notebooks files we use for experimentation. Finally, config folder could contain parameters within yaml or json or ini files and can be accessed by the code module files using configparser . requirements.txt contains a list of all external python packages needed by the project. One advantage of maintaining this file is that all of these packages can be easily installed using pip install -r requirements.txt command. (No need of manually installing each and every external packages!) . One example requirements.txt file is shown below (with package_name==package_version format) , 1 2 3 4 5 6 BeautifulSoup == 3.2.0 Django == 1.3 Fabric == 1.2.0 Jinja2 == 2.5.5 PyYAML == 3.09 Pygments == 1.4 Finally, README.MD contains the what, why and how of the project, with some dummy codes on how to run the project and sample use cases. Type 2: Kedro Kedro is not a project structuring strategy, it's a python tool released by QuantumBlack Labs , which does project structuring for you \ud83d\ude0e. On top of it, they provide a plethora of features to make our project organization and even code execution process super-easy, so that we can truly focus on what matters the most -- the experimentations and implementations! Their project structure is shown below. And btw, we can create a blank project by running kedro new command (don't forget to install kedro first by pip install kedro ) get-started # Parent directory of the template \u251c\u2500\u2500 conf # Project configuration files \u251c\u2500\u2500 data # Local project data (not committed to version control) \u251c\u2500\u2500 docs # Project documentation \u251c\u2500\u2500 logs # Project output logs (not committed to version control) \u251c\u2500\u2500 notebooks # Project related Jupyter notebooks (can be used for experimental code before moving the code to src) \u251c\u2500\u2500 README.md # Project README \u251c\u2500\u2500 setup.cfg # Configuration options for `pytest` when doing `kedro test` and for the `isort` utility when doing `kedro lint` \u2514\u2500\u2500 src # Project source code While most of the directories are similar to other types, a few points should be noted. Kedro's way of grouping different modules is by creating different \"pipelines\" . These pipelines are present within src folder, which in turn contains the module files. Furthermore, they have clear segregation of individual functions which are executed - these are stored within nodes.py file, and these functions are later connected with the input and output within pipeline.py file (all within the individual pipeline folder) . Kedro also segregates the code and the parameters, by storing the parameters within conf folder. Apart from just helping with organizing the project, they also provide options for sequential or parallel executions. We can execute individual functions (within nodes.py ) , or individual pipelines (which are a combination of functions) , or the complete project at one go. We can also create doc of the complete project or compile and package the project as a python .whl file, with just a single command run. For more details, and believe me we have just touched the surface, refer to their official documentation . Code formatting With a top-down approach, let's first have a look at a neat piece of code. We will discuss individual aspects of the code in more detail later. For now, just assume if someone asks you to do some scripting, what an ideal piece of code file should look like. Following code is take from csv_column_operations.py module file. It was generated for the prompt: \"write a function which takes csv file as input and returns the sum of a column\" . 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 \"\"\"Return sum of a column from CSV file A module with \"perform_column_sum\" main function that computes and return sum of an user defined numerical column from an csv file passed as pandas dataframe Author: Mohit Mayank Created: 4th August, 2021 \"\"\" # imports import sys # to exit execution import pandas as pd # to handle csv files # modules def perform_column_sum ( csv_file , column_to_perform_operation_on , operation_to_perform = 'sum' ): \"\"\"Performs numerical operation over a column of pandas dataframe Parameters ------------- csv_file: pandas.DataFrame the csv file which contains the column on which operation is to be performed column_to_perform_operation_on: string the column name (must be present in the csv file) operation_to_perform: string which operation to be performed on the column. Supported: ['sum'] Returns -------- column_operation_result: numeric the result after applying numeric operation on csv column \"\"\" # Step 1: data check and break # check 1: column should be present in the csv_file check_flag_col_presence = column_to_perform_operation_on in csv_file . columns # break the code if incorrect data is provided if not check_flag_col_presence : print ( f \"Column { column_to_perform_operation_on } is absent from the csv_file! Breaking code!\" ) sys . exit () # check 2: all values in the column should be of type numeric check_flag_data_type = pd . to_numeric ( csv_file [ column_to_perform_operation_on ], errors = 'coerce' ) . notnull () . all () # break the code if incorrect data is provided if not check_flag_data_type : print ( f \"One or more values in column { column_to_perform_operation_on } is not numeric! Breaking code!\" ) sys . exit () # Step 2: extract the column column_data = csv_file [ column_to_perform_operation_on ] # Step 3: Perform the operation column_operation_result = None if operation_to_perform == 'sum' : column_operation_result = sum ( column_data ) # Step 4: return the result return column_operation_result # run when file is directly executed if __name__ == '__main__' : # create a dummy dataframe df = pd . DataFrame ({ 'col1' : [ 1 , 2 , 3 ], 'col2' : [ 'a' , 'a' , 'a' ]}) # call the function answer = perform_column_sum ( df , 'col1' , 'sum' ) # match if the answer is correct assert ( answer == 6 ) Note Some might argue why do such an overkill for a simple piece of code. Note, it's a dummy example. In real life, you will develop more complex pieces of codes and hence it become quite important that we understand the gist. Now let's take a deeper dive into the individual aspect of the above code. Module structure A module is a python file with .py extension that contains the executable code or functions or classes, etc. Usually, we start the module with module definition, which is an area where we provide some basic details of the module. We can do so using the following template (and it can be easily compared to a real code shown above) 1 2 3 4 5 6 7 8 \"\"\" Author: Created: \"\"\" Next, we should clearly segregate the parts of the module such as imports, code area, etc using comment lines. Finally, at the bottom, we could include some examples on how to run the code. Including these scripts within if __name__ == '__main__': makes sure that they only run when the file is directly executed (like python csv_column_operations.py ) . So these pieces of code doesn't run when you say import the module in another script. Functions structure Functions are the basic block of code that performs a specific task. A module consists of several functions. To inform the user what a particular block of code does, we start the function with a function definition. A sample template is provided below, 1 2 3 4 5 6 7 8 9 10 11 12 \"\"\"Description Paramters --------- : Returns --------- : \"\"\" After this, we can start adding the relevant code lines. Make sure to separate different logical blocks of code within the functions using comments. One important thing to handle at the start of the coding section is to check the parameters and input data for some data type or data content related basic issues. A majority of code break happens due to silly mistakes like when someone provides wrong input, in which case we should print or log warning message and gracefully exit. The above same code contains two such preliminary but important checks inside the step 1 section. Naming convention There are several formatting conventions that we can follow, like Camel Case , Snake case , etc. It's quite subjective and depends on the developer. Below are some examples of naming different entities of a python code (taken from PIP8 conventions - with some modifications) \ud83d\ude07, Module name: Modules should have short, all-lowercase names (ex: csv_column_operations.py ) Function or method name: Function names should be lowercase, with words separated by underscores as necessary to improve readability. Also, don't forget to add your verbs! (ex: perform_column_sum() ) Variable name: Similar to function name but without the verbs! (ex: list_of_news ) Class name: Class names should normally use the CapWords convention. (ex: FindMax ) Constant name: Constants are usually defined on a module level and written in all capital letters with underscores separating words. (ex: MAX_OVERFLOW and TOTAL ). Add comments PEP-8 defines three types of comments, Block comments: which is written for a single or a collection of code lines. This can be done either when you want to explain a set of lines or just want to segregate code. In the above example, you can see # Step {1, 2, 3} used as segregation comment and # run when file is directly executed used to explain a set of code lines. Inline comments: which are added on the same line as the code. For example, see how # to handle csv files is used to justify the pandas package import. PEP-8 suggests using inline comments sparingly. Documentation Strings: these are used for documentation for module, functions or classes. PEP-257 suggests using multiline comment for docstring (using \"\"\") . An example of module and function docstrings (short for documentation strings) is provided in the sample code above. We should be as descriptive in our comments as possible. Try to separate functional sections of your code, provide explanations for complex code lines, provide details about the input/output of functions, etc. How do you know you have enough comments? - If you think someone with half your expertise can understand the code without calling you middle of the night! \ud83d\ude24 Indentations - Tabs vs Spaces Frankly, I am only going to touch this topic with a long stick \ud83e\uddf9. There are already several articles , reddit threads and even tv series (Silicon valley \ud83d\udcfa) where this topic has been discussed a lot! Want my 2 cents? Pick any modern IDE (like VSCode, Sublime, etc) , set indentations to tabs, and set 1 tab = 4 spaces. Done \ud83d\ude0f Additional tips Till now we have discussed how to either structure the project or format the code. Next, we will cover a generic set of good practices which will save you some pain down the line \ud83d\ude2c Logging Instead of printing statements in the console which is temporary (do a cls and poof it's gone\ud83d\udca8), a better idea is to save these statements in a separate file, which you can always go back and refer to. This is logging \ud83d\udcdc Python provides an inbuilt function for logging. By referring to the official how to , logging to a file is super easy, 1 2 3 4 5 6 7 8 9 # import import logging # config the logging behavior logging . basicConfig ( filename = 'example.log' , level = logging . DEBUG ) # some sample log messages logging . debug ( 'This message should go to the log file' , exc_info = True ) logging . info ( 'So should this' , exc_info = True ) logging . warning ( 'And this, too' , exc_info = True ) # we add \"exc_info=True\" to capture the stack trace Note, there is a hierarchical levelling of logs to segregate different severity of logs. In the example shown above, the level parameter denotes the minimal level that is tracked and hence saved to the file. As per the official how to , these are the different logging levels with some details about when to use which (in increasing order of severity) , Level When it\u2019s used DEBUG Detailed information, typically of interest only when diagnosing problems. INFO Confirmation that things are working as expected. WARNING An indication that something unexpected happened, or indicative of some problem in the near future (e.g. \u2018disk space low\u2019). The software is still working as expected. ERROR Due to a more serious problem, the software has not been able to perform some function. CRITICAL A serious error, indicating that the program itself may be unable to continue running. While the above code is good for normal testing, for production you might want to have more control -- like formatting the output slightly differently (formatter) or have multiple places to publish logs (handlers) . One such use case is convered below, where we want to log to console as well as a file in a detailed json format. # import import sys import logging import json_log_formatter # create formatter (using json_log_formatter) formatter = json_log_formatter . VerboseJSONFormatter () # create two handlers (console and file) logger_sys_handler = logging . StreamHandler ( sys . stdout ) logger_file_handler = logging . FileHandler ( filename = 'log.json' ) # perform the same formatting for both handler logger_sys_handler . setFormatter ( formatter ) logger_file_handler . setFormatter ( formatter ) # get the logger and add handlers logger = logging . getLogger ( 'my_logger' ) logger . addHandler ( logger_sys_handler ) logger . addHandler ( logger_file_handler ) # set level logger . setLevel ( logging . INFO ) Documentation Documentation of the code is an absolute must, if you are planning to maintain the code or hand it over to someone else in the foreseeable future. Just ask any developer about their excitement on finding a ready-made and well curated documentation for a package they are planning to use! On the other hand, it looks quite difficult to create one yourself, isn't it? I mean, look at the beautiful docs of sklearn or pandas . \ud83d\ude2e Well, sorry for the scare there, but actually it's quite simple \ud83d\ude09. Remember all the function and module docstring and the formatting we followed before? As it turns out, we can leverage many open source tools like pydoc and sphinx to create full-fledged HTML documentations! Going into practical details is out of scope of this article, but it is fairly easy to follow the \"how to\" steps of both the packages and get your doc ready. One last thing, if you are using Kedro, this process is even simpler. All you have to do is run one command - kedro build-docs --open to create the documentation and automatically open it in your default browser! Virtual environment Virtual environments (VE) can be thought of as the local copy of python environment, created specifically for a project. This local copy is like a blank slate, as any required package can be installed here separately. It is extremely important to create a new virtual environment for any new project, because, each project has its own dependency tree (one package with a specific version needs another package to run, with its own specific version) while developing a project we may need to downgrade or upgrade a package, which if done on the base python environment, will affect your system! hence, a separate copy of python (VE), where you install whatever you want, seems to be the most logical solution. Using VE basically requires two steps, Create a VE: this can be done by running command python3 -m venv tutorial-env at the project root directory. (note, tutorial-env is the name of the VE, you can use rename it to anything) Activate VE: this can be done by running command tutorial-env\\Scripts\\activate.bat on Windows and source tutorial-env/bin/activate on Unix or MacOS. And that's it! Install, uninstall, upgrade or downgrade whatever you want! Note Remember to switch to another VE when you start working on another project or/and to deactivate the VE when you want to move to base VE. References PEP8 Style Guide for Python Code Kedro Python 3 documentation","title":"Good practices"},{"location":"python/good_practices/#python-good-practices","text":"","title":"Python Good Practices"},{"location":"python/good_practices/#introduction","text":"Writing code that works now is easy. Writing code that will work tomorrow is hard. Writing code that will work tomorrow and is intuitive enough for anyone to understand and follow\u200a\u2014\u200awell now we have hit the super hard stuff \ud83d\ude00. Observing several ML engineers and Data scientists working with me, I have noticed nearly all of them have their own unique style of coding. Well, don't get me wrong, subjectively is a good thing and I think it is what leads to innovations. That said while working in a team or even in open source collaboration, it helps to agree to a certain set of rules. And that's the idea behind this article, to provide python practitioners with a set of curated guidelines, from which they can pick and choose. With that, let's cover some of the good practices, which will not only help us to create a working but also a beautiful piece of code \ud83d\ude00 To cover this topic, we will go through three parts, Project structuring: ideas on how to organize your code Code formatting: ideas on how to make your code easy to follow Additional tips: a few things which will help you in the longer run","title":"Introduction"},{"location":"python/good_practices/#project-structuring","text":"In this part, we will basically talk about some good practices on how the complete python project can be structured. For this, we will look at two different possibilities, which anyone can choose based on how simple or complex their project is going to be.","title":"Project Structuring"},{"location":"python/good_practices/#type-1-the-classic","text":"This is the most basic format and yet gives the hint of organized structuring. This can be followed when our project consists of only a few scripts. The directory of a sample project could look something like this: 1 2 3 4 5 6 7 8 my_project # Root directory of the project \u251c\u2500\u2500 code # Source codes \u251c\u2500\u2500 input # Input files \u251c\u2500\u2500 output # Output files \u251c\u2500\u2500 config # Configuration files \u251c\u2500\u2500 notebooks # Project related Jupyter notebooks (for experimental code) \u251c\u2500\u2500 requirements . txt # List of external package which are project dependency \u2514\u2500\u2500 README . md # Project README As obvious from the names, folder code contains the individual modules ( .py files), input and output contains the input and output files respectively, and notebook contains the .ipynb notebooks files we use for experimentation. Finally, config folder could contain parameters within yaml or json or ini files and can be accessed by the code module files using configparser . requirements.txt contains a list of all external python packages needed by the project. One advantage of maintaining this file is that all of these packages can be easily installed using pip install -r requirements.txt command. (No need of manually installing each and every external packages!) . One example requirements.txt file is shown below (with package_name==package_version format) , 1 2 3 4 5 6 BeautifulSoup == 3.2.0 Django == 1.3 Fabric == 1.2.0 Jinja2 == 2.5.5 PyYAML == 3.09 Pygments == 1.4 Finally, README.MD contains the what, why and how of the project, with some dummy codes on how to run the project and sample use cases.","title":"Type 1: The Classic"},{"location":"python/good_practices/#type-2-kedro","text":"Kedro is not a project structuring strategy, it's a python tool released by QuantumBlack Labs , which does project structuring for you \ud83d\ude0e. On top of it, they provide a plethora of features to make our project organization and even code execution process super-easy, so that we can truly focus on what matters the most -- the experimentations and implementations! Their project structure is shown below. And btw, we can create a blank project by running kedro new command (don't forget to install kedro first by pip install kedro ) get-started # Parent directory of the template \u251c\u2500\u2500 conf # Project configuration files \u251c\u2500\u2500 data # Local project data (not committed to version control) \u251c\u2500\u2500 docs # Project documentation \u251c\u2500\u2500 logs # Project output logs (not committed to version control) \u251c\u2500\u2500 notebooks # Project related Jupyter notebooks (can be used for experimental code before moving the code to src) \u251c\u2500\u2500 README.md # Project README \u251c\u2500\u2500 setup.cfg # Configuration options for `pytest` when doing `kedro test` and for the `isort` utility when doing `kedro lint` \u2514\u2500\u2500 src # Project source code While most of the directories are similar to other types, a few points should be noted. Kedro's way of grouping different modules is by creating different \"pipelines\" . These pipelines are present within src folder, which in turn contains the module files. Furthermore, they have clear segregation of individual functions which are executed - these are stored within nodes.py file, and these functions are later connected with the input and output within pipeline.py file (all within the individual pipeline folder) . Kedro also segregates the code and the parameters, by storing the parameters within conf folder. Apart from just helping with organizing the project, they also provide options for sequential or parallel executions. We can execute individual functions (within nodes.py ) , or individual pipelines (which are a combination of functions) , or the complete project at one go. We can also create doc of the complete project or compile and package the project as a python .whl file, with just a single command run. For more details, and believe me we have just touched the surface, refer to their official documentation .","title":"Type 2: Kedro"},{"location":"python/good_practices/#code-formatting","text":"With a top-down approach, let's first have a look at a neat piece of code. We will discuss individual aspects of the code in more detail later. For now, just assume if someone asks you to do some scripting, what an ideal piece of code file should look like. Following code is take from csv_column_operations.py module file. It was generated for the prompt: \"write a function which takes csv file as input and returns the sum of a column\" . 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 \"\"\"Return sum of a column from CSV file A module with \"perform_column_sum\" main function that computes and return sum of an user defined numerical column from an csv file passed as pandas dataframe Author: Mohit Mayank Created: 4th August, 2021 \"\"\" # imports import sys # to exit execution import pandas as pd # to handle csv files # modules def perform_column_sum ( csv_file , column_to_perform_operation_on , operation_to_perform = 'sum' ): \"\"\"Performs numerical operation over a column of pandas dataframe Parameters ------------- csv_file: pandas.DataFrame the csv file which contains the column on which operation is to be performed column_to_perform_operation_on: string the column name (must be present in the csv file) operation_to_perform: string which operation to be performed on the column. Supported: ['sum'] Returns -------- column_operation_result: numeric the result after applying numeric operation on csv column \"\"\" # Step 1: data check and break # check 1: column should be present in the csv_file check_flag_col_presence = column_to_perform_operation_on in csv_file . columns # break the code if incorrect data is provided if not check_flag_col_presence : print ( f \"Column { column_to_perform_operation_on } is absent from the csv_file! Breaking code!\" ) sys . exit () # check 2: all values in the column should be of type numeric check_flag_data_type = pd . to_numeric ( csv_file [ column_to_perform_operation_on ], errors = 'coerce' ) . notnull () . all () # break the code if incorrect data is provided if not check_flag_data_type : print ( f \"One or more values in column { column_to_perform_operation_on } is not numeric! Breaking code!\" ) sys . exit () # Step 2: extract the column column_data = csv_file [ column_to_perform_operation_on ] # Step 3: Perform the operation column_operation_result = None if operation_to_perform == 'sum' : column_operation_result = sum ( column_data ) # Step 4: return the result return column_operation_result # run when file is directly executed if __name__ == '__main__' : # create a dummy dataframe df = pd . DataFrame ({ 'col1' : [ 1 , 2 , 3 ], 'col2' : [ 'a' , 'a' , 'a' ]}) # call the function answer = perform_column_sum ( df , 'col1' , 'sum' ) # match if the answer is correct assert ( answer == 6 ) Note Some might argue why do such an overkill for a simple piece of code. Note, it's a dummy example. In real life, you will develop more complex pieces of codes and hence it become quite important that we understand the gist. Now let's take a deeper dive into the individual aspect of the above code.","title":"Code formatting"},{"location":"python/good_practices/#module-structure","text":"A module is a python file with .py extension that contains the executable code or functions or classes, etc. Usually, we start the module with module definition, which is an area where we provide some basic details of the module. We can do so using the following template (and it can be easily compared to a real code shown above) 1 2 3 4 5 6 7 8 \"\"\" Author: Created: \"\"\" Next, we should clearly segregate the parts of the module such as imports, code area, etc using comment lines. Finally, at the bottom, we could include some examples on how to run the code. Including these scripts within if __name__ == '__main__': makes sure that they only run when the file is directly executed (like python csv_column_operations.py ) . So these pieces of code doesn't run when you say import the module in another script.","title":"Module structure"},{"location":"python/good_practices/#functions-structure","text":"Functions are the basic block of code that performs a specific task. A module consists of several functions. To inform the user what a particular block of code does, we start the function with a function definition. A sample template is provided below, 1 2 3 4 5 6 7 8 9 10 11 12 \"\"\"Description Paramters --------- : Returns --------- : \"\"\" After this, we can start adding the relevant code lines. Make sure to separate different logical blocks of code within the functions using comments. One important thing to handle at the start of the coding section is to check the parameters and input data for some data type or data content related basic issues. A majority of code break happens due to silly mistakes like when someone provides wrong input, in which case we should print or log warning message and gracefully exit. The above same code contains two such preliminary but important checks inside the step 1 section.","title":"Functions structure"},{"location":"python/good_practices/#naming-convention","text":"There are several formatting conventions that we can follow, like Camel Case , Snake case , etc. It's quite subjective and depends on the developer. Below are some examples of naming different entities of a python code (taken from PIP8 conventions - with some modifications) \ud83d\ude07, Module name: Modules should have short, all-lowercase names (ex: csv_column_operations.py ) Function or method name: Function names should be lowercase, with words separated by underscores as necessary to improve readability. Also, don't forget to add your verbs! (ex: perform_column_sum() ) Variable name: Similar to function name but without the verbs! (ex: list_of_news ) Class name: Class names should normally use the CapWords convention. (ex: FindMax ) Constant name: Constants are usually defined on a module level and written in all capital letters with underscores separating words. (ex: MAX_OVERFLOW and TOTAL ).","title":"Naming convention"},{"location":"python/good_practices/#add-comments","text":"PEP-8 defines three types of comments, Block comments: which is written for a single or a collection of code lines. This can be done either when you want to explain a set of lines or just want to segregate code. In the above example, you can see # Step {1, 2, 3} used as segregation comment and # run when file is directly executed used to explain a set of code lines. Inline comments: which are added on the same line as the code. For example, see how # to handle csv files is used to justify the pandas package import. PEP-8 suggests using inline comments sparingly. Documentation Strings: these are used for documentation for module, functions or classes. PEP-257 suggests using multiline comment for docstring (using \"\"\") . An example of module and function docstrings (short for documentation strings) is provided in the sample code above. We should be as descriptive in our comments as possible. Try to separate functional sections of your code, provide explanations for complex code lines, provide details about the input/output of functions, etc. How do you know you have enough comments? - If you think someone with half your expertise can understand the code without calling you middle of the night! \ud83d\ude24","title":"Add comments"},{"location":"python/good_practices/#indentations-tabs-vs-spaces","text":"Frankly, I am only going to touch this topic with a long stick \ud83e\uddf9. There are already several articles , reddit threads and even tv series (Silicon valley \ud83d\udcfa) where this topic has been discussed a lot! Want my 2 cents? Pick any modern IDE (like VSCode, Sublime, etc) , set indentations to tabs, and set 1 tab = 4 spaces. Done \ud83d\ude0f","title":"Indentations - Tabs vs Spaces"},{"location":"python/good_practices/#additional-tips","text":"Till now we have discussed how to either structure the project or format the code. Next, we will cover a generic set of good practices which will save you some pain down the line \ud83d\ude2c","title":"Additional tips"},{"location":"python/good_practices/#logging","text":"Instead of printing statements in the console which is temporary (do a cls and poof it's gone\ud83d\udca8), a better idea is to save these statements in a separate file, which you can always go back and refer to. This is logging \ud83d\udcdc Python provides an inbuilt function for logging. By referring to the official how to , logging to a file is super easy, 1 2 3 4 5 6 7 8 9 # import import logging # config the logging behavior logging . basicConfig ( filename = 'example.log' , level = logging . DEBUG ) # some sample log messages logging . debug ( 'This message should go to the log file' , exc_info = True ) logging . info ( 'So should this' , exc_info = True ) logging . warning ( 'And this, too' , exc_info = True ) # we add \"exc_info=True\" to capture the stack trace Note, there is a hierarchical levelling of logs to segregate different severity of logs. In the example shown above, the level parameter denotes the minimal level that is tracked and hence saved to the file. As per the official how to , these are the different logging levels with some details about when to use which (in increasing order of severity) , Level When it\u2019s used DEBUG Detailed information, typically of interest only when diagnosing problems. INFO Confirmation that things are working as expected. WARNING An indication that something unexpected happened, or indicative of some problem in the near future (e.g. \u2018disk space low\u2019). The software is still working as expected. ERROR Due to a more serious problem, the software has not been able to perform some function. CRITICAL A serious error, indicating that the program itself may be unable to continue running. While the above code is good for normal testing, for production you might want to have more control -- like formatting the output slightly differently (formatter) or have multiple places to publish logs (handlers) . One such use case is convered below, where we want to log to console as well as a file in a detailed json format. # import import sys import logging import json_log_formatter # create formatter (using json_log_formatter) formatter = json_log_formatter . VerboseJSONFormatter () # create two handlers (console and file) logger_sys_handler = logging . StreamHandler ( sys . stdout ) logger_file_handler = logging . FileHandler ( filename = 'log.json' ) # perform the same formatting for both handler logger_sys_handler . setFormatter ( formatter ) logger_file_handler . setFormatter ( formatter ) # get the logger and add handlers logger = logging . getLogger ( 'my_logger' ) logger . addHandler ( logger_sys_handler ) logger . addHandler ( logger_file_handler ) # set level logger . setLevel ( logging . INFO )","title":"Logging"},{"location":"python/good_practices/#documentation","text":"Documentation of the code is an absolute must, if you are planning to maintain the code or hand it over to someone else in the foreseeable future. Just ask any developer about their excitement on finding a ready-made and well curated documentation for a package they are planning to use! On the other hand, it looks quite difficult to create one yourself, isn't it? I mean, look at the beautiful docs of sklearn or pandas . \ud83d\ude2e Well, sorry for the scare there, but actually it's quite simple \ud83d\ude09. Remember all the function and module docstring and the formatting we followed before? As it turns out, we can leverage many open source tools like pydoc and sphinx to create full-fledged HTML documentations! Going into practical details is out of scope of this article, but it is fairly easy to follow the \"how to\" steps of both the packages and get your doc ready. One last thing, if you are using Kedro, this process is even simpler. All you have to do is run one command - kedro build-docs --open to create the documentation and automatically open it in your default browser!","title":"Documentation"},{"location":"python/good_practices/#virtual-environment","text":"Virtual environments (VE) can be thought of as the local copy of python environment, created specifically for a project. This local copy is like a blank slate, as any required package can be installed here separately. It is extremely important to create a new virtual environment for any new project, because, each project has its own dependency tree (one package with a specific version needs another package to run, with its own specific version) while developing a project we may need to downgrade or upgrade a package, which if done on the base python environment, will affect your system! hence, a separate copy of python (VE), where you install whatever you want, seems to be the most logical solution. Using VE basically requires two steps, Create a VE: this can be done by running command python3 -m venv tutorial-env at the project root directory. (note, tutorial-env is the name of the VE, you can use rename it to anything) Activate VE: this can be done by running command tutorial-env\\Scripts\\activate.bat on Windows and source tutorial-env/bin/activate on Unix or MacOS. And that's it! Install, uninstall, upgrade or downgrade whatever you want! Note Remember to switch to another VE when you start working on another project or/and to deactivate the VE when you want to move to base VE.","title":"Virtual environment"},{"location":"python/good_practices/#references","text":"PEP8 Style Guide for Python Code Kedro Python 3 documentation","title":"References"},{"location":"python/python_snippets/","text":"Python Snippets Add numpy array as Pandas column References from stackoverflow answer 1 2 3 4 5 6 7 8 import numpy as np import pandas as pd import scipy.sparse as sparse df = pd . DataFrame ( np . arange ( 1 , 10 ) . reshape ( 3 , 3 )) arr = sparse . coo_matrix (([ 1 , 1 , 1 ], ([ 0 , 1 , 2 ], [ 1 , 2 , 0 ])), shape = ( 3 , 3 )) df [ 'newcol' ] = arr . toarray () . tolist () print ( df ) Get members of python object 1 2 3 4 5 6 7 8 # import import networkx as nx from inspect import getmembers # Fetching the name of all drawing related members of NetworkX class. for x in getmembers ( nx ): if 'draw' in x [ 0 ]: print ( x ) Install packages using code References from here 1 2 3 4 5 6 7 8 9 10 # import import sys import subprocess # install package function def install ( package ): subprocess . check_call ([ sys . executable , \"-m\" , \"pip\" , \"install\" , package ]) # example # install(\"pathfinding\") Python packaging 1 2 3 4 5 6 # clean the previous build files python setup . py clean -- all # build the new distribution files python setup . py sdist bdist_wheel # upload the latest version to pypi twine upload -- skip - existing dist /* Python virtual environment 1 2 3 4 5 6 7 # Create the virtual environment in the current directory python - m venv projectnamevenv # pick one of the below based on your OS (default: Linux) # activate the virtual environment - Linux . projectnamevenv \\ Scripts \\ activate # activate the virtual environment - windows # .\\\\projectnamevenv\\\\Scripts\\\\activate.bat Where is my Python installed? To know the exact location of where the python distribution is installed, follow the steps as suggested here 1 2 3 import os import sys print ( os . path . dirname ( sys . executable )) Get list of installed Python packages To know exactly which packages are current installed (and their version) in your VE, try 1 pip list --format = freeze > reqirements.txt Find files or folders glob is a very efficient way to extract relevant files or folders using python. A few example are shown below. 1 2 3 4 5 6 7 8 9 10 11 12 # import from glob import glob # Ex 1: fetch all files within a directory glob ( \"../data/01_raw/CoAID/*\" ) # Ex 2: fetch all files within a directory with a pattern 'News*COVID-19.csv' glob ( \"../data/01_raw/CoAID/folder_1/News*COVID-19.csv\" ) # Ex 2: fetch all files within multiple directories which # follow a pattern 'News*COVID-19.csv' glob ( \"../data/01_raw/CoAID/**/News*COVID-19.csv\" ) Increase the pandas column width in jupyter lab or notebook Most of the times, we have text in a dataframe column, which while displaying gets truncated. One way to handle this to increase the max width of all columns in the dataframe (as shown below) 1 2 import pandas as pd pd . set_option ( 'max_colwidth' , 100 ) # increase 100 to add more space for bigger text Parse date and time from string There are basically 2 ways to do this, (1) Trust machine \ud83e\udd16: for majority of the 'famous' date writing styles, you can use dateparser package that automatically extracts the date and parse it into datetime format. 1 2 3 4 5 6 # import from dateparser import parse # parse text = 'October 05, 2021' dateparser . parse ( text ) # output - datetime.datetime(2021, 10, 5, 0, 0) which will return output ``. Another way is (2) DIY \ud83d\udcaa: if you can create the date time format ), you can use datetime package directly. 1 2 3 4 5 6 7 # import from datetime import datetime # parse text = 'October 05, 2021' date_format = '%B %d , %Y' datetime . strptime ( text , date_format ) # output - datetime.datetime(2021, 10, 5, 0, 0) Bulk document insert in MongoDB While pymongo provides insert_many function for bulk insert, it breaks in case of duplicate key. We can handle it with following function, which in its worse case is similar to insert_one , but shines otherwise. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 # import import pymongo # function def insert_many_wrapper ( df , col ): \"\"\"bulk insert docs into the MongoDB while handling duplicate docs Parameters - df (pandas.dataframe): row as a doc with `_id` col - col (pymongo.db.col): pymongo collection object in which insertion is to be done \"\"\" # make a copy and reset index df = df . copy () . reset_index ( drop = True ) # vars all_not_inserted = True duplicate_count = 0 ttl_docs = df . shape [ 0 ] # iterate till all insertions are done (or passed in case of duplicates) while all_not_inserted : # try insertion try : col . insert_many ( df . to_dict ( orient = 'records' ), ordered = True ) all_not_inserted = False except pymongo . errors . BulkWriteError as e : id_till_inserted = e . details [ 'writeErrors' ][ 0 ][ 'keyValue' ][ '_id' ] index_in_df = df [ df [ '_id' ] == id_till_inserted ] . index [ 0 ] print ( f \"Duplicate id: { id_till_inserted } , Current index: { index_in_df } \" ) df = df . loc [ index_in_df + 1 :, :] duplicate_count += 1 # final status print ( f \"Total docs: { ttl_docs } , Inserted: { ttl_docs - len ( duplicate_count ) } , Duplicate found: { len ( duplicate_count ) } \" ) Search top StackExchange questions Stack Exchange exposes several API endpoints to process the questions, answers or posts from their website. A simple implementation to search and download the latest (from yesterday) and top voted questions is shown below. For more such API endpoints, consult their official doc . 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 \"\"\" Request StackExchange API to get the top 10 most voted questions and their answer from yesterday \"\"\" import requests import json import datetime import time # Get the current date today = datetime . date . today () yesterday = today - datetime . timedelta ( days = 1 ) # Get the current time now = datetime . datetime . now () # Get the time of yesterday yesterday_time = now . replace ( day = yesterday . day , month = yesterday . month , year = yesterday . year ) # Convert the time to epoch time yesterday_epoch = int ( time . mktime ( yesterday_time . timetuple ())) # Get the time of today today_time = now . replace ( day = today . day , month = today . month , year = today . year ) # Convert the time to epoch time today_epoch = int ( time . mktime ( today_time . timetuple ())) # Get the top 10 most voted questions and their answer from yesterday url = \"https://api.stackexchange.com/2.2/questions?pagesize=10&fromdate=\" + \\ str ( yesterday_epoch ) + \"&todate=\" + str ( today_epoch ) + \\ \"&order=desc&sort=votes&site=stackoverflow\" # Get the response from the API response = requests . get ( url ) # Convert the response to JSON data = response . json () # Print the data print ( json . dumps ( data , indent = 4 )) Export complete data from ElasticSearch Due to several memory and efficiency related limitations, it is non-trivial to export complete data from ElasticSearch database. That said, it is not impossible. PFB an scan based implementation that does the same for a dummy test_news index. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 # import import pandas as pd from elasticsearch import Elasticsearch from elasticsearch.helpers import scan from tqdm import tqdm # config index_name = 'test_news' db_ip = 'http://localhost:9200' # connect to elasticsearch es = Elasticsearch ([ db_ip ]) # fetch all data from elasticsearch scroll = scan ( es , index = index_name , query = { \"query\" : { \"match_all\" : {}}}) data = [] for res in tqdm ( scroll ): data . append ( res [ '_source' ]) # convert to pandas dataframe and export as csv pd . DataFrame ( data ) . to_csv ( \"news_dump.csv\" , index = False ) Convert python literals from string While I/O from database or config files, we may get some literals (ex list) in form of string, wherein they maintain their structure but the type. We can use ast package to convert them back to their correct type. Quoting the documentation, \"With ast.literal_eval you can safely evaluate an expression node or a string containing a Python literal or container display. The string or node provided may only consist of the following Python literal structures: strings, bytes, numbers, tuples, lists, dicts, booleans, and None.\" 1 2 3 4 5 6 # import import ast # list literal in string format list_as_string = '[\"a\", \"b\"]' # convert list_as_list = ast . literal_eval ( list_as_string ) # Output: [\"a\", \"b\"] Plotly visualization on Pandas dataframe If you want to visualize your pandas dataframe using plotly package, there is no need to use the package explicitly. It can be done right from the pandas dataframe object, with just a couple of lines of code as shown below: 1 2 3 4 # set the backend plotting option pd . options . plotting . backend = \"plotly\" # do a normal plot! pd . DataFrame ( result ) . plot ( x = 'size' , y = 'mean' ) Conda cheat sheet Conda an open-source, cross-platform, language-agnostic package manager and environment management system. Therein again we have multiple varieties, Miniconda: it's a minimilistic package with python, conda and some base packages. Anaconda: it's a bigger package with all the things in Miniconda plus around 150 high quality packages. While the complete documentation can be accessed from here , some important snippets are: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 # list all supported python versions conda search python # create a new conda environment (with new python version) # note, py39 is the name of the env conda create - n py39 python = 3.9 # list all of the environments conda info -- envs # activate an environment conda activate py39 # where py39 is the name of the env # deactivate the current environment conda deactivate # delete an environment conda env remove - n py39 Requirement files Requirement file is a collection of packages you want to install for a Project. A sample file is shown below, # fine name requirements.txt package-one == 1 .9.4 git+https://github.com/path/to/package-two@41b95ec#egg = package-two package-three == 1 .0.1 package-four - Note three ways of defining packages, (1) with version number, (2) with github source and (3) without version number (installs the latest). Once done, you can install all these packages at one go by pip install -r requirements.txt Pandas Groupby Function Pandas can be utilised for fast analysis of categorical data using groupby. Let's have a look. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 #import import numpy as np import pandas as pd # load a dummy df df = pd . Dataframe ( 'dummy.csv' ) # example below ## Name | Gender | Salary ## Ravi | Male | $20,000 ## Sita | Female | $40,000 ## Kito | Female | $11,000 # perform groupby to get average salary per gender ## Option 1 df . groupby ([ 'Gender' ]) . agg ({ 'Salary' : [ np . mean ]}) ## Option 2 df . groupby ([ 'Gender' ]) . mean () ## Option 3 df . groupby ([ 'Gender' ]) . apply ( lambda x : x [ 'Salary' ] . mean ()) Save and Load from Pickle Pickle can be used to efficiently store and load python objects and data. Refer StackOverflow 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 # import import pickle # create data a = { 'a' : 1 , 'b' : [ 1 , 2 , 3 , 4 ]} # save pickle with open ( 'filename.pickle' , 'wb' ) as handle : pickle . dump ( a , handle , protocol = pickle . HIGHEST_PROTOCOL ) # load pickle with open ( 'filename.pickle' , 'rb' ) as handle : b = pickle . load ( handle ) # check assert print ( a == b ) Download Youtube video Youtube video can be downloaded using the pytube package. Here is an example. 1 2 3 4 5 6 7 8 9 10 11 12 # import from pytube import YouTube ## var: link to download video_url = \"https://www.youtube.com/watch?v=JP41nYZfekE\" # create instance yt = YouTube ( video_url ) # download abs_video_path = yt . streams . filter ( progressive = True , file_extension = 'mp4' ) . order_by ( 'resolution' ) . desc () . first () . download () ## print(f\"Video downloaded at {abs_video_path}\") Machine Translation EasyNMT lets you perform state-of-the-art machine translation with just 3 lines of python code! It supports translation between 150+ languages and automatic language detection for 170+ languages. Pre-trained machine translation models are auto-downloaded and can perform sentence and document translations! 1 2 3 4 5 6 7 8 9 # import from easynmt import EasyNMT # load model model = EasyNMT ( 'opus-mt' ) #Translate a single sentence to German print ( model . translate ( 'This is a sentence we want to translate to German' , target_lang = 'de' )) ## Output: Dies ist ein Satz, den wir ins Deutsche \u00fcbersetzen wollen Pandas read excel file While pandas is quite famous for CSV analysis, it can be used to read and process Excel files as well. Here are some snippets, 1 2 3 4 5 6 7 8 9 10 11 12 13 # import import pandas as pd # if you just want to read one sheet, by default it reads the first one. df = pd . read_excel ( \"file.xlsx\" , sheet_name = \"Page1\" ) # if you want to get the names of sheet and do more selective reading excel_data = pd . ExcelFile ( \"file.xlsx\" ) # get the sheet names print ( excel_data . sheet_names ) # read one sheet (decide using last print result) sheet_name = '..' df = excel_data . parse ( sheet_name ) Send Slack Messages One of the easiest way to send Slack message is via unique Incoming Webhook. Basically, you need to create a Slack App, register an incoming webhook with the app and whenever you want to post a message - just send a payload to the webhook. For more details on setup, you can refer the official page Once done, you just need to send the message like shown below, 1 2 3 4 5 6 7 8 9 10 11 12 # import requests (needed to connect with webhook) import requests # func def send_message_to_slack ( message ): # set the webhook webhook_url = \"...enter incoming webhook url here...\" # modify the message payload payload = '{\"text\": \" %s \"}' % message # send the message response = requests . post ( webhook_url , payload ) # test send_message_to_slack ( \"test\" )","title":"Python snippets"},{"location":"python/python_snippets/#python-snippets","text":"","title":"Python Snippets"},{"location":"python/python_snippets/#add-numpy-array-as-pandas-column","text":"References from stackoverflow answer 1 2 3 4 5 6 7 8 import numpy as np import pandas as pd import scipy.sparse as sparse df = pd . DataFrame ( np . arange ( 1 , 10 ) . reshape ( 3 , 3 )) arr = sparse . coo_matrix (([ 1 , 1 , 1 ], ([ 0 , 1 , 2 ], [ 1 , 2 , 0 ])), shape = ( 3 , 3 )) df [ 'newcol' ] = arr . toarray () . tolist () print ( df )","title":"Add numpy array as Pandas column"},{"location":"python/python_snippets/#get-members-of-python-object","text":"1 2 3 4 5 6 7 8 # import import networkx as nx from inspect import getmembers # Fetching the name of all drawing related members of NetworkX class. for x in getmembers ( nx ): if 'draw' in x [ 0 ]: print ( x )","title":"Get members of python object"},{"location":"python/python_snippets/#install-packages-using-code","text":"References from here 1 2 3 4 5 6 7 8 9 10 # import import sys import subprocess # install package function def install ( package ): subprocess . check_call ([ sys . executable , \"-m\" , \"pip\" , \"install\" , package ]) # example # install(\"pathfinding\")","title":"Install packages using code"},{"location":"python/python_snippets/#python-packaging","text":"1 2 3 4 5 6 # clean the previous build files python setup . py clean -- all # build the new distribution files python setup . py sdist bdist_wheel # upload the latest version to pypi twine upload -- skip - existing dist /*","title":"Python packaging"},{"location":"python/python_snippets/#python-virtual-environment","text":"1 2 3 4 5 6 7 # Create the virtual environment in the current directory python - m venv projectnamevenv # pick one of the below based on your OS (default: Linux) # activate the virtual environment - Linux . projectnamevenv \\ Scripts \\ activate # activate the virtual environment - windows # .\\\\projectnamevenv\\\\Scripts\\\\activate.bat","title":"Python virtual environment"},{"location":"python/python_snippets/#where-is-my-python-installed","text":"To know the exact location of where the python distribution is installed, follow the steps as suggested here 1 2 3 import os import sys print ( os . path . dirname ( sys . executable ))","title":"Where is my Python installed?"},{"location":"python/python_snippets/#get-list-of-installed-python-packages","text":"To know exactly which packages are current installed (and their version) in your VE, try 1 pip list --format = freeze > reqirements.txt","title":"Get list of installed Python packages"},{"location":"python/python_snippets/#find-files-or-folders","text":"glob is a very efficient way to extract relevant files or folders using python. A few example are shown below. 1 2 3 4 5 6 7 8 9 10 11 12 # import from glob import glob # Ex 1: fetch all files within a directory glob ( \"../data/01_raw/CoAID/*\" ) # Ex 2: fetch all files within a directory with a pattern 'News*COVID-19.csv' glob ( \"../data/01_raw/CoAID/folder_1/News*COVID-19.csv\" ) # Ex 2: fetch all files within multiple directories which # follow a pattern 'News*COVID-19.csv' glob ( \"../data/01_raw/CoAID/**/News*COVID-19.csv\" )","title":"Find files or folders"},{"location":"python/python_snippets/#increase-the-pandas-column-width-in-jupyter-lab-or-notebook","text":"Most of the times, we have text in a dataframe column, which while displaying gets truncated. One way to handle this to increase the max width of all columns in the dataframe (as shown below) 1 2 import pandas as pd pd . set_option ( 'max_colwidth' , 100 ) # increase 100 to add more space for bigger text","title":"Increase the pandas column width in jupyter lab or notebook"},{"location":"python/python_snippets/#parse-date-and-time-from-string","text":"There are basically 2 ways to do this, (1) Trust machine \ud83e\udd16: for majority of the 'famous' date writing styles, you can use dateparser package that automatically extracts the date and parse it into datetime format. 1 2 3 4 5 6 # import from dateparser import parse # parse text = 'October 05, 2021' dateparser . parse ( text ) # output - datetime.datetime(2021, 10, 5, 0, 0) which will return output ``. Another way is (2) DIY \ud83d\udcaa: if you can create the date time format ), you can use datetime package directly. 1 2 3 4 5 6 7 # import from datetime import datetime # parse text = 'October 05, 2021' date_format = '%B %d , %Y' datetime . strptime ( text , date_format ) # output - datetime.datetime(2021, 10, 5, 0, 0)","title":"Parse date and time from string"},{"location":"python/python_snippets/#bulk-document-insert-in-mongodb","text":"While pymongo provides insert_many function for bulk insert, it breaks in case of duplicate key. We can handle it with following function, which in its worse case is similar to insert_one , but shines otherwise. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 # import import pymongo # function def insert_many_wrapper ( df , col ): \"\"\"bulk insert docs into the MongoDB while handling duplicate docs Parameters - df (pandas.dataframe): row as a doc with `_id` col - col (pymongo.db.col): pymongo collection object in which insertion is to be done \"\"\" # make a copy and reset index df = df . copy () . reset_index ( drop = True ) # vars all_not_inserted = True duplicate_count = 0 ttl_docs = df . shape [ 0 ] # iterate till all insertions are done (or passed in case of duplicates) while all_not_inserted : # try insertion try : col . insert_many ( df . to_dict ( orient = 'records' ), ordered = True ) all_not_inserted = False except pymongo . errors . BulkWriteError as e : id_till_inserted = e . details [ 'writeErrors' ][ 0 ][ 'keyValue' ][ '_id' ] index_in_df = df [ df [ '_id' ] == id_till_inserted ] . index [ 0 ] print ( f \"Duplicate id: { id_till_inserted } , Current index: { index_in_df } \" ) df = df . loc [ index_in_df + 1 :, :] duplicate_count += 1 # final status print ( f \"Total docs: { ttl_docs } , Inserted: { ttl_docs - len ( duplicate_count ) } , Duplicate found: { len ( duplicate_count ) } \" )","title":"Bulk document insert in MongoDB"},{"location":"python/python_snippets/#search-top-stackexchange-questions","text":"Stack Exchange exposes several API endpoints to process the questions, answers or posts from their website. A simple implementation to search and download the latest (from yesterday) and top voted questions is shown below. For more such API endpoints, consult their official doc . 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 \"\"\" Request StackExchange API to get the top 10 most voted questions and their answer from yesterday \"\"\" import requests import json import datetime import time # Get the current date today = datetime . date . today () yesterday = today - datetime . timedelta ( days = 1 ) # Get the current time now = datetime . datetime . now () # Get the time of yesterday yesterday_time = now . replace ( day = yesterday . day , month = yesterday . month , year = yesterday . year ) # Convert the time to epoch time yesterday_epoch = int ( time . mktime ( yesterday_time . timetuple ())) # Get the time of today today_time = now . replace ( day = today . day , month = today . month , year = today . year ) # Convert the time to epoch time today_epoch = int ( time . mktime ( today_time . timetuple ())) # Get the top 10 most voted questions and their answer from yesterday url = \"https://api.stackexchange.com/2.2/questions?pagesize=10&fromdate=\" + \\ str ( yesterday_epoch ) + \"&todate=\" + str ( today_epoch ) + \\ \"&order=desc&sort=votes&site=stackoverflow\" # Get the response from the API response = requests . get ( url ) # Convert the response to JSON data = response . json () # Print the data print ( json . dumps ( data , indent = 4 ))","title":"Search top StackExchange questions"},{"location":"python/python_snippets/#export-complete-data-from-elasticsearch","text":"Due to several memory and efficiency related limitations, it is non-trivial to export complete data from ElasticSearch database. That said, it is not impossible. PFB an scan based implementation that does the same for a dummy test_news index. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 # import import pandas as pd from elasticsearch import Elasticsearch from elasticsearch.helpers import scan from tqdm import tqdm # config index_name = 'test_news' db_ip = 'http://localhost:9200' # connect to elasticsearch es = Elasticsearch ([ db_ip ]) # fetch all data from elasticsearch scroll = scan ( es , index = index_name , query = { \"query\" : { \"match_all\" : {}}}) data = [] for res in tqdm ( scroll ): data . append ( res [ '_source' ]) # convert to pandas dataframe and export as csv pd . DataFrame ( data ) . to_csv ( \"news_dump.csv\" , index = False )","title":"Export complete data from ElasticSearch"},{"location":"python/python_snippets/#convert-python-literals-from-string","text":"While I/O from database or config files, we may get some literals (ex list) in form of string, wherein they maintain their structure but the type. We can use ast package to convert them back to their correct type. Quoting the documentation, \"With ast.literal_eval you can safely evaluate an expression node or a string containing a Python literal or container display. The string or node provided may only consist of the following Python literal structures: strings, bytes, numbers, tuples, lists, dicts, booleans, and None.\" 1 2 3 4 5 6 # import import ast # list literal in string format list_as_string = '[\"a\", \"b\"]' # convert list_as_list = ast . literal_eval ( list_as_string ) # Output: [\"a\", \"b\"]","title":"Convert python literals from string"},{"location":"python/python_snippets/#plotly-visualization-on-pandas-dataframe","text":"If you want to visualize your pandas dataframe using plotly package, there is no need to use the package explicitly. It can be done right from the pandas dataframe object, with just a couple of lines of code as shown below: 1 2 3 4 # set the backend plotting option pd . options . plotting . backend = \"plotly\" # do a normal plot! pd . DataFrame ( result ) . plot ( x = 'size' , y = 'mean' )","title":"Plotly visualization on Pandas dataframe"},{"location":"python/python_snippets/#conda-cheat-sheet","text":"Conda an open-source, cross-platform, language-agnostic package manager and environment management system. Therein again we have multiple varieties, Miniconda: it's a minimilistic package with python, conda and some base packages. Anaconda: it's a bigger package with all the things in Miniconda plus around 150 high quality packages. While the complete documentation can be accessed from here , some important snippets are: 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 # list all supported python versions conda search python # create a new conda environment (with new python version) # note, py39 is the name of the env conda create - n py39 python = 3.9 # list all of the environments conda info -- envs # activate an environment conda activate py39 # where py39 is the name of the env # deactivate the current environment conda deactivate # delete an environment conda env remove - n py39","title":"Conda cheat sheet"},{"location":"python/python_snippets/#requirement-files","text":"Requirement file is a collection of packages you want to install for a Project. A sample file is shown below, # fine name requirements.txt package-one == 1 .9.4 git+https://github.com/path/to/package-two@41b95ec#egg = package-two package-three == 1 .0.1 package-four - Note three ways of defining packages, (1) with version number, (2) with github source and (3) without version number (installs the latest). Once done, you can install all these packages at one go by pip install -r requirements.txt","title":"Requirement files"},{"location":"python/python_snippets/#pandas-groupby-function","text":"Pandas can be utilised for fast analysis of categorical data using groupby. Let's have a look. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 #import import numpy as np import pandas as pd # load a dummy df df = pd . Dataframe ( 'dummy.csv' ) # example below ## Name | Gender | Salary ## Ravi | Male | $20,000 ## Sita | Female | $40,000 ## Kito | Female | $11,000 # perform groupby to get average salary per gender ## Option 1 df . groupby ([ 'Gender' ]) . agg ({ 'Salary' : [ np . mean ]}) ## Option 2 df . groupby ([ 'Gender' ]) . mean () ## Option 3 df . groupby ([ 'Gender' ]) . apply ( lambda x : x [ 'Salary' ] . mean ())","title":"Pandas Groupby Function"},{"location":"python/python_snippets/#save-and-load-from-pickle","text":"Pickle can be used to efficiently store and load python objects and data. Refer StackOverflow 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 # import import pickle # create data a = { 'a' : 1 , 'b' : [ 1 , 2 , 3 , 4 ]} # save pickle with open ( 'filename.pickle' , 'wb' ) as handle : pickle . dump ( a , handle , protocol = pickle . HIGHEST_PROTOCOL ) # load pickle with open ( 'filename.pickle' , 'rb' ) as handle : b = pickle . load ( handle ) # check assert print ( a == b )","title":"Save and Load from Pickle"},{"location":"python/python_snippets/#download-youtube-video","text":"Youtube video can be downloaded using the pytube package. Here is an example. 1 2 3 4 5 6 7 8 9 10 11 12 # import from pytube import YouTube ## var: link to download video_url = \"https://www.youtube.com/watch?v=JP41nYZfekE\" # create instance yt = YouTube ( video_url ) # download abs_video_path = yt . streams . filter ( progressive = True , file_extension = 'mp4' ) . order_by ( 'resolution' ) . desc () . first () . download () ## print(f\"Video downloaded at {abs_video_path}\")","title":"Download Youtube video"},{"location":"python/python_snippets/#machine-translation","text":"EasyNMT lets you perform state-of-the-art machine translation with just 3 lines of python code! It supports translation between 150+ languages and automatic language detection for 170+ languages. Pre-trained machine translation models are auto-downloaded and can perform sentence and document translations! 1 2 3 4 5 6 7 8 9 # import from easynmt import EasyNMT # load model model = EasyNMT ( 'opus-mt' ) #Translate a single sentence to German print ( model . translate ( 'This is a sentence we want to translate to German' , target_lang = 'de' )) ## Output: Dies ist ein Satz, den wir ins Deutsche \u00fcbersetzen wollen","title":"Machine Translation"},{"location":"python/python_snippets/#pandas-read-excel-file","text":"While pandas is quite famous for CSV analysis, it can be used to read and process Excel files as well. Here are some snippets, 1 2 3 4 5 6 7 8 9 10 11 12 13 # import import pandas as pd # if you just want to read one sheet, by default it reads the first one. df = pd . read_excel ( \"file.xlsx\" , sheet_name = \"Page1\" ) # if you want to get the names of sheet and do more selective reading excel_data = pd . ExcelFile ( \"file.xlsx\" ) # get the sheet names print ( excel_data . sheet_names ) # read one sheet (decide using last print result) sheet_name = '..' df = excel_data . parse ( sheet_name )","title":"Pandas read excel file"},{"location":"python/python_snippets/#send-slack-messages","text":"One of the easiest way to send Slack message is via unique Incoming Webhook. Basically, you need to create a Slack App, register an incoming webhook with the app and whenever you want to post a message - just send a payload to the webhook. For more details on setup, you can refer the official page Once done, you just need to send the message like shown below, 1 2 3 4 5 6 7 8 9 10 11 12 # import requests (needed to connect with webhook) import requests # func def send_message_to_slack ( message ): # set the webhook webhook_url = \"...enter incoming webhook url here...\" # modify the message payload payload = '{\"text\": \" %s \"}' % message # send the message response = requests . post ( webhook_url , payload ) # test send_message_to_slack ( \"test\" )","title":"Send Slack Messages"},{"location":"python/scraping_websites/","text":"Scraping websites using Scrapy Introduction Scraping is the process of traversing and collecting data from the web pages. People scrap websites for many reasons -- be it to get information about companies or fetch latest news or get stock prices informations or just create a dataset for the next big AI model In this article, we will be using Scrapy to scrape data from the Devgan website that hosts details of difference sections in Indian Penal Code (IPC). I think this example provides the perfect blend of basic and advanced scraping techniques. So let's get started! Tip The complete code is also available on GitHub Understanding the website Before we even start scraping, we need to understand the structure of the website. This is very important, as we want to (1) get an idea of what we want to scrap and (2) where those data are located. The flow of scraping section descriptions from Devgan website Our goal is to scrap the description for each section in IPC. As per the website flow above, the complete process can be divided into two parts, First, we need to traverse to the main page and extract the link of each sections. Next, we need to traverse to each individual section page and extract the description details present there. Data extraction methods Now, let's also look into different methods exposed by Scrapy to extract data from the web pages. The basic idea is that scrapy downloads the web page source code in HTML and parse it using different parsers. For this, we can either use XPaths or CSS selectors. The choice is purely up to us. We can begin with the main page and try to find the link of each section. For this, you can open inspect option from your browser by right clicking on any of the sections and select inspect . This should show you the source code. Next, try to find the position of the tag where each section is defined. Refer the image below, and you can see that each section is within tag inside the
    tag. The href component will give you the link of the section and the tag inside gives the section name. Inspecting the source code of the first relevant page in Devgan website To try out the extraction, we can utilize the interpreter functionality of Scapy. For that just activate your Scrapy VM and type scrapy shell '{website-link}' , here it will be scrapy shell http://devgan.in/all_sections_ipc.php . This opens a playground where we can play around with response variable to experiment with different extraction queries. To extract the individual sections we can use CSS query - response.css('div#content').css('a') . Note, here we define the {tag_type}#{id} as the first CSS query and then use another CSS query - a . This will give us a list of all the tags inside the
    tag. Now from within section, to extract the title, we can use CSS query - section.css('span.sectionlink::text').extract() . For this to work, you should save the last query as section variable. Similar approach can be applied to extract the description from the next page. Just re-run the shell with one of the section's link and try out building CSS query. Once you have all the queries ready, we can move on to the main coding part Note You can refer the Scrapy official doc for more details on creating CSS or XPath queries. Setup Scrapy project First, let us install the Scapy package using pip. It can be easily done by running the following command in the terminal: pip install scrapy . Do make sure to create your own virtual environment (VE), activate it and then install the package in that environment. For confusion regarding VE, refer my snippets on the same topic. Next, let us setup the Scrapy project. Go to your directory of choice and run the command scrapy startproject tutorial . This will create a project with folder structure as shown below. We can ignore most of the files created here, our main focus will be on the spiders/ directory. tutorial/ scrapy.cfg # deploy configuration file tutorial/ # project's Python module, you'll import your code from here __init__.py items.py # project items definition file middlewares.py # project middlewares file pipelines.py # project pipelines file settings.py # project settings file spiders/ # a directory where you'll later put your spiders __init__.py Note The above folder structure is taken from the Scrapy Official Tutorial Create your Spider Usually we create one spider to scrap one website. For this example we will do exactly the same for Devgan website. So let's create a spider spiders/devgan.py . The code is shown below, 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 # import import scrapy # function class DevganSpider ( scrapy . Spider ): name = \"devgan\" allowed_domains = [ \"devgan.in\" ] def start_requests ( self ): urls = [ 'http://devgan.in/all_sections_ipc.php' , ] for url in urls : yield scrapy . Request ( url = url , callback = self . parse_mainpage ) def parse_mainpage ( self , response ): # identify the links to the individual section pages sections = response . css ( 'div#content' ) . css ( 'a' ) #.getall() # for each section for section in sections : # loc var loc = { 'title' : section . xpath ( '@title' ) . extract (), 'link' : 'http://devgan.in' + section . xpath ( '@href' ) . extract ()[ 0 ], 'section' : section . css ( 'span.sectionlink::text' ) . extract (), } # traverse again and extract the description yield scrapy . Request ( loc [ 'link' ], callback = self . parse_section , cb_kwargs = dict ( meta = loc )) def parse_section ( self , response , meta ): # extract the description meta [ 'description' ] = \" \" . join ( response . css ( 'tr.mys-desc' ) . css ( '::text' ) . extract ()) # return return meta Now lets us try to understand the code line by line, Line 2: Importing the scrapy package. Line 5: Defining the spider class that inherits the scrapy.Spider class. Line 6-7: We define the name of the spider and allow the spider to crawl the domain devgan.in . Line 9-14: We define the start_requests method. This method is called when the spider is executed. It calls the scraping function for each url to scrap, it is done using scrapy.Request function call. For each url, the scraping function set within callback parameter is called. Line 16: We define the scraping function parse_mainpage for the main page. Note, this function receives response as an argument, that contains the source code from the server for the main page url. Line 18-29: We start with identifying the links to the individual section pages, store them in sections , and call the scraping function parse_section for each section. Before that, we also extract the title, link and section name from the main page using the queries we created before. One point to remember, this particular example is little complex as we want to traverse further inside into the section pages. For this, we again call the scrapy.Request for the individual section links. Finally, we want to pass the data collected form this page to the section page, as we will consolidate all data for individual section there and return it. For this, we use cb_kwargs parameter to pass the meta data to the next function. Line 31-35: We extract the description from the section page using the CSS query. We add description detail to the metadata and return the complete data that is to be persisted. Executing the spider To run the spider, traverse to the root directory and execure the following command, scrapy crawl devgan -O sections_details.csv -t csv . Here, devgan is the name of the spider we created earlier, -O is used to set the output file name as sections_details.csv . -t is used to define the output format as csv . This will create the csv file with all details of the sections as separate columns as shown below (only 2 rows) title link section description IPC Section 1... http://... Section 1 This Act shall be called the Indian Penal Code... IPC Section 2... http://... Section 2 Every person shall be liable to punishment un.... And that's it! Cheers!","title":"Scraping websites using Scrapy"},{"location":"python/scraping_websites/#scraping-websites-using-scrapy","text":"","title":"Scraping websites using Scrapy"},{"location":"python/scraping_websites/#introduction","text":"Scraping is the process of traversing and collecting data from the web pages. People scrap websites for many reasons -- be it to get information about companies or fetch latest news or get stock prices informations or just create a dataset for the next big AI model In this article, we will be using Scrapy to scrape data from the Devgan website that hosts details of difference sections in Indian Penal Code (IPC). I think this example provides the perfect blend of basic and advanced scraping techniques. So let's get started! Tip The complete code is also available on GitHub","title":"Introduction"},{"location":"python/scraping_websites/#understanding-the-website","text":"Before we even start scraping, we need to understand the structure of the website. This is very important, as we want to (1) get an idea of what we want to scrap and (2) where those data are located. The flow of scraping section descriptions from Devgan website Our goal is to scrap the description for each section in IPC. As per the website flow above, the complete process can be divided into two parts, First, we need to traverse to the main page and extract the link of each sections. Next, we need to traverse to each individual section page and extract the description details present there.","title":"Understanding the website"},{"location":"python/scraping_websites/#data-extraction-methods","text":"Now, let's also look into different methods exposed by Scrapy to extract data from the web pages. The basic idea is that scrapy downloads the web page source code in HTML and parse it using different parsers. For this, we can either use XPaths or CSS selectors. The choice is purely up to us. We can begin with the main page and try to find the link of each section. For this, you can open inspect option from your browser by right clicking on any of the sections and select inspect . This should show you the source code. Next, try to find the position of the tag where each section is defined. Refer the image below, and you can see that each section is within tag inside the
    tag. The href component will give you the link of the section and the tag inside gives the section name. Inspecting the source code of the first relevant page in Devgan website To try out the extraction, we can utilize the interpreter functionality of Scapy. For that just activate your Scrapy VM and type scrapy shell '{website-link}' , here it will be scrapy shell http://devgan.in/all_sections_ipc.php . This opens a playground where we can play around with response variable to experiment with different extraction queries. To extract the individual sections we can use CSS query - response.css('div#content').css('a') . Note, here we define the {tag_type}#{id} as the first CSS query and then use another CSS query - a . This will give us a list of all the tags inside the
    tag. Now from within section, to extract the title, we can use CSS query - section.css('span.sectionlink::text').extract() . For this to work, you should save the last query as section variable. Similar approach can be applied to extract the description from the next page. Just re-run the shell with one of the section's link and try out building CSS query. Once you have all the queries ready, we can move on to the main coding part Note You can refer the Scrapy official doc for more details on creating CSS or XPath queries.","title":"Data extraction methods"},{"location":"python/scraping_websites/#setup-scrapy-project","text":"First, let us install the Scapy package using pip. It can be easily done by running the following command in the terminal: pip install scrapy . Do make sure to create your own virtual environment (VE), activate it and then install the package in that environment. For confusion regarding VE, refer my snippets on the same topic. Next, let us setup the Scrapy project. Go to your directory of choice and run the command scrapy startproject tutorial . This will create a project with folder structure as shown below. We can ignore most of the files created here, our main focus will be on the spiders/ directory. tutorial/ scrapy.cfg # deploy configuration file tutorial/ # project's Python module, you'll import your code from here __init__.py items.py # project items definition file middlewares.py # project middlewares file pipelines.py # project pipelines file settings.py # project settings file spiders/ # a directory where you'll later put your spiders __init__.py Note The above folder structure is taken from the Scrapy Official Tutorial","title":"Setup Scrapy project"},{"location":"python/scraping_websites/#create-your-spider","text":"Usually we create one spider to scrap one website. For this example we will do exactly the same for Devgan website. So let's create a spider spiders/devgan.py . The code is shown below, 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 # import import scrapy # function class DevganSpider ( scrapy . Spider ): name = \"devgan\" allowed_domains = [ \"devgan.in\" ] def start_requests ( self ): urls = [ 'http://devgan.in/all_sections_ipc.php' , ] for url in urls : yield scrapy . Request ( url = url , callback = self . parse_mainpage ) def parse_mainpage ( self , response ): # identify the links to the individual section pages sections = response . css ( 'div#content' ) . css ( 'a' ) #.getall() # for each section for section in sections : # loc var loc = { 'title' : section . xpath ( '@title' ) . extract (), 'link' : 'http://devgan.in' + section . xpath ( '@href' ) . extract ()[ 0 ], 'section' : section . css ( 'span.sectionlink::text' ) . extract (), } # traverse again and extract the description yield scrapy . Request ( loc [ 'link' ], callback = self . parse_section , cb_kwargs = dict ( meta = loc )) def parse_section ( self , response , meta ): # extract the description meta [ 'description' ] = \" \" . join ( response . css ( 'tr.mys-desc' ) . css ( '::text' ) . extract ()) # return return meta Now lets us try to understand the code line by line, Line 2: Importing the scrapy package. Line 5: Defining the spider class that inherits the scrapy.Spider class. Line 6-7: We define the name of the spider and allow the spider to crawl the domain devgan.in . Line 9-14: We define the start_requests method. This method is called when the spider is executed. It calls the scraping function for each url to scrap, it is done using scrapy.Request function call. For each url, the scraping function set within callback parameter is called. Line 16: We define the scraping function parse_mainpage for the main page. Note, this function receives response as an argument, that contains the source code from the server for the main page url. Line 18-29: We start with identifying the links to the individual section pages, store them in sections , and call the scraping function parse_section for each section. Before that, we also extract the title, link and section name from the main page using the queries we created before. One point to remember, this particular example is little complex as we want to traverse further inside into the section pages. For this, we again call the scrapy.Request for the individual section links. Finally, we want to pass the data collected form this page to the section page, as we will consolidate all data for individual section there and return it. For this, we use cb_kwargs parameter to pass the meta data to the next function. Line 31-35: We extract the description from the section page using the CSS query. We add description detail to the metadata and return the complete data that is to be persisted.","title":"Create your Spider"},{"location":"python/scraping_websites/#executing-the-spider","text":"To run the spider, traverse to the root directory and execure the following command, scrapy crawl devgan -O sections_details.csv -t csv . Here, devgan is the name of the spider we created earlier, -O is used to set the output file name as sections_details.csv . -t is used to define the output format as csv . This will create the csv file with all details of the sections as separate columns as shown below (only 2 rows) title link section description IPC Section 1... http://... Section 1 This Act shall be called the Indian Penal Code... IPC Section 2... http://... Section 2 Every person shall be liable to punishment un.... And that's it! Cheers!","title":"Executing the spider"},{"location":"reinforcement_learning/interview_questions/","text":"Here are some questions and their answers to make you ready for your next interview. Best of luck Question Answer Explain Reinforcement learning (RL) in deep learning. Reinforcement learning is a type of machine learning where an agent learns to make decisions by interacting with an environment. The agent receives rewards or penalties based on its actions, and the goal is to learn a policy that maximizes the total reward. Deep reinforcement learning combines reinforcement learning with deep neural networks, allowing the agent to make decisions based on complex, high-dimensional input such as images or speech. Question Answer What is the use of the deep reinforcement learning in machine learning? Deep reinforcement learning (DRL) is a type of reinforcement learning that uses deep neural networks as function approximators to learn policies or value functions. DRL allows for the use of complex, high-dimensional observations such as images or speech and can be used for tasks such as playing games, controlling robots, and optimizing resource allocation. Question Answer What is the difference between model-free and model-based RL? Model-based RL has an agent that tries to understand (or model) the world by learning state transition function and reward function. So if an agent can predict the next state and the reward before taking the action, it is a model-based RL algorithm. If not then it is model-free algorithm. Refer this AI StackExchange QA Question Answer What is the difference between value-based and policy-based reinforcement learning? Value-based reinforcement learning methods, such as Q-learning, estimate the value of different actions given a certain state, and then take the action that maximizes this value. Policy-based methods, such as REINFORCE, directly learn a policy that maps states to actions. Question Answer How does Q-learning work in reinforcement learning? Q-learning is a value-based reinforcement learning algorithm that estimates the value of different actions given a certain state. It uses the Bellman equation to update the Q-value, which is the expected future reward of taking an action in a certain state. Over time, the algorithm converges to the optimal Q-value function that represents the best action to take in each state. Question Answer What is the difference between on-policy and off-policy reinforcement learning? On-policy reinforcement learning (ex: SARSA) methods learn from the actions taken by the current policy, while off-policy (ex: Q-learning) methods learn from actions taken by a different policy (like greedy approach). On-policy methods are often used when an agent can easily interact with its environment, while off-policy methods are used when it is difficult or expensive to interact with the environment. Refer this StackOverflow QA for more details. Question Answer What is the use of the SARSA algorithm in reinforcement learning? SARSA (State-Action-Reward-State-Action) is a type of on-policy algorithm that estimates the value of different actions given a certain state. It updates the Q-value based on the action taken in the next state, rather than the optimal action as in Q-learning. This makes SARSA more suitable for problems with non-deterministic environments. Question Answer Explain actor-critic algorithm. Actor-Critic is a type of algorithm that combines both policy-based and value-based methods in reinforcement learning. The actor network is used to output a probability distribution over actions given a certain state, while the critic network is used to estimate the value of the actions taken by the actor network. The actor network is then updated according to the critic network's evaluation of its actions. Question Answer Explain A3C algorithm in reinforcement learning. A3C (Asynchronous Advantage Actor-Critic) is an algorithm that uses multiple parallel agents to train a neural network in an asynchronous manner. A3C is an extension of the actor-critic algorithm that allows for faster and more stable training by reducing the correlation between the updates of different agents. It's useful for training agents for tasks that require handling high dimensional and continuous action spaces, such as robotics and gaming. Question Answer What is the use of the Q-network in reinforcement learning? A Q-network is a neural network used in Q-learning, a value-based reinforcement learning algorithm. The Q-network is trained to estimate the value of taking different actions given a certain state. It's used to approximate the Q-value function, which represents the expected future rewards of taking different actions in different states, and to make decisions based on the current state. Question Answer What is Monte Carlo Tree Search in reinforcement learning? Monte Carlo Tree Search (MCTS) is a method used to select the next action in a reinforcement learning problem. MCTS works by building a search tree, where each node represents a state and each edge represents an action. The algorithm explores the tree by selecting the best node based on a combination of the current value estimates and the uncertainty of the estimates. MCTS is particularly useful for problems with large and complex action spaces, such as game playing.","title":"Interview Questions"},{"location":"reinforcement_learning/interview_questions/#explain-reinforcement-learning-rl-in-deep-learning","text":"Reinforcement learning is a type of machine learning where an agent learns to make decisions by interacting with an environment. The agent receives rewards or penalties based on its actions, and the goal is to learn a policy that maximizes the total reward. Deep reinforcement learning combines reinforcement learning with deep neural networks, allowing the agent to make decisions based on complex, high-dimensional input such as images or speech. Question Answer","title":"Explain Reinforcement learning (RL) in deep learning."},{"location":"reinforcement_learning/interview_questions/#what-is-the-use-of-the-deep-reinforcement-learning-in-machine-learning","text":"Deep reinforcement learning (DRL) is a type of reinforcement learning that uses deep neural networks as function approximators to learn policies or value functions. DRL allows for the use of complex, high-dimensional observations such as images or speech and can be used for tasks such as playing games, controlling robots, and optimizing resource allocation. Question Answer","title":"What is the use of the deep reinforcement learning in machine learning?"},{"location":"reinforcement_learning/interview_questions/#what-is-the-difference-between-model-free-and-model-based-rl","text":"Model-based RL has an agent that tries to understand (or model) the world by learning state transition function and reward function. So if an agent can predict the next state and the reward before taking the action, it is a model-based RL algorithm. If not then it is model-free algorithm. Refer this AI StackExchange QA Question Answer","title":"What is the difference between model-free and model-based RL?"},{"location":"reinforcement_learning/interview_questions/#what-is-the-difference-between-value-based-and-policy-based-reinforcement-learning","text":"Value-based reinforcement learning methods, such as Q-learning, estimate the value of different actions given a certain state, and then take the action that maximizes this value. Policy-based methods, such as REINFORCE, directly learn a policy that maps states to actions. Question Answer","title":"What is the difference between value-based and policy-based reinforcement learning?"},{"location":"reinforcement_learning/interview_questions/#how-does-q-learning-work-in-reinforcement-learning","text":"Q-learning is a value-based reinforcement learning algorithm that estimates the value of different actions given a certain state. It uses the Bellman equation to update the Q-value, which is the expected future reward of taking an action in a certain state. Over time, the algorithm converges to the optimal Q-value function that represents the best action to take in each state. Question Answer","title":"How does Q-learning work in reinforcement learning?"},{"location":"reinforcement_learning/interview_questions/#what-is-the-difference-between-on-policy-and-off-policy-reinforcement-learning","text":"On-policy reinforcement learning (ex: SARSA) methods learn from the actions taken by the current policy, while off-policy (ex: Q-learning) methods learn from actions taken by a different policy (like greedy approach). On-policy methods are often used when an agent can easily interact with its environment, while off-policy methods are used when it is difficult or expensive to interact with the environment. Refer this StackOverflow QA for more details. Question Answer","title":"What is the difference between on-policy and off-policy reinforcement learning?"},{"location":"reinforcement_learning/interview_questions/#what-is-the-use-of-the-sarsa-algorithm-in-reinforcement-learning","text":"SARSA (State-Action-Reward-State-Action) is a type of on-policy algorithm that estimates the value of different actions given a certain state. It updates the Q-value based on the action taken in the next state, rather than the optimal action as in Q-learning. This makes SARSA more suitable for problems with non-deterministic environments. Question Answer","title":"What is the use of the SARSA algorithm in reinforcement learning?"},{"location":"reinforcement_learning/interview_questions/#explain-actor-critic-algorithm","text":"Actor-Critic is a type of algorithm that combines both policy-based and value-based methods in reinforcement learning. The actor network is used to output a probability distribution over actions given a certain state, while the critic network is used to estimate the value of the actions taken by the actor network. The actor network is then updated according to the critic network's evaluation of its actions. Question Answer","title":"Explain actor-critic algorithm."},{"location":"reinforcement_learning/interview_questions/#explain-a3c-algorithm-in-reinforcement-learning","text":"A3C (Asynchronous Advantage Actor-Critic) is an algorithm that uses multiple parallel agents to train a neural network in an asynchronous manner. A3C is an extension of the actor-critic algorithm that allows for faster and more stable training by reducing the correlation between the updates of different agents. It's useful for training agents for tasks that require handling high dimensional and continuous action spaces, such as robotics and gaming. Question Answer","title":"Explain A3C algorithm in reinforcement learning."},{"location":"reinforcement_learning/interview_questions/#what-is-the-use-of-the-q-network-in-reinforcement-learning","text":"A Q-network is a neural network used in Q-learning, a value-based reinforcement learning algorithm. The Q-network is trained to estimate the value of taking different actions given a certain state. It's used to approximate the Q-value function, which represents the expected future rewards of taking different actions in different states, and to make decisions based on the current state. Question Answer","title":"What is the use of the Q-network in reinforcement learning?"},{"location":"reinforcement_learning/interview_questions/#what-is-monte-carlo-tree-search-in-reinforcement-learning","text":"Monte Carlo Tree Search (MCTS) is a method used to select the next action in a reinforcement learning problem. MCTS works by building a search tree, where each node represents a state and each edge represents an action. The algorithm explores the tree by selecting the best node based on a combination of the current value estimates and the uncertainty of the estimates. MCTS is particularly useful for problems with large and complex action spaces, such as game playing.","title":"What is Monte Carlo Tree Search in reinforcement learning?"},{"location":"reinforcement_learning/introduction/","text":"Getting started Before we deep-dive into Reinforcement Learning (RL), let's go through some videos that showcase how RL is solving some really complicated real world problems: Waymo 360 Experience of Autonomous Driving System DeepMind AI that plays 57 Atari games - Two Minute Papers QT-Opt - Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation As obvious from the videos, RL is used across domains - it can control cars, play games and even interact with real world using robot. Isn't it interesting?! Now let's answer the first question - What is Reinforcement Learning? Contrary to basic supervised and unsupervised problems, RL is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. [1] I know this must be too much to unpack, so let's dissect some important keywords in the next section. Important terms Agent: it is the entity that takes some action which is influenced by current scenario and it's past experience. We try to create an algorithm or AI model that can act as an agent. Environment: this is where agent takes actions. It is the complete world wherein the agent lives and traverse. The environment considers the agent's actions and return rewards (positive or negative based on type of impact) and changes the state of the agent. An environment could be deterministic (taking an action from a state leads to a fixed state always) or non-deteministic (state transition wrt action selection is probabilistic) . graph LR A[Agent] -- state 's' --> B[Environment]; A -- action 'a' --> B; B -- reward 'r' --> A; B -- new state 'sn' --> A; States: An environment consists of multiple states, infact this denotes the different scenarios an agent could be in. States could be (1) engineered (ex: In MountainCar env, combination of calculated position and velocity forms the state) , (2) raw (ex: using screen images for video games) , or (3) combination of both (ex: autonomus driving system considering raw video feeds and some extracted features) . States could be discrete (ex: cell A to cell B) or continous (ex: screenshot of a game, no two images could be same) . Action: These are the possible interactions an agents can make with the environment. Actions could differ wrt to the current state of the agent. It can also be discrete (ex: left turn and right turn) or continous (ex: -120 degree turn) . Reward: Agent performs some actions and enviroment returns rewards. This is usually programmed wrt the behavior we want the agent to learn. If we want the agent to reach a goal -- give +1 point at that point. If we want the agent to move faster -- give -1 for every step it takes (so that it tries ot finish the game asap) . Reward designing is a very important part of solving RL problem. Policy: It is the rulebook that tells the agent what to do for a given state and possible set of actions. Policy based RL agents tries to directly learn the best policy for a given state, whereas value based RL agents learns the value function (estimated reward for each action in a state) and then select action based on their strategy. Episodes: One complete iteration over the environment by the agent, either till it passed or failed, is called an epsiode. In game terminology, think of a round as one episode. The Paradigms in RL Forward Reinforcement Learning It refers to the process of learning a policy or control strategy in an environment by taking actions and receiving feedback or rewards from the environment. The objective of this approach is to maximize the expected cumulative reward obtained from the environment by learning a policy that maps the state of the environment to an action to be taken. Inverse Reinforcement Learning Inverse Reinforcement Learning (IRL) aims to learn the underlying reward function of a given environment from a set of observed trajectories, instead of learning a policy directly. This approach can be used to infer the reward function from human demonstrations or expert behavior, and then learn a policy that maximizes the inferred reward function. Behavior Cloning Behavior cloning refers to a supervised learning technique where a model is trained to mimic the behavior of an expert agent or human in a given task. In this approach, the model is trained on a dataset of state-action pairs, where each action is a direct imitation of the expert's actions in the corresponding state. Behavior cloning can be used as a pre-training step for more complex reinforcement learning algorithms, or as a standalone approach in cases where the expert's behavior is sufficient for solving the task. Apprenticeship Learning Apprenticeship learning, also known as imitation learning, is a machine learning technique where a learner tries to imitate the behavior of an expert agent or human in a given task. Unlike behavior cloning, which only learns to mimic the expert's actions, apprenticeship learning aims to learn the underlying decision-making process or policy of the expert by observing their behavior in a given environment. This approach is useful when the expert's behavior is not easily captured by a simple state-action mapping, and when the reward function of the task is unknown or difficult to specify. Hint One easy way to differentiate the above mentioned domains is as follows, [3] In IRL, we recover a Reward function In Apprenticeship Learning, we find a good policy using the recovered reward function from IRL In Behavior cloning, we directly learn the teacher's policy using supervised learning Resources If you are looking for a deep dive into Reinforcement learning, here are some good materials, I cannot think of a better free course than David Silver on Youtube . In case you prefer books, here is the best of them all - Reinforcement Learning: An Introduction . If you love the API doc way of learning, here is OpenAI Spinning Up in Deep RL References [1] Reinforcement Learning - Wikipedia [2] Apprenticeship Learning via Inverse Reinforcement Learning - Paper [3] Inverse Reinforcement Learning - Lecture PDF","title":"Introduction"},{"location":"reinforcement_learning/introduction/#getting-started","text":"Before we deep-dive into Reinforcement Learning (RL), let's go through some videos that showcase how RL is solving some really complicated real world problems: Waymo 360 Experience of Autonomous Driving System DeepMind AI that plays 57 Atari games - Two Minute Papers QT-Opt - Scalable Deep Reinforcement Learning for Vision-Based Robotic Manipulation As obvious from the videos, RL is used across domains - it can control cars, play games and even interact with real world using robot. Isn't it interesting?! Now let's answer the first question - What is Reinforcement Learning? Contrary to basic supervised and unsupervised problems, RL is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. [1] I know this must be too much to unpack, so let's dissect some important keywords in the next section.","title":"Getting started"},{"location":"reinforcement_learning/introduction/#important-terms","text":"Agent: it is the entity that takes some action which is influenced by current scenario and it's past experience. We try to create an algorithm or AI model that can act as an agent. Environment: this is where agent takes actions. It is the complete world wherein the agent lives and traverse. The environment considers the agent's actions and return rewards (positive or negative based on type of impact) and changes the state of the agent. An environment could be deterministic (taking an action from a state leads to a fixed state always) or non-deteministic (state transition wrt action selection is probabilistic) . graph LR A[Agent] -- state 's' --> B[Environment]; A -- action 'a' --> B; B -- reward 'r' --> A; B -- new state 'sn' --> A; States: An environment consists of multiple states, infact this denotes the different scenarios an agent could be in. States could be (1) engineered (ex: In MountainCar env, combination of calculated position and velocity forms the state) , (2) raw (ex: using screen images for video games) , or (3) combination of both (ex: autonomus driving system considering raw video feeds and some extracted features) . States could be discrete (ex: cell A to cell B) or continous (ex: screenshot of a game, no two images could be same) . Action: These are the possible interactions an agents can make with the environment. Actions could differ wrt to the current state of the agent. It can also be discrete (ex: left turn and right turn) or continous (ex: -120 degree turn) . Reward: Agent performs some actions and enviroment returns rewards. This is usually programmed wrt the behavior we want the agent to learn. If we want the agent to reach a goal -- give +1 point at that point. If we want the agent to move faster -- give -1 for every step it takes (so that it tries ot finish the game asap) . Reward designing is a very important part of solving RL problem. Policy: It is the rulebook that tells the agent what to do for a given state and possible set of actions. Policy based RL agents tries to directly learn the best policy for a given state, whereas value based RL agents learns the value function (estimated reward for each action in a state) and then select action based on their strategy. Episodes: One complete iteration over the environment by the agent, either till it passed or failed, is called an epsiode. In game terminology, think of a round as one episode.","title":"Important terms"},{"location":"reinforcement_learning/introduction/#the-paradigms-in-rl","text":"","title":"The Paradigms in RL"},{"location":"reinforcement_learning/introduction/#forward-reinforcement-learning","text":"It refers to the process of learning a policy or control strategy in an environment by taking actions and receiving feedback or rewards from the environment. The objective of this approach is to maximize the expected cumulative reward obtained from the environment by learning a policy that maps the state of the environment to an action to be taken.","title":"Forward Reinforcement Learning"},{"location":"reinforcement_learning/introduction/#inverse-reinforcement-learning","text":"Inverse Reinforcement Learning (IRL) aims to learn the underlying reward function of a given environment from a set of observed trajectories, instead of learning a policy directly. This approach can be used to infer the reward function from human demonstrations or expert behavior, and then learn a policy that maximizes the inferred reward function.","title":"Inverse Reinforcement Learning"},{"location":"reinforcement_learning/introduction/#behavior-cloning","text":"Behavior cloning refers to a supervised learning technique where a model is trained to mimic the behavior of an expert agent or human in a given task. In this approach, the model is trained on a dataset of state-action pairs, where each action is a direct imitation of the expert's actions in the corresponding state. Behavior cloning can be used as a pre-training step for more complex reinforcement learning algorithms, or as a standalone approach in cases where the expert's behavior is sufficient for solving the task.","title":"Behavior Cloning"},{"location":"reinforcement_learning/introduction/#apprenticeship-learning","text":"Apprenticeship learning, also known as imitation learning, is a machine learning technique where a learner tries to imitate the behavior of an expert agent or human in a given task. Unlike behavior cloning, which only learns to mimic the expert's actions, apprenticeship learning aims to learn the underlying decision-making process or policy of the expert by observing their behavior in a given environment. This approach is useful when the expert's behavior is not easily captured by a simple state-action mapping, and when the reward function of the task is unknown or difficult to specify. Hint One easy way to differentiate the above mentioned domains is as follows, [3] In IRL, we recover a Reward function In Apprenticeship Learning, we find a good policy using the recovered reward function from IRL In Behavior cloning, we directly learn the teacher's policy using supervised learning","title":"Apprenticeship Learning"},{"location":"reinforcement_learning/introduction/#resources","text":"If you are looking for a deep dive into Reinforcement learning, here are some good materials, I cannot think of a better free course than David Silver on Youtube . In case you prefer books, here is the best of them all - Reinforcement Learning: An Introduction . If you love the API doc way of learning, here is OpenAI Spinning Up in Deep RL","title":"Resources"},{"location":"reinforcement_learning/introduction/#references","text":"[1] Reinforcement Learning - Wikipedia [2] Apprenticeship Learning via Inverse Reinforcement Learning - Paper [3] Inverse Reinforcement Learning - Lecture PDF","title":"References"},{"location":"reinforcement_learning/q_learning/","text":"Introduction Q-learning is a RL algorithm that computes an expected reward for taking an action from any given state. The expected reward is a composition of the immediate reward and the future rewards possible from the new transitioned state. It is an iteration based algorithm that adjusts the scores over time, and given infinite exploration time it can identify an optimal action-selection policy. Note Q-learning does not requires to model the environment, hence it is model-free (check here for more details) . To better grasp the intuition, let's understand it through a Grid world example before further complications creeps in. The Beer game in Grid world Think of Grid World as a nD world (environment) where an agent will live and traverse. The environment consists of multiple cells, where each cell denotes one state, and it could deliver certain reward (positive or negative) to reach that state. The agent at any given time, will only have the knowledge of the current state and the information it gained in previous episodes. Now imagine we drop an agent in a 2D grid world that contains a beer (that we want to find) and a pit (that we want to avoid) . The agent traverse the world until it either finds the beer or drops into the pit. If we make the agent play this game 1000s of times, how should the agent behave overtime? The answer seems simple -- at first the agent will have no knowledge of the world, so it will stumble across the env, like a person performing coin toss to decide his fate . Sometimes, it will find the beer or the pit, and at either points, it starts again. The interesting part is, after each episode the agent will become more and more aware of the world. And after many episodes it would have developed a detailed map, that guides to the beer with details of the areas you should not go to (as it contains the pit) \ud83d\ude0e A technical recreation of The Beer Game with left most cell denoting the pit, the 7th cell contains beer and the rest of the cell denotes the expected reward if we move in the suggested direction. [1] Bellman's Equation The next question is how to capture the details of the map? That's where bellman's equation comes into the picture. Basically it learns Q (quality) scores, that represents expected reward, for taking an action \\(a\\) from a state \\(s\\) . If we capture this for all the states and actions in the world then viola, we have the map! The formulation of Q-score is given below. \\[Q(s_{t},a_{t}) = r_{t} + \\gamma \\cdot \\max _{a}Q(s_{t+1},a)\\] Here, \\(Q(s_{t},a_{t})\\) is the Q-score for taking action \\(a_t\\) in state \\(s_t\\) . \\(r_t\\) is the reward we get to perform the action. \\(\\gamma\\) is the discount factor which is multiplied with the current reward estimation for the best possible action from the next step \\(s_{t+1}\\) . The discount factor (range 0 to \\(\\inf\\) ) is used to adjust the importance to be given to immediate reward (low \\(\\gamma\\) ) or future reward (high \\(\\gamma\\) ) . The formulation to update the Q-score is given below, where \\(\\alpha\\) is the learning rate (yes, like the Neural networks ) \\[Q^{new}(s_{t},a_{t}) = Q^{old}(s_{t},a_{t}) + \\alpha \\cdot (r_{t} + \\gamma \\cdot \\max _{a}Q(s_{t+1},a) - Q^{old}(s_{t},a_{t}))\\] Below is an example of a 2D grid world, where we have set the middle cell with a reward of +1 and we perform multiple iterations of the q-learning. Agent performing Q learning over the 2D grid world. [1] Hint If you want to create and play around with your 2D grid world, try this Interactive Q-Learning playgorund by yours truly. [1] Code Exploring MountainCar env Now let's get started with some action i.e. coding. We will train a RL agent using Q-learning to beat the game of MountainCar available on OpenAI's Gym package that provides a simple interface to represent and play with general RL problems. Before that, let's understand some fundamentals of the MountainCar game environment. Aim: To get the car to reach the yellow flag Observation (states) : Position: -1.2 to 0.6 Velocity: -0.07 to 0.07 Actions: Push left: 0 No push: 1 Right push: 2 Reward: Reached yellow flag: +1 Other: -0.5 Why is this difficult? Bcoz a normal continous right push will not work, to win we need to oscillate as we need momentum from left. Untrained agent in the MountainCar env We can explore the same via code as shown below, 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 \"\"\" Reference: https://blog.paperspace.com/getting-started-with-openai-gym/ Requirements; (don't forget to create new VE) pip install gym pip install \"gym[box2d]\" pip install \"gym[other]\" \"\"\" # import import gym # create an env env = gym . make ( \"MountainCar-v0\" , render_mode = \"human\" ) # seed env . action_space . seed ( 42 ) # Observation and action space obs_space = env . observation_space action_space = env . action_space print ( \"The observation space: {} \" . format ( obs_space )) print ( \"The action space: {} \" . format ( action_space )) # reset the space observation , info = env . reset ( seed = 42 ) print ( f \"Observation: { observation } \" ) print ( f \"Info: { info } \" ) print ( f \"Sample action; { env . action_space . sample () } \" ) ## OUTPUT # The observation space: Box([-1.2 -0.07], [0.6 0.07], (2,), float32) # The action space: Discrete(3) # Observation: [-0.4452088 0. ] # Info: {} # Sample action; 0 Training Agent using Q-learning Hint Before starting with this section, why not try to code up some basic logic to beat the game? You could try to always push right or oscillate left and right at certain intervals. Give it try, it will be fun The code shared below (credits to gkhayes ) trains a Q-learning agent to play the MountainCar game. It tries to create a Q-table (that contains Q-scores) for all the combination of states and actions. As the states are continous (they are in float) , it is first discretized to make it a finite state problem. The final Q-table is of the shape [19, 15, 3] , with 19 possible states for position, 15 states for velocity and 3 actions. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 \"\"\" Reference and Credit: https://gist.github.com/gkhayes/3d154e0505e31d6367be22ed3da2e955 Requirements; (don't forget to create new VE) pip install gym pip install \"gym[box2d]\" pip install \"gym[other]\" pip install numpy, matplotlib \"\"\" # import import numpy as np import gym import matplotlib.pyplot as plt # Import and initialize Mountain Car Environment env = gym . make ( 'MountainCar-v0' ) env . reset () # Define Q-learning function def QLearning ( env , learning , discount , epsilon , min_eps , episodes ): # Determine size of discretized state space num_states = ( env . observation_space . high - env . observation_space . low ) * \\ np . array ([ 10 , 100 ]) num_states = np . round ( num_states , 0 ) . astype ( int ) + 1 # Initialize Q table Q = np . random . uniform ( low = - 1 , high = 1 , size = ( num_states [ 0 ], num_states [ 1 ], env . action_space . n )) # Initialize variables to track rewards reward_list = [] ave_reward_list = [] # Calculate episodic reduction in epsilon reduction = ( epsilon - min_eps ) / episodes # Run Q learning algorithm for i in range ( episodes ): # Initialize parameters done = False tot_reward , reward = 0 , 0 state = env . reset () # Discretize state state_adj = ( state [ 0 ] - env . observation_space . low ) * np . array ([ 10 , 100 ]) state_adj = np . round ( state_adj , 0 ) . astype ( int ) while done != True : # Render environment for last five episodes if i >= ( episodes - 20 ): env . render () # Determine next action - epsilon greedy strategy if np . random . random () < 1 - epsilon : action = np . argmax ( Q [ state_adj [ 0 ], state_adj [ 1 ]]) else : action = np . random . randint ( 0 , env . action_space . n ) # Get next state and reward state2 , reward , done , info , _ = env . step ( action ) # Discretize state2 state2_adj = ( state2 - env . observation_space . low ) * np . array ([ 10 , 100 ]) state2_adj = np . round ( state2_adj , 0 ) . astype ( int ) #Allow for terminal states if done and state2 [ 0 ] >= 0.5 : Q [ state_adj [ 0 ], state_adj [ 1 ], action ] = reward # Adjust Q value for current state else : delta = learning * ( reward + discount * np . max ( Q [ state2_adj [ 0 ], state2_adj [ 1 ]]) - Q [ state_adj [ 0 ], state_adj [ 1 ], action ]) Q [ state_adj [ 0 ], state_adj [ 1 ], action ] += delta # Update variables tot_reward += reward state_adj = state2_adj # Decay epsilon if epsilon > min_eps : epsilon -= reduction # Track rewards reward_list . append ( tot_reward ) if ( i + 1 ) % 100 == 0 : ave_reward = np . mean ( reward_list ) ave_reward_list . append ( ave_reward ) reward_list = [] if ( i + 1 ) % 100 == 0 : print ( 'Episode {} Average Reward: {} ' . format ( i + 1 , ave_reward )) env . close () # save the Q table np . save ( 'MountainCar-v0-q-learning' , Q ) return ave_reward_list # Run Q-learning algorithm rewards = QLearning ( env , 0.2 , 0.9 , 0.8 , 0 , 5000 ) # Plot Rewards plt . plot ( 100 * ( np . arange ( len ( rewards )) + 1 ), rewards ) plt . xlabel ( 'Episodes' ) plt . ylabel ( 'Average Reward' ) plt . title ( 'Average Reward vs Episodes' ) plt . savefig ( 'rewards.jpg' ) plt . close () Running the above code trains a RL agent for 5000 episodes and saves the Q-table as numpy array. It also computes the average reward vs episodes graph that is shown below. Do note how the agent is able to get more rewards over more training! Increasing reward over time for the agent in training. The average rewards went up from -4300 to -250 under just 5000 episodes. Inference of trained agent To run the trained model, we can use the saved Q-table to select the best action given any state in the game. Below is the code for the same, 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 \"\"\" Requirements; (don't forget to create new VE) pip install gym pip install \"gym[box2d]\" pip install \"gym[other]\" MountainCar-v0-q-table.txt.npy file that contains the modelled environment \"\"\" # import import gym import numpy as np from gym.utils import play # load trained Q table Q = np . load ( 'MountainCar-v0-q-table.txt.npy' ) # create an env env = gym . make ( \"MountainCar-v0\" , render_mode = \"human\" ) # seed env . action_space . seed ( 42 ) observation , info = env . reset ( seed = 42 ) # total number of steps for s in range ( 200 ): state_adj = ( observation - env . observation_space . low ) * np . array ([ 10 , 100 ]) state_adj = np . round ( state_adj , 0 ) . astype ( int ) # define the next step to take next_action = np . argmax ( Q [ state_adj [ 0 ], state_adj [ 1 ]]) # perform one step observation , reward , terminated , truncated , info = env . step ( next_action ) print ( s , observation , reward , terminated , truncated , info ) # if the game ends, restart the game if terminated or truncated : observation , info = env . reset () env . close () Trained RL agent performing as expected There you go!! References Interactive Q Learning - Mohit Mayank","title":"Q-Learning"},{"location":"reinforcement_learning/q_learning/#introduction","text":"Q-learning is a RL algorithm that computes an expected reward for taking an action from any given state. The expected reward is a composition of the immediate reward and the future rewards possible from the new transitioned state. It is an iteration based algorithm that adjusts the scores over time, and given infinite exploration time it can identify an optimal action-selection policy. Note Q-learning does not requires to model the environment, hence it is model-free (check here for more details) . To better grasp the intuition, let's understand it through a Grid world example before further complications creeps in.","title":"Introduction"},{"location":"reinforcement_learning/q_learning/#the-beer-game-in-grid-world","text":"Think of Grid World as a nD world (environment) where an agent will live and traverse. The environment consists of multiple cells, where each cell denotes one state, and it could deliver certain reward (positive or negative) to reach that state. The agent at any given time, will only have the knowledge of the current state and the information it gained in previous episodes. Now imagine we drop an agent in a 2D grid world that contains a beer (that we want to find) and a pit (that we want to avoid) . The agent traverse the world until it either finds the beer or drops into the pit. If we make the agent play this game 1000s of times, how should the agent behave overtime? The answer seems simple -- at first the agent will have no knowledge of the world, so it will stumble across the env, like a person performing coin toss to decide his fate . Sometimes, it will find the beer or the pit, and at either points, it starts again. The interesting part is, after each episode the agent will become more and more aware of the world. And after many episodes it would have developed a detailed map, that guides to the beer with details of the areas you should not go to (as it contains the pit) \ud83d\ude0e A technical recreation of The Beer Game with left most cell denoting the pit, the 7th cell contains beer and the rest of the cell denotes the expected reward if we move in the suggested direction. [1]","title":"The Beer game in Grid world"},{"location":"reinforcement_learning/q_learning/#bellmans-equation","text":"The next question is how to capture the details of the map? That's where bellman's equation comes into the picture. Basically it learns Q (quality) scores, that represents expected reward, for taking an action \\(a\\) from a state \\(s\\) . If we capture this for all the states and actions in the world then viola, we have the map! The formulation of Q-score is given below. \\[Q(s_{t},a_{t}) = r_{t} + \\gamma \\cdot \\max _{a}Q(s_{t+1},a)\\] Here, \\(Q(s_{t},a_{t})\\) is the Q-score for taking action \\(a_t\\) in state \\(s_t\\) . \\(r_t\\) is the reward we get to perform the action. \\(\\gamma\\) is the discount factor which is multiplied with the current reward estimation for the best possible action from the next step \\(s_{t+1}\\) . The discount factor (range 0 to \\(\\inf\\) ) is used to adjust the importance to be given to immediate reward (low \\(\\gamma\\) ) or future reward (high \\(\\gamma\\) ) . The formulation to update the Q-score is given below, where \\(\\alpha\\) is the learning rate (yes, like the Neural networks ) \\[Q^{new}(s_{t},a_{t}) = Q^{old}(s_{t},a_{t}) + \\alpha \\cdot (r_{t} + \\gamma \\cdot \\max _{a}Q(s_{t+1},a) - Q^{old}(s_{t},a_{t}))\\] Below is an example of a 2D grid world, where we have set the middle cell with a reward of +1 and we perform multiple iterations of the q-learning. Agent performing Q learning over the 2D grid world. [1] Hint If you want to create and play around with your 2D grid world, try this Interactive Q-Learning playgorund by yours truly. [1]","title":"Bellman's Equation"},{"location":"reinforcement_learning/q_learning/#code","text":"","title":"Code"},{"location":"reinforcement_learning/q_learning/#exploring-mountaincar-env","text":"Now let's get started with some action i.e. coding. We will train a RL agent using Q-learning to beat the game of MountainCar available on OpenAI's Gym package that provides a simple interface to represent and play with general RL problems. Before that, let's understand some fundamentals of the MountainCar game environment. Aim: To get the car to reach the yellow flag Observation (states) : Position: -1.2 to 0.6 Velocity: -0.07 to 0.07 Actions: Push left: 0 No push: 1 Right push: 2 Reward: Reached yellow flag: +1 Other: -0.5 Why is this difficult? Bcoz a normal continous right push will not work, to win we need to oscillate as we need momentum from left. Untrained agent in the MountainCar env We can explore the same via code as shown below, 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 \"\"\" Reference: https://blog.paperspace.com/getting-started-with-openai-gym/ Requirements; (don't forget to create new VE) pip install gym pip install \"gym[box2d]\" pip install \"gym[other]\" \"\"\" # import import gym # create an env env = gym . make ( \"MountainCar-v0\" , render_mode = \"human\" ) # seed env . action_space . seed ( 42 ) # Observation and action space obs_space = env . observation_space action_space = env . action_space print ( \"The observation space: {} \" . format ( obs_space )) print ( \"The action space: {} \" . format ( action_space )) # reset the space observation , info = env . reset ( seed = 42 ) print ( f \"Observation: { observation } \" ) print ( f \"Info: { info } \" ) print ( f \"Sample action; { env . action_space . sample () } \" ) ## OUTPUT # The observation space: Box([-1.2 -0.07], [0.6 0.07], (2,), float32) # The action space: Discrete(3) # Observation: [-0.4452088 0. ] # Info: {} # Sample action; 0","title":"Exploring MountainCar env"},{"location":"reinforcement_learning/q_learning/#training-agent-using-q-learning","text":"Hint Before starting with this section, why not try to code up some basic logic to beat the game? You could try to always push right or oscillate left and right at certain intervals. Give it try, it will be fun The code shared below (credits to gkhayes ) trains a Q-learning agent to play the MountainCar game. It tries to create a Q-table (that contains Q-scores) for all the combination of states and actions. As the states are continous (they are in float) , it is first discretized to make it a finite state problem. The final Q-table is of the shape [19, 15, 3] , with 19 possible states for position, 15 states for velocity and 3 actions. 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 \"\"\" Reference and Credit: https://gist.github.com/gkhayes/3d154e0505e31d6367be22ed3da2e955 Requirements; (don't forget to create new VE) pip install gym pip install \"gym[box2d]\" pip install \"gym[other]\" pip install numpy, matplotlib \"\"\" # import import numpy as np import gym import matplotlib.pyplot as plt # Import and initialize Mountain Car Environment env = gym . make ( 'MountainCar-v0' ) env . reset () # Define Q-learning function def QLearning ( env , learning , discount , epsilon , min_eps , episodes ): # Determine size of discretized state space num_states = ( env . observation_space . high - env . observation_space . low ) * \\ np . array ([ 10 , 100 ]) num_states = np . round ( num_states , 0 ) . astype ( int ) + 1 # Initialize Q table Q = np . random . uniform ( low = - 1 , high = 1 , size = ( num_states [ 0 ], num_states [ 1 ], env . action_space . n )) # Initialize variables to track rewards reward_list = [] ave_reward_list = [] # Calculate episodic reduction in epsilon reduction = ( epsilon - min_eps ) / episodes # Run Q learning algorithm for i in range ( episodes ): # Initialize parameters done = False tot_reward , reward = 0 , 0 state = env . reset () # Discretize state state_adj = ( state [ 0 ] - env . observation_space . low ) * np . array ([ 10 , 100 ]) state_adj = np . round ( state_adj , 0 ) . astype ( int ) while done != True : # Render environment for last five episodes if i >= ( episodes - 20 ): env . render () # Determine next action - epsilon greedy strategy if np . random . random () < 1 - epsilon : action = np . argmax ( Q [ state_adj [ 0 ], state_adj [ 1 ]]) else : action = np . random . randint ( 0 , env . action_space . n ) # Get next state and reward state2 , reward , done , info , _ = env . step ( action ) # Discretize state2 state2_adj = ( state2 - env . observation_space . low ) * np . array ([ 10 , 100 ]) state2_adj = np . round ( state2_adj , 0 ) . astype ( int ) #Allow for terminal states if done and state2 [ 0 ] >= 0.5 : Q [ state_adj [ 0 ], state_adj [ 1 ], action ] = reward # Adjust Q value for current state else : delta = learning * ( reward + discount * np . max ( Q [ state2_adj [ 0 ], state2_adj [ 1 ]]) - Q [ state_adj [ 0 ], state_adj [ 1 ], action ]) Q [ state_adj [ 0 ], state_adj [ 1 ], action ] += delta # Update variables tot_reward += reward state_adj = state2_adj # Decay epsilon if epsilon > min_eps : epsilon -= reduction # Track rewards reward_list . append ( tot_reward ) if ( i + 1 ) % 100 == 0 : ave_reward = np . mean ( reward_list ) ave_reward_list . append ( ave_reward ) reward_list = [] if ( i + 1 ) % 100 == 0 : print ( 'Episode {} Average Reward: {} ' . format ( i + 1 , ave_reward )) env . close () # save the Q table np . save ( 'MountainCar-v0-q-learning' , Q ) return ave_reward_list # Run Q-learning algorithm rewards = QLearning ( env , 0.2 , 0.9 , 0.8 , 0 , 5000 ) # Plot Rewards plt . plot ( 100 * ( np . arange ( len ( rewards )) + 1 ), rewards ) plt . xlabel ( 'Episodes' ) plt . ylabel ( 'Average Reward' ) plt . title ( 'Average Reward vs Episodes' ) plt . savefig ( 'rewards.jpg' ) plt . close () Running the above code trains a RL agent for 5000 episodes and saves the Q-table as numpy array. It also computes the average reward vs episodes graph that is shown below. Do note how the agent is able to get more rewards over more training! Increasing reward over time for the agent in training. The average rewards went up from -4300 to -250 under just 5000 episodes.","title":"Training Agent using Q-learning"},{"location":"reinforcement_learning/q_learning/#inference-of-trained-agent","text":"To run the trained model, we can use the saved Q-table to select the best action given any state in the game. Below is the code for the same, 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 \"\"\" Requirements; (don't forget to create new VE) pip install gym pip install \"gym[box2d]\" pip install \"gym[other]\" MountainCar-v0-q-table.txt.npy file that contains the modelled environment \"\"\" # import import gym import numpy as np from gym.utils import play # load trained Q table Q = np . load ( 'MountainCar-v0-q-table.txt.npy' ) # create an env env = gym . make ( \"MountainCar-v0\" , render_mode = \"human\" ) # seed env . action_space . seed ( 42 ) observation , info = env . reset ( seed = 42 ) # total number of steps for s in range ( 200 ): state_adj = ( observation - env . observation_space . low ) * np . array ([ 10 , 100 ]) state_adj = np . round ( state_adj , 0 ) . astype ( int ) # define the next step to take next_action = np . argmax ( Q [ state_adj [ 0 ], state_adj [ 1 ]]) # perform one step observation , reward , terminated , truncated , info = env . step ( next_action ) print ( s , observation , reward , terminated , truncated , info ) # if the game ends, restart the game if terminated or truncated : observation , info = env . reset () env . close () Trained RL agent performing as expected There you go!!","title":"Inference of trained agent"},{"location":"reinforcement_learning/q_learning/#references","text":"Interactive Q Learning - Mohit Mayank","title":"References"},{"location":"reinforcement_learning/rlhf/","text":"Reinforcement Learning from Human Feedback (RLHF) Introduction In Reinforcement learning , an agent learns to perform a task by interacting with an environment and receiving rewards or penalties for its actions. While this approach has shown great success in a wide range of applications, it can be difficult to design reward functions that accurately capture the user's preferences. Take autonomous driving car as an example, instead of creating a complicated reward function (with metrics like follow the lane, on wet road drive slow, stop at stop sign, don't hit anyone, etc and their weightage) we might want to let AI learn it. One point to note, we also do not want AI to learn to imitate human drivers, but rather learn what humans value in driving behavior and then optimize against those preferences. Because of this, we not only want trajectories (episodes or examples) , but also some form of feedbacks on different trajectories stating which one is better than the other. Once we have this, we can train a model to first learn the reward function ( Inverse Reinforcement Learning ) and later use the same reward to train an expert model ( Apprenticeship learning ) In this article, we will start with understanding the fundamentals of different types of human feedback, how to use them with RL and their pros and cons. Finally, we will discuss application of RLHF in NLP with tasks like Summarization. Let's start Types of human feedback There are different types of human feedback that can be used in reinforcement learning. These include: Explicit feedback: This type of feedback is direct and clear. It involves a human providing a specific reward or penalty to reinforce or discourage certain behaviors. For example, a teacher might provide explicit feedback to a student by giving them a grade for their performance. Implicit feedback: This type of feedback is more subtle and indirect. It involves a human providing information about what they like or dislike without explicitly stating it. For example, a customer might provide implicit feedback to a restaurant by choosing to visit it again or not. Comparison-based feedback: This type of feedback involves a human comparing the performance of an agent to that of another agent or a benchmark. For example, a manager might provide comparison-based feedback to an employee by comparing their performance to that of other employees in the same position. Incorporating human feedback There are several methods for incorporating human feedback in reinforcement learning, including: Reward shaping: This method involves modifying the reward function to incorporate human feedback. The goal is to manually guide the learning process towards behaviors that are more aligned with the user's preferences. For example, if a user wants a robot to clean a room quickly and efficiently, the reward function can be modified to encourage the robot to complete the task as quickly as possible. Imitation learning: This method involves learning from demonstration. The agent observes a human expert performing a task and learns to mimic their behavior. This method is particularly useful when the task is complex and difficult to learn from scratch. For example, a robot can learn to fold laundry by watching a human expert do it. Inverse reinforcement learning: This method involves inferring the reward function from human demonstrations. The agent observes a human expert performing a task and tries to learn the underlying reward function that motivated the expert's behavior. This method is particularly useful when the user's preferences are not easily expressed in terms of a reward function. For example, a robot can infer the reward function that motivated a human expert to perform a task and then optimize its behavior accordingly. These methods can be used alone or in combination to incorporate human feedback in reinforcement learning. The choice of method depends on the nature of the task, the type of human feedback available, and the computational resources available for learning. Benefits and challenges of using human feedback Using human feedback in reinforcement learning has several benefits, but also presents some challenges. Benefits Improved efficiency: Incorporating human feedback can accelerate the learning process and reduce the number of trials required to learn a task. By providing feedback that guides the agent towards desirable behaviors, human feedback can help the agent focus on the most promising strategies. Better performance: Human feedback can improve the quality of the learned policy and increase the success rate of the task. By incorporating the user's preferences, the agent can learn to optimize its behavior for the specific context of the task. Increased interpretability: Human feedback can make the learned policy more transparent and interpretable. By understanding the user's preferences, the agent can provide explanations for its actions and provide insights into how it works. Challenges Quality of human feedback: The quality of human feedback can vary depending on the user's expertise, knowledge, and ability to articulate their preferences. Some users may have conflicting preferences or provide ambiguous feedback, which can make it difficult for the agent to learn effectively. Bias and subjectivity: Human feedback can be biased and subjective, depending on the user's cultural background, personal beliefs, and emotional state. These factors can introduce bias into the learning process and make it difficult to generalize the learned policy to different contexts. Limited scalability: Incorporating human feedback can be resource-intensive and may not be feasible for large-scale applications. Collecting and processing feedback from multiple users can be time-consuming, and the resulting models may not be generalizable to new users. RLHF in NLP Reinforcement learning from human feedback (RLHF) has shown great potential in improving natural language processing (NLP) tasks. In NLP, the use of human feedback can help to capture the nuances of language and better align the agent's behavior with the user's expectations. Summarization Summarization aims to generate summaries that capture the most important information from a longer text. In RLHF, human feedback can be used to evaluate the quality of summaries and guide the agent towards more informative and concise summaries. This is quite difficult to capture using the metrics like ROUGE as they miss the human preferences. One such approach was proposed in [1], where researchers improved a summarization model using human feedback. The overall process was as follows, First, an autoregressive model is trained via supervised learning on the dataset (TL;DR dataset with >120k post from reddits and their summaries were taken) . The resulting model is termed as initial policy. Then the following steps are performed in iteration, For each reddit post, samples from initial policy, current policy (for step 0 its same as initial policy) , other baselines, and original summaries are taken and send over to human labelers. Based on human labelers's feedback, we now have candidate summary for each post. We use this data to train a reward function (linear layer on initial policy) using supervised learning. The output of reward function is treated as the reward to optimize using the reinforcement learning (authors use PPO algorithm) . To shed more light on the how policies are trained using rewards, the finetuned model is treated as initial policy. In RL terminology, state is the prompt plus the generations so far, action is token to generate, each step is one token generation and one episode terminates when policy returns token. Also, the reward function gives score for the complete summary and not individual generations. Finally, a conditioning term in added to the final reward that penalizes the KL divergence between the learned RL policy and the original supervised model. Quoting the paper, \"This KL term serves two purposes. First, it acts as an entropy bonus, encouraging the policy to explore and deterring it from collapsing to a single mode. Second, it ensures the policy doesn\u2019t learn to produce outputs that are too different from those that the reward model has seen during training.\" Diagram of human feedback, reward model training, and policy training procedure in [1] Others RLHF has been utlised for other NLP tasks as well. For example, As Dialogue systems in ChatGPT. Here the aim is to generate responses to user inputs that are coherent, informative, and relevant to the user's goals. In RLHF, human feedback can be used to evaluate the quality of generated responses and guide the agent towards more effective communication strategies. For example, a user can provide explicit feedback on the relevance of a response, or implicit feedback by continuing or ending the conversation. While RLHF has shown promise in improving NLP tasks, there are still challenges related to the quality of human feedback and the scalability of the approach. Collecting and processing human feedback can be time-consuming and may not be feasible for large-scale applications. Furthermore, human feedback can be subjective and may not capture the full range of user preferences. However, as RLHF continues to be refined, it has the potential to greatly enhance the quality and effectiveness of NLP systems. References [1] Learning to summarize from human feedback","title":"RLHF"},{"location":"reinforcement_learning/rlhf/#reinforcement-learning-from-human-feedback-rlhf","text":"","title":"Reinforcement Learning from Human Feedback (RLHF)"},{"location":"reinforcement_learning/rlhf/#introduction","text":"In Reinforcement learning , an agent learns to perform a task by interacting with an environment and receiving rewards or penalties for its actions. While this approach has shown great success in a wide range of applications, it can be difficult to design reward functions that accurately capture the user's preferences. Take autonomous driving car as an example, instead of creating a complicated reward function (with metrics like follow the lane, on wet road drive slow, stop at stop sign, don't hit anyone, etc and their weightage) we might want to let AI learn it. One point to note, we also do not want AI to learn to imitate human drivers, but rather learn what humans value in driving behavior and then optimize against those preferences. Because of this, we not only want trajectories (episodes or examples) , but also some form of feedbacks on different trajectories stating which one is better than the other. Once we have this, we can train a model to first learn the reward function ( Inverse Reinforcement Learning ) and later use the same reward to train an expert model ( Apprenticeship learning ) In this article, we will start with understanding the fundamentals of different types of human feedback, how to use them with RL and their pros and cons. Finally, we will discuss application of RLHF in NLP with tasks like Summarization. Let's start","title":"Introduction"},{"location":"reinforcement_learning/rlhf/#types-of-human-feedback","text":"There are different types of human feedback that can be used in reinforcement learning. These include: Explicit feedback: This type of feedback is direct and clear. It involves a human providing a specific reward or penalty to reinforce or discourage certain behaviors. For example, a teacher might provide explicit feedback to a student by giving them a grade for their performance. Implicit feedback: This type of feedback is more subtle and indirect. It involves a human providing information about what they like or dislike without explicitly stating it. For example, a customer might provide implicit feedback to a restaurant by choosing to visit it again or not. Comparison-based feedback: This type of feedback involves a human comparing the performance of an agent to that of another agent or a benchmark. For example, a manager might provide comparison-based feedback to an employee by comparing their performance to that of other employees in the same position.","title":"Types of human feedback"},{"location":"reinforcement_learning/rlhf/#incorporating-human-feedback","text":"There are several methods for incorporating human feedback in reinforcement learning, including: Reward shaping: This method involves modifying the reward function to incorporate human feedback. The goal is to manually guide the learning process towards behaviors that are more aligned with the user's preferences. For example, if a user wants a robot to clean a room quickly and efficiently, the reward function can be modified to encourage the robot to complete the task as quickly as possible. Imitation learning: This method involves learning from demonstration. The agent observes a human expert performing a task and learns to mimic their behavior. This method is particularly useful when the task is complex and difficult to learn from scratch. For example, a robot can learn to fold laundry by watching a human expert do it. Inverse reinforcement learning: This method involves inferring the reward function from human demonstrations. The agent observes a human expert performing a task and tries to learn the underlying reward function that motivated the expert's behavior. This method is particularly useful when the user's preferences are not easily expressed in terms of a reward function. For example, a robot can infer the reward function that motivated a human expert to perform a task and then optimize its behavior accordingly. These methods can be used alone or in combination to incorporate human feedback in reinforcement learning. The choice of method depends on the nature of the task, the type of human feedback available, and the computational resources available for learning.","title":"Incorporating human feedback"},{"location":"reinforcement_learning/rlhf/#benefits-and-challenges-of-using-human-feedback","text":"Using human feedback in reinforcement learning has several benefits, but also presents some challenges.","title":"Benefits and challenges of using human feedback"},{"location":"reinforcement_learning/rlhf/#benefits","text":"Improved efficiency: Incorporating human feedback can accelerate the learning process and reduce the number of trials required to learn a task. By providing feedback that guides the agent towards desirable behaviors, human feedback can help the agent focus on the most promising strategies. Better performance: Human feedback can improve the quality of the learned policy and increase the success rate of the task. By incorporating the user's preferences, the agent can learn to optimize its behavior for the specific context of the task. Increased interpretability: Human feedback can make the learned policy more transparent and interpretable. By understanding the user's preferences, the agent can provide explanations for its actions and provide insights into how it works.","title":"Benefits"},{"location":"reinforcement_learning/rlhf/#challenges","text":"Quality of human feedback: The quality of human feedback can vary depending on the user's expertise, knowledge, and ability to articulate their preferences. Some users may have conflicting preferences or provide ambiguous feedback, which can make it difficult for the agent to learn effectively. Bias and subjectivity: Human feedback can be biased and subjective, depending on the user's cultural background, personal beliefs, and emotional state. These factors can introduce bias into the learning process and make it difficult to generalize the learned policy to different contexts. Limited scalability: Incorporating human feedback can be resource-intensive and may not be feasible for large-scale applications. Collecting and processing feedback from multiple users can be time-consuming, and the resulting models may not be generalizable to new users.","title":"Challenges"},{"location":"reinforcement_learning/rlhf/#rlhf-in-nlp","text":"Reinforcement learning from human feedback (RLHF) has shown great potential in improving natural language processing (NLP) tasks. In NLP, the use of human feedback can help to capture the nuances of language and better align the agent's behavior with the user's expectations.","title":"RLHF in NLP"},{"location":"reinforcement_learning/rlhf/#summarization","text":"Summarization aims to generate summaries that capture the most important information from a longer text. In RLHF, human feedback can be used to evaluate the quality of summaries and guide the agent towards more informative and concise summaries. This is quite difficult to capture using the metrics like ROUGE as they miss the human preferences. One such approach was proposed in [1], where researchers improved a summarization model using human feedback. The overall process was as follows, First, an autoregressive model is trained via supervised learning on the dataset (TL;DR dataset with >120k post from reddits and their summaries were taken) . The resulting model is termed as initial policy. Then the following steps are performed in iteration, For each reddit post, samples from initial policy, current policy (for step 0 its same as initial policy) , other baselines, and original summaries are taken and send over to human labelers. Based on human labelers's feedback, we now have candidate summary for each post. We use this data to train a reward function (linear layer on initial policy) using supervised learning. The output of reward function is treated as the reward to optimize using the reinforcement learning (authors use PPO algorithm) . To shed more light on the how policies are trained using rewards, the finetuned model is treated as initial policy. In RL terminology, state is the prompt plus the generations so far, action is token to generate, each step is one token generation and one episode terminates when policy returns token. Also, the reward function gives score for the complete summary and not individual generations. Finally, a conditioning term in added to the final reward that penalizes the KL divergence between the learned RL policy and the original supervised model. Quoting the paper, \"This KL term serves two purposes. First, it acts as an entropy bonus, encouraging the policy to explore and deterring it from collapsing to a single mode. Second, it ensures the policy doesn\u2019t learn to produce outputs that are too different from those that the reward model has seen during training.\" Diagram of human feedback, reward model training, and policy training procedure in [1]","title":"Summarization"},{"location":"reinforcement_learning/rlhf/#others","text":"RLHF has been utlised for other NLP tasks as well. For example, As Dialogue systems in ChatGPT. Here the aim is to generate responses to user inputs that are coherent, informative, and relevant to the user's goals. In RLHF, human feedback can be used to evaluate the quality of generated responses and guide the agent towards more effective communication strategies. For example, a user can provide explicit feedback on the relevance of a response, or implicit feedback by continuing or ending the conversation. While RLHF has shown promise in improving NLP tasks, there are still challenges related to the quality of human feedback and the scalability of the approach. Collecting and processing human feedback can be time-consuming and may not be feasible for large-scale applications. Furthermore, human feedback can be subjective and may not capture the full range of user preferences. However, as RLHF continues to be refined, it has the potential to greatly enhance the quality and effectiveness of NLP systems.","title":"Others"},{"location":"reinforcement_learning/rlhf/#references","text":"[1] Learning to summarize from human feedback","title":"References"}]} \ No newline at end of file diff --git a/sitemap.xml b/sitemap.xml new file mode 100644 index 00000000..f981f9e3 --- /dev/null +++ b/sitemap.xml @@ -0,0 +1,268 @@ + + + + http://mohitmayank.com/a_lazy_data_science_guide/ + 2023-10-03 + daily + + + http://mohitmayank.com/a_lazy_data_science_guide/audio_intelligence/connectionist_temporal_classification/ + 2023-10-03 + daily + + + http://mohitmayank.com/a_lazy_data_science_guide/audio_intelligence/interview_questions/ + 2023-10-03 + daily + + + http://mohitmayank.com/a_lazy_data_science_guide/audio_intelligence/speaker_diarization/ + 2023-10-03 + daily + + + http://mohitmayank.com/a_lazy_data_science_guide/audio_intelligence/stt/ + 2023-10-03 + daily + + + 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