Assignment+3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "_You are currently looking at **version 1.2** of this notebook. To download notebooks and datafiles, as well as get help on Jupyter notebooks in the Coursera platform, visit the [Jupyter Notebook FAQ](https://www.coursera.org/learn/python-machine-learning/resources/bANLa) course resource._\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Assignment 3 - Evaluation\n",
    "\n",
    "In this assignment you will train several models and evaluate how effectively they predict instances of fraud using data based on [this dataset from Kaggle](https://www.kaggle.com/dalpozz/creditcardfraud).\n",
    " \n",
    "Each row in `fraud_data.csv` corresponds to a credit card transaction. Features include confidential variables `V1` through `V28` as well as `Amount` which is the amount of the transaction. \n",
    " \n",
    "The target is stored in the `class` column, where a value of 1 corresponds to an instance of fraud and 0 corresponds to an instance of not fraud."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 1\n",
    "Import the data from `fraud_data.csv`. What percentage of the observations in the dataset are instances of fraud?\n",
    "\n",
    "*This function should return a float between 0 and 1.* "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.016410823768035772"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('fraud_data.csv')\n",
    "\n",
    "def answer_one():\n",
    "   \n",
    "    per = df.iloc[:,-1].mean()\n",
    "    return per\n",
    "answer_one()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Use X_train, X_test, y_train, y_test for all of the following questions\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "X = df.iloc[:,:-1]\n",
    "y = df.iloc[:,-1]\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 2\n",
    "\n",
    "Using `X_train`, `X_test`, `y_train`, and `y_test` (as defined above), train a dummy classifier that classifies everything as the majority class of the training data. What is the accuracy of this classifier? What is the recall?\n",
    "\n",
    "*This function should a return a tuple with two floats, i.e. `(accuracy score, recall score)`.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.98525073746312686, 0.0)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def answer_two():\n",
    "    from sklearn.dummy import DummyClassifier\n",
    "    from sklearn.metrics import recall_score\n",
    "    \n",
    "    clf = DummyClassifier(\"most_frequent\",random_state = 0).fit(X_train,y_train)\n",
    "    acc = clf.score(X_test,y_test)\n",
    "    recall = recall_score(y_test,clf.predict(X_test),'binary')\n",
    "    \n",
    "    return (acc,recall)\n",
    "answer_two()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 3\n",
    "\n",
    "Using X_train, X_test, y_train, y_test (as defined above), train a SVC classifer using the default parameters. What is the accuracy, recall, and precision of this classifier?\n",
    "\n",
    "*This function should a return a tuple with three floats, i.e. `(accuracy score, recall score, precision score)`.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0.99078171091445433, 0.375, 1.0)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def answer_three():\n",
    "    from sklearn.metrics import recall_score, precision_score\n",
    "    from sklearn.svm import SVC\n",
    "\n",
    "    clf = SVC().fit(X_train,y_train)\n",
    "    acc = clf.score(X_test,y_test)\n",
    "    recall = recall_score(y_test,clf.predict(X_test),'binary')\n",
    "    precision = precision_score(y_test,clf.predict(X_test),'binary')\n",
    "    \n",
    "    return (acc,recall,precision)\n",
    "answer_three()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 4\n",
    "\n",
    "Using the SVC classifier with parameters `{'C': 1e9, 'gamma': 1e-07}`, what is the confusion matrix when using a threshold of -220 on the decision function. Use X_test and y_test.\n",
    "\n",
    "*This function should return a confusion matrix, a 2x2 numpy array with 4 integers.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5320,   24],\n",
       "       [  14,   66]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def answer_four():\n",
    "    from sklearn.metrics import confusion_matrix\n",
    "    from sklearn.svm import SVC\n",
    "\n",
    "    clf = SVC(C=1e9,gamma=1e-07).fit(X_train,y_train)\n",
    "    temp = clf.decision_function(X_test)\n",
    "    ans = confusion_matrix(y_test,np.greater(temp,-220),)\n",
    "    \n",
    "    return ans\n",
    "answer_four()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 5\n",
    "\n",
    "Train a logisitic regression classifier with default parameters using X_train and y_train.\n",
    "\n",
    "For the logisitic regression classifier, create a precision recall curve and a roc curve using y_test and the probability estimates for X_test (probability it is fraud).\n",
    "\n",
    "Looking at the precision recall curve, what is the recall when the precision is `0.75`?\n",
    "\n",
    "Looking at the roc curve, what is the true positive rate when the false positive rate is `0.16`?\n",
    "\n",
    "*This function should return a tuple with two floats, i.e. `(recall, true positive rate)`.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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w6nHX0qUkZwHvB9445lJGbQu9JaTL6XWD9yT51ar6r7FW1a29wEer6u+SvJLe5fgvrqqf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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9ad9ebbe48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(0.8, 0.9)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def answer_five():\n",
    "        \n",
    "    from sklearn.linear_model import LogisticRegression\n",
    "    from sklearn.metrics import precision_recall_curve\n",
    "    from sklearn.metrics import roc_curve\n",
    "    import matplotlib.pyplot as plt\n",
    "    %matplotlib inline\n",
    "    \n",
    "    clf = LogisticRegression(n_jobs=-1).fit(X_train,y_train)\n",
    "    p,r,_ = precision_recall_curve(y_test,clf.predict_proba(X_test)[:,1],)\n",
    "    fpr,tpr,_ = roc_curve(y_test,clf.predict_proba(X_test)[:,1],)\n",
    "    \n",
    "    plt.plot(fpr,tpr)\n",
    "    plt.xlabel('fpr')\n",
    "    plt.ylabel('tpr')\n",
    "    plt.show()\n",
    "    return (0.8,0.9)# Return your answer\n",
    "answer_five()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Question 6\n",
    "\n",
    "Perform a grid search over the parameters listed below for a Logisitic Regression classifier, using recall for scoring and the default 3-fold cross validation.\n",
    "\n",
    "`'penalty': ['l1', 'l2']`\n",
    "\n",
    "`'C':[0.01, 0.1, 1, 10, 100]`\n",
    "\n",
    "From `.cv_results_`, create an array of the mean test scores of each parameter combination. i.e.\n",
    "\n",
    "|      \t| `l1` \t| `l2` \t|\n",
    "|:----:\t|----\t|----\t|\n",
    "| **`0.01`** \t|    ?\t|   ? \t|\n",
    "| **`0.1`**  \t|    ?\t|   ? \t|\n",
    "| **`1`**    \t|    ?\t|   ? \t|\n",
    "| **`10`**   \t|    ?\t|   ? \t|\n",
    "| **`100`**   \t|    ?\t|   ? \t|\n",
    "\n",
    "<br>\n",
    "\n",
    "*This function should return a 5 by 2 numpy array with 10 floats.* \n",
    "\n",
    "*Note: do not return a DataFrame, just the values denoted by '?' above in a numpy array. You might need to reshape your raw result to meet the format we are looking for.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.66666667,  0.76086957],\n",
       "       [ 0.80072464,  0.80434783],\n",
       "       [ 0.8115942 ,  0.8115942 ],\n",
       "       [ 0.80797101,  0.8115942 ],\n",
       "       [ 0.80797101,  0.80797101]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def answer_six():    \n",
    "    from sklearn.model_selection import GridSearchCV\n",
    "    from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "    params = {'penalty': ['l1', 'l2'],'C':[0.01, 0.1, 1, 10, 100]}\n",
    "    clf = GridSearchCV(LogisticRegression(n_jobs=-1),params,scoring='recall',n_jobs=-1).fit(X_train,y_train)\n",
    "    ans = clf.cv_results_['mean_test_score'].reshape(5,2)\n",
    "    return ans\n",
    "answer_six()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support.' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        this.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option)\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width, fig.canvas.height);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to  previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>')\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
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     "output_type": "display_data"
    },
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\" width=\"576\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use the following function to help visualize results from the grid search\n",
    "def GridSearch_Heatmap(scores):\n",
    "    %matplotlib notebook\n",
    "    import seaborn as sns\n",
    "    import matplotlib.pyplot as plt\n",
    "    plt.figure()\n",
    "    sns.heatmap(scores.reshape(5,2), xticklabels=['l1','l2'], yticklabels=[0.01, 0.1, 1, 10, 100])\n",
    "    plt.yticks(rotation=0);\n",
    "\n",
    "GridSearch_Heatmap(answer_six())"
   ]
  }
 ],
 "metadata": {
  "coursera": {
   "course_slug": "python-machine-learning",
   "graded_item_id": "5yX9Z",
   "launcher_item_id": "eqnV3",
   "part_id": "Msnj0"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}