ALI_Task0_Text_Classification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Loading various libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.naive_bayes import BernoulliNB\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from nltk.stem.snowball import SnowballStemmer\n",
    "from nltk import word_tokenize          \n",
    "from nltk.stem import WordNetLemmatizer\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "import nltk\n",
    "import pandas as pd\n",
    "import random\n",
    "from sklearn.model_selection import GridSearchCV"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing the 20newsgroup training data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Loading the data set - training data.\n",
    "from sklearn.datasets import fetch_20newsgroups\n",
    "# mydata_train = fetch_20newsgroups(subset='train', shuffle=True)\n",
    "mydata_train = fetch_20newsgroups(subset='train', shuffle=True, remove = ('headers', 'footers', 'quotes'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training data size: 11314\n"
     ]
    }
   ],
   "source": [
    "print('Training data size:', len(mydata_train['data']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11314"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(mydata_train['target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['alt.atheism',\n",
       " 'comp.graphics',\n",
       " 'comp.os.ms-windows.misc',\n",
       " 'comp.sys.ibm.pc.hardware',\n",
       " 'comp.sys.mac.hardware',\n",
       " 'comp.windows.x',\n",
       " 'misc.forsale',\n",
       " 'rec.autos',\n",
       " 'rec.motorcycles',\n",
       " 'rec.sport.baseball',\n",
       " 'rec.sport.hockey',\n",
       " 'sci.crypt',\n",
       " 'sci.electronics',\n",
       " 'sci.med',\n",
       " 'sci.space',\n",
       " 'soc.religion.christian',\n",
       " 'talk.politics.guns',\n",
       " 'talk.politics.mideast',\n",
       " 'talk.politics.misc',\n",
       " 'talk.religion.misc']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Printing all the categories\n",
    "mydata_train.target_names "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "        17, 18, 19], dtype=int64),\n",
       " array([480, 584, 591, 590, 578, 593, 585, 594, 598, 597, 600, 595, 591,\n",
       "        594, 593, 599, 546, 564, 465, 377], dtype=int64))"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Finding frequency of each category\n",
    "targets, frequency = np.unique(mydata_train.target, return_counts=True)\n",
    "targets, frequency "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('alt.atheism', 480), ('comp.graphics', 584), ('comp.os.ms-windows.misc', 591), ('comp.sys.ibm.pc.hardware', 590), ('comp.sys.mac.hardware', 578), ('comp.windows.x', 593), ('misc.forsale', 585), ('rec.autos', 594), ('rec.motorcycles', 598), ('rec.sport.baseball', 597), ('rec.sport.hockey', 600), ('sci.crypt', 595), ('sci.electronics', 591), ('sci.med', 594), ('sci.space', 593), ('soc.religion.christian', 599), ('talk.politics.guns', 546), ('talk.politics.mideast', 564), ('talk.politics.misc', 465), ('talk.religion.misc', 377)]\n"
     ]
    }
   ],
   "source": [
    "targets_str = np.array(mydata_train.target_names)\n",
    "print(list(zip(targets_str, frequency)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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rmDFjBrp16wYgbYSmdOnSWLx4MdRqNW7evImZM2dq/X7RokURFRWF169fZxgjICAAv//+O/bu3YuUlBTs3r0bS5YsQWBg4H86Z10dU61Ww8/PD3v37sWBAwdga2v73s/4+/vjypUr+O2335CUlIQDBw7g999/R58+fT45zoABAwBA68tKzpw50atXLwwZMgSXL1+GEAKvXr3C/v37ce3aNeXnGjZsiKlTp6JevXrInj07PD09cfnyZezcuRMNGzZUfi6j5yYAdO7cGbNnz8a1a9eQlJSEiRMnKpeGM5ItWzZ89913GD16NG7evImkpCTMnj0b169fR6dOnQAAVapUwfnz53Hw4EGkpKRg7dq12L9//3vHWr58OY4cOQK1Wo1ly5bhzJkzyjE+dN5fKi4uDmZmZrCyssKLFy/w008//edR2f8ie/bs6NixI8aPH48HDx7g5cuX+PHHHz/pC93b7t27h6lTp2Lq1KmYMWMGLCwsPun9qmjRonjy5InW/+u4uDiYmJigcOHCUKlU2LNnz2dP0yD6XExOif6/unXr4tixY7h37x6qVasGc3NzODs7Y9u2bfD19X3v583MzLB48WJMnDhRqZvYrl07rZ/Zs2cPqlWrBjMzM7i4uKBmzZpK2aF169ahUqVKyJs3L7y8vODv759hPcROnTphyJAhaNeuHQoWLIijR49qrcJ+161bt9CqVStYWlqiRIkSePToEZYsWQIAKFeuHPr164d69erB0tISU6dO/ay/U/Xq1ZGUlARra2vUqVMHrVq1wrBhw5T9y5cvx969e2FpaQk/Pz8EBARo/f63334LBwcHFC9eHJaWljh48OB7Mdq1a4dZs2bh+++/h6WlJfr164e5c+eiTZs2n3Wuuj7moUOHsGrVKty4cQNly5bVqqOpYWNjg3///RcLFy5Evnz50LlzZ0yePPmz4piammLWrFl48uSJ1vaZM2eiQ4cOyqV9W1tbTJkyRasMUsOGDREXF6ckojlz5kTt2rWRmpoKDw8P5ec+9Nz86aef0KxZM3h6eqJEiRJ49eoVXF1dkStXrg+e98yZM9GwYUPUq1cPhQsXxvr167Fz505lSoCXlxeGDx+ONm3aoFChQti7dy/atm373nF69eqFESNGwNLSEjNmzMCGDRuU6QIfOu8vNW/ePERERMDKygoVK1ZEiRIl9F73c+7cuShXrhwqVaqEsmXLomLFisifP/9H//Z9+/aFubk5LCwsUKtWLZw9exbh4eHo3bs3gE97v6pfvz5atmyJcuXKwdLSEqtWrYK/vz8aNGgAJycnFCxYEAsWLNCqrkAkg0pwbJ6IiD5ArVajWLFimDdvnvTOWba2thg5cuR7X2qyqtjYWBQoUACHDh1CzZo1DX06RHrBkVMiItKSkJCATZs2ISkpCYmJiRg6dChSU1P/U1cl+jxRUVHYt28fUlJS8PTpU3z//fcoW7YsqlatauhTI9IbJqdERKQlNTUVEyZMQMGCBVGiRAkcP34c//7773slskj3kpKS0K9fP1haWqJs2bJ4/vw5/vnnH53NqyX6GvCyPhEREREZDY6cEhEREZHRYHJKREREREaDySkRERERGY1MNcM6Z86cKFSokKFPg4iIiIgy8PjxY7x58ybD/ZkqOS1UqNBn90YnIiIiIv35WHMLXtYnIiIiIqPB5JSIiIiIjAaTUyIiIiIyGkxOiYiIiMhoMDklIiIiIqPB5JSIiIiIjAaTUyIiIiIyGkxOiYiIiMhoSE9O37x5g759+6Js2bJwcnKCn58fACAyMhIeHh4oV64cqlatiosXLyq/86F9RERERJR5SU9Ohw0bBpVKhWvXruH8+fOYOXMmAKBnz54IDAzEtWvXMHToUPj7+yu/86F9RERERJR5qYQQQtbBX7x4gWLFiuHu3buwsLBQtsfExMDe3h7Pnj2DiYkJhBAoVqwYDh48CAsLiwz32dvbfzCetbU125cSERERGbGP5WtSR05v3LiB/PnzY/LkyahSpQo8PT2xa9cuREdHo1ixYjAxMQEAqFQqlCpVClFRUR/cR0RERESZm4nMg6vVaty5cwcVK1bE1KlTcebMGfj4+GDLli06OX5wcDCCg4OV+4mJiTo5LhGRMbIdppv3zvTcntpM2rGJiD6H1OS0VKlSyJYtGzp16gQAcHV1RenSpXHnzh08ePAAarVauXQfFRWFUqVKwcLCIsN97woKCkJQUJBy39raWubDISIjxsSNKGOyXh98bZAMUpPTggULokGDBti+fTuaNm2KW7du4datW6hVqxbc3NwQGhoKf39/rF+/HtbW1sqc0g/tIyIyFkyIiYh0T2pyCgALFixA9+7dMXToUGTLlg0hISEoUaIEQkJC4O/vj8mTJ8PCwgJLly5VfudD+4jo68DELXPICiNuWeExEn1NpCendnZ22LNnz3vbHRwccOTIkXR/50P7iDIDJm5EWRdf/0QfJj05pa+bId5E+cZNlHXx9U9EbF9KREREREaDySkRERERGQ1e1v/K8JIX/Rd83hAR0deCI6dEREREZDSYnBIRERGR0eBlfcryeMmbiIjIeHDklIiIiIiMBpNTIiIiIjIaTE6JiIiIyGgwOSUiIiIio8HklIiIiIiMBpNTIiIiIjIaLCVFREREXzWWBMxcOHJKREREREaDySkRERERGQ0mp0RERERkNJicEhEREZHRYHJKREREREaDySkRERERGQ0mp0RERERkNJicEhEREZHRYBF+IiIi0ikWxacvwZFTIiIiIjIaTE6JiIiIyGgwOSUiIiIio8HklIiIiIiMBpNTIiIiIjIaTE6JiIiIyGgwOSUiIiIio8HklIiIiIiMBpNTIiIiIjIaTE6JiIiIyGgwOSUiIiIio8HklIiIiIiMBpNTIiIiIjIaTE6JiIiIyGgwOSUiIiIio8HklIiIiIiMBpNTIiIiIjIa0pNTW1tbODg4oHLlyqhcuTL+/PNPAEBkZCQ8PDxQrlw5VK1aFRcvXlR+50P7iIiIiCjz0svI6Z9//omzZ8/i7Nmz+PbbbwEAPXv2RGBgIK5du4ahQ4fC399f+fkP7SMiIiKizMsgl/VjYmJw8uRJ+Pn5AQDatm2L6OhoXL9+/YP7iIiIiChz00ty2qVLFzg5OaF79+54/PgxoqOjUaxYMZiYmAAAVCoVSpUqhaioqA/uIyIiIqLMTXpyun//fpw7dw6nT59GwYIF0bVrV50dOzg4GNbW1sotMTFRZ8cmIiIiIv2TnpyWKlUKAJAjRw4MHDgQBw4cQMmSJfHgwQOo1WoAgBACUVFRKFWq1Af3vSsoKAh3795VbmZmZrIfDhERERFJJDU5ffHiBZ4/f67cX716NVxdXVG4cGG4ubkhNDQUALB+/XpYW1vD3t7+g/uIiIiIKHMzkXnwR48eoW3btkhJSYEQAnZ2dli+fDkAICQkBP7+/pg8eTIsLCywdOlS5fc+tI+IiIiIMi+pyamdnR3OnDmT7j4HBwccOXLks/cRERERUebFDlFEREREZDSYnBIRERGR0WBySkRERERGg8kpERERERkNJqdEREREZDSkrtbPCmyHbZFy3NtTm0k5LhEREZEx48gpERERERkNJqdEREREZDSYnBIRERGR0WBySkRERERGg8kpERERERkNJqdEREREZDRYSoqIiIjoM8kqJQmwnCRHTomIiIjIaDA5JSIiIiKjweSUiIiIiIwGk1MiIiIiMhpMTomIiIjIaDA5JSIiIiKjweSUiIiIiIwGk1MiIiIiMhpMTomIiIjIaDA5JSIiIiKjweSUiIiIiIwGk1MiIiIiMhpMTomIiIjIaDA5JSIiIiKjweSUiIiIiIwGk1MiIiIiMhpMTomIiIjIaDA5JSIiIiKjweSUiIiIiIwGk1MiIiIiMhpMTomIiIjIaDA5JSIiIiKjweSUiIiIiIwGk1MiIiIiMhpMTomIiIjIaDA5JSIiIiKjweSUiIiIiIyG3pLTpUuXQqVS4e+//wYAxMTEoHHjxihbtiwcHR2xf/9+5Wc/tI+IiIiIMi+9JKe3b9/GokWLUKNGDWXbsGHDUKNGDURGRmLp0qXo2LEjkpOTP7qPiIiIiDIv6clpamoqAgICMH/+fOTMmVPZHhYWhl69egEAqlatiuLFi2Pfvn0f3UdEREREmZf05DQ4OBi1atWCu7u7su3p06dITk5G0aJFlW22traIior64D4iIiIiytxMZB78woULWL9+vbQ5o8HBwQgODlbuJyYmSolDRERERPohdeT0wIEDuH37NsqWLQtbW1scPXoUgYGBCAsLg4mJCR4+fKj87O3bt1GqVCkUKFAgw33vCgoKwt27d5WbmZmZzIdDRERERJJJTU579+6NBw8e4Pbt27h9+zZq1KiBhQsXonfv3mjXrh0WLFgAADhx4gTu3bsHLy8vAPjgPiIiIiLKvKRe1v+QadOmoXPnzihbtixMTU0RGhqKHDlyfHQfEREREWVeek1O9+7dq/y7SJEi2LFjR7o/96F9RERERJR5sUMUERERERkNJqdEREREZDSYnBIRERGR0WBySkRERERGg8kpERERERkNJqdEREREZDQMVueUiIiIiD6N7bAt0o59e2ozacf+LzhySkRERERGg8kpERERERkNJqdEREREZDSYnBIRERGR0fjk5HT79u0yz4OIiIiI6NOT0/Hjx8PBwQFz585FfHy8zHMiIiIioizqk5PTQ4cOYc2aNbhw4QLKlSuH77//HpcuXZJ5bkRERESUxXzWnFNXV1csWrQI27Ztw+bNm+Hs7AwfHx+cP39e1vkRERERURbyWclpeHg4WrZsiTZt2qBPnz54+PAhevbsidatW8s6PyIiIiLKQj65Q1SFChVQsGBB9O/fH23atEH27NkBAL6+vliyZIm0EyQiIiKirOOTk9PQ0FC4u7unu2/r1q06OyEiIiIiyro++bL+qVOn8OzZM+X+06dPsWjRIiknRURERERZ0ycnp7/++ivy58+v3C9QoAB+/fVXKSdFRERERFnTJyenQoj3tqWkpOj0ZIiIiIgoa/vk5LRYsWIICwtT7v/5558oVqyYlJMiIiIioqzpkxdEzZkzBy1btsSQIUMAAHny5MHGjRulnRgRERERZT2fnJyWL18ely5dwtWrVwEADg4OSjkpIiIiIiJd+OTkFABUKhUsLS2hVqtx7949AECpUqWknBgRERERZT2fnJwuW7YM/fv3R44cOZAtW9pUVZVKhZiYGGknR0RERERZyycnpxMmTMCJEyfg4OAg83yIiIiIKAv75NX6BQsWZGJKRERERFJ9cnLaqlUrzJkzBzExMYiPj1duRERERES68smX9UeMGAEACAoKgkqlghACKpWKhfiJiIiISGc+OTlNTU2VeR5ERERERJ9+WR8ATp06hRUrVgAAnj9/jgcPHkg5KSIiIiLKmj45Of3111/x3XffYezYsQCAp0+fomPHjrLOi4iIiIiyoE9OThcuXIijR4/CwsICAFCmTBk8fvxY2okRERERUdbzyclpzpw5kTt3bq1tJiaf1WCKiIiIiOiDPjk5LVSoEK5duwaVSgUgrWMUW5cSERERkS598tDnnDlz0KFDB1y5cgUlS5aEhYUFNm/eLPPciIiIiCiL+eTk1N7eHseOHcPVq1chhICDgwOyZ88u89yIiIiIKIv55OQ0KioKAJA3b14AwL179wCAl/aJiIiISGc+OTl1d3dXOkO9fv0aL1++RIECBRATEyPz/IiIiIgoC/nkBVGPHz9GTEwMHj9+jISEBKxduxa9e/f+6O81bNgQzs7OqFy5Mjw9PXHmzBkAQGRkJDw8PFCuXDlUrVoVFy9eVH7nQ/uIiIiIKPP6rA5Rb2vTpg22bNny0Z8LCwvDuXPncPbsWQQFBcHf3x8A0LNnTwQGBuLatWsYOnSosv1j+4iIiIgo8/rk5DQ+Pl65xcbGYtu2bYiPj//o71laWir/jouLg0qlQkxMDE6ePAk/Pz8AQNu2bREdHY3r169/cB8RERERZW6fPOfU0tJSmXOaPXt2lC1bFvPmzfuk3+3SpQv27NkDAPj3338RHR2NYsWKKUX8VSoVSpUqhaioKOTLly/Dffb29p/7+IiIiIjoK/LJyWlqaup/DrJ8+XIAwB9//IGhQ4diwoQJ//lYbwsODkZwcLByPzExUSfHJSIiIiLD+M9zTv+Lrl27Ys+ePbC2tsaDBw+gVqsBAEIIREVFoVSpUihZsmSG+94VFBSEu3fvKjczMzN9PhwiIiIi0rFPTk6zZcuG7Nmzv3fTbE/P8+fPcf/+feX+33//jQIFCqBw4cJwc3NDaGgoAGD9+vWwtraGvb39B/cRERERUeb2yZf1x48fj1evXinloxYsWIDcuXNj4MCBGf5OXFwc2rVrh1evXiFbtmwoVKgQNm/eDJVKhZCQEPj7+2Py5MmwsLDA0qVLld/70D4iIiIiyrw+OTndsGEDTp06pdyfOHEi3N3dMWLEiAx/x8bGBsePH093n4ODA44cOfLZ+4iIiIgo8/rky/oJCQla3aBiYmKQkJAg5aSIiIiIKGv65JHTQYMGwcXFBU2bNgUAbNu2DWPHjpV1XkRERESUBX1yctqzZ0/UqlVLqVcaFBSESpUqSTsxIiIiIsp6Pjk5BYACBQrAyckJdevWhVqtRlJSEkxNTWWdGxERERFlMZ8853TdunWoUaMGunXrBgC4ePEiWrVqJeu8iIiIiCgL+uTkdMqUKTh9+jQsLS0BAC4uLrhz546s8yIiIiKiLOiTk9Ps2bOjQIECWtt4SZ+IiIiIdOmTk1Nzc3M8evQIKpUKALBr1y7kz59f2okRERERUdbzyQuipk2bhiZNmuDmzZuoXbs2bt26hS1btsg8NyIiIiLKYj4pOU1NTUVKSgr27NmDw4cPQwgBDw8PZf4pEREREZEufFJymi1bNgQGBiIiIgJNmjSRfU5ERERElEV98pzTsmXL4vr16zLPhYiIiIiyuE+ec/rs2TNUrlwZHh4eMDMzU7b/9ddfUk6MiIiIiLKejyangYGBWLhwIbp27YoWLVrAyspKH+dFRERERFnQR5PTkydPAgC6du0KNzc3nD59WvpJEREREVHW9MlzTgFACCHrPIiIiIiIPj5y+urVK5w/fx5CCLx+/Vr5t4azs7PUEyQiIiKirOOTktMWLVoo99/+t0qlws2bN+WcGRERERFlOR9NTm/fvq2H0yAiIiIi+sw5p0REREREMjE5JSIiIiKjweSUiIiIiIwGk1MiIiIiMhpMTomIiIjIaDA5JSIiIiKjweSUiIiIiIwGk1MiIiIiMhpMTomIiIjIaDA5JSIiIiKjweSUiIiIiIwGk1MiIiIiMhpMTomIiIjIaDA5JSIiIiKjweSUiIiIiIwGk1MiIiIiMhpMTomIiIjIaDA5JSIiIiKjweSUiIiIiIwGk1MiIiIiMhpMTomIiIjIaEhNTl+/fo1WrVqhXLlycHFxgY+PD65fvw4AiImJQePGjVG2bFk4Ojpi//79yu99aB8RERERZV7SR04DAwNx9epVREREoGXLlggICAAADBs2DDVq1EBkZCSWLl2Kjh07Ijk5+aP7iIiIiCjzkpqc5sqVC02bNoVKpQIA1KhRA7dv3wYAhIWFoVevXgCAqlWronjx4ti3b99H9xERERFR5qXXOadz585Fy5Yt8fTpUyQnJ6No0aLKPltbW0RFRX1wHxERERFlbib6CjR58mRcv34du3btwqtXr3RyzODgYAQHByv3ExMTdXJcIiIiIjIMvYyczpw5E3/99Re2bt2KPHnyoECBAjAxMcHDhw+Vn7l9+zZKlSr1wX3vCgoKwt27d5WbmZmZPh4OEREREUkiPTkNDg7G6tWrsXPnTlhaWirb27VrhwULFgAATpw4gXv37sHLy+uj+4iIiIgo85J6Wf/u3bsYNGgQ7OzsUK9ePQBAzpw5cezYMUybNg2dO3dG2bJlYWpqitDQUOTIkQMAPriPiIiIiDIvqcmptbU1hBDp7itSpAh27Njx2fuIiIiIKPNihygiIiIiMhpMTomIiIjIaDA5JSIiIiKjweSUiIiIiIwGk1MiIiIiMhpMTomIiIjIaDA5JSIiIiKjweSUiIiIiIwGk1MiIiIiMhpMTomIiIjIaDA5JSIiIiKjweSUiIiIiIwGk1MiIiIiMhpMTomIiIjIaDA5JSIiIiKjweSUiIiIiIwGk1MiIiIiMhpMTomIiIjIaDA5JSIiIiKjweSUiIiIiIwGk1MiIiIiMhpMTomIiIjIaDA5JSIiIiKjweSUiIiIiIwGk1MiIiIiMhpMTomIiIjIaDA5JSIiIiKjweSUiIiIiIwGk1MiIiIiMhpMTomIiIjIaDA5JSIiIiKjweSUiIiIiIwGk1MiIiIiMhpMTomIiIjIaDA5JSIiIiKjweSUiIiIiIwGk1MiIiIiMhpMTomIiIjIaDA5JSIiIiKjweSUiIiIiIyG9OS0f//+sLW1hUqlwtmzZ5XtkZGR8PDwQLly5VC1alVcvHjxk/YRERERUeYlPTn19fXFwYMHYWNjo7W9Z8+eCAwMxLVr1zB06FD4+/t/0j4iIiIiyrykJ6d16tSBtbW11raYmBicPHkSfn5+AIC2bdsiOjoa169f/+A+IiIiIsrcDDLnNDo6GsWKFYOJiQkAQKVSoVSpUoiKivrgPiIiIiLK3L7qBVHBwcGwtrZWbomJiYY+JSIiIiL6AgZJTkuWLIkHDx5ArVYDAIQQiIqKQqlSpT64711BQUG4e/eucjMzM9Pr4yAiIiIi3TJIclq4cGG4ubkhNDQUALB+/XpYW1vD3t7+g/uIiIiIKHMzkR2gZ8+e2LJlCx4+fIhGjRrB3Nwc169fR0hICPz9/TF58mRYWFhg6dKlyu98aB8RERERZV7Sk9OQkJB0tzs4OODIkSOfvY+IiIiIMq+vekEUEREREWUuTE6JiIiIyGgwOSUiIiIio8HklIiIiIiMBpNTIiIiIjIaTE6JiIiIyGgwOSUiIiIio8HklIiIiIiMBpNTIiIiIjIaTE6JiIiIyGgwOSUiIiIio8HklIiIiIiMBpNTIiIiIjIaTE6JiIiIyGgwOSUiIiIio8HklIiIiIiMBpNTIiIiIjIaTE6JiIiIyGgwOSUiIiIio8HklIiIiIiMBpNTIiIiIjIaTE6JiIiIyGgwOSUiIiIio8HklIiIiIiMBpNTIiIiIjIaTE6JiIiIyGgwOSUiIiIio8HklIiIiIiMBpNTIiIiIjIaTE6JiIiIyGgwOSUiIiIio8HklIiIiIiMBpNTIiIiIjIaTE6JiIiIyGgwOSUiIiIio8HklIiIiIiMBpNTIiIiIjIaTE6JiIiIyGgwOSUiIiIio8HklIiIiIiMhtEmp5GRkfDw8EC5cuVQtWpVXLx40dCnRERERESSGW1y2rNnTwQGBuLatWsYOnQo/P39DX1KRERERCSZUSanMTExOHnyJPz8/AAAbdu2RXR0NK5fv27gMyMiIiIimYwyOY2OjkaxYsVgYmICAFCpVChVqhSioqIMfGZEREREJJNKCCEMfRLvOnXqFDp27IirV68q26pVq4apU6eifv36yrbg4GAEBwcr9x8+fIiiRYvq9Vw/R2JiIszMzDJtPEPE5GPMHDH5GDNHTD7Grz+eIWLyMWaemJ/q8ePHePPmTYb7jTI5jYmJgb29PZ49ewYTExMIIVCsWDEcPHgQ9vb2hj69/8za2hp3797NtPEMEZOPMXPE5GPMHDH5GL/+eIaIyceYeWLqilFe1i9cuDDc3NwQGhoKAFi/fj2sra2/6sSUiIiIiD7OxNAnkJGQkBD4+/tj8uTJsLCwwNKlSw19SkREREQkmdEmpw4ODjhy5IihT0OngoKCMnU8Q8TkY8wcMfkYM0dMPsavP54hYvIxZp6YumKUc06JiIiIKGsyyjmnRERERJQ1MTklIiIiIqPB5JSIiIiIjAaTUz2Jj4/Hs2fPlFtm8fr1a7w9bVkIgdevXxvwjDKH9IoTx8bGGuBM5ElKSlL+ffPmTWzevBkpKSl6ix8XF4cLFy7oLZ6+XLlyRa/xnjx58knbiDIihEBCQoLUGOfOnfukbWQcmJxK9ueff6Jw4cIoUKAAihYtiiJFikjtYnXixAm8fPkSABAWFobBgwfj/v370uLVr18f8fHxyv34+Hh4e3tLiwcAarUas2bNQu/evQEAN27cwO7du6XFi4uLQ9++fdG8eXMAwKVLl7B69Wpp8QCgRo0auHHjhnL/8OHDqFq1qtSY0dHRaN68OSpXrgwAOHv2LGbPni0tXq1atZCQkICnT5/C09MTU6ZMQZ8+faTFA4DGjRvj+fPnSExMhIuLC5o3b47Ro0frPE7r1q3Rpk2bDG8yNW7cGD4+Pti4cSP0sd61YcOGn7RNVzZt2qS858ycORO+vr7SvmSMHz/+gzdZ9P0ed/v2bfTu3RsNGzZE/fr1lZtM3bt3x/Pnz5GUlITKlSujSJEi+PXXX6XF8/f3/6RtujRmzBg8ffpUuf/kyROMGzdOWrymTZu+F0/zufW1YXIq2U8//YR///0XycnJSEpKUv4rS0BAAHLmzInIyEiMGDECOXLkQLdu3aTFe/nyJfLly6fcz5cvHxITE6XFA4C+ffviypUr2Lt3LwCgQIECGDJkiLR4PXv2RNGiRXH79m0AQOnSpTFt2jRp8QBgxIgRqFevHv7880/MmDEDnTt3xooVK6TG7NmzJ9q3b68kNI6Ojvj999+lxVOr1TA3N8eWLVvQtWtXHDp0CIcOHZIWDwAePXoES0tL/Pvvv2jZsiUiIyOxYcMGncdp1aoVWrZsmeFNpps3b+L777/Hzz//jDJlymD69OlSrtYkJSUhPj4eKSkpSEhIQHx8POLj4xEdHY0XL17oPJ7GiBEjYGFhgYiICISGhsLHx0dJ4nQtISEBCQkJuHz5Mn7++WdERUUhOjoav/zyi9QRan2/x/3vf/+DpaUl+vbti0GDBik3mU6dOgVLS0ts27YNrq6uePjwIRYsWKDzODExMTh37hxevXqF8+fP49y5czh37hwOHDgg9XkKABs3bkSBAgWU+wULFsTGjRulxXvw4MF78WQOTkklSKqaNWvqNZ6rq6sQQoi5c+eKWbNmCSGEqFy5srR4Tk5OIiEhQbkfFxcnKlWqJC2eEEK4uLgIIbQfl7Ozs7R4mr+pvuJpHD9+XJiYmIjixYuLx48fS4/n7u4uhNB+nDKfOxUrVhRCCNGtWzexc+dOIcT//b+VRfPc7NOnj9i0aZMQQu5jNLSTJ0+KkiVLity5c4uAgABx7949nR177NixQqVSiWzZsgmVSqXc8uXLJ8aPH6+zOO/SvB6nT58ufvnlF61tsvj4+Gj97e7fvy8aNmwoLZ6+3+OcnJykHTsjmsczePBgsXbtWiGEnNfinDlzhK2trTA1NRW2trbKzdnZWSxevFjn8d6W3t9V5uejo6OjSE5OVu6/efNG+uexLEZbhD+zCAwMxOTJk+Hr64tcuXIp20uVKiUl3ps3b/Do0SNs2rRJGd2TOY+vU6dO8Pb2Rq9evQAACxYsQNeuXaXFA6D1dwTSHl9qaqq0eKamplr3X716Jf1y6fHjx9GpUyeMHz8eZ86cQceOHbFq1SoULFhQWkwTExOtxxUbGyv1cdarVw8VK1ZESkoKQkJCEBsbCxMTuW9Jjo6OaNKkCS5fvozp06crU2B07WPFr4ODg6XE1YiMjMQvv/yCtWvXokWLFujRowd2796Nxo0b62ye3ZgxYzBmzBj07t0bv/32m06O+SlSUlJw7NgxrF+/XukcmJycLDXm/fv3Ubx4ceV+sWLFcO/ePWnx9P0e5+joiKioKGmfS+kpWrQoevfuja1bt2LEiBFITk6W8lk1YMAADBgwABMmTMCoUaN0fvwPcXBwwPTp0zFo0CAIITBr1iyUL19eWrwmTZqgXbt2GDBgAABg7ty5aNq0qbR4MjE5lezNmzeYOHEiZs6ciezZswMAVCoVYmJipMT74Ycf4ODgAG9vb7i5ueHGjRuwsrKSEgsAhg4diqJFi2LLli0A0i5H+fn5SYsHAM7OzggNDUVqaiquX7+OadOmoW7dutLi1atXD5MmTcLr168RHh6O2bNnS583+O2332LFihWoXbs2AODnn39GtWrVcPPmTWkx27Vrh549eyI+Ph6LFy/GggULEBAQIC3e/PnzERERATs7O+TIkQMpKSlYtGiRtHgAsGzZMmzbtg0uLi7IkycP7t27hylTpug8zttTXfStUaNGiIyMRO/evXHx4kVYWloCANzc3LBs2TKdx/vtt98QFRWF/fv3Q6VSoU6dOihZsqTO42hMnDgRPXv2hLe3NypUqICrV6+iXLly0uIBgLW1NcaMGaO8HpYsWQJra2tp8fT9Hvf48WO4uLigZs2aWonxX3/9JS3mypUrERoaiq5du8LS0hK3b9+W2tGocePGePnyJfLkyYOwsDAcP34cQUFBWl86dG3u3Lnw8/PDyJEjldfG8uXLpcWbNGkSJk+erEwBadGiBYYOHSotnkzsECWZjY0Ndu/ejTJlyhgkfkpKClJSUt4b/ZNBCIHExESYm5tLjZOYmIhBgwbh77//BpA2v2/27NnIkyePlHhqtRozZszA33//DSEEWrVqhaFDhypfNmR48uTJe6OkZ86cgaurq7SYALB69Wqtx9mxY0ep8Y4fP47w8HAAgI+Pj/RFX0DaXLdLly6hc+fOiI2NxevXr1GsWDHpcfVl/fr1aN26NbJl08+SglWrVqFfv36oU6cOAODgwYOYP38+2rdvr5f4+vDw4UP0798fu3btgkqlgre3N+bMmSNtcWt673HBwcHImzevlHh//PFHuttlXwXTJxcXF5w+fRo3b95E06ZN4evri9OnT2P79u3SY2vmtsr6/5cpGWxCQRbh4eGh13ijR48WT548Ue4/fvxYjB07Vlq87777TsTGxoo3b94IZ2dnkTt3bmUemAxqtVoMGTJE2vHTi+fn56e3eFlJSEiIKFmypBgwYIAYOHCgKFWqlFi0aJHUmL/88otwdnYWdnZ2Qgghrl+/LurWrSs15rFjx0RISIiYO3eucpNt3bp1YtKkSUIIIe7evSvOnTsnLZaDg4O4efOmcv/WrVvCwcFBWrzu3bu/9x4XGBgoLR7JcerUKdG4cWNRtmxZUbp0aeUmi77XYwghxD///CPi4uKEEELMmDFDtG3bVpw/f15avFGjRonY2FiRmpoqmjZtKgoUKCDWrVsnLZ5MTE4lGzlypAgKChLHjh0TERERyk2W9BaUyFwsoIm3ceNG0bVrVxEXFyd9cn3VqlWlHv9d1apV02u8jDRr1kzKcX/44YcP3mRxcnISMTExyv2YmBjpzx0XFxfx8uVLrQ8lmQsGJk2aJFxdXUWBAgVEmzZthLm5uWjVqpW0eEKkfUBpPvSFSFu8I3NhZnrvLzLfc9JLKGQviHr+/Lno06ePaN68uRBCiIsXL4pVq1ZJi6fvQQYhhPjzzz/FTz/9pJfXvhBpi3cWLFggIiIixIULF5SbLBUrVhQPHz4U3t7e4tSpU0II+QvBNMc/e/ascHFxEQsWLBC1a9eWFk+zyGzHjh2iRYsW4sqVK1/tgk+WkpIsNDQUf/31F7799luljEyrVq2kxUtv0rzM0lXi/88KOXDgAJo3bw4LCwupl7uBtFpukyZNwv3795XyNW/XWtW1evXqITAwEIcPH1bKkBiieLOs+nj58uX74E2mQoUKpftvWXLmzIncuXNrbZO5CGvVqlU4fPgwrK2tsX79epw4cUL65faNGzdi8+bNyiXEYsWKSS3v1qxZM4wdOxZ3795FdHQ0xo8fj2+++Uba61KtVmvdF0JIfY8D/q+c3K1btwDILyen7xJE/fv3x4oVK7Bs2TKoVCqsW7cOcXFx0uIBQPbs2dGzZ084OzujUqVKyk0WzXqMfPny6WU9BvB/7y07duxAYGAgevbsKbV8lea9Zd++fWjXrh0cHBygUqmkxZPK0Nkx6Zavr6+YNm2aUKvVIjk5WUydOlW0bdtWWryGDRuKXr16CRsbGxEbGyuSkpKkfxt9u2yNpoxNtmzZpMV7u/yI5ibz8lNW0aZNGzF8+HBx584dcefOHTFy5EjRpk0bqTGbNWsmrl69qoy0LV26VHzzzTfS4mnKczk5OYnU1FQhhPxyWdWrVxdCaI8wOjo6Sov39uvx3ZuM12VAQIDo06ePuH37trh165bo06eP6N69u87jvE3f5eQMUYIoJSVFeUwPHjyQWipLCCG+//57ceLECakxPkStVos3b95IjeHs7CyOHj0qqlevLi5duiSEkPtarFWrlpg6daooU6aMePjwoUhNTWUpKUrfiRMnUKlSJb2tENT36kB9r7gE0h8dlkkzWqJPo0ePRlBQEPLly4fmzZvj2LFjCAkJQdu2baXGPX78OM6ePavVgrZ///5SYi1YsAD9+vWDm5ubsshEdkmiOXPmoEOHDrhy5QpKliwJCwsLbN68WVq83LlzIzk5GZUrV8bgwYNhbW0tvUWrjY0NDhw4AJVKheTkZEyePFnp+iWDvl+Ps2bNwoABA1C1alWoVCq0aNFCaiczQP/l5PRdgihXrlzIli2b8pwpWrSo9OLt+/fvx6JFi2Bvb69VIeD06dPSYt6/fx8XLlzQen9r0aKFtHiayhINGjTQS2WJZcuW4eeff8b06dNRpEgRXL9+XXr1HGkMnR1nds7OzkKtVotr164Je3t7MWzYMOnfSIUQIjExUSQmJkqPk5W8fv1axMXFKTeZDDF3SN/zI9NrLKCPZgMpKSni0qVL4uLFi0KtVkuNdf78eZGYmChiYmJEQECAaNu2rThz5ozUmA8fPhSNGjUSJiYmIkeOHKJhw4ZS/64LFiwQL1++lHZ8YzBs2DAxceJEUb58ebFz507RtGlTMWbMGGnx7t27J+rVqydy5MghTE1Nhbe3t7h//760ePXq1RMvXrwQffr0Ef/73/9E//79pc+137t3b7o3WZYsWSJsbGyEubm5cHNzE9mzZxe1atWSFo++DEtJSebm5obTp09j3rx5UKvVCAoKgqurK86cOaPTOJGRkShbtmyGcyGdnZ11Gq9Dhw5YvXo1XF1d053TIvPbb2RkJPr374+IiAitb8AyWjQCwNGjR9GtWzdcu3ZNa7vMETDNc2TkyJEoX748/Pz8lOeSLI6Ojjh58iRq1KiBs2fP4urVqxg+fDjWr18vJV56j0fWY/zY3EcLCwudx3yb5m1WH/O/nj59igIFCuDly5cQQiBv3ry4du2atBGbTp06YdeuXejQoQP69OkDe3t7KXH27dsHLy8v/PPPP+nulzkCZohycoD+ShBp2vqmpqYiODgYsbGxGDBggNR6tfrm5OSE/fv3o379+jhz5gz279+PZcuWSWnRvHr1anTo0AHz5s1Ld7+ur0YNGjQIs2bNQuvWrdN9j5FZr1YWXtaXTF8dm3744Qds3rw53b7dKpVK58XbBw8eDCDtMqm+9ejRA71798b48eOxZs0azJ8/H7a2ttLiDRgwAMuWLUOvXr2wf/9+zJs3770OLrqWN29eTJs2DWvWrMGhQ4f0sugjV65cyJUrF1JTUyGEgIODA27cuKHzOElJSXj9+rXSk12TuMXFxUlbLGBpaQmVSqV1KVZzX6VSSfuiERUVhR49emDv3r1QqVSoV68eQkJCpHbiadWqFXbt2qXU/b1z5w6++eYbXL16VUq8lStX4tGjR1i0aBEaNGiAihUrol+/fjrvTBMaGgovL690L+FrLu/LYmJigp9++gk//fSTtBjvSk5Oxr1797S+gOt6kEGjSJEiyr9HjBghJca76tWrl24itXv3binxTE1NYWVlpSyoq1OnDgYOHCgl1pUrVwBA54NQGdE0aJC52FrvDDVkm1UsWrRI5MuXT1mUdP36dVGnTh0Dn9XXTbM4QTOxPDU1VWp5qXfjCSFElSpVpMUTQojIyEgxYMAAsX79euX+lClTpMasXbu2SEpKEp07dxZBQUEiODhYyuR9Q/VkNwQvLy8xffp08fz5cxEbGytmzJghvLy8pMacN2+esrDs/v37onz58mLLli1SY2rs2bNHlCxZUlhZWQkHBwcRHh6ul7iyvXjxQvz666/ihx9+EP369VNusmzatEkULVpU5MyZU+TPn1+oVCpha2ur8zhBQUFCCCFatWolWrdu/d5Nps2bNyu3devWiTZt2ohhw4ZJi1ezZk2Rmpoq2rRpI2bPni3++usvpdwaGR9e1tez1NRUqNVqqR2bUlNT8fDhQ62SK7JGai5duoRx48YhMjJSK57MUkvVq1fHsWPH4OHhgVWrVqFo0aKoWLGitNaemnh169ZFcHAwSpYsiapVq+L27dtS4gHA3bt3pbZHTM+FCxdQunRpvHz5EsOHD0dsbCxGjhwpbTGNvnuyA2kLFMuXL690MUtISMDVq1dRpUoVKfEqVaqEixcvam1zdHTEhQsXpMTTGDRoEBITE3Hs2DGMGjVK6kK6169fIzQ0FL/88gvy5MmD/v37w9fXF2fOnIGvr6+U18mDBw9w69YtrfccTYcqGRo3bowcOXKgatWqWpfyZY0yurq64u+//0arVq1w5swZhIaGIiIiAjNmzNBpnE2bNuGbb74xig5RarUa9evXx/79+6Ucf/fu3XB3d8eTJ0/Qq1cvPH/+HFOmTIG3t7eUeBpbt2597/NR1qLhBw8eYM6cOe/Fy2gqjDHjZX1JDDU/atmyZejfvz9y5Mih1DxTqVSIiYmREq99+/bo0qUL+vTpI33+lUadOnXw9OlT9O3bF+7u7jA1NZXaKrF9+/Z4+vQphg8fDi8vLyQnJ2PixInS4gGAl5cXcuTIgQYNGqBBgwaoX7++0iNdFkdHRwBpUwoWLlwovRWtvhNTIK1e5YkTJ5T7uXPnRq9evXDy5Ekp8ezt7bXme167dg1ly5aVEuvtL4R+fn4IDAyEj4+PMhdd1iVhW1tb+Pj4YOHChVrtZ6tUqQIfHx+dx5s0aRJmzJgBOzs75T1HpVLh+PHjOo+lERUVhUuXLkk7/ruyZcsGGxsbJcHw8/OTUpHgm2++AZB2Wb9x48Za+7Zt26bzeB+SkpIitUJA/fr1AaTVdd65c6e0OG/r2LEjLl++DFdXV63nqixt2rSBu7s7WrVqpbfPY1k4cipJjx49sGjRItSrV++9fSqVStq8mjJlyuDff/+Fg4ODlOO/S8birs8RHR2NuLg4JbGSLTk5Ga9fv5aatGncvHkT4eHhCA8Px759+2BjYyP1A7h79+6YNWsW8uTJg6pVqyIyMhIzZ87E999/LyWepnTNu2QuNKtcuTLOnj370W1fSrMwIS4uDocPH4aHhwcA4MiRI/Dw8EB4eLhO4wFpheEzImPeOZD2/2rlypXo0qWLzo+dkTJlyuD48eNaRepla9OmDRYuXIiCBQvqJV6NGjVw9OhRNGnSBIGBgbC1tUXbtm2lXR3S5+JEjbcX76SkpODcuXNo2rQpfvnlFynxoqOj0bt3b9y9exdnz57F2bNnsWfPHvzwww9S4gFA+fLlcfHiRb0lis7OzgZpECMDR04lWbRoEQBgz549eo1bsGBBvSWmQNqk9v3790u9pPauiRMnwtvbG9WqVUPJkiWlrygNCAiAt7c36tevj8KFCyNHjhxS42kkJSXhzZs3ePPmDSwsLKSNuGmcOnUKlpaW+Oeff+Dq6ooDBw6gdu3a0pLThIQE5d+vXr3C8uXLpdcANTU1VSpbAGkjmTL+f769MOHtxE1mEmeIerzZs2fHnDlz9JqcFilSRK+JKZA2WluzZk1UrVpVazGkjJXeQNoizNjYWEyYMAEdOnTA8+fPMXfuXJ3HuXbtGq5cuYK4uDitq3xxcXF4+fKlzuO97e3XiImJCYYPH47q1atLi9ezZ0907NhRmRrh6OiIzp07S01ObW1t8ebNG2Vxomzu7u5a729fM46cSqZWqzF37lzcuHEDv/76K27cuIE7d+4olxh0RVMq55dffkHu3LnRsWNHrTdRWaVyDh8+jEaNGsHc3By5cuVSVj/L+oYPABMmTMDOnTtx6dIleHh4wNvbGw0aNJDW+m7FihXYuXMndu/ejfz588Pb2xve3t46X438Nmtra5QsWRL/+9//4O3tDScnJ2mxNFxcXBAREYEff/wR1atXh6+vr95HxjXze2XZsmULvvvuOzRp0gQAsH37dixduvS9S5q69ubNG+TMmVNqDI1NmzbB09NTmQYSGxuLw4cPo1mzZlLiBQQEwN/fH7Vr15Zy/HeNGTMGcXFx773HyZq2AAC1a9eGnZ0dqlSpojUK1qdPH2kx9eGPP/7AsmXLcPLkSa151xYWFggMDJT2nDGEKlWq4OTJk1rvabLf3y5evIgePXqgbt26Ws/V0aNHS4l3/vx51K9fH2XLltWKJ+tKrUxMTiXr1asXUlJScPDgQVy+fBnPnz+Ht7e3zue4aS6Rpve/U2apHAcHBwwdOvS9N22ZPZI1Xrx4gfXr12PMmDGIioqSPuomhEBoaChGjx4tPd6QIUOwZ88emJiYoEGDBvD29katWrWkjto2atQIdnZ22Lp1K86ePYu8efPC3d1db5eJrly5gmbNmkkpX/W2a9euKZfVGzVqhDJlykiLdf78eWXk6+7duzh16hT+/PNPTJ8+XVrMd6cpCCHg7u4u7RKto6Mjrly5Ajs7O5iZmSnbZcVLb/qC7C/E6S1sk+nNmzeYPXs2wsPDoVKp4OPjgwEDBkj7grNkyRJ0795dyrEz8t133723zdLSEjVr1kS7du10Hq9GjRo4cuQI3NzccObMGcTGxqJevXo6n9LzthYtWiA+Ph5ubm5an4+6Xtim4eLigpYtW773efw1fsngZX3Jjh49irNnz8LV1RVA2osvOTlZ53H03UJQw8zMLN03GZk08zB3796N1NRUtGvXTsrCC43FixcjPDwcp0+fhpOTE3788Uep8QAoycvTp0+xceNGdOvWDY8fP0ZiYqK0mPpuRWtlZaU150wIgfnz50uLB6SNlDZq1EhqC8G39evXT2nTCqTN4+vSpYvU5PRdMr+cApA2RzAjhpi+4OLignv37qFEiRJ6iderVy88ffpUed4sXboUly9fxtKlS6XEK1y4MOLj42FhYYGZM2fi6NGjGDt2rNS5/Kampjh8+DC+/fZbqFQqhIWFoXLlypg3bx7OnDmDyZMn6zReu3bt0LNnT8THx2Px4sVYsGABAgICdBrjXVevXpVWXzgj48eP12s8aQxSwCoLqV69uhBCKK0n1Wq1lNqRb7tz545YsWKFCA0NFVFRUVJjjRo1Svzzzz9SY7xLpVKJWrVqif379+slXo4cOUStWrXEpk2b9Nam8ciRI2LChAnCy8tL5M+fXzRt2lQEBwdLi6dWq0WDBg2kHT89t2/fVm53796V3kpUCCE8PDxEuXLlxNy5c6W3oBVCCHd3dyGE0Go9K7sNbe3atcWhQ4eU+wcPHpTapvHVq1ciNTVVuZ+SkiJevXolLZ4QQhw7dkxMmjRJTJo0SZw4cUJqLCGE8Pb2FpaWlqJJkyZ6qQPq4OCg9TdVq9XCwcFBWjwnJychhBBnz54VLi4uYsGCBaJ27drS4gkhRJ06dURCQoJyPyEhQXh6eooXL16IChUqSIm5atUq8b///U+0a9dOrFy5UkqMt33zzTd6eZ/R6N27t/T2yPrCkVPJnJ2dERoaitTUVFy/fh3Tpk1TujnIsGrVKvTr109ZoDRw4EDMnz9fWqml+fPnIy4uDrlz50bOnDmVOaeyWokCafU4w8PDMWPGDPTs2RPVqlWDj48POnXqJCXe06dPsWfPHuzYsQOjRo2CpaUlvL29pXZSGTRoELy9vTFhwgTUrFkTJiZyX6rZs2fHy5cvkZqaqpQgk83GxkYvcd526NAhnDlzBr/++ivKli2Ltm3bom/fvqhYsaKUeCYmJkhOTlZGiKOjo6Wv3J0+fTpat26N8uXLQwiB69evY8OGDdLi1a9fH1u3bkW+fPkApC10a9asGQ4ePCgl3sKFCzFx4kS0adMGKpUKbdu2xahRo6SOgvn5+cHPz0/a8d9VoEABvHr1SllI8+bNG6mVAjTvLzt27EBgYCB69uyJkJAQafEA4PHjx1rTQMzMzPDkyRPkyZNH2vSFDh06oEOHDlKOnZ7cuXPDzc0NDRs21JoDGhwcLCXegQMHsHjxYtjb22vFk1l1QRbOOZUsMTERgwYNwt9//w0gbYVicHCwtF7J5cuXx9atW5V5Wbdv30bjxo2Vdmq6dufOnXS36yPxuHXrFrZu3YoZM2YgOjpaq+iwriUnJ+PgwYMIDw/HqlWrEB8fj6dPn0qLpyH02JN9wIABiIyMhJ+fn9aHhqyavDExMRgzZgwiIiK0WjTq64307NmzaNGiBe7fv4969eohODhY5wvPQkNDsXr1apw7dw5du3ZFaGgopk+fjv/97386jfOu2NhYHDlyBADg4eEhtUauvspzaTg7O2PXrl0oVKgQgLQkp0GDBpmmhA6QVtbt+PHjyvNk3bp1qFq1qrLoS9e92V1cXLBw4UIMGDAAS5cuRYUKFeDk5ITz58/rNM7b2rdvjzx58qBbt24A0hZnJSQkYPny5ahVq5bO1mV069btg++fsiouAMC4cePS3T5mzBgp8fbt25fudi8vLynxZOLIqWRmZmYICQmR/i1UI0+ePFoLBmxtbaWWsTDE6FfPnj2xa9cuAIC3tzemT5+OBg0aSIvXqFEjnDp1CpUrV4a3tzfCwsKkdRTSePDgAbp3747du3dDpVKhQYMGWLRoEYoVKyYtpubDXVMGDZDbs7x79+6oXbs2du3ahVmzZiEkJESZmy1TeHg45s+fj/Pnz6NPnz7o3r079u7di9atW+P69es6jeXn5wc7Ozts3LgRSUlJCA0N1cuq9sTERDx//hxA2sJBmclpamoqEhMTlS808fHxUr8oAlAS03f/rU+bN29G8+bNpRw7NTUVVapUURZ5ubm5ISUlBWfOnJHyRXXixIno2bMnGjRogAoVKuDq1avS52UvXrwY48ePV/rb169fH7Nnz0b27NmxdetWncXRvFefP38e+/fvR8eOHaFSqbB69Wp4enrqLE56ZCWhGfkak9CMcORUD44dO4YbN25ovWHLqgs4atQoZM+eHQEBARBCYOnSpUhJScGgQYMAyCsp9baxY8di7Nix0o6/aNEieHt7f7DouC7t3LkTnp6eWpdJZPvmm29Qs2ZNpcboggULcOjQIWzatElv5yCbZnRNM0KTlJQELy8vZcRPhgoVKqBgwYLo378/2rRpo3WJvUmTJjr9UHxXXFwcoqOjpTeM2LhxI7p376588B46dAhLlixRugHp2rRp07Bhwwb06tULQNpztW3btvjxxx+lxGvbti3Kly+Pnj17Akh7P7h06RLWr18vJV5GNI1W6OtQp04dbN68WfkMjI+PR/PmzaW1S83I6dOn4ebmprd4CxcuRGBgoN7i6QqTU8l69+6N7du3o3Llylrty8LCwqTE+9B8QdmrdjVCQkKUD46vWVRU1Af3lypVSlpsfV8q1Vi7dq3S2q9Ro0ZSe7JXq1YNx48fR9WqVbFt2zZYWVnBwcEBkZGR0mKeOnUK7u7u0o7/rsaNG2PNmjUwMTFRktIuXbpIXVHr5uaGsLAw2NvbAwCuX7+O//3vf1KnS/zxxx/YsmULgLRpIDLnZz5+/Bj9+vVTyix5e3tj7ty5KFy4sLSY+hYSEoL27dsjX7586Nu3L44ePYrg4GCdNztZvXo1OnTogHnz5qW7X9fTBz5GZuJWoUIFXL58+aPbZGvWrJnyWtGHMWPGZDi9wJjxsr5k4eHhuHTpkt5G3QxVUuptshLTjNpdaug68XZ3d1fiPX36VKkxmpycjAIFCiAmJkan8d4mhMDDhw9RtGhRAMDDhw/TrWGrS+PHj8fff/+NLl26QKVSYcqUKbh8+TJGjhwpJV65cuXw9OlT+Pn5oXr16rCwsJCeOJ46dQqlS5dG/vz5AaT9f/3rr7/Qo0cPKfEePXoES0tLhIWFoWXLlpg5cybc3NykJqcpKSlKYgoA9vb20t8Xunbtiq5du0qNAaQ9tpkzZ2LNmjXSYxnSL7/8gp49e+LQoUM4f/48Jk2ahMGDB+u8fbFmLUJ6hej1Mc/9XaNGjZKWuLm4uMDf31+p57p06VK4uLhIifUh+kxMgYznvRo9wxQJyDo8PT21SoJkNv/8849SKmPGjBmibdu24vz581JiJSYmioSEBDFhwgQxfPhwER0dLaKjo8WIESPExIkTpcQUQoghQ4aIhQsXipSUFJGSkiIWLVokhg0bJi2eEEIsX75cFC1aVHTr1k1069ZNFCtWTISGhkqN6eTkJF68eKHcT0xMVErMyHbw4EGxadMmkZycLDWOi4vLe9tklnaqVKmSEEKIPn36iE2bNkmPJ0Ra2aNFixYpz9fFixcLb29vqTHfpXmsMlStWlXasd9Vp04dIYQQlpaWwsrKSrlp7svi6uoqhBBi/PjxYvHixVrbdE2tVot169ZJObYxSUhIEIMHDxZubm7Czc1NDBkyRKuUlQxv3rxR/n3jxg2xadMm6SXzUlJSxL1798SdO3eU29eIl/Ul0fQpPnDgAG7evIlvv/1Wa/RU1iKTyMhI9O/f/70V0LJKOzk7O+PcuXOIiIhA165d0bt3b4SGhuLAgQNS4gFpI5qnTp366DZdSe9yuj7ael64cAF79+4FANSrV0961630VufKXrGrb5oWrW/TPIdlaN++PeLi4nD58mVcunQJAFCrVi2pz50bN26gU6dOOH36NFQqFdzc3BAaGiq1E9a7ZM7HHDt2LHLkyIFu3bppVZWQMZ/+wYMHKFasmN6rklSpUgU//vgjJkyYgC1btsDGxgaOjo64cOGClHhubm56LzeUlJQEU1NTAMDNmzdx6dIlNGnSRHqpNX2qWrUqdu/ejaSkJDg7O8PW1hZOTk5YsGCBlHjLli1D//79kSNHDmWKn0qlknqVTxZe1pdk9uzZWvd/++035d8yV0D36NEDvXv3xvjx47FmzRrMnz8ftra2UmIBhqmPl5CQgJiYGGWOWUxMDBISEqTFS0pKwtWrV+Hg4AAgrf3lmzdvpMXTcHR0lL545m3VqlVD586dlUvcS5YsQbVq1fQWHwACAwOxcOFCaccvVqwYwsLClBI9f/75p9QKCMuWLcO2bdvg4uKCPHny4N69e5gyZYq0eABQpkwZHD16VOkm9nYCpy8yFwpppkSMGjVKadksaz695rmhUqlQuHBhZYDh9evXePz4sc7jafzyyy+YMmUKevToARsbG1y7dg3169eXFs/NzQ0HDx7USyUJjVq1aimJm6enJ2xtbbF582adJ26GnFerVqthbm6O5cuXo2vXrpg8ebLOy9W9bcKECThx4oTyWfU1Y3IqyZ49ewwSNz4+Ht9++y0mTpwIJycnhISEoHr16hg+fLiUeCkpKTh27BjWr1+vtNaT0Z71bYMGDYKLiwuaNm0KANi2bZvU6gBTp05FrVq1lPlJ586dk1Ybr3Tp0h+c6yWzf/i8efMwYcIEpWWpt7c3Ro0aJS1eemStKNeYM2cOWrZsiSFDhgBIK722ceNGafFy5cqFVq1aKfdLlCihlxaYDx48wK1bt7QqhOh6MY2hGGJeva+vr9aqbiEEfH19cezYMSnxqlevrtTGBtLmZ2eUXOnC0aNHsWzZMtjZ2Wl9mZE5mqqvxM2Q82qTkpIAAHv37kXHjh0BQOrIcMGCBTNFYgowOZVOsyL5Y9t0RbNox9zcHLdv30bRokXx5MkTKbGA/6uP5+3trZf6eEIItGjRArVr18bu3bsBAEFBQdIueaempqJIkSK4fPkyjh49CgCoWbOmtG4tmzdvBgCsWbMGt2/f1iqXI+sSYr9+/TB//nxs2LABU6dOlRLjU8lMTlNSUnDx4kVcunRJ6Xft4OAg9cNC39NsAGDSpEmYMWMG7OzstCqE6Po9x8vLC/v27YOVlZXWh7yQ3CXu+++/x6+//vrRbbqUlJSkNS0rd+7cerl68jaZdVV/+eUXKcf9EH0lbpoFQZrBE32qV68eKlasiJSUFISEhCA2NlZqt79WrVphzpw56Nixo9bzVR8lJHXOkBNes4J3J7EnJSWJ8uXLS4s3ePBg8eTJE7Fy5UqRP39+UbRoUTFw4EBp8fQtNTVVWWSiL87OznqNJ4QQVapU0bqfmpr63jZd0Tw+WQsuMjJ69Gjx5MkT5f7jx4/F2LFjpcbU92P08vISa9asERUrVhTnzp0TPXr0EJMmTZIa087OTuvvKsv9+/eFEELcvn073Zss6f0/TG+hmy5VrlxZPHr0SLn/4MEDvb8vBAQE6DWebH369BEVKlQQ5cqVE0lJSeLZs2fC3d1dasx///1XzJ07V8yaNUu5yZSamirOnDmjLBp+/PixOH36tLR4KpXqvVu2bNmkxZOJI6eSTJs2DVOnTkViYqJStgYAXr16Ja0AvxACQUFBKFCgADp27AhPT0/ExcVJnbdob28Pb29vNGjQAPXr10eBAgWkxQLSRoCsra3x5MkTqb2m31a2bFlcv35dqzyPbHFxcXjx4oXS5vbFixeIi4uTEqtkyZKoUKECoqKi0q0xKOvS3saNG7XKnBQsWBAbN26U2lVF33Pr9D3NBgCKFCki/XUI6H8+5p9//ok1a9bg1q1baNOmjbI9Li5O+rza/v37o2bNmujcuTOAtLa0+u7+I3Me7+3btzFt2rT3msVork7JMH/+fERERMDOzg45cuRASkqK1MfYsWNHXL58Ga6urlpXFGTavHkzPD09lZHL7Nmz4/79+9I64RlDKUldYXIqSa9evfDtt9+id+/eWhO8LSwsYGVlJS2uj4+PsqKzZMmSKFmypLRYQNrc2p07d2LDhg0YMGAAihYtqrQUlcXMzAyVK1dG06ZNtT6UgoODpcR79uwZKleuDA8PD614f/31l5R4QNobaY0aNZSFO2vXrpVW2HzDhg04ffo0/Pz83lvIJ1N6b6SaS32y6HtunT6n2WgqDvj4+GDgwIHvXdrT9GXXNX3NxyxfvjxatmyJ06dPo2XLlsp2CwsLqe2LgbT+7HZ2dtiyZQvi4uKwbNkyvS4eku1///sfGjRogL59++pttby+E7fTp0/j4sWLeq0GMGrUKK1KL5aWlhg1ahSaNWsmLWZ0dLRSLcfLy0svc9xlYCmpTKZx48YIDQ3V26iixvnz57Fz507Mnz8farUa0dHR0mJlVFRY1kjGH3/8ke522UXHt2zZooxceHt7o0mTJlLjXb58GRUqVJAa423t2rVD1apVMWjQIAghMGvWLJw4cQLr1q2TFnPfvn3pbpfVk/rHH3/EsGHDsH37dvTr1w+mpqZo3769lC8BH2rnq1KppC2m03c3s8ePH6NQoUJSjp0RfXX6MtQ8Xpnl1DLy7nNECAF3d3dpXxQbN26Mv/76C3ny5JFy/PSk9zpIr5ydrmhaF9euXRsqlUp662KZmJxKFhMTgzFjxry3IELWC9DX1xdHjx7V26iin58fTpw4gfLlyyuX9ytWrCglFumfzHaC9+/fh5+fHw4ePAiVSoU6depg+fLlUks7vR0bAIoXLy49lkZ0dLT0aTaG4Orqiu3btyul3R4+fIhGjRpJ+wBu2rQpVqxYoUxdePLkCfz9/ZXFhDJo6hqHhYXh8OHDmDFjBtzc3HReA9hQdVU7duyIqVOnSm3J/C59J24XL15Ejx49ULduXa0rCqNHj5YSDwA8PT0xbdo0eHh4AAAOHTqEoUOH4uDBg1LiGaJ1sSy8rC+Z5lvMrl27MGvWLISEhEi7bAGkFU2XWUftXdeuXUOePHlQvnx5VKhQQW+Fvo8fP46zZ89qJfwy69WFhYW9F09Wwg8YZg5YemS2EyxevDh2796NFy9eAIAyv1amy5cvw9fXV0lOra2tsXbtWpQvX15azKNHjyp94GVffjYEfc/HvH//vtac2oIFCyr/P2XRlMfbv38/GjdujBw5ckhZda3vebytW7eGSqVCQkICXFxcULNmTa3ETebUJXNzcxw+fFgrcTM3N5cW76effoKpqSlev34tvdyhxvTp09G6dWvl/SUyMhIbNmyQFs8QrYtl4cipZJpvh5pOO0lJSfDy8sKRI0cMfWo68/z5c+zevRu7du3Czp07YWNjg507d0qLN3nyZKxbtw5RUVHw8vLCzp070aBBA2kv+v79++PWrVs4deoUOnTogLVr18LHxwdLliyREg9IKzfWoEED1KxZU2uOlMy5SvqmVqsxd+5c3LhxA7/++itu3LiBO3fuSC02Xq9ePfTo0UMpXbNmzRqEhIRIq0s8c+ZMzJ8/X1nAs2HDBvTv31+pJasvzZs3lzqyuG/fPmU+ZufOnaXOx3RycsKZM2eU5DApKQlubm7SuicB+u/0Va1aNezfv19JFF+9eoW6devqfB5vRlOWNGROXTpy5Ei6iZusxh8ODg5KCTl9io2NVT7vPTw8YGlpKS2Wj48Pvv32W3z33XcA0spnrVmzRurnsTQGqhKQZWj6QFepUkU8efJEpKSkCHt7e2nxxo0b995t7ty54vjx41LiqdVqceDAATF69Gjh4eEhChQoIFq2bCkllkalSpXEq1evlPIxV65cEW3atJEWz9HRUaSkpCilYx48eCAaNmwoLZ4QQm897d92584d8erVK+X+y5cvRVRUlLR4PXv2FAEBAUpptdjYWOmlZNIrOSSzDFHZsmW1yjo9ffpUlC1bVlq8jJw8eVLasRs1aiRiY2NFQkKCsLGxETY2NmLUqFHS4v3444+iVatWYs+ePWLPnj2iVatW4scff5QWTwghXr16JTZs2CBu3rwphBDi7t27YuvWrdLi6ft5aijPnj0TW7ZsEVu2bBGxsbFSY33zzTdKSafM6vr166J69eoiR44cwtTUVNSoUUNcv37d0Kf1n2QzdHKc2ZUrVw5Pnz6Fn58fqlevjipVqsDd3V1avMuXL+O3335DdHQ07t69iwULFmDv3r3o1KmTlA4jhQsXxsiRI2FiYoKZM2fi0aNHWp1NZMiVKxdy5cqF1NRUCCHg4OCAGzduSI2XLVs2qFQqJCcno2jRotIvIzo6OiIqKkpqjHf5+vp+0jZdOXr0KBYtWqSMDllaWkq/3JY9e3Zl5AsALl26JHX1roWFhdYl6Pz580sviJ3ewhZN1QAZHj16BEtLS/z7779o1aqV9EuXkyZNQuXKlTFkyBAMGTIE7u7umDRpkrR4wP91+tIsOitRogQaN24sLd67/dAfPnwIIfEiZ9OmTfH06VPl/pMnT6QV/H+blZUVmjZtiqZNm0odUQTSGie4ubnh+++/R1BQkHKTQbPA0srKCvnz51dumvuyaFoXP3v2DE+fPsWRI0f0NtVO1zjnVLLQ0FAAwIABA1ClShXExsZKfVOLjY3F2bNnUaRIEQBpHxydO3fG0aNH4enpqfN5mdHR0Xpd/QikvckkJyejcuXKGDx4MKytraX01dYwNzfHy5cvUbt2bfj5+aFo0aLSH/Pjx4/1PgdM311w3o4FpM2Xkj0/avLkyahTp45SVun8+fNYuXKlzuNoEsT69evD398f3bt3BwAsW7YM3t7eOo/3Nn9///cWQKS3TVf0NR9TI0eOHBgzZoze64zqU2aex2uoigQVK1bU22LdNWvWAIC0ihXvioyMRNmyZTOsuCCrjJxMTE71qFatWtJj3L17V0lMgbSC3Pfv30f+/PmljJ6YmppixowZypyWhg0bYuDAgVI/nH777TckJSVh1qxZGD58OA4dOoQVK1ZIi7d69WqYmJhgxowZCA4ORmxsrNRyR0BaFQRZdU0zohmteXvVtczRGmdnZ4SGhiI1NRXXr1/HtGnTULduXWnxAKBRo0a4fPmyMnevRo0aUsquvV2HE9AuYaVSqaS0iY2JicHDhw/x6tUrnD9/Xvl/p2noIIujoyOaNGmCy5cvY/r06Xj58qW0WEDaF+LevXvj7t27OHv2LM6ePYs9e/bghx9+kBpXn/RdVzUlJQVqtVprHq+smsP6Ttw09PllRrOwbcWKFRg5cqTWvokTJ7637Uv98MMP2Lx583vvO4DcMnJSGXRSQRbVo0cPacdu2LChGDt2rIiKihJRUVFi3LhxwsfHR6jValG5cmWdx+vXr59o2rSp2LBhg/j7779F8+bNRb9+/XQeh+T7/fffhZ2dnRgzZowYM2aMKFOmjFi+fLm0eAkJCSIwMFAULlxYFC5cWAQGBooXL15IiyeEEL179/6kbV+jOXPmCFtbW2FqaipsbW2Vm4uLi1i8eLG0uPqej9mkSROxYsUKZQ54cnKycHR0lBbPELLCPN4JEyZ80jaZTp06JfX46bXa1XcL5a8VV+sbwKZNm6QVxX348CH69++PXbt2QaVSoX79+pgzZw7y58+PyMhInZeZcnZ2xtmzZ5EtW9r0ZbVaDTc3N6kFnZ8/f46QkJD3yiz9/vvvUuLps6zTrFmzMGjQoAznQsksXwUAe/fuxb///gsA+Oabb+Dp6SklTkpKCoYPH45p06ZJOX5G3Nzc3ru8LbNgvL6lpqZi4sSJUms3GlqVKlVw8uRJpfYoAK1/Zwb6qquqkZycjMmTJytl41q0aIGhQ4dKnauc3msxvW0yNWvWTEqpvO3bt2Pbtm1YtWoVOnXqpGyPi4vDmTNnpD3G9NYpWFpaSp/nLgMv60uWXg/4mjVrSotXtGhRhIWFpbtPRv1TIQRSU1OV5FQIIfVSMJC2SKdQoULvlVmSRZ+t/TSNE/Llyyc1Tkbq1q2LunXrIi4uTmqXr+zZs0sr35QeQ/Zlf5fMsk4qlQphYWGZOjk1MTHReo+JjY2V/p6jb5l5Hq8mcbt3757Wl/C4uDjpsd8lq4Zzrly5YGlpiWzZsmm9l5csWRKjRo2SEhMA3N3d8ezZM+VLRXJyMszMzGBtbY2VK1eicuXK0mLrGpNTyRo2bPjet6T0tsn08OFDFC1aVMqxGzdujIYNG8Lf3x8AsHz5cultNh88eIDw8HCpMd72+vVrTJkyRS+xevbsCUC/86M03m7R6OLiAkBOi0aNpk2bYtKkSejWrZtWgijjW74h+7K/K6P2u7qgUqlgbW2d7pfizKJdu3bo2bMn4uPjsXjxYixYsAABAQGGPi2d0vc83vTI6g5nqMQtKSkJpqamAICbN2/i0qVLaNKkiZQBBy8vL3h5eaFVq1bKe6k+dO/eHeXLl0fXrl0hhEBoaCguXLiAWrVqoW/fvtI6U8nAy/qSJCUl4fXr1/D09MTBgwe1Fid4e3vrtRiwzEteqampCAkJwa5duwCk9YAPDAxURlJlaNGiBZYvXy699IiGIVr72dvbK+1g69evr7WSVpa3LyUeOnQIM2fOlHop8e3niEqlUlbryqy8oOnLrnk9vr1aWIZz5869t1I2vW26pO8WxoawevVq/P333xBCoFWrVkpThczi9evX2LZtG1xcXFC6dGncu3cP58+fl1rp5V2yLnlrRERE6DVxq1q1Knbv3o2kpCQ4OzvD1tYWTk5OWLBggc5jrV69Gh06dMiwfKOsbobpfdZrpkpoGgF9LThyKsmUKVMwbtw4qFQqrW+HFhYWGDRokF7PReZcrGzZsqF3797o3bu3tBgamktAefLkgZubGxo3bqxVjkjXH76GbO23e/duhIeHY8OGDRgwYACKFi0Kb29vTJ8+XVpMfV9KNERbPbVajaZNmypTCho0aIBFixYpq2t1Td9lnQD9tzA2hA4dOqBDhw6GPg1pNHVVNUqUKIESJUro9RxkJaaaxG3fvn1aVSw0ZCVuarUa5ubmWL58Obp27YrJkydLe51cuXIFQPqfvTK/EL9580YpKwWklZjStNzWxxQ4XWJyKolm/k7v3r3x22+/6T3+3bt3oVKppL2hdevW7YMvMhmLkzRJfr58+VChQgWdH/9db3846LusU6lSpfDdd9+hatWqqFKlCubPn4/Vq1dLTU6N4VKibIGBgahduzZWrVoFAFiwYAECAwOxadMmncYxVFknwDBTQvThYwXTM9PIcGZmqMRNUxpr7969yki7rIRNM3Vn6dKlUo6fkSlTpqBmzZrKiPS5c+ewePFiJCYm4ttvv9XruXwpXtbPZCIiItC+fXs8fPgQKpUKRYsWxerVq3V++eSXX34BkFbEfP/+/ejYsSNUKhVWr14NT09PgyTkmYmfnx9OnDiB8uXLK5f3ZReQNoZLibKltzJfxmr9uXPnYs6cObh//z6KFy+ubM+XLx/69eunFOWXISEhAcOGDVNqDzdq1AiTJ0+Gubm5tJj68LG5upk1KdeH06dPY/jw4bh586ZWRZKvsj5mBvr27Yvdu3cjJSUFFy5cQGJiInx8fHDy5Emdx/rnn38+uL9FixY6j6kRExOjVce5UKFC0mLJxORUkoy6XwjJXTCqVq2KIUOGoF27dgCAdevWYdq0aThx4oSUeHXq1MHmzZuVRSzx8fFo3rw59u/fLyVeRmRN3s/I5s2bpbb3q1atGpKTk9GwYUP4+PjA09MTOXPmlBYvq3BxccH27duVBYIPHz5Eo0aNEBERofNYhirr1LlzZ+TJkwe9e/eGSqVCSEgIEhISpDaqoK+bk5MT+vbt+14FlEqVKuk8lqESNyEEIiIiYGdnBwsLCzx58gTR0dFwdXXVeax69epluE+lUkkpQ5jZMDmV5M6dOx/cb2NjIyVuepOenZ2dpdUdrVChAi5fvvzRbbLJnrz/rh49emDRokVSYzx//hy7d+/Grl27sHPnTtjY2CijYbpkqHaChrBixQoMGTJEqSixbds2zJgxQ6sWoa4IIeDk5IQLFy7o/Ngf4uLi8l6ynd62r1VW6BClb/qs9WuoxG3Tpk3w9PRUFtLGxsbi8OHDaNasmZR4+pQZ38OZnEoWExODsWPH4uzZs8rEZADSFkR07doV3bp1U9pA7tu3D8uWLZM296V9+/bIlSuXcply6dKlePnypdKijv6blJQUHDlyBDt37kR4eDiuXr2K2rVr4++//9Z5rAcPHqBYsWIZfqGS9UXKUC5cuIC9e/cCSPuglDE6pNG4cWOEhobqtayTo6Mjjhw5olzGT0xMRI0aNfSeJMvStGlTdOzYETNmzEBERATUajVcXV2/qpXIxqZPnz7o1q0bqlSpYuhTkebdBFwIAXd3d+llHdeuXas1xaZt27Y6j5EZ38O5IEqy7t27o3bt2ggPD8esWbMQEhIi5TKCxunTpxEaGgpbW1sAad2NKlasqFzy1vULcfHixRg/fjwGDhwIIK2UlMxadUBaF4zChQsrK+dfvXqFJ0+eoGTJklLiaeZ+aj7s4+Pjce3aNalv5IULF4aTkxMaNGiAmTNnolq1atIm72tWqn+Nb2D/haOjIxwdHfUSy8zMDJUrV9ZrWaeuXbuiRo0aygKIsLAwdOvWTVo8fYuJiYGfnx9mzZoFIK0ov8yqElnB/v37sWjRItjb22tVJMkMiVtGZJetA4Dx48fj77//RpcuXaBSqTBlyhRcvnwZI0eO1GmczPgezpFTyTTf1jSX25OSkuDl5YUjR45IiZdeaY63eXl5SYmrT9WqVcP+/fu1ktO6desqk8B1zc3NDSdOnFCSQ7VajRo1akiZSK/x8uVL5MmTR9rx07N//34MHjwY169fh1qtVi4JxcfH6/U8ZNL3Y8xoEY/sxTtbt27Vqj2cmRa11ahRA0eOHIGbmxvOnDmD2NhY1KtXL9O0oDWEjD43ZH5evJu4rVixAq1atdJ54qbh6emJadOmwcPDAwBw6NAhDB06VGphemdnZxw9elR5L3/x4gVq1qyp82l2rq6uH6x0oM+mP7rCr5uSaTpS5MqVC0+fPoWVlRWePHkiLZ6+k0+1Wo3169e/13de5iKQpKQkrW/3uXPnxps3b6TFS01N1Rq1NDEx0XqsMqSXmMrs9AWkzaOdNGmS1FFaQ9P3YzTUCvImTZrAyclJajk5Q8kKHaL0TfO5cf/+fQDQqjAhy7p167QSt4CAANSsWVNacjp9+nS0bt0a5cuXB5BWA3TDhg1SYmkIIbTey/PmzSul1e6cOXN0fkxDY3IqWbly5fD06VP4+fmhevXqsLCwgLu7u7R4ly5dwrhx4xAZGamVQMlaEKUpW6XPhEalUiEmJgaFCxcGkJa0ybwAYGpqqlXY+Nq1a0rvYn1q0qSJ1IYKFhYW8PX1lXZ8Y6Dvx2iIsk6acnKPHj0CAGnl5Axl0KBBWL16NeLi4rBjxw4EBQVlug5R+nb58mX4+voqyam1tTXWrl2rJHIy6Ctx06hZsyYuX76sXLX08PCQ3mWwWrVq6Ny5M3r06AEAWLJkCapVq6bzOG8PSiUnJyMqKgplypTReRx94mV9PTp06BBiY2PRuHFjaXOknJ2d0aVLl/eSxVq1akmJ5+DggCtXrkhvA/m2pUuXYuLEiejcuTMAIDQ0FGPGjFHu69qWLVvw3XffKSu8t2/fjqVLl2aqS6UAMHXqVJQoUQLffvutMuKf2ej7MRqirJO+y8nR169evXro0aOHkuSvWbMGISEhSic1Gbp3746kpCStxM3ExARLliyRFlPfXrx4gfHjx2tNsRk1ahTy5s0rJZ6mwYCJiQmioqJw4sQJzJ07F6GhoVLiycTkNJNJr7euTN7e3tiyZYvea3Du3bsX//77LwDgm2++gaenp9R4165dQ3h4OIC00S99fSuV3enrbRs3boSfn5/SGUofve71Td+P0RBlnfRdTs4YyK47nNnpqznF2/SVuGXGMksZqVGjBlauXAlfX18lD6hUqRIuXrxo4DP7fLysn8nUq1cP+/fvR506dfQSz97eHnXr1kXr1q215oHK6o+sUbduXdjb2+stcStXrhzKlSsnPY6Gvjp9ve2HH37Axo0bUaVKlUw751TfjzElJQUJCQlaZZ1kJ/tubm7Yu3evVjk5mVOJjMHGjRuZnH6B7Nmz49KlS0oXukuXLkl/feTNmxfTpk2TGgOAUtbQEAvmYmNj8dNPPyE8PBwqlQo+Pj6YNGkSrKyspMRLSUl5b+Dka70KxuQ0k/H19UWjRo1gbm6OXLlyKd8OZbWhe/PmDcqXL69VdF/2JX59z6mLiYnBmDFjEBERoZdatUDa4oDx48drXZoNCAiQemm2cOHCqF+/vrTjGwN9P0ZDlHXSdzk5YyC7IUZmN3nyZNSpUwfOzs4A0tpSr1y5UmpMfSVumjJLK1aseG+x1cSJE6UtwAIAf39/WFtbY/369QDSSi/6+/tj48aNUuLlypULiYmJymfw+fPnkTt3bimxZONl/UzGwcEBQ4cOfW9kSGahcX3T95y6b775BrVr18aSJUu0atVOmDBBSjzAMJdmJ0+eDAsLC/zvf//TGgXXtKbNDAzxGPVd1ikrlJMj3YuJicHx48cBpF0elt04omXLlrC2tkZgYCCAtMQtKipKWuLm5ub23hez9Lbpkr47KO7YsQNjx47FjRs34OPjg/DwcKxateqrHHRgcprJuLu749SpU9Lj7Nu3D15eXhn2SZbVHxnQf+Km71q1gP47fQFAtmzZlH+rVKpMOefUUI9Rn3OH3xYXF4fo6Gi9NR2QKSvNHcwK9JW4bd++Hdu2bcOqVau02hTHxcXhzJkzUpPTBg0aYM2aNShUqBAA4PHjx+jQoYOyfkGXhBB4+PAhXr9+jW3btkEIodf1EbrGy/qZTLNmzbBp0yZ88803UuOEhobCy8sLs2fPfm+fSqWSmpzqe06dvmvVAoa5NJuamqrzYxobfT9GQ5R1aty4MdasWQMTExMlTpcuXTB+/HhpMfXBkHMHMytDJvzFixfH48ePtRI3GV/ecuXKBUtLS2TLlg358uVTtpcsWVJ6N0MrKys4OTmhWbNmAIB///0Xnp6eCAoKAqD7TnE+Pj64cOECevfurdPjGgJHTjMZKysrxMXFIXfu3MiZM2emHFVwcnLCpUuX3kvcNLVHdZ24+fn5KeU45s+fDwsLC5QrV075sJSBl2YzB0OUddJU7AgLC8OhQ4cwc+ZMuLm5ZZre8++2L379+jUeP34srX1xZmbInuy+vr44ePDge4mbtbU1AN0nbhEREXqv9ZtRhzgNXTfpaNy4MUJDQ6VPydAHJqeZjL7fZAICAuDj44N69eopRfFlM2Tipo9ataRfgYGBWLhwoZRjG2LusKOjIy5cuIC+ffuicePGaN68ud5LzMmk7/bFJIe+ErfVq1ejQ4cOmDdvXrr7ZVeW0SdfX18cPXoUTZs2hZmZmbJd14m+PvDTNZOxsbHBy5cvlUtflStXltqj3cvLC1u2bMGgQYOQP39+eHt7w9vbG02bNpUa0xDi4+NRoUIF5d/58+eXFkvfnb6yMplTYAxR1snR0RFNmjTB5cuXMX36dKWma2ah7/bFWUHp0qXfq7JiaWmJmjVrYsKECVLe6/TV2vfKlSsAkO6XM302j9E4ffq0Mj1L15ycnODk5CTl2PrGkdNM5vDhw2jbtq3Sg/3Ro0dYv349atasKTWuEAKhoaEYPXo0oqKi9L6IZuHChcqqT137888/0a9fP8TGxmotoklKSpISD9B/py+SQ99TUAAoCyJcXFxQunRp3Lt3D+fPn880Hc1cXV2xfft2rfbFjRo1ktrYILMbNWoU7t27h+7duwNI68JnaWkJIQSio6MRFhaml/OQmbgZi2bNmmHLli2GPg2jx+Q0k6lRowZmzZqlJDGHDx9GUFAQjh49KiXe4sWLER4ejtOnT8PJyQkNGjSAj4+P0odeX8aMGfPRy0T/lZ2dHcLCwlClShUpx09PZroMa0zGjBmD/v37o0CBAgCAJ0+e4JdffpE2isO5w7qn7/bFWUH16tW1pkUIIVC9enUcP34cFStWxKVLl/RyHjISt4wqymjIXLxrDL7WhJ/JaSaTXmtEmW3oTE1NUa1aNQwbNgwNGjT4agv+foiHhwcOHz6s15hBQUFo1aqV3jp9ZRXpvRZk1zrUMGRZJ5nzag1h37592LJlC+Li4tC5c2fUrl3b0Kf0VStbtiwiIiKUKWAvXryAq6srrl27prfXhyz16tXLcJ9KpcLu3bulxU4vMdywYQNat24tLea7vtaRWs45zWTMzMwQHh4Ob29vAMCuXbt03qv4bU+fPsWePXuwY8cOjBo1CpaWlvD29saIESOkxQTS5nzevn1baz6mrG+HgYGBmDx5Mnx9fbXmupUqVUpKPED/nb6yivRKScmcnmEsZZ1kl5bTJ83f1N3dHY6Ojti+fXumKJVlSB07dkSNGjWUqhLr169Hhw4dkJiYqExJ0TV9JW579uzR6fE+R+fOnbF161bls2L79u0YPny4XpPTrzExBQAIylROnjwpSpUqJUqXLi1Kly4tbGxsxKlTp6TGTEpKErt37xbDhw8Xtra2In/+/FLjBQcHCzMzM1G6dGlhb28v7O3tRdmyZaXFW7BggcidO7ewsrISBQsWFAULFhSFChWSFk8IIcqVKyeWLFkiIiIixIULF5QbfRlfX18xbdo0oVarRXJyspg6dapo27attHiVK1cWQgjx559/iv79+4ukpCTh6OgoLV5W8PbfdMCAAfyb6sjmzZtFUFCQCAoKEps3b5Yer2LFiuLOnTvK/W3btony5ctLjRkWFiZ69OghevToIdatWyc1lhBC7N+/X7i6uoq4uDixf/9+YW9vL27cuCEtXnqf9X/99Ze0eDIxOc1k/vnnHxETEyPOnz8vzp8/L2JiYqS+0TRs2FAUKFBANGjQQEyePFkcP35cpKamSosnhBClS5cW9+7dkxrjbaVKlRLXr1/XWzwhhHBzc9NrvKzi3r17ol69eiJHjhzC1NRUeHt7i/v370uLV6lSJSGEEH369BGbNm0SQvxfciXL6NGjxZMnT5T7jx8/FmPHjpUaU58M8Tcl3dN34jZu3Djh6uoqZs+eLebMmSPc3d3FhAkTpMXT+PPPP0X16tVF2bJlxZUrV6TGMkTCLwvnnGYy786pE0LA3d1d2pyhnTt3wtPTU+tyt2yenp44cOCA3uLVqlULhw4d0ls8ABg9ejSqVq2aqS7HGpMXL14AgNQpLwDQvn17xMXF4fLly8qiklq1akld7GbIebX6YIi/aWY1aNAgzJo1C61bt063rNJff/0lNX5YWBiCg4Px7NkzbNq0CQ4ODtJiOTs74+jRo1rzamvWrCmlPN+7NVWXL18OFxcXZWqPrNqqBw4cwIABA7B3715ERETgu+++w/bt22FnZyclnkycc5rJye4b7uPjo/x7ypQp+Omnn6TF0hg+fDj69++P5s2bayXFshYP1a9fH4MGDcK3336rFc/Z2VlKPACYP39+pu/0ZQhqtRpz587FjRs38Ouvv+LGjRu4c+cO6tevLyXesmXLlLJOefLkwb179zBlyhQpsTT0Pa9W3wzxN82sNPV3W7VqpbeY7yZuarUanp6e2L59O7Zv3y4tcRNCaNX8zps3L2SNzb37RcnJyQmpqak4c+aM1Nqqnp6eGDZsGBo2bIhnz55h8+bNX2ViCnC1fqbj6emJadOmwcPDA0BaR6OhQ4fi4MGD0mPra3Rm9OjRmDdvHuzs7JQaoCqVCsePH5cSr3Tp0u9tk704yRDtBLOCXr16ISUlBQcPHsTly5fx/PlzeHt74+TJk4Y+NZ1p164dqlatikGDBkEIgVmzZuHEiRNYt26doU+NCN26dctwn0qlwu+//y4lbvfu3ZGUlIQePXoAAJYsWQITExMsWbJESjx9MtRIrUxMTjOZI0eOoHXr1ihfvjwAIDIyEhs2bEC1atWkx9ZXbU5bW1ucPXsWlpaW0mMZkj47fWUVmkvebz9X0yu/JpPssk7379+Hn58fDh48CJVKhTp16mD58uUoVqyYtJj0dQoKCvrg/q+x7WVGXrx4gfHjx2PXrl0AAG9vb4waNUrK1B5911Y1VMIvEy/rZzI1a9bE5cuXceTIEQBpNTr1lcTNnDlTL3FsbGwyfWJqqE5fmd27c6NTUlLSvQwuk+x5xMWLF8fu3bv1Nq+Wvl758uXTe0xDFcXPmzcvpk2bJuXY75o9e3aG+1Qqlc4f49KlS3V6PGPAkVP6IidOnEClSpWQJ08ehIWF4fjx4wgKCkLx4sWlxRw8eDCioqLeqzuqz04fzZs3x+bNm6UdX9+dvrKKwMBA1KlTBzNmzMD69esxbdo05MqVC/Pnzzf0qemMvufVEn0OQxXFj42NxU8//YTw8HCoVCr4+Phg0qRJsLKykhJPnzJjFywmp/RFXFxccPr0ady8eRNNmzaFr68vTp8+je3bt0uLmd6bm+xOH+86deoU3N3dpR1f352+sorExEQMGjQIf//9N4C0hSCzZ8+WNmVC3+1Sgawxr5Z0Kzo6Gr1798bdu3dx9uxZnD17Fnv27MEPP/xg6FPTmZYtW8La2hqBgYEA0lpvR0VFYePGjVLjJicn49atW3j9+rWyTdeLaQ3ZBUsaA5SvokzE1dVVCCHE3LlzxaxZs4QQrDmoCx4eHmLnzp3K/fDwcOHh4WHAM/r6qdVqMWTIEL3GdHFxeW+b5jUjO+bbr0NnZ2epMenr1qRJE7FixQrleZKcnKyXxgZJSUni6tWrIiIiQrnJkl69T9k1QDdt2iSKFi0qcubMKfLnzy9UKpWwtbWVGjOzyGbo5Ji+bm/evMGjR4+wadMmpSyJzNJVGmvWrEFAQAB69OiBsLAwqbFGjx6N58+fQwiBZs2aoWDBgli/fr3UmPPmzUP37t1hZ2cHOzs7dO/ePVNdejaE7Nmz672VoSHKOhnDvFr6usTExMDPzw/ZsqWlBCYmJjAxkbskZfPmzShVqhScnZ1Rr149VK5cGS1btpQWr3jx4nj8+LFy//HjxyhRooS0eAAwatQoHD16FBUqVMDTp0+xfPly+Pr6So2ZnJyMa9eu4dy5c8rta8TklL7IDz/8AAcHB+TLlw9ubm64ceOG9Dk8gwcPxqxZs+Dk5ARHR0fMmjULQ4YMkRZv48aNsLS0RHh4OExMTHDo0CFMnDhRWjwgbcX1yZMn8c8//+Cff/7BiRMn8ODBA6kxs4KmTZti0qRJuH//PuLj45WbLA4ODpg+fTpSUlKgVqsxbdo0pZKGLM7OzggNDUVqaiquX7+OXr16KV8cidJjYmKiVfMzNjZWWg1QDX0nblZWVnByckL37t3RvXt3ODs7I3/+/AgKCvpo1YL/Klu2bLCxsYFarQYA+Pn5Sb3Eru+EXybOOSWdSk1NhVqthqmpqbQY5cqVQ0REBHLnzg0AePXqFVxcXHDt2jUp8TRlh0aOHIny5cvDz89Pek1XfXf6yio0I0NA2lws8f+bG8ga7TdEWSd9z6ulr9+sWbNw9epV7Nq1Cz/99BMWLFgAf39/9O3bV1pMd3d3nDp1Ck5OTjh//rzWNhnGjRv3wf0y5oHXqFEDR48eRZMmTRAYGAhbW1u0bdtWWo1sV1dX/P3332jVqhXOnDmD0NBQREREYMaMGVLiycRSUvTFtm7disjISOXbIfDx+nlfwtLSEjlz5lTu58iRQ+poraYEyZo1a3Do0CEIIfTecUd2p6+sQt+Xt/Vd1iklJQUTJkxASEgIQkJCpMaizGPQoEFYvXo14uLisGPHDgQFBaFjx45SY+bIkQMAYG1tjQ0bNsDW1haxsbHS4slchJiRPn36IDY2FhMnTkT79u3x/PnzD5aZ+lLpjdTKjCcTk1P6Ip06dcKlS5fg6uqq1a1JBk25jBo1aqBRo0bo0qULACA0NFRq/c9ly5bh559/xvTp01GkSBFcv34dfn5+0uIBgLm5OQ4fPqzV6cvc3FxqTNI9fZd1MsS8Wvq6paSkoFGjRggPD0eHDh30FlffiVt6Tp8+DTc3N2nHnz17Njp37gx3d3dERkYCSOukKOvzQ98Jv0y8rE9fpHz58rh48aKSmMpkqHIZV65ckT5P8F2G7PRFumOIsk5jx45Fjhw50K1bN5iZmSnbLSwspMWkr5uHhwcOHjyoNe1FtvSmRumrBbZGs2bNsGXLFp0fNykpCa9fv4anpycOHjyoTB/SvP6vXr2q85gAsGLFCjRv3hw3b97USvhlD6bIwOSUvkjjxo3x119/Zer5bLa2tihbtiz69euHb775RtrI8LtiY2MN0umLdMcQ7VL1Pa+Wvn4DBgxAZGQk/Pz8tL7QyCjebqjETZ/GjRuHcePGKa8/DQsLCwwaNAijRo2SEtcYEn5d4WV9+iKzZs2Ct7c36tatq1XCZvTo0TqPFRkZibJly2ZYGkPXhY01bt68iY0bN2L+/PkYOHAgevXqhYCAAOTPn19KPA0rKys0bdpUagySyxBlnVg2ij6X5j110aJFyjYZbTYBYMqUKUri9nb7VE3iJkt6l/A3bNiA1q1b6zzWmDFjMGbMGPTu3Ru//fabzo//Lk3Cn5KSgoSEBK2EXzPf/WvDkVP6Ii1atEB8fDzc3Ny0Lu3LWB2oaRlaunTp9/apVCppKyDfdurUKbRu3RpPnjxBp06dMG7cOKmtWunrlhXapRL9F/pK3DQqVaqErVu3olSpUgCA7du3Y+DAgbh8+bLezkEWQ43UysTklL6Ig4NDprgM8zGRkZH45ZdfsHbtWrRo0QI9evTA7t27sXz58q+2yDHJx7JORMbhwIEDGDBgAPbu3YuIiAh899132L59O+zs7Ax9ajqj74RfJian9EVatGiB0NDQTL3YolGjRoiMjMT333+PgIAArbmfjo6OuHDhguFOjoxWSkoKhg8fjmnTphn6VIg+29c6V/FDwsLCEBwcjGfPnmHTpk1wcHAw9ClRBjjnlL5I7ty54ebmhoYNG2rNrwsODtbreWgu+csQGBiI1q1bp7uSlYkpZYRlnehrJmMVuyHMmzdP675arYanpye2b9+O7du3o3///gY6M/oQJqf0RSpWrIiKFSsa+jQ+2v3jS5QqVQqvX79Gnjx5EBYWhuPHjyMoKIhzTemjNO1SWdaJvjYyu5jpk6ZKhoaTkxNSU1Nx5swZvVVeoc/Hy/pEH+Hi4oLTp0/j5s2baNq0KXx9fXH69Gls377d0KdGRo5lnehr0LRpU6xYsQIFChQAADx58gT+/v7SrkYRfQxHTknnZHfdGD16NIKCgpAvXz40b94cx44dQ0hICNq2bSslXvbs2ZE9e3Zs3boVvXv3RlBQEFxdXaXEosyFZZ3oa/DgwQMlMQWAggUL4v79+wY8I93RdBbMiIxyWfTlmJySzo0aNUrqfKWNGzdi/Pjx2LlzJ0xMTHDo0CG0b99eWnL65s0bPHr0CJs2bVIWt3Dki4gyC7VaDbVaDROTtJQgKSkJSUlJBj4r3fhQS1RZtVzpyzE5JZ2TPZFec6l03759aNeuHRwcHKTOHfrhhx/g4OAAb29vuLm54caNG7CyspIWj4hIn5o0aYJ27dphwIABAIC5c+dmmgYgXJT4deKcU/oiSUlJMDU1BZDWSenSpUto0qSJVkF+Xatduza++eYbLFq0CIcOHULhwoXh5OSkt5XzKSkpSElJUR43EdHXLDk5GZMnT1YGFlq0aIGhQ4ciR44cBj4z3UpOTsatW7fw+vVrZZuszoL0ZZic0hepWrUqdu/ejaSkJDg7O8PW1hZOTk5YsGCBtJjXr1/Hzz//jDp16qBNmza4fv061q1bh2HDhkmL+a6HDx+iaNGieotHRET/3ebNm9GjRw/ExsYib968iI2NhY2NDW7dumXoU6N0vF+4kegzqNVqmJubY8uWLejatSsOHTqEQ4cOSY85Z84ctGnTBgBgb2+v18QUSLsMRkSUGSQkJKBPnz4oV64cypUrh379+iEhIcHQp6VTo0aNwtGjR1GhQgU8ffoUy5cvh6+vr6FPizLA5JS+iGbS/N69e1G/fn0AkHpJHwAaN24MHx8f/PPPPzDUwP+7tfOIiL5W33//PdRqNcLCwrB27VqkpKTg+++/N/Rp6VS2bNlgY2MDtVoNAPDz88Pu3bsNfFaUES6Ioi9Sr149VKxYESkpKQgJCUFsbKyy4lOWmzdvYuPGjZg/fz4GDhyIXr16ISAgAPnz55ca9+7du1CpVChRooTUOERE+nTu3DlEREQo93/99Ve4uLgY8Ix0TzN/1traGhs2bICtrS1iY2MNfFaUEc45pS8ihEBERATs7OxgYWGBJ0+eIDo6Wm91QE+dOoXWrVvjyZMn6NSpE8aNG6fzzk0RERFo3749Hj58CJVKhaJFi2L16tWZ7s2biLImR0dHHDlyBObm5gDSLvPXrFkzU7VnXrFiBZo3b46bN2+iffv2eP78OWbPng0/Pz9Dnxqlg8kpfbHjx48jPDwcAODj44OqVatKjxkZGYlffvkFa9euRYsWLdCjRw/s3r0by5cvx7lz53Qaq2rVqhgyZAjatWsHAFi3bh2mTZuGEydO6DQOEZEhzJgxA8uWLcO3334LAAgLC4O/vz8GDx5s4DPTHTc3N5w+ffqj28g4cM4pfZGFCxfC19cXMTExePz4MXx9fbF48WKpMRs1aoRGjRrB2toaFy9exG+//QY3NzcMHjxYSkee169fK4kpAPj6+uLNmzc6j0NEZAg//vgjZs2ahfj4eERHR6Nnz56ZJjFNSkpCfHw8UlJSkJCQgPj4eCQkJCA6OhovXrww9OlRBjjnlL7Izz//jFOnTqFQoUIAgOHDh6NBgwYICAiQFjMwMBCtW7fW6luuIeMylJubG/bu3Yu6desCSCv+7+7urvM4RESG0LhxY6xZswa1a9eGo6Mjdu7cicePH2P8+PGGPrUvNmXKFIwbNw4qlQr58uVTtltYWGDQoEEGPDP6EI6c0hfTJKbv/luWUqVKKUWUw8LCMHjwYKl9oE+fPo0GDRqgTJkyKFOmDOrXr4+TJ0/Czc0Nbm5u0uISEenDo0ePYGlpiX///RetWrVCZGQkNmzYYOjT0okxY8YgNTUVgYGBSE1NVW7Pnz/HqFGjDH16lAGOnNIXKVu2LEaMGIGePXsCABYtWoSyZctKjRkQEIDTp08jMjISI0aMgK+vL7p164bt27dLiffzzz9LOS4RkTFITk4GAOzfvx+NGzdGjhw5pFdd0bfffvvN0KdAn4ELouiLPH78GP369UN4eDhUKhW8vb0xd+5cFC5cWFpMzST2efPmQa1WIygoCK6urqw9SkT0H7Rv3x5xcXG4fPkyLl26BACoVasW31PJYDLXVyPSu0KFCmHNmjV6jfnmzRs8evQImzZtwrRp0wCk9buX5dKlSxg3bhwiIyOVAs4AdF4VgIjIEJYtW4Zt27bBxcUFefLkwb179zBlyhRDnxZlYUxOSedk953/4Ycf4ODgAG9vb7i5ueHGjRuwsrKSFq99+/bo0qUL+vTpI737FRGRvuXKlQutWrVS7pcoUYLNRsigeFmfdE7fl9hTUlKQkpICU1NTKcfnlAEiIiL94Wp90jl9J3LZs2fHs2fPpB2/Xr162L9/v7TjExER0f/hyCnphKH7zssc3Tx8+DAaNWoEc3Nz5MqVC0IIqFQq3Lx5U0o8IiKirIzJKX2RrNB33sHBAUOHDkWVKlW05pxWqlTJgGdFRESUOTE5pS9iyL7z+hqtdXd3x6lTp6TGICIiojScc0pfxBB95yMiIlChQgU4OTnByckJFStWREREhLR4zZo1w6ZNm6Qdn4iIiP4Pk1P6Ipq+8xr66DsfEBCA8ePHIzY2Fs+ePcP48eMREBAgLd78+fPRsmVL5M2bF/nz54eVlRXy588vLR4REVFWxsv69EWcnJxw6dIl2NraAgBu376NihUrIkeOHADS+tLLiHn+/Hmtbc7OztKK4t+5cyfd7TY2NlLiERERZWUswk9fxBB95zWjtXXr1gUgf7TWxsYGL1++xNmzZwEAlStXRp48eaTFIyIiyso4ckpfHX2P1h4+fBht27ZVul49evQI69evR82aNXUah4iIiJic0hcyRN/5ffv2fXC/l5eXTuPVqFEDs2bNQq1atQCkJatBQUE4evSoTuMQERERk1P6Qs7OzujSpQuqVaumVQNUk8hlBi4uLu9VA6hcubJymZ+IiIh0h3NO6Ytkz54dgwcP1mtMfY/WmpmZITw8HN7e3gCAXbt2IW/evFJiERERZXVMTumLaPrO16lTR28x27dvjy5duqBPnz5ao7WyzJs3D23atFFipaam4q+//pIel4iIKCtickpfxNfXV+995/U9Wnv//n2cPHkSjx49AgAUKVIEx48f11t8IiKirOT/tXc3IVG1bxzHf/4zRNIoMIygmCJkKtPxJUtRNB0kCsGoICTDDN1EkqaLIsNQgmphs2gTBhHRSikoJHAmREnJGLU2hS9oLZQIG0MoRxx9Fg8eMuVRmObM9Pf7WTn3mXOue2Z18ZtzvLjnFH4Jxtz5qqoqFRYWmpbW/n5/6fz8vFJSUgLyP1wBAFjrSE7hl6ioKJWWlppaMxhp7a/CwsLk8/lMqQUAwFpDcwq/LMydLygoMK3muXPn5HA4lqS1gRIdHa2uri5lZGRIkl6/fq3o6OiA1wUAYC3iZ334ZfPmzfr+/bsiIyMVERFhpJjfvn0LWM2UlBS53e6AXf933d3dOn78uKxWqyRpcHBQT58+VVpamml7AABgraA5hV+CMXf++vXrOnDggKlprcfjUXd3tyQpIyNDmzZtMq02AABrCc0p/Gb23PlgpLUAAMAc3HMKvwRj7jyTmQAA+P9Fcgq/BGvuvNlpLQAAMAfJKfzy8+dPozGV/r0fc3p6OqA1g5HWAgAAc/wv2BvA321h7vwCM+bOV1VVqbm5WX19ferr61Nzc7MqKysDWhMAAJiD5BR+Ccbc+WCktQAAwBw0p/BLMObOL6S1drtdkjlpLQAAMAcPRMEvwZg773a7l01rk5OTA1YTAACYg+QUf5QZc+eDkdYCAABz8EAU/LIwd36BGXPna2trtWXLFsXHxys+Pl4xMTGqra0NaE0AAGAOklP45fbt28vOnTeTGWktAAAwB80p/JKenq4PHz6YOnd+Ia3NyMiQZE5aCwAAzMEDUfjrdHd3L5vWpqWlBXlnAADAXzSn+Ct5PB5T01oAAGAOmlMAAACEDJ7WBwAAQMigOQUAAEDIoDkFgP9gsVhktVo1OztrrKWmpqq9vd3UfQwNDenUqVPauXOnkpKSlJiYqJqaGnm9XlP3AQCBRnMKACvwer168OBB0OqPj48rMzNTR44c0cjIiPr6+tTV1aWNGzdqampqyft/baQB4G9DcwoAK6irq1N9fb1+/Pix5NjU1JTKysqUlpamhIQElZeXa2ZmRgMDA4qLi5Mkzc/PKzY2VlevXpUkdXR0KDc3V5LU1NSkvXv3ymazaf/+/Xrz5s2SGvfu3VNOTo7Onz9vrG3YsEG1tbWKiYmRJOXk5KiiokLp6enKz8+Xz+dTTU2NMUnt4sWLmpmZkSSVlJTo7t27xrWqq6tVV1dnfNYTJ04oNzdXVqtVBQUFmpiY8P9LBIBVojkFgBUkJibq8OHDamxsXHLs8uXLysrKUk9Pj969e6e5uTk5HA7FxcXJ6/Xq8+fPev/+vXbt2iWXyyVJamtrk91uN853uVzq7+9Xb2+v9u3bt6RGb2+vDh48uOI+BwYG1NHRoVevXun+/ft6+/at3G63+vv7NTw8vOz+l9PZ2aknT57o48eP2r59u65cubKq8wDgT6A5BYBVqK+vl8PhWJIiPnv2THfu3JHNZlNSUpI6Ozs1NDQkScrLy5PT6ZTT6VRxcbG8Xq8mJyfldDqVl5dnvKe4uFgOh0MjIyOKiopacS+NjY2y2WzasWOHXr58aayfOXNG69evlyQ5nU6VlJQoIiJC4eHhKisrU1tb26o+67Fjx7R161ZJUnl5uZxO56rOA4A/gfGlALAKFotFRUVFamhoWLQ+Pz+vlpYW4yf8X9ntdr148UIej0cOh8OYZjY4OKjU1FRJUktLi9xut9rb23X06FE1NDTo9OnTi66TlJSknp4e43VlZaUqKyuVk5Oj6elpY/2/GtuwsDDj7/DwcPl8PuP19PT0qs8FgEAjOQWAVbp27ZoeP36ssbExY62wsFC3bt0yHkLyeDyLklOXy6XR0VHFxcXJbrfrxo0byszM1Lp16zQ7O6vh4WGlpqaqurpaJ0+eXNSELrhw4YJcLpcePnxorM3NzS1qTH9nt9v16NEjzczMaHZ2Vk1NTcrPz5ck7d6926gzMTGh1tbWRee2trbqy5cvkv69J3bhFgQAMAPNKQCsUkxMjCoqKjQ+Pm6sNTY2KjIyUjabTQkJCcrLy9Po6KgkKTY2VrGxsUpPT5ckZWdna2xszPhJ3+fzqbS0VPHx8bLZbHK73aqqqlpSd9u2bers7NTz589lsViUkpKizMxMZWdnKysra9m9lpeXKzk5WcnJybLZbLJYLLp06ZJx7OvXr9qzZ4/Onj2rQ4cOLTo3KytLRUVFslqt+vTpk27evOnvVwcAq8b4UgCAoa6uTpOTk4ue5gcAM5GcAgAAIGSQnAIAACBkkJwCAAAgZNCcAgAAIGTQnAIAACBk0JwCAAAgZNCcAgAAIGTQnAIAACBk0JwCAAAgZPwDk9TRn8WRR50AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Training data class distribution\n",
    "fig=plt.figure(figsize=(10, 5), dpi= 80, facecolor='w', edgecolor='k')\n",
    "plt.bar(targets_str,frequency)\n",
    "plt.xticks(rotation=90)\n",
    "plt.title('Class distribution of 20 Newsgroups Training Data')\n",
    "plt.xlabel('News Group')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that all of the categories have decent amount of samples and doesn't have high imbalance. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Importing the 20newsgroup test data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mydata_test = fetch_20newsgroups(subset='test', shuffle=True)\n",
    "mydata_test = fetch_20newsgroups(subset='test', shuffle=True, remove = ('headers', 'footers', 'quotes'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Testing data size: 7532\n"
     ]
    }
   ],
   "source": [
    "print('Testing data size:', len(mydata_test['data']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[('alt.atheism', 319), ('comp.graphics', 389), ('comp.os.ms-windows.misc', 394), ('comp.sys.ibm.pc.hardware', 392), ('comp.sys.mac.hardware', 385), ('comp.windows.x', 395), ('misc.forsale', 390), ('rec.autos', 396), ('rec.motorcycles', 398), ('rec.sport.baseball', 397), ('rec.sport.hockey', 399), ('sci.crypt', 396), ('sci.electronics', 393), ('sci.med', 396), ('sci.space', 394), ('soc.religion.christian', 398), ('talk.politics.guns', 364), ('talk.politics.mideast', 376), ('talk.politics.misc', 310), ('talk.religion.misc', 251)]\n"
     ]
    }
   ],
   "source": [
    "# Finding frequency of each category\n",
    "targets_test, frequency_test = np.unique(mydata_test.target, return_counts=True)\n",
    "targets_test_str = np.array(mydata_test.target_names)\n",
    "print(list(zip(targets_test_str, frequency_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Testing data class distribution\n",
    "fig=plt.figure(figsize=(10, 5), dpi= 80, facecolor='w', edgecolor='k')\n",
    "plt.bar(targets_test_str,frequency_test)\n",
    "plt.xticks(rotation=90)\n",
    "plt.title('Class distribution of 20 Newsgroups Test Data')\n",
    "plt.xlabel('News Group')\n",
    "plt.ylabel('Frequency')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data Preparation: \n",
    "\n",
    "We will perform preparation steps such as Tokenization, Convert everything to lowercase, Removing stopwords, Stemming, Lemmatizing, and converting text data to a vector representation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I was wondering if anyone out there could enli...</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A fair number of brave souls who upgraded thei...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>well folks, my mac plus finally gave up the gh...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>\\nDo you have Weitek's address/phone number?  ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>From article &lt;C5owCB.n3p@world.std.com&gt;, by to...</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                data  target\n",
       "0  I was wondering if anyone out there could enli...       7\n",
       "1  A fair number of brave souls who upgraded thei...       4\n",
       "2  well folks, my mac plus finally gave up the gh...       4\n",
       "3  \\nDo you have Weitek's address/phone number?  ...       1\n",
       "4  From article <C5owCB.n3p@world.std.com>, by to...      14"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mydata_train_df = pd.DataFrame({'data': mydata_train.data, 'target': mydata_train.target})\n",
    "mydata_train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>i was wondering if anyone out there could enli...</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a fair number of brave souls who upgraded thei...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>well folks  my mac plus finally gave up the gh...</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>\\ndo you have weitek s address phone number   ...</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>from article      world std com   by tombaker ...</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                data  target\n",
       "0  i was wondering if anyone out there could enli...       7\n",
       "1  a fair number of brave souls who upgraded thei...       4\n",
       "2  well folks  my mac plus finally gave up the gh...       4\n",
       "3  \\ndo you have weitek s address phone number   ...       1\n",
       "4  from article      world std com   by tombaker ...      14"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Text preprocessing steps - remove numbers, captial letters and punctuation\n",
    "import re\n",
    "import string\n",
    "\n",
    "alphanumeric = lambda x: re.sub(r\"\"\"\\w*\\d\\w*\"\"\", ' ', x)\n",
    "punc_lower = lambda x: re.sub('[%s]' % re.escape(string.punctuation), ' ', x.lower())\n",
    "\n",
    "mydata_train_df['data'] = mydata_train_df.data.map(alphanumeric).map(punc_lower)\n",
    "mydata_train_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>data</th>\n",
       "      <th>target</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>I am a little confused on all of the models of...</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>I'm not familiar at all with the format of the...</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>\\nIn a word, yes.\\n</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>\\nThey were attacking the Iraqis to drive them...</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>\\nI've just spent two solid months arguing tha...</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                data  target\n",
       "0  I am a little confused on all of the models of...       7\n",
       "1  I'm not familiar at all with the format of the...       5\n",
       "2                                \\nIn a word, yes.\\n       0\n",
       "3  \\nThey were attacking the Iraqis to drive them...      17\n",
       "4  \\nI've just spent two solid months arguing tha...      19"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Preparing test data\n",
    "mydata_test_df = pd.DataFrame({'data': mydata_test.data, 'target': mydata_test.target})\n",
    "mydata_test_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "                                                data  target\n",
       "0  i am a little confused on all of the models of...       7\n",
       "1  i m not familiar at all with the format of the...       5\n",
       "2                                \\nin a word  yes \\n       0\n",
       "3  \\nthey were attacking the iraqis to drive them...      17\n",
       "4  \\ni ve just spent two solid months arguing tha...      19"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Text preprocessing steps - remove numbers, captial letters and punctuation\n",
    "alphanumeric = lambda x: re.sub(r\"\"\"\\w*\\d\\w*\"\"\", ' ', x)\n",
    "punc_lower = lambda x: re.sub('[%s]' % re.escape(string.punctuation), ' ', x.lower())\n",
    "\n",
    "mydata_test_df['data'] = mydata_test_df.data.map(alphanumeric).map(punc_lower)\n",
    "mydata_test_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Count Vectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(11314, 67822)\n",
      "<class 'scipy.sparse._csr.csr_matrix'>\n"
     ]
    }
   ],
   "source": [
    "# Extracting features from text files\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "count_vect = CountVectorizer(stop_words='english')\n",
    "\n",
    "X_train_cv = count_vect.fit_transform(mydata_train_df.data)  # fit_transform learns the vocab and one-hot encodes\n",
    "X_test_cv = count_vect.transform(mydata_test_df.data) # transform uses the same vocab and one-hot encodes\n",
    "\n",
    "print(X_train_cv.shape)\n",
    "print(type(X_train_cv))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
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     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_cv_df = pd.DataFrame(X_train_cv.todense())\n",
    "X_train_cv_df.columns = sorted(count_vect.vocabulary_)\n",
    "X_train_cv_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### TF-IDF Vectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(11314, 67822)\n",
      "<class 'scipy.sparse._csr.csr_matrix'>\n"
     ]
    }
   ],
   "source": [
    "# Creating a document-term matrix using TF-IDF\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "\n",
    "tfidfV = TfidfVectorizer(stop_words='english') \n",
    "# tfidfV = TfidfVectorizer(ngram_range=(1, 2), binary =True, stop_words='english') \n",
    "\n",
    "X_train_tfidfV = tfidfV.fit_transform(mydata_train_df.data) # fit_transform learns the vocab and one-hot encodes \n",
    "X_test_tfidfV = tfidfV.transform(mydata_test_df.data) # transform uses the same vocab and one-hot encodes \n",
    "\n",
    "# print the dimensions of the training set (text messages, terms) \n",
    "print(X_train_tfidfV.shape)\n",
    "print(type(X_train_tfidfV))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
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       "\n",
       "   zzz  zzzoh  zzzzzz  zzzzzzt  ³ation   ýé  \n",
       "0  0.0    0.0     0.0      0.0     0.0  0.0  \n",
       "1  0.0    0.0     0.0      0.0     0.0  0.0  \n",
       "2  0.0    0.0     0.0      0.0     0.0  0.0  \n",
       "3  0.0    0.0     0.0      0.0     0.0  0.0  \n",
       "4  0.0    0.0     0.0      0.0     0.0  0.0  \n",
       "\n",
       "[5 rows x 67822 columns]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_tfidfV_df = pd.DataFrame(X_train_tfidfV.todense())\n",
    "X_train_tfidfV_df.columns = sorted(tfidfV.vocabulary_)\n",
    "X_train_tfidfV_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Classification Models"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 78.1 ms\n",
      "Wall time: 82.2 ms\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MultinomialNB()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;MultinomialNB<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.naive_bayes.MultinomialNB.html\">?<span>Documentation for MultinomialNB</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>MultinomialNB()</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "MultinomialNB()"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# Use a Naive Bayes model \n",
    "from sklearn.naive_bayes import MultinomialNB \n",
    "\n",
    "mnb = MultinomialNB() \n",
    "\n",
    "# Train the model \n",
    "y_train = mydata_train_df.target\n",
    "mnb.fit(X_train_cv, y_train) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 7,  1, 15, ..., 15,  3, 15], dtype=int64)"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Take the model that was trained on the X_train_cv data and apply it to the X_test_cv data \n",
    "y_pred_cv_mnb = mnb.predict(X_test_cv) \n",
    "y_pred_cv_mnb # The output is all of the predictions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6399362719065321\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.60      0.36      0.45       319\n",
      "           1       0.57      0.70      0.63       389\n",
      "           2       0.67      0.01      0.02       394\n",
      "           3       0.50      0.71      0.58       392\n",
      "           4       0.72      0.58      0.64       385\n",
      "           5       0.58      0.80      0.67       395\n",
      "           6       0.85      0.66      0.75       390\n",
      "           7       0.82      0.70      0.76       396\n",
      "           8       0.90      0.65      0.75       398\n",
      "           9       0.93      0.76      0.84       397\n",
      "          10       0.58      0.92      0.71       399\n",
      "          11       0.58      0.78      0.66       396\n",
      "          12       0.67      0.49      0.57       393\n",
      "          13       0.83      0.79      0.81       396\n",
      "          14       0.76      0.75      0.76       394\n",
      "          15       0.48      0.90      0.63       398\n",
      "          16       0.55      0.65      0.60       364\n",
      "          17       0.66      0.78      0.72       376\n",
      "          18       0.44      0.44      0.44       310\n",
      "          19       0.44      0.06      0.11       251\n",
      "\n",
      "    accuracy                           0.64      7532\n",
      "   macro avg       0.66      0.62      0.60      7532\n",
      "weighted avg       0.66      0.64      0.62      7532\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report, accuracy_score\n",
    "\n",
    "y_test = mydata_test_df.target\n",
    "print(accuracy_score(y_test, y_pred_cv_mnb))\n",
    "print(classification_report(y_test, y_pred_cv_mnb))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using pipeline now onwards to do the modeling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## I. Not using Stemming and Lemmatization "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Terms in TF-IDF Vectorizer are unigrams (single words)\n",
    "With TfidfVectorizer we can extract \"bag of words\" from the text and apply TF-IDF (term frequency - inverse document frequency) weights. Experimentaly we found that applying sublinear tf scaling (1 + log(tf)) improves overall results. We will first extract only unigrams from the text data to form the vectorizer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A. Using Multinominal Naive Bayes Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6692777482740307\n",
      "CPU times: total: 3.34 s\n",
      "Wall time: 3.34 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Performance of NB Classifier with No Stemming & Lemmatization\n",
    "text_clf = Pipeline([\n",
    "        ('vect', TfidfVectorizer(stop_words='english', sublinear_tf=True)), # CountVectorizer, TfidfVectorizer\n",
    "        ('clf', MultinomialNB())])  # MultinomialNB, LogisticRegression, SGDClassifier, KNeighborsClassifier\n",
    "\n",
    "text_clf.fit(mydata_train_df.data, mydata_train_df.target)\n",
    "print(text_clf.score(mydata_test_df.data, mydata_test_df.target))\n",
    "# print(classification_report(mydata_test_df.target, text_clf.predict(mydata_test_df.data)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### B. Using Logistic Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6902549123738715\n",
      "CPU times: total: 39.7 s\n",
      "Wall time: 19.3 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Performance of LR Classifier with No Stemming & Lemmatization\n",
    "text_clf = Pipeline([\n",
    "        ('vect', TfidfVectorizer(stop_words='english', sublinear_tf=True)), # CountVectorizer, TfidfVectorizer\n",
    "        ('clf', LogisticRegression())])  # MultinomialNB, LogisticRegression, SGDClassifier, KNeighborsClassifier\n",
    "\n",
    "text_clf.fit(mydata_train_df.data, mydata_train_df.target)\n",
    "print(text_clf.score(mydata_test_df.data, mydata_test_df.target))\n",
    "# print(classification_report(mydata_test_df.target, text_clf.predict(mydata_test_df.data)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Performing grid search on the 'penalty' parameter of Logistic Regression Classifier. We get best performance when L2-norm is used in the penalization.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:547: FitFailedWarning: \n",
      "5 fits failed out of a total of 10.\n",
      "The score on these train-test partitions for these parameters will be set to nan.\n",
      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "5 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 895, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\base.py\", line 1474, in wrapper\n",
      "    return fit_method(estimator, *args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\pipeline.py\", line 475, in fit\n",
      "    self._final_estimator.fit(Xt, y, **last_step_params[\"fit\"])\n",
      "  File \"d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\base.py\", line 1474, in wrapper\n",
      "    return fit_method(estimator, *args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 1172, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 67, in _check_solver\n",
      "    raise ValueError(\n",
      "ValueError: Solver lbfgs supports only 'l2' or None penalties, got l1 penalty.\n",
      "\n",
      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:1051: UserWarning: One or more of the test scores are non-finite: [       nan 0.74120552]\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 4min 14s\n",
      "Wall time: 1min 57s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(estimator=Pipeline(steps=[(&#x27;vect&#x27;,\n",
       "                                        TfidfVectorizer(stop_words=&#x27;english&#x27;,\n",
       "                                                        sublinear_tf=True)),\n",
       "                                       (&#x27;clf&#x27;, LogisticRegression())]),\n",
       "             param_grid={&#x27;clf__penalty&#x27;: (&#x27;l1&#x27;, &#x27;l2&#x27;)})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(estimator=Pipeline(steps=[(&#x27;vect&#x27;,\n",
       "                                        TfidfVectorizer(stop_words=&#x27;english&#x27;,\n",
       "                                                        sublinear_tf=True)),\n",
       "                                       (&#x27;clf&#x27;, LogisticRegression())]),\n",
       "             param_grid={&#x27;clf__penalty&#x27;: (&#x27;l1&#x27;, &#x27;l2&#x27;)})</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: Pipeline</label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;vect&#x27;,\n",
       "                 TfidfVectorizer(stop_words=&#x27;english&#x27;, sublinear_tf=True)),\n",
       "                (&#x27;clf&#x27;, LogisticRegression())])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" ><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;TfidfVectorizer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html\">?<span>Documentation for TfidfVectorizer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>TfidfVectorizer(stop_words=&#x27;english&#x27;, sublinear_tf=True)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-5\" type=\"checkbox\" ><label for=\"sk-estimator-id-5\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(estimator=Pipeline(steps=[('vect',\n",
       "                                        TfidfVectorizer(stop_words='english',\n",
       "                                                        sublinear_tf=True)),\n",
       "                                       ('clf', LogisticRegression())]),\n",
       "             param_grid={'clf__penalty': ('l1', 'l2')})"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# Performing Grid Search to find best LR parameters\n",
    "pipeline = Pipeline([\n",
    "        ('vect', TfidfVectorizer(stop_words='english', sublinear_tf=True)), # CountVectorizer, TfidfVectorizer\n",
    "        ('clf', LogisticRegression())])  # MultinomialNB, LogisticRegression, SGDClassifier\n",
    "\n",
    "parameters = {\n",
    "       'clf__penalty': ('l1', 'l2')\n",
    "}\n",
    "\n",
    "grid_search = GridSearchCV(pipeline, parameters)\n",
    "grid_search.fit(mydata_train_df.data, mydata_train_df.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best training score 0.7412\n"
     ]
    }
   ],
   "source": [
    "# grid_search.cv_results_\n",
    "print('Best training score %0.4f' % grid_search.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters = \n",
      "clf__penalty: 'l2'\n"
     ]
    }
   ],
   "source": [
    "print('Best parameters = ')\n",
    "best_parameters = grid_search.best_estimator_.get_params()\n",
    "for param_name in sorted(parameters.keys()):\n",
    "    print(\"%s: %r\" % (param_name, best_parameters[param_name]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6902549123738715"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test Accuracy\n",
    "grid_search.best_estimator_.score(mydata_test_df.data, mydata_test_df.target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### C. Using Stochastic Gradient Descent Classifier Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6959638874137015\n",
      "CPU times: total: 5.08 s\n",
      "Wall time: 4.77 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Performance of SGD Classifier with No Stemming & Lemmatization\n",
    "random.seed(1)\n",
    "text_clf = Pipeline([\n",
    "        ('vect', TfidfVectorizer(stop_words='english', sublinear_tf=True)), # CountVectorizer, TfidfVectorizer\n",
    "        ('clf', SGDClassifier())])  # MultinomialNB, LogisticRegression, SGDClassifier, KNeighborsClassifier\n",
    "\n",
    "text_clf.fit(mydata_train_df.data, mydata_train_df.target)\n",
    "print(text_clf.score(mydata_test_df.data, mydata_test_df.target))\n",
    "# print(classification_report(mydata_test_df.target, text_clf.predict(mydata_test_df.data)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### D. Using k Nearest Neighbors Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.05284121083377589\n",
      "CPU times: total: 7.69 s\n",
      "Wall time: 7.79 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Performance of kNN Classifier with No Stemming & Lemmatization\n",
    "text_clf = Pipeline([\n",
    "        ('vect', TfidfVectorizer(stop_words='english', sublinear_tf=True)), # CountVectorizer, TfidfVectorizer\n",
    "        ('clf', KNeighborsClassifier(n_neighbors=100))])  # MultinomialNB, LogisticRegression, SGDClassifier, KNeighborsClassifier\n",
    "\n",
    "text_clf.fit(mydata_train_df.data, mydata_train_df.target)\n",
    "print(text_clf.score(mydata_test_df.data, mydata_test_df.target))\n",
    "# print(classification_report(mydata_test_df.target, text_clf.predict(mydata_test_df.data)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Performing grid search on the 'n_neighbors' and 'weights' parameters of kNN Classifier. We get best performance when n_neighbors is set to 5 and distance weights is used.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 2min 14s\n",
      "Wall time: 2min 16s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-3 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-3 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-3 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-3 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-3 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-3 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-3 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-3 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-3 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-3 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(estimator=Pipeline(steps=[(&#x27;vect&#x27;,\n",
       "                                        TfidfVectorizer(stop_words=&#x27;english&#x27;,\n",
       "                                                        sublinear_tf=True)),\n",
       "                                       (&#x27;clf&#x27;, KNeighborsClassifier())]),\n",
       "             param_grid={&#x27;clf__n_neighbors&#x27;: (5, 10, 100, 200),\n",
       "                         &#x27;clf__weights&#x27;: (&#x27;uniform&#x27;, &#x27;distance&#x27;)})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-6\" type=\"checkbox\" ><label for=\"sk-estimator-id-6\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(estimator=Pipeline(steps=[(&#x27;vect&#x27;,\n",
       "                                        TfidfVectorizer(stop_words=&#x27;english&#x27;,\n",
       "                                                        sublinear_tf=True)),\n",
       "                                       (&#x27;clf&#x27;, KNeighborsClassifier())]),\n",
       "             param_grid={&#x27;clf__n_neighbors&#x27;: (5, 10, 100, 200),\n",
       "                         &#x27;clf__weights&#x27;: (&#x27;uniform&#x27;, &#x27;distance&#x27;)})</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-7\" type=\"checkbox\" ><label for=\"sk-estimator-id-7\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: Pipeline</label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;vect&#x27;,\n",
       "                 TfidfVectorizer(stop_words=&#x27;english&#x27;, sublinear_tf=True)),\n",
       "                (&#x27;clf&#x27;, KNeighborsClassifier())])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-8\" type=\"checkbox\" ><label for=\"sk-estimator-id-8\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;TfidfVectorizer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html\">?<span>Documentation for TfidfVectorizer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>TfidfVectorizer(stop_words=&#x27;english&#x27;, sublinear_tf=True)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-9\" type=\"checkbox\" ><label for=\"sk-estimator-id-9\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier()</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(estimator=Pipeline(steps=[('vect',\n",
       "                                        TfidfVectorizer(stop_words='english',\n",
       "                                                        sublinear_tf=True)),\n",
       "                                       ('clf', KNeighborsClassifier())]),\n",
       "             param_grid={'clf__n_neighbors': (5, 10, 100, 200),\n",
       "                         'clf__weights': ('uniform', 'distance')})"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# Performing Grid Search to find best kNN parameters\n",
    "pipeline = Pipeline([\n",
    "        ('vect', TfidfVectorizer(stop_words='english', sublinear_tf=True)), # CountVectorizer, TfidfVectorizer\n",
    "        ('clf', KNeighborsClassifier())])  # MultinomialNB, LogisticRegression, SGDClassifier\n",
    "\n",
    "parameters = {\n",
    "       'clf__n_neighbors': (5, 10, 100, 200),\n",
    "       'clf__weights': ('uniform', 'distance')\n",
    "}\n",
    "\n",
    "grid_search = GridSearchCV(pipeline, parameters)\n",
    "grid_search.fit(mydata_train_df.data, mydata_train_df.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best training score 0.1197\n"
     ]
    }
   ],
   "source": [
    "# grid_search.cv_results_\n",
    "print('Best training score %0.4f' % grid_search.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters = \n",
      "clf__n_neighbors: 5\n",
      "clf__weights: 'distance'\n"
     ]
    }
   ],
   "source": [
    "print('Best parameters = ')\n",
    "best_parameters = grid_search.best_estimator_.get_params()\n",
    "for param_name in sorted(parameters.keys()):\n",
    "    print(\"%s: %r\" % (param_name, best_parameters[param_name]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08576739245884227"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test Accuracy\n",
    "grid_search.best_estimator_.score(mydata_test_df.data, mydata_test_df.target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Terms in TF-IDF Vectorizer are unigrams and bigrams, and values are binary values\n",
    "Now we will extract unigrams as well as bigrams from the text to form the TF-IDF vectorizer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### A. Using Multinominal Naive Bayes Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6557355284121084\n",
      "CPU times: total: 10.3 s\n",
      "Wall time: 10.4 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Performance of NB Classifier with No Stemming & Lemmatization, and ngram_range = (1,2)\n",
    "text_clf = Pipeline([\n",
    "        ('vect', TfidfVectorizer(stop_words='english', ngram_range = (1,2), binary = True, sublinear_tf=True)), # CountVectorizer, TfidfVectorizer\n",
    "        ('clf', MultinomialNB())])  # BernoulliNB, MultinomialNB, LogisticRegression, SGDClassifier, KNeighborsClassifier\n",
    "\n",
    "text_clf.fit(mydata_train_df.data, mydata_train_df.target)\n",
    "print(text_clf.score(mydata_test_df.data, mydata_test_df.target))\n",
    "# print(classification_report(mydata_test_df.target, text_clf.predict(mydata_test_df.data)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### B. Using Logistic Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6836165693043016\n",
      "CPU times: total: 3min 37s\n",
      "Wall time: 2min 12s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Performance of LR Classifier with No Stemming & Lemmatization, and ngram_range = (1,2)\n",
    "text_clf = Pipeline([\n",
    "        ('vect', TfidfVectorizer(stop_words='english', ngram_range = (1,2), binary = True, sublinear_tf=True)), # CountVectorizer, TfidfVectorizer\n",
    "        ('clf', LogisticRegression())])  # BernoulliNB, MultinomialNB, LogisticRegression, SGDClassifier, KNeighborsClassifier\n",
    "\n",
    "text_clf.fit(mydata_train_df.data, mydata_train_df.target)\n",
    "print(text_clf.score(mydata_test_df.data, mydata_test_df.target))\n",
    "# print(classification_report(mydata_test_df.target, text_clf.predict(mydata_test_df.data)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Performing grid search on the 'penalty' parameter of Logistic Regression Classifier. We get best performance when L2-norm is used in the penalization.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:547: FitFailedWarning: \n",
      "5 fits failed out of a total of 10.\n",
      "The score on these train-test partitions for these parameters will be set to nan.\n",
      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "5 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 895, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\base.py\", line 1474, in wrapper\n",
      "    return fit_method(estimator, *args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\pipeline.py\", line 475, in fit\n",
      "    self._final_estimator.fit(Xt, y, **last_step_params[\"fit\"])\n",
      "  File \"d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\base.py\", line 1474, in wrapper\n",
      "    return fit_method(estimator, *args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 1172, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 67, in _check_solver\n",
      "    raise ValueError(\n",
      "ValueError: Solver lbfgs supports only 'l2' or None penalties, got l1 penalty.\n",
      "\n",
      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:1051: UserWarning: One or more of the test scores are non-finite: [       nan 0.73024533]\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 13min 14s\n",
      "Wall time: 8min 7s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-4 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-4 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-4 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-4 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-4 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-4 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-4 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-4 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-4 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-4 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(estimator=Pipeline(steps=[(&#x27;vect&#x27;,\n",
       "                                        TfidfVectorizer(binary=True,\n",
       "                                                        ngram_range=(1, 2),\n",
       "                                                        stop_words=&#x27;english&#x27;,\n",
       "                                                        sublinear_tf=True)),\n",
       "                                       (&#x27;clf&#x27;, LogisticRegression())]),\n",
       "             param_grid={&#x27;clf__penalty&#x27;: (&#x27;l1&#x27;, &#x27;l2&#x27;)})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-10\" type=\"checkbox\" ><label for=\"sk-estimator-id-10\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(estimator=Pipeline(steps=[(&#x27;vect&#x27;,\n",
       "                                        TfidfVectorizer(binary=True,\n",
       "                                                        ngram_range=(1, 2),\n",
       "                                                        stop_words=&#x27;english&#x27;,\n",
       "                                                        sublinear_tf=True)),\n",
       "                                       (&#x27;clf&#x27;, LogisticRegression())]),\n",
       "             param_grid={&#x27;clf__penalty&#x27;: (&#x27;l1&#x27;, &#x27;l2&#x27;)})</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-11\" type=\"checkbox\" ><label for=\"sk-estimator-id-11\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: Pipeline</label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;vect&#x27;,\n",
       "                 TfidfVectorizer(binary=True, ngram_range=(1, 2),\n",
       "                                 stop_words=&#x27;english&#x27;, sublinear_tf=True)),\n",
       "                (&#x27;clf&#x27;, LogisticRegression())])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-12\" type=\"checkbox\" ><label for=\"sk-estimator-id-12\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;TfidfVectorizer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html\">?<span>Documentation for TfidfVectorizer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>TfidfVectorizer(binary=True, ngram_range=(1, 2), stop_words=&#x27;english&#x27;,\n",
       "                sublinear_tf=True)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-13\" type=\"checkbox\" ><label for=\"sk-estimator-id-13\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(estimator=Pipeline(steps=[('vect',\n",
       "                                        TfidfVectorizer(binary=True,\n",
       "                                                        ngram_range=(1, 2),\n",
       "                                                        stop_words='english',\n",
       "                                                        sublinear_tf=True)),\n",
       "                                       ('clf', LogisticRegression())]),\n",
       "             param_grid={'clf__penalty': ('l1', 'l2')})"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# Performing Grid Search to find best LR parameters\n",
    "pipeline = Pipeline([\n",
    "        ('vect', TfidfVectorizer(stop_words='english', ngram_range = (1,2), binary = True, sublinear_tf=True)), # CountVectorizer, TfidfVectorizer\n",
    "        ('clf', LogisticRegression())])  # MultinomialNB, LogisticRegression, SGDClassifier\n",
    "\n",
    "parameters = {\n",
    "       'clf__penalty': ('l1', 'l2')\n",
    "}\n",
    "\n",
    "grid_search = GridSearchCV(pipeline, parameters)\n",
    "grid_search.fit(mydata_train_df.data, mydata_train_df.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best training score 0.7302\n"
     ]
    }
   ],
   "source": [
    "# grid_search.cv_results_\n",
    "print('Best training score %0.4f' % grid_search.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters = \n",
      "clf__penalty: 'l2'\n"
     ]
    }
   ],
   "source": [
    "print('Best parameters = ')\n",
    "best_parameters = grid_search.best_estimator_.get_params()\n",
    "for param_name in sorted(parameters.keys()):\n",
    "    print(\"%s: %r\" % (param_name, best_parameters[param_name]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6836165693043016"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test Accuracy\n",
    "grid_search.best_estimator_.score(mydata_test_df.data, mydata_test_df.target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### C. Using Stochastic Gradient Descent Classifier Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7036643653744026\n",
      "CPU times: total: 16.6 s\n",
      "Wall time: 16 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "random.seed(1)\n",
    "# Performance of SGD Classifier with No Stemming & Lemmatization, and ngram_range = (1,2)\n",
    "text_clf = Pipeline([\n",
    "        ('vect', TfidfVectorizer(stop_words='english', ngram_range = (1,2), binary = True, sublinear_tf=True)), # CountVectorizer, TfidfVectorizer\n",
    "        ('clf', SGDClassifier())])  # BernoulliNB, MultinomialNB, LogisticRegression, SGDClassifier, KNeighborsClassifier\n",
    "\n",
    "text_clf.fit(mydata_train_df.data, mydata_train_df.target)\n",
    "print(text_clf.score(mydata_test_df.data, mydata_test_df.target))\n",
    "# print(classification_report(mydata_test_df.target, text_clf.predict(mydata_test_df.data)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### D. Using k Nearest Neighbors Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.05138077535847053\n",
      "CPU times: total: 14.4 s\n",
      "Wall time: 15 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Performance of KNN Classifier with No Stemming & Lemmatization, and ngram_range = (1,2)\n",
    "text_clf = Pipeline([\n",
    "        ('vect', TfidfVectorizer(stop_words='english', ngram_range = (1,2), binary = True, sublinear_tf=True)), # CountVectorizer, TfidfVectorizer\n",
    "        ('clf', KNeighborsClassifier(n_neighbors=100))])  # BernoulliNB, MultinomialNB, LogisticRegression, SGDClassifier, KNeighborsClassifier\n",
    "\n",
    "text_clf.fit(mydata_train_df.data, mydata_train_df.target)\n",
    "print(text_clf.score(mydata_test_df.data, mydata_test_df.target))\n",
    "# print(classification_report(mydata_test_df.target, text_clf.predict(mydata_test_df.data)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Performing grid search on the 'n_neighbors' and 'weights' parameters of kNN Classifier. We get best performance when n_neighbors is set to 5 and distance weights is used.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 4min 22s\n",
      "Wall time: 4min 25s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-5 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-5 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-5 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-5 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-5 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-5 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-5 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-5 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-5 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-5 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-5 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-5 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-5 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-5 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-5\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(estimator=Pipeline(steps=[(&#x27;vect&#x27;,\n",
       "                                        TfidfVectorizer(binary=True,\n",
       "                                                        ngram_range=(1, 2),\n",
       "                                                        stop_words=&#x27;english&#x27;,\n",
       "                                                        sublinear_tf=True)),\n",
       "                                       (&#x27;clf&#x27;, KNeighborsClassifier())]),\n",
       "             param_grid={&#x27;clf__n_neighbors&#x27;: (5, 10, 100, 200),\n",
       "                         &#x27;clf__weights&#x27;: (&#x27;uniform&#x27;, &#x27;distance&#x27;)})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-14\" type=\"checkbox\" ><label for=\"sk-estimator-id-14\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(estimator=Pipeline(steps=[(&#x27;vect&#x27;,\n",
       "                                        TfidfVectorizer(binary=True,\n",
       "                                                        ngram_range=(1, 2),\n",
       "                                                        stop_words=&#x27;english&#x27;,\n",
       "                                                        sublinear_tf=True)),\n",
       "                                       (&#x27;clf&#x27;, KNeighborsClassifier())]),\n",
       "             param_grid={&#x27;clf__n_neighbors&#x27;: (5, 10, 100, 200),\n",
       "                         &#x27;clf__weights&#x27;: (&#x27;uniform&#x27;, &#x27;distance&#x27;)})</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-15\" type=\"checkbox\" ><label for=\"sk-estimator-id-15\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: Pipeline</label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;vect&#x27;,\n",
       "                 TfidfVectorizer(binary=True, ngram_range=(1, 2),\n",
       "                                 stop_words=&#x27;english&#x27;, sublinear_tf=True)),\n",
       "                (&#x27;clf&#x27;, KNeighborsClassifier())])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-16\" type=\"checkbox\" ><label for=\"sk-estimator-id-16\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;TfidfVectorizer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html\">?<span>Documentation for TfidfVectorizer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>TfidfVectorizer(binary=True, ngram_range=(1, 2), stop_words=&#x27;english&#x27;,\n",
       "                sublinear_tf=True)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-17\" type=\"checkbox\" ><label for=\"sk-estimator-id-17\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier()</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(estimator=Pipeline(steps=[('vect',\n",
       "                                        TfidfVectorizer(binary=True,\n",
       "                                                        ngram_range=(1, 2),\n",
       "                                                        stop_words='english',\n",
       "                                                        sublinear_tf=True)),\n",
       "                                       ('clf', KNeighborsClassifier())]),\n",
       "             param_grid={'clf__n_neighbors': (5, 10, 100, 200),\n",
       "                         'clf__weights': ('uniform', 'distance')})"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# Performing Grid Search to find best kNN parameters\n",
    "pipeline = Pipeline([\n",
    "        ('vect', TfidfVectorizer(stop_words='english', ngram_range = (1,2), binary = True, sublinear_tf=True)), # CountVectorizer, TfidfVectorizer\n",
    "        ('clf', KNeighborsClassifier())])  # MultinomialNB, LogisticRegression, SGDClassifier\n",
    "\n",
    "parameters = {\n",
    "       'clf__n_neighbors': (5, 10, 100, 200),\n",
    "       'clf__weights': ('uniform', 'distance')\n",
    "}\n",
    "\n",
    "grid_search = GridSearchCV(pipeline, parameters)\n",
    "grid_search.fit(mydata_train_df.data, mydata_train_df.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best training score 0.1142\n"
     ]
    }
   ],
   "source": [
    "# grid_search.cv_results_\n",
    "print('Best training score %0.4f' % grid_search.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters = \n",
      "clf__n_neighbors: 5\n",
      "clf__weights: 'distance'\n"
     ]
    }
   ],
   "source": [
    "print('Best parameters = ')\n",
    "best_parameters = grid_search.best_estimator_.get_params()\n",
    "for param_name in sorted(parameters.keys()):\n",
    "    print(\"%s: %r\" % (param_name, best_parameters[param_name]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.07939458311205523"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test Accuracy\n",
    "grid_search.best_estimator_.score(mydata_test_df.data, mydata_test_df.target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## II. Using Stemming\n",
    "Now we'll use stemming technique, i.e. cutting the words to their root form. We use Snowball english stemmer algorithm from the nltk package. We define a class to represent the Snowball stemmer as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "stemmer = SnowballStemmer('english', ignore_stopwords=True)\n",
    "\n",
    "class StemmedTfidfVectorizer(TfidfVectorizer):\n",
    "    \n",
    "    def __init__(self, stemmer, *args, **kwargs):\n",
    "        super(StemmedTfidfVectorizer, self).__init__(*args, **kwargs)\n",
    "        self.stemmer = stemmer\n",
    "        \n",
    "    def build_analyzer(self):\n",
    "        analyzer = super(StemmedTfidfVectorizer, self).build_analyzer()\n",
    "        return lambda doc: (self.stemmer.stem(word) for word in analyzer(doc.replace('\\n', ' ')))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Terms in TF-IDF Vectorizer are unigrams (single words)\n",
    "First we will apply stemming to extract only unigrams from the text data to form the vectorizer.\n",
    "### A. Using Multinominal Naive Bayes Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "ename": "RuntimeError",
     "evalue": "scikit-learn estimators should always specify their parameters in the signature of their __init__ (no varargs). <class '__main__.StemmedTfidfVectorizer'> with constructor (self, stemmer, *args, **kwargs) doesn't  follow this convention.",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
      "Cell \u001b[1;32mIn[49], line 14\u001b[0m\n\u001b[0;32m      7\u001b[0m start_time \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n\u001b[0;32m      9\u001b[0m text_clf \u001b[38;5;241m=\u001b[39m Pipeline([\n\u001b[0;32m     10\u001b[0m         (\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mvect\u001b[39m\u001b[38;5;124m'\u001b[39m, StemmedTfidfVectorizer(stemmer\u001b[38;5;241m=\u001b[39mstemmer, stop_words\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124menglish\u001b[39m\u001b[38;5;124m'\u001b[39m, sublinear_tf\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)),\n\u001b[0;32m     11\u001b[0m         (\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mclf\u001b[39m\u001b[38;5;124m'\u001b[39m, MultinomialNB())\n\u001b[0;32m     12\u001b[0m ])\n\u001b[1;32m---> 14\u001b[0m \u001b[43mtext_clf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmydata_train_df\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mmydata_train_df\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtarget\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m     15\u001b[0m score \u001b[38;5;241m=\u001b[39m text_clf\u001b[38;5;241m.\u001b[39mscore(mydata_test_df\u001b[38;5;241m.\u001b[39mdata, mydata_test_df\u001b[38;5;241m.\u001b[39mtarget)\n\u001b[0;32m     17\u001b[0m end_time \u001b[38;5;241m=\u001b[39m time\u001b[38;5;241m.\u001b[39mtime()\n",
      "File \u001b[1;32md:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\base.py:1474\u001b[0m, in \u001b[0;36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[1;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1467\u001b[0m     estimator\u001b[38;5;241m.\u001b[39m_validate_params()\n\u001b[0;32m   1469\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[0;32m   1470\u001b[0m     skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m   1471\u001b[0m         prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[0;32m   1472\u001b[0m     )\n\u001b[0;32m   1473\u001b[0m ):\n\u001b[1;32m-> 1474\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfit_method\u001b[49m\u001b[43m(\u001b[49m\u001b[43mestimator\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32md:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\pipeline.py:471\u001b[0m, in \u001b[0;36mPipeline.fit\u001b[1;34m(self, X, y, **params)\u001b[0m\n\u001b[0;32m    428\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Fit the model.\u001b[39;00m\n\u001b[0;32m    429\u001b[0m \n\u001b[0;32m    430\u001b[0m \u001b[38;5;124;03mFit all the transformers one after the other and sequentially transform the\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    468\u001b[0m \u001b[38;5;124;03m    Pipeline with fitted steps.\u001b[39;00m\n\u001b[0;32m    469\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    470\u001b[0m routed_params \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_method_params(method\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfit\u001b[39m\u001b[38;5;124m\"\u001b[39m, props\u001b[38;5;241m=\u001b[39mparams)\n\u001b[1;32m--> 471\u001b[0m Xt \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mrouted_params\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    472\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _print_elapsed_time(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPipeline\u001b[39m\u001b[38;5;124m\"\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_log_message(\u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msteps) \u001b[38;5;241m-\u001b[39m \u001b[38;5;241m1\u001b[39m)):\n\u001b[0;32m    473\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_final_estimator \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpassthrough\u001b[39m\u001b[38;5;124m\"\u001b[39m:\n",
      "File \u001b[1;32md:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\pipeline.py:408\u001b[0m, in \u001b[0;36mPipeline._fit\u001b[1;34m(self, X, y, routed_params)\u001b[0m\n\u001b[0;32m    406\u001b[0m     cloned_transformer \u001b[38;5;241m=\u001b[39m clone(transformer)\n\u001b[0;32m    407\u001b[0m \u001b[38;5;66;03m# Fit or load from cache the current transformer\u001b[39;00m\n\u001b[1;32m--> 408\u001b[0m X, fitted_transformer \u001b[38;5;241m=\u001b[39m \u001b[43mfit_transform_one_cached\u001b[49m\u001b[43m(\u001b[49m\n\u001b[0;32m    409\u001b[0m \u001b[43m    \u001b[49m\u001b[43mcloned_transformer\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    410\u001b[0m \u001b[43m    \u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    411\u001b[0m \u001b[43m    \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    412\u001b[0m \u001b[43m    \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m,\u001b[49m\n\u001b[0;32m    413\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmessage_clsname\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mPipeline\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[0;32m    414\u001b[0m \u001b[43m    \u001b[49m\u001b[43mmessage\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_log_message\u001b[49m\u001b[43m(\u001b[49m\u001b[43mstep_idx\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    415\u001b[0m \u001b[43m    \u001b[49m\u001b[43mparams\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mrouted_params\u001b[49m\u001b[43m[\u001b[49m\u001b[43mname\u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[0;32m    416\u001b[0m \u001b[43m\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m    417\u001b[0m \u001b[38;5;66;03m# Replace the transformer of the step with the fitted\u001b[39;00m\n\u001b[0;32m    418\u001b[0m \u001b[38;5;66;03m# transformer. This is necessary when loading the transformer\u001b[39;00m\n\u001b[0;32m    419\u001b[0m \u001b[38;5;66;03m# from the cache.\u001b[39;00m\n\u001b[0;32m    420\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msteps[step_idx] \u001b[38;5;241m=\u001b[39m (name, fitted_transformer)\n",
      "File \u001b[1;32md:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\joblib\\memory.py:312\u001b[0m, in \u001b[0;36mNotMemorizedFunc.__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m    311\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m__call__\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs):\n\u001b[1;32m--> 312\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfunc\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
      "File \u001b[1;32md:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\pipeline.py:1303\u001b[0m, in \u001b[0;36m_fit_transform_one\u001b[1;34m(transformer, X, y, weight, message_clsname, message, params)\u001b[0m\n\u001b[0;32m   1301\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m _print_elapsed_time(message_clsname, message):\n\u001b[0;32m   1302\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(transformer, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfit_transform\u001b[39m\u001b[38;5;124m\"\u001b[39m):\n\u001b[1;32m-> 1303\u001b[0m         res \u001b[38;5;241m=\u001b[39m \u001b[43mtransformer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_transform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mparams\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mfit_transform\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1304\u001b[0m     \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m   1305\u001b[0m         res \u001b[38;5;241m=\u001b[39m transformer\u001b[38;5;241m.\u001b[39mfit(X, y, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mparams\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mfit\u001b[39m\u001b[38;5;124m\"\u001b[39m, {}))\u001b[38;5;241m.\u001b[39mtransform(\n\u001b[0;32m   1306\u001b[0m             X, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mparams\u001b[38;5;241m.\u001b[39mget(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mtransform\u001b[39m\u001b[38;5;124m\"\u001b[39m, {})\n\u001b[0;32m   1307\u001b[0m         )\n",
      "File \u001b[1;32md:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:2138\u001b[0m, in \u001b[0;36mTfidfVectorizer.fit_transform\u001b[1;34m(self, raw_documents, y)\u001b[0m\n\u001b[0;32m   2131\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_check_params()\n\u001b[0;32m   2132\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_tfidf \u001b[38;5;241m=\u001b[39m TfidfTransformer(\n\u001b[0;32m   2133\u001b[0m     norm\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnorm,\n\u001b[0;32m   2134\u001b[0m     use_idf\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39muse_idf,\n\u001b[0;32m   2135\u001b[0m     smooth_idf\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msmooth_idf,\n\u001b[0;32m   2136\u001b[0m     sublinear_tf\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39msublinear_tf,\n\u001b[0;32m   2137\u001b[0m )\n\u001b[1;32m-> 2138\u001b[0m X \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit_transform\u001b[49m\u001b[43m(\u001b[49m\u001b[43mraw_documents\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   2139\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_tfidf\u001b[38;5;241m.\u001b[39mfit(X)\n\u001b[0;32m   2140\u001b[0m \u001b[38;5;66;03m# X is already a transformed view of raw_documents so\u001b[39;00m\n\u001b[0;32m   2141\u001b[0m \u001b[38;5;66;03m# we set copy to False\u001b[39;00m\n",
      "File \u001b[1;32md:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\base.py:1467\u001b[0m, in \u001b[0;36m_fit_context.<locals>.decorator.<locals>.wrapper\u001b[1;34m(estimator, *args, **kwargs)\u001b[0m\n\u001b[0;32m   1462\u001b[0m partial_fit_and_fitted \u001b[38;5;241m=\u001b[39m (\n\u001b[0;32m   1463\u001b[0m     fit_method\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mpartial_fit\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mand\u001b[39;00m _is_fitted(estimator)\n\u001b[0;32m   1464\u001b[0m )\n\u001b[0;32m   1466\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m global_skip_validation \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m partial_fit_and_fitted:\n\u001b[1;32m-> 1467\u001b[0m     \u001b[43mestimator\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_validate_params\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[0;32m   1469\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m config_context(\n\u001b[0;32m   1470\u001b[0m     skip_parameter_validation\u001b[38;5;241m=\u001b[39m(\n\u001b[0;32m   1471\u001b[0m         prefer_skip_nested_validation \u001b[38;5;129;01mor\u001b[39;00m global_skip_validation\n\u001b[0;32m   1472\u001b[0m     )\n\u001b[0;32m   1473\u001b[0m ):\n\u001b[0;32m   1474\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m fit_method(estimator, \u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n",
      "File \u001b[1;32md:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\base.py:668\u001b[0m, in \u001b[0;36mBaseEstimator._validate_params\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m    658\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_validate_params\u001b[39m(\u001b[38;5;28mself\u001b[39m):\n\u001b[0;32m    659\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Validate types and values of constructor parameters\u001b[39;00m\n\u001b[0;32m    660\u001b[0m \n\u001b[0;32m    661\u001b[0m \u001b[38;5;124;03m    The expected type and values must be defined in the `_parameter_constraints`\u001b[39;00m\n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    664\u001b[0m \u001b[38;5;124;03m    accepted constraints.\u001b[39;00m\n\u001b[0;32m    665\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m    666\u001b[0m     validate_parameter_constraints(\n\u001b[0;32m    667\u001b[0m         \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_parameter_constraints,\n\u001b[1;32m--> 668\u001b[0m         \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_params\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdeep\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;28;43;01mFalse\u001b[39;49;00m\u001b[43m)\u001b[49m,\n\u001b[0;32m    669\u001b[0m         caller_name\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m,\n\u001b[0;32m    670\u001b[0m     )\n",
      "File \u001b[1;32md:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\base.py:243\u001b[0m, in \u001b[0;36mBaseEstimator.get_params\u001b[1;34m(self, deep)\u001b[0m\n\u001b[0;32m    228\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    229\u001b[0m \u001b[38;5;124;03mGet parameters for this estimator.\u001b[39;00m\n\u001b[0;32m    230\u001b[0m \n\u001b[1;32m   (...)\u001b[0m\n\u001b[0;32m    240\u001b[0m \u001b[38;5;124;03m    Parameter names mapped to their values.\u001b[39;00m\n\u001b[0;32m    241\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[0;32m    242\u001b[0m out \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m()\n\u001b[1;32m--> 243\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m key \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_param_names\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m:\n\u001b[0;32m    244\u001b[0m     value \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mgetattr\u001b[39m(\u001b[38;5;28mself\u001b[39m, key)\n\u001b[0;32m    245\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m deep \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mhasattr\u001b[39m(value, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mget_params\u001b[39m\u001b[38;5;124m\"\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(value, \u001b[38;5;28mtype\u001b[39m):\n",
      "File \u001b[1;32md:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\base.py:217\u001b[0m, in \u001b[0;36mBaseEstimator._get_param_names\u001b[1;34m(cls)\u001b[0m\n\u001b[0;32m    215\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m p \u001b[38;5;129;01min\u001b[39;00m parameters:\n\u001b[0;32m    216\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m p\u001b[38;5;241m.\u001b[39mkind \u001b[38;5;241m==\u001b[39m p\u001b[38;5;241m.\u001b[39mVAR_POSITIONAL:\n\u001b[1;32m--> 217\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\n\u001b[0;32m    218\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mscikit-learn estimators should always \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    219\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mspecify their parameters in the signature\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    220\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m of their __init__ (no varargs).\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    221\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m with constructor \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m doesn\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m    222\u001b[0m             \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m follow this convention.\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m (\u001b[38;5;28mcls\u001b[39m, init_signature)\n\u001b[0;32m    223\u001b[0m         )\n\u001b[0;32m    224\u001b[0m \u001b[38;5;66;03m# Extract and sort argument names excluding 'self'\u001b[39;00m\n\u001b[0;32m    225\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28msorted\u001b[39m([p\u001b[38;5;241m.\u001b[39mname \u001b[38;5;28;01mfor\u001b[39;00m p \u001b[38;5;129;01min\u001b[39;00m parameters])\n",
      "\u001b[1;31mRuntimeError\u001b[0m: scikit-learn estimators should always specify their parameters in the signature of their __init__ (no varargs). <class '__main__.StemmedTfidfVectorizer'> with constructor (self, stemmer, *args, **kwargs) doesn't  follow this convention."
     ]
    }
   ],
   "source": [
    "import time\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "\n",
    "# Assuming stemmer is already defined as SnowballStemmer('english', ignore_stopwords=True)\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    "text_clf = Pipeline([\n",
    "        ('vect', StemmedTfidfVectorizer(stemmer=stemmer, stop_words='english', sublinear_tf=True)),\n",
    "        ('clf', MultinomialNB())\n",
    "])\n",
    "\n",
    "text_clf.fit(mydata_train_df.data, mydata_train_df.target)\n",
    "score = text_clf.score(mydata_test_df.data, mydata_test_df.target)\n",
    "\n",
    "end_time = time.time()\n",
    "execution_time = end_time - start_time\n",
    "\n",
    "print(f\"Accuracy: {score}\")\n",
    "print(f\"Execution time: {execution_time} seconds\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### B. Using Logistic Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6905204460966543\n",
      "CPU times: total: 50.3 s\n",
      "Wall time: 37.1 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Performance of LR Classifier with Stemming\n",
    "text_clf = Pipeline([\n",
    "        ('vect', StemmedTfidfVectorizer(stemmer=stemmer, stop_words='english', sublinear_tf=True)), # CountVectorizer, TfidfVectorizer\n",
    "        ('clf', LogisticRegression())])  # MultinomialNB, LogisticRegression, SGDClassifier, KNeighborsClassifier\n",
    "\n",
    "text_clf.fit(mydata_train_df.data, mydata_train_df.target)\n",
    "print(text_clf.score(mydata_test_df.data, mydata_test_df.target))\n",
    "# print(classification_report(mydata_test_df.target, text_clf.predict(mydata_test_df.data)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Performing grid search on the 'penalty' parameter of Logistic Regression Classifier. We get best performance when L2-norm is used in the penalization.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %%time\n",
    "# # Performing Grid Search to find best LR parameters\n",
    "# pipeline = Pipeline([\n",
    "#         ('vect', StemmedTfidfVectorizer(stemmer=stemmer, stop_words='english', sublinear_tf=True)), # CountVectorizer, TfidfVectorizer\n",
    "#         ('clf', LogisticRegression())])  # MultinomialNB, LogisticRegression, SGDClassifier\n",
    "\n",
    "# parameters = {\n",
    "#        'clf__penalty': ('l1', 'l2')\n",
    "# }\n",
    "\n",
    "# grid_search = GridSearchCV(pipeline, parameters)\n",
    "# grid_search.fit(mydata_train_df.data, mydata_train_df.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # grid_search.cv_results_\n",
    "# print('Best training score %0.4f' % grid_search.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print('Best parameters = ')\n",
    "# best_parameters = grid_search.best_estimator_.get_params()\n",
    "# for param_name in sorted(parameters.keys()):\n",
    "#     print(\"%s: %r\" % (param_name, best_parameters[param_name]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Test Accuracy\n",
    "# grid_search.best_estimator_.score(mydata_test_df.data, mydata_test_df.target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### C. Using Stochastic Gradient Descent Classifier Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7007434944237918\n",
      "CPU times: total: 24.7 s\n",
      "Wall time: 24.8 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Performance of SGD Classifier with Stemming\n",
    "random.seed(1)\n",
    "text_clf = Pipeline([\n",
    "        ('vect', StemmedTfidfVectorizer(stemmer=stemmer, stop_words='english', sublinear_tf=True)), # CountVectorizer, TfidfVectorizer\n",
    "        ('clf', SGDClassifier())])  # MultinomialNB, LogisticRegression, SGDClassifier, KNeighborsClassifier\n",
    "\n",
    "text_clf.fit(mydata_train_df.data, mydata_train_df.target)\n",
    "print(text_clf.score(mydata_test_df.data, mydata_test_df.target))\n",
    "# print(classification_report(mydata_test_df.target, text_clf.predict(mydata_test_df.data)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### D. Using k Nearest Neighbors Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.09147636749867233\n",
      "CPU times: total: 25.8 s\n",
      "Wall time: 25.9 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Performance of kNN Classifier with Stemming\n",
    "text_clf = Pipeline([\n",
    "        ('vect', StemmedTfidfVectorizer(stemmer=stemmer, stop_words='english', sublinear_tf=True)), # CountVectorizer, TfidfVectorizer\n",
    "        ('clf', KNeighborsClassifier(n_neighbors=5, weights='distance'))])  # MultinomialNB, LogisticRegression, SGDClassifier, KNeighborsClassifier\n",
    "\n",
    "text_clf.fit(mydata_train_df.data, mydata_train_df.target)\n",
    "print(text_clf.score(mydata_test_df.data, mydata_test_df.target))\n",
    "# print(classification_report(mydata_test_df.target, text_clf.predict(mydata_test_df.data)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Performing grid search on the 'n_neighbors' and 'weights' parameters of kNN Classifier. We get best performance when n_neighbors is set to 5 and distance weights is used.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %%time\n",
    "# # Performing Grid Search to find best kNN parameters\n",
    "# pipeline = Pipeline([\n",
    "#         ('vect', StemmedTfidfVectorizer(stemmer=stemmer, stop_words='english', sublinear_tf=True)), # CountVectorizer, TfidfVectorizer\n",
    "#         ('clf', KNeighborsClassifier())])  # MultinomialNB, LogisticRegression, SGDClassifier\n",
    "\n",
    "# parameters = {\n",
    "#        'clf__n_neighbors': (5, 10, 100, 200),\n",
    "#        'clf__weights': ('uniform', 'distance')\n",
    "# }\n",
    "\n",
    "# grid_search = GridSearchCV(pipeline, parameters)\n",
    "# grid_search.fit(mydata_train_df.data, mydata_train_df.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # grid_search.cv_results_\n",
    "# print('Best training score %0.4f' % grid_search.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print('Best parameters = ')\n",
    "# best_parameters = grid_search.best_estimator_.get_params()\n",
    "# for param_name in sorted(parameters.keys()):\n",
    "#     print(\"%s: %r\" % (param_name, best_parameters[param_name]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Test Accuracy\n",
    "# grid_search.best_estimator_.score(mydata_test_df.data, mydata_test_df.target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Terms in TF-IDF Vectorizer are unigrams and bigrams, and values are binary values\n",
    "Now we will extract unigrams as well as bigrams from the text to form the TF-IDF vectorizer.\n",
    "\n",
    "### A. Using Multinominal Naive Bayes Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6558682952734998\n",
      "CPU times: total: 52.9 s\n",
      "Wall time: 53.6 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Performance of NB Classifier with Stemming, and ngram_range = (1,2)\n",
    "text_clf = Pipeline([\n",
    "        ('vect', StemmedTfidfVectorizer(stemmer=stemmer, stop_words='english', ngram_range = (1,2), binary = True, sublinear_tf=True)), # CountVectorizer, TfidfVectorizer\n",
    "        ('clf', MultinomialNB())])  # BernoulliNB, MultinomialNB, LogisticRegression, SGDClassifier, KNeighborsClassifier\n",
    "\n",
    "text_clf.fit(mydata_train_df.data, mydata_train_df.target)\n",
    "print(text_clf.score(mydata_test_df.data, mydata_test_df.target))\n",
    "# print(classification_report(mydata_test_df.target, text_clf.predict(mydata_test_df.data)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### B. Using Logistic Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.68441317047265\n",
      "CPU times: total: 2min 33s\n",
      "Wall time: 1min 54s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Performance of LR Classifier with Stemming, and ngram_range = (1,2)\n",
    "text_clf = Pipeline([\n",
    "        ('vect', StemmedTfidfVectorizer(stemmer=stemmer, stop_words='english', ngram_range = (1,2), binary = True, sublinear_tf=True)), # CountVectorizer, TfidfVectorizer\n",
    "        ('clf', LogisticRegression())])  # BernoulliNB, MultinomialNB, LogisticRegression, SGDClassifier, KNeighborsClassifier\n",
    "\n",
    "text_clf.fit(mydata_train_df.data, mydata_train_df.target)\n",
    "print(text_clf.score(mydata_test_df.data, mydata_test_df.target))\n",
    "# print(classification_report(mydata_test_df.target, text_clf.predict(mydata_test_df.data)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Performing grid search on the 'penalty' parameter of Logistic Regression Classifier. We get best performance when L2-norm is used in the penalization.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %%time\n",
    "# # Performing Grid Search to find best LR parameters\n",
    "# pipeline = Pipeline([\n",
    "#         ('vect', StemmedTfidfVectorizer(stemmer=stemmer, stop_words='english', ngram_range = (1,2), binary = True, sublinear_tf=True)), # CountVectorizer, TfidfVectorizer\n",
    "#         ('clf', LogisticRegression())])  # MultinomialNB, LogisticRegression, SGDClassifier\n",
    "\n",
    "# parameters = {\n",
    "#        'clf__penalty': ('l1', 'l2')\n",
    "# }\n",
    "\n",
    "# grid_search = GridSearchCV(pipeline, parameters)\n",
    "# grid_search.fit(mydata_train_df.data, mydata_train_df.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # grid_search.cv_results_\n",
    "# print('Best training score %0.4f' % grid_search.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print('Best parameters = ')\n",
    "# best_parameters = grid_search.best_estimator_.get_params()\n",
    "# for param_name in sorted(parameters.keys()):\n",
    "#     print(\"%s: %r\" % (param_name, best_parameters[param_name]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Test Accuracy\n",
    "# grid_search.best_estimator_.score(mydata_test_df.data, mydata_test_df.target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### C. Using Stochastic Gradient Descent Classifier Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7065852363250132\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.58      0.44      0.50       319\n",
      "           1       0.66      0.69      0.67       389\n",
      "           2       0.65      0.62      0.64       394\n",
      "           3       0.71      0.64      0.67       392\n",
      "           4       0.73      0.73      0.73       385\n",
      "           5       0.78      0.75      0.77       395\n",
      "           6       0.72      0.83      0.77       390\n",
      "           7       0.79      0.71      0.75       396\n",
      "           8       0.83      0.74      0.78       398\n",
      "           9       0.55      0.88      0.68       397\n",
      "          10       0.87      0.91      0.89       399\n",
      "          11       0.82      0.75      0.78       396\n",
      "          12       0.65      0.56      0.60       393\n",
      "          13       0.79      0.81      0.80       396\n",
      "          14       0.77      0.79      0.78       394\n",
      "          15       0.58      0.88      0.70       398\n",
      "          16       0.58      0.70      0.63       364\n",
      "          17       0.82      0.81      0.81       376\n",
      "          18       0.69      0.41      0.52       310\n",
      "          19       0.60      0.16      0.25       251\n",
      "\n",
      "    accuracy                           0.71      7532\n",
      "   macro avg       0.71      0.69      0.69      7532\n",
      "weighted avg       0.71      0.71      0.70      7532\n",
      "\n",
      "CPU times: total: 1min 18s\n",
      "Wall time: 1min 19s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "random.seed(1)\n",
    "# Performance of SGD Classifier with Stemming, and ngram_range = (1,2)\n",
    "text_clf = Pipeline([\n",
    "        ('vect', StemmedTfidfVectorizer(stemmer=stemmer, stop_words='english', ngram_range = (1,2), binary = True, sublinear_tf=True)), # CountVectorizer, TfidfVectorizer\n",
    "        ('clf', SGDClassifier())])  # BernoulliNB, MultinomialNB, LogisticRegression, SGDClassifier, KNeighborsClassifier\n",
    "\n",
    "text_clf.fit(mydata_train_df.data, mydata_train_df.target)\n",
    "print(text_clf.score(mydata_test_df.data, mydata_test_df.target))\n",
    "print(classification_report(mydata_test_df.target, text_clf.predict(mydata_test_df.data)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### D. Using k Nearest Neighbors Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.07753584705257567\n",
      "CPU times: total: 56 s\n",
      "Wall time: 56.5 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Performance of KNN Classifier with Stemming, and ngram_range = (1,2)\n",
    "text_clf = Pipeline([\n",
    "        ('vect', StemmedTfidfVectorizer(stemmer=stemmer, stop_words='english', ngram_range = (1,2), binary = True, sublinear_tf=True)), # CountVectorizer, TfidfVectorizer\n",
    "        ('clf', KNeighborsClassifier(n_neighbors=5, weights='distance'))])  # BernoulliNB, MultinomialNB, LogisticRegression, SGDClassifier, KNeighborsClassifier\n",
    "\n",
    "text_clf.fit(mydata_train_df.data, mydata_train_df.target)\n",
    "print(text_clf.score(mydata_test_df.data, mydata_test_df.target))\n",
    "# print(classification_report(mydata_test_df.target, text_clf.predict(mydata_test_df.data)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Performing grid search on the 'n_neighbors' and 'weights' parameters of kNN Classifier. We get best performance when n_neighbors is set to 5 and distance weights is used.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %%time\n",
    "# # Performing Grid Search to find best kNN parameters\n",
    "# pipeline = Pipeline([\n",
    "#         ('vect', StemmedTfidfVectorizer(stemmer=stemmer, stop_words='english', ngram_range = (1,2), binary = True, sublinear_tf=True)), # CountVectorizer, TfidfVectorizer\n",
    "#         ('clf', KNeighborsClassifier())])  # MultinomialNB, LogisticRegression, SGDClassifier\n",
    "\n",
    "# parameters = {\n",
    "#        'clf__n_neighbors': (5, 10, 100, 200),\n",
    "#        'clf__weights': ('uniform', 'distance')\n",
    "# }\n",
    "\n",
    "# grid_search = GridSearchCV(pipeline, parameters)\n",
    "# grid_search.fit(mydata_train_df.data, mydata_train_df.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # grid_search.cv_results_\n",
    "# print('Best training score %0.4f' % grid_search.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# print('Best parameters = ')\n",
    "# best_parameters = grid_search.best_estimator_.get_params()\n",
    "# for param_name in sorted(parameters.keys()):\n",
    "#     print(\"%s: %r\" % (param_name, best_parameters[param_name]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# # Test Accuracy\n",
    "# grid_search.best_estimator_.score(mydata_test_df.data, mydata_test_df.target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## III. Using Lemmatization\n",
    "Now we apply lemmatization, i.e. getting grammatically correct normal form of the word with the use of morphology. We will use WordNetLemmatizer from nltk package and part-of-speech word tagging. We define the class LemmaTokenizer as follows."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "class LemmaTokenizer(object):\n",
    "    def __init__(self):\n",
    "        self.wnl = WordNetLemmatizer()\n",
    "    def __call__(self, articles):\n",
    "        return [self.wnl.lemmatize(t) for t in word_tokenize(articles)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Terms in TF-IDF Vectorizer are unigrams (single words)\n",
    "First we will apply lemmetization to extract only unigrams from the text data to form the vectorizer.\n",
    "\n",
    "### A. Using Multinominal Naive Bayes Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.6687466808284652\n",
      "Execution time: 31.198439359664917 seconds\n"
     ]
    }
   ],
   "source": [
    "import time\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from nltk.tokenize import word_tokenize\n",
    "from nltk.stem import WordNetLemmatizer\n",
    "\n",
    "class LemmaTokenizer:\n",
    "    def __init__(self):\n",
    "        self.wnl = WordNetLemmatizer()\n",
    "    \n",
    "    def __call__(self, doc):\n",
    "        return [self.wnl.lemmatize(t) for t in word_tokenize(doc)]\n",
    "\n",
    "# Assuming mydata_train_df and mydata_test_df are already defined\n",
    "\n",
    "start_time = time.time()\n",
    "\n",
    "text_clf = Pipeline([\n",
    "        ('vect', TfidfVectorizer(tokenizer=LemmaTokenizer(), stop_words='english', sublinear_tf=True)),\n",
    "        ('clf', MultinomialNB())\n",
    "])\n",
    "\n",
    "text_clf.fit(mydata_train_df.data, mydata_train_df.target)\n",
    "accuracy = text_clf.score(mydata_test_df.data, mydata_test_df.target)\n",
    "\n",
    "end_time = time.time()\n",
    "execution_time = end_time - start_time\n",
    "\n",
    "print(f\"Accuracy: {accuracy}\")\n",
    "print(f\"Execution time: {execution_time} seconds\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### B. Using Logistic Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6915825809877855\n",
      "CPU times: total: 53.3 s\n",
      "Wall time: 39.6 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Performance of LR Classifier with Lemmatization\n",
    "text_clf = Pipeline([\n",
    "        ('vect', TfidfVectorizer(tokenizer=LemmaTokenizer(), stop_words='english', sublinear_tf=True)), # CountVectorizer, TfidfVectorizer\n",
    "        ('clf', LogisticRegression())])  # MultinomialNB, LogisticRegression, SGDClassifier, KNeighborsClassifier\n",
    "\n",
    "text_clf.fit(mydata_train_df.data, mydata_train_df.target)\n",
    "print(text_clf.score(mydata_test_df.data, mydata_test_df.target))\n",
    "# print(classification_report(mydata_test_df.target, text_clf.predict(mydata_test_df.data)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Performing grid search on the 'penalty' parameter of Logistic Regression Classifier. We get best performance when L2-norm is used in the penalization.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:547: FitFailedWarning: \n",
      "5 fits failed out of a total of 10.\n",
      "The score on these train-test partitions for these parameters will be set to nan.\n",
      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "5 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 895, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\base.py\", line 1474, in wrapper\n",
      "    return fit_method(estimator, *args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\pipeline.py\", line 475, in fit\n",
      "    self._final_estimator.fit(Xt, y, **last_step_params[\"fit\"])\n",
      "  File \"d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\base.py\", line 1474, in wrapper\n",
      "    return fit_method(estimator, *args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 1172, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 67, in _check_solver\n",
      "    raise ValueError(\n",
      "ValueError: Solver lbfgs supports only 'l2' or None penalties, got l1 penalty.\n",
      "\n",
      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:1051: UserWarning: One or more of the test scores are non-finite: [       nan 0.74226645]\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 5min 5s\n",
      "Wall time: 3min 57s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-6 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-6 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-6 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-6 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-6 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-6 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-6 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-6 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-6 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-6 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-6 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-6 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-6 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-6 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-6 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-6 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-6\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(estimator=Pipeline(steps=[(&#x27;vect&#x27;,\n",
       "                                        TfidfVectorizer(stop_words=&#x27;english&#x27;,\n",
       "                                                        sublinear_tf=True,\n",
       "                                                        tokenizer=&lt;__main__.LemmaTokenizer object at 0x000002554911BB60&gt;)),\n",
       "                                       (&#x27;clf&#x27;, LogisticRegression())]),\n",
       "             param_grid={&#x27;clf__penalty&#x27;: (&#x27;l1&#x27;, &#x27;l2&#x27;)})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-18\" type=\"checkbox\" ><label for=\"sk-estimator-id-18\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(estimator=Pipeline(steps=[(&#x27;vect&#x27;,\n",
       "                                        TfidfVectorizer(stop_words=&#x27;english&#x27;,\n",
       "                                                        sublinear_tf=True,\n",
       "                                                        tokenizer=&lt;__main__.LemmaTokenizer object at 0x000002554911BB60&gt;)),\n",
       "                                       (&#x27;clf&#x27;, LogisticRegression())]),\n",
       "             param_grid={&#x27;clf__penalty&#x27;: (&#x27;l1&#x27;, &#x27;l2&#x27;)})</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-19\" type=\"checkbox\" ><label for=\"sk-estimator-id-19\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: Pipeline</label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;vect&#x27;,\n",
       "                 TfidfVectorizer(stop_words=&#x27;english&#x27;, sublinear_tf=True,\n",
       "                                 tokenizer=&lt;__main__.LemmaTokenizer object at 0x000002554911BB60&gt;)),\n",
       "                (&#x27;clf&#x27;, LogisticRegression())])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-20\" type=\"checkbox\" ><label for=\"sk-estimator-id-20\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;TfidfVectorizer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html\">?<span>Documentation for TfidfVectorizer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>TfidfVectorizer(stop_words=&#x27;english&#x27;, sublinear_tf=True,\n",
       "                tokenizer=&lt;__main__.LemmaTokenizer object at 0x000002554911BB60&gt;)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-21\" type=\"checkbox\" ><label for=\"sk-estimator-id-21\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(estimator=Pipeline(steps=[('vect',\n",
       "                                        TfidfVectorizer(stop_words='english',\n",
       "                                                        sublinear_tf=True,\n",
       "                                                        tokenizer=<__main__.LemmaTokenizer object at 0x000002554911BB60>)),\n",
       "                                       ('clf', LogisticRegression())]),\n",
       "             param_grid={'clf__penalty': ('l1', 'l2')})"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# Performing Grid Search to find best LR parameters\n",
    "pipeline = Pipeline([\n",
    "        ('vect', TfidfVectorizer(tokenizer=LemmaTokenizer(), stop_words='english', sublinear_tf=True)), # CountVectorizer, TfidfVectorizer\n",
    "        ('clf', LogisticRegression())])  # MultinomialNB, LogisticRegression, SGDClassifier\n",
    "\n",
    "parameters = {\n",
    "       'clf__penalty': ('l1', 'l2')\n",
    "}\n",
    "\n",
    "grid_search = GridSearchCV(pipeline, parameters)\n",
    "grid_search.fit(mydata_train_df.data, mydata_train_df.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best training score 0.7423\n"
     ]
    }
   ],
   "source": [
    "# grid_search.cv_results_\n",
    "print('Best training score %0.4f' % grid_search.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters = \n",
      "clf__penalty: 'l2'\n"
     ]
    }
   ],
   "source": [
    "print('Best parameters = ')\n",
    "best_parameters = grid_search.best_estimator_.get_params()\n",
    "for param_name in sorted(parameters.keys()):\n",
    "    print(\"%s: %r\" % (param_name, best_parameters[param_name]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6915825809877855"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test Accuracy\n",
    "grid_search.best_estimator_.score(mydata_test_df.data, mydata_test_df.target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### C. Using Stochastic Gradient Descent Classifier Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6996813595326606\n",
      "CPU times: total: 28.5 s\n",
      "Wall time: 27.7 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Performance of SGD Classifier with Lemmatization\n",
    "random.seed(1)\n",
    "text_clf = Pipeline([\n",
    "        ('vect', TfidfVectorizer(tokenizer=LemmaTokenizer(), stop_words='english', sublinear_tf=True)), # CountVectorizer, TfidfVectorizer\n",
    "        ('clf', SGDClassifier())])  # MultinomialNB, LogisticRegression, SGDClassifier, KNeighborsClassifier\n",
    "\n",
    "text_clf.fit(mydata_train_df.data, mydata_train_df.target)\n",
    "print(text_clf.score(mydata_test_df.data, mydata_test_df.target))\n",
    "# print(classification_report(mydata_test_df.target, text_clf.predict(mydata_test_df.data)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### D. Using k Nearest Neighbors Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.05523101433882103\n",
      "CPU times: total: 30.5 s\n",
      "Wall time: 30.7 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Performance of kNN Classifier with Lemmatization\n",
    "text_clf = Pipeline([\n",
    "        ('vect', TfidfVectorizer(tokenizer=LemmaTokenizer(), stop_words='english', sublinear_tf=True)), # CountVectorizer, TfidfVectorizer\n",
    "        ('clf', KNeighborsClassifier(n_neighbors=100))])  # MultinomialNB, LogisticRegression, SGDClassifier, KNeighborsClassifier\n",
    "\n",
    "text_clf.fit(mydata_train_df.data, mydata_train_df.target)\n",
    "print(text_clf.score(mydata_test_df.data, mydata_test_df.target))\n",
    "# print(classification_report(mydata_test_df.target, text_clf.predict(mydata_test_df.data)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Performing grid search on the 'n_neighbors' and 'weights' parameters of kNN Classifier. We get best performance when n_neighbors is set to 5 and distance weights is used.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 12min 17s\n",
      "Wall time: 12min 26s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-7 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-7 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-7 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-7 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-7 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-7 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-7 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-7 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-7 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-7 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-7 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-7 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-7 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-7 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-7 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-7 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-7\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(estimator=Pipeline(steps=[(&#x27;vect&#x27;,\n",
       "                                        TfidfVectorizer(stop_words=&#x27;english&#x27;,\n",
       "                                                        sublinear_tf=True,\n",
       "                                                        tokenizer=&lt;__main__.LemmaTokenizer object at 0x000002554376BB60&gt;)),\n",
       "                                       (&#x27;clf&#x27;, KNeighborsClassifier())]),\n",
       "             param_grid={&#x27;clf__n_neighbors&#x27;: (5, 10, 100, 200),\n",
       "                         &#x27;clf__weights&#x27;: (&#x27;uniform&#x27;, &#x27;distance&#x27;)})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-22\" type=\"checkbox\" ><label for=\"sk-estimator-id-22\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(estimator=Pipeline(steps=[(&#x27;vect&#x27;,\n",
       "                                        TfidfVectorizer(stop_words=&#x27;english&#x27;,\n",
       "                                                        sublinear_tf=True,\n",
       "                                                        tokenizer=&lt;__main__.LemmaTokenizer object at 0x000002554376BB60&gt;)),\n",
       "                                       (&#x27;clf&#x27;, KNeighborsClassifier())]),\n",
       "             param_grid={&#x27;clf__n_neighbors&#x27;: (5, 10, 100, 200),\n",
       "                         &#x27;clf__weights&#x27;: (&#x27;uniform&#x27;, &#x27;distance&#x27;)})</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-23\" type=\"checkbox\" ><label for=\"sk-estimator-id-23\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: Pipeline</label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;vect&#x27;,\n",
       "                 TfidfVectorizer(stop_words=&#x27;english&#x27;, sublinear_tf=True,\n",
       "                                 tokenizer=&lt;__main__.LemmaTokenizer object at 0x000002554376BB60&gt;)),\n",
       "                (&#x27;clf&#x27;, KNeighborsClassifier())])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-24\" type=\"checkbox\" ><label for=\"sk-estimator-id-24\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;TfidfVectorizer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html\">?<span>Documentation for TfidfVectorizer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>TfidfVectorizer(stop_words=&#x27;english&#x27;, sublinear_tf=True,\n",
       "                tokenizer=&lt;__main__.LemmaTokenizer object at 0x000002554376BB60&gt;)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-25\" type=\"checkbox\" ><label for=\"sk-estimator-id-25\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier()</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(estimator=Pipeline(steps=[('vect',\n",
       "                                        TfidfVectorizer(stop_words='english',\n",
       "                                                        sublinear_tf=True,\n",
       "                                                        tokenizer=<__main__.LemmaTokenizer object at 0x000002554376BB60>)),\n",
       "                                       ('clf', KNeighborsClassifier())]),\n",
       "             param_grid={'clf__n_neighbors': (5, 10, 100, 200),\n",
       "                         'clf__weights': ('uniform', 'distance')})"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# Performing Grid Search to find best kNN parameters\n",
    "pipeline = Pipeline([\n",
    "        ('vect', TfidfVectorizer(tokenizer=LemmaTokenizer(), stop_words='english', sublinear_tf=True)), # CountVectorizer, TfidfVectorizer\n",
    "        ('clf', KNeighborsClassifier())])  # MultinomialNB, LogisticRegression, SGDClassifier\n",
    "\n",
    "parameters = {\n",
    "       'clf__n_neighbors': (5, 10, 100, 200),\n",
    "       'clf__weights': ('uniform', 'distance')\n",
    "}\n",
    "\n",
    "grid_search = GridSearchCV(pipeline, parameters)\n",
    "grid_search.fit(mydata_train_df.data, mydata_train_df.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best training score 0.1290\n"
     ]
    }
   ],
   "source": [
    "# grid_search.cv_results_\n",
    "print('Best training score %0.4f' % grid_search.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters = \n",
      "clf__n_neighbors: 5\n",
      "clf__weights: 'distance'\n"
     ]
    }
   ],
   "source": [
    "print('Best parameters = ')\n",
    "best_parameters = grid_search.best_estimator_.get_params()\n",
    "for param_name in sorted(parameters.keys()):\n",
    "    print(\"%s: %r\" % (param_name, best_parameters[param_name]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08775889537971322"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test Accuracy\n",
    "grid_search.best_estimator_.score(mydata_test_df.data, mydata_test_df.target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Terms in TF-IDF Vectorizer are unigrams and bigrams, and values are binary values\n",
    "Now we will extract unigrams as well as bigrams from the text to form the TF-IDF vectorizer.\n",
    "\n",
    "### A. Using Multinominal Naive Bayes Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6571959638874137\n",
      "CPU times: total: 33.5 s\n",
      "Wall time: 33.8 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Performance of NB Classifier with Lemmatization, and ngram_range = (1,2)\n",
    "text_clf = Pipeline([\n",
    "        ('vect', TfidfVectorizer(tokenizer=LemmaTokenizer(), stop_words='english', ngram_range = (1,2), binary = True, sublinear_tf=True)), # CountVectorizer, TfidfVectorizer\n",
    "        ('clf', MultinomialNB())])  # BernoulliNB, MultinomialNB, LogisticRegression, SGDClassifier, KNeighborsClassifier\n",
    "\n",
    "text_clf.fit(mydata_train_df.data, mydata_train_df.target)\n",
    "print(text_clf.score(mydata_test_df.data, mydata_test_df.target))\n",
    "# print(classification_report(mydata_test_df.target, text_clf.predict(mydata_test_df.data)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### B. Using Logistic Regression Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6845459373340415\n",
      "CPU times: total: 2min 9s\n",
      "Wall time: 1min 30s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Performance of LR Classifier with Lemmatization, and ngram_range = (1,2)\n",
    "text_clf = Pipeline([\n",
    "        ('vect', TfidfVectorizer(tokenizer=LemmaTokenizer(), stop_words='english', ngram_range = (1,2), binary = True, sublinear_tf=True)), # CountVectorizer, TfidfVectorizer\n",
    "        ('clf', LogisticRegression())])  # BernoulliNB, MultinomialNB, LogisticRegression, SGDClassifier, KNeighborsClassifier\n",
    "\n",
    "text_clf.fit(mydata_train_df.data, mydata_train_df.target)\n",
    "print(text_clf.score(mydata_test_df.data, mydata_test_df.target))\n",
    "# print(classification_report(mydata_test_df.target, text_clf.predict(mydata_test_df.data)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Performing grid search on the 'penalty' parameter of Logistic Regression Classifier. We get best performance when L2-norm is used in the penalization.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py:547: FitFailedWarning: \n",
      "5 fits failed out of a total of 10.\n",
      "The score on these train-test partitions for these parameters will be set to nan.\n",
      "If these failures are not expected, you can try to debug them by setting error_score='raise'.\n",
      "\n",
      "Below are more details about the failures:\n",
      "--------------------------------------------------------------------------------\n",
      "5 fits failed with the following error:\n",
      "Traceback (most recent call last):\n",
      "  File \"d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\model_selection\\_validation.py\", line 895, in _fit_and_score\n",
      "    estimator.fit(X_train, y_train, **fit_params)\n",
      "  File \"d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\base.py\", line 1474, in wrapper\n",
      "    return fit_method(estimator, *args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\pipeline.py\", line 475, in fit\n",
      "    self._final_estimator.fit(Xt, y, **last_step_params[\"fit\"])\n",
      "  File \"d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\base.py\", line 1474, in wrapper\n",
      "    return fit_method(estimator, *args, **kwargs)\n",
      "           ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 1172, in fit\n",
      "    solver = _check_solver(self.solver, self.penalty, self.dual)\n",
      "             ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^\n",
      "  File \"d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\linear_model\\_logistic.py\", line 67, in _check_solver\n",
      "    raise ValueError(\n",
      "ValueError: Solver lbfgs supports only 'l2' or None penalties, got l1 penalty.\n",
      "\n",
      "  warnings.warn(some_fits_failed_message, FitFailedWarning)\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\model_selection\\_search.py:1051: UserWarning: One or more of the test scores are non-finite: [       nan 0.73192495]\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 15min 44s\n",
      "Wall time: 11min 3s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-8 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-8 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-8 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-8 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-8 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-8 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-8 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-8 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-8 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-8 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-8 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-8 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-8 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-8 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-8 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-8 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-8\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(estimator=Pipeline(steps=[(&#x27;vect&#x27;,\n",
       "                                        TfidfVectorizer(binary=True,\n",
       "                                                        ngram_range=(1, 2),\n",
       "                                                        stop_words=&#x27;english&#x27;,\n",
       "                                                        sublinear_tf=True,\n",
       "                                                        tokenizer=&lt;__main__.LemmaTokenizer object at 0x0000025554835FD0&gt;)),\n",
       "                                       (&#x27;clf&#x27;, LogisticRegression())]),\n",
       "             param_grid={&#x27;clf__penalty&#x27;: (&#x27;l1&#x27;, &#x27;l2&#x27;)})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-26\" type=\"checkbox\" ><label for=\"sk-estimator-id-26\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(estimator=Pipeline(steps=[(&#x27;vect&#x27;,\n",
       "                                        TfidfVectorizer(binary=True,\n",
       "                                                        ngram_range=(1, 2),\n",
       "                                                        stop_words=&#x27;english&#x27;,\n",
       "                                                        sublinear_tf=True,\n",
       "                                                        tokenizer=&lt;__main__.LemmaTokenizer object at 0x0000025554835FD0&gt;)),\n",
       "                                       (&#x27;clf&#x27;, LogisticRegression())]),\n",
       "             param_grid={&#x27;clf__penalty&#x27;: (&#x27;l1&#x27;, &#x27;l2&#x27;)})</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-27\" type=\"checkbox\" ><label for=\"sk-estimator-id-27\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: Pipeline</label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;vect&#x27;,\n",
       "                 TfidfVectorizer(binary=True, ngram_range=(1, 2),\n",
       "                                 stop_words=&#x27;english&#x27;, sublinear_tf=True,\n",
       "                                 tokenizer=&lt;__main__.LemmaTokenizer object at 0x0000025554835FD0&gt;)),\n",
       "                (&#x27;clf&#x27;, LogisticRegression())])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-28\" type=\"checkbox\" ><label for=\"sk-estimator-id-28\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;TfidfVectorizer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html\">?<span>Documentation for TfidfVectorizer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>TfidfVectorizer(binary=True, ngram_range=(1, 2), stop_words=&#x27;english&#x27;,\n",
       "                sublinear_tf=True,\n",
       "                tokenizer=&lt;__main__.LemmaTokenizer object at 0x0000025554835FD0&gt;)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-29\" type=\"checkbox\" ><label for=\"sk-estimator-id-29\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;LogisticRegression<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.linear_model.LogisticRegression.html\">?<span>Documentation for LogisticRegression</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>LogisticRegression()</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(estimator=Pipeline(steps=[('vect',\n",
       "                                        TfidfVectorizer(binary=True,\n",
       "                                                        ngram_range=(1, 2),\n",
       "                                                        stop_words='english',\n",
       "                                                        sublinear_tf=True,\n",
       "                                                        tokenizer=<__main__.LemmaTokenizer object at 0x0000025554835FD0>)),\n",
       "                                       ('clf', LogisticRegression())]),\n",
       "             param_grid={'clf__penalty': ('l1', 'l2')})"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# Performing Grid Search to find best LR parameters\n",
    "pipeline = Pipeline([\n",
    "        ('vect', TfidfVectorizer(tokenizer=LemmaTokenizer(), stop_words='english', ngram_range = (1,2), binary = True, sublinear_tf=True)), # CountVectorizer, TfidfVectorizer\n",
    "        ('clf', LogisticRegression())])  # MultinomialNB, LogisticRegression, SGDClassifier\n",
    "\n",
    "parameters = {\n",
    "       'clf__penalty': ('l1', 'l2')\n",
    "}\n",
    "\n",
    "grid_search = GridSearchCV(pipeline, parameters)\n",
    "grid_search.fit(mydata_train_df.data, mydata_train_df.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best training score 0.7319\n"
     ]
    }
   ],
   "source": [
    "# grid_search.cv_results_\n",
    "print('Best training score %0.4f' % grid_search.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters = \n",
      "clf__penalty: 'l2'\n"
     ]
    }
   ],
   "source": [
    "print('Best parameters = ')\n",
    "best_parameters = grid_search.best_estimator_.get_params()\n",
    "for param_name in sorted(parameters.keys()):\n",
    "    print(\"%s: %r\" % (param_name, best_parameters[param_name]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.6845459373340415"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test Accuracy\n",
    "grid_search.best_estimator_.score(mydata_test_df.data, mydata_test_df.target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### C. Using Stochastic Gradient Descent Classifier Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7044609665427509\n",
      "CPU times: total: 40.9 s\n",
      "Wall time: 41.2 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "random.seed(1)\n",
    "# Performance of SGD Classifier with Lemmatization, and ngram_range = (1,2)\n",
    "text_clf = Pipeline([\n",
    "        ('vect', TfidfVectorizer(tokenizer=LemmaTokenizer(), stop_words='english', ngram_range = (1,2), binary = True, sublinear_tf=True)), # CountVectorizer, TfidfVectorizer\n",
    "        ('clf', SGDClassifier())])  # BernoulliNB, MultinomialNB, LogisticRegression, SGDClassifier, KNeighborsClassifier\n",
    "\n",
    "text_clf.fit(mydata_train_df.data, mydata_train_df.target)\n",
    "print(text_clf.score(mydata_test_df.data, mydata_test_df.target))\n",
    "# print(classification_report(mydata_test_df.target, text_clf.predict(mydata_test_df.data)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### D. Using k Nearest Neighbors Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.054168879447689854\n",
      "CPU times: total: 41.1 s\n",
      "Wall time: 42 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "# Performance of KNN Classifier with Lemmatization, and ngram_range = (1,2)\n",
    "text_clf = Pipeline([\n",
    "        ('vect', TfidfVectorizer(tokenizer=LemmaTokenizer(), stop_words='english', ngram_range = (1,2), binary = True, sublinear_tf=True)), # CountVectorizer, TfidfVectorizer\n",
    "        ('clf', KNeighborsClassifier(n_neighbors=100))])  # BernoulliNB, MultinomialNB, LogisticRegression, SGDClassifier, KNeighborsClassifier\n",
    "\n",
    "text_clf.fit(mydata_train_df.data, mydata_train_df.target)\n",
    "print(text_clf.score(mydata_test_df.data, mydata_test_df.target))\n",
    "# print(classification_report(mydata_test_df.target, text_clf.predict(mydata_test_df.data)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Performing grid search on the 'n_neighbors' and 'weights' parameters of kNN Classifier. We get best performance when n_neighbors is set to 5 and distance weights is used.**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:525: UserWarning: The parameter 'token_pattern' will not be used since 'tokenizer' is not None'\n",
      "  warnings.warn(\n",
      "d:\\Users\\aliba\\AppData\\Local\\Programs\\Python\\Python312\\Lib\\site-packages\\sklearn\\feature_extraction\\text.py:408: UserWarning: Your stop_words may be inconsistent with your preprocessing. Tokenizing the stop words generated tokens ['ha', 'le', 'u', 'wa'] not in stop_words.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: total: 16min 35s\n",
      "Wall time: 16min 51s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-9 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-9 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-9 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-9 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-9 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-9 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-9 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-9 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-9 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-9 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-9 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-9 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-9 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-9 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-9 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-9 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-9 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-9 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-9 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-9\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(estimator=Pipeline(steps=[(&#x27;vect&#x27;,\n",
       "                                        TfidfVectorizer(binary=True,\n",
       "                                                        ngram_range=(1, 2),\n",
       "                                                        stop_words=&#x27;english&#x27;,\n",
       "                                                        sublinear_tf=True,\n",
       "                                                        tokenizer=&lt;__main__.LemmaTokenizer object at 0x0000025557E03B60&gt;)),\n",
       "                                       (&#x27;clf&#x27;, KNeighborsClassifier())]),\n",
       "             param_grid={&#x27;clf__n_neighbors&#x27;: (5, 10, 100, 200),\n",
       "                         &#x27;clf__weights&#x27;: (&#x27;uniform&#x27;, &#x27;distance&#x27;)})</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-30\" type=\"checkbox\" ><label for=\"sk-estimator-id-30\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(estimator=Pipeline(steps=[(&#x27;vect&#x27;,\n",
       "                                        TfidfVectorizer(binary=True,\n",
       "                                                        ngram_range=(1, 2),\n",
       "                                                        stop_words=&#x27;english&#x27;,\n",
       "                                                        sublinear_tf=True,\n",
       "                                                        tokenizer=&lt;__main__.LemmaTokenizer object at 0x0000025557E03B60&gt;)),\n",
       "                                       (&#x27;clf&#x27;, KNeighborsClassifier())]),\n",
       "             param_grid={&#x27;clf__n_neighbors&#x27;: (5, 10, 100, 200),\n",
       "                         &#x27;clf__weights&#x27;: (&#x27;uniform&#x27;, &#x27;distance&#x27;)})</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-31\" type=\"checkbox\" ><label for=\"sk-estimator-id-31\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: Pipeline</label><div class=\"sk-toggleable__content fitted\"><pre>Pipeline(steps=[(&#x27;vect&#x27;,\n",
       "                 TfidfVectorizer(binary=True, ngram_range=(1, 2),\n",
       "                                 stop_words=&#x27;english&#x27;, sublinear_tf=True,\n",
       "                                 tokenizer=&lt;__main__.LemmaTokenizer object at 0x0000025557E03B60&gt;)),\n",
       "                (&#x27;clf&#x27;, KNeighborsClassifier())])</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-32\" type=\"checkbox\" ><label for=\"sk-estimator-id-32\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;TfidfVectorizer<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.feature_extraction.text.TfidfVectorizer.html\">?<span>Documentation for TfidfVectorizer</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>TfidfVectorizer(binary=True, ngram_range=(1, 2), stop_words=&#x27;english&#x27;,\n",
       "                sublinear_tf=True,\n",
       "                tokenizer=&lt;__main__.LemmaTokenizer object at 0x0000025557E03B60&gt;)</pre></div> </div></div><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-33\" type=\"checkbox\" ><label for=\"sk-estimator-id-33\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;KNeighborsClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.neighbors.KNeighborsClassifier.html\">?<span>Documentation for KNeighborsClassifier</span></a></label><div class=\"sk-toggleable__content fitted\"><pre>KNeighborsClassifier()</pre></div> </div></div></div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(estimator=Pipeline(steps=[('vect',\n",
       "                                        TfidfVectorizer(binary=True,\n",
       "                                                        ngram_range=(1, 2),\n",
       "                                                        stop_words='english',\n",
       "                                                        sublinear_tf=True,\n",
       "                                                        tokenizer=<__main__.LemmaTokenizer object at 0x0000025557E03B60>)),\n",
       "                                       ('clf', KNeighborsClassifier())]),\n",
       "             param_grid={'clf__n_neighbors': (5, 10, 100, 200),\n",
       "                         'clf__weights': ('uniform', 'distance')})"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# Performing Grid Search to find best kNN parameters\n",
    "pipeline = Pipeline([\n",
    "        ('vect', TfidfVectorizer(tokenizer=LemmaTokenizer(), stop_words='english', ngram_range = (1,2), binary = True, sublinear_tf=True)), # CountVectorizer, TfidfVectorizer\n",
    "        ('clf', KNeighborsClassifier())])  # MultinomialNB, LogisticRegression, SGDClassifier\n",
    "\n",
    "parameters = {\n",
    "       'clf__n_neighbors': (5, 10, 100, 200),\n",
    "       'clf__weights': ('uniform', 'distance')\n",
    "}\n",
    "\n",
    "grid_search = GridSearchCV(pipeline, parameters)\n",
    "grid_search.fit(mydata_train_df.data, mydata_train_df.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best training score 0.1106\n"
     ]
    }
   ],
   "source": [
    "# grid_search.cv_results_\n",
    "print('Best training score %0.4f' % grid_search.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters = \n",
      "clf__n_neighbors: 5\n",
      "clf__weights: 'distance'\n"
     ]
    }
   ],
   "source": [
    "print('Best parameters = ')\n",
    "best_parameters = grid_search.best_estimator_.get_params()\n",
    "for param_name in sorted(parameters.keys()):\n",
    "    print(\"%s: %r\" % (param_name, best_parameters[param_name]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.07939458311205523"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Test Accuracy\n",
    "grid_search.best_estimator_.score(mydata_test_df.data, mydata_test_df.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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