TextClassificatoinFinal.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "b71b015f",
   "metadata": {},
   "source": [
    "<h1> Table of Contents</h1>\n",
    "\n",
    "<h2>1. Importing Libraries and dependencies</h2>\n",
    "<p>pandas, numpy, matplotlib, seaborn, nltk, sklearn</p>\n",
    "<h2>2. Text Data Preparation</h2>\n",
    "<p>Text extraction, Tokenization, Stop-words and punctuatiion removal</p>\n",
    "<h2>3. Transforming Text Data</h2>\n",
    "<p><ol><li>Bag of Words,<li> Tf-Idf </ol></p>\n",
    "<h2>4. Model Building and Classifcation</h2>\n",
    "<p><ol><li>bag of words, <li>tf-idf vector space <li>without stop words <li>with less words per document</ol></p>\n",
    "<h2>5. Model Evaluation</h2>\n",
    "<p>Confusion matrix, classification report, champion model</p>\n",
    "<h2>Error Analysis with champion Model</h2>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "66b63064",
   "metadata": {},
   "source": [
    "# 1. Importing Libraries and Dependience"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7f7350de",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Data Manipulation\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import regex as re\n",
    "import random\n",
    "import string\n",
    "import glob\n",
    "\n",
    "# Data Visualization\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from wordcloud import WordCloud, STOPWORDS\n",
    "\n",
    "# Feature Engineering\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "\n",
    "# Data Splitting\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.model_selection import KFold\n",
    "\n",
    "# Model Building \n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "# Models\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "\n",
    "# Evaluation Metrics\n",
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.metrics import accuracy_score as asc\n",
    "from sklearn.metrics import classification_report"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "52e1ce49",
   "metadata": {},
   "source": [
    "# 2. Text Data Preparation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "546d45d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'A': 'Adventures of Sherlock Holmes.txt',\n",
       " 'B': 'Arsene Lupin.txt',\n",
       " 'C': 'The Last Stroke.txt',\n",
       " 'D': 'The Mysterious Affairs at Styles.txt',\n",
       " 'E': 'The Red House Mystery.txt'}"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "textFiles = glob.glob('Books/*.txt')\n",
    "\n",
    "BookLabels = {}\n",
    "for index, book in enumerate(textFiles):\n",
    "    BookLabels[chr(65 + index)] = re.split(r\"Books\\\\\", book)[-1]\n",
    "    \n",
    "BookLabels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b29bd36f",
   "metadata": {},
   "outputs": [],
   "source": [
    "def Txt2List(filename, partitions,wordinRec):\n",
    "    np.random.seed(42)\n",
    "    # Randomly selection\n",
    "    f = open(filename, 'r',  encoding=\"utf8\")\n",
    "    content = f.read().replace('\\n', ' ')\n",
    "    f.close\n",
    "    \n",
    "    # Select only the words from the file\n",
    "    word_ls = re.split(r'\\W+', content)\n",
    "    \n",
    "    partitions = 200\n",
    "    wordinRec = 100\n",
    "    recs = []\n",
    "    \n",
    "    for i in range(int(len(word_ls)/wordinRec)):\n",
    "        recs.append(' '.join(word_ls[i*wordinRec : i*wordinRec + wordinRec]))\n",
    "    np.random.seed(42)    \n",
    "    # return 200 random samples from the recs list excluding the index page \n",
    "    return random.sample(recs[10:], partitions)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "56853c6a",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "files = glob.glob('books/*.txt')\n",
    "df = pd.DataFrame(columns=['text','label'])\n",
    "Label = 65\n",
    "BookLabels = {}\n",
    "for book in files:\n",
    "    tdf = pd.DataFrame()\n",
    "    tdf['text'] = Txt2List(book,200,100)\n",
    "    tdf['label'] = chr(Label)\n",
    "    df = df.append(tdf, ignore_index=True)\n",
    "    \n",
    "    BookLabels[re.split(r\"books\\\\\", book)[-1]] = chr(Label)\n",
    "    Label += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "489fd6c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        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>text</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>it is Kate Whitney How you startled me Kate I ...</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>hollow of his hand Sherlock Holmes sat up with...</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>quite so bulky but if he had remained I might ...</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>was late before Sherlock Holmes returned He ca...</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>a day and at such an hour But I take it that i...</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text label\n",
       "0  it is Kate Whitney How you startled me Kate I ...     A\n",
       "1  hollow of his hand Sherlock Holmes sat up with...     A\n",
       "2  quite so bulky but if he had remained I might ...     A\n",
       "3  was late before Sherlock Holmes returned He ca...     A\n",
       "4  a day and at such an hour But I take it that i...     A"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_unclean = df.copy()\n",
    "df_unclean.to_csv('unfilteredRecords.csv', index=False)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3e9ef029",
   "metadata": {},
   "outputs": [],
   "source": [
    "def textfilter(text):\n",
    "    nopuncText = removePunctuations(text)\n",
    "    filteredWords = [word for word in nltk.word_tokenize(nopuncText) if not word in stopwords.words()]\n",
    "    return re.sub(r\"\\b\\d+\\b\",\"\",(\" \".join(filteredWords)))\n",
    "def removePunctuations(text):\n",
    "    return re.sub(r'[^\\w\\s]','',text.lower())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9cdbbe12",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "##############Before removing stop Words\n",
      "it is Kate Whitney How you startled me Kate I had not an idea who you were when you came in I didn t know what to do so I came straight to you That was always the way Folk who were in grief came to my wife like birds to a lighthouse It was very sweet of you to come Now you must have some wine and water and sit here comfortably and tell us all about it Or should you rather that I sent James off to bed Oh no no I want the doctor s advice and\n",
      "\n",
      "##############After removing stop words\n",
      "kate whitney startled kate idea came know came straight always way folk grief came wife like birds lighthouse sweet must wine water comfortably tell us rather sent james bed oh doctor advice\n",
      "Wall time: 7min 50s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "print('##############Before removing stop Words\\n'+df.text[0])\n",
    "\n",
    "df.text = df['text'].apply(textfilter)\n",
    "\n",
    "print('\\n##############After removing stop words\\n'+df.text[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5a263627",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('filteredRecords2.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8d57536e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='count'>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "def countWords(text):\n",
    "    return len(nltk.word_tokenize(text))\n",
    "\n",
    "df['count'] = df['text'].apply(countWords)\n",
    "\n",
    "plt.figure(figsize=(15,9))\n",
    "sns.scatterplot(x =df.index, y = 'count', hue = 'label', data = df)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "533f41e0",
   "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>text</th>\n",
       "      <th>label</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>kate whitney startled kate idea came know came...</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>hollow hand sherlock holmes sat whistle jove p...</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>quite bulky remained might shown grip much fee...</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>late sherlock holmes returned came back alone ...</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>day hour likely crony landlady sherlock holmes...</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                text label\n",
       "0  kate whitney startled kate idea came know came...     A\n",
       "1  hollow hand sherlock holmes sat whistle jove p...     A\n",
       "2  quite bulky remained might shown grip much fee...     A\n",
       "3  late sherlock holmes returned came back alone ...     A\n",
       "4  day hour likely crony landlady sherlock holmes...     A"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('filteredRecords2.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b261a741",
   "metadata": {},
   "source": [
    "# 3. Transforming Text Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ed0835f",
   "metadata": {},
   "source": [
    "## 3.1 Bag of Words"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "d5a20050",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 1500)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "vectorizer = CountVectorizer(max_features = 1500)\n",
    "X_bow = vectorizer.fit_transform(df.text).toarray()\n",
    "X_bow.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "fc733962",
   "metadata": {
    "scrolled": true
   },
   "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>8vo</th>\n",
       "      <th>_mon</th>\n",
       "      <th>_was_</th>\n",
       "      <th>able</th>\n",
       "      <th>ablett</th>\n",
       "      <th>absence</th>\n",
       "      <th>absolute</th>\n",
       "      <th>absolutely</th>\n",
       "      <th>absurd</th>\n",
       "      <th>accepted</th>\n",
       "      <th>...</th>\n",
       "      <th>wrong</th>\n",
       "      <th>wrote</th>\n",
       "      <th>www</th>\n",
       "      <th>yard</th>\n",
       "      <th>year</th>\n",
       "      <th>years</th>\n",
       "      <th>yes</th>\n",
       "      <th>yesterday</th>\n",
       "      <th>yet</th>\n",
       "      <th>young</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1500 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   8vo  _mon  _was_  able  ablett  absence  absolute  absolutely  absurd  \\\n",
       "0    0     0      0     0       0        0         0           0       0   \n",
       "1    0     0      0     0       0        0         0           1       0   \n",
       "2    0     0      0     0       0        0         0           0       0   \n",
       "3    0     0      0     1       0        0         0           0       0   \n",
       "4    0     0      0     0       0        0         0           0       0   \n",
       "\n",
       "   accepted  ...  wrong  wrote  www  yard  year  years  yes  yesterday  yet  \\\n",
       "0         0  ...      0      0    0     0     0      0    0          0    0   \n",
       "1         0  ...      0      0    0     0     0      0    0          0    0   \n",
       "2         0  ...      0      0    0     0     0      0    0          0    0   \n",
       "3         0  ...      0      0    0     0     0      0    0          0    0   \n",
       "4         0  ...      1      0    0     0     0      0    0          0    0   \n",
       "\n",
       "   young  \n",
       "0      0  \n",
       "1      0  \n",
       "2      0  \n",
       "3      1  \n",
       "4      1  \n",
       "\n",
       "[5 rows x 1500 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "BOW_df = pd.DataFrame(X_bow,columns = vectorizer.get_feature_names())\n",
    "BOW_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "f744dce8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'CountVectorzer')]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18,7))\n",
    "sns.heatmap(BOW_df, cbar =False,yticklabels = False, cmap = 'rocket_r').set(title='CountVectorzer')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "24b8e489",
   "metadata": {},
   "source": [
    "## 3.2 TF-IDF"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3b8b432f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1000, 1500)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tfidf_transformer = TfidfTransformer()\n",
    "X_tfidf = tfidf_transformer.fit_transform(X_bow).toarray()\n",
    "X_tfidf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5d2694d8",
   "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>8vo</th>\n",
       "      <th>_mon</th>\n",
       "      <th>_was_</th>\n",
       "      <th>able</th>\n",
       "      <th>ablett</th>\n",
       "      <th>absence</th>\n",
       "      <th>absolute</th>\n",
       "      <th>absolutely</th>\n",
       "      <th>absurd</th>\n",
       "      <th>accepted</th>\n",
       "      <th>...</th>\n",
       "      <th>wrong</th>\n",
       "      <th>wrote</th>\n",
       "      <th>www</th>\n",
       "      <th>yard</th>\n",
       "      <th>year</th>\n",
       "      <th>years</th>\n",
       "      <th>yes</th>\n",
       "      <th>yesterday</th>\n",
       "      <th>yet</th>\n",
       "      <th>young</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.152283</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.204722</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.170209</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>...</td>\n",
       "      <td>0.227317</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.186764</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 1500 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   8vo  _mon  _was_      able  ablett  absence  absolute  absolutely  absurd  \\\n",
       "0  0.0   0.0    0.0  0.000000     0.0      0.0       0.0    0.000000     0.0   \n",
       "1  0.0   0.0    0.0  0.000000     0.0      0.0       0.0    0.152283     0.0   \n",
       "2  0.0   0.0    0.0  0.000000     0.0      0.0       0.0    0.000000     0.0   \n",
       "3  0.0   0.0    0.0  0.204722     0.0      0.0       0.0    0.000000     0.0   \n",
       "4  0.0   0.0    0.0  0.000000     0.0      0.0       0.0    0.000000     0.0   \n",
       "\n",
       "   accepted  ...     wrong  wrote  www  yard  year  years  yes  yesterday  \\\n",
       "0       0.0  ...  0.000000    0.0  0.0   0.0   0.0    0.0  0.0        0.0   \n",
       "1       0.0  ...  0.000000    0.0  0.0   0.0   0.0    0.0  0.0        0.0   \n",
       "2       0.0  ...  0.000000    0.0  0.0   0.0   0.0    0.0  0.0        0.0   \n",
       "3       0.0  ...  0.000000    0.0  0.0   0.0   0.0    0.0  0.0        0.0   \n",
       "4       0.0  ...  0.227317    0.0  0.0   0.0   0.0    0.0  0.0        0.0   \n",
       "\n",
       "   yet     young  \n",
       "0  0.0  0.000000  \n",
       "1  0.0  0.000000  \n",
       "2  0.0  0.000000  \n",
       "3  0.0  0.170209  \n",
       "4  0.0  0.186764  \n",
       "\n",
       "[5 rows x 1500 columns]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "TFIDF_df = pd.DataFrame(X_tfidf, columns = vectorizer.get_feature_names())\n",
    "TFIDF_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "f2651693",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'TF-IDF')]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1296x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(18,7))\n",
    "sns.heatmap(TFIDF_df, cbar =False,yticklabels = False, cmap = 'rocket_r').set(title='TF-IDF')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "80e7a237",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "813832dd",
   "metadata": {},
   "source": [
    "# 4. Model Building and Classification"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "4c6d537e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Defining Algorithms for using in all below code blocks\n",
    "algorithms = {\n",
    "            'SVM' : SVC(random_state = 21),\n",
    "            'DTR' : DecisionTreeClassifier(random_state = 21),\n",
    "            'RFC' : RandomForestClassifier(random_state = 21),\n",
    "            'KNN' : KNeighborsClassifier(),\n",
    "            'GNB' : GaussianNB()\n",
    "        }\n",
    "\n",
    "def modelRes(algorithms, X_train, y_train, X_test, y_test):\n",
    "    testScores = {}\n",
    "    testPreds = {}\n",
    "    for modelName, model in algorithms.items():\n",
    "        pipelineModel = Pipeline([('MODEL', model)])         \n",
    "        pipelineModel.fit(X_train, y_train)\n",
    "        testPreds[modelName] = pipelineModel.predict(X_test) \n",
    "        testScores[modelName] = cross_val_score(pipelineModel, X_test, y_test, cv=10, scoring=\"accuracy\")\n",
    "    avgTestScores = {}\n",
    "    for name, model in testScores.items():\n",
    "        avgTestScores[name] = np.mean(model)\n",
    "    return avgTestScores, testPreds"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e787b627",
   "metadata": {},
   "source": [
    "## 4.1 Using Bag of Words vector Space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "77b0286c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 2.55 s\n"
     ]
    },
    {
     "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>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SVM</th>\n",
       "      <td>0.670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DTR</th>\n",
       "      <td>0.640</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RFC</th>\n",
       "      <td>0.755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNN</th>\n",
       "      <td>0.365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GNB</th>\n",
       "      <td>0.745</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Accuracy\n",
       "SVM     0.670\n",
       "DTR     0.640\n",
       "RFC     0.755\n",
       "KNN     0.365\n",
       "GNB     0.745"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# Using the data we got from the BOW transformation (refer 3.1)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_bow, df.label, test_size=0.2, random_state = 42)\n",
    "\n",
    "# Store the cross validation scores and prediction\n",
    "bowTestScores, bowTestPreds = modelRes(algorithms=algorithms,X_train=X_train,y_train=y_train,X_test=X_test,y_test=y_test)\n",
    "\n",
    "\n",
    "# Converting it into a dataframe\n",
    "avgBowTestScores_df = pd.DataFrame(bowTestScores, index=[\"Accuracy\"]).T\n",
    "avgBowTestScores_df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bd3ef39e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM               precision    recall  f1-score   support\n",
      "\n",
      "           A       0.64      0.91      0.75        33\n",
      "           B       0.95      0.79      0.86        48\n",
      "           C       0.86      0.81      0.83        37\n",
      "           D       1.00      0.79      0.88        38\n",
      "           E       0.83      0.91      0.87        44\n",
      "\n",
      "    accuracy                           0.84       200\n",
      "   macro avg       0.86      0.84      0.84       200\n",
      "weighted avg       0.87      0.84      0.84       200\n",
      "\n",
      "DTR               precision    recall  f1-score   support\n",
      "\n",
      "           A       0.52      0.73      0.61        33\n",
      "           B       0.79      0.62      0.70        48\n",
      "           C       0.83      0.81      0.82        37\n",
      "           D       0.86      0.79      0.82        38\n",
      "           E       0.91      0.93      0.92        44\n",
      "\n",
      "    accuracy                           0.78       200\n",
      "   macro avg       0.78      0.78      0.77       200\n",
      "weighted avg       0.79      0.78      0.78       200\n",
      "\n",
      "RFC               precision    recall  f1-score   support\n",
      "\n",
      "           A       0.57      0.94      0.71        33\n",
      "           B       0.91      0.65      0.76        48\n",
      "           C       0.86      0.81      0.83        37\n",
      "           D       0.97      0.82      0.89        38\n",
      "           E       0.96      0.98      0.97        44\n",
      "\n",
      "    accuracy                           0.83       200\n",
      "   macro avg       0.85      0.84      0.83       200\n",
      "weighted avg       0.87      0.83      0.83       200\n",
      "\n",
      "KNN               precision    recall  f1-score   support\n",
      "\n",
      "           A       0.37      0.67      0.48        33\n",
      "           B       0.44      0.83      0.58        48\n",
      "           C       0.62      0.27      0.38        37\n",
      "           D       0.92      0.32      0.47        38\n",
      "           E       0.81      0.39      0.52        44\n",
      "\n",
      "    accuracy                           0.51       200\n",
      "   macro avg       0.63      0.49      0.48       200\n",
      "weighted avg       0.64      0.51      0.49       200\n",
      "\n",
      "GNB               precision    recall  f1-score   support\n",
      "\n",
      "           A       0.61      0.67      0.64        33\n",
      "           B       0.89      0.67      0.76        48\n",
      "           C       0.76      0.76      0.76        37\n",
      "           D       0.82      0.82      0.82        38\n",
      "           E       0.74      0.89      0.80        44\n",
      "\n",
      "    accuracy                           0.76       200\n",
      "   macro avg       0.76      0.76      0.76       200\n",
      "weighted avg       0.77      0.76      0.76       200\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for key in bowTestPreds.keys():\n",
    "    print(key, classification_report(y_test, bowTestPreds[key]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7f2863ef",
   "metadata": {},
   "source": [
    "## 4.2 Using IF-IDF vector Space"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0cabff59",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 2.75 s\n"
     ]
    },
    {
     "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>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SVM</th>\n",
       "      <td>0.595</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DTR</th>\n",
       "      <td>0.635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RFC</th>\n",
       "      <td>0.795</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNN</th>\n",
       "      <td>0.730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GNB</th>\n",
       "      <td>0.680</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Accuracy\n",
       "SVM     0.595\n",
       "DTR     0.635\n",
       "RFC     0.795\n",
       "KNN     0.730\n",
       "GNB     0.680"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "# Using the data we got from the IF-IDF transformation (refer 3.2)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_tfidf, df.label, test_size=0.2, random_state = 42)\n",
    "\n",
    "# Store the cross validation scores and prediction\n",
    "tfidfTestScores, tfidfTestPreds = modelRes(algorithms=algorithms,X_train=X_train,y_train=y_train,X_test=X_test,y_test=y_test)\n",
    "\n",
    "# Converting it into a dataframe\n",
    "avgTfidfTestScores_df = pd.DataFrame(tfidfTestScores, index=[\"Accuracy\"]).T\n",
    "avgTfidfTestScores_df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "442dc1ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM               precision    recall  f1-score   support\n",
      "\n",
      "           A       0.56      0.94      0.70        33\n",
      "           B       0.92      0.71      0.80        48\n",
      "           C       0.88      0.76      0.81        37\n",
      "           D       0.97      0.82      0.89        38\n",
      "           E       0.91      0.91      0.91        44\n",
      "\n",
      "    accuracy                           0.82       200\n",
      "   macro avg       0.85      0.83      0.82       200\n",
      "weighted avg       0.86      0.82      0.83       200\n",
      "\n",
      "DTR               precision    recall  f1-score   support\n",
      "\n",
      "           A       0.49      0.79      0.60        33\n",
      "           B       0.86      0.62      0.72        48\n",
      "           C       0.82      0.73      0.77        37\n",
      "           D       0.94      0.79      0.86        38\n",
      "           E       0.87      0.93      0.90        44\n",
      "\n",
      "    accuracy                           0.77       200\n",
      "   macro avg       0.80      0.77      0.77       200\n",
      "weighted avg       0.81      0.77      0.78       200\n",
      "\n",
      "RFC               precision    recall  f1-score   support\n",
      "\n",
      "           A       0.66      0.88      0.75        33\n",
      "           B       0.92      0.73      0.81        48\n",
      "           C       0.86      0.84      0.85        37\n",
      "           D       0.92      0.89      0.91        38\n",
      "           E       0.96      0.98      0.97        44\n",
      "\n",
      "    accuracy                           0.86       200\n",
      "   macro avg       0.86      0.86      0.86       200\n",
      "weighted avg       0.87      0.86      0.86       200\n",
      "\n",
      "KNN               precision    recall  f1-score   support\n",
      "\n",
      "           A       0.66      0.76      0.70        33\n",
      "           B       0.92      0.71      0.80        48\n",
      "           C       0.71      0.65      0.68        37\n",
      "           D       0.87      0.87      0.87        38\n",
      "           E       0.72      0.86      0.78        44\n",
      "\n",
      "    accuracy                           0.77       200\n",
      "   macro avg       0.77      0.77      0.77       200\n",
      "weighted avg       0.78      0.77      0.77       200\n",
      "\n",
      "GNB               precision    recall  f1-score   support\n",
      "\n",
      "           A       0.54      0.64      0.58        33\n",
      "           B       0.84      0.65      0.73        48\n",
      "           C       0.69      0.59      0.64        37\n",
      "           D       0.65      0.79      0.71        38\n",
      "           E       0.76      0.80      0.78        44\n",
      "\n",
      "    accuracy                           0.69       200\n",
      "   macro avg       0.70      0.69      0.69       200\n",
      "weighted avg       0.71      0.69      0.70       200\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for key in tfidfTestPreds.keys():\n",
    "    print(key, classification_report(y_test, tfidfTestPreds[key]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f604e608",
   "metadata": {},
   "source": [
    "## 4.3 When stop words and punctuations are not removed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "6c011c35",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "############## Uncleaned Data\n",
      "it is Kate Whitney How you startled me Kate I had not an idea who you were when you came in I didn t know what to do so I came straight to you That was always the way Folk who were in grief came to my wife like birds to a lighthouse It was very sweet of you to come Now you must have some wine and water and sit here comfortably and tell us all about it Or should you rather that I sent James off to bed Oh no no I want the doctor s advice and\n",
      "\n",
      "############## Cleaned Data\n",
      "kate whitney startled kate idea came know came straight always way folk grief came wife like birds lighthouse sweet must wine water comfortably tell us rather sent james bed oh doctor advice\n"
     ]
    }
   ],
   "source": [
    "print('############## Uncleaned Data\\n'+df_unclean.text[0])\n",
    "print('\\n############## Cleaned Data\\n'+df.text[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "dca67654",
   "metadata": {},
   "outputs": [],
   "source": [
    "def modelRes20(algorithms, X_train, y_train, X_test, y_test):\n",
    "    testScores = {}\n",
    "    testPreds = {}\n",
    "    for modelName, model in algorithms.items():\n",
    "        pipelineModel = Pipeline([('MODEL', model)])         \n",
    "        pipelineModel.fit(X_train, y_train)\n",
    "        testPreds[modelName] = pipelineModel.predict(X_test) \n",
    "        testScores[modelName] = cross_val_score(pipelineModel, X_test, y_test, cv=10, scoring=\"accuracy\")\n",
    "    avgTestScores = {}\n",
    "    for name, model in testScores.items():\n",
    "        avgTestScores[name] = np.mean(model)\n",
    "    return avgTestScores, testPreds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f436a1bd",
   "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>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SVM</th>\n",
       "      <td>0.620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DTR</th>\n",
       "      <td>0.635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RFC</th>\n",
       "      <td>0.745</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNN</th>\n",
       "      <td>0.495</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GNB</th>\n",
       "      <td>0.735</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Accuracy\n",
       "SVM     0.620\n",
       "DTR     0.635\n",
       "RFC     0.745\n",
       "KNN     0.495\n",
       "GNB     0.735"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using the uncleaned data\n",
    "\n",
    "vectorizer = CountVectorizer()\n",
    "X_unclean = vectorizer.fit_transform(df.text).toarray()\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_unclean, df_unclean.label, test_size=0.2, random_state = 42)\n",
    "\n",
    "uncleanScores, uncleanPreds = modelRes20(algorithms=algorithms,X_train=X_train,y_train=y_train,X_test=X_test,y_test=y_test)\n",
    "\n",
    "# Converting it into a dataframe\n",
    "uncleanScores_df = pd.DataFrame(uncleanScores, index=[\"Accuracy\"]).T\n",
    "uncleanScores_df "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a9240a2a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SVM               precision    recall  f1-score   support\n",
      "\n",
      "           A       0.63      0.94      0.76        33\n",
      "           B       0.89      0.81      0.85        48\n",
      "           C       0.88      0.81      0.85        37\n",
      "           D       0.97      0.79      0.87        38\n",
      "           E       0.95      0.91      0.93        44\n",
      "\n",
      "    accuracy                           0.85       200\n",
      "   macro avg       0.86      0.85      0.85       200\n",
      "weighted avg       0.87      0.85      0.85       200\n",
      "\n",
      "DTR               precision    recall  f1-score   support\n",
      "\n",
      "           A       0.56      0.73      0.63        33\n",
      "           B       0.88      0.62      0.73        48\n",
      "           C       0.75      0.81      0.78        37\n",
      "           D       0.88      0.79      0.83        38\n",
      "           E       0.88      0.98      0.92        44\n",
      "\n",
      "    accuracy                           0.79       200\n",
      "   macro avg       0.79      0.79      0.78       200\n",
      "weighted avg       0.80      0.79      0.79       200\n",
      "\n",
      "RFC               precision    recall  f1-score   support\n",
      "\n",
      "           A       0.56      0.94      0.70        33\n",
      "           B       0.94      0.65      0.77        48\n",
      "           C       0.88      0.76      0.81        37\n",
      "           D       0.94      0.84      0.89        38\n",
      "           E       0.93      0.98      0.96        44\n",
      "\n",
      "    accuracy                           0.82       200\n",
      "   macro avg       0.85      0.83      0.83       200\n",
      "weighted avg       0.86      0.82      0.83       200\n",
      "\n",
      "KNN               precision    recall  f1-score   support\n",
      "\n",
      "           A       0.24      0.91      0.38        33\n",
      "           B       0.73      0.17      0.27        48\n",
      "           C       0.79      0.30      0.43        37\n",
      "           D       1.00      0.34      0.51        38\n",
      "           E       0.86      0.70      0.78        44\n",
      "\n",
      "    accuracy                           0.47       200\n",
      "   macro avg       0.72      0.48      0.47       200\n",
      "weighted avg       0.74      0.47      0.47       200\n",
      "\n",
      "GNB               precision    recall  f1-score   support\n",
      "\n",
      "           A       0.74      0.76      0.75        33\n",
      "           B       0.90      0.77      0.83        48\n",
      "           C       0.79      0.89      0.84        37\n",
      "           D       0.82      0.82      0.82        38\n",
      "           E       0.87      0.89      0.88        44\n",
      "\n",
      "    accuracy                           0.82       200\n",
      "   macro avg       0.82      0.82      0.82       200\n",
      "weighted avg       0.83      0.82      0.83       200\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for key in uncleanPreds.keys():\n",
    "    print(key, classification_report(y_test, uncleanPreds[key]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f637b45",
   "metadata": {},
   "source": [
    "## 4.4 When no of words are reduced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "4c245e96",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.random.seed(42)\n",
    "files = glob.glob('books/*.txt')\n",
    "df_reduced = pd.DataFrame(columns=['text','label'])\n",
    "Label = 65\n",
    "BookLabels = {}\n",
    "for book in files:\n",
    "    tdf = pd.DataFrame()\n",
    "    tdf['text'] = Txt2List(book,200,50)\n",
    "    tdf['label'] = chr(Label)\n",
    "    df_reduced = df_reduced.append(tdf, ignore_index=True)\n",
    "    \n",
    "    BookLabels[re.split(r\"books\\\\\", book)[-1]] = chr(Label)\n",
    "    Label += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "c1305525",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "##############Before removing stop Words\n",
      "his pocket There are several other indications but these may be enough to aid us in our search Lestrade laughed I am afraid that I am still a sceptic he said Theories are all very well but we have to deal with a hard headed British jury _Nous verrons_ answered Holmes calmly You work your own method and I shall work mine I shall be busy this afternoon and shall probably return to London by the evening train And leave your case unfinished No finished But the mystery It is solved Who was the criminal then The gentleman I describe\n",
      "\n",
      "##############After removing stop words\n",
      "pocket several indications may enough aid us search lestrade laughed afraid still sceptic said theories well deal hard headed british jury _nous verrons_ answered holmes calmly work method shall work shall busy afternoon shall probably return london evening train leave case unfinished finished mystery solved criminal gentleman describe\n",
      "Wall time: 7min 58s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "print('##############Before removing stop Words\\n'+df_reduced.text[0])\n",
    "\n",
    "df_reduced.text = df_reduced['text'].apply(textfilter)\n",
    "\n",
    "print('\\n##############After removing stop words\\n'+df_reduced.text[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9ebba7f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_reduced.to_csv(\"reduced.csv\",index = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "5478c5bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "def modelReduced(algorithms, X_train, y_train, X_test, y_test):\n",
    "    testScores = {}\n",
    "    testPreds = {}\n",
    "    for modelName, model in algorithms.items():\n",
    "        pipelineModel = Pipeline([('MODEL', model)])         \n",
    "        pipelineModel.fit(X_train, y_train)\n",
    "        testPreds[modelName] = pipelineModel.predict(X_test) \n",
    "        testScores[modelName] = cross_val_score(pipelineModel, X_test, y_test, cv=10, scoring=\"accuracy\")\n",
    "    avgTestScores = {}\n",
    "    for name, model in testScores.items():\n",
    "        avgTestScores[name] = np.mean(model)\n",
    "    return avgTestScores, testPreds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "c5e4588b",
   "metadata": {
    "scrolled": false
   },
   "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>Accuracy</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SVM</th>\n",
       "      <td>0.545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DTR</th>\n",
       "      <td>0.665</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>RFC</th>\n",
       "      <td>0.735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>KNN</th>\n",
       "      <td>0.425</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GNB</th>\n",
       "      <td>0.660</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Accuracy\n",
       "SVM     0.545\n",
       "DTR     0.665\n",
       "RFC     0.735\n",
       "KNN     0.425\n",
       "GNB     0.660"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Using the uncleaned data\n",
    "\n",
    "vectorizer = CountVectorizer()\n",
    "X_unclean = vectorizer.fit_transform(df_reduced.text).toarray()\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(X_unclean, df_reduced.label, test_size=0.2, random_state = 42)\n",
    "\n",
    "redScores, redPreds = modelReduced(algorithms=algorithms,X_train=X_train,y_train=y_train,X_test=X_test,y_test=y_test)\n",
    "\n",
    "# Converting it into a dataframe\n",
    "redScores_df = pd.DataFrame(redScores, index=[\"Accuracy\"]).T\n",
    "redScores_df "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20f043cf",
   "metadata": {},
   "source": [
    "## Experimenting with max_features parameter of CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "0df5f3f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "def modelIteration(algorithms,X_train, y_train, X_test, y_test):\n",
    "    testScores = {}\n",
    "    for modelName, model in algorithms.items():\n",
    "        pipelineModel = Pipeline([('MODEL', model)])         \n",
    "        pipelineModel.fit(X_train, y_train)\n",
    "        testScores[modelName] = cross_val_score(pipelineModel, X_test, y_test, cv=10, scoring=\"accuracy\")\n",
    "    avgTestScores = []\n",
    "    for name, model in testScores.items():\n",
    "        avgTestScores.append(np.mean(model))\n",
    "    return avgTestScores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "bc5e22c4",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Wall time: 33.8 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "scoresList = []\n",
    "indexList = []\n",
    "for step in range(10,8000,1000):\n",
    "    vectorizer = CountVectorizer(max_features = step)\n",
    "    X_iter = vectorizer.fit_transform(df.text).toarray()\n",
    "    X_train, X_test, y_train, y_test = train_test_split(X_iter, df.label, test_size=0.2, random_state = 42)\n",
    "    scoresList.append(modelIteration(algorithms,X_train, y_train, X_test, y_test))\n",
    "    indexList.append(step)\n",
    "    \n",
    "iter_res_df = pd.DataFrame(scoresList, index = indexList, columns = ['SVM','DTR','RFC','KNN','GNB'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "7a198ab8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5, 1.0, 'Max Features vs Performance')]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x504 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,7))\n",
    "sns.lineplot(data = iter_res_df).set(title='Max Features vs Performance')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0313a565",
   "metadata": {},
   "source": [
    "# 5. Evaluation, Results, The Champion model"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2be24cf9",
   "metadata": {},
   "source": [
    "## Accuracy Scores Comparision in different Scenarios"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "f6e847be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Reduced Words Scenario'}>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x1080 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(nrows=2, ncols=2, figsize=(15,15))\n",
    "avgBowTestScores_df.plot(kind='bar', ax=axes[0,0], rot=0, title=\"CountVectors\")\n",
    "avgTfidfTestScores_df.plot(kind='bar', ax=axes[0,1], rot=0, legend=False, title=\"TF-IDF\") \n",
    "uncleanScores_df.plot(kind='bar', ax=axes[1, 0], rot=0, legend=False, title=\"Unclean Scenario\") \n",
    "redScores_df.plot(kind='bar', ax=axes[1, 1], rot=0, legend=False, title=\"Reduced Words Scenario\") "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff11246e",
   "metadata": {},
   "source": [
    "# Champion Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f00d2c05",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(df.text, df.label, test_size=0.2, random_state = 42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "d4147ff6",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.7150000000000001\n"
     ]
    }
   ],
   "source": [
    "pipeline_champion= Pipeline([('Bow',CountVectorizer()),\n",
    "                             ('tf-idf',TfidfTransformer()),\n",
    "                            ('RFC',RandomForestClassifier(n_jobs=-1,random_state = 21))])\n",
    "pipeline_champion.fit(X_train,y_train)\n",
    "value = cross_val_score(pipeline_champion,X_test,y_test, cv =10,scoring=\"accuracy\")\n",
    "print(np.mean(value))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "7f1b446e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('from sklearn.model_selection import RandomizedSearchCV'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 10 folds for each of 10 candidates, totalling 100 fits\n",
      "Wall time: 13.1 s\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "RandomizedSearchCV(cv=10,\n",
       "                   estimator=Pipeline(steps=[('Bow', CountVectorizer()),\n",
       "                                             ('tf-idf', TfidfTransformer()),\n",
       "                                             ('RFC',\n",
       "                                              RandomForestClassifier(n_jobs=-1,\n",
       "                                                                     random_state=21))]),\n",
       "                   param_distributions={'RFC__max_depth': [None, 3, 5, 10],\n",
       "                                        'RFC__max_features': [0.5, 1, 'sqrt',\n",
       "                                                              'auto'],\n",
       "                                        'RFC__min_samples_leaf': array([ 1,  3,  5,  7,  9, 11, 13, 15, 17, 19]),\n",
       "                                        'RFC__min_samples_split': array([ 2,  4,  6,  8, 10, 12, 14, 16, 18]),\n",
       "                                        'RFC__n_estimators': array([10, 20, 30, 40, 50, 60, 70, 80, 90])},\n",
       "                   verbose=True)"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%time\n",
    "RandomForest_grid = {\"RFC__n_estimators\": np.arange(10, 100, 10),\n",
    "           \"RFC__max_depth\": [None, 3, 5, 10],\n",
    "           \"RFC__min_samples_split\": np.arange(2, 20, 2),\n",
    "           \"RFC__min_samples_leaf\": np.arange(1, 20, 2),\n",
    "           \"RFC__max_features\": [0.5, 1, \"sqrt\", \"auto\"]}\n",
    "RFC = RandomizedSearchCV(pipeline_champion,\n",
    "                              param_distributions=RandomForest_grid,\n",
    "                              n_iter=10,\n",
    "                              cv=10,\n",
    "                              verbose=True)\n",
    "RFC.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "b05e8138",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'RFC__n_estimators': 60,\n",
       " 'RFC__min_samples_split': 12,\n",
       " 'RFC__min_samples_leaf': 1,\n",
       " 'RFC__max_features': 1,\n",
       " 'RFC__max_depth': None}"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RFC.best_params_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "b52b5e29",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.775"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RFC.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45553d84",
   "metadata": {},
   "source": [
    "# 6. Error Analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b60c43c9",
   "metadata": {},
   "source": [
    "Refer the ErrorAnalysis.ipynb"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}