10 - TF IDF and Naives Bayes.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn.svm import SVC\n",
    "from sklearn.neighbors import KNeighborsClassifier\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>label</th>\n",
       "      <th>sub_mssg</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>job post apple research center content length ...</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>query letter for text identification I be post...</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td>risk a colleague and I be the of risk by our h...</td>\n",
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       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>request book information this morning I be on ...</td>\n",
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       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>call for in syntactic theory content length ca...</td>\n",
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      "text/plain": [
       "   label                                           sub_mssg\n",
       "0      0  job post apple research center content length ...\n",
       "1      0  query letter for text identification I be post...\n",
       "2      0  risk a colleague and I be the of risk by our h...\n",
       "3      0  request book information this morning I be on ...\n",
       "4      0  call for in syntactic theory content length ca..."
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reading the file \n",
    "df = pd.read_csv(r\"06 - Updated.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "(4196, 2)"
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     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# printing the shape of the dataframe\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# storing the sub and message clumn in a variable named corpus\n",
    "corpus = df['sub_mssg'].values.astype('U')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "# definning the count vectorizer\n",
    "vectorizer = CountVectorizer()\n",
    "# fitting the corpus and transforming it to vector\n",
    "X = vectorizer.fit_transform(corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       ...,\n",
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       "       [0, 0, 0, ..., 0, 0, 0],\n",
       "       [0, 0, 0, ..., 0, 0, 0]], dtype=int64)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Corpus after count vectorizer\n",
    "X.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4196, 21740)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# getting the shape of the count vectorizer\n",
    "l = X.toarray()\n",
    "l.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# now definning the Tf Idf vectorizer\n",
    "vectorizer = TfidfVectorizer(max_features=5000)\n",
    "X = vectorizer.fit_transform(corpus)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "scipy.sparse.csr.csr_matrix"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\Users\\Prakhar Jadaun\\AppData\\Local\\Programs\\Python\\Python39\\lib\\site-packages\\sklearn\\utils\\deprecation.py:87: FutureWarning: Function get_feature_names is deprecated; get_feature_names is deprecated in 1.0 and will be removed in 1.2. Please use get_feature_names_out instead.\n",
      "  warnings.warn(msg, category=FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'ait'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# getting the feature names\n",
    "vectorizer.get_feature_names()[144]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = X.toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4196, 5000)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# getting the shape of the output \n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<PandasArray>\n",
       "[0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
       " ...\n",
       " 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]\n",
       "Length: 4196, dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = df['label'].array\n",
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, ..., 1, 1, 1], dtype=int64)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = np.array(temp)\n",
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "(4196,)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "temp.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = np.reshape(temp,(4196,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4196, 1)"
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     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0],\n",
       "       [0],\n",
       "       [0],\n",
       "       ...,\n",
       "       [1],\n",
       "       [1],\n",
       "       [1]], dtype=int64)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "temp = np.hstack([X,temp])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4196, 5001)"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5001, 4196)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp = temp.transpose()\n",
    "temp.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# now coverting the output of tf idf to a csv file\n",
    "np.savetxt('Tf Idf for cpp.csv',temp,delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# y = df['label'].array\n",
    "# y = np.array(y)\n",
    "# y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# y = y.reshape((1,4196))\n",
    "# y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "x_train, x_test, y_train, y_test = \\\n",
    "    train_test_split(X, df['label'].array, test_size=0.3, random_state=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2937, 5000)"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1259, 5000)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_test.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Naive Bayes classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "# #converting sparse matrix to matrix\n",
    "# x_train = x_train.A\n",
    "# x_test = x_test.A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "model = MultinomialNB().fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, ..., 1, 1, 0], dtype=int64)"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = model.predict(x_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "lAcc = []\n",
    "lModel = []"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9896743447180302"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "acc_NB = accuracy_score(y_test,y_pred)\n",
    "lAcc.append(acc_NB)\n",
    "lModel.append(\"Naive Bayes\")\n",
    "acc_NB"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **MLP**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.neural_network import MLPClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "MLPClassifier(max_iter=300)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model =MLPClassifier(solver='adam',max_iter=300)\n",
    "model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, ..., 1, 1, 0], dtype=int64)"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = model.predict(x_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9912629070691025"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "MLPacc = accuracy_score(y_test,y_pred)\n",
    "lAcc.append(MLPacc)\n",
    "lModel.append(\"MLP classifier\")\n",
    "MLPacc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **SVM**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn import svm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC()"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = svm.SVC()\n",
    "model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 1, ..., 1, 1, 0], dtype=int64)"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = model.predict(x_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9904686258935663"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "SVMacc = accuracy_score(y_test,y_pred)\n",
    "lAcc.append(SVMacc)\n",
    "lModel.append(\"SVM\")\n",
    "SVMacc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# **Random forest**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RandomForestClassifier()"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = RandomForestClassifier()\n",
    "model.fit(x_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1, ..., 1, 1, 0], dtype=int64)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred = model.predict(x_test)\n",
    "y_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9809372517871326"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "RFacc = accuracy_score(y_test,y_pred)\n",
    "lAcc.append(RFacc)\n",
    "lModel.append(\"Random Forest Classifier\")\n",
    "RFacc"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data visualization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 504x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(7,5))\n",
    "plt.bar(lModel,lAcc)\n",
    "plt.grid(linestyle='--',color='black')\n",
    "plt.xlabel(\"Classifiers\")\n",
    "plt.ylabel(\"Accuracy score\")\n",
    "plt.title(\"Comparisons of different classifiers\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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