Day 91.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Text Classification using cornell sentiment analysis dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Part 1: Data Preprocessing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing the Libraries\n",
    "import numpy as np\n",
    "import re\n",
    "import pickle\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from sklearn.datasets import load_files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing dataset \n",
    "reviews = load_files(\"txt_sentoken/\")\n",
    "X,y = reviews.data, reviews.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "                                              # Persisting the dataset\n",
    "# storing as pickle files\n",
    "with open('X.pickle','wb') as f:\n",
    "    pickle.dump(X,f)\n",
    "    \n",
    "with open('y.pickle','wb') as f:\n",
    "    pickle.dump(y,f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "                                              # Persisting the dataset\n",
    "# unpickling the dataset\n",
    "with open('X.pickle', 'rb') as f:\n",
    "    X = pickle.load(f)\n",
    "    \n",
    "with open('y.pickle', 'rb') as f:\n",
    "    y = pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# creating the corpus\n",
    "corpus = []\n",
    "for i in range(0, len(X)):\n",
    "    review = re.sub(r'\\W', ' ',str(X[i]))\n",
    "    review = review.lower()\n",
    "    review = re.sub(r'\\s+[a-z]\\s+',' ', review)\n",
    "    review = re.sub(r'^[a-z]\\s+',' ', review)\n",
    "    review = re.sub(r'\\s+',' ', review)\n",
    "    corpus.append(review)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Part 2: Building the Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import our model(BOW) from sklean \n",
    "from sklearn.feature_extraction.text import CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "# initialize our Bag of Word Model\n",
    "vectorizer = CountVectorizer(max_features= 2000, min_df = 3, max_df = 0.6, stop_words = stopwords.words('english'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "# fit the model\n",
    "X = vectorizer.fit_transform(corpus).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Transform BOW Model into TF-IDF Model\n",
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "transformer = TfidfTransformer()\n",
    "X = transformer.fit_transform(X).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "# for Save our model which we done in part 5 \n",
    "from sklearn.feature_extraction.text import TfidfVectorizer\n",
    "vectorizer = TfidfVectorizer(max_features= 2000, min_df = 3, max_df = 0.6, stop_words = stopwords.words('english'))\n",
    "X = vectorizer.fit_transform(corpus).toarray()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Split the data into train_test_split\n",
    "from sklearn.model_selection import train_test_split\n",
    "text_train, text_test, sent_train, sent_test = train_test_split(X, y ,test_size = 0.2, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Part 3: Train our Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\amira\\Anaconda3\\lib\\site-packages\\sklearn\\linear_model\\logistic.py:433: FutureWarning: Default solver will be changed to 'lbfgs' in 0.22. Specify a solver to silence this warning.\n",
      "  FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='warn',\n",
       "          n_jobs=None, penalty='l2', random_state=None, solver='warn',\n",
       "          tol=0.0001, verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# import Logistic ression model for train our data\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "classifier = LogisticRegression()\n",
    "classifier.fit(text_train,sent_train )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Part 4: Making a prediction and Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 0, 1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 0, 0, 1, 1, 0, 0, 1, 1, 0, 1,\n",
       "       1, 1, 0, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 0, 1, 1, 1, 0, 0,\n",
       "       1, 1, 0, 0, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0,\n",
       "       0, 0, 1, 0, 0, 1, 0, 1, 0, 0, 0, 1, 0, 0, 1, 1, 0, 0, 1, 0, 1, 1,\n",
       "       1, 1, 0, 1, 1, 1, 0, 0, 0, 0, 0, 0, 1, 0, 1, 1, 0, 1, 1, 1, 0, 0,\n",
       "       1, 0, 1, 1, 0, 0, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1, 0, 0, 0, 1,\n",
       "       0, 1, 0, 0, 1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 1,\n",
       "       1, 0, 1, 1, 0, 1, 0, 1, 0, 0, 0, 0, 1, 0, 0, 0, 1, 1, 1, 1, 0, 0,\n",
       "       1, 0, 0, 1, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 1, 0, 1, 0, 0, 1, 0, 0,\n",
       "       1, 0, 0, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 1,\n",
       "       1, 1, 0, 0, 1, 1, 0, 0, 1, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0,\n",
       "       1, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 1, 0,\n",
       "       0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 0, 1, 1, 0, 0, 0,\n",
       "       0, 1, 1, 0, 0, 0, 1, 0, 1, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 0, 1, 0,\n",
       "       1, 0, 0, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 0,\n",
       "       1, 0, 1, 1, 1, 1, 0, 1, 1, 0, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1,\n",
       "       1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 0, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1,\n",
       "       0, 0, 1, 0, 1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 0, 0, 1, 1, 1, 1, 0, 1,\n",
       "       0, 0, 1, 0])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# predict the result\n",
    "sent_pred = classifier.predict(text_test)\n",
    "sent_pred"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[168,  40],\n",
       "       [ 21, 171]], dtype=int64)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# making a confusion matrix\n",
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(sent_test, sent_pred)\n",
    "cm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8475"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# accuracy score\n",
    "from sklearn.metrics import accuracy_score\n",
    "accuracy = accuracy_score(sent_test, sent_pred)\n",
    "accuracy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Part 5: Saving Our Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pickling the Classifier\n",
    "with open('classifier.pickle', 'wb') as f:\n",
    "    pickle.dump(classifier, f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Pickling the Vectorizer\n",
    "with open('tfidfmodel.pickle', 'wb') as f:\n",
    "    pickle.dump(vectorizer, f)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Part 6: Importing and Using our Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Unpickling the classifier and vectorizer\n",
    "with open('classifier.pickle', 'rb') as f:\n",
    "    clf= pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('tfidfmodel.pickle', 'rb') as f:\n",
    "    tfidf= pickle.load(f)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1]\n"
     ]
    }
   ],
   "source": [
    "# sample data to check model\n",
    "sample = [ \"you are a nice person man, have a good life\"]\n",
    "sample = tfidf.transform(sample).toarray()\n",
    "print(clf.predict(sample))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1 So It's mean our sentence is positive"
   ]
  }
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