Comparison of 5 Different Classifiers for Covid19 Fake News Detection (17BCE1073).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import itertools\n",
    "from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer, HashingVectorizer\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.linear_model import PassiveAggressiveClassifier, SGDClassifier\n",
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "from sklearn import metrics\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv('covid19_articles.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df.label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop('label', axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(df['title'], y, test_size=0.33)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Comparing Accuracies of 5 Different Classifiers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "count_vectorizer = CountVectorizer(stop_words='english')\n",
    "count_train = count_vectorizer.fit_transform(X_train)\n",
    "count_test = count_vectorizer.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "tfidf_vectorizer = TfidfVectorizer(stop_words='english', max_df=0.7)\n",
    "tfidf_train = tfidf_vectorizer.fit_transform(X_train)\n",
    "tfidf_test = tfidf_vectorizer.transform(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#MultinomialNB with Count Vectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "mn_count_clf = MultinomialNB(alpha=0.1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy:   0.913\n"
     ]
    }
   ],
   "source": [
    "mn_count_clf.fit(count_train, y_train)\n",
    "pred = mn_count_clf.predict(count_test)\n",
    "score = metrics.accuracy_score(y_test, pred)\n",
    "print(\"accuracy:   %0.3f\" % score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#MultinomialNB with Tfidf Vectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "mn_tfidf_clf = MultinomialNB(alpha=0.1) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy:   0.890\n"
     ]
    }
   ],
   "source": [
    "mn_tfidf_clf.fit(tfidf_train, y_train)\n",
    "pred = mn_tfidf_clf.predict(tfidf_test)\n",
    "score = metrics.accuracy_score(y_test, pred)\n",
    "print(\"accuracy:   %0.3f\" % score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Passive Aggressive Classifier with Tfidf Vectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "pa_tfidf_clf = PassiveAggressiveClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy:   0.941\n"
     ]
    }
   ],
   "source": [
    "pa_tfidf_clf.fit(tfidf_train, y_train)\n",
    "pred = pa_tfidf_clf.predict(tfidf_test)\n",
    "score = metrics.accuracy_score(y_test, pred)\n",
    "print(\"accuracy:   %0.3f\" % score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Linear SVC with Tfidf Vectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "svc_tfidf_clf = LinearSVC()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy:   0.952\n"
     ]
    }
   ],
   "source": [
    "svc_tfidf_clf.fit(tfidf_train, y_train)\n",
    "pred = svc_tfidf_clf.predict(tfidf_test)\n",
    "score = metrics.accuracy_score(y_test, pred)\n",
    "print(\"accuracy:   %0.3f\" % score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "#SGD Classifier with Tfidf Vectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "sgd_tfidf_clf = SGDClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "accuracy:   0.947\n"
     ]
    }
   ],
   "source": [
    "sgd_tfidf_clf.fit(tfidf_train, y_train)\n",
    "pred = sgd_tfidf_clf.predict(tfidf_test)\n",
    "score = metrics.accuracy_score(y_test, pred)\n",
    "print(\"accuracy:   %0.3f\" % score)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x228e11951d0>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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HFpE7RSRHRHLy8/N98sE1SyAtYRaSOrnV22tuWmpCZZWDB7J3sP+ks8XAiu8N90lcSinV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\nUgFIk7tSSgUgTe5KKRWANLkrpVQA+v82G839QU/T5AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x228e10f97b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(0).clf()\n",
    "\n",
    "for model, name in [ (mn_count_clf, 'multinomial nb count'),\n",
    "                     (mn_tfidf_clf, 'multinomial nb tfidf'),\n",
    "                     (pa_tfidf_clf, 'passive aggressive'),\n",
    "                     (svc_tfidf_clf, 'svc'),\n",
    "                     (sgd_tfidf_clf, 'sgd')]:\n",
    "    if 'count' in name:\n",
    "        pred = model.predict_proba(count_test)[:,1]\n",
    "    elif 'multinomial' in name:\n",
    "        pred = model.predict_proba(tfidf_test)[:,1]\n",
    "    else: \n",
    "        pred = model.decision_function(tfidf_test)\n",
    "    fpr, tpr, thresh = metrics.roc_curve(y_test.values, pred, pos_label='REAL')\n",
    "    plt.plot(fpr,tpr,label=\"{}\".format(name))\n",
    "\n",
    "plt.legend(loc=0)"
   ]
  }
 ],
 "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": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}