text_classification.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "skilled-prime",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Loading the data set - training data.\n",
    "from sklearn.datasets import fetch_20newsgroups\n",
    "twenty_train = fetch_20newsgroups(subset='train', shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "jewish-bankruptcy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['alt.atheism',\n",
       " 'comp.graphics',\n",
       " 'comp.os.ms-windows.misc',\n",
       " 'comp.sys.ibm.pc.hardware',\n",
       " 'comp.sys.mac.hardware',\n",
       " 'comp.windows.x',\n",
       " 'misc.forsale',\n",
       " 'rec.autos',\n",
       " 'rec.motorcycles',\n",
       " 'rec.sport.baseball',\n",
       " 'rec.sport.hockey',\n",
       " 'sci.crypt',\n",
       " 'sci.electronics',\n",
       " 'sci.med',\n",
       " 'sci.space',\n",
       " 'soc.religion.christian',\n",
       " 'talk.politics.guns',\n",
       " 'talk.politics.mideast',\n",
       " 'talk.politics.misc',\n",
       " 'talk.religion.misc']"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# You can check the target names (categories) and some data files by following commands.\n",
    "twenty_train.target_names #prints all the categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "above-technical",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "From: lerxst@wam.umd.edu (where's my thing)\n",
      "Subject: WHAT car is this!?\n",
      "Nntp-Posting-Host: rac3.wam.umd.edu\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n\".join(twenty_train.data[0].split(\"\\n\")[:3])) #prints first line of the first data file\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "integral-order",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11314, 130107)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Extracting features from text files\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "count_vect = CountVectorizer()\n",
    "X_train_counts = count_vect.fit_transform(twenty_train.data)\n",
    "X_train_counts.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "bearing-pilot",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(11314, 130107)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# TF-IDF\n",
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "tfidf_transformer = TfidfTransformer()\n",
    "X_train_tfidf = tfidf_transformer.fit_transform(X_train_counts)\n",
    "X_train_tfidf.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "baking-roots",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Machine Learning\n",
    "# Training Naive Bayes (NB) classifier on training data.\n",
    "from sklearn.naive_bayes import MultinomialNB\n",
    "clf = MultinomialNB().fit(X_train_tfidf, twenty_train.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "colonial-produce",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Building a pipeline: We can write less code and do all of the above, by building a pipeline as follows:\n",
    "# The names ‘vect’ , ‘tfidf’ and ‘clf’ are arbitrary but will be used later.\n",
    "# We will be using the 'text_clf' going forward.\n",
    "from sklearn.pipeline import Pipeline\n",
    "\n",
    "text_clf = Pipeline([('vect', CountVectorizer()), ('tfidf', TfidfTransformer()), ('clf', MultinomialNB())])\n",
    "\n",
    "text_clf = text_clf.fit(twenty_train.data, twenty_train.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "activated-chuck",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7738980350504514"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "# Performance of NB Classifier\n",
    "import numpy as np\n",
    "twenty_test = fetch_20newsgroups(subset='test', shuffle=True)\n",
    "predicted = text_clf.predict(twenty_test.data)\n",
    "np.mean(predicted == twenty_test.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "coastal-plastic",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 7, 11,  0, ...,  9,  3, 15])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predicted"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "respected-territory",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ana/.local/lib/python3.7/site-packages/sklearn/linear_model/_stochastic_gradient.py:577: ConvergenceWarning: Maximum number of iteration reached before convergence. Consider increasing max_iter to improve the fit.\n",
      "  ConvergenceWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "0.8248805098247477"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Training Support Vector Machines - SVM and calculating its performance\n",
    "\n",
    "from sklearn.linear_model import SGDClassifier\n",
    "text_clf_svm = Pipeline([('vect', CountVectorizer()), ('tfidf', TfidfTransformer()),\n",
    "                         ('clf-svm', SGDClassifier(loss='hinge', penalty='l2',alpha=1e-3, max_iter=5, random_state=42))])\n",
    "\n",
    "text_clf_svm = text_clf_svm.fit(twenty_train.data, twenty_train.target)\n",
    "predicted_svm = text_clf_svm.predict(twenty_test.data)\n",
    "np.mean(predicted_svm == twenty_test.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "split-gazette",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8167817312798725"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import nltk\n",
    "\n",
    "# NLTK\n",
    "# Removing stop words\n",
    "from sklearn.pipeline import Pipeline\n",
    "text_clf = Pipeline([('vect', CountVectorizer(stop_words='english')), ('tfidf', TfidfTransformer()), \n",
    "                     ('clf', MultinomialNB())])\n",
    "\n",
    "from nltk.stem.snowball import SnowballStemmer\n",
    "stemmer = SnowballStemmer(\"english\", ignore_stopwords=True)\n",
    "\n",
    "class StemmedCountVectorizer(CountVectorizer):\n",
    "    def build_analyzer(self):\n",
    "        analyzer = super(StemmedCountVectorizer, self).build_analyzer()\n",
    "        return lambda doc: ([stemmer.stem(w) for w in analyzer(doc)])\n",
    "    \n",
    "stemmed_count_vect = StemmedCountVectorizer(stop_words='english')\n",
    "\n",
    "text_mnb_stemmed = Pipeline([('vect', stemmed_count_vect), ('tfidf', TfidfTransformer()), \n",
    "                             ('mnb', MultinomialNB(fit_prior=False))])\n",
    "\n",
    "text_mnb_stemmed = text_mnb_stemmed.fit(twenty_train.data, twenty_train.target)\n",
    "\n",
    "predicted_mnb_stemmed = text_mnb_stemmed.predict(twenty_test.data)\n",
    "\n",
    "np.mean(predicted_mnb_stemmed == twenty_test.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "detailed-disability",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import accuracy_score, precision_score, recall_score, classification_report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "traditional-engineering",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8167817312798725"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "accuracy_score(predicted_mnb_stemmed, twenty_test.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "hispanic-myanmar",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.803538189701977"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "precision_score(predicted_mnb_stemmed, twenty_test.target, average='macro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "affecting-roulette",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.8335586735879094"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "recall_score(predicted_mnb_stemmed, twenty_test.target, average='macro')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "retained-lightning",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                          precision    recall  f1-score   support\n",
      "\n",
      "             alt.atheism       0.70      0.80      0.75       278\n",
      "           comp.graphics       0.72      0.78      0.75       357\n",
      " comp.os.ms-windows.misc       0.69      0.82      0.75       329\n",
      "comp.sys.ibm.pc.hardware       0.79      0.69      0.74       447\n",
      "   comp.sys.mac.hardware       0.83      0.85      0.84       378\n",
      "          comp.windows.x       0.79      0.86      0.83       361\n",
      "            misc.forsale       0.75      0.88      0.81       333\n",
      "               rec.autos       0.92      0.88      0.90       418\n",
      "         rec.motorcycles       0.96      0.93      0.94       410\n",
      "      rec.sport.baseball       0.92      0.93      0.92       395\n",
      "        rec.sport.hockey       0.98      0.91      0.94       432\n",
      "               sci.crypt       0.97      0.72      0.83       531\n",
      "         sci.electronics       0.64      0.83      0.72       304\n",
      "                 sci.med       0.78      0.92      0.84       336\n",
      "               sci.space       0.94      0.83      0.88       445\n",
      "  soc.religion.christian       0.96      0.64      0.77       599\n",
      "      talk.politics.guns       0.95      0.65      0.77       529\n",
      "   talk.politics.mideast       0.95      0.93      0.94       385\n",
      "      talk.politics.misc       0.54      0.90      0.67       185\n",
      "      talk.religion.misc       0.29      0.93      0.45        80\n",
      "\n",
      "                accuracy                           0.82      7532\n",
      "               macro avg       0.80      0.83      0.80      7532\n",
      "            weighted avg       0.85      0.82      0.82      7532\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(predicted_mnb_stemmed, twenty_test.target, target_names=twenty_train.target_names))"
   ]
  }
 ],
 "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": "3.7.10"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}