textClassification.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Text Classification"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# loading data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "categories = ['alt.atheism', 'soc.religion.christian', 'comp.graphics', 'sci.med']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading 20news dataset. This may take a few minutes.\n",
      "Downloading dataset from https://ndownloader.figshare.com/files/5975967 (14 MB)\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import fetch_20newsgroups\n",
    "twenty_train = fetch_20newsgroups(subset='train', categories=categories, shuffle=True, random_state=42)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# statistics about the loaded data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['alt.atheism', 'comp.graphics', 'sci.med', 'soc.religion.christian']\n",
      "2257\n",
      "2257\n"
     ]
    }
   ],
   "source": [
    "print twenty_train.target_names\n",
    "print len(twenty_train.data)\n",
    "print len(twenty_train.filenames)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# first lines of the first loaded file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "From: sd345@city.ac.uk (Michael Collier)\n",
      "Subject: Converting images to HP LaserJet III?\n",
      "Nntp-Posting-Host: hampton\n"
     ]
    }
   ],
   "source": [
    "print(\"\\n\".join(twenty_train.data[0].split(\"\\n\")[:3]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "comp.graphics\n"
     ]
    }
   ],
   "source": [
    "print(twenty_train.target_names[twenty_train.target[0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 1, 3, 3, 3, 3, 3, 2, 2, 2], dtype=int64)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "twenty_train.target[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "comp.graphics\n",
      "comp.graphics\n",
      "soc.religion.christian\n",
      "soc.religion.christian\n",
      "soc.religion.christian\n",
      "soc.religion.christian\n",
      "soc.religion.christian\n",
      "sci.med\n",
      "sci.med\n",
      "sci.med\n"
     ]
    }
   ],
   "source": [
    "for t in twenty_train.target[:10]:\n",
    "    print(twenty_train.target_names[t])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# representing text files as a matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "count_vect = CountVectorizer()\n",
    "X_train_counts = count_vect.fit_transform(twenty_train.data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# see the matrix dimension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2257, 35788)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train_counts.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "16082"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "count_vect.vocabulary_.get(u'hampton')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training a classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.naive_bayes import MultinomialNB\n",
    "clf = MultinomialNB().fit(X_train_counts, twenty_train.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.996455471865\n"
     ]
    }
   ],
   "source": [
    "print clf.score(X_train_counts, twenty_train.target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Testing the classifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "twenty_test = fetch_20newsgroups(subset='test', categories=categories, shuffle=True, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.934087882823\n"
     ]
    }
   ],
   "source": [
    "X_test_counts = count_vect.transform(twenty_test.data)\n",
    "print clf.score(X_test_counts, twenty_test.target)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# getting detailed performance report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                        precision    recall  f1-score   support\n",
      "\n",
      "           alt.atheism       0.92      0.90      0.91       319\n",
      "         comp.graphics       0.95      0.95      0.95       389\n",
      "               sci.med       0.96      0.91      0.93       396\n",
      "soc.religion.christian       0.91      0.97      0.94       398\n",
      "\n",
      "           avg / total       0.93      0.93      0.93      1502\n",
      "\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "predicted = clf.predict(X_test_counts)\n",
    "print(metrics.classification_report(twenty_test.target, predicted, target_names=twenty_test.target_names))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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