Classification_SMS.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# SciPy 2016 Scikit-learn Tutorial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Case Study - Text classification for SMS spam detection"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first load the text data from the `dataset` directory that should be located in your notebooks directory, which we created by running the `fetch_data.py` script from the top level of the GitHub repository.\n",
    "\n",
    "Furthermore, we perform some simple preprocessing and split the data array into two parts:\n",
    "\n",
    "1. `text`: A list of lists, where each sublists contains the contents of our emails\n",
    "2. `y`: our SPAM vs HAM labels stored in binary; a 1 represents a spam message, and a 0 represnts a ham (non-spam) message. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "with open(os.path.join(\"datasets\", \"smsspam\", \"SMSSpamCollection\")) as f:\n",
    "    lines = [line.strip().split(\"\\t\") for line in f.readlines()]\n",
    "\n",
    "text = [x[1] for x in lines]\n",
    "y = [int(x[0] == \"spam\") for x in lines]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Go until jurong point, crazy.. Available only in bugis n great world la e buffet... Cine there got amore wat...',\n",
       " 'Ok lar... Joking wif u oni...',\n",
       " \"Free entry in 2 a wkly comp to win FA Cup final tkts 21st May 2005. Text FA to 87121 to receive entry question(std txt rate)T&C's apply 08452810075over18's\",\n",
       " 'U dun say so early hor... U c already then say...',\n",
       " \"Nah I don't think he goes to usf, he lives around here though\",\n",
       " \"FreeMsg Hey there darling it's been 3 week's now and no word back! I'd like some fun you up for it still? Tb ok! XxX std chgs to send, $1.50 to rcv\",\n",
       " 'Even my brother is not like to speak with me. They treat me like aids patent.',\n",
       " \"As per your request 'Melle Melle (Oru Minnaminunginte Nurungu Vettam)' has been set as your callertune for all Callers. Press *9 to copy your friends Callertune\",\n",
       " 'WINNER!! As a valued network customer you have been selected to receivea $900 prize reward! To claim call 09061701461. Claim code KL341. Valid 12 hours only.',\n",
       " 'Had your mobile 11 months or more? U R entitled to Update to the latest colour mobiles with camera for Free! Call The Mobile Update Co FREE on 08002986030']"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "text[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 0, 1, 0, 0, 1, 0, 0, 1, 1]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of ham and spam messages: [4827  747]\n"
     ]
    }
   ],
   "source": [
    "print('Number of ham and spam messages:', np.bincount(y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we split our dataset into 2 parts, the test and training dataset:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "text_train, text_test, y_train, y_test = train_test_split(text, y, \n",
    "                                                          random_state=42,\n",
    "                                                          test_size=0.25,\n",
    "                                                          stratify=y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, we use the CountVectorizer to parse the text data into a bag-of-words model."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CountVectorizer defaults\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "CountVectorizer(analyzer='word', binary=False, decode_error='strict',\n",
       "        dtype=<class 'numpy.int64'>, encoding='utf-8', input='content',\n",
       "        lowercase=True, max_df=1.0, max_features=None, min_df=1,\n",
       "        ngram_range=(1, 1), preprocessor=None, stop_words=None,\n",
       "        strip_accents=None, token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b',\n",
       "        tokenizer=None, vocabulary=None)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "print('CountVectorizer defaults')\n",
    "CountVectorizer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vectorizer = CountVectorizer()\n",
    "vectorizer.fit(text_train)\n",
    "\n",
    "X_train = vectorizer.transform(text_train)\n",
    "X_test = vectorizer.transform(text_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "7453\n"
     ]
    }
   ],
   "source": [
    "print(len(vectorizer.vocabulary_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4180, 7453)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X_train.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['00', '000', '000pes', '008704050406', '0089', '01223585236', '01223585334', '02', '0207', '02072069400', '02073162414', '02085076972', '021', '03', '04', '0430', '05', '050703', '0578', '06']\n"
     ]
    }
   ],
   "source": [
    "print(vectorizer.get_feature_names()[:20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['crammed', 'cramps', 'crap', 'crash', 'crashed', 'crashing', 'crave', 'craving', 'craziest', 'crazy', 'crazyin', 'crckt', 'cream', 'created', 'creative', 'credit', 'credited', 'credits', 'creep', 'creepy']\n"
     ]
    }
   ],
   "source": [
    "print(vectorizer.get_feature_names()[2000:2020])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(4180, 7453)\n",
      "(1394, 7453)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(X_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training a Classifier on Text Features"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now train a classifier, for instance a logistic regression classifier, which is a fast baseline for text classification tasks:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "clf = LogisticRegression()\n",
    "clf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n",
       "          intercept_scaling=1, max_iter=100, multi_class='ovr', n_jobs=1,\n",
       "          penalty='l2', random_state=None, solver='liblinear', tol=0.0001,\n",
       "          verbose=0, warm_start=False)"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now evaluate the classifier on the testing set. Let's first use the built-in score function, which is the rate of correct classification in the test set:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.98493543758967006"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also compute the score on the training set to see how well we do there:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.99832535885167462"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Visualizing important features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def visualize_coefficients(classifier, feature_names, n_top_features=25):\n",
    "    # get coefficients with large absolute values \n",
    "    coef = classifier.coef_.ravel()\n",
    "    positive_coefficients = np.argsort(coef)[-n_top_features:]\n",
    "    negative_coefficients = np.argsort(coef)[:n_top_features]\n",
    "    interesting_coefficients = np.hstack([negative_coefficients, positive_coefficients])\n",
    "    # plot them\n",
    "    plt.figure(figsize=(15, 5))\n",
    "    colors = [\"red\" if c < 0 else \"blue\" for c in coef[interesting_coefficients]]\n",
    "    plt.bar(np.arange(50), coef[interesting_coefficients], color=colors)\n",
    "    feature_names = np.array(feature_names)\n",
    "    plt.xticks(np.arange(1, 51), feature_names[interesting_coefficients], rotation=60, ha=\"right\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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6zN7+34YsfP4N2GfUPqu/704W8l8lK7DbkQ0Yh4xZ54eTPSIHA/Ma718EbN7y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\n1u1eZ0T6c8nW25vJ/O4osoD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7fWrDw6hrs15/X2DiGV7z6va+nDot/lLY1lXJSv0zyNbq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\nSewiIiIiIiIWuSR2ERERERERi9z/AXmEirG+a1DmAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f15b66f2048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_coefficients(clf, vectorizer.get_feature_names())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.995454545455\n",
      "0.98350071736\n"
     ]
    }
   ],
   "source": [
    "vectorizer = CountVectorizer(min_df=2)\n",
    "vectorizer.fit(text_train)\n",
    "\n",
    "X_train = vectorizer.transform(text_train)\n",
    "X_test = vectorizer.transform(text_test)\n",
    "\n",
    "clf = LogisticRegression()\n",
    "clf.fit(X_train, y_train)\n",
    "\n",
    "print(clf.score(X_train, y_train))\n",
    "print(clf.score(X_test, y_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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SpGkysivmMKWUPwE79Zk+Dzi4/n81sNVUliNJ0pQUb9uWJC3ephTYSZK00BicSZI0kIGd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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f15b65ff048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "visualize_coefficients(clf, vectorizer.get_feature_names())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"figures/supervised_scikit_learn.png\" width=\"100%\">"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Exercises\n",
    "\n",
    "Use TfidfVectorizer instead of CountVectorizer. Are the results better? How are the coefficients different?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Code here"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Change the parameters min_df and ngram_range of the TfidfVectorizer and CountVectorizer. How does that change the important features?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Code here"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}