02_Homework_ws19.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Build a spam classifier using Naive Bayes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Project Description: \n",
    "- There are two datasets \n",
    "    - The ham-and-spam-dataset.zip dataset contains two folders with \"good\" (ham) and \"bad\" (spam) emails. \n",
    "    - If you rather prefer to work with messages, you can use SPAM text message 20170820 - Data.csv dataset.\n",
    "- Use one of these datasets in order to build your own Naive Bayes classifier.\n",
    "- Verify your Classifier using new email messages/emails (create your own messages).\n",
    "\n",
    "## Project Duration: 2 weeks\n",
    "## Project Deliverables:\n",
    "1. End of the first week: \n",
    "    - Load the dataset using pandas, \n",
    "    - Analysis it, visualise\n",
    "    - Prepare the dataset for the usage by Naive Bayes Classifier\n",
    "2. End of the second week:\n",
    "    - Train the classifier,\n",
    "    - Validate it,\n",
    "    - Apply it to new messages,\n",
    "    - Try to cheat the classifier by adding \"good words\" to the end of your own message."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can use the following link can be used as guidance for implementation:\n",
    "https://towardsdatascience.com/spam-filtering-using-naive-bayes-98a341224038"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "import string\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.pipeline import Pipeline\n",
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "from sklearn.feature_extraction.text import TfidfTransformer\n",
    "from sklearn.naive_bayes import MultinomialNB, GaussianNB\n",
    "from sklearn.metrics import classification_report,confusion_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([<matplotlib.axes._subplots.AxesSubplot object at 0x7f788949af28>,\n",
       "       <matplotlib.axes._subplots.AxesSubplot object at 0x7f78893f3668>],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "data = pd.read_csv('/home/srini/hbrs/Secondsem/machinlearning/Assignments/assignment2/SPAM text message 20170820 - Data.csv')\n",
    "\n",
    "data = data.rename(columns={'v1': 'class','v2': 'text'})\n",
    "data.head()\n",
    "data.groupby('Category').describe()\n",
    "data['length'] = data['Message'].apply(len)\n",
    "data.hist(column='length', by='Category', bins=50)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "def process_text(text):\n",
    "    \n",
    "    nopunctuation = [char for char in text if char not in string.punctuation]\n",
    "    nopunctuation = ''.join(nopunctuation)\n",
    "    \n",
    "    cleaning_word = [words for words in nopunctuation.split() if words.lower() not in stopwords.words('english')]\n",
    "        \n",
    "    return cleaning_word\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       [Go, jurong, point, crazy, Available, bugis, n...\n",
       "1                          [Ok, lar, Joking, wif, u, oni]\n",
       "2       [Free, entry, 2, wkly, comp, win, FA, Cup, fin...\n",
       "3           [U, dun, say, early, hor, U, c, already, say]\n",
       "4       [Nah, dont, think, goes, usf, lives, around, t...\n",
       "5       [FreeMsg, Hey, darling, 3, weeks, word, back, ...\n",
       "6       [Even, brother, like, speak, treat, like, aids...\n",
       "7       [per, request, Melle, Melle, Oru, Minnaminungi...\n",
       "8       [WINNER, valued, network, customer, selected, ...\n",
       "9       [mobile, 11, months, U, R, entitled, Update, l...\n",
       "10      [Im, gonna, home, soon, dont, want, talk, stuf...\n",
       "11      [SIX, chances, win, CASH, 100, 20000, pounds, ...\n",
       "12      [URGENT, 1, week, FREE, membership, £100000, P...\n",
       "13      [Ive, searching, right, words, thank, breather...\n",
       "14                                         [DATE, SUNDAY]\n",
       "15      [XXXMobileMovieClub, use, credit, click, WAP, ...\n",
       "16                                    [Oh, kim, watching]\n",
       "17      [Eh, u, remember, 2, spell, name, Yes, v, naug...\n",
       "18      [Fine, thats, way, u, feel, Thats, way, gota...\n",
       "19      [England, v, Macedonia, dont, miss, goalsteam,...\n",
       "20                               [seriously, spell, name]\n",
       "21           [I‘m, going, try, 2, months, ha, ha, joking]\n",
       "22                 [ü, pay, first, lar, da, stock, comin]\n",
       "23      [Aft, finish, lunch, go, str, lor, Ard, 3, smt...\n",
       "24               [Ffffffffff, Alright, way, meet, sooner]\n",
       "25      [forced, eat, slice, Im, really, hungry, tho, ...\n",
       "26                              [Lol, always, convincing]\n",
       "27      [catch, bus, frying, egg, make, tea, eating, m...\n",
       "28      [Im, back, amp, packing, car, Ill, let, know, ...\n",
       "29       [Ahhh, Work, vaguely, remember, feel, like, Lol]\n",
       "                              ...                        \n",
       "5542                    [Armand, says, get, ass, epsilon]\n",
       "5543          [U, still, havent, got, urself, jacket, ah]\n",
       "5544    [Im, taking, derek, amp, taylor, walmart, Im, ...\n",
       "5545                          [Hi, durban, still, number]\n",
       "5546                         [Ic, lotta, childporn, cars]\n",
       "5547    [contract, mobile, 11, Mnths, Latest, Motorola...\n",
       "5548                                 [trying, weekend, V]\n",
       "5549    [know, wot, people, wear, shirts, jumpers, hat...\n",
       "5550                             [Cool, time, think, get]\n",
       "5551            [Wen, get, spiritual, deep, Thats, great]\n",
       "5552    [safe, trip, Nigeria, Wish, happiness, soon, c...\n",
       "5553                             [Hahahause, brain, dear]\n",
       "5554    [Well, keep, mind, Ive, got, enough, gas, one,...\n",
       "5555    [Yeh, Indians, nice, Tho, kane, bit, shud, go,...\n",
       "5556          [Yes, thats, u, texted, Pshewmissing, much]\n",
       "5557    [meant, calculation, ltgt, units, ltgt, school...\n",
       "5558                            [Sorry, Ill, call, later]\n",
       "5559         [arent, next, ltgt, hours, imma, flip, shit]\n",
       "5560                        [Anything, lor, Juz, us, lor]\n",
       "5561    [Get, dump, heap, mom, decided, come, lowes, B...\n",
       "5562    [Ok, lor, Sony, ericsson, salesman, ask, shuhu...\n",
       "5563                             [Ard, 6, like, dat, lor]\n",
       "5564        [dont, wait, til, least, wednesday, see, get]\n",
       "5565                                           [Huh, lei]\n",
       "5566    [REMINDER, O2, get, 250, pounds, free, call, c...\n",
       "5567    [2nd, time, tried, 2, contact, u, U, £750, Pou...\n",
       "5568                   [ü, b, going, esplanade, fr, home]\n",
       "5569                     [Pity, mood, Soany, suggestions]\n",
       "5570    [guy, bitching, acted, like, id, interested, b...\n",
       "5571                                   [Rofl, true, name]\n",
       "Name: Message, Length: 5572, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data['Message'].apply(process_text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "msg_train, msg_test, cat_train, cat_test = \\\n",
    "train_test_split(data['Message'],data['Category'],test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "pipeline = Pipeline([\n",
    "    ('bow',CountVectorizer(analyzer=process_text)), # converts strings to integer counts\n",
    "    ('tfidf',TfidfTransformer()), # converts integer counts to weighted TF-IDF scores\n",
    "    ('classifier',MultinomialNB()) # train on TF-IDF vectors with Naive Bayes classifier\n",
    "])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Pipeline(memory=None,\n",
       "         steps=[('bow',\n",
       "                 CountVectorizer(analyzer=<function process_text at 0x7f7889286730>,\n",
       "                                 binary=False, decode_error='strict',\n",
       "                                 dtype=<class 'numpy.int64'>, encoding='utf-8',\n",
       "                                 input='content', lowercase=True, max_df=1.0,\n",
       "                                 max_features=None, min_df=1,\n",
       "                                 ngram_range=(1, 1), preprocessor=None,\n",
       "                                 stop_words=None, strip_accents=None,\n",
       "                                 token_pattern='(?u)\\\\b\\\\w\\\\w+\\\\b',\n",
       "                                 tokenizer=None, vocabulary=None)),\n",
       "                ('tfidf',\n",
       "                 TfidfTransformer(norm='l2', smooth_idf=True,\n",
       "                                  sublinear_tf=False, use_idf=True)),\n",
       "                ('classifier',\n",
       "                 MultinomialNB(alpha=1.0, class_prior=None, fit_prior=True))],\n",
       "         verbose=False)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pipeline.fit(msg_train,cat_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "predictions = pipeline.predict(msg_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         ham       0.98      1.00      0.99       982\n",
      "        spam       1.00      0.81      0.90       133\n",
      "\n",
      "    accuracy                           0.98      1115\n",
      "   macro avg       0.99      0.91      0.94      1115\n",
      "weighted avg       0.98      0.98      0.98      1115\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(cat_test,predictions))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f7888bbe5c0>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "sns.heatmap(confusion_matrix(cat_test,predictions),annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "         ham       0.98      1.00      0.99       968\n",
      "        spam       1.00      0.87      0.93       147\n",
      "\n",
      "    accuracy                           0.98      1115\n",
      "   macro avg       0.99      0.94      0.96      1115\n",
      "weighted avg       0.98      0.98      0.98      1115\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f78846126a0>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# for testing the classifier we used dataset from kaggle\n",
    "\n",
    "test_csv = pd.read_csv('/home/srini/hbrs/Secondsem/machinlearning/Assignments/assignment2/spam.csv', encoding='latin-1')\n",
    "\n",
    "# messages = pd.read_csv('../input/spam.csv', encoding='latin-1')\n",
    "test_csv.drop(['Unnamed: 2','Unnamed: 3','Unnamed: 4'],axis=1,inplace=True)\n",
    "test_csv = test_csv.rename(columns={'v1': 'class','v2': 'text'})\n",
    "# print(test_csv)\n",
    "test_csv.groupby('class').describe()\n",
    "check =test_csv['text'].apply(process_text)\n",
    "# print(check)\n",
    "msg_train1, msg_test1, cat_train1, cat_test1 = \\\n",
    "train_test_split(test_csv['text'],test_csv['class'],test_size=0.2)\n",
    "\n",
    "# print(msg_test1)\n",
    "# print(check)\n",
    "pred1 = pipeline.predict(msg_test1)\n",
    "# for i in pred1:\n",
    "#     print(i)\n",
    "print(classification_report(cat_test1,pred1))\n",
    "sns.heatmap(confusion_matrix(cat_test1,pred1),annot=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "ename": "NotFittedError",
     "evalue": "CountVectorizer - Vocabulary wasn't fitted.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNotFittedError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-6-3066b2c3cb25>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0mvectorizer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mCountVectorizer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 8\u001b[0;31m \u001b[0mtest_counts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvectorizer\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransform\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnew_df\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      9\u001b[0m \u001b[0mactual_category\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     10\u001b[0m \u001b[0mpredictions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpipeline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtest_counts\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/sklearn/feature_extraction/text.py\u001b[0m in \u001b[0;36mtransform\u001b[0;34m(self, raw_documents)\u001b[0m\n\u001b[1;32m   1107\u001b[0m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_validate_vocabulary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1108\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1109\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_check_vocabulary\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1110\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1111\u001b[0m         \u001b[0;31m# use the same matrix-building strategy as fit_transform\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/sklearn/feature_extraction/text.py\u001b[0m in \u001b[0;36m_check_vocabulary\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    387\u001b[0m         \u001b[0;34m\"\"\"Check if vocabulary is empty or missing (not fit-ed)\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    388\u001b[0m         \u001b[0mmsg\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"%(name)s - Vocabulary wasn't fitted.\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 389\u001b[0;31m         \u001b[0mcheck_is_fitted\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'vocabulary_'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmsg\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    390\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    391\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvocabulary_\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/anaconda3/lib/python3.7/site-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_is_fitted\u001b[0;34m(estimator, attributes, msg, all_or_any)\u001b[0m\n\u001b[1;32m    912\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    913\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mall_or_any\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mhasattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mattr\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mattr\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mattributes\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 914\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mNotFittedError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m'name'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mestimator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    915\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    916\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNotFittedError\u001b[0m: CountVectorizer - Vocabulary wasn't fitted."
     ]
    }
   ],
   "source": [
    "from sklearn.feature_extraction.text import CountVectorizer\n",
    "\n",
    "new_df = ['Hello Rose, hope u had a good vacation','You won the money lottery, claim it today. Pay','Find your horoscope for free','Montly rent and expenses details shared'],\n",
    "        \n",
    "\n",
    "\n",
    "vectorizer = CountVectorizer()\n",
    "test_counts = vectorizer.transform(new_df)\n",
    "actual_category = [1,0,0,1]\n",
    "predictions = pipeline.predict(test_counts)\n",
    "category = ['ham','spam']\n",
    "predictions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Authors:\n",
    "\n",
    "Shravanthi Arvind Patil <br>\n",
    "Sreenivasa Hikkal Venugopala"
   ]
  }
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