spam_detection.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "#Detecting Spam with Spark\n",
    "\n",
    "##  Classifying messages to detect spam.\n",
    "\n",
    "### By Shakeel Raja \n",
    "\n",

    "Sample outputs from each cell have been added as for inspection. Test script for each cell has been commented out which can be un-commented to run it with sample data.   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Task a) & b) Read some files and prepare a (f,w) RDD \n",
    "\n",
    "\n",
    "Following code reads file from given directory, splits the words per file, changes all words to lowercase and creates (f,w) pairs as shown in the output "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import re \n",
    "import numpy as np\n",
    "\n",
    "def read_fw_RDD(argDir): # package tasks a/b into a function for later use\n",
    "    \n",
    "    fwL_RDD = sc.wholeTextFiles(argDir)#<<< task a) read the files\n",
    "\n",
    "    #print('Read {} files from directory {}'.format(fwL_RDD.count(),argDir)) # status message for testing, can be disabled later on\n",
    "    #print('file word count histogram') # the histogram can be useful for checking later \n",
    "    #print(fwL_RDD.map(lambda fwL: (len(fwL[1]))).histogram([0,10,100,1000,10000]))\n",
    "\n",
    "    #Splitting into (f,wL) pairs while changinging words to lowercase\n",
    "    fwL_RDD = (fwL_RDD.map(lambda x: (x[0], re.split('\\W+', x[1].lower()))))\n",
    "    #Generating (f,w) pairs\n",
    "    fwL_RDD = fwL_RDD.flatMapValues(lambda x: x)\n",
    "\n",
    "    return fwL_RDD\n",
    "\n",
    "# TEST SCRIPT  \n",
    "#dirPath = 'hdfs://saltdean.nsqdc.city.ac.uk./data/spam/bare/part1'\n",
    "#fw_RDD = read_fw_RDD(dirPath) # for testing\n",
    "#print(fw_RDD.take(3)) # for testing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### SAMPLE OUTPUT\n",
    "\n",
    "This output shows (f,w) tuples generated from code above. \n",
    "\n",
    "[('hdfs://saltdean.nsqdc.city.ac.uk./data/spam/bare/part1/3-1msg1.txt', 'subject'), ('hdfs://saltdean.nsqdc.city.ac.uk./data/spam/bare/part1/3-1msg1.txt', 're'), ('hdfs://saltdean.nsqdc.city.ac.uk./data/spam/bare/part1/3-1msg1.txt', '2')]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Task c) Normalised word count lists\n",
    "\n",
    "Following code generates a [file[(w,c), ... ]] list for each file and creates a normalised term frequency vector for words in each file. This involves reading the data, adding 1 to each word, adding words to get term frequency and normalizing calculated term frequency. This cell gives output as [f(w,cn)....]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from operator import add\n",
    "\n",
    "#Rearrange data into [f(w,c)..]\n",
    "def reGrpLst(fw_c): # reorganise the tuples\n",
    "    fw,c = fw_c \n",
    "    f,w = fw \n",
    "    return (f,[(w,c)]) \n",
    "\n",
    "def normalizer (wordCount): # calculate normalised term frequency for (w,c) pairs.\n",
    "    tSum=0\n",
    "    for word,count in wordCount :\n",
    "        tSum += count\n",
    "    return [(w,float(c/tSum)) for w,c in wordCount]\n",
    "\n",
    "# this function reads the content of given directory and calculates \n",
    "# term frequency for for each word per file, regroups data into [f(w,c)....] for each file, \n",
    "# and normalises as [f[(w,cN)...] \n",
    "def make_f_tfLn_RDD(argDir):\n",
    "    \n",
    "    fw_RDD = read_fw_RDD( argDir ) \\ \n",
    "    .map(lambda x: (x,1)) \\\n",
    "    .reduceByKey(add)\\\n",
    "    .map(reGrpLst) \\\n",
    "    .reduceByKey(add)\n",
    "\n",
    "    #Sort words by frequency and normalize\n",
    "    f_wcL_RDD = fw_RDD.map(lambda f_wcL: (f_wcL[0], sorted(f_wcL[1],key=lambda x: x[1]))) \n",
    "    f_wcLn_RDD = f_wcL_RDD.map(lambda fwC: (fwC[0], normalizer(fwC[1])))\n",
    "    \n",
    "    #return f_wVec_RDD #Word count List (normalized)\n",
    "    return f_wcLn_RDD\n",
    "    \n",
    "# TEST SCRIPT  \n",
    "#f_wcLn_RDD = make_f_tfLn_RDD('hdfs://saltdean/data/spam/bare/part1') # for testing\n",
    "#print(f_wcLn_RDD.top(1)) # for testing\n",
    "#wcLn = f_wcLn_RDD.take(1)[0][1] # get the first normalised word count list\n",
    "#print('Sum of normalized word count: {} (roughly equal to 1).'.format(sum([cn for (w,cn) in wcLn]))) # the sum of normalised counts should be close to 1 "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### SAMPLE OUTPUT:\n",
    "\n",
    "[('hdfs://saltdean/data/spam/bare/part1/spmsga141.txt', [('murkowski', 0.005434782608695652), ('sending', 0.005434782608695652), ('see', 0.005434782608695652), ('cost', 0.005434782608695652), ('please', 0.005434782608695652), ('hand', 0.005434782608695652), ('reply', 0.005434782608695652), ('time', 0.005434782608695652), ('places', 0.005434782608695652), ('http', 0.005434782608695652), ('hard', 0.005434782608695652), ('under', 0.005434782608695652), ('list', 0.005434782608695652), ('if', 0.005434782608695652), ('from', 0.005434782608695652), ('bill', 0.005434782608695652), ('avoid', 0.005434782608695652), ('federal', 0.005434782608695652), ('outlined', 0.005434782608695652), ('else', 0.005434782608695652), ('each', 0.005434782608695652), ('address', 0.005434782608695652), ('e', 0.005434782608695652), ('any', 0.005434782608695652), ('creditime', 0.005434782608695652), ('immediately', 0.005434782608695652), ('states', 0.005434782608695652), ('town', 0.005434782608695652), ('mail', 0.005434782608695652), ('message', 0.005434782608695652), ('get', 0.005434782608695652), ('inability', 0.005434782608695652), ('cancel', 0.005434782608695652), ('can', 0.005434782608695652), ('aforementioned', 0.005434782608695652), ('receiving', 0.005434782608695652), ('attempts', 0.005434782608695652), ('finding', 0.005434782608695652), ('receive', 0.005434782608695652), ('com', 0.005434782608695652), ('that', 0.005434782608695652), ('try', 0.005434782608695652), ('complies', 0.005434782608695652), ('no', 0.005434782608695652), ('browser', 0.005434782608695652), ('like', 0.005434782608695652), ('may', 0.005434782608695652), ('would', 0.005434782608695652), ('accounts', 0.005434782608695652), ('search', 0.005434782608695652), ('include', 0.005434782608695652), ('do', 0.005434782608695652), ('starting', 0.005434782608695652), ('stopped', 0.005434782608695652), ('type', 0.005434782608695652), ('additional', 0.005434782608695652), ('deal', 0.005434782608695652), ('honored', 0.005434782608695652), ('united', 0.005434782608695652), ('point', 0.005434782608695652), ('must', 0.005434782608695652), ('help', 0.005434782608695652), ('proposed', 0.005434782608695652), ('', 0.005434782608695652), ('s', 0.005434782608695652), ('saying', 0.005434782608695652), ('result', 0.005434782608695652), ('anyone', 0.005434782608695652), ('is', 0.005434782608695652), ('www', 0.005434782608695652), ('haveing', 0.005434782608695652), ('commercial', 0.005434782608695652), ('of', 0.005434782608695652), ('good', 0.005434782608695652), ('in', 0.005434782608695652), ('state', 0.005434782608695652), ('addresses', 0.005434782608695652), ('where', 0.005434782608695652), ('advertisement', 0.005434782608695652), ('maybe', 0.005434782608695652), ('merchant', 0.005434782608695652), ('requirements', 0.005434782608695652), ('service', 0.005434782608695652), ('process', 0.005434782608695652), ('by', 0.010869565217391304), ('so', 0.010869565217391304), ('credit', 0.010869565217391304), ('or', 0.010869565217391304), ('and', 0.010869565217391304), ('then', 0.010869565217391304), ('information', 0.010869565217391304), ('be', 0.010869565217391304), ('this', 0.010869565217391304), ('further', 0.010869565217391304), ('are', 0.010869565217391304), ('us', 0.010869565217391304), ('give', 0.010869565217391304), ('mailings', 0.010869565217391304), ('at', 0.010869565217391304), ('subject', 0.010869565217391304), ('as', 0.010869565217391304), ('have', 0.010869565217391304), ('requests', 0.010869565217391304), ('all', 0.016304347826086956), ('will', 0.016304347826086956), ('for', 0.016304347826086956), ('email', 0.016304347826086956), ('with', 0.016304347826086956), ('remove', 0.016304347826086956), ('card', 0.016304347826086956), ('on', 0.021739130434782608), ('we', 0.021739130434782608), ('the', 0.02717391304347826), ('your', 0.02717391304347826), ('to', 0.03804347826086957), ('a', 0.03804347826086957), ('you', 0.04891304347826087)])]\n",
    "\n",
    "Sum of normalized word count: 1.0000000000000009 (roughly equal to 1)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Task d) Creating hashed feature vectors \n",
    "Using the hashing trick to create fixed size TF vectors.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# function for generating hash values for each file vector based on given size N\n",
    "def hashing_vectorizer(word_count_list, N): \n",
    "    v = [0] * N  # create fixed size vector of 0s\n",
    "    \n",
    "    for word_count in word_count_list:         \n",
    "        word,count = word_count # unpack tuple\n",
    "        h = hash(word) # get hash value\n",
    "        v[h % N] = v[h % N] + count # add count\n",
    "        \n",
    "    return v # return hashed word vector\n",
    "\n",
    "# creating hash vector per file by sending (w,cn) to hash function and appending it to filename\n",
    "def make_f_wVn_RDD(f_wcLn_RDD, argN): \n",
    "    f_wVec2_RDD = f_wcLn_RDD.map(lambda f_wc: (f_wc[0], hashing_vectorizer(f_wc[1],argN)))\n",
    "    return f_wVec2_RDD \n",
    "    \n",
    "    \n",
    "# TEST SCRIPT    \n",
    "#N=100\n",
    "#dirPath = 'hdfs://saltdean.nsqdc.city.ac.uk/data/spam/bare/part1'\n",
    "#f_wVn_RDD = make_f_wVn_RDD(make_f_tfLn_RDD(dirPath),N) # for testing\n",
    "#print(f_wVn_RDD.top(1)) # for testing\n",
    "#print( sum(f_wVn_RDD.take(1)[0][1])) # for testing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### SAMPLE OUTPUT\n",
    "\n",
    "[('hdfs://saltdean.nsqdc.city.ac.uk/data/spam/bare/part1/spmsga141.txt', [0.021739130434782608, 0.010869565217391304, 0, 0.03260869565217391, 0, 0, 0.005434782608695652, 0, 0.005434782608695652, 0.005434782608695652, 0.005434782608695652, 0, 0.005434782608695652, 0, 0, 0.03260869565217391, 0.010869565217391304, 0.02717391304347826, 0.005434782608695652, 0.016304347826086956, 0.005434782608695652, 0.06521739130434782, 0.021739130434782608, 0.010869565217391304, 0.010869565217391304, 0.010869565217391304, 0, 0.021739130434782608, 0.016304347826086956, 0.005434782608695652, 0, 0.005434782608695652, 0, 0.005434782608695652, 0.010869565217391304, 0.005434782608695652, 0.010869565217391304, 0.016304347826086956, 0, 0.005434782608695652, 0.016304347826086956, 0.02717391304347826, 0.021739130434782608, 0, 0.005434782608695652, 0.005434782608695652, 0.005434782608695652, 0.043478260869565216, 0.005434782608695652, 0.005434782608695652, 0, 0.005434782608695652, 0.005434782608695652, 0.005434782608695652, 0, 0.010869565217391304, 0.021739130434782608, 0.016304347826086956, 0, 0.005434782608695652, 0.010869565217391304, 0.005434782608695652, 0.005434782608695652, 0.005434782608695652, 0.016304347826086956, 0.03804347826086957, 0.010869565217391304, 0.005434782608695652, 0.005434782608695652, 0.016304347826086956, 0.021739130434782608, 0, 0.021739130434782608, 0.010869565217391304, 0.010869565217391304, 0, 0, 0.016304347826086956, 0, 0.005434782608695652, 0.005434782608695652, 0.005434782608695652, 0, 0, 0.005434782608695652, 0, 0.005434782608695652, 0, 0.021739130434782608, 0.043478260869565216, 0.016304347826086956, 0, 0.021739130434782608, 0.005434782608695652, 0, 0.02717391304347826, 0, 0, 0.010869565217391304, 0.005434782608695652])]\n",
    "\n",
    "0.9999999999999997"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Task e) Create Labeled Points\n",
    "\n",
    "Following code determines whether the file is spam (i.e. the filename contains ’spmsg’) and replace the filename by a 1 (spam) or 0 (ham) accordingly using mllib LabelPoint function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "#Import labeledpoint from mllib to attach labels to data\n",
    "from pyspark.mllib.regression import LabeledPoint\n",
    "\n",
    "#Function to attach labels to data\n",
    "def make_lp_RDD(f_tfLn_RDD,argN):\n",
    "\n",
    "    # make a vector\n",
    "    hash_RDD = make_f_wVn_RDD(f_tfLn_RDD, argN)\n",
    "    # detect spam by filename and transform into LabeledPoint objects\n",
    "    lp_RDD = hash_RDD.map(lambda f_wVec: LabeledPoint(1 if re.search ('spmsg' ,f_wVec[0]) else 0,f_wVec[1])) \n",
    "\n",
    "    return lp_RDD \n",
    "\n",
    "# TEST SCRIPT    \n",
    "#lp_RDD = make_lp_RDD(make_f_tfLn_RDD('hdfs://saltdean.nsqdc.city.ac.uk./data/spam/bare/part1'),100)\n",
    "#print(lp_RDD.take(1))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### SAMPLE OUTPUT\n",
    "\n",
    "Labels of 0 or 1 attached to the hash vector\n",
    "\n",
    "[LabeledPoint(0.0, [0.019801980198,0.0792079207921,0.00990099009901,0.00990099009901,0.00990099009901,0.0,0.00990099009901,0.0,0.0,0.00990099009901,0.0,0.0,0.0,0.0,0.0,0.0,0.00990099009901,0.039603960396,0.00990099009901,0.0,0.019801980198,0.049504950495,0.019801980198,0.0,0.00990099009901,0.00990099009901,0.00990099009901,0.019801980198,0.00990099009901,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.0,0.00990099009901,0.0,0.00990099009901,0.0,0.0,0.0,0.0,0.039603960396,0.0,0.00990099009901,0.00990099009901,0.00990099009901,0.029702970297,0.0,0.00990099009901,0.039603960396,0.00990099009901,0.00990099009901,0.029702970297,0.019801980198,0.0,0.00990099009901,0.00990099009901,0.0,0.0,0.0,0.019801980198,0.0,0.019801980198,0.00990099009901,0.00990099009901,0.0,0.00990099009901,0.019801980198,0.00990099009901,0.0,0.019801980198,0.0,0.00990099009901,0.0,0.00990099009901,0.00990099009901,0.0,0.0693069306931,0.0,0.00990099009901,0.0,0.00990099009901,0.0,0.029702970297,0.0,0.0,0.019801980198,0.0,0.00990099009901,0.0,0.00990099009901,0.0,0.00990099009901,0.00990099009901,0.00990099009901,0.029702970297,0.019801980198])]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Task f) Train a classifier \n",
    "\n",
    "Use the `LabeledPoint` objects to train the `LogisticRegression` and calculate the accuracy of the model on the training set using normalised word list as input again.  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from pyspark.mllib.classification import LogisticRegressionWithLBFGS, NaiveBayes\n",
    "\n",
    "path = 'hdfs://saltdean/data/spam/stop/part1'\n",
    "\n",
    "N=100 # size of hash vector \n",
    "def trainModel(trainpath,N):\n",
    "    \n",
    "    #Create train directory from training path by getting term frequency and labelled point for each file\n",
    "    train_RDD = make_lp_RDD(make_f_tfLn_RDD(path), N)\n",
    "    \n",
    "    #Apply logistic regression and Naive Bayes Models \n",
    "    model = LogisticRegressionWithLBFGS.train(train_RDD)\n",
    "    #model = NaiveBayes.train(train_RDD) # uncomment to try naive bayes\n",
    "    \n",
    "    #Predict with training data and calculate accuracy (this will get close to 100% as the model is trained on same data)\n",
    "    labels_Preds = train_RDD.map(lambda p: (p.label, model.predict(p.features)))\n",
    "    trainErr = labels_Preds.filter(lambda x: x[0] != x[1]).count() / float(train_RDD.count())\n",
    "    correct = labels_Preds.filter(lambda x: x[0] == x[1]).count()\n",
    "    accuracy = correct/labels_Preds.count()\n",
    "\n",
    "    print('Training {} data items gave {} correct predictions with training accuracy of {:.1%} and error of {:.1%} '.format(labels_Preds.count(), correct, accuracy, trainErr)) # output raw numbers\n",
    "    return model \n",
    "\n",
    "#f_wcLn_RDD = make_f_tfLn_RDD(path) # for testing\n",
    "#model = trainModel(path,N) # for testing\n",
    "#print(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### SAMPLE OUTPUT\n",
    "Training 289 data items gave 289 correct predictions with training accuracy of 100.0% and error of 0.0% \n",
    "\n",
    "(weights=[-363.307005267,-125.088207321,-165.072227295,287.388859907,122.30305052,-812.454534754,107.318355618,13.2426632602,227.865310925,-453.707612367,41.9848605925,-550.657329394,-650.426435918,404.052173404,-340.774956292,531.362347587,310.73511211,-58.1812844056,-570.055169614,-224.248795405,-91.6697147098,-26.625201643,425.509051547,269.135534,472.577950296,66.8670545364,-205.110715293,628.670296566,516.672511653,20.6506929531,1.86023040465,-101.744379394,-210.895681769,221.075858226,44.5045272705,492.667734878,688.035426155,157.669489159,1105.36027481,-634.939707953,378.321292732,219.473940462,-264.642571976,143.875341616,-809.728660427,-146.788839695,-106.203126231,-479.845256628,-81.6894429518,-411.494460719,45.8975507382,-15.8226101493,-365.155830102,-601.188343865,-417.668225645,377.100961044,-585.645516942,31.7354911258,195.328859431,87.7341097968,403.842009206,-257.317664047,-249.930713537,440.323910261,123.937673549,488.026152557,-199.023731806,415.790155674,-110.863600975,148.164444405,-117.218226185,9.08258659071,-162.306375591,-258.239434578,-384.256327793,11.3816505206,-78.7843162392,-27.4581887945,-76.6924148055,-19.0701382672,139.002600618,-558.789734616,1301.33846644,-116.735338192,367.459529957,112.837914435,-301.771559049,-123.50212083,121.70576053,173.679906734,446.422213089,-798.814469198,349.612258584,-424.226640714,108.789830458,-108.016603174,-555.510911002,-570.098055546,-164.228863695,-316.565701242], intercept=0.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Task g) Test the classifier\n",
    "\n",
    "Use the files from \\texttt{.../data/extra/spam/bare/part10} and prepare them like in task~a)-e) (use the function you created in task e) and before. Then use the trained model to predict the label for each vector you have and compare it to the original to test the performance of your classifier. (10\\%) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "path = 'hdfs://saltdean/data/spam/stop/part10'\n",
    "\n",
    "def testModel(model,testPath,N):\n",
    "    test_RDD = make_lp_RDD(make_f_tfLn_RDD(path), N)\n",
    "    \n",
    "    # calculating performance measures for testing phase\n",
    "    labels_Preds = test_RDD.map(lambda p: (p.label, model.predict(p.features)))\n",
    "    testErr = labels_Preds.filter(lambda x: x[0] != x[1]).count() / float(test_RDD.count())\n",
    "    correct = labels_Preds.filter(lambda x: x[0] == x[1]).count()\n",
    "    accuracy = correct/labels_Preds.count()\n",
    "\n",
    "    print('Testing {} data items gave {} correct predictions with testing accuracy: {:.1%} , error: {:.1%} '.format(labels_Preds.count(), correct, accuracy, testErr)) # output raw numbers\n",
    "\n",
    "    return model \n",
    "\n",
    "# pass the model to test function alongwith trained model and hash vector size\n",
    "#tmodel = testModel(model,make_f_tfLn_RDD(path),N) # for testing\n",
    "#print(tmodel)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### SAMPLE OUTPUT\n",
    "\n",
    "Testing 291 data items gave 219 correct predictions with testing accuracy: 75.3% , error: 24.7% \n",
    "\n",
    "(weights=[-363.307005267,-125.088207321,-165.072227295,287.388859907,122.30305052,-812.454534754,107.318355618,13.2426632602,227.865310925,-453.707612367,41.9848605925,-550.657329394,-650.426435918,404.052173404,-340.774956292,531.362347587,310.73511211,-58.1812844056,-570.055169614,-224.248795405,-91.6697147098,-26.625201643,425.509051547,269.135534,472.577950296,66.8670545364,-205.110715293,628.670296566,516.672511653,20.6506929531,1.86023040465,-101.744379394,-210.895681769,221.075858226,44.5045272705,492.667734878,688.035426155,157.669489159,1105.36027481,-634.939707953,378.321292732,219.473940462,-264.642571976,143.875341616,-809.728660427,-146.788839695,-106.203126231,-479.845256628,-81.6894429518,-411.494460719,45.8975507382,-15.8226101493,-365.155830102,-601.188343865,-417.668225645,377.100961044,-585.645516942,31.7354911258,195.328859431,87.7341097968,403.842009206,-257.317664047,-249.930713537,440.323910261,123.937673549,488.026152557,-199.023731806,415.790155674,-110.863600975,148.164444405,-117.218226185,9.08258659071,-162.306375591,-258.239434578,-384.256327793,11.3816505206,-78.7843162392,-27.4581887945,-76.6924148055,-19.0701382672,139.002600618,-558.789734616,1301.33846644,-116.735338192,367.459529957,112.837914435,-301.771559049,-123.50212083,121.70576053,173.679906734,446.422213089,-798.814469198,349.612258584,-424.226640714,108.789830458,-108.016603174,-555.510911002,-570.098055546,-164.228863695,-316.565701242], intercept=0.0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Task h) Run experiments \n",
    "\n",
    "Packaging the whole classifier training and evaluation in one function. \n",
    "Following experiments will be conducted using TrainTestEval() function:\n",
    "\n",
    "1. Effect of differently sized training sets\n",
    "2. Effect of increasing hash vector size\n",
    "3. Effect of differently processed training datasets\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "# Combine Training, Testing and Evaluation in a single function which takes in training and testing directories\n",
    "# alongwith hash vector size as N\n",
    "import glob\n",
    "import time\n",
    "from pyspark.mllib.classification import LogisticRegressionWithLBFGS\n",
    "\n",
    "def TrainTestEval(trainPath, testPath, N):\n",
    "\n",
    "    # make train and test RDDs by calcultaing term frequencies and applying labeled points training and test directories  \n",
    "    \n",
    "    train_RDD = make_lp_RDD(make_f_tfLn_RDD(trainPath), N)\n",
    "    test_RDD = make_lp_RDD(make_f_tfLn_RDD(testPath), N)\n",
    "    \n",
    "    # run the classifier with training data\n",
    "    start_time = time.time()\n",
    "    model = LogisticRegressionWithLBFGS.train(train_RDD)\n",
    "    trainTime = time.time() - start_time\n",
    "    \n",
    "    # apply the trained classifier on training data and calculate evaluation data\n",
    "    train_labels_Preds = train_RDD.map(lambda p: (p.label, model.predict(p.features)))\n",
    "    trainCorrect = train_labels_Preds.filter(lambda x: x[0] == x[1]).count()\n",
    "    trainAccuracy = trainCorrect/train_labels_Preds.count()\n",
    "\n",
    "    # apply the trained classifier on testing data and calculate evaluation data\n",
    "    test_labels_Preds = test_RDD.map(lambda p: (p.label, model.predict(p.features)))\n",
    "    testCorrect = test_labels_Preds.filter(lambda x: x[0] == x[1]).count()\n",
    "    testAccuracy = testCorrect/test_labels_Preds.count()\n",
    "\n",
    "    # return selected performance measures for comparison and evaluation in following experiments\n",
    "    return [train_labels_Preds.count(), trainTime, testAccuracy, trainAccuracy]\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Experiment 1\n",
    "\n",
    "Increase the size of training data in steps and monitor the effect on training time and test accuracy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EXPERIMENT 1: Testing different training set sizes\n",
      "**************************************************\n",
      "Constant Test Path = hdfs://saltdean/data/spam/bare/part10\n",
      "\n",
      "Train path\t\t\t\t\tExamples\tTrain Time\tTrain Accuracy\tTest Accuracy\n",
      "hdfs://saltdean/data/spam/bare/part[1-1]\t289\t\t4.209\t\t100.0%\t\t87.3%\n",
      "hdfs://saltdean/data/spam/bare/part[1-2]\t578\t\t6.512\t\t100.0%\t\t91.4%\n",
      "hdfs://saltdean/data/spam/bare/part[1-3]\t867\t\t8.953\t\t100.0%\t\t91.1%\n",
      "hdfs://saltdean/data/spam/bare/part[1-4]\t1156\t\t11.908\t\t100.0%\t\t91.1%\n",
      "hdfs://saltdean/data/spam/bare/part[1-5]\t1446\t\t15.286\t\t100.0%\t\t90.7%\n",
      "hdfs://saltdean/data/spam/bare/part[1-6]\t1735\t\t17.631\t\t98.4%\t\t94.2%\n",
      "hdfs://saltdean/data/spam/bare/part[1-7]\t2024\t\t20.877\t\t98.3%\t\t96.2%\n",
      "hdfs://saltdean/data/spam/bare/part[1-8]\t2313\t\t22.565\t\t97.6%\t\t94.5%\n",
      "hdfs://saltdean/data/spam/bare/part[1-9]\t2602\t\t24.095\t\t97.6%\t\t94.8%\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA00AAAGJCAYAAABW5dinAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc75bacbeb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0cAAAGJCAYAAABBx0huAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc75b7d2d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dirPattern = 'hdfs://saltdean/data/spam/bare/part[1-{}]' \n",
    "testPath = 'hdfs://saltdean/data/spam/bare/part10'\n",
    "\n",
    "print('EXPERIMENT 1: Testing different training set sizes')\n",
    "print('**************************************************')\n",
    "print('Constant Test Path = {}'.format(testPath))\n",
    "print('\\nTrain path\\t\\t\\t\\t\\tExamples\\tTrain Time\\tTrain Accuracy\\tTest Accuracy')\n",
    "\n",
    "N = 100\n",
    "evalData = [] # empty array for storing performance data\n",
    "\n",
    "for i in range(9): # increase training size by adding directories recursively\n",
    "    trainPath = dirPattern.format(i+1) \n",
    "    data = TrainTestEval(trainPath, testPath, N)   \n",
    "    print('{}\\t{}\\t\\t{}\\t\\t{:.1%}\\t\\t{:.1%}'.format(str(trainPath), data[0],(\"%0.3f\" % data[1]),data[3], data[2]))\n",
    "    evalData.append(data) # collect performance measures\n",
    "    \n",
    "#Storing the collected performance measures into a dataframe for plotting comparison graph\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "df = pd.DataFrame(evalData)\n",
    "df.columns = ['trainEx', 'trainTime', 'testAccuracy', 'trainAccuracy']\n",
    "\n",
    "plt.figure(figsize=(14,6))\n",
    "training_accuracy, = plt.plot(df.trainEx, df.trainAccuracy*100,label=\"Training Accuracy\");\n",
    "test_accuracy, = plt.plot(df.trainEx, df.testAccuracy*100,label=\"Test Accuracy\");\n",
    "plt.legend(handles=[training_accuracy,test_accuracy])\n",
    "plt.title('Fig (a). Training Examples vs. Test/Train Accuracy'), plt.xlabel('No. of Training Examples'), plt.ylabel('% Accuracy')\n",
    "plt.ylim([85,101]), plt.show()\n",
    "plt.figure(figsize=(14,6))\n",
    "plt.plot(df.trainEx, df.trainTime)\n",
    "plt.title('Fig (b).Training Examples vs. Training Time'),plt.xlabel('No. of Training Examples'), plt.ylabel('Training Time')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Comments on Experiment 1\n",
    "\n",
    "This experiment highlights the effect of training data size on training time and testing accuracy. Fig (a) highlights a high training accuracy of 100% with a lower test accuracy with only one training directory. The overfitting of the training data is reduced as the sample size gets close to 2000 and testing accuracy starts to increase. This fact demonstrates that size of training data plays a crucial role towards improved classification and to reduce overfitting.   \n",
    "\n",
    "The output in Fig (b) shows an almost proportional relationship between size of training data and time taken during the training phase. A slight increase in overall test accuracy can also be viewed from the output and the plot as training data increases. With a roughly ten-fold increase in data size, the training time increases by a factor of 6 and a 2% overall increase in test accuracy is also observed. \n",
    "\n",
    "These observations can lead to a conclusion that for critical applications, i.e. scientific and technical document classifications, it would be advisable to use to use a bigger data size to ensure highest possible accuracy of results. For time critical text analytics (i.e. online document classification), a compromise would have to be made between perfromance and accuracy as training on increasingly large datasets may become computationally expensive and may not fulfil required level of agility. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### EXPERIMENT 2\n",
    "\n",
    "Increase the size of Hash Vector size and monitor the impact on accuracy and training time. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EXPERIMENT 2: Testing different vector sizes\n",
      "********************************************\n",
      "N\tTrain Time\tTrain Accuracy\tTest Acuracy\n",
      "10\t25.978\t\t84.3%\t\t85.2%\n",
      "30\t24.917\t\t90.5%\t\t87.6%\n",
      "100\t25.393\t\t97.6%\t\t94.8%\n",
      "300\t26.077\t\t100.0%\t\t97.6%\n",
      "1000\t26.481\t\t100.0%\t\t98.6%\n",
      "1500\t26.117\t\t100.0%\t\t98.3%\n",
      "2000\t27.260\t\t100.0%\t\t99.3%\n",
      "3000\t26.622\t\t100.0%\t\t99.0%\n",
      "10000\t36.324\t\t100.0%\t\t97.6%\n",
      "30000\t50.788\t\t100.0%\t\t96.9%\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1EAAAGJCAYAAABrd7nBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc75b7cadd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA0oAAAGJCAYAAAC0OcPeAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nOwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc75b70cdd8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "print('EXPERIMENT 2: Testing different vector sizes')\n",
    "print('********************************************')\n",
    "print('N\\tTrain Time\\tTrain Accuracy\\tTest Acuracy')\n",
    "\n",
    "# array with increasing values of N to try as hash vector size\n",
    "NSize = [10,30,100,300,1000,1500,2000,3000,10000,30000]\n",
    "\n",
    "trainPath = 'hdfs://saltdean/data/spam/bare/part[1-9]'\n",
    "testPath = 'hdfs://saltdean/data/spam/bare/part10'\n",
    "\n",
    "evalData2 = [] # for storing evaluation data\n",
    "\n",
    "for n in NSize: # loop through NSize, increasing the value in each iteration\n",
    "    data2 = TrainTestEval(trainPath, testPath, n)   \n",
    "    print('{}\\t{}\\t\\t{:.1%}\\t\\t{:.1%}'.format(n,(\"%0.3f\" % data2[1]),data2[3],data2[2]))\n",
    "    data2.extend([n])\n",
    "    evalData2.append(data2)\n",
    "\n",
    "#Storing the collected performance measures into a dataframe and plotting evaluation graphs\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "df2 = pd.DataFrame(evalData2)\n",
    "df2.columns = ['trainEx','trainTime', 'testAccuracy', 'trainAccuracy','N']\n",
    "plt.figure(figsize=(14,6))\n",
    "training_accuracy, = plt.plot(df2.N, df2.trainAccuracy*100,label=\"Training Accuracy\");\n",
    "test_accuracy, = plt.plot(df2.N, df2.testAccuracy*100,label=\"Test Accuracy\");\n",
    "plt.legend(handles=[training_accuracy,test_accuracy])\n",
    "plt.title('Fig (c). Hash Vector Size vs. Test/Train Accuracy'), plt.xlabel('Hash Vector Sizes'), plt.ylabel('% Accuracy')\n",
    "plt.ylim([85,101])\n",
    "plt.show()\n",
    "plt.figure(figsize=(14,6))\n",
    "plt.plot(df2.N, df2.trainTime)\n",
    "plt.title('Fig (d).Hash Vector Size vs. vs. Training Time'),plt.xlabel('Hash Vector Sizes'), plt.ylabel('Training Time')\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Comments on Experiment 2\n",
    "\n",
    "This experiment monitors the impact of hash vector size of testing and training accuracy, as well as time taken during the training phase. Fig (c) alongwith the output shows lower training and testing accuracy at very small size of N(i.e. 10). With an increase of training size according to defined steps, training accuracy quickly moves to 100% while the test accuracy peaks 99.3% at N = 2000. Fig (c) shows the effect of hash vector size on training time which increases from 20 sec (N= 10) to 47 sec (N= 30000). The optimal value of N is seen to be at N = 2000 where the classifier gives a training accuacy of 99.3% with a training time of 22.37 seconds. \n",
    "\n",
    "An increasing hash vector size would ensure the inclusion of new words, differently spelled words from new data which may result in better classification, but may become become computationally more expensive in a performance oriented context. The hash vector size, therefore, must be chosen based on availability of computing resources, nature of problem while finding the right balance between time and accuracy.   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### EXPERIMENT 3\n",
    "\n",
    "Monitor the impact of differently pre-processed datasets on training, testing accuracy and training time."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "EXPERIMENT 3: Testing differently preprocessed data sets\n",
      "**********************************************************\n",
      "\n",
      "training on parts 1-9, testing on part 10 with N = 2000\n",
      "\n",
      "Pre-Processing\t\t\t\tTraining Time\t\tTrain Accuracy\tTest Accuracy\n",
      "\n",
      "No preprocessing            \t\t26.884\t\t\t100.0%\t\t99.3%\n",
      "Stopwords removed           \t\t26.718\t\t\t100.0%\t\t99.0%\n",
      "Lemmatised                  \t\t26.492\t\t\t100.0%\t\t99.0%\n",
      "Lemmatised+stopwords removed\t\t24.704\t\t\t100.0%\t\t99.0%\n"
     ]
    }
   ],
   "source": [
    "N = 2000 # based on observation from experiment 2\n",
    "\n",
    "print('\\nEXPERIMENT 3: Testing differently preprocessed data sets')\n",
    "print('**********************************************************')\n",
    "print('\\ntraining on parts 1-9, testing on part 10 with N = {}'.format(N))\n",
    "\n",
    "prefix = 'hdfs://saltdean/data/spam/'\n",
    "\n",
    "# Dictionary with labels and training data locations\n",
    "setDict = {'No preprocessing            ': prefix + 'bare/',\n",
    "           'Stopwords removed           ': prefix + 'stop/',\n",
    "           'Lemmatised                  ': prefix + 'lemm/',\n",
    "           'Lemmatised+stopwords removed': prefix + 'lemm_stop/'}\n",
    "\n",
    "print('\\nPre-Processing\\t\\t\\t\\tTraining Time\\t\\tTrain Accuracy\\tTest Accuracy\\n')\n",
    "evalData3 = []\n",
    "\n",
    "for sp in setDict: # loop through setDict trying different training locations  \n",
    "\n",
    "    testPath = setDict[sp] + 'part' + str(10)\n",
    "    trainPath = setDict[sp] + 'part[1-9]' \n",
    "    data3 = TrainTestEval(trainPath, testPath, N)\n",
    "    evalData3.append(data)\n",
    "    \n",
    "    # print the output \n",
    "    print('{}\\t\\t{}\\t\\t\\t{:.1%}\\t\\t{:.1%}'.format(sp,\"%0.3f\" % data3[1], data3[3], data3[2]))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Comments on Experiment 3\n",
    "Training on different pre-processed datasets highlights some changes in testing accuracy and training times with a hash vector size of 2000 as indicated by previous experiment. Withoutany pre-processing, the training data gave highest test accuracy of 99.3% but took slightly longer time than other datasets. The effect of lemmatisation and stop word removal slightly reduces training time and fastest training is seen with both lemmatisation and stop word removal with 24.704 and an accuracy of 99%. \n",
    "\n",
    "The effect may scale up with much larger datasets and must be considered in depth to choose the right balance between pre-processing and requred level of accuracy. Pre-Processing text according to domain needs may reduce the training time while not reducing the testing accuracy too much. \n",
    "\n",
    "## Conclusion\n",
    "\n",
    "Above experiments highlight some basic yet important practices in text classification of big data. Logistic regression has been applied to a number of training sets based on data size, hash vector size and pre-processing. The results re-affirm the important principles of text analytics i.e. increasing testing accuracy and training time with larger training datasets and hashvector sizes and reduced computation while training on pre-processed data. "
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "PySpark",
   "language": "python",
   "name": "pyspark"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}