btomlin_834198.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# The University of Melbourne, School of Computing and Information Systems\n",
    "# COMP30027 Machine Learning, 2018 Semester 1\n",
    "-----\n",
    "## Project 1: What is labelled data worth to Naive Bayes?\n",
    "-----\n",
    "###### Student Name(s): Ben Tomlin (834198)\n",
    "###### Python version: 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This iPython notebook is a template which you may use for your Project 1 submission. (You are not required to use it; in particular, there is no need to use iPython if you do not like it.)\n",
    "\n",
    "Marking will be applied on the seven functions that are defined in this notebook, and to your responses to the questions at the end of this notebook.\n",
    "\n",
    "You may change the prototypes of these functions, and you may write other functions, according to your requirements. We would appreciate it if the required functions were prominent/easy to find. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Notes to marker:\n",
    "\n",
    "## Function Locations:\n",
    "### evaluate()\n",
    ">This function is in Classifier.evaluate() and is the same for both models.\n",
    ">The evaluate class method is the first method (for easy access) after the initializer\n",
    "        \n",
    "### __init__()/preprocess(), train(), predict()\n",
    ">Each classifier object has functions in the following order for convenient access\n",
    ">__init__(), preprocess(), train(), predict()\n",
    "        \n",
    "## Run speed\n",
    "A decision was made to use Pandas DataFrame structures for holding the raw data. This has slowed the implementation down. The larger datasets take 10min to run five iterations of unsupervised training. iloc was used to prevent minimize dict key hashing etc. In hindsight - i would not use Pandas again for heavy processing like this project requires. \n",
    "\n",
    "To accomodate for this - the program results have been copied into markdown cells containing answers to the questions. This way you don't need to run the whole program. \n",
    "\n",
    "The smaller breast cancer dataset will run quickly so that the code can be tested. Results will differ slightly due to random seeds (unsupervised) and holdout (supervised)\n",
    "\n",
    "## General Notes\n",
    "Holdout and add k smoothing were implemented however Q1 and Q7 are the ones answered here. Unknown attribute values are treated as an attribute value '?'. \n",
    "\n",
    "There's no need to run all the code as the outputs were copied into the markdown of the answers - though running the code will produce the output used in the answers."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Preliminaries & Helper Functions\n",
    "---\n",
    "\n",
    "This contains utility methods for object creation, manipulation and printing of results - auxillary to the actual project."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Utility\n",
    "import csv\n",
    "import numbers\n",
    "from collections import defaultdict\n",
    "from math import exp, log, acos, sqrt, pi\n",
    "from re import sub\n",
    "\n",
    "# Dataframes for raw data\n",
    "import numpy as np\n",
    "import pandas as p\n",
    "\n",
    "# Random seeding for unsupervised model\n",
    "import random\n",
    "\n",
    "# relative path to folder containing data csv files\n",
    "dataPath = './'\n",
    "\n",
    "# Datasets to analyze\n",
    "dataSet = {\n",
    "        'breastCancer': {'nClassValue':2,\n",
    "                         'file':'breast-cancer.csv'},\n",
    "        'car':          {'nClassValue':4,\n",
    "                         'file':'car.csv'},\n",
    "        'hypothyroid':  {'nClassValue':2,\n",
    "                         'file':'hypothyroid.csv'},\n",
    "        'mushroom':     {'nClassValue':2,\n",
    "                         'file':'mushroom.csv'}} \n",
    "\n",
    "    \n",
    "def printComparisionTable(classifierDict):\n",
    "    \"\"\" Print a comparision table for two classifiers given in a dict structure,\n",
    "    \n",
    "    Keys of dict structure are printed as identifiers for the clasifier\n",
    "    \"\"\"\n",
    "    \n",
    "    def _stringifyMetric(m):\n",
    "        \"\"\"Turn all metrics to strings for simple printing. (Some could be 'NA'\n",
    "        others will be floats)\"\"\"\n",
    "        if isinstance(m, numbers.Number):\n",
    "            return('{:.2f}'.format(m))\n",
    "        else:\n",
    "            return(str(m))\n",
    "                \n",
    "    COLUMNW = '25'\n",
    "    WIDTH=str(int(COLUMNW)*3)\n",
    "\n",
    "    metricTitle = 'Metric Name'\n",
    "    tableRow = []\n",
    "    \n",
    "    classifierType = list(classifierDict.keys())\n",
    "    \n",
    "    # Add header row to metric table\n",
    "    tableRow.append([metricTitle])\n",
    "    for classifierName in classifierType:\n",
    "        tableRow[0].append(classifierName)\n",
    "\n",
    "    # Add metric identifier data to metric table\n",
    "    rx=1\n",
    "    metrics = classifierDict[classifierType[0]].metrics[Classifier.CLASSAVERAGE_IDENTIFIER]\n",
    "    for key in metrics.keys():\n",
    "        tableRow.append([])\n",
    "        tableRow[rx].append(key)\n",
    "        \n",
    "        for classifierName in classifierType:\n",
    "            metric = classifierDict[classifierName].metrics[Classifier.CLASSAVERAGE_IDENTIFIER][key]\n",
    "            tableRow[rx].append(_stringifyMetric(metric))\n",
    "        rx+=1\n",
    "\n",
    "    def _printLine():\n",
    "        print('_'*(int(WIDTH)+7))\n",
    "\n",
    "    def _printHeader():\n",
    "        _printLine()\n",
    "        titleFormat = '|{:^'+str(int(WIDTH)+5)+'}|'\n",
    "        print(titleFormat.format(classifierDict[classifierType[0]].dataKey.upper()))\n",
    "        _printLine()\n",
    "        headerFormat = '| {0[0]:<'+COLUMNW+'s}'+'| {0[1]:<'+COLUMNW+'s}| {0[2]:<'+COLUMNW+'s}|'\n",
    "        print(headerFormat.format(tableRow[0]))\n",
    "        _printLine()\n",
    "    \n",
    "    # Print table \n",
    "    _printHeader()\n",
    "    \n",
    "    # Print data\n",
    "    for ix in range(-1+len(tableRow)):\n",
    "        rowFormat = '| {0[0]:<'+COLUMNW+'s}| {0[1]:<'+COLUMNW+'s}| {0[2]:<'+COLUMNW+'s}|'\n",
    "        printRow = rowFormat.format(tableRow[ix+1])\n",
    "        print(printRow)\n",
    "    _printLine()\n",
    "       \n",
    "\n",
    "def createTrainAndEvaluateClassifier(file, dataKey, nIter = 5, nClassValue=-1):\n",
    "    \"\"\"Create, train and evaluate classifiers on a dataset. \n",
    "    \n",
    "    ARGUMENT:\n",
    "        file - path to dataset\n",
    "        dataKey - identifier for dataset (used when printing)\n",
    "        nClassValue - if not -1, create an unsupervised classifier. Else create\n",
    "                        a supervised classifier. Indicates number of classes to \n",
    "                        use for the unsupervised model\n",
    "                        \n",
    "    RETURN:\n",
    "        The resultant classifier\n",
    "    \"\"\"\n",
    "\n",
    "    if nClassValue>0:\n",
    "        classifier = UnsupervisedNaiveBayes(file, dataKey, nClassValue)\n",
    "        classifier.train(nIter)\n",
    "\n",
    "    else:\n",
    "        classifier = NaiveBayes(file, dataKey)\n",
    "        classifier.train()\n",
    "\n",
    "\n",
    "    classifier.evaluate()\n",
    "    return(classifier)\n",
    "\n",
    "\n",
    "def prepareClassifiersForSet(key, nIteration=5):\n",
    "    \"\"\"Evaluate both classifiers on datasets and print the results\n",
    "    \"\"\"\n",
    "    \n",
    "    filepath = getDatasetPath(dataSet[key]['file'])\n",
    "    nClassValue = dataSet[key]['nClassValue']\n",
    "        \n",
    "    sModel = createTrainAndEvaluateClassifier(filepath, key)\n",
    "    uModel = createTrainAndEvaluateClassifier(filepath, key, nClassValue=nClassValue, nIter=nIteration)\n",
    "    classifierObject[key][\"Unsupervised\"]=uModel\n",
    "    classifierObject[key][\"Supervised\"]=sModel\n",
    "            \n",
    "\n",
    "def getDatasetPath(file):\n",
    "    return(dataPath+file)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Classifier Class\n",
    "---\n",
    "This contains ancestor methods common to both classifiers\n",
    ">* Raw data management\n",
    ">* Data preprocessing\n",
    ">* Data partitioning\n",
    ">* Evaluation of a model\n",
    ">* Testing for smoothing requirements\n",
    ">* Model smoothing logic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "class Classifier:\n",
    "    \"\"\"Generic container for data used by a classifier\n",
    "    \n",
    "    The classifier contains methods for smoothing count data structures and \n",
    "    evaluation of models\n",
    " \n",
    "    ---'Raw Data' Structure--- \n",
    "    The datasets (training, source, evaluation) are organized in Pandas \n",
    "    DataFrames. These have the following structure;\n",
    "    \n",
    "    __________________________________________________________________________\n",
    "    |Index     | Attribute Identifier    | ...        | Class Identifier       |\n",
    "    __________________________________________________________________________\n",
    "    |ix        | Attribute Value        | ...        | Class Value           | \n",
    "    |ix        | Attribute Value        | ...        | Class Value           | \n",
    "    |ix        | Attribute Value        | ...        | Class Value           | \n",
    "    |ix        | Attribute Value        | ...        | Class Value           | \n",
    "    _________________________________________________________________________|\n",
    "    \n",
    "\n",
    "    \"\"\"\n",
    "    MARK_ATTR='attr'        #Attribute column identifier\n",
    "    MARK_CLASS='class'      #Class column identifier \n",
    "    DEFAULT_SMOOTHING = 1\n",
    "    DEFAULT_INITIAL_PARTITION=0.75\n",
    "    DEFAULT_CLASS_COLUMN=-1\n",
    "    CLASSAVERAGE_IDENTIFIER = 'Class Average'\n",
    "    \n",
    "    def __init__(self, sourcePath, dataKey, hasHeader=False, \n",
    "                    initialPartition=DEFAULT_INITIAL_PARTITION,\n",
    "                    classColumn=DEFAULT_CLASS_COLUMN,\n",
    "                    smoothingK=DEFAULT_SMOOTHING):\n",
    "        \"\"\"Initializes a classifier from source data\n",
    "         \n",
    "        Partition raw data and initialize metadata. Place data into DataFrames.\n",
    "\n",
    "        ARGUMENT\n",
    "            sourcePath: \n",
    "                Absolute or relative path to the csv file containing dataset. \n",
    "                \n",
    "            dataKey: Arbitrary identifier or name for the dataset\n",
    "                \n",
    "            hasHeader:\n",
    "                Indicates whether a header is present in CSV file or not. Header \n",
    "                assumed to be first row of the data file. If present, the data \n",
    "                frame will use the header as column identifiers.\n",
    "            \n",
    "            initialPartition: \n",
    "                Fraction of dataset to use as training in initial split.\n",
    "                \n",
    "            classColumn:\n",
    "                Integer column index indentifying the CSV column that holds class\n",
    "                data.\n",
    "                \n",
    "            smoothingK:\n",
    "                The 'add k' value to use for smoothing. Default is 1.\n",
    "        \"\"\"\n",
    "        # Load data\n",
    "        self.sourcePath = sourcePath\n",
    "        self.hasHeader = hasHeader\n",
    "        self.dataKey = dataKey\n",
    "\n",
    "        # Wraparound class column index\n",
    "        self.nCol = self._getColumnCount()\n",
    "        self.classColumnIndex = (classColumn+self.nCol)%self.nCol\n",
    "        self.sourceData = self.preprocess()\n",
    "        \n",
    "        title = self._getHeader()\n",
    "        self.classColumnId = title[classColumn]\n",
    "\n",
    "        # Set of all possible class values\n",
    "        self.classValue = list(self.sourceData.iloc[:,self.classColumnIndex].unique())\n",
    "\n",
    "        # Get attribute & feature sets\n",
    "        self.featureSet = {(ix, title[ix]) for ix in range(len(title))}\n",
    "        self.attributeSet={(ix,title[ix]) for ix in range(len(title)) if ix != self.classColumnIndex}\n",
    "\n",
    "        self.model=None             \n",
    "\n",
    "        # Split source data into samples\n",
    "        self.trainSample, self.evalSample = self._partitionData(initialPartition)\n",
    "        \n",
    "        # Add k value to use if smoothing required\n",
    "        self.smoothingApplied=False\n",
    "        self.smoothingK = smoothingK\n",
    "        self.metrics = None\n",
    " \n",
    "\n",
    "    def preprocess(self):\n",
    "        \"\"\" Load data into pandas dataframe \"\"\"\n",
    "        return p.read_csv(self.sourcePath, header=0, names=self._getHeader())\n",
    "    \n",
    "\n",
    "    def evaluate(self):\n",
    "        \"\"\"Evaluate model and return a dictionary of performance metrics \n",
    "                \n",
    "        RETURN:\n",
    "            score\n",
    "                A dictionary with scoring parameters\n",
    "        \"\"\"\n",
    "         \n",
    "        def _getMetric(prediction, interestingClass):\n",
    "            \"\"\" Return a tuple of (TP, TN, FP, FN) metrics for an evaluation set\n",
    "            \n",
    "            Predictions are discrete, that is, they predict one class for each \n",
    "            instance. Metrics are measured against an interesting class value,\n",
    "            which is the class value we are measuring performance against\n",
    "            \n",
    "            DEFINITIONS:\n",
    "                TP - True Positive\n",
    "                TN - True Negative\n",
    "                FP - False Positive\n",
    "                FN - False Negative\n",
    "                TC - Total Correct classifications\n",
    "                \n",
    "            ARGUMENT:\n",
    "                prediction\n",
    "                    List of predictions made against the evaluation set. An \n",
    "                    assumption is made that the evaluation set is ordered \n",
    "                    in the same way as the prediction list. Predictions are \n",
    "                    discrete\n",
    "\n",
    "                interestingClass\n",
    "                    The class value which we are interested in. Metrics are \n",
    "                    calculated against this value. \n",
    "                    \n",
    "            RETURN:\n",
    "                (TP, TN, FP, FN, TC)\n",
    "                \n",
    "            ASSUMPTION:\n",
    "                *) Prediction is a list with an ordering consistent with \n",
    "                    self.evalSample\n",
    "            \"\"\"\n",
    "            \n",
    "            if (len(prediction) != len(self.evalSample)):\n",
    "                ValueError('Require the same amount of predictions as samples.')\n",
    "\n",
    "            TP=TN=FP=FN=TC=0\n",
    "            for ix in range(self.evalSample.shape[0]):\n",
    "                instanceClass = self.evalSample.iloc[ix][self.classColumnId]\n",
    "                predictedClass = prediction[ix]\n",
    "\n",
    "                TP+=(predictedClass == instanceClass \n",
    "                     and predictedClass == interestingClass)\n",
    "\n",
    "                TN+=(predictedClass != interestingClass \n",
    "                     and instanceClass != interestingClass)\n",
    "\n",
    "                FP+=(predictedClass == interestingClass\n",
    "                     and instanceClass != interestingClass)\n",
    "\n",
    "                FN+=(predictedClass != interestingClass\n",
    "                     and instanceClass == interestingClass)\n",
    "\n",
    "                TC+=(predictedClass == instanceClass)\n",
    "                \n",
    "            return((TP,TN,FP,FN,TC))\n",
    "       \n",
    "\n",
    "        def _processMetric(sampleSize, metric):\n",
    "            \"\"\"Given a metric tuple, derive performance statistics\n",
    "            \n",
    "            ARGUMENT:\n",
    "                sampleSize = integer size of sample metric applies to\n",
    "                metric = (TP TN FP FN TC)\n",
    "                \n",
    "            RETURN:\n",
    "                where s = number of instances in sample\n",
    "                (\n",
    "                    (BA) Basic Accuracy: TC / s ,\n",
    "                    (CVA) Class Value Accuracy: (TP + TN) / s , \n",
    "                    (CVE) Class Value Error Rate: (FP + FN) /s , \n",
    "                    (CVP) Class Value Precision: TP / (TP + FP) , \n",
    "                    (CVR) Class Value Recall: TP / (TP + FN) ,\n",
    "                    (CVF) Class Value FScore: CVP*CVR*2 / (CVP + CVR)\n",
    "                )\n",
    "            \"\"\" \n",
    "            BA = 100*metric[4]/sampleSize\n",
    "            CVA = 100*sum(metric[0:2])/sampleSize\n",
    "            CVE = 100*sum(metric[2:4])/sampleSize\n",
    "\n",
    "            # Get the precision. 'NA' if the classifier made no predictions on the\n",
    "            # evaluation set for the class of interest.\n",
    "            if (metric[0]+metric[2])==0:\n",
    "                CVP='NA'\n",
    "            else:\n",
    "                CVP = 100*metric[0]/(metric[0]+metric[2])\n",
    "                \n",
    "            # Get recall. If there are no evaluation instances with a particular\n",
    "            # class then we return an 'NA' value\n",
    "            if (metric[0]+metric[3])==0:\n",
    "                CVR='NA'\n",
    "            else:\n",
    "                CVR = 100*metric[0]/(metric[0]+metric[3]) \n",
    "                \n",
    "            # Find the F-Score. 'NA' if there is no value available for precision\n",
    "            # or recall. 'NA' if the precision and recall are zero.\n",
    "            if CVP=='NA' or CVR=='NA' or (CVR+CVP==0):\n",
    "                CVF='NA'\n",
    "            else:\n",
    "                try:\n",
    "\n",
    "                    CVF = CVP*CVR*2/(CVP+CVR)\n",
    "                except ZeroDivisionError:\n",
    "                    CVF = 'NA'\n",
    "\n",
    "            return((BA, CVA, CVE, CVP, CVR, CVF))\n",
    "                \n",
    "\n",
    "        score = {}\n",
    "\n",
    "\n",
    "        # Evaluate model against all class values\n",
    "        for interestingClass in self.classValue:\n",
    "            prediction = []\n",
    "            \n",
    "            # Make predictions on trained model, in order of self.evalSample\n",
    "            for _, instance in self.evalSample.iterrows():\n",
    "                predicted = self.predict(instance)\n",
    "                prediction.append(predicted)\n",
    "                \n",
    "            # (TP TN FP FN TC)\n",
    "            metric = _getMetric(prediction, interestingClass)\n",
    "            score[interestingClass] = {}\n",
    "\n",
    "            score[interestingClass]['Smoothing Applied'] = self.smoothingApplied\n",
    "            score[interestingClass]['True Positive'] = metric[0]\n",
    "            score[interestingClass]['True Negative'] = metric[1]\n",
    "            score[interestingClass]['False Positive'] = metric[2]\n",
    "            score[interestingClass]['False Negative'] = metric[3]\n",
    "            score[interestingClass]['Total Correct'] = metric[4]\n",
    "\n",
    "            sampleSize = self.evalSample.shape[0]\n",
    "            processedMetric = _processMetric(sampleSize, metric)\n",
    "\n",
    "            score[interestingClass]['Basic Class Value Accuracy'] = processedMetric[0]\n",
    "            score[interestingClass]['Class Value Accuracy'] = processedMetric[1]\n",
    "            score[interestingClass]['Class Value Error Rate'] = processedMetric[2]\n",
    "            score[interestingClass]['Class Value Precision'] = processedMetric[0]\n",
    "            score[interestingClass]['Class Value Recall'] = processedMetric[4]\n",
    "            score[interestingClass]['Class Value F-Score'] = processedMetric[5]\n",
    "            \n",
    "\n",
    "        # Average performance for the class\n",
    "        score[self.CLASSAVERAGE_IDENTIFIER] = {}\n",
    "        for processedMetricName in ['Basic Class Value Accuracy',\n",
    "                           'Class Value Accuracy',\n",
    "                           'Class Value Error Rate',\n",
    "                           'Class Value Precision',\n",
    "                           'Class Value Recall',\n",
    "                           'Class Value F-Score']:\n",
    "            \n",
    "            # List of all metric values for a given processed metric\n",
    "            metricValues = [score[iClass][processedMetricName] for iClass \n",
    "                            in score.keys() if iClass != self.CLASSAVERAGE_IDENTIFIER]\n",
    "\n",
    "            # The average is undefined if the metric has any 'NA' values\n",
    "            if len([x for x in metricValues if type(x) is str])>0:\n",
    "                metricAverage='NA'\n",
    "\n",
    "            # Average all values for the metric\n",
    "            else:\n",
    "                metricAverage=(sum(metricValues)/len(metricValues))\n",
    "\n",
    "            # Put average metric score into our score structure\n",
    "            score[self.CLASSAVERAGE_IDENTIFIER][sub(r'Value ','',processedMetricName)]=metricAverage\n",
    "                                    \n",
    "        self.metrics=score\n",
    "\n",
    "        return(score)\n",
    "\n",
    "\n",
    "    def _getColumnCount(self):\n",
    "        \"\"\"Return number of class and attribute columns\"\"\"\n",
    "        with open(self.sourcePath) as file:\n",
    "            return len(next(csv.reader(file)))\n",
    "  \n",
    "\n",
    "    def _partitionData(self, trainingPortion):\n",
    "        \"\"\"Shuffle and partition classifier dataset into two partitions\n",
    "        \n",
    "        Return tuple of two DataFrames, the first the training set and the second\n",
    "        the evaluation set. \n",
    "        \n",
    "        ARGUMENT\n",
    "            trainingPortion - the portion of the the dataset that will be \n",
    "            returned as the first element of the return tuple. The other element\n",
    "            will be the remainder.\n",
    "                \n",
    "        RETURN\n",
    "            (trainingPartition, evaluationPartition)\n",
    "        \"\"\"\n",
    "        \n",
    "        if (trainingPortion < 0 or trainingPortion > 1):\n",
    "            raise ValueError('Tried to partition dataset with non fractional \\\n",
    "                                            portion {}'.format(trainingPortion))\n",
    "\n",
    "        nInstance = self.sourceData.shape[0]\n",
    "        nTrain = (int)(nInstance * trainingPortion)\n",
    "        shuffleIndex = np.random.permutation(nInstance)\n",
    "\n",
    "        trainSample = self.sourceData.iloc[shuffleIndex[0:nTrain]]\n",
    "        evalSample = self.sourceData.iloc[shuffleIndex[nTrain:nInstance]]\n",
    "\n",
    "        return((trainSample,evalSample))\n",
    " \n",
    "\n",
    "    def _getHeader(self):\n",
    "        \"\"\"Return list of data column titles. \n",
    "\n",
    "        Return list of numeric column titles for a given CSV file. \n",
    "        Titles start from one and are labeled \"attrN\". A CSV file will have;\n",
    "\n",
    "            o = m + n columns\n",
    "            n attribute columns\n",
    "            m class columns \n",
    "        \"\"\"\n",
    "            \n",
    "        xAttr=0\n",
    "        xClass=0\n",
    "        title=[]\n",
    "\n",
    "        # Build column headings\n",
    "        for ix in range(self.nCol):\n",
    "\n",
    "            # Discern column type\n",
    "            if (ix == self.classColumnIndex):\n",
    "                mark=self.MARK_CLASS\n",
    "                counter=xClass\n",
    "                xClass+=1\n",
    "            else:\n",
    "                mark=self.MARK_ATTR\n",
    "                counter=xAttr\n",
    "                xAttr+=1\n",
    "                \n",
    "            title.append(\"{}{}\".format(mark,counter))\n",
    "\n",
    "        return(title)\n",
    "\n",
    "\n",
    "    def smoothingRequired(self, count, classValueList):\n",
    "        \"\"\"Given count, check there are no zero count attribute values\n",
    "        \n",
    "        Attribute value counts are for a given class value priors. Check against\n",
    "        all known attribute and class values\n",
    "        \n",
    "        ARGUMENT:\n",
    "            count - A structure holding prior and posterior probabilities \n",
    "            \n",
    "            { classValue: [ classValuePrior, attrData ] }\n",
    "            \n",
    "                attrData =\n",
    "                { attrId: { attrValue: attrPosterior (given classValue) } }\n",
    "                \n",
    "            classValueList - list of class values to verify the count dstr for.\n",
    "        \n",
    "        RETURN:\n",
    "            True - if a zero count exists\n",
    "            False - otherwise\n",
    "        \n",
    "        ASSUMPTION:\n",
    "            *) All of the possible values for a class are represented in the \n",
    "                training set - this is checked by __init__()\n",
    "        \"\"\"\n",
    "        \n",
    "        # Check there is an instance for every attribute value seen in the \n",
    "        # source dataset given a some posterior class value\n",
    "        for classValueX in classValueList:\n",
    "            for _,attrId in self.attributeSet:\n",
    "                \n",
    "                seenAttributeValue = set(self.sourceData[attrId].unique())\n",
    "                \n",
    "                for aValue in seenAttributeValue:\n",
    "                \n",
    "                    # Check every seen attribute value is represented in this class\n",
    "                    if count[classValueX][1][attrId][aValue] == 0:\n",
    "                        return(True)\n",
    "        return(False)\n",
    "           \n",
    "\n",
    "    def getSmoothedModel(self, count, k=DEFAULT_SMOOTHING):\n",
    "        \"\"\"Yield a natural log transformed probability model with add k smoothing. \n",
    "        \n",
    "        Add k to all attribute value posteriors in count. Mutates count into \n",
    "        model. Count can be cumulative probabilities also\n",
    "        \n",
    "        ARGUMENT:\n",
    "            count - count structure from which to derive a model\n",
    "            \n",
    "                Count has the following structure;\n",
    "                    \n",
    "                    { classValue: [ valueCount, attributeData ] }\n",
    "                    \n",
    "                    attributeData;\n",
    "                        { attributeId: attributeValueData }\n",
    "                        \n",
    "                        attributeValueData;\n",
    "                            { attributeValue: attribute posterior count }\n",
    "\n",
    "            k - value of k to add for smoothing\n",
    "            \n",
    "        RETURN:\n",
    "            Mutate count into log transformed probability model for NB classifier. \n",
    "\n",
    "                { classValue: [ valueProbability, attributeData ] }\n",
    "                \n",
    "                attributeData;\n",
    "                    { attributeId: attributeValueData }\n",
    "                    \n",
    "                    attributeValueData;\n",
    "                        { attributeValue: posterior probability }\n",
    "                        \n",
    "        NOTE:\n",
    "            All probabilities are natural log transformed. This happens here for \n",
    "            optimization reasons. Predictions only require log transformed \n",
    "            probabilities. Model evaluation only requires predictions.\n",
    "            \n",
    "            The prediciton code is hammered for evaluation and general object\n",
    "            usage. Thus its calculation overhead is minimized with a \n",
    "            transformed model for optimality.\n",
    "        \"\"\"\n",
    "\n",
    "        # Check k is valid\n",
    "        if (k>1 or k<0):\n",
    "            raise ValueError(\"k must be [0, 1] for smoothing. {} given\".format(k))\n",
    "        \n",
    "        nInstance = sum([x[0] for x in count.values()])\n",
    "        \n",
    "        # For class values of a given class\n",
    "        for _, classValueData in count.items():\n",
    "            \n",
    "            # Convert class value count to probability\n",
    "            classValueFreq = classValueData[0]\n",
    "            classValueData[0]/=nInstance\n",
    "            \n",
    "            # Apply transform to class value count\n",
    "            classValueData[0]=log(classValueData[0])\n",
    "\n",
    "            # Add k to all attribute value counts for all seen attribute \n",
    "            # values of a given attribute.\n",
    "            for _,attrId in self.attributeSet:\n",
    "\n",
    "                seenAttributeValue = set(self.sourceData[attrId].unique())\n",
    "                \n",
    "                for attrValue in seenAttributeValue:\n",
    "                \n",
    "                    # Divisor is number of instances for the prior \n",
    "                    # (ie the class value) plus the product of k and the \n",
    "                    # number of k additions (ie, the number of unique \n",
    "                    # attribute values for the given attribute)\n",
    "                    divisor = classValueFreq + (len(seenAttributeValue)*k)\n",
    "\n",
    "                    # Convert to attribute value counts to add k probabilities\n",
    "                    classValueData[1][attrId][attrValue]+=k\n",
    "                    classValueData[1][attrId][attrValue]/=divisor\n",
    "\n",
    "                    # Apply transform to posterior probabilities\n",
    "                    classValueData[1][attrId][attrValue] = log(classValueData[1][attrId][attrValue])\n",
    "            \n",
    "\n",
    "    def getClassDistribution(self, dataSet):\n",
    "        \"\"\"Return a vector indicating the ratios of each class value for dataset\n",
    "        \n",
    "        ARGUMENT:\n",
    "            dataSet - A pandas dataframe containing data to analyze\n",
    "            \n",
    "        RETURN:\n",
    "            A sorted list of tuples. The first element of each tuple is the ratio, \n",
    "            the second element is the class value which the ratio represents. \n",
    "            The list is sorted in order of ratios\n",
    "            \n",
    "        NOTE:\n",
    "            This only applies to datasets that have a class column corresponding\n",
    "            with label corresponding to self.classColumnId\n",
    "            \n",
    "        \"\"\"\n",
    "\n",
    "\n",
    "        nItem = dataSet.shape[0]\n",
    "        distributionMap = []\n",
    "\n",
    "        # Find ratio for each class value\n",
    "        for classValue in self.classValue:\n",
    "            classRatio = sum(dataSet[self.classColumnId]==classValue)/nItem\n",
    "            distributionMap.append((classRatio, classValue))\n",
    "            \n",
    "        distributionMap.sort(key = lambda x: x[0])\n",
    "        return(distributionMap)\n",
    " "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Naive Bayes Class\n",
    "---\n",
    "This contains methods specific to NB\n",
    ">* Training logic\n",
    ">* Prediction logic\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "class NaiveBayes(Classifier):\n",
    "    \"\"\"Class abstracting Naive Bayes class prediction model\n",
    "    :\n",
    "    Contains and maintains a data model used for prediction, and a structure\n",
    "    containing raw data. This class is responsible for interacting with the \n",
    "    classifier model. (Making predictions, updating model, evaluating model etc)\n",
    "\n",
    "    --- 'model' DataStructure ---\n",
    "    The output of training, this contains all of the information required to \n",
    "    make a prediction for an instance\n",
    "\n",
    "        { Class Value:\n",
    "            [ Probability(Class Value), \n",
    "              Attribute Value Posterior Probabilities (for class value) \n",
    "            ]\n",
    "        }\n",
    "        \n",
    "        ---'Attribute Value Posterior Probabilities' DataStructure---\n",
    "        \n",
    "        { Attribute Identifier:\n",
    "            { Attribute Value:\n",
    "                Attribute Value Posterior Probability\n",
    "            }\n",
    "        }\n",
    "        \n",
    "          \n",
    "    ---'Data Features'---\n",
    "    The available features tracked by an attribute, class and feature set. The \n",
    "    feature set is the union of the class and attribute sets. Each of these are \n",
    "    encapsulated in the following structures for targeted access to 'Raw Data'\n",
    "\n",
    "        #    feature - class or  attribute of data\n",
    "        #    class   - some classification of a data instance \n",
    "        #    attribute - some feature of a data instance that is  not a class\n",
    "    \n",
    "    [ (Feature DataFrame Column Index, Feature Identifier), ... ] \n",
    "    \n",
    "    The Feature Identifier could be a class or attribute column identifier\n",
    "    \"\"\"\n",
    "\n",
    "\n",
    "    def __init__(self, sourcePath, dataKey,\n",
    "                    hasHeader=False,\n",
    "                    initialPartition=Classifier.DEFAULT_INITIAL_PARTITION,\n",
    "                    classColumn=Classifier.DEFAULT_CLASS_COLUMN,\n",
    "                    smoothingK = Classifier.DEFAULT_SMOOTHING):\n",
    "        \"\"\" Initialize classifier and class metadata. Check training data valid\n",
    "        \n",
    "        See parent initializer for arguments not presented here. \n",
    "\n",
    "        Valid training data passes the following: Check the training partition \n",
    "        contains all of the class values seen in the data set. Raise an error \n",
    "        if not. \n",
    "\n",
    "        ARGUMENTS\n",
    "            As per the Classifier class initializer docstring\n",
    "\n",
    "            classColumn:\n",
    "                Integer indicating the CSV class column index. Negative \n",
    "                values count from the last column. (-1 : last column ) etc\n",
    "                \n",
    "            smoothingK:\n",
    "                some value in [0,1] for add k smoothing.\n",
    "       \"\"\"\n",
    "        \n",
    "        Classifier.__init__(self, sourcePath, dataKey,\n",
    "                            hasHeader,\n",
    "                            initialPartition,\n",
    "                            classColumn,\n",
    "                            smoothingK)\n",
    "\n",
    "\n",
    "        def _checkTrainingSampleHasAllClassValue():\n",
    "            \"\"\"Asserts the classifiers training sample contains all class values\n",
    "            \"\"\"\n",
    "            if self.trainSample is None: \n",
    "                raise ValueError(\"Sample needs to be partitioned\")\n",
    "            \n",
    "            sampleClassValue=set(self.trainSample[self.classColumnId].unique())\n",
    "            if sampleClassValue != set(self.classValue):\n",
    "                raise ValueError('Not all possible class values are represented\\\n",
    "                                    in the training data partition. Get more \\\n",
    "                                    source data or try a different paritioning \\\n",
    "                                    strategy.')\n",
    "\n",
    "        # Ensure the training sample has all class values represented\n",
    "        _checkTrainingSampleHasAllClassValue()\n",
    "        \n",
    "\n",
    "    def predict(self, instance):\n",
    "        \"\"\"Predicts the class of valid given instance\n",
    "        \n",
    "        A valid instance has some value for each non-class attribute. \n",
    "        A valid instance does not contain any unseen attribute \n",
    "        value (all attribute values in the supplied source data set are 'seen')\n",
    "        \n",
    "        It may or may not contain class data - which will be ignored. \n",
    "        \n",
    "        Prediction made with log transformed Naive Bayes formula;\n",
    "            \n",
    "            for all c\n",
    "            max( c ): p(c) * p(x1|c) * p(x2|c) * p(x3|c) * ... * p(xi|c)\n",
    "            \n",
    "            or, transformed;\n",
    "            max( c ): log(p(c)) + log(p(x1|c)) + ... + log(p(xi|c))\n",
    "\n",
    "            where c is some class value\n",
    "            \n",
    "        ARGUMENT:\n",
    "            instance - valid instance for which a prediction is to be made. \n",
    "            A Pandas series with column keys that are consistent with \n",
    "            self.featureSet column identifiers\n",
    "            \n",
    "        RETURN:\n",
    "            Returns the class with the greatest value as per the above formula\n",
    "            Return None if the instance was invalid\n",
    "            \n",
    "        ASSUMPTION:\n",
    "            *) The model's probabilities are assumed to be log transformed.\n",
    "            \n",
    "        NOTE: \n",
    "            See self.train() for rationale of a log transformed model.\n",
    "            \n",
    "        \"\"\"\n",
    "        \n",
    "        model = self.model \n",
    "        predictedClass = None\n",
    "        maxScore = None\n",
    "            \n",
    "        # Probabilities for each class value\n",
    "        for classValue, cData in model.items():\n",
    "\n",
    "            score = cData[0]\n",
    "            \n",
    "            # Product of class value posteriors for each attribute of the instance\n",
    "            for _,attrId in self.attributeSet:\n",
    "                instanceAttrValue = instance[attrId]\n",
    "                \n",
    "                # Assuming the model is log transformed, product becomes a sum\n",
    "                score += cData[1][attrId][instanceAttrValue]\n",
    "                \n",
    "            if (maxScore is None) or (score>maxScore):\n",
    "                maxScore=score\n",
    "                predictedClass = classValue\n",
    "                \n",
    "        return(predictedClass)\n",
    "      \n",
    "      \n",
    "    def train(self):\n",
    "        \"\"\"Build model from the training dataset\n",
    "        \"\"\"\n",
    "\n",
    "        def _countOccurence():\n",
    "            \"\"\"Count class value and attribute value (given a class value) occurences\n",
    "            \n",
    "            Always returns a valid count datastructure. See _validateCount()\n",
    "            \n",
    "            RETURN:\n",
    "                Count data structure;\n",
    "                    { classValue:\n",
    "                        [classValueCount, classValueData] }\n",
    "                        \n",
    "                ----classValueData structure;\n",
    "                        { attrId: attrData }\n",
    "                        \n",
    "                ----attrData structure;\n",
    "                        { attrValue: count }\n",
    "            \"\"\"\n",
    "\n",
    "\n",
    "            def _validateCount(count):\n",
    "                \"\"\"Asserts the count structure is valid. Raise a value error if not\n",
    "                \n",
    "                A valid count structure is such that;\n",
    "                \n",
    "                    A -> for every given class, the sum of class value counts is \n",
    "                        equal to the number of training instances\n",
    "                        \n",
    "                    B -> for every given class value, the sum of attribute value\n",
    "                        counts for any given attribute and some given class value \n",
    "                        is such that it is equal to the occurence count of the\n",
    "                        posterior class value\n",
    "                        \n",
    "                RETURN:\n",
    "                    true if variable count is a valid structure\n",
    "                \"\"\"\n",
    "                \n",
    "                # Check condition A\n",
    "                sumCountInstance = sum([x[0] for x in count.values()])\n",
    "                if sumCountInstance != self.trainSample.shape[0]:\n",
    "                    raise ValueError('Model size not consistent with training set')\n",
    "                \n",
    "                # Check condition B\n",
    "                for classValue in count.keys():\n",
    "                    classValueCount = count[classValue][0]\n",
    "\n",
    "                    for attrId in count[classValue][1].keys():\n",
    "\n",
    "                        totalAttrValueCount = sum(count[classValue][1][attrId].values())\n",
    "                        if totalAttrValueCount != classValueCount:\n",
    "                            raise ValueError('Attribute counts not for class value')\n",
    "                return\n",
    "\n",
    "\n",
    "            nInstance = self.trainSample.shape[0]\n",
    "\n",
    "            # count;            { classValue: [valueCount, valueData] }\n",
    "            # (class)valueData; { attrId: attrData }\n",
    "            # attrData;         { attrValue: (int)count }\n",
    "            count = defaultdict(lambda: [0, defaultdict(lambda: defaultdict(int))])\n",
    "            # Count instances\n",
    "            for ix in range(nInstance):\n",
    "\n",
    "                classValue = self.trainSample.iloc[ix, self.classColumnIndex]\n",
    "\n",
    "                # Count class value occurence \n",
    "                count[classValue][0]+=1\n",
    "                \n",
    "                # Count attribute value occurence for a given class value\n",
    "                for attrIndex, attrId in self.attributeSet:\n",
    "                    attrValue = self.trainSample.iloc[ix,attrIndex]\n",
    "                    count[classValue][1][attrId][attrValue]+=1\n",
    "                    \n",
    "            _validateCount(count)\n",
    "            return(count)\n",
    "\n",
    "       \n",
    "        # Check a train sample was partitioned\n",
    "        if self.trainSample is None:\n",
    "            raise ValueError('Partition a training sample before training')\n",
    "\n",
    "        count = _countOccurence()\n",
    "        \n",
    "        # Apply smoothing if required\n",
    "        smoothingK = 0 # Default no smoothing\n",
    "        if self.smoothingRequired(count,self.classValue):\n",
    "            self.smoothingApplied=True\n",
    "            smoothingK = self.smoothingK\n",
    "        \n",
    "        # Yield  model from counts\n",
    "        self.getSmoothedModel(count,smoothingK)\n",
    "        self.model=count\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "\n",
    "# Unsupervised Classifier Class\n",
    "---\n",
    "Contains unsupervised specific logic related to;\n",
    "* Seeding probabilities\n",
    "* Probability predictions\n",
    "* Discrete predictions from probability predictions\n",
    "* Iterative model refinement (part of training)\n",
    "* Model training metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "class UnsupervisedNaiveBayes(Classifier):\n",
    "    \"\"\"Class for creating, manipulating and evaluating UNB classifier \n",
    " \n",
    "    The unsupervised classifier uses a labelled evaluation set and and unlabelled\n",
    "    training set. A model is built from the unlabelled set and seeded with \n",
    "    random class probabilities. The model is then iteratively refined by \n",
    "    predicting class values for all training set instances and then \n",
    "    being recalculated on the updated training set. \n",
    "\n",
    "    --- 'model' DataStructure ---\n",
    "\n",
    "    The output of training, this contains all of the information required to \n",
    "    make a prediction for an instance\n",
    "\n",
    "        { Class Value:\n",
    "            [ Probability(Class Value), Attribute Value Posterior Probabilities (for class value) ]\n",
    "        }\n",
    "        \n",
    "        ---'Attribute Value Posterior Probabilities' DataStructure---\n",
    "        \n",
    "        { Attribute Identifier:\n",
    "            { Attribute Value:\n",
    "                Attribute Value Posterior Probability\n",
    "            }\n",
    "        }\n",
    "        \n",
    "    There is only one Class in the model typically\n",
    " \n",
    "    ---'Raw Data' Structure--- \n",
    "    The datasets (training, source, evaluation) are organized in Pandas DataFrames. These have the following structure;\n",
    "    \n",
    "    __________________________________________________________________________________________\n",
    "    |Index     | Attribute Identifier    | ...        | Class Value Identifier    | ...        |\n",
    "    __________________________________________________________________________________________\n",
    "    |ix        | Attribute Value        | ...        | Probability for value    | ...        |\n",
    "    |ix        | Attribute Value        | ...        | Probability for value    | ...        |\n",
    "    |ix        | Attribute Value        | ...        | Probability for value    | ...        |\n",
    "    |ix        | Attribute Value        | ...        | Probability for value    | ...        |\n",
    "    __________________________________________________________________________________________\n",
    "    \n",
    "    \n",
    "    ---'Data Features'---\n",
    "    The available features tracked by an attribute, class and feature set. The feature set is \n",
    "    the union of the class and attribute sets. Each of these are encapsulated in the following\n",
    "    structures for targeted access to 'Raw Data'\n",
    "    \n",
    "    [ (Feature DataFrame Column Index, Feature Identifier), ... ] \n",
    "    \n",
    "    The Feature Identifier could be a class value identifier or an attribute identifier\n",
    "    \"\"\"\n",
    "    \n",
    "    #Sets bound on the minimum possible probability any class value can have for\n",
    "    #any instance when randomly seeding probabilities into training data.\n",
    "    EPSILON=0.001\n",
    "    DEFAULT_ITERATIONS = 5\n",
    "\n",
    "\n",
    "    def __init__(self, sourcePath, dataKey, nClassValue, \n",
    "                    hasHeader=False, \n",
    "                    initialPartition=Classifier.DEFAULT_INITIAL_PARTITION,\n",
    "                    classColumnIndex=Classifier.DEFAULT_CLASS_COLUMN,\n",
    "                    smoothingK=Classifier.DEFAULT_SMOOTHING):\n",
    "        \"\"\"Prepare and validate training data structure. Randomly seed class probabilities.\n",
    "        \n",
    "        Add a column for each possible class value to the training data frame\n",
    "        to hold class value probabilities.\n",
    "\n",
    "        ARGUMENT:\n",
    "        See parent 'Classifier' class for parent args\n",
    "\n",
    "            nClassValue\n",
    "                This is the number of class values the unsupervised classifer \n",
    "                will use to make predictions. There should be as many of these\n",
    "                as there are known class values. See train for how class values \n",
    "                are mapped. \n",
    "                \n",
    "                If this is not consistent with how many class values are known\n",
    "                about, a ValueError will be raised.\n",
    "                \n",
    "        \"\"\"\n",
    "        Classifier.__init__(self, \n",
    "                            sourcePath, dataKey,\n",
    "                            hasHeader,\n",
    "                            initialPartition,\n",
    "                            classColumnIndex)\n",
    "        \n",
    "        self.nClassValue = nClassValue\n",
    "        \n",
    "        # Remove the class column form the training sample (unsupervised)\n",
    "        self.trainSample.drop(self.classColumnId, axis=1, inplace=True)\n",
    "        \n",
    "\n",
    "        # Track indices and id's of class value columns\n",
    "        self.classSet = list()\n",
    "\n",
    "        # Enumerate columns for class values and add them into our training data\n",
    "        for ix in range(nClassValue):\n",
    "            className = Classifier.MARK_CLASS+str(ix)\n",
    "            classColumnIndex = self.trainSample.shape[1]\n",
    "            self.classSet.append((classColumnIndex,className))\n",
    "            self.trainSample.loc[:,className]=p.Series()\n",
    "        \n",
    "        # Seed training data class columns with noise\n",
    "        self._randomSeed()\n",
    "        \n",
    "        # This holds a map between class columns and actual classes. This is\n",
    "        # set once the model has trained\n",
    "        self.classMap = None\n",
    "        self.trainingMetrics=None\n",
    "                 \n",
    "\n",
    "    def train(self, nIter = DEFAULT_ITERATIONS):\n",
    "        \"\"\"Refine the model by training iteratively\n",
    "        \n",
    "        Make a prediction for each class value of every instance in the \n",
    "        training set. Normalize the result such that the sum of probabilities \n",
    "        across all class values sums to one.\n",
    "        \n",
    "        Recreates model with new resultant training set and scores it with the\n",
    "        following metrics;\n",
    "\n",
    "            1) Certainty: sum of all dominant class value probabilities divided\n",
    "                by the number of training instances. Certainty should increase \n",
    "                over iterations\n",
    "                \n",
    "            2) ClassDistributionVector dTheta: The change in the class distribution\n",
    "                vector direction. This reflects the distribution of class values\n",
    "                over the training set. This should stabilize after some iteration\n",
    "                \n",
    "                dot(a,b) = abs(a)abs(b)cos(th)\n",
    "                    th = acos(dot(a,b)/(abs(a)abs(b)))\n",
    "                    \n",
    "                    dTheta = th1 - th0 [rad]\n",
    "                    \n",
    "        ARGUMENT:\n",
    "            nIter\n",
    "                Number of iterations to train the model for\n",
    "                \n",
    "        RETURN:\n",
    "            A dictionary with each key being an iteration number. The value for \n",
    "            each key contains the two metrics presented above.\n",
    "\n",
    "        \"\"\"\n",
    "        def _getClassDistributionVector():\n",
    "            \"\"\"Return unit normal class distribution vector \"\"\"\n",
    "            v = []\n",
    "            for _,cId in self.classSet:\n",
    "                v.append(self.model[cId][0])\n",
    "            return v\n",
    "        \n",
    "\n",
    "        def _getTheta(u, v):\n",
    "            \"\"\"Return theta, angle between two ndim vectors\"\"\"\n",
    "            if(len(u)!=len(v)):raise ValueError(\"Cannot get dTheta for vectors of \\\n",
    "                inconsistent dimension\")\n",
    "            \n",
    "            def _dot(q,r):\n",
    "                \"\"\"Return dot product of two n dim vectors\"\"\"\n",
    "                prod = 0\n",
    "                for ix in range(len(q)):\n",
    "                    prod+=(q[ix]*r[ix])\n",
    "                return(prod)\n",
    "                    \n",
    "            def _abs(s):\n",
    "                \"\"\"Return magnitude of a vector\"\"\"\n",
    "                summand=0\n",
    "                for si in s:\n",
    "                    summand+=pow(si,2)\n",
    "                return(sqrt(summand))\n",
    "\n",
    "            return((acos(_dot(u,v)/(_abs(u)*_abs(v))))/pi)\n",
    "\n",
    "\n",
    "        def _getCertainty(prediction):\n",
    "            \"\"\"Return the certainty of a set of predictions \"\"\"\n",
    "            summand=0\n",
    "            for ix in range(len(prediction)):\n",
    "                summand+=max(prediction[ix].values())\n",
    "            return(summand/len(prediction))\n",
    "                \n",
    "\n",
    "        # Derive model from randomly seeded training data set\n",
    "        self._trainOnData()\n",
    "        prediction = {}\n",
    "        modelMetric = {}\n",
    "\n",
    "        # Iteratively refine model\n",
    "        trainSample = self.trainSample\n",
    "        for rx in range(nIter):\n",
    "            \n",
    "            # Get class distribution vector from the current model\n",
    "            priorClassDistributionVector = _getClassDistributionVector()\n",
    "            \n",
    "            # Make predictions for training set with current model\n",
    "            for ix in range(trainSample.shape[0]):\n",
    "                \n",
    "                instance = trainSample.iloc[ix]\n",
    "                prediction[ix]=self._normalizedProbabilityPredict(instance)\n",
    "                \n",
    "                # Update the training dataframe with new prediction\n",
    "                for classIndex, classValue in self.classSet:\n",
    "                    trainSample.iloc[ix,classIndex]=prediction[ix][classValue]\n",
    "                \n",
    "            # Train/derive model from updated training dataset\n",
    "            self._trainOnData()\n",
    "            nextClassDistributionVector = _getClassDistributionVector()\n",
    "                \n",
    "            modelMetric['Iteration {}'.format(rx+1)]={}\n",
    "            modelMetric['Iteration {}'.format(rx+1)][\"Certainty [%]\"] = _getCertainty(prediction)\n",
    "            modelMetric['Iteration {}'.format(rx+1)][\"Class Distribution Delta [deg]\"] = _getTheta(\n",
    "                priorClassDistributionVector,\n",
    "                nextClassDistributionVector)\n",
    "            \n",
    "        # Map unidentified class values to actual class values\n",
    "        self.trainingMetrics=modelMetric\n",
    "        self.classMap = self._mapClassValues(nextClassDistributionVector)\n",
    "\n",
    "        return modelMetric\n",
    "\n",
    "         \n",
    "    def predict(self,instance):\n",
    "        \"\"\"Return the predicted class value with the highest probability\"\"\"\n",
    "        prediction = self._probabilityPredict(instance)\n",
    "        maxClass = max(prediction.items(), key=lambda x:x[1])[0]\n",
    "        return(self.classMap[maxClass])\n",
    "\n",
    "\n",
    "    def _normalizedProbabilityPredict(self, instance):\n",
    "        \"\"\"Predicts an instance with model. Return dict of probabilities\n",
    "        \n",
    "        Normalized such that the sum of all class value predictions for the \n",
    "        instance add to 1.\n",
    "        \n",
    "        ARGUMENT:\n",
    "            instance - An instance having only previously seen attribute values\n",
    "        \n",
    "        RETURN:\n",
    "            A dictionary with keys being the available class values column \n",
    "            identifiers, and values being the probability of the instance having that value\n",
    "            \n",
    "        ASSUMPTION:\n",
    "            Assumes the model is natural logarithm transformed\n",
    "        \"\"\"\n",
    "\n",
    "\n",
    "        def _normalizeClassValueScore(classScore):\n",
    "            \"\"\"Recover normalized probabilities from log transformed ones.\n",
    "            \n",
    "            Given a dictionary with class value keys, and log transformed \n",
    "            probability values.\n",
    "            \n",
    "            THEORY: \n",
    "                If the Naive Bayes probabilities are;\n",
    "                    score=p(classValue) \n",
    "                    score*=p(ai | classValue) for all i in [i0.. iN] \n",
    "                    \n",
    "                    such that p(instance is classValue) = score\n",
    "                    \n",
    "                And the score vector is log transformed such that\n",
    "                    logScore=log(p(classValue))\n",
    "                    logScore+=log(p_i) for all i in [i0 .. iN]\n",
    "                    \n",
    "                    where p_i is a score for some class value\n",
    "                    \n",
    "                Then the percentage ratio of each class value (ie class value \n",
    "                predictions) are as follows;\n",
    "                    p(classValueNorm) for given instance\n",
    "                        = exp(logScore for given class value)\n",
    "                            / sum(exp(logScore) for all class values)\n",
    "            \"\"\"\n",
    "            \n",
    "            # Convert to normalized probability\n",
    "            totalSum = sum([exp(x) for x in classScore.values()])\n",
    "            for classValue, classValueScore in classScore.items():\n",
    "                classScore[classValue]= exp(classValueScore)/totalSum\n",
    "        classScore = self._probabilityPredict(instance)\n",
    "        _normalizeClassValueScore(classScore)    \n",
    "\n",
    "        return(classScore)\n",
    "        \n",
    "\n",
    "    def _probabilityPredict(self, instance):\n",
    "        \"\"\"Predicts an instance with model. Return dict of probabilities\n",
    "        \n",
    "        This prediction uses a log transformed model and does not revert the \n",
    "        transform in it's predictions. This is for quicker evaluation of the\n",
    "        \n",
    "        ARGUMENT:\n",
    "            instance - An instance having only previously seen attribute values\n",
    "        \n",
    "        RETURN:\n",
    "            A dictionary with keys being the available class values column \n",
    "            identifiers, and values being the probability of the instance having that value\n",
    "            \n",
    "        ASSUMPTION:\n",
    "            Assumes the model is natural logarithm transformed\n",
    "        \"\"\"\n",
    "\n",
    "        if self.model is None:\n",
    "            raise ValueError(\"Train classifier before using it to make predictions\")\n",
    "        \n",
    "        classScore = {}\n",
    "        \n",
    "        # Score each possible class value\n",
    "        for cx in range(self.nClassValue):\n",
    "\n",
    "            classValue = self.classSet[cx][1]\n",
    "            \n",
    "            # Probability an instance will be 'classValue'\n",
    "            classValueScore = self.model[classValue][0]\n",
    "            \n",
    "            # Get product of all posteriors (n attributes, and nxm attribute values)\n",
    "            for attrIndex, attrId in self.attributeSet:\n",
    "                attrValue = instance[attrIndex]\n",
    "                classValueScore+=self.model[classValue][1][attrId][attrValue]\n",
    "                \n",
    "            classScore[classValue]=classValueScore\n",
    "        return(classScore)\n",
    "\n",
    "           \n",
    "    def _randomSeed(self):\n",
    "        \"\"\"Seeds the class values of each training item with a random probability \n",
    "        \n",
    "        The sum of all training item class value probabilities is be 1. \n",
    "        \n",
    "        Probabilities will be generated by randomly bisecting the remaining \n",
    "        probability quota for each class value available. For example, \n",
    "\n",
    "            probabilityQuota = 1\n",
    "            classValueX-1 = rand(0,probabilityQuota) \n",
    "            prbabilityQuota -= classValueX-1\n",
    "            classValueX = rand(0, probabilityQuota)\n",
    "            so on\n",
    "            ...\n",
    "            \n",
    "        The order in which class values will be assigned will also be randomized. \n",
    "        This is done to accomodate the \"diminishing probability quota\" forcing\n",
    "        lower ordinal class values from assuming higher probability sums.\n",
    "        \n",
    "        NOTE:\n",
    "            Seeding will be such that no class value is seeded with zero \n",
    "            probability. The margin for this will be set by self.EPSILON\n",
    "        \"\"\"\n",
    "        nTrain = self.trainSample.shape[0]\n",
    "        \n",
    "        # Seed every instance\n",
    "        for ix in range(nTrain):\n",
    "            probabilityQuota = 1\n",
    "            classCount = 0\n",
    "\n",
    "            # Seed class columns in a random order for every instance\n",
    "            for cx in np.random.permutation(range(self.nClassValue)):\n",
    "                classColumn = self.classSet[cx][0]\n",
    "                \n",
    "                # Assign quota remainder to final class value probability\n",
    "                if classCount == (self.nClassValue - 1):\n",
    "                    self.trainSample.iloc[ix, classColumn] = probabilityQuota\n",
    "\n",
    "                # Assign random bisection of class value quota to probability\n",
    "                else:\n",
    "                    # Epsilon ensures that no class value will be seeded with\n",
    "                    # zero probability\n",
    "                    probability = random.uniform(0+self.EPSILON, \n",
    "                                                 probabilityQuota-self.EPSILON)\n",
    "                    self.trainSample.iloc[ix, classColumn] = probability\n",
    "\n",
    "                probabilityQuota -= probability\n",
    "                classCount+=1\n",
    "\n",
    "\n",
    "    def _mapClassValues(self, classDistributionVector):\n",
    "        \"\"\"Maps model's unidentified class values to actual class values. \n",
    "        \n",
    "        Mapping is based on the class distribution. The class distribution of \n",
    "        the iteratively classified training set is matched with the distribution\n",
    "        of the evaluation data set. The identity of a class value in the \n",
    "        unsupervised model is the identity of a class value in the evaluation \n",
    "        training set that has the closes matching distribution.\n",
    "        \n",
    "        INPUT:\n",
    "            classDistributionVector - Vector with elements corresponding to \n",
    "            the occurence ratio of a class column in the training data set. \n",
    "            Ordering of elements as per ordering of self.classSet\n",
    "            \n",
    "        OUTPUT:\n",
    "            A dictionary \n",
    "             { realClassIdentity: trainingSetUnidentifiedClassValueColumnName }\n",
    "        \"\"\"\n",
    "        classMap = {}\n",
    "        \n",
    "        # Get actual class distribution\n",
    "        actualClassDistribution = self.getClassDistribution(self.sourceData)\n",
    "        \n",
    "        # Build training class distribution/class value id pair list\n",
    "        trainingDistribution = [(classDistributionVector[ix], self.classSet[ix][1]) for ix in range(len(classDistributionVector))]\n",
    "        \n",
    "        # Sort the training distribution class value column id's in distribution order\n",
    "        trainingDistribution.sort(key=lambda x: x[0])\n",
    "        \n",
    "        if len(trainingDistribution) != len(actualClassDistribution):\n",
    "            raise ValueError('The unsupervised model needs to have the same number of class values as the dataset')\n",
    "        \n",
    "        for ix in range(len(trainingDistribution)):\n",
    "            classMap[trainingDistribution[ix][1]]=actualClassDistribution[ix][1]\n",
    "            \n",
    "        return(classMap)\n",
    "        \n",
    "\n",
    "    def _trainOnData(self):\n",
    "        \"\"\"Train the model on the current training dataset\n",
    "\n",
    "        Fill \"count\" data structure containing posterior and class counts\n",
    "        \n",
    "        Apply smoothing if necessary and convert to a log transformed model.\n",
    "        \n",
    "        \n",
    "        RETURN:\n",
    "            Update model of self.\n",
    "\n",
    "        \"\"\"\n",
    "        \n",
    "        def _accumulateProbabilities():\n",
    "            \"\"\"Accumulate probabilities for class and attribute values\n",
    "            \n",
    "            Analogous to counting for the supervised NB classifier, accumulate\n",
    "            probabilities for each class value and each attribute value given\n",
    "            a class value.\n",
    "            \n",
    "            RETURN:\n",
    "                data structure with same structure as the model. However, \n",
    "                contains cumulative probabilities\n",
    "\n",
    "            \"\"\"\n",
    "            \n",
    "            \n",
    "            # Model data structure as per class docstring\n",
    "            cumulateModel = defaultdict(lambda: \n",
    "                                        [0, defaultdict(lambda: \n",
    "                                                        defaultdict(int))])\n",
    "\n",
    "            nTrainingInstance = trainSample.shape[0]\n",
    "\n",
    "            # Cumulate probabilities instance by instance \n",
    "            for ix in range(nTrainingInstance):\n",
    "                \n",
    "                # For each class value\n",
    "                for classIndex, classId in self.classSet:\n",
    "\n",
    "                    # For the unsupervised model, a column represents the class \n",
    "                    # value. Thus the class value is the class id\n",
    "                    classValue = classId\n",
    "                    classValueProbability = trainSample.iloc[ix, classIndex]\n",
    "\n",
    "                    # Class value total probability, and total instances\n",
    "                    cumulateModel[classValue][0]+=classValueProbability\n",
    "                \n",
    "                    # Cumulate attribute value posteriors\n",
    "                    for attrIndex, attrId in self.attributeSet:\n",
    "                        attrValue = trainSample.iloc[ix,attrIndex]\n",
    "                            \n",
    "                        # Attribute value probability given class value\n",
    "                        cumulateModel[classValue][1][attrId][attrValue] \\\n",
    "                                        +=classValueProbability\n",
    "            \n",
    "            return cumulateModel\n",
    "             \n",
    "        trainSample = self.trainSample\n",
    "\n",
    "        # The __init__() call should have intialized a training data set. \n",
    "        # to continue. Check it is available.\n",
    "        if trainSample is None:\n",
    "            raise ValueError('Tried to train with no training sample initialized.')\n",
    "\n",
    "        \n",
    "        # Train\n",
    "        model = _accumulateProbabilities()\n",
    "     \n",
    "        # Apply smoothing to cumulate probability count model if required\n",
    "        smoothingK = 0\n",
    "        if self.smoothingRequired(model, model.keys()):\n",
    "            self.smoothingApplied=True\n",
    "            smoothingK = self.smoothingK\n",
    "        \n",
    "        # Yield smoothed, log transformed model from counts\n",
    "        self.getSmoothedModel(model,smoothingK)\n",
    "\n",
    "        self.model = model\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# A global variable to hold the results of our calculations below\n",
    "classifierObject=defaultdict(dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3.6/site-packages/ipykernel_launcher.py:91: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/usr/lib/python3.6/site-packages/pandas/core/indexing.py:357: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[key] = _infer_fill_value(value)\n",
      "/usr/lib/python3.6/site-packages/pandas/core/indexing.py:537: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n"
     ]
    }
   ],
   "source": [
    "prepareClassifiersForSet('breastCancer',5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________\n",
      "|                                  BREASTCANCER                                  |\n",
      "__________________________________________________________________________________\n",
      "| Metric Name              | Unsupervised             | Supervised               |\n",
      "__________________________________________________________________________________\n",
      "| Basic Class Accuracy     | 63.89                    | 72.22                    |\n",
      "| Class Accuracy           | 63.89                    | 72.22                    |\n",
      "| Class Error Rate         | 36.11                    | 27.78                    |\n",
      "| Class Precision          | 63.89                    | 72.22                    |\n",
      "| Class Recall             | 60.50                    | 65.93                    |\n",
      "| Class F-Score            | 59.37                    | 66.39                    |\n",
      "__________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "printComparisionTable(classifierObject['breastCancer'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Unsupervised Breast Cancer - Per Class Value Metrics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{   'Class Average': {   'Basic Class Accuracy': 45.833333333333336,\n",
      "                         'Class Accuracy': 45.833333333333336,\n",
      "                         'Class Error Rate': 54.166666666666664,\n",
      "                         'Class F-Score': 44.00797607178465,\n",
      "                         'Class Precision': 45.833333333333336,\n",
      "                         'Class Recall': 45.20851818988465},\n",
      "    'no-recurrence-events': {   'Basic Class Value Accuracy': 45.833333333333336,\n",
      "                                'Class Value Accuracy': 45.833333333333336,\n",
      "                                'Class Value Error Rate': 54.166666666666664,\n",
      "                                'Class Value F-Score': 54.11764705882353,\n",
      "                                'Class Value Precision': 45.833333333333336,\n",
      "                                'Class Value Recall': 46.93877551020408,\n",
      "                                'False Negative': 26,\n",
      "                                'False Positive': 13,\n",
      "                                'Smoothing Applied': True,\n",
      "                                'Total Correct': 33,\n",
      "                                'True Negative': 10,\n",
      "                                'True Positive': 23},\n",
      "    'recurrence-events': {   'Basic Class Value Accuracy': 45.833333333333336,\n",
      "                             'Class Value Accuracy': 45.833333333333336,\n",
      "                             'Class Value Error Rate': 54.166666666666664,\n",
      "                             'Class Value F-Score': 33.898305084745765,\n",
      "                             'Class Value Precision': 45.833333333333336,\n",
      "                             'Class Value Recall': 43.47826086956522,\n",
      "                             'False Negative': 13,\n",
      "                             'False Positive': 26,\n",
      "                             'Smoothing Applied': True,\n",
      "                             'Total Correct': 33,\n",
      "                             'True Negative': 23,\n",
      "                             'True Positive': 10}}\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "dict_keys(['breastCancer'])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pp.pprint(classifierObject['breastCancer']['Unsupervised'].metrics)\n",
    "\n",
    "classifierObject.keys()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "-1\n",
      "10\n",
      "4\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3.6/site-packages/ipykernel_launcher.py:91: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/usr/lib/python3.6/site-packages/pandas/core/indexing.py:357: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[key] = _infer_fill_value(value)\n",
      "/usr/lib/python3.6/site-packages/pandas/core/indexing.py:537: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n"
     ]
    }
   ],
   "source": [
    "prepareClassifiersForSet('car',5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________\n",
      "|                                      CAR                                       |\n",
      "__________________________________________________________________________________\n",
      "| Metric Name              | Unsupervised             | Supervised               |\n",
      "__________________________________________________________________________________\n",
      "| Basic Class Accuracy     | 35.42                    | 83.33                    |\n",
      "| Class Accuracy           | 67.71                    | 91.67                    |\n",
      "| Class Error Rate         | 32.29                    | 8.33                     |\n",
      "| Class Precision          | 35.42                    | 83.33                    |\n",
      "| Class Recall             | 25.14                    | 53.04                    |\n",
      "| Class F-Score            | 21.97                    | 56.05                    |\n",
      "__________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "printComparisionTable(classifierObject['car'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "-1\n",
      "10\n",
      "2\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/lib/python3.6/site-packages/ipykernel_launcher.py:91: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "/usr/lib/python3.6/site-packages/pandas/core/indexing.py:357: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[key] = _infer_fill_value(value)\n",
      "/usr/lib/python3.6/site-packages/pandas/core/indexing.py:537: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self.obj[item] = s\n"
     ]
    }
   ],
   "source": [
    "prepareClassifiersForSet('hypothyroid',5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________\n",
      "|                                  HYPOTHYROID                                   |\n",
      "__________________________________________________________________________________\n",
      "| Metric Name              | Unsupervised             | Supervised               |\n",
      "__________________________________________________________________________________\n",
      "| Basic Class Accuracy     | 88.24                    | 95.20                    |\n",
      "| Class Accuracy           | 88.24                    | 95.20                    |\n",
      "| Class Error Rate         | 11.76                    | 4.80                     |\n",
      "| Class Precision          | 88.24                    | 95.20                    |\n",
      "| Class Recall             | 45.68                    | 49.93                    |\n",
      "| Class F-Score            | NA                       | NA                       |\n",
      "__________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "printComparisionTable(classifierObject['hypothyroid'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'prepareClassifiersForSet' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-a12645bf4c09>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mprepareClassifiersForSet\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'mushroom'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m: name 'prepareClassifiersForSet' is not defined"
     ]
    }
   ],
   "source": [
    "prepareClassifiersForSet('mushroom',5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "__________________________________________________________________________________\n",
      "|                                    MUSHROOM                                    |\n",
      "__________________________________________________________________________________\n",
      "| Metric Name              | Unsupervised             | Supervised               |\n",
      "__________________________________________________________________________________\n",
      "| Basic Class Accuracy     | 48.10                    | 95.77                    |\n",
      "| Class Accuracy           | 48.10                    | 95.77                    |\n",
      "| Class Error Rate         | 51.90                    | 4.23                     |\n",
      "| Class Precision          | 48.10                    | 95.77                    |\n",
      "| Class Recall             | 48.12                    | 95.65                    |\n",
      "| Class F-Score            | 48.09                    | 95.75                    |\n",
      "__________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "printComparisionTable(classifierObject['mushroom'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "pprint(classifierObject['hypothyroid']['Unsupervised'].metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Class Average': {'Basic Class Accuracy': 95.19595448798988,\n",
       "  'Class Accuracy': 95.19595448798988,\n",
       "  'Class Error Rate': 4.804045512010114,\n",
       "  'Class F-Score': 'NA',\n",
       "  'Class Precision': 95.19595448798988,\n",
       "  'Class Recall': 49.93368700265252},\n",
       " 'hypothyroid': {'Basic Class Value Accuracy': 95.19595448798988,\n",
       "  'Class Value Accuracy': 95.19595448798988,\n",
       "  'Class Value Error Rate': 4.804045512010114,\n",
       "  'Class Value F-Score': 'NA',\n",
       "  'Class Value Precision': 95.19595448798988,\n",
       "  'Class Value Recall': 0.0,\n",
       "  'False Negative': 37,\n",
       "  'False Positive': 1,\n",
       "  'Smoothing Applied': True,\n",
       "  'Total Correct': 753,\n",
       "  'True Negative': 753,\n",
       "  'True Positive': 0},\n",
       " 'negative': {'Basic Class Value Accuracy': 95.19595448798988,\n",
       "  'Class Value Accuracy': 95.19595448798988,\n",
       "  'Class Value Error Rate': 4.804045512010114,\n",
       "  'Class Value F-Score': 97.53886010362693,\n",
       "  'Class Value Precision': 95.19595448798988,\n",
       "  'Class Value Recall': 99.86737400530504,\n",
       "  'False Negative': 1,\n",
       "  'False Positive': 37,\n",
       "  'Smoothing Applied': True,\n",
       "  'Total Correct': 753,\n",
       "  'True Negative': 0,\n",
       "  'True Positive': 753}}"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "classifierObject['hypothyroid']['Supervised'].metrics\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Questions (you may respond in a cell or cells below):\n",
    "\n",
    "1. Since we’re starting off with random guesses, it might be surprising that the unsupervised NB works at all. Explain what characteristics of the data cause it to work pretty well (say, within 10% Accuracy of the supervised NB) most of the time; also, explain why it utterly fails sometimes.\n",
    "2. When evaluating supervised NB across the four different datasets, you will observe some variation in effectiveness (e.g. Accuracy). Explain what causes this variation. Describe and explain any particularly suprising results.\n",
    "3. Evaluating the model on the same data that we use to train the model is considered to be a major mistake in Machine Learning. Implement a hold–out (hint: check out numpy.shuffle()) or cross–validation evaluation strategy. How does your estimate of Accuracy change, compared to testing on the training data? Explain why. (The result might surprise you!)\n",
    "4. Implement one of the advanced smoothing regimes (add-k, Good-Turing). Do you notice any variation in the predictions made by either the supervised or unsupervised NB classifiers? Explain why, or why not.\n",
    "5. The lecture suggests that deterministically labelling the instances in the initialisation phase of the unsupervised NB classifier “doesn’t work very well”. Confirm this for yourself, and then demonstrate why.\n",
    "6. Rather than evaluating the unsupervised NB classifier by assigning a class deterministically, instead calculate how far away the probabilistic estimate of the true class is from 1 (where we would be certain of the correct class), and take the average over the instances. Does this performance estimate change, as we alter the number of iterations in the method? Explain why.\n",
    "7. Explore what causes the unsupervised NB classifier to converge: what proportion of instances change their prediction from the random assignment, to the first iteration? From the first to the second? What is the latest iteration where you observe a prediction change? Make some conjecture(s) as to what is occurring here.\n",
    "\n",
    "Don't forget that groups of 1 student should respond to question (1), and one other question. Groups of 2 students should respond to question (1), and three other questions. Your responses should be about 100-200 words each."
   ]
  },
  {
   "attachments": {
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"
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    "## Intro\n",
    "Both the supervised and unsupervised classifiers (with 5 training iterations) have been run on each of the datasets and their performance summarized in the below tables. Graphs showing unsupervised training data follow after these tables.\n",
    "\n",
    "Briefly, some theory is presented to clarify the conceptual framework used to reason about the classifier behaviour. This framework is used in the discussion for both questions. \n",
    "\n",
    "Observations are made about the results obtained from running the classifiers and the characteristics of the datasets.\n",
    "\n",
    "Following the presentation of results and observations, a discussion responding to questions 1 and 7 is presented. \n",
    "\n",
    "## A Conceptual Framework\n",
    "\n",
    "The Naive Bayes (NB) classifier assigns a class value to an instance, such that the class value maximizes the below formula.\n",
    "\n",
    " $$ score = p(c_j) \\Pi_i p(x_i\\mid c_j)  $$\n",
    " \n",
    "Unsupervised Naive Bayes (UNB) works in similarly to NB except each point (instance) is given a class value weighting at random such that it's weightings sum to one.\n",
    "\n",
    "Each instance, a datapoint, can be thought of as existing in N-Dimensional space where N is the number of attributes available.\n",
    "\n",
    "Considering all the points belonging to one class value (or their weighting for that class value in the unsupervised case), we will have a point cloud in this NDimensional space. Given a new instance to classify, NB compares it to these point clouds (one for each possible class value) and then assigns it a class value. Lets define the \"laxation\" of one of these point clouds as how coherent (concentrated in NDimensional space) it is. Let \"Similarity\" be defined as how similar the instance for classification is to an instance \"prototypical\" to a point cloud (for a given class value - ie how close is it to the point clouds most common/archetypal kind of point)\n",
    "\n",
    ">1. __Laxation/Coherence__ $\\approx p(c_i)$ - This is how concentrated the point cloud for a class value is.j\n",
    ">2. __Similarity__ $\\approx \\Pi_i p(x_i \\mid c_j)$ - How close the instance being evaluated is to the point cloud prototype/centroid\n",
    "\n",
    "Given the above definitions, we can state;\n",
    "\n",
    "* More common class value's will have greater point cloud laxation (be more decoherent)\n",
    "    - This is because a high probability for the class value makes the classifier more tolerant to dis-similar instances. (lower similarity items).\n",
    "    \n",
    "* Less common class value point clouds will be more coherent. \n",
    "    - This is because a low probability for the class value will make it's point cloud less forgiving to dissimilar items. (relative to other point clouds)\n",
    "    \n",
    "* Instances similar to a class value point cloud prototype will be more likely to be assigned that class value.\n",
    "* The larger a point cloud is, the higher it's laxation and lower it's density.\n",
    "* The smaller a point cloud is, the lower it's laxation and higher it's density.\n",
    "\n",
    "## The Results\n",
    "\n",
    "### Breast Cancer Dataset\n",
    "\n",
    "#### After 5 iterations\n",
    "\n",
    "|Metric|Unsupervised|Supervised|\n",
    "|:---|---:|---:|\n",
    "| Basic Class Accuracy     | 63.89                    | 76.39                    |\n",
    "| Class Accuracy           | 63.89                    | 76.39                    |\n",
    "| Class Error Rate         | 36.11                    | 23.61                    |\n",
    "| Class Precision          | 63.89                    | 76.39                    |\n",
    "| Class Recall             | 64.71                    | 68.27                    |\n",
    "| Class F-Score            | 61.48                    | 69.08                    |\n",
    "\n",
    "\n",
    "#### After 20 iterations\n",
    "|Metric|Unsupervised|Supervised|\n",
    "|:---|---:|---:|\n",
    "| Basic Class Accuracy     | 65.28                    | 75.00                    |\n",
    "| Class Accuracy           | 65.28                    | 75.00                    |\n",
    "| Class Error Rate         | 34.72                    | 25.00                    |\n",
    "| Class Precision          | 65.28                    | 75.00                    |\n",
    "| Class Recall             | 62.12                    | 65.48                    |\n",
    "| Class F-Score            | 60.52                    | 66.67                    |\n",
    "\n",
    "\n",
    "\n",
    "### Car Dataset\n",
    "\n",
    "#### After 5 Iterations\n",
    "|Metric|Unsupervised|Supervised|\n",
    "|:---|---:|---:|\n",
    "| Basic Class Accuracy     |       25.14               | 84.1                    |\n",
    "| Class Accuracy           |   62.57                | 92.05                      |\n",
    "| Class Error Rate         |37.43                   |  7.948                     |\n",
    "| Class Precision          |         25.14             | 84.1                    |\n",
    "| Class Recall             |      21.61               | 55.21                    |\n",
    "| Class F-Score            |  16.03              |      60.8                     |\n",
    "\n",
    "#### After 10 Iterations\n",
    "\n",
    "|Metric|Unsupervised|Supervised|\n",
    "|:---|---:|---:|\n",
    "| Basic Class Accuracy     | 35.42                    | 83.33                    |\n",
    "| Class Accuracy           | 67.71                    | 91.67                    |\n",
    "| Class Error Rate         | 32.29                    | 8.33                     |\n",
    "| Class Precision          | 35.42                    | 83.33                    |\n",
    "| Class Recall             | 25.14                    | 53.04                    |\n",
    "| Class F-Score            | 21.97                    | 56.05                    |\n",
    "\n",
    "### Hypothyroid Dataset\n",
    "\n",
    "#### After 5 iterations\n",
    "\n",
    "|Metric|Unsupervised|Supervised|\n",
    "|:---|---:|---:|\n",
    "| Basic Class Accuracy     | 79.65                    | 95.83                    |\n",
    "| Class Accuracy           | 79.65                    | 95.83                    |\n",
    "| Class Error Rate         | 20.35                    | 4.17                     |\n",
    "| Class Precision          | 79.65                    | 95.83                    |\n",
    "| Class Recall             | 41.50                    | 50.00                    |\n",
    "| Class F-Score            | NA                       | NA                       |\n",
    "\n",
    "#### After 10 iterations\n",
    "\n",
    "|Metric|Unsupervised|Supervised|\n",
    "|:---|---:|---:|\n",
    "| Basic Class Accuracy     | 88.24                    | 95.20                    |\n",
    "| Class Accuracy           | 88.24                    | 95.20                    |\n",
    "| Class Error Rate         | 11.76                    | 4.80                     |\n",
    "| Class Precision          | 88.24                    | 95.20                    |\n",
    "| Class Recall             | 45.68                    | 49.93                    |\n",
    "| Class F-Score            | NA                       | NA                       |\n",
    "__________________________________________________________________________________\n",
    "\n",
    "\n",
    "### Mushroom Dataset\n",
    "\n",
    "#### After 5 iterations\n",
    "|Metric|Unsupervised|Supervised|\n",
    "|:---|---:|---:|\n",
    "| Basic Class Accuracy     | 66.17                    | 95.37                    |\n",
    "| Class Accuracy           | 66.17                    | 95.37                    |\n",
    "| Class Error Rate         | 33.83                    | 4.63                     |\n",
    "| Class Precision          | 66.17                    | 95.37                    |\n",
    "| Class Recall             | 64.96                    | 95.28                    |\n",
    "| Class F-Score            | 63.64                    | 95.35                    |\n",
    "\n",
    "\n",
    "#### After 10 iterations\n",
    "|Metric|Unsupervised|Supervised|\n",
    "|:---|---:|---:|\n",
    "| Basic Class Accuracy     | 48.10                    | 95.77                    |\n",
    "| Class Accuracy           | 48.10                    | 95.77                    |\n",
    "| Class Error Rate         | 51.90                    | 4.23                     |\n",
    "| Class Precision          | 48.10                    | 95.77                    |\n",
    "| Class Recall             | 48.12                    | 95.65                    |\n",
    "| Class F-Score            | 48.09                    | 95.75                    |\n",
    "\n",
    "\n",
    "\n",
    "### Unsupervised Model Training Convergence\n",
    "The below graph demonstrates how the unsupervised training went. Red lines show the class value distribution cosine similarity between iterations and blue lines show the certainty of predictions. \n",
    "\n",
    "1. Certainty (blue line) - Measures for each prediction that the model makes, how certain is it? The average certainty of the model applied to some dataset.\n",
    "> Where $x_i$ corresponds to an instance\n",
    "> $$ certainty = \\frac{\\Sigma_{i=0}^N (max(p(x_i = classLabel)))}{N} $$\n",
    "2. Change in class value distribution (red line) - measures how the class value distribution changes between iterations. This is the change in angle between the class value distribution vector of one iteration and the next.\n",
    "> Where i corresponds to a class value/label, and $ d_i $ to the probability ratio for class label 'i'\n",
    ">$$ \\mathbf D = [d_0, d_1, .. d_i] $$\n",
    ">\n",
    "> Where j corresponds to a training iteration\n",
    ">$$ \\delta\\theta_j = arcos( \\frac{\\mathbf{D}_{j-1} \\cdot \\mathbf{D}_j}{\\mid\\mid \\mathbf{D}_j\\mid\\mid \\mid\\mid \\mathbf{D}_{j-1}\\mid\\mid}) $$\n",
    "\n",
    "\n",
    "![image.png](attachment:image.png)\n",
    "\n",
    "\n",
    "## Dataset Observations\n",
    "Briefly some observations about the datasets are presented.\n",
    "\n",
    "#### Breast Cancer\n",
    "The breast cancer dataset has very little data (~280 instance) and few (9) attributes. Attributes have an average of about 5 possible values.\n",
    "\n",
    "#### Car\n",
    "The dataset is moderate size (~1700 instances) and has fewer (6) attributes with an average of about 3 possible attribute values. This dataset has four class values.\n",
    "\n",
    "#### Hypothyroid\n",
    "F-Score was not available due to a zero recall value. The class value/label distribution was ~95%, 5%. \n",
    "\n",
    "The hypothyroid dataset has many instances (~3000), many attributes (~18). Its attributes are mostly binary with only two possible attribute values. \n",
    "\n",
    "### Mushroom\n",
    "The mushroom dataset has many attributes (22), is large (8000+ instances) and an average of ~5 attribute values per attributee. \n",
    "\n",
    "## Q1\n",
    "#### What characteristics of the data cause it to work well with an unsupervsed model?\n",
    "\n",
    "##### Breast Cancer\n",
    "The breast cancer unsupervised model shows comparable performance to the supervised model. This dataset has high class value distribution (~75%, 25%) and higher attribute arity (~5). Unlike the hypothyroid dataset - each class value point cloud has a large enough mass to not be dominated by the other competing cloud. That is, both clouds have tighter laxation and thus are more sensitive to the attribute profiles of an instance. High arity complements this allowing for greater distinction of similarity between an instance and a point cloud (more detailes attribute profiles). We see the unsupervised model become certain after 20 iterations and its class value/label distribution become more settled. This suggests the point clouds were able to separate themselves from one another during training and that datasets with separable point clouds work better with unsupervised models. This separability increased with arity and the number of attributes - as the increase the size of the NDimensional space.\n",
    "\n",
    "##### Hypothyroid\n",
    "The unsupervised model shows zero recall for the 'hypothyroid' classification. The model settles with high certainty and little change in class label distribution between iterations. We also see zero recall for the supervised model. The poor performance of both models here could be due to the small number of instances with the 'hypothyroid' class value. (~5%, 95% initial class value distribution) This could be because the 'negative' class value point cloud has too higher laxation and overwhelms the 'hypothyroid' class value point cloud when making comparisions. \n",
    "\n",
    "We see every prediction for the 'hypothyroid' class is wrong in both models. This suggests that neither model learnt what 'hypothyroid' might look like and that a model, unsupervised or supervised, will need to see enough of a given class label (ie hypthyroid) to be able to learn what that class label is. Datasets characterized by balanced class value/label distributions should work better with an unsupervised classifier. \n",
    "\n",
    "This set has many attributes but they mostly of binary arity. With low arity, it's harder for a point cloud to have a distinct prototype and thus harder to measure similarity of an instance between one point cloud and the next making training harder. We should look then for datasets with higher arity when using an unsupervised model.\n",
    "\n",
    "##### Car\n",
    "The car dataset performs poorly The dataset class value distribution is ~(4%, 4%, 22%, 70%), it has few attributes and low arity but many class values. The smaller class value clouds will need to be more distinct from the larger ones for the classifier to assign to them - otherwise the laxation of the larger clouds will offset any similarity an instance has to a smaller cloud. The space in which the clouds exist is limited in this data set due to low arity and few attributes. This could cause point clouds to be crowded and poorly defined. The result is a model with low certainty, and poor performance. We can say that the point clouds of this dataset are not linearly separable and that the training set has an unbalanced class value distribution. Our observations here suggest that datasets characterized by fewer class labels and more attributes with higher arity are desirable for unuspervised models.\n",
    "\n",
    "##### Mushroom\n",
    "The unsupervised model for the mushroom dataset quickly converges on class value distribution with a high certainty. Confusingly though - we see better performance with fewer training iterations. The mushroom dataset has many attributes, high arity and a ~50/50 class value distribution. \n",
    "\n",
    "Based on our prior observations - this should be an optimal dataset for unsupervised learning. Although - observations show that it's performance is poor compared to the supervised model. The unsupervised model relies on the assumption that attributes are independant when making predictions. (the 'naive' assumption) Attributes of mushroom physiology could be related - and thus errors accumulate on every training iteration due to our naive assumption. Models produced with less training iterations rely less heavily on the 'naive' assumption. However, the competing element here is that more training iterations allow the unsupervised model to better learn class values. As a result, datasets characterized by interdependant attributes are less suited to an unsupervised model.\n",
    "\n",
    "In summary, datasets with the following charachteristics are better suited to unsupervised Naive Bayes models;\n",
    "* More attributes.\n",
    "* Higher attribute arity.\n",
    "* Datasets with better balanced actual class value distributions.\n",
    "* Datasets where the assumption that attributes are not related holds. (This is important for NB too - but more important for UNB)\n",
    "* Datasets with linearly separable class label point clouds.\n",
    "\n",
    "\n",
    "## Q7\n",
    "#### What causes the Unsupervised Model to converge and what is happening for convergence?\n",
    "\n",
    "Graphs of training data are presented in the results above for inspecting convergence of the unsupervised model. Two metrics are used for evaluating how class value distributions change between iterations (Distribution Delta) and how stable the model is (Certainty)\n",
    "\n",
    "1. Certainty (blue line) - Measures for each prediction that the model makes, how certain is it? The average certainty of the model applied to some dataset.\n",
    "> where $x_i$ corresponds to an instance\n",
    "> $$ certainty = \\frac{\\Sigma_{i=0}^N (max(p(x_i = classLabel)))}{N} $$\n",
    "2. Change in class value distribution (red line) - measures how the class value distribution changes between iterations. This is the change in angle between the class value distribution vector of one iteration and the next.\n",
    "> Where i corresponds to a class value/label, and $ d_i $ to the probability ratio for class label 'i'\n",
    ">$$ \\mathbf D = [d_0, d_1, .. d_i] $$\n",
    ">\n",
    "> Where j corresponds to a training iteration\n",
    ">$$ \\delta\\theta_j = arcos( \\frac{\\mathbf{D}_{j-1} \\cdot \\mathbf{D}_j}{\\mid\\mid \\mathbf{D}_j\\mid\\mid \\mid\\mid \\mathbf{D}_{j-1}\\mid\\mid}) $$\n",
    "\n",
    "If we define convergence as the settling of class label distribution.\n",
    "\n",
    "Visualizing what is happening during the training - we have point clouds with each point having a weighting between zero and one for each possible class value. At first they are randomly weighted. Our (finite) NDimensional space is like a sponge filled with a coloured dye - one colour for each class value, and each point is the 'material' of the sponge. At the end of each iteration, the dispersal of the 'coloured dye' becomes more ordered (loses entropy). Initially the dyes are evenly dispersed (random probability distribution, high entropy). Ideally - each colour of dye will separate into it's own distinct region of the sponge, and there will be minimum overlap with other colours at it's boundary. So what causes the dye to lose entropy (become less random) for a training iteration and when does it settle (converge)? \n",
    "\n",
    "Considering an example with two class values. One theory is that - whichever point cloud is largest after random seeding will have a prototype close to the training set prototype but slightly different (random variation). Ditto for the other cloud however it will be smaller. The larger cloud will collect more points on the first training iteration than the other clouds (it has a higher laxation) and these points will be tend to be closer to it's prototype than the training set's average prototype. However it will be marginally less sensitive to the dis-similarity between points it collects and it's own prototype when compared the other point cloud (which has a slightly lower laxation due to it's smaller size). The other point cloud will have been more sensitive to differences in similarity between points and it's prototype. Thus it will have collected less points overall (because of it's lower laxation) but more points closer to it's own prototype. (greater point weightings actually but here linguistic precision is sacrificed for simplicity of explanation) \n",
    "\n",
    "At the end of the iteration, after accounting for the new point probability weightings, the larger cloud will tend to have an increased size (total class label probability weighting, due to its higher laxation) and the smaller cloud will tend to have a decresed size (lower total class label probability weighting due to it's lower laxation). The larger cloud will tend to have a minor overall shift of it's prototype point in the direction formed by a vector pointing from the origin to its original protoype point (it weakly preferred points with greater similarity to its prototype). The smaller cloud will have a greater shift of it's prototype point in the direction formed by a vector pointing from the origin to it's prototype point. (it more strongly preferred points with greater similarity to its protoype due to its lower laxation) As training iteration continue, this process will repeat and the dyes (point clouds) will draw apart. The more distinct the point clouds become - the lower the distribution delta and the higher the certainty. This due to a lessened interference/competition from neighbouring clouds (which are far away and have only dissimilar points.) \n",
    "\n",
    "When considering a single cloud (colour) - the dye stops changing its distribution when the laxation of a cloud and similarity of points in a cloud to the clouds prototype become balanced. When considering the dynamic between clouds, they become balanced when they become sufficiently distinct. \n",
    "\n",
    "Given the above, we hypothesize that convergence requires data be separable in n dimensional space into as many partitions as there are class labels and we require a balanced (guessed) starting label distribution that is random.\n",
    "\n",
    "We observe high certainty and low distribution deltas in the graphs shown above for the breast cancer, hypothyroid and mushroom datasets. According to our theory - this means the point clouds were separable. All of these sets have a large NDimensional space (lots of attributes with high arity for breast cancer and mushrooms, lots of attributes for hypothyroid.) In contrast the graph above shows the car dataset has no increase in certainty and no sign of a decreasing distribution delta. This suggests its data was not separable. Consistent with this - we observed that the car dataset has few attributes and they are of low arity. \n",
    "\n",
    "A final comment is that convergence does not guarantee an optimal model. The mushroom results demonstrate this. A hypothesis is suggested for why this might be in the discussion for question one. (namely, each training iteration relies on the 'naive' assumption and errors accumulate). Consequently - there is a trade off between training iterations and lowering entropy. More training does not always result in a better unsupervised model.\n",
    "\n",
    "In summary\n",
    "* Larger clouds grow in an iteration with less sensitivity to dissimilaritys between new members and their prototype. \n",
    "* Smaller clouds shrink and more strongly prefer points more similar to their prototype. \n",
    "* Clouds tend to move in the direction of their prototypes each iteration.\n",
    "* Clouds change (prototype location and laxation) untill a balance is struck between laxation and similarity of members to the cloud prototype. \n",
    "* Datasets need to be linearly seperable in their NDimensional space for convergence to occur.\n",
    "* Convergence does not mean a model is optical. Less training can be better for datasets with highly related attributes.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Code to generate graphs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "# This is the code used to plot unsupervised training metric graphics.\n",
    "import matplotlib.pyplot as pypl\n",
    "def printTrainingConvergence():\n",
    "    '''Print training iteration performance for any unsupervised data classifiers\n",
    "    '''\n",
    "    \n",
    "    # Get figure to plot on\n",
    "    NCOL = 1\n",
    "    NROW = len(classifierObject)//NCOL\n",
    "    fig, axes = pypl.subplots(NROW,NCOL, figsize=(16,32))\n",
    "    fig.figsize=(10,10)\n",
    "    px=0\n",
    "    \n",
    "    # Plot each dataset in classifier object\n",
    "    for dataset in classifierObject.keys():\n",
    "        \n",
    "        classifier = classifierObject[dataset]['Unsupervised']\n",
    "        trainingData = classifier.trainingMetrics\n",
    "        #pprint(trainingData)\n",
    "        datasetName = classifier.dataKey\n",
    "        \n",
    "        xtick = []\n",
    "        metricName = list(trainingData[list(trainingData.keys())[0]].keys())\n",
    "        y = [ [] for metric in metricName ]\n",
    "\n",
    "        # Extract data\n",
    "        for iteration in range(len(list(trainingData.keys()))):\n",
    "            iterationkey = 'Iteration {}'.format((iteration+1))\n",
    "            yx=0\n",
    "            for metric in metricName:\n",
    "                y[yx].append(trainingData[iterationkey][metric])\n",
    "                yx+=1\n",
    "        \n",
    "        # Plot\n",
    "        \n",
    "        #axisLeft = axes[px//NCOL, (px%NCOL)]\n",
    "        axisLeft=axes[px]\n",
    "        axisLeft.set_ylim([0,1])\n",
    "        axisRight = axisLeft.twinx()\n",
    "        #axisRight.set_ylim([0,0.01])\n",
    "        axisLeft.set_ylabel('Certainty [%]')\n",
    "        axisLeft.set_xlabel('Iteration Nbr (-1 offset)')\n",
    "        axisRight.set_ylabel('Distribution Vector Delta Theta [deg]')\n",
    "            \n",
    "        axisLeft.plot(range(len(y[0])),y[0], 'bx-', label=metricName[0])\n",
    "        axisRight.plot(range(len(y[1])),y[1], 'rx-', label=metricName[1])\n",
    "\n",
    "        axisLeft.set_title(datasetName)\n",
    "            \n",
    "        px+=1\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x2304 with 8 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "printTrainingConvergence()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Code demonstrating more detailed evaluation metrics for hypothyroid"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{   'Class Average': {   'Basic Class Accuracy': 88.24273072060683,\n",
      "                         'Class Accuracy': 88.24273072060683,\n",
      "                         'Class Error Rate': 11.757269279393173,\n",
      "                         'Class F-Score': 'NA',\n",
      "                         'Class Precision': 88.24273072060683,\n",
      "                         'Class Recall': 45.68062827225131},\n",
      "    'hypothyroid': {   'Basic Class Value Accuracy': 88.24273072060683,\n",
      "                       'Class Value Accuracy': 88.24273072060683,\n",
      "                       'Class Value Error Rate': 11.757269279393173,\n",
      "                       'Class Value F-Score': 'NA',\n",
      "                       'Class Value Precision': 88.24273072060683,\n",
      "                       'Class Value Recall': 0.0,\n",
      "                       'False Negative': 27,\n",
      "                       'False Positive': 66,\n",
      "                       'Smoothing Applied': True,\n",
      "                       'Total Correct': 698,\n",
      "                       'True Negative': 698,\n",
      "                       'True Positive': 0},\n",
      "    'negative': {   'Basic Class Value Accuracy': 88.24273072060683,\n",
      "                    'Class Value Accuracy': 88.24273072060683,\n",
      "                    'Class Value Error Rate': 11.757269279393173,\n",
      "                    'Class Value F-Score': 93.75419744795165,\n",
      "                    'Class Value Precision': 88.24273072060683,\n",
      "                    'Class Value Recall': 91.36125654450262,\n",
      "                    'False Negative': 66,\n",
      "                    'False Positive': 27,\n",
      "                    'Smoothing Applied': True,\n",
      "                    'Total Correct': 698,\n",
      "                    'True Negative': 0,\n",
      "                    'True Positive': 698}}\n"
     ]
    }
   ],
   "source": [
    "import pprint\n",
    "\n",
    "pp=pprint.PrettyPrinter(indent=4)\n",
    "#pp.pprint(classifierObject['mushroom']['Supervised'].model)\n",
    "pp.pprint(classifierObject['hypothyroid']['Unsupervised'].metrics)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}