naviebayes.py

import csv
import random
import math

## step 1: load data...
def loadCsv(filename):
    lines = csv.reader(open(filename, "rb"))
##    for lin in lines:
##        print lin
    dataset = list(lines)
    for i in range(len(dataset)):
        dataset[i] = [float(x) for x in dataset[i]]
    return dataset

##filename = 'pima-indians-diabetes.data.csv'
##dataset = loadCsv(filename)
##print('Loaded data file {0} with {1} rows').format(filename, len(dataset))

## step 2: process data, divide data into two parts, trainSet and testSet
def splitDataset(dataset, splitRatio):
    trainSize = int(len(dataset) * splitRatio)
    trainSet = []
    copy = list(dataset)
    while len(trainSet) < trainSize:
        index = random.randrange(len(copy))
        trainSet.append(copy.pop(index))
    return [trainSet, copy]

##dataset = [[1], [2], [3], [4], [5]]
##splitRatio = 0.67
##train, test = splitDataset(dataset, splitRatio)
##print('Split {0} rows into train with {1} and test with {2}').format(len(dataset), train, test)

def separateByClass(dataset):
    separated = {}
    for i in range(len(dataset)):
        vector = dataset[i]
        if (vector[-1] not in separated):
            separated[vector[-1]] = []
        separated[vector[-1]].append(vector)
    return separated

##dataset = [[1, 20, 1], [2, 21, 0], [3, 22, 1]]
##separated = separateByClass(dataset)
##print('Separated instances: {0}').format(separated)

def mean(numbers):
    return sum(numbers)/float(len(numbers))

def stdev(numbers):
    avg = mean(numbers)
    variance = sum([pow(x-avg,2) for x in numbers])/float(len(numbers)-1)
    return math.sqrt(variance)

##numbers = [1, 2, 3, 4, 5]
##print('Summary of {0}: mean={1}, stdev={2}').format(numbers, mean(numbers), stdev(numbers))

def summarize(dataset):
    summaries = [(mean(attribute), stdev(attribute)) for attribute in zip(*dataset)]
    del summaries[-1]
    return summaries

##dataset = [[1,20,0], [2,21,1], [3,22,0]]
##summary = summarize(dataset)
##print('Attribute summaries: {0}').format(summary)

def summarizeByClass(dataset):
    separated = separateByClass(dataset)
    summaries = {}
    for classValue, instances in separated.iteritems():
        summaries[classValue] = summarize(instances)
    return summaries

##dataset = [[1,20,1], [2,21,0], [3,22,1], [4,22,0]]
##summary = summarizeByClass(dataset)
##print('Summary by class value: {0}').format(summary)

def calculateProb(x, mean, stdev):
    exponent = math.exp(-(math.pow(x-mean,2)/(2*math.pow(stdev,2))))
    return (1/(math.sqrt(2*math.pi)*stdev))*exponent

##x = 71.5
##mean = 73
##stdev = 6.2
##prob = calculateProb(x, mean, stdev)
##print('Probability of belonging to this class: {0}').format(prob)

def calculateClassProb(summaries, inputVector):
    prob = {}
    for classValue, classSummaries in summaries.iteritems():
        prob[classValue] = 1
        for i in range(len(classSummaries)):
            mean, stdev = classSummaries[i]
            x = inputVector[i]
            prob[classValue] *= calculateProb(x, mean, stdev)
    return prob

##summaries = {0:[(1, 0.5)], 1:[(20, 5.0)]}
##inputVector = [1.1, '?']
##prob = calculateClassProb(summaries, inputVector)
##print('Prob for each class:{0}').format(prob)

def predict(summaries, inputVector):
    prob = calculateClassProb(summaries, inputVector)
    bestLabel, bestProb = None, -1
    for classValue, prob in prob.iteritems():
        if bestLabel is None or prob > bestProb:
            bestProb = prob
            bestLabel = classValue
    return bestLabel

##summaries = {'A':[(1, 0.5)], 'B':[(20, 5.0)]}
##inputVector = [1.1, '?']
##result = predict(summaries, inputVector)
##print('Prediction:{0}').format(result)

def getPredictions(summaries, testSet):
    predictions = []
    for i in range(len(testSet)):
        result = predict(summaries, testSet[i])
        predictions.append(result)
    return predictions

##summaries = {'A':[(1,0.5)], 'B':[(20, 5.0)]}
##testSet =[[1.1, '?'], [19.1, '?']]
##predictions = getPredictions(summaries, testSet)
##print('Predictions:{0}').format(predictions)

def getAccuracy(testSet, predictions):
    correct = 0
    for x in range(len(testSet)):
        if testSet[x][-1] == predictions[x]:
            correct += 1
    return (correct/float(len(testSet)))*100.0
##testSet = [[1,1,1,'a'], [2,2,2,'a'], [3,3,3,'b']]
##predictions = ['a', 'a', 'a']
##accuracy = getAccuracy(testSet, predictions)
##print('Accuracy: {0}').format(accuracy)

def main():
    filename = 'pima-indians-diabetes.data.csv'
    splitRatio = 0.67
    dataset = loadCsv(filename)
    trainingSet, testSet = splitDataset(dataset, splitRatio)
    print('Split {0} rows into train={1} and test={2} rows').format(len(dataset), len(trainingSet), len(testSet))
    #prepare model
    summaries = summarizeByClass(trainingSet)
    #test model
    predictions = getPredictions(summaries, testSet)
    accuracy = getAccuracy(testSet, predictions)
    print('Accuracy: {0}%').format(accuracy)

main()