# -*- coding: utf-8 -*-
"""
Created on Mon Oct 30 19:58:47 2017

@author: SARTHAK BABBAR
"""

# Example of Naive Bayes implemented from Scratch in Python
import csv
import random
import math
from sklearn.metrics import classification_report


def loadCsv(filename):
    lines=csv.reader(open(filename,"r"))
    dataset=list(lines)
    for i in range(len(dataset)):
        dataset[i] = [float(x) for x in dataset[i]]
    return dataset


def addcsv(file1,file2):
    lines2=csv.reader(open(file2,"r"))
    dataset2=list(lines2)
    for i in range(len(dataset2)):
        dataset2[i] = [float(x) for x in dataset2[i]]
    dataset= file1 + dataset2   
    
    return dataset

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]

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

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)

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

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

def calculateProbability(x, mean, stdev):
    if mean==0: 
        return 1
    if stdev==0:
        return 1
    exponent = math.exp(-(math.pow(x-mean,2)/(2*math.pow(stdev,2))))
    return (1/(math.sqrt(2*math.pi) * stdev))*exponent

def calculateClassProbabilities(summaries, inputVector):
    probabilities = {}
    for classValue, classSummaries in summaries.items():
        probabilities[classValue] = 1
        for i in range(len(classSummaries)):
            mean, stdev = classSummaries[i]
            x = inputVector[i]
            probabilities[classValue] *= calculateProbability(x,mean,stdev)
    return probabilities
			
def predict(summaries, inputVector):
    probabilities = calculateClassProbabilities(summaries, inputVector)
    bestLabel, bestProb = None, -1
    for classValue, probability in probabilities.items():
        if bestLabel is None or probability > bestProb:
            tProb = probability
            bestLabel = classValue
    return bestLabel

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

def getAccuracy(testSet, predictions):
    correct = 0
    for i in range(len(testSet)):
        if testSet[i][-1] == predictions[i]:
            correct += 1
    return (correct/float(len(testSet))) * 100.0

def main():
    file1 ='smote&tree.csv'
    splitRatio = 0.8
    dataset = loadCsv(file1)
    trainingSet,testSet = splitDataset(dataset,splitRatio)
    #trainingSet=addcsv(train,file2)   
    #testSet=addcsv(test,file3) 
    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)
    report = classification_report(testSet,predictions)
    print(report)
    print('Accuracy: {0}%'.format(accuracy))
    
 
main()