from math import log

import operator

import pickle

import os

import numpy as np

def debug(value_name,value):

    print("debuging for %s" % value_name)

    print(value)

# feature map  and wind_label

def loadDateset():

    with open('./wine.data') as f:

        wine = [eaxm.strip().split(',') for  eaxm in f.readlines()]

        #for i in range(len(wine)):

        #    wine[i] = list(map(float,wine[i]))

        wine = np.array(wine)

        wine_label = wine[...,:1]

        wine_data = wine[...,1:]

        # get the map of wine_feature

        featLabels = []

        for i in range(len(wine_data)):

            #print(i)

            featLabels.append(i)

        #

        wine_data = np.concatenate((wine_data,wine_label),axis=1)

        #  这里的label需要做一定的修改　需要的label是属性对应的字典

    return wine_data,featLabels

#  wine_data = dateset[:-1] wine_label = dateset[-1:]

def informationEntropy(dataSet):

    m = len(dataSet)

    labelMap = {}

    for wine in dataSet:

        nowLabel = wine[-1]

        if nowLabel not in labelMap.keys():

            labelMap[nowLabel] = 0

        labelMap[nowLabel] += 1

    shannoEnt = 0.0

    for key in labelMap.keys():

        prop = float(labelMap[key]/m)

        shannoEnt -= prop*(log(prop,2))

    return shannoEnt

# split the subDataSet  Improve reusability

def splitDataSet(dataSet,axis,feature):

    subDataSet = []

    # date type

    for featVec in dataSet:

        if(featVec[axis] == feature):

            reduceVec = featVec[:axis]

            if(isinstance(reduceVec,np.ndarray)):

                reduceVec = np.ndarray.tolist(reduceVec)

            reduceVec.extend(featVec[axis+1:])

            subDataSet.append(reduceVec)

    return subDataSet

# choose the best Feature to split

def chooseFeature(dataSet):

    numFeature = len(dataSet[0])-1

    baseEntorpy = informationEntropy(dataSet)

    bestInfoGain = 0.0

    bestFeature = -1

    for i in range(numFeature):

        #valueList = wine_data[:,i:i+1]

        valueList = [value[i] for value in dataSet]

        # debug

        # print("valueList is:")

        # print(len(valueList))

        uniqueVals = set(valueList)

        newEntropy = 0.0

        for value in uniqueVals:

            subDataSet = splitDataSet(dataSet,i,value)

            #debug

            #print("subDataSet is :")

            #print(subDataSet)

            #print(len(subDataSet[0]))

            # 数值部分要注意

            prop = len(subDataSet)/float(len(dataSet))

            newEntropy += prop*informationEntropy(subDataSet)

        infoGain = baseEntorpy - newEntropy

        if(infoGain > bestInfoGain):

            bestInfoGain = infoGain

            bestFeature = i

    return bestFeature

def majorityCnt(classList):

    classMap = {}

    for vote in classList:

        if vote not in classMap.keys():

            classMap[vote] = 0

        classMap[vote] += 1

    #tempMap = sorted(classMap.items(),key = operator.itemgetter(1),reverse = True)

    tempMap = sorted(classMap.items(), key=lambda  x:x[1], reverse=True)

    return tempMap[0][0]

# labels for map of Feature

def createTree(dataSet,Featlabels):

    classList = [example[-1] for example in dataSet]

    # if all of the attribute of classList is same

    if(classList.count(classList[0])) == len(classList):

        #print("all is same")

        return classList[0]

    # print("debug after")

    # feature is empty

    if len(dataSet[0]) == 1:

        print("len is zero")

        return majorityCnt(classList)

    # print("debug pre")

    bestFeat = chooseFeature(dataSet)

    #debug

    #print("debug")

    #print(bestFeat)

    bestFeatLabel = Featlabels[bestFeat]

    # print(bestFeatLabel)

    # python tree use dict for index of feature to build the tree

    myTree = {bestFeatLabel:{}}

    # del redundant label

    del(Featlabels[bestFeat])

    valueList = [example[bestFeat] for example in dataSet]

    uniqueVals = set(valueList)

    # print(uniqueVals)

    # 取值都一样的话就没有必要继续划分

    if(len(uniqueVals) == 1):

        return majorityCnt(dataSet)

    for value in uniqueVals:

        #if(bestFeat == 6):

        #    print(value)

        subFeatLabels = Featlabels[:]

        # print(sublabels)

        subdataSet = splitDataSet(dataSet,bestFeat,value)

        if(bestFeatLabel == 6 and value == '3.06'):

            #print("debuging ")

            myTree[bestFeatLabel][value] = createTree(subdataSet, subFeatLabels)

            #print(myTree[bestFeatLabel][value])

            #print("len of build")

            #print(len(uniqueVals))

        #    print(value)

        else:

            myTree[bestFeatLabel][value] = createTree(subdataSet,subFeatLabels)

    return myTree

# classity fuction   featLabel and testVes is used to get featvalue of test

def classify(inputTree,featLabels,testVec):

    # get the node

    nowNode = list(inputTree.keys())[0]

    # debug

    #debug(nowNode)

    # print(featLabels)

    featIndex = featLabels.index(nowNode)

    # print(featIndex)

    #find the value of  testVec in feature

    keyValue = testVec[featIndex]

    #print("len of input")

    #print(len(inputTree[nowNode].keys()))

    keyValue = str(keyValue)

    subTree = inputTree[nowNode][keyValue]

    if(isinstance(subTree,dict)):

        classLabel = classify(subTree,featLabels,testVec)

    else:

        classLabel = subTree

    return classLabel

if __name__ == '__main__':

    wine_data, featLabels = loadDateset()

    #print(featLabels)

    #print(wine_data)

    myTree = createTree(wine_data,featLabels.copy())

    #print(type(myTree))

    # the type of value

    test = [14.23,1.71,2.43,15.6,127,2.8,3.06,.28,2.29,5.64,1.04,3.92,1065]

    #print(featLabels)

    print(classify(myTree,featLabels,test))