#!/usr/bin/env python3

# -*- coding: utf-8 -*-

from pandas import read_csv

def loadDataSet():

    dataset = read_csv("F:/goverment/Aprior/No Number.csv")

    data = dataset.values[:,:]

    Data=[]

    for line in data:

        ls=[]

        for i in line:

            ls.append(i)

        Data.append(ls)

    #print(Data)

    return Data

    '''

    return [['a', 'c', 'e'], ['b', 'd'], ['b', 'c'], ['a', 'b', 'c', 'd'], ['a', 'b'], ['b', 'c'], ['a', 'b'],

            ['a', 'b', 'c', 'e'], ['a', 'b', 'c'], ['a', 'c', 'e']]'''

def createC1(dataSet):

    C1 = []

    for transaction in dataSet:

        for item in transaction:

            if not [item] in C1:

                C1.append([item])

    C1.sort()

    '''??????????????????????????????????????????????????????'''

    # 映射为frozenset唯一性的，可使用其构造字典

    return list(map(frozenset, C1))      

# 从候选K项集到频繁K项集（支持度计算）

def scanD(D, Ck, minSupport):

    ssCnt = {}

    for tid in D:

        for can in Ck:

            if can.issubset(tid):

                if not can in ssCnt:

                    ssCnt[can] = 1

                else:

                    ssCnt[can] += 1

    numItems = float(len(D))

    retList = []

    supportData = {}

    for key in ssCnt:

        support = ssCnt[key] / numItems

        if support >= minSupport:

            retList.insert(0, key)

            supportData[key] = support

    return retList, supportData

def calSupport(D, Ck, min_support):

    dict_sup = {}

    for i in D:

        for j in Ck:

            if j.issubset(i):

                if not j in dict_sup:

                    dict_sup[j] = 1

                else:

                    dict_sup[j] += 1

    sumCount = float(len(D))

    supportData = {}

    relist = []

    for i in dict_sup:

        temp_sup = dict_sup[i] / sumCount

        if temp_sup >= min_support:

            relist.append(i)

            supportData[i] = temp_sup  # 此处可设置返回全部的支持度数据（或者频繁项集的支持度数据）

    return relist, supportData

# 改进剪枝算法

def aprioriGen(Lk, k):  # 创建候选K项集 ##LK为频繁K项集

    retList = []

    lenLk = len(Lk)

    for i in range(lenLk):

        for j in range(i + 1, lenLk):

            L1 = list(Lk[i])[:k - 2]

            L2 = list(Lk[j])[:k - 2]

            L1.sort()

            L2.sort()

            if L1 == L2:  # 前k-1项相等，则可相乘，这样可防止重复项出现

                #  进行剪枝（a1为k项集中的一个元素，b为它的所有k-1项子集）

                a = Lk[i] | Lk[j]  # a为frozenset()集合

                a1 = list(a)

                b = []

                # 遍历取出每一个元素，转换为set，依次从a1中剔除该元素，并加入到b中

                for q in range(len(a1)):

                    t = [a1[q]]

                    tt = frozenset(set(a1) - set(t))

                    b.append(tt)

                t = 0

                for w in b:

                    # 当b（即所有k-1项子集）都是Lk（频繁的）的子集，则保留，否则删除。

                    if w in Lk:

                        t += 1

                if t == len(b):

                    retList.append(b[0] | b[1])

    return retList

def apriori(dataSet, minSupport=0.2):

    C1 = createC1(dataSet)

    D = list(map(set, dataSet))  # 使用list()转换为列表

    L1, supportData = calSupport(D, C1, minSupport)

    L = [L1]  # 加列表框，使得1项集为一个单独元素

    k = 2

    while (len(L[k - 2]) > 0):

        Ck = aprioriGen(L[k - 2], k)

        Lk, supK = scanD(D, Ck, minSupport)  # scan DB to get Lk

        supportData.update(supK)

        L.append(Lk)  # L最后一个值为空集

        k += 1

    del L[-1]  # 删除最后一个空集

    return L, supportData  # L为频繁项集，为一个列表，1，2，3项集分别为一个元素。

# 生成集合的所有子集

def getSubset(fromList, toList):

    for i in range(len(fromList)):

        t = [fromList[i]]

        tt = frozenset(set(fromList) - set(t))

        if not tt in toList:

            toList.append(tt)

            tt = list(tt)

            if len(tt) > 1:

                getSubset(tt, toList)

#def calcConf(freqSet, H, supportData, ruleList, minConf=0.7):

def calcConf(freqSet, H, supportData, Rule, minConf=0.7):

    for conseq in H:

        conf = supportData[freqSet] / supportData[freqSet - conseq]  # 计算置信度

        # 提升度lift计算lift = p(a & b) / p(a)*p(b)

        lift = supportData[freqSet] / (supportData[conseq] * supportData[freqSet - conseq])

        ls=[]

        if conf >= minConf and lift > 3:

            for i in freqSet - conseq:

                #print(i," ",end="")

                ls.append(i)

                ls.append(" ")

            #print('-->',end="")

            ls.append('-->')

            for i in conseq:

                #print(i," ",end="")

                ls.append(i)

                ls.append(" ")

            #print('支持度:', round(supportData[freqSet - conseq]*100, 1), "%",'  置信度:', round(conf*100,1),"%",'  lift值为', round(lift, 2))

            #ls.append(' 支持度:')

            #ls.append(round(supportData[freqSet - conseq]*100, 1))

            #ls.append("% ")

            #ls.append(' 置信度:')

            ls.append( round(conf*100,1))

            ls.append("% ")

            #ls.append( round(lift, 2))

            #ls.append(round(lift, 2))

            #ruleList.append((freqSet - conseq, conseq, conf))

        if ls!=[]:

            #print(len(ls))

            Rule.append(ls)

# =============================================================================

#     for line in Rule:

#         for i in line:

#             print(i,end="")

#         print("")

# =============================================================================

    return Rule

# =============================================================================

#             print(freqSet - conseq, '-->', conseq, '支持度', round(supportData[freqSet - conseq], 2), '置信度：', round(conf,3),

#                   'lift值为：', round(lift, 2))

# =============================================================================

# 生成规则

def gen_rule(L, supportData, minConf=0.7):

    bigRuleList = []

    for i in range(1, len(L)):  # 从二项集开始计算

        for freqSet in L[i]:  # freqSet为所有的k项集

            # 求该三项集的所有非空子集，1项集，2项集，直到k-1项集，用H1表示，为list类型,里面为frozenset类型，

            H1 = list(freqSet)

            all_subset = []

            getSubset(H1, all_subset)  # 生成所有的子集

            calcConf(freqSet, all_subset, supportData, bigRuleList, minConf)

    return bigRuleList

if __name__ == '__main__':

    dataSet = loadDataSet()

    #print(dataSet)

    L, supportData = apriori(dataSet, minSupport=0.05)

    rule = gen_rule(L, supportData, minConf=0.5)

    for i in rule:

        for j in i:

            if j==',':

                continue

            else:

                print(j,end="")

        print("")

'''

具体公式：

P(B|A)/P(B)

称为A条件对于B事件的提升度,如果该值=1,说明两个条件没有任何关联,

如果<1,说明A条件(或者说A事件的发生)与B事件是相斥的,

一般在数据挖掘中当提升度大于3时,我们才承认挖掘出的关联规则是有价值的。

'''