直接暴力实现！

#!/usr/bin/env python3
# -*- coding: utf-8 -*-
# @Time    : 2021/10/28 11:32
# @Author  : 邱天衡
# @FileName: main.py
# @Software: PyCharm
# @Blog    ：https://blog.csdn.net/Mr_Clutchimport itertools
import json
import pandas as pd# 非频繁项
global non_frequency_d
# 频繁项集
global frequency_ddef Apriori(d, support, confidence):global non_frequency_dglobal frequency_dconfidence_dic = {}dir_data = {}item_d = {}  # 存储候选n项集for index, rows in d.iterrows():dir_data[index] = rows['items'][1:-1]item = rows['items'][1:-1].split(',')for i in item:if i in item_d:item_d[i] += 1else:item_d[i] = 1# 生成频繁1项集frequency_d = {k: v for k, v in item_d.items() if v >= support}confidence_dic[1] = frequency_dprint(1, len(frequency_d))non_frequency_d = {k: v for k, v in item_d.items() if v < support}.keys()count = 2while 1:k_list = []  # 存储当前项集合item_d = {}  # 存储候选n项集for k, _ in frequency_d.items():k_list.extend(k.split(','))# 去重k_list = list(set(k_list))combination_data = list(itertools.combinations(k_list, count))for item in combination_data:# 利用apriori性质去除一些候选项, 优化算法f_keys = list(itertools.combinations(item, count - 1))flag = Falsefor f in f_keys:for j in non_frequency_d:if all(i in j for i in f):flag = Truebreakif flag:continue# 生成频繁count项集for _, v in dir_data.items():if all(i in v.split(',') for i in item):key = ','.join(item)if key in item_d:item_d[key] += 1else:item_d[key] = 1# 统计频繁count项集frequency_d = {k: v for k, v in item_d.items() if v >= support}non_frequency_d = {k: v for k, v in item_d.items() if v < support}.keys()# 打印对应频繁项集数目print(count, len(frequency_d))if len(frequency_d) == 0:breakelif len(frequency_d) == 1:confidence_dic[count] = frequency_delse:  # 进行强关联规则分析confidence_dic[count] = frequency_dcount += 1print('----------频繁项目集----------')print(json.dumps(confidence_dic, indent=4, ensure_ascii=False, sort_keys=False, separators=(',', ':')))if confidence_dic.get(count, 0) == 0:count -= 1print('----------强关联规则----------')for i in range(count, 1, -1):item = confidence_dic[i]for k, v in item.items():for j in range(1, i):group = list(itertools.combinations(k.split(','), j))# 生成强关联规则for g in group:names = k.split(',')for z in g:names.remove(z)A = confidence_dic[len(names)][','.join(names)]union = confidence_dic[len(k.split(','))][k]# 计算置信度percent = round(union / A, 2)if percent > confidence:print(','.join(names) + ' -> ' + ','.join(g), '\t|confidence|\t', percent)if __name__ == '__main__':# 最小支持度计数minsup_count = 150# 最小置信度min_confidence = 0.3data = pd.read_csv('./Groceries.csv', header=0, index_col=0)Apriori(data, minsup_count, min_confidence)

运行结果