如果客户买了 xx 物品,那么他可能买YY物品
规则常用的方法,支持度和置信度
支持度是指规则的应验次数
置信度就是应验次数所占的比例
直接上代码
# 面包,牛奶,奶酪,苹果,香蕉 from collections import OrderedDict import numpy as np from pyexcel_xls import get_data from pyexcel_xls import save_data xls_data = get_data(r"777.xls") features = ["bread", "milk", "cheese", "apples", "bananas"] # print (xls_data['Sheet1']) lis =xls_data['Sheet1'] X= np.array(lis) n_samples,n_features=X.shape # 获取行数 print(n_samples) print(n_features) # print(X) # 统计买苹果的人数 num_apple_purchaes =0 for sample in X: if sample[3]==1: num_apple_purchaes +=1 print("{0} people bought Apples".format(num_apple_purchaes)) from collections import defaultdict valid_rules =defaultdict(int) # 接受应验次数 invalid_rules =defaultdict(int) # 接受不应验次数 num_occurences =defaultdict(int) # 接受出现次数 for sample in X: #对每一行进行循环 for premise in range(n_features): #对每列进行循环 if sample[premise] == 0: continue #判断该行的某一列列元素是否位0，即是否购买，若为0，跳出本轮循环，测试下一列 num_occurences[premise] += 1 #记录有购买的一列 sample[premise] for conclusion in range(n_features): #当读取到某一列有购买后，再次循环每一列的值 if premise == conclusion: #排除相同的一列，若循环到同一列，则跳出循环，比较下一列 continue if sample[conclusion] == 1: #当sample[conclusion] 的值为1时，满足了当顾客购买前一件商品时也买了这种商品 valid_rules[(premise, conclusion)] += 1 #记录下该规则出现的次数 else: invalid_rules[(premise, conclusion)] += 1 #当不满足时即 sample[conclusion]=0 时，记录下不满足该规则的次数 support = valid_rules #支持度=规则出现的次数 confidence = defaultdict(float) #强制将置信度转为浮点型 for premise, conclusion in valid_rules.keys(): confidence[(premise, conclusion)] = valid_rules[(premise, conclusion)] / num_occurences[premise] #计算某一规则的置信度，并将其存在字典confidence中 for premise, conclusion in confidence: #根据字典的两个参数来取值 premise_name = features[premise] #我们之前定义了features列表，它的每一列都对应数组的每一列，即商品名称 conclusion_name = features[conclusion] #商品名称 print("Rule: 如果顾客购买 {0} 那么他可能同时购买 {1}".format(premise_name, conclusion_name)) print(" - Confidence: {0:.3f}".format(confidence[(premise, conclusion)])) print(" - Support: {0}".format(support[(premise, conclusion)])) print("")
结果: 通过 置信度和支持度即可 知道 当买了什么时候,客户更喜欢在买什么
25 5 18 people bought Apples Rule: 如果顾客购买 bread 那么他可能同时购买 milk - Confidence: 0.533 - Support: 8 Rule: 如果顾客购买 milk 那么他可能同时购买 cheese - Confidence: 0.222 - Support: 2 Rule: 如果顾客购买 apples 那么他可能同时购买 cheese - Confidence: 0.333 - Support: 6 Rule: 如果顾客购买 milk 那么他可能同时购买 apples - Confidence: 0.444 - Support: 4 Rule: 如果顾客购买 bread 那么他可能同时购买 apples - Confidence: 0.667 - Support: 10 Rule: 如果顾客购买 apples 那么他可能同时购买 bread - Confidence: 0.556 - Support: 10 Rule: 如果顾客购买 apples 那么他可能同时购买 bananas - Confidence: 0.611 - Support: 11 Rule: 如果顾客购买 apples 那么他可能同时购买 milk - Confidence: 0.222 - Support: 4 Rule: 如果顾客购买 milk 那么他可能同时购买 bananas - Confidence: 0.556 - Support: 5 Rule: 如果顾客购买 cheese 那么他可能同时购买 bananas - Confidence: 0.556 - Support: 5 Rule: 如果顾客购买 cheese 那么他可能同时购买 bread - Confidence: 0.556 - Support: 5 Rule: 如果顾客购买 cheese 那么他可能同时购买 apples - Confidence: 0.667 - Support: 6 Rule: 如果顾客购买 cheese 那么他可能同时购买 milk - Confidence: 0.222 - Support: 2 Rule: 如果顾客购买 bananas 那么他可能同时购买 apples - Confidence: 0.647 - Support: 11 Rule: 如果顾客购买 bread 那么他可能同时购买 bananas - Confidence: 0.467 - Support: 7 Rule: 如果顾客购买 bananas 那么他可能同时购买 cheese - Confidence: 0.294 - Support: 5 Rule: 如果顾客购买 milk 那么他可能同时购买 bread - Confidence: 0.889 - Support: 8 Rule: 如果顾客购买 bananas 那么他可能同时购买 milk - Confidence: 0.294 - Support: 5 Rule: 如果顾客购买 bread 那么他可能同时购买 cheese - Confidence: 0.333 - Support: 5 Rule: 如果顾客购买 bananas 那么他可能同时购买 bread - Confidence: 0.412 - Support: 7
最后按照置信度排序
