数据分析与挖掘（二）：实现关联规则Aproiri算法应用、使用算法实现关联规则度量统计

数据分析与挖掘（二）：实现关联规则Aproiri算法应用、使用算法实现关联规则度量统计

一、实验目的及要求

安装函数包、调用函数实现关联规则Aproiri算法应用

使用算法实现关联规则度量统计，显示结果

二、实验设备（环境）及要求

Jupyter notebook、百度 AI studio

三、实验内容

1.调用函数实现对数据集数据的分析。

数据集：

[[’apple’,’banana’]

[’milk’,’bread’,’banana’,’ham’]

[’milk’,’bread’,’apple’,’banana’]

[’milk’,’apple’,’banana’,’ham’]

[’bread’,’banana’]]
1
2
3
4
5
6
7
8
9

(1) 实现基本步骤

安装函数包代码：

pip install apyori
1

调用函数代码段：

data_file = "goods.txt"# 接收要导入的文件

Data = np.loadtxt(data_file, delimiter=",")

min_supp = 0.5

min_conf = 0.5

min_lift = 0.0

res=apriori(transactions=Data,min_support=min_supp,min_confidence=min_conf,min_lift=min_lift)
1
2
3
4
5
6
7
8
9
10
11

显示结果代码段：

for rule in res:

print(str(rule))

print("data")

print(Data)
1
2
3
4
5
6
7

(2) 结果查看与分析

2.从文本获取数据，统计输出：

多少人买了牛奶

多少人买了面包

多少人既买了牛奶又买了面包

(1) 实施基本步骤

获取数据代码：

import numpy as np

from apyori import apriori

data_file = "goods.txt"# 接收要导入的文件

Data = np.loadtxt(data_file, delimiter=",")
1
2
3
4
5
6
7

统计代码：

num_milk = 0

num_bread = 0

num_milk_bread = 0

for sample in Data: # 取出数据中的每一行

  if sample[0] == 1: # 检测sample[0]的值是否为1，即顾客是否购买牛奶

    num_milk += 1

  if sample[1] == 1: # 检测sample[1]的值是否为1，即顾客是否购买面包

    num_bread += 1

  if sample[0] == 1 and sample[1] == 1:

    num_milk_bread += 1

print("{0} people bought milk".format(num_milk))

print("{0} people bought bread".format(num_bread))

print("{0} people bought both milk and bread".format(num_milk_bread))
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25

(2) 结果查看与分析：

3.从文本获取数据，计算每条规则的支持度和置信度，置信度为float浮点型，输出保留3位小数。

(1) 基本步骤

导入数据：

import numpy as np

from apyori import apriori

data_file = "goods.txt"# 接收要导入的文件

Data = np.loadtxt(data_file, delimiter=",")
1
2
3
4
5
6
7

计算置信度：

from collections import defaultdict

 

features = ["milk", "bread", "apple", "banana", "ham"] # 存放商品名称

valid_rules = defaultdict(int) # 存放所有的规则应验的情况

invalid_rules = defaultdict(int) # 存放规则无效

num_occurances = defaultdict(int) # 存放条件相同的规则数量

 

for sample in Data: # 第一层循环：购买了X商品的作为前提条件

  for premise in range(4):

​    if sample[premise] == 0:

​      continue # 没买当前商品，忽略以下内容，进入下一次循环

​    num_occurances[premise] += 1 # 买了X商品，又买了当前商品

​    for conclusion in range(premise, 5):

​      if premise == conclusion:

​        continue

​      if sample[conclusion] == 1:

​        valid_rules[(premise, conclusion)] += 1

​      else:

​        invalid_rules[(premise, conclusion)] += 1

 

support = valid_rules

confidence = defaultdict(int)

for premise, conclusion in valid_rules.keys():

  confidence[premise, conclusion] = valid_rules[premise, conclusion]/num_occurances[premise]

 

 

def print_rule(premise, conclusion, support, confidence, features):

  print("Rule: If a person buys " +

​     features[premise]+" they will also buy "+features[conclusion])

  print("- Confidence: {0:.3f}".format(confidence[premise, conclusion]))

  print("- Support: {0}".format(support[premise, conclusion]))

 

premise = int(input()) # 获取条件

conclusion = int(input()) # 获取结论

print_rule(premise, conclusion, support, confidence, features)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69

（2）结果查看与分析