pip install mlxtend
The following command must be run outside of the IPython shell:
$ pip install mlxtend
The Python package manager (pip) can only be used from outside of IPython.
Please reissue the `pip` command in a separate terminal or command prompt.
See the Python documentation for more information on how to install packages:
https://docs.python.org/3/installing/
!pip install mlxtend
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Installing collected packages: numpy, scipy, joblib, threadpoolctl, scikit-learn, pandas, mlxtend
Found existing installation: numpy 1.15.4
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Found existing installation: pandas 0.23.4
Uninstalling pandas-0.23.4:
Successfully uninstalled pandas-0.23.4
Successfully installed joblib-1.0.1 mlxtend-0.18.0 numpy-1.20.3 pandas-1.2.4 scikit-learn-0.24.2 scipy-1.6.3 threadpoolctl-2.1.0
import pandas as pd
item_list = [['牛奶','面包'],
['面包','尿布','啤酒','土豆'],
['牛奶','尿布','啤酒','可乐'],
['面包','牛奶','尿布','啤酒'],
['面包','牛奶','尿布','可乐']]
item_df = pd.DataFrame(item_list)
from mlxtend.preprocessing import TransactionEncoder
te = TransactionEncoder()
df_tf = te.fit_transform(item_list)
df = pd.DataFrame(df_tf,columns=te.columns_)
print(df)
可乐 啤酒 土豆 尿布 牛奶 面包
0 False False False False True True
1 False True True True False True
2 True True False True True False
3 False True False True True True
4 True False False True True True
from mlxtend.frequent_patterns import apriori
frequent_itemsets = apriori(df, min_support=0.05, use_colnames=True)
frequent_itemsets.sort_values(by='support', ascending=False, inplace=True)
print(frequent_itemsets[frequent_itemsets.itemsets.apply(lambda x: len(x)) == 2])
support itemsets
17 0.6 (面包, 尿布)
18 0.6 (面包, 牛奶)
11 0.6 (啤酒, 尿布)
16 0.6 (牛奶, 尿布)
13 0.4 (面包, 啤酒)
7 0.4 (可乐, 尿布)
12 0.4 (啤酒, 牛奶)
8 0.4 (可乐, 牛奶)
14 0.2 (尿布, 土豆)
6 0.2 (可乐, 啤酒)
15 0.2 (面包, 土豆)
9 0.2 (面包, 可乐)
10 0.2 (啤酒, 土豆)
from mlxtend.frequent_patterns import association_rules
association_rule = association_rules(frequent_itemsets,metric='confidence',min_threshold=0.9)
association_rule.sort_values(by='lift',ascending=False,inplace=True)
association_rule
|
antecedents |
consequents |
antecedent support |
consequent support |
support |
confidence |
lift |
leverage |
conviction |
| 15 |
(土豆) |
(面包, 啤酒, 尿布) |
0.2 |
0.4 |
0.2 |
1.0 |
2.500000 |
0.12 |
inf |
| 30 |
(土豆) |
(面包, 啤酒) |
0.2 |
0.4 |
0.2 |
1.0 |
2.500000 |
0.12 |
inf |
| 12 |
(土豆, 尿布) |
(面包, 啤酒) |
0.2 |
0.4 |
0.2 |
1.0 |
2.500000 |
0.12 |
inf |
| 24 |
(土豆) |
(面包, 尿布) |
0.2 |
0.6 |
0.2 |
1.0 |
1.666667 |
0.08 |
inf |
| 36 |
(土豆) |
(啤酒) |
0.2 |
0.6 |
0.2 |
1.0 |
1.666667 |
0.08 |
inf |
| 21 |
(可乐, 啤酒) |
(牛奶, 尿布) |
0.2 |
0.6 |
0.2 |
1.0 |
1.666667 |
0.08 |
inf |
| 25 |
(土豆, 尿布) |
(啤酒) |
0.2 |
0.6 |
0.2 |
1.0 |
1.666667 |
0.08 |
inf |
| 5 |
(可乐) |
(牛奶, 尿布) |
0.4 |
0.6 |
0.4 |
1.0 |
1.666667 |
0.16 |
inf |
| 18 |
(可乐, 面包) |
(牛奶, 尿布) |
0.2 |
0.6 |
0.2 |
1.0 |
1.666667 |
0.08 |
inf |
| 27 |
(土豆) |
(啤酒, 尿布) |
0.2 |
0.6 |
0.2 |
1.0 |
1.666667 |
0.08 |
inf |
| 9 |
(面包, 土豆, 尿布) |
(啤酒) |
0.2 |
0.6 |
0.2 |
1.0 |
1.666667 |
0.08 |
inf |
| 28 |
(面包, 土豆) |
(啤酒) |
0.2 |
0.6 |
0.2 |
1.0 |
1.666667 |
0.08 |
inf |
| 13 |
(面包, 土豆) |
(啤酒, 尿布) |
0.2 |
0.6 |
0.2 |
1.0 |
1.666667 |
0.08 |
inf |
| 14 |
(啤酒, 土豆) |
(面包, 尿布) |
0.2 |
0.6 |
0.2 |
1.0 |
1.666667 |
0.08 |
inf |
| 26 |
(啤酒, 土豆) |
(尿布) |
0.2 |
0.8 |
0.2 |
1.0 |
1.250000 |
0.04 |
inf |
| 0 |
(啤酒) |
(尿布) |
0.6 |
0.8 |
0.6 |
1.0 |
1.250000 |
0.12 |
inf |
| 23 |
(面包, 土豆) |
(尿布) |
0.2 |
0.8 |
0.2 |
1.0 |
1.250000 |
0.04 |
inf |
| 31 |
(土豆) |
(面包) |
0.2 |
0.8 |
0.2 |
1.0 |
1.250000 |
0.04 |
inf |
| 32 |
(可乐, 面包) |
(牛奶) |
0.2 |
0.8 |
0.2 |
1.0 |
1.250000 |
0.04 |
inf |
| 33 |
(可乐, 面包) |
(尿布) |
0.2 |
0.8 |
0.2 |
1.0 |
1.250000 |
0.04 |
inf |
| 34 |
(可乐, 啤酒) |
(牛奶) |
0.2 |
0.8 |
0.2 |
1.0 |
1.250000 |
0.04 |
inf |
| 35 |
(可乐, 啤酒) |
(尿布) |
0.2 |
0.8 |
0.2 |
1.0 |
1.250000 |
0.04 |
inf |
| 29 |
(啤酒, 土豆) |
(面包) |
0.2 |
0.8 |
0.2 |
1.0 |
1.250000 |
0.04 |
inf |
| 19 |
(可乐, 啤酒, 牛奶) |
(尿布) |
0.2 |
0.8 |
0.2 |
1.0 |
1.250000 |
0.04 |
inf |
| 22 |
(土豆, 尿布) |
(面包) |
0.2 |
0.8 |
0.2 |
1.0 |
1.250000 |
0.04 |
inf |
| 20 |
(可乐, 啤酒, 尿布) |
(牛奶) |
0.2 |
0.8 |
0.2 |
1.0 |
1.250000 |
0.04 |
inf |
| 1 |
(面包, 啤酒) |
(尿布) |
0.4 |
0.8 |
0.4 |
1.0 |
1.250000 |
0.08 |
inf |
| 17 |
(可乐, 面包, 尿布) |
(牛奶) |
0.2 |
0.8 |
0.2 |
1.0 |
1.250000 |
0.04 |
inf |
| 16 |
(可乐, 面包, 牛奶) |
(尿布) |
0.2 |
0.8 |
0.2 |
1.0 |
1.250000 |
0.04 |
inf |
| 11 |
(面包, 啤酒, 土豆) |
(尿布) |
0.2 |
0.8 |
0.2 |
1.0 |
1.250000 |
0.04 |
inf |
| 10 |
(啤酒, 土豆, 尿布) |
(面包) |
0.2 |
0.8 |
0.2 |
1.0 |
1.250000 |
0.04 |
inf |
| 8 |
(土豆) |
(尿布) |
0.2 |
0.8 |
0.2 |
1.0 |
1.250000 |
0.04 |
inf |
| 7 |
(可乐) |
(牛奶) |
0.4 |
0.8 |
0.4 |
1.0 |
1.250000 |
0.08 |
inf |
| 6 |
(可乐) |
(尿布) |
0.4 |
0.8 |
0.4 |
1.0 |
1.250000 |
0.08 |
inf |
| 4 |
(可乐, 尿布) |
(牛奶) |
0.4 |
0.8 |
0.4 |
1.0 |
1.250000 |
0.08 |
inf |
| 3 |
(可乐, 牛奶) |
(尿布) |
0.4 |
0.8 |
0.4 |
1.0 |
1.250000 |
0.08 |
inf |
| 2 |
(啤酒, 牛奶) |
(尿布) |
0.4 |
0.8 |
0.4 |
1.0 |
1.250000 |
0.08 |
inf |
| 37 |
(面包, 啤酒, 牛奶) |
(尿布) |
0.2 |
0.8 |
0.2 |
1.0 |
1.250000 |
0.04 |
inf |
