关于python中apriori算法编码关联规则的问题答案

【问题标题】：Question about coding association rules for an apriori algorithm in python关于python中apriori算法编码关联规则的问题
【发布时间】：2020-09-29 04:33:21
【问题描述】：

我只是想知道是否有办法只显示“支持”和“信心”列？我不需要显示前件、后件或提升列。

下面是我的代码：

import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
from apyori import apriori


from mlxtend.preprocessing import TransactionEncoder
from mlxtend.frequent_patterns import apriori
from mlxtend.frequent_patterns import association_rules

dataset = [['plates', 'forks', 'knives'],
 ['plates', 'bowls', 'glasses'],
 ['forks', 'knives'],
 ['glasses', 'forks', 'knives'],
 ['microwave', 'blender'],
 ['dumbbell', 'workout bands', 'water bottle'],
 ['dumbbell', 'sneakers'],
 ['workout bands', 'sneakers'],
 ['bike', 'earbuds'],
 ['yoga mat', 'headphones'],
 ['camera'],
 ['iPad', 'earbuds', 'phone charger'],
 ['iPad', 'laptop', 'laptop charger'],
 ['headphones', 'laptop', 'laptop charger'],
 ['iPad', 'bluetooth speaker', 'phone charger'],
 ['microwave', 'coffee maker'],
 ['camping tent', 'water bottle', 'flashlight'],
 ['sleeping bag', 'yoga mat', 'sneakers'],
 ['tv ', 'tv remote'],
 ['tv', 'tv remote', 'bluetooth speaker']]

te = TransactionEncoder()
te_ary = te.fit(dataset).transform(dataset)

df = pd.DataFrame(te_ary, columns=te.columns_)
frequent_itemsets = apriori(df, min_support=0.1, use_colnames=True)

frequent_itemsets

association_rules(frequent_itemsets, metric="confidence", min_threshold=0.25)

谢谢！

【问题讨论】：

标签： python database pandas data-mining apriori

【解决方案1】：

我不知道这个库，但是 API 文档说 association_rules 的返回类型是一个熊猫数据框。

所以你可以用它做标准的熊猫东西：

df = association_rules(frequent_itemsets, metric="confidence", min_threshold=0.25)
print(df[["support", "confidence"]])

【讨论】：