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【小沐学NLP】关联规则分析Apriori算法（association_rules代码）

cnlinkchina 2023-12-21 阅读 38
ci插入图片python游戏开发yyds干货盘点

1、简介

官网地址： https://rasbt.github.io/mlxtend/

Mlxtend (machine learning extensions) is a Python library of useful tools for the day-to-day data science tasks.

2、 association_rules

Association rules generation from frequent itemsets. Function to generate association rules from frequent itemsets. 模型构建，挖掘关联规则

2.1 示例 1 -- 从频繁项集生成关联规则

  • 我们首先创建一个由 fpgrowth 函数生成的频繁项集的pandas.
import pandas as pd
from mlxtend.preprocessing import TransactionEncoder
from mlxtend.frequent_patterns import apriori, fpmax, fpgrowth

dataset = [['Milk', 'Onion', 'Nutmeg', 'Kidney Beans', 'Eggs', 'Yogurt'],
           ['Dill', 'Onion', 'Nutmeg', 'Kidney Beans', 'Eggs', 'Yogurt'],
           ['Milk', 'Apple', 'Kidney Beans', 'Eggs'],
           ['Milk', 'Unicorn', 'Corn', 'Kidney Beans', 'Yogurt'],
           ['Corn', 'Onion', 'Onion', 'Kidney Beans', 'Ice cream', 'Eggs']]

te = TransactionEncoder()
te_ary = te.fit(dataset).transform(dataset)
df = pd.DataFrame(te_ary, columns=te.columns_)

frequent_itemsets = fpgrowth(df, min_support=0.6, use_colnames=True)
### alternatively:
#frequent_itemsets = apriori(df, min_support=0.6, use_colnames=True)
#frequent_itemsets = fpmax(df, min_support=0.6, use_colnames=True)

print(frequent_itemsets)

在这里插入图片描述

  • 该函数允许您 （1） 指定您感兴趣的指标和 （2） 相应的阈值。目前实施的措施是信心和提升。假设，仅当置信度高于 70% 阈值 时，您才对从频繁项集派生的规则感兴趣。
from mlxtend.frequent_patterns import association_rules

rules = association_rules(frequent_itemsets, metric="confidence", min_threshold=0.7)
print(rules)

在这里插入图片描述

2.2 示例 2 -- 规则生成和选择标准

如果您对根据不同兴趣指标的规则感兴趣，您可以简单地调整和参数。例如，如果您只对提升分数为 >= 1.2 的规则感兴趣，则可以执行以下操作：

from mlxtend.frequent_patterns import association_rules

rules = association_rules(frequent_itemsets, metric="lift", min_threshold=1.2)
print(rules)

在这里插入图片描述 我们可以按如下方式计算先行长度：

from mlxtend.frequent_patterns import association_rules

rules = association_rules(frequent_itemsets, metric="lift", min_threshold=1.2)
# print(rules)
rules["antecedent_len"] = rules["antecedents"].apply(lambda x: len(x))
print(rules)

在这里插入图片描述 假设我们对满足以下条件的规则感兴趣：

  • 至少 2 个前因
  • 置信度> 0.75
  • 提升得分> 1.2
from mlxtend.frequent_patterns import association_rules

rules = association_rules(frequent_itemsets, metric="lift", min_threshold=1.2)
# print(rules)
rules["antecedent_len"] = rules["antecedents"].apply(lambda x: len(x))
# print(rules)
rules = rules[ (rules['antecedent_len'] >= 2) &
       (rules['confidence'] > 0.75) &
       (rules['lift'] > 1.2) ]
print(rules)

在这里插入图片描述 同样，使用 Pandas API，我们可以根据“前因”或“后因”列选择条目：

from mlxtend.frequent_patterns import association_rules

rules = association_rules(frequent_itemsets, metric="lift", min_threshold=1.2)
# print(rules)
rules["antecedent_len"] = rules["antecedents"].apply(lambda x: len(x))
# print(rules)
rules[rules['antecedents'] == {'Eggs', 'Kidney Beans'}]
print(rules)

在这里插入图片描述

2.3 示例 3 -- 具有不完整的先前和后续信息的频繁项集

计算的大多数指标取决于频繁项集输入数据帧中提供的给定规则的结果和先前支持分数。

import pandas as pd

dict = {'itemsets': [['177', '176'], ['177', '179'],
                     ['176', '178'], ['176', '179'],
                     ['93', '100'], ['177', '178'],
                     ['177', '176', '178']],
        'support':[0.253623, 0.253623, 0.217391,
                   0.217391, 0.181159, 0.108696, 0.108696]}

freq_itemsets = pd.DataFrame(dict)
print(freq_itemsets)

在这里插入图片描述

import pandas as pd

dict = {'itemsets': [['177', '176'], ['177', '179'],
                     ['176', '178'], ['176', '179'],
                     ['93', '100'], ['177', '178'],
                     ['177', '176', '178']],
        'support':[0.253623, 0.253623, 0.217391,
                   0.217391, 0.181159, 0.108696, 0.108696]}

freq_itemsets = pd.DataFrame(dict)
print(freq_itemsets)

from mlxtend.frequent_patterns import association_rules
res = association_rules(freq_itemsets, support_only=True, min_threshold=0.1)
print(res)

在这里插入图片描述

import pandas as pd

dict = {'itemsets': [['177', '176'], ['177', '179'],
                     ['176', '178'], ['176', '179'],
                     ['93', '100'], ['177', '178'],
                     ['177', '176', '178']],
        'support':[0.253623, 0.253623, 0.217391,
                   0.217391, 0.181159, 0.108696, 0.108696]}

freq_itemsets = pd.DataFrame(dict)
print(freq_itemsets)

from mlxtend.frequent_patterns import association_rules
res = association_rules(freq_itemsets, support_only=True, min_threshold=0.1)
print(res)

res = res[['antecedents', 'consequents', 'support']]
print(res)

在这里插入图片描述

2.4 示例 4 -- 修剪关联规则

没有用于修剪的特定 API。相反，可以在生成的数据帧上使用 pandas API 来删除单个行。

import pandas as pd
from mlxtend.preprocessing import TransactionEncoder
from mlxtend.frequent_patterns import apriori, fpmax, fpgrowth
from mlxtend.frequent_patterns import association_rules


dataset = [['Milk', 'Onion', 'Nutmeg', 'Kidney Beans', 'Eggs', 'Yogurt'],
           ['Dill', 'Onion', 'Nutmeg', 'Kidney Beans', 'Eggs', 'Yogurt'],
           ['Milk', 'Apple', 'Kidney Beans', 'Eggs'],
           ['Milk', 'Unicorn', 'Corn', 'Kidney Beans', 'Yogurt'],
           ['Corn', 'Onion', 'Onion', 'Kidney Beans', 'Ice cream', 'Eggs']]

te = TransactionEncoder()
te_ary = te.fit(dataset).transform(dataset)
df = pd.DataFrame(te_ary, columns=te.columns_)

frequent_itemsets = fpgrowth(df, min_support=0.6, use_colnames=True)
rules = association_rules(frequent_itemsets, metric="lift", min_threshold=1.2)
print(rules)

在这里插入图片描述 我们想删除规则“（洋葱、芸豆）->（鸡蛋）”。为此，我们可以定义选择掩码并删除此行

import pandas as pd
from mlxtend.preprocessing import TransactionEncoder
from mlxtend.frequent_patterns import apriori, fpmax, fpgrowth
from mlxtend.frequent_patterns import association_rules


dataset = [['Milk', 'Onion', 'Nutmeg', 'Kidney Beans', 'Eggs', 'Yogurt'],
           ['Dill', 'Onion', 'Nutmeg', 'Kidney Beans', 'Eggs', 'Yogurt'],
           ['Milk', 'Apple', 'Kidney Beans', 'Eggs'],
           ['Milk', 'Unicorn', 'Corn', 'Kidney Beans', 'Yogurt'],
           ['Corn', 'Onion', 'Onion', 'Kidney Beans', 'Ice cream', 'Eggs']]

te = TransactionEncoder()
te_ary = te.fit(dataset).transform(dataset)
df = pd.DataFrame(te_ary, columns=te.columns_)

frequent_itemsets = fpgrowth(df, min_support=0.6, use_colnames=True)
rules = association_rules(frequent_itemsets, metric="lift", min_threshold=1.2)
print(rules)

antecedent_sele = rules['antecedents'] == frozenset({'Onion', 'Kidney Beans'}) # or  frozenset({'Kidney Beans', 'Onion'})
consequent_sele = rules['consequents'] == frozenset({'Eggs'})
final_sele = (antecedent_sele & consequent_sele)

rules = rules.loc[ ~final_sele ]
print(rules)

在这里插入图片描述

结语

如果您觉得该方法或代码有一点点用处，可以给作者点个赞，或打赏杯咖啡；╮(￣▽￣)╭ 如果您感觉方法或代码不咋地//(ㄒoㄒ)//，就在评论处留言，作者继续改进；o_O??? 如果您需要相关功能的代码定制化开发，可以留言私信作者；(✿◡‿◡) 感谢各位大佬童鞋们的支持！( ´ ▽´ )ﾉ ( ´ ▽´)っ！！！

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