Python apriori 返回 Generator 而不是 Dataframe

Question

我正在编写代码，获取数据集的一小部分（购物篮），将其转换为热编码数据帧，我想运行 mlxtend 的先验算法对其进行获取频繁项集。

但是，每当我运行先验算法时，它似乎立即运行并且它 returns 生成器对象而不是数据框。我按照 documentation 的说明进行操作，在他们的示例中，它显示了 apriori returns 数据框。我做错了什么？

这是我的代码：

import numpy as np
import pandas as pd
import csv
from mlxtend.frequent_patterns import apriori
from mlxtend.frequent_patterns import association_rules
from mlxtend.preprocessing import TransactionEncoder
from apyori import apriori

def simpleRandomisedSample(filename, support_frac, sample_frac):
    df1 = pd.read_csv("%s.csv" % filename, header=None) #Saving csv file into a dataframe in memory
    size = len(df1)
    support = support_frac * len(df1) #Sets the original support value to x% of the original dataset
    sample_support = support * sample_frac #Support for our reduced sample as a fraction of the original support
    sample = df1.sample(frac=sample_frac) #Saving x% (randomised) of the dataset as our sample
    sample = sample.reset_index(drop = True) #Reseting indexes (which previously got randomised along with the data)
    del df1 #Deleting original dataframe from memory to clear up space
    sample_size = len(sample)
    return size, support, sample_size, sample_support, sample

def main():
    size, support, sample_size, sample_support, sample = simpleRandomisedSample("chess",0.01,0.1)
    print("The original dataset had %d rows and a support of %.2f" % (size, support))
    print("The dataset was reduced to %d rows and the sample has a support of %.2f" % (sample_size, sample_support)) 

    sample_list = sample.values.tolist() #Converting Dataframe to list of lists for use with Apriori
    te = TransactionEncoder()
    te_ary = te.fit(sample_list).transform(sample_list) #Preprocessing our sample to work with Apriori algorithm
    df = pd.DataFrame(te_ary, columns=te.columns_)
    print(df)
    frequent_itemsets = apriori(df, min_support=0.6, use_colnames=True)
    print(frequent_itemsets)
    
if __name__ == "__main__":
    main()

Answer 1

您的导入中存在名称冲突：

from mlxtend.frequent_patterns import apriori
[...]
from apyori import apriori

您的代码没有使用 mlxtend 算法，而是 apyori 提供的算法，延迟导入的算法会覆盖之前的算法。

您可以删除不使用的那个，或者，如果您以后想同时访问这两个，您可以给其中一个起一个不同的名字：

from mlxtend.frequent_patterns import apriori as mlx_apriori
from apyori import apriori as apy_apriori

Python apriori 返回 Generator 而不是 Dataframe

Python apriori returning Generator instead of Dataframe

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