Python 先验返回生成器而不是 Dataframe

Question

我正在编写获取数据集（购物篮）的一小部分的代码，将其转换为热编码的 dataframe 并且我想在其上运行 mlxtend 的先验算法以获得频繁项集。

但是，每当我运行 apriori 算法时，它似乎会立即运行并返回生成器 object 而不是 dataframe。 我按照文档中的说明进行操作，在他们的示例中，它显示 apriori 返回 dataframe。 我究竟做错了什么？

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

您的导入中有名称冲突：

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

您的代码没有使用mlxtend算法，而是使用apyori提供的算法，后期导入的算法会覆盖前一个算法。

您可以删除您不使用的那个，或者，如果您想稍后访问这两个，您可以给一个不同的名称：

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