Python 先驗返回生成器而不是 Dataframe

Question

我正在編寫獲取數據集（購物籃）的一小部分的代碼，將其轉換為熱編碼的 dataframe 並且我想在其上運行 mlxtend 的先驗算法以獲得頻繁項集。

但是，每當我運行 apriori 算法時，它似乎會立即運行並返回生成器 object 而不是 dataframe。 我按照文檔中的說明進行操作，在他們的示例中，它顯示 apriori 返回 dataframe。 我究竟做錯了什么？

這是我的代碼：

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

您的導入中有名稱沖突：

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

您的代碼沒有使用mlxtend算法，而是使用apyori提供的算法，后期導入的算法會覆蓋前一個算法。

您可以刪除您不使用的那個，或者，如果您想稍后訪問這兩個，您可以給一個不同的名稱：

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