CV-Target-Mean-Encode 的 PySpark 动态均值计算

Question

使用 - Python 3.6，Spark 2.3

原DF-

key     a_fold_0    b_fold_0    a_fold_1    b_fold_1    a_fold_2    b_fold_2
1   1   2   3   4   5   6
2   7   5   3   5   2   1

我想从下面的数据框中计算均值，如下所示（所有列和所有折叠都是这样）-

key     a_fold_0    b_fold_0    a_fold_1    b_fold_1    a_fold_2    b_fold_2    a_fold_0_mean   b_fold_0_mean   a_fold_1_mean
1   1   2   3   4   5   6   3 + 5 / 2   4 + 6 / 2   1 + 5 / 2
2   7   5   3   5   2   1   3 + 2 / 2   5 + 1 / 2   7 + 2 / 2

进程-

对于 fold_0 我的意思应该是 fold_1 + fold_2 / 2

对于 fold_1 我的意思应该是 fold_0 + fold_2 / 2

对于 fold_2 我的意思应该是 fold_0 + fold_1 / 2

对于每一列。

而我的列数，没有。折叠，一切都将是动态的。

如何在 pyspark 数据帧上解决这个问题？

尝试通过交叉验证目标均值编码技术创建新特征。

Answer 1

自己解决了。

万一有人需要重用代码 -

orig_list = ['Married-spouse-absent', 'Married-AF-spouse', 'Separated', 'Married-civ-spouse', 'Widowed', 'Divorced', 'Never-married']
k_folds = 3

cols = df.columns  # ['fnlwgt_bucketed', 'Married-spouse-absent_fold_0', 'Married-AF-spouse_fold_0', 'Separated_fold_0', 'Married-civ-spouse_fold_0', 'Widowed_fold_0', 'Divorced_fold_0', 'Never-married_fold_0', 'Married-spouse-absent_fold_1', 'Married-AF-spouse_fold_1', 'Separated_fold_1', 'Married-civ-spouse_fold_1', 'Widowed_fold_1', 'Divorced_fold_1', 'Never-married_fold_1', 'Married-spouse-absent_fold_2', 'Married-AF-spouse_fold_2', 'Separated_fold_2', 'Married-civ-spouse_fold_2', 'Widowed_fold_2', 'Divorced_fold_2', 'Never-married_fold_2']

for folds in range(k_folds):
    for column in orig_list:
        col_namer = []
        for fold in range(k_folds):
            if fold != folds:
                col_namer.append(column+'_fold_'+str(fold))
        df = df.withColumn(column+'_fold_'+str(folds)+'_mean', (sum(df[col] for col in col_namer)/(k_folds-1)))
        print(col_namer)
df.show(1)

CV-Target-Mean-Encode 的 PySpark 动态均值计算

PySpark Dynamic mean calculation for CV-Target-Mean-Encode

python

apache-spark-sql

cross-validation