CV目标均值编码的PySpark动态均值计算
CV目标均值编码的PySpark动态均值计算
提问于 2018-05-31 18:16:51
使用- Python 3.6，Spark 2.3
原始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平均值，如下所示(就像这样计算所有列和所有折叠)-
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流程-
对于fold_0，我的平均值应该是fold_1 + fold_2 /2
对于fold_1，我的平均值应该是fold_0 + fold_2 /2
对于fold_2，我的平均值应该是fold_0 + fold_1 /2
对于每一列。
和我的列数，否。折叠，一切都是动态的。
如何在pyspark数据框架上解决这个问题？
尝试使用交叉验证-目标-均值-编码技术来创建新特征。
回答 1
Stack Overflow用户
回答已采纳
发布于 2018-05-31 19:54:50
我自己解决的。
以防任何人需要重用代码-
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)页面原文内容由Stack Overflow提供。腾讯云小微IT领域专用引擎提供翻译支持
原文链接：
https://stackoverflow.com/questions/50621933
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