python-3.x - H2O 目标均值编码器 "frames are being sent in the same order"错误

Question

我按照 H2O 示例在 Sparking Water 中运行目标均值编码(sparking water 2.4.2 和 H2O 3.22.04)。它在以下所有段落中运行良好

from h2o.targetencoder import TargetEncoder

# change label to factor
input_df_h2o['label'] = input_df_h2o['label'].asfactor()

# add fold column for Target Encoding
input_df_h2o["cv_fold_te"] = input_df_h2o.kfold_column(n_folds = 5, seed = 54321)

# find all categorical features
cat_features = [k for (k,v) in input_df_h2o.types.items() if v in ('string')]
# convert string to factor
for i in cat_features:
    input_df_h2o[i] = input_df_h2o[i].asfactor()

# target mean encode
targetEncoder = TargetEncoder(x= cat_features, y = y, fold_column = "cv_fold_te", blending_avg=True)
targetEncoder.fit(input_df_h2o)

但是当我开始使用与目标编码器相同的数据集来运行转换代码时(请参阅下面的代码):

ext_input_df_h2o = targetEncoder.transform(frame=input_df_h2o,
                                    holdout_type="kfold", # mean is calculating on out-of-fold data only; loo means leave one out
                                    is_train_or_valid=True,
                                    noise = 0, # determines if random noise should be added to the target average
                                    seed=54321)

我会遇到错误，例如

Traceback (most recent call last):
  File "/tmp/zeppelin_pyspark-6773422589366407956.py", line 331, in <module>
    exec(code)
  File "<stdin>", line 5, in <module>
  File "/usr/lib/envs/env-1101-ver-1619-a-4.2.9-py-3.5.3/lib/python3.5/site-packages/h2o/targetencoder.py", line 97, in transform
    assert self._encodingMap.map_keys['string'] == self._teColumns
AssertionError

我在它的源代码中找到了代码http://docs.h2o.ai/h2o/latest-stable/h2o-py/docs/_modules/h2o/targetencoder.html 但如何解决这个问题呢？它与用于运行拟合的表相同。

Answer 1

问题是因为您正在尝试对多个分类特征进行编码。我认为这是 H2O 的一个错误，但您可以将转换器放入迭代所有类别名称的 for 循环中来解决。

import numpy as np
import pandas as pd
import h2o
from h2o.targetencoder import TargetEncoder
h2o.init()

df = pd.DataFrame({
    'x_0': ['a'] * 5 + ['b'] * 5,
    'x_1': ['c'] * 9 + ['d'] * 1,
    'x_2': ['a'] * 3 + ['b'] * 7,
    'y_0': [1, 1, 1, 1, 0, 1, 0, 0, 0, 0]
})

hf = h2o.H2OFrame(df)
hf['cv_fold_te'] = hf.kfold_column(n_folds=2, seed=54321)
hf['y_0'] = hf['y_0'].asfactor()
cat_features = ['x_0', 'x_1', 'x_2']

for item in cat_features:
    target_encoder = TargetEncoder(x=[item], y='y_0', fold_column = 'cv_fold_te')
    target_encoder.fit(hf)
    hf = target_encoder.transform(frame=hf, holdout_type='kfold',
                                  seed=54321, noise=0.0)
hf