当转换器包含嵌入式管道时如何从 ELI5 获取功能名称

Question

ELI5 library provides the function transform_feature_names to retrieve the feature names for the output of an sklearn transformer. The documentation 表示当转换器包含嵌套管道时，该函数开箱即用。

我正在尝试让函数在中示例的简化版本上运行。我的简化示例不需要 Pipeline，但在现实生活中我需要它来向 categorical_transformer 添加步骤，并且我还想向 ColumnTransformer 添加转换器。

import eli5
import pandas as pd
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import Pipeline
from sklearn.preprocessing import OneHotEncoder, StandardScaler

X_train = pd.DataFrame({'age': [23, 12, 12, 18],
                        'gender': ['M', 'F', 'F', 'F'],
                        'income': ['high', 'low', 'low', 'medium'],
                        'y': [0, 1, 1, 1]})

categorical_features = ['gender', 'income']
categorical_transformer = Pipeline(
    steps=[('onehot', OneHotEncoder(handle_unknown='ignore'))])

transformers=[('categorical', categorical_transformer, categorical_features)]
preprocessor = ColumnTransformer(transformers)
X_train_transformed = preprocessor.fit(X_train)

eli5.transform_feature_names(preprocessor, list(X_train.columns))

这会随着消息消失

AttributeError: Transformer categorical (type Pipeline) does not provide get_feature_names.

由于 Pipeline 嵌套在 ColumnTransformer 中，我从 ELI5 文档中了解到它会被处理。

我是否需要使用 get_feature_names 方法创建 Pipeline 的修改版本或进行其他自定义修改才能利用 ELI5 功能？

我正在使用 python 3.7.6、eli5 0.10.1、pandas 0.25.3 和 sklearn 0.22.1。

Answer 1

我认为问题在于 eli5 依赖于 ColumnTransformer 方法 get_feature_names，它本身要求 Pipeline 到 get_feature_names，尚未实现sklearn.

I've opened an Issue with eli5 with your example.

一个可能的修复：为 ColumnTransformer 添加 transform_feature_names 分派；这可能只是对其现有 get_feature_names 的修改，以为其每个组件转换器调用 eli5 transform_feature_names（而不是 sklearn 自己的 get_feature_names）。以下似乎有效，尽管我不确定当 input_names 与训练数据框列不同时如何处理，在 ColumnTransformer 中可用 _df_columns.

from eli5 import transform_feature_names

@transform_feature_names.register(ColumnTransformer)
def col_tfm_names(transformer, in_names=None):
    if in_names is None:
        from eli5.sklearn.utils import get_feature_names
        # generate default feature names
        in_names = get_feature_names(transformer, num_features=transformer._n_features)
    # return a list of strings derived from in_names
    feature_names = []
    for name, trans, column, _ in transformer._iter(fitted=True):
        if hasattr(transformer, '_df_columns'):
            if ((not isinstance(column, slice))
                    and all(isinstance(col, str) for col in column)):
                names = column
            else:
                names = transformer._df_columns[column]
        else:
            indices = np.arange(transformer._n_features)
            names = ['x%d' % i for i in indices[column]]
        # erm, want to be able to override with in_names maybe???

        if trans == 'drop' or (
                hasattr(column, '__len__') and not len(column)):
            continue
        if trans == 'passthrough':
            feature_names.extend(names)
            continue
        feature_names.extend([name + "__" + f for f in
                              transform_feature_names(trans, in_names=names)])
    return feature_names

我还需要为 OneHotEncoder 创建调度，因为它的 get_feature_names 需要参数 input_features:

@transform_feature_names.register(OneHotEncoder)
def _ohe_names(est, in_names=None):
    return est.get_feature_names(input_features=in_names)

相关链接：
https://eli5.readthedocs.io/en/latest/autodocs/eli5.html#eli5.transform_feature_names
https://github.com/TeamHG-Memex/eli5/blob/4839d1927c4a68aeff051935d1d4d8a4fb69b46d/eli5/sklearn/transform.py

当转换器包含嵌入式管道时如何从 ELI5 获取功能名称

How to get feature names from ELI5 when transformer includes an embedded pipeline

python

pipeline

scikit-learn

eli5