对于这个库来说，这似乎是一个非常重要的问题，到目前为止，我还没有看到决定性的答案，尽管在大多数情况下，答案似乎是“不”。

现在，任何使用transformer api in sklearn返回一个numpy数组作为其结果。通常这很好，但如果您将扩展或减少列数的多步骤过程链接在一起，则没有一种干净的方法来跟踪它们与原始列标签的关系，因此很难使用该部分充分发挥图书馆的作用。

作为一个例子，这是我最近使用的一个片段，其中无法将新列映射到数据集中最初的列是一个很大的缺点：

numeric_columns = train.select_dtypes(include=np.number).columns.tolist()
cat_columns     = train.select_dtypes(include=np.object).columns.tolist()

numeric_pipeline = make_pipeline(SimpleImputer(strategy='median'), StandardScaler())
cat_pipeline     = make_pipeline(SimpleImputer(strategy='most_frequent'), OneHotEncoder())

transformers = [
('num', numeric_pipeline, numeric_columns),
('cat', cat_pipeline, cat_columns)
]

combined_pipe = ColumnTransformer(transformers)

train_clean = combined_pipe.fit_transform(train)

test_clean  = combined_pipe.transform(test)

在此示例中，我使用以下方法分割数据集ColumnTransformer然后使用添加额外的列OneHotEncoder，所以我的列排列与我开始时的排列不同。

如果我使用使用相同 API 的不同模块，我可以轻松地进行不同的安排。OrdinalEncoer, select_k_best, etc.

如果您正在进行多步转换，是否有办法一致地查看新列与原始数据集的关系？

对此有广泛的讨论here https://github.com/scikit-learn/scikit-learn/issues/5523，但我认为一切还没有最终确定。

是的，你是对的，没有完全支持跟踪 feature_namessklearn截至目前。最初，决定将其保留为通用级别numpy大批。可以跟踪 sklearn 估计器中添加功能名称的最新进展here https://github.com/scikit-learn/scikit-learn/pull/13307/files.

无论如何，我们可以创建包装器来获取特征名称ColumnTransformer。我不确定它是否可以捕获所有可能的类型ColumnTransformers。但至少，它可以解决你的问题。

From Documentation of ColumnTransformer https://scikit-learn.org/stable/modules/generated/sklearn.compose.ColumnTransformer.html#sklearn.compose.ColumnTransformer.get_feature_names:

Notes

转换后的特征矩阵中的列顺序遵循转换器列表中指定列的顺序。除非在 passthrough 关键字中指定，否则原始特征矩阵中未指定的列将从生成的转换特征矩阵中删除。使用 passthrough 指定的那些列将添加到变压器输出的右侧。

尝试这个！

import pandas as pd
import numpy as np
from sklearn.compose import ColumnTransformer
from sklearn.pipeline import make_pipeline, Pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import StandardScaler, OneHotEncoder, MinMaxScaler
from sklearn.feature_extraction.text import _VectorizerMixin
from sklearn.feature_selection._base import SelectorMixin
from sklearn.feature_selection import SelectKBest
from sklearn.feature_extraction.text import CountVectorizer

train = pd.DataFrame({'age': [23,12, 12, np.nan],
                      'Gender': ['M','F', np.nan, 'F'],
                      'income': ['high','low','low','medium'],
                      'sales': [10000, 100020, 110000, 100],
                      'foo' : [1,0,0,1],
                      'text': ['I will test this',
                               'need to write more sentence',
                               'want to keep it simple',
                               'hope you got that these sentences are junk'],
                      'y': [0,1,1,1]})
numeric_columns = ['age']
cat_columns     = ['Gender','income']

numeric_pipeline = make_pipeline(SimpleImputer(strategy='median'), StandardScaler())
cat_pipeline     = make_pipeline(SimpleImputer(strategy='most_frequent'), OneHotEncoder())
text_pipeline = make_pipeline(CountVectorizer(), SelectKBest(k=5))

transformers = [
    ('num', numeric_pipeline, numeric_columns),
    ('cat', cat_pipeline, cat_columns),
    ('text', text_pipeline, 'text'),
    ('simple_transformer', MinMaxScaler(), ['sales']),
]

combined_pipe = ColumnTransformer(
    transformers, remainder='passthrough')

transformed_data = combined_pipe.fit_transform(
    train.drop('y',1), train['y'])

def get_feature_out(estimator, feature_in):
    if hasattr(estimator,'get_feature_names'):
        if isinstance(estimator, _VectorizerMixin):
            # handling all vectorizers
            return [f'vec_{f}' \
                for f in estimator.get_feature_names()]
        else:
            return estimator.get_feature_names(feature_in)
    elif isinstance(estimator, SelectorMixin):
        return np.array(feature_in)[estimator.get_support()]
    else:
        return feature_in


def get_ct_feature_names(ct):
    # handles all estimators, pipelines inside ColumnTransfomer
    # doesn't work when remainder =='passthrough'
    # which requires the input column names.
    output_features = []

    for name, estimator, features in ct.transformers_:
        if name!='remainder':
            if isinstance(estimator, Pipeline):
                current_features = features
                for step in estimator:
                    current_features = get_feature_out(step, current_features)
                features_out = current_features
            else:
                features_out = get_feature_out(estimator, features)
            output_features.extend(features_out)
        elif estimator=='passthrough':
            output_features.extend(ct._feature_names_in[features])
                
    return output_features

pd.DataFrame(transformed_data, 
             columns=get_ct_feature_names(combined_pipe))