使用 StandarScaler 和 One-Hot-Encoding 获取 ColumnTransformer 的特征名称

Question

我正在使用带有 StandardScaler 和 OneHotEncoder 的简单 ColumnTransformer，例如：

from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import OneHotEncoder

num_features = ['num_feat_1',
                'num_feat_2',
                'num_feat_3']
cat_features = ['cat_feat_1',
                'cat_feat_2',
                'cat_feat_3']

ct = ColumnTransformer([
    ("scaler", StandardScaler(), num_features),
    ("onehot", OneHotEncoder(sparse=False,
                             handle_unknown='ignore'), cat_features)], 
    remainder='passthrough') 

ct.fit(X_train)
X_train_trans = ct.transform(X_train)
X_test_trans = ct.transform(X_test)

要映射线性回归的系数，我需要 ct.get_feature_names()，但我收到错误 Transformer scaler (type StandardScaler) does not provide get_feature_names。为什么会这样，我该如何解决？

Answer 1

在您的情况下，get_feature_names() 仅适用于 onehot ，而对于 StandardScaler() 您不会更改转换后变量的名称，因此我们通过转换器，如果 get_feature 不起作用，我们保留原始特征名称。

使用示例数据集：

import pandas as pd
import numpy as np
X = pd.concat([
    pd.DataFrame(np.random.uniform(0,1,(100,3)),columns=num_features),
    pd.DataFrame(np.random.choice(['a','b'],(100,3)),columns=cat_features)
],axis=1)

X_train = X.iloc[:50,:]
X_test = X.iloc[50:,:]

from sklearn.compose import ColumnTransformer
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import OneHotEncoder

num_features = ['num_feat_1',
                'num_feat_2',
                'num_feat_3']
cat_features = ['cat_feat_1',
                'cat_feat_2',
                'cat_feat_3']

ct = ColumnTransformer([
    ("scaler", StandardScaler(), num_features),
    ("onehot", OneHotEncoder(sparse=False,
                             handle_unknown='ignore'), cat_features)], 
    remainder='passthrough') 

ct.fit(X_train)

我们试试这个：

tx = ct.get_params()['transformers']
feature_names = []
for name,transformer,features in tx:
    try:
        Var = ct.named_transformers_[name].get_feature_names().tolist()
    except AttributeError:
        Var = features
    feature_names = feature_names + Var

feature_names
['num_feat_1',
 'num_feat_2',
 'num_feat_3',
 'x0_a',
 'x0_b',
 'x1_a',
 'x1_b',
 'x2_a',
 'x2_b']