python - Sklearn 管道 : Get feature names after OneHotEncode In ColumnTransformer

Question

我想在安装管道后获取特征名称。

categorical_features = ['brand', 'category_name', 'sub_category']
categorical_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),
    ('onehot', OneHotEncoder(handle_unknown='ignore'))])
    
numeric_features = ['num1', 'num2', 'num3', 'num4']
numeric_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='median')),
    ('scaler', StandardScaler())])

preprocessor = ColumnTransformer(
    transformers=[
        ('num', numeric_transformer, numeric_features),
        ('cat', categorical_transformer, categorical_features)])

然后

clf = Pipeline(steps=[('preprocessor', preprocessor),
                      ('regressor', GradientBoostingRegressor())])

用 pandas dataframe 拟合后，我可以从中获取特征重要性

clf.steps[1][1].feature_importances_

我尝试了 clf.steps[0][1].get_feature_names() 但我遇到了错误

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

如何从中获取特征名称？

Answer 1

您可以使用以下代码段访问 feature_names:

clf.named_steps['preprocessor'].transformers_[1][1]\
   .named_steps['onehot'].get_feature_names(categorical_features)

使用 sklearn >= 0.21 版本，我们可以使它更简单:

clf['preprocessor'].transformers_[1][1]\
    ['onehot'].get_feature_names(categorical_features)

可重现的例子:

import numpy as np
import pandas as pd
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder, StandardScaler
from sklearn.pipeline import Pipeline
from sklearn.compose import ColumnTransformer
from sklearn.linear_model import LinearRegression

df = pd.DataFrame({'brand': ['aaaa', 'asdfasdf', 'sadfds', 'NaN'],
                   'category': ['asdf', 'asfa', 'asdfas', 'as'],
                   'num1': [1, 1, 0, 0],
                   'target': [0.2, 0.11, 1.34, 1.123]})

numeric_features = ['num1']
numeric_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='median')),
    ('scaler', StandardScaler())])

categorical_features = ['brand', 'category']
categorical_transformer = Pipeline(steps=[
    ('imputer', SimpleImputer(strategy='constant', fill_value='missing')),
    ('onehot', OneHotEncoder(handle_unknown='ignore'))])

preprocessor = ColumnTransformer(
    transformers=[
        ('num', numeric_transformer, numeric_features),
        ('cat', categorical_transformer, categorical_features)])

clf = Pipeline(steps=[('preprocessor', preprocessor),
                      ('regressor',  LinearRegression())])
clf.fit(df.drop('target', 1), df['target'])

clf.named_steps['preprocessor'].transformers_[1][1]\
   .named_steps['onehot'].get_feature_names(categorical_features)

# ['brand_NaN' 'brand_aaaa' 'brand_asdfasdf' 'brand_sadfds' 'category_as'
#  'category_asdf' 'category_asdfas' 'category_asfa']