在scikitlearn中，如何在转换的目标聚集管道中从经过训练的估计器访问属性？

Question

我用scikit设置了一个小管道，了解我包装在一个TransforedTargetRegressor对象中。在训练之后，我想从我训练过的估计器中访问属性（例如feature_importances_）。谁能告诉我怎么做

from sklearn.pipeline import Pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.ensemble import RandomForestRegressor
from sklearn.preprocessing import MinMaxScaler
from sklearn.compose import TransformedTargetRegressor

# setup the pipeline
Pipeline(steps = [('scale', StandardScaler(with_mean=True, with_std=True)),
                  ('estimator', RandomForestRegressor())])

# tranform target variable
model = TransformedTargetRegressor(regressor=pipeline, 
                                   transformer=MinMaxScaler())
           
# fit model
model.fit(X_train, y_train)

我尝试了以下方法：

# try to access the attribute of the fitted estimator
model.get_params()['regressor__estimator'].feature_importances_
model.regressor.named_steps['estimator'].feature_importances_

但这会导致以下结果NotFittedError：

NotFittedError: This RandomForestRegressor instance is not fitted yet. Call 'fit' with appropriate arguments before using this method.

Answer 1

当您查看^{}的文档时，它说属性.regressor_（注意后面的下划线）返回拟合的回归器。因此，您的呼叫应该如下所示：

model.regressor_.named_steps['estimator'].feature_importances_

您以前的呼叫只是返回一个未安装的克隆。这就是错误的来源