GridSearchCV:无法从GridSearchCV param_grid访问特征选择流水线的分类器参数

Question

我使用SelectFromModel进行特征选择，LogisticRegression作为估计器。我有一个调整数值列和分类列的预处理管道。并将该特征选择管与GridSearchCV管道中的模型结合使用。

在param_grid中，我希望在SelectFromModel方法中访问LogisticRegression的max_iter。所以我试了一下'selectfrommodel__logisticregression__max_iter': [400, 500],。

preprocessor = make_column_transformer(
    (num_transformer, make_column_selector(dtype_include=np.number)),
    (cat_transformer, make_column_selector(dtype_include=object))
)

fs_pipe = make_pipeline(
        preprocessor,
        SelectFromModel(estimator=LogisticRegression(solver='saga'))
)

lr_pipe = make_pipeline(fs_pipe, LogisticRegression(n_jobs=-1))

param_grid = {
    'selectfrommodel__logisticregression__max_iter': [400, 500],
    'logisticregression__penalty': ['l1', 'l2'],
    'logisticregression__solver': ['saga'],
    'logisticregression__max_iter': [400, 500],
}

lr_grid = GridSearchCV(
    estimator=lr_pipe,
    param_grid=param_grid,
    verbose=1, scoring='f1_micro',
    error_score='raise')

lr_grid.fit(trainX, trainY)

但是它抛出了这个错误：

ValueError: Invalid parameter selectfrommodel for estimator Pipeline(steps=[('pipeline',
                 Pipeline(steps=[('columntransformer',
                                  ColumnTransformer(transformers=[('pipeline-1',
                                                                   Pipeline(steps=[('simpleimputer',
                                                                                    SimpleImputer()),
                                                                                   ('minmaxscaler',
                                                                                    MinMaxScaler())]),
                                                                   <sklearn.compose._column_transformer.make_column_selector object at 0x0000027DBB9E1C48>),
                                                                  ('pipeline-2',
                                                                   Pipeline(steps=[('simpleimputer',
                                                                                    SimpleImputer(fill_value='missing',
                                                                                                  strategy='constant')),
                                                                                   ('onehotencoder',
                                                                                    OneHotEncoder(handle_unknown='ignore'))]),
                                                                   <sklearn.compose._column_transformer.make_column_selector object at 0x0000027DCA939D88>)])),
                                 ('selectfrommodel',
                                  SelectFromModel(estimator=LogisticRegression(solver='saga')))])),
                ('logisticregression', LogisticRegression(n_jobs=-1))]). Check the list of available parameters with `estimator.get_params().keys()`.

如何从max_iter param_grid访问LogisticRegression()的estimator参数(在SelectFromModel()中用作estimator)

Answer 1

您的selectfrommodel嵌套在lr_pipe下面。按照错误的建议运行lr_pipe.get_params().keys()将提示您注意到这一点。看起来你需要pipeline__selectfrommodel__logisticregression__max_iter。(也许可以考虑跳过make_pipeline和make_column_transformer，这样您就可以为这些步骤指定更短的名称了？您还可以创建一个三步管道，而不是嵌套管道fs_pipe和lr_pipe。