使用 `estimator.get_params().keys()` 检查可用参数列表

Question

当我尝试使用 Pipeline 和 param_grid 运行 RandomForestClassifier 时：

pipeline = Pipeline([("scaler" , StandardScaler()),
                     ("rf",RandomForestClassifier())])
from sklearn.model_selection import GridSearchCV
param_grid = {
    'max_depth': [4, 5, 10],
    'max_features': [2, 3],
    'min_samples_leaf': [3, 4, 5],
    'n_estimators': [100, 200, 300]
}
# initialize
grid_pipeline = GridSearchCV(pipeline,param_grid,n_jobs=-1, verbose=1, cv=3, scoring='f1')
# fit
grid_pipeline.fit(X_train,y_train)
grid_pipeline.best_params_

我收到以下错误：

ValueError: Invalid parameter max_depth for estimator Pipeline(memory=None,
         steps=[('scaler',
                 StandardScaler(copy=True, with_mean=True, with_std=True)),
                ('rf',
                 RandomForestClassifier(bootstrap=True, ccp_alpha=0.0,
                                        class_weight=None, criterion='gini',
                                        max_depth=None, max_features='auto',
                                        max_leaf_nodes=None, max_samples=None,
                                        min_impurity_decrease=0.0,
                                        min_impurity_split=None,
                                        min_samples_leaf=1, min_samples_split=2,
                                        min_weight_fraction_leaf=0.0,
                                        n_estimators=100, n_jobs=None,
                                        oob_score=False, random_state=None,
                                        verbose=0, warm_start=False))],
         verbose=False). Check the list of available parameters with `estimator.get_params().keys()`.

尽管我已经查看了 scikit learn 文档和几篇文章，但我在代码中找不到错误。

Answer 1

当您使用管道时，GridSearchCV()必须在参数键中包含名称。只需用双下划线将名称与参数名称分开即可。在你的情况下：

param_grid = {
    'rf__max_depth': [4, 5, 10],
    'rf__max_features': [2, 3],
    'rf__min_samples_leaf': [3, 4, 5],
    'rf__n_estimators': [100, 200, 300]
}

sklearn 文档中的示例：https://scikit-learn.org/stable/tutorial/statistical_inference/putting_together.html