python - 使用 `GridSearchCV`来测试从管道中完全删除步骤的效果？

Question

假设我正在使用GridSearchCV搜索超参数，我还使用 Pipeline因为我(认为我)想要预处理我的数据:

param_grid = {
    'svc__gamma': np.linspace(0.2, 1, 5)
}

pipeline = Pipeline(steps=[('scaler', StandardScaler()), ('svc', SVC())])

search = GridSearchCV(pipeline, param_grid, cv=10)
search.fit(train_x, train_y)

有没有办法测试我的假设，即包含 scaler步骤实际上有帮助(不仅仅是删除它并重新运行它)？

即有没有办法写:

param_grid = {
    'svc__gamma': np.linspace(0.2, 1, 5),
    'scaler': [On, Off]
}

或者我应该采取不同的方式来解决这个问题？

Answer 1

您可以通过将 passthrough 传递到您的 param_grid 来完成此操作，如下所示:

param_grid = {
    'svc__gamma': np.linspace(0.2, 1, 5),
    'scaler': ['passthrough', StandardScaler()]
}

如sklearn pipelines docs中所示

Individual steps may also be replaced as parameters, and non-final steps may be ignored by setting them to 'passthrough':

>>> from sklearn.pipeline import Pipeline
>>> from sklearn.svm import SVC
>>> from sklearn.decomposition import PCA
>>> from sklearn.linear_model import LogisticRegression
>>> estimators = [('reduce_dim', PCA()), ('clf', SVC())]
>>> pipe = Pipeline(estimators)
>>> param_grid = dict(reduce_dim=['passthrough', PCA(5), PCA(10)],
...                   clf=[SVC(), LogisticRegression()],
...                   clf__C=[0.1, 10, 100])
>>> grid_search = GridSearchCV(pipe, param_grid=param_grid)