使用 `GridSearchCV` 来测试完全从管道中删除一个步骤的效果？

Question

假设我正在使用 GridSearchCV 来搜索超参数，并且我也在使用 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)

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

也就是说，有没有办法写：

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

或者我应该用不同的方式来处理这个问题吗？

Answer 1

您可以通过将 passthrough 传递给您的 param_grid 来做到这一点，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)

使用 `GridSearchCV` 来测试完全从管道中删除一个步骤的效果？

Use `GridSearchCV` to test effect of removing a step from the pipeline entirely?

python

machine-learning

python-3.x

scikit-learn

gridsearchcv