sklearn - 如何从传递给 GridSearchCV 的管道内部检索 PCA 组件和解释方差 [英] sklearn - How to retrieve PCA components and explained variance from inside a Pipeline passed to GridSearchCV

问题描述

我将 GridSearchCV 与管道一起使用，如下所示:

I am using GridSearchCV with a pipeline as follows:

grid = GridSearchCV(
    Pipeline([
        ('reduce_dim', PCA()),
        ('classify', RandomForestClassifier(n_jobs = -1))
        ]),
    param_grid=[
        {
            'reduce_dim__n_components': range(0.7,0.9,0.1),
            'classify__n_estimators': range(10,50,5),
            'classify__max_features': ['auto', 0.2],
            'classify__min_samples_leaf': [40,50,60],
            'classify__criterion': ['gini', 'entropy']
        }
    ],
    cv=5, scoring='f1')

grid.fit(X,y)

我现在如何从 grid.best_estimator_ 模型中检索 PCA 详细信息，例如 components 和 explained_variance?

How do I now retrieve PCA details like components and explained_variance from the grid.best_estimator_ model?

此外，我还想使用 pickle 将 best_estimator_ 保存到一个文件中，然后再加载它.如何从此加载的估算器中检索 PCA 详细信息?我怀疑它会和上面一样.

Furthermore, I also want to save the best_estimator_ to a file using pickle and later load it. How do I retrieve the PCA details from this loaded estimator? I suspect it will be the same as above.

推荐答案

grid.best_estimator_ 是访问具有最佳参数的管道.

grid.best_estimator_ is to access the pipeline with the best parameters.

现在使用 named_steps[]attribute 访问管道的内部估算器.

Now use named_steps[]attribute to access the internal estimators of the pipeline.

所以 grid.best_estimator_.named_steps['reduce_dim'] 会给你 pca 对象.现在您可以简单地使用它来访问此 pca 对象的 components_ 和 explained_variance_ 属性，如下所示:

So grid.best_estimator_.named_steps['reduce_dim'] will give you the pca object. Now you can simply use this to access the components_ and explained_variance_ attibutes for this pca object like this:

grid.best_estimator_.named_steps['reduce_dim'].components_grid.best_estimator_.named_steps['reduce_dim'].explained_variance_

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