GridSearchCV output problems in Scikit-learn

user_4495790 0
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I'd like to perform a hyperparameter search for selecting preprocessing steps and models in sklearn as follows:

pipeline = Pipeline([("combiner", PolynomialFeatures()),
                     ("dimred", PCA()),
                     ("classifier", RandomForestClassifier())])

parameters = [{"combiner": [None]},
              {"combiner": [PolynomialFeatures()], "combiner__degree": [2], "combiner__interaction_only": [False, True]},

              {"dimred": [None]},
              {"dimred": [PCA()], "dimred__n_components": [.95, .75]},

              {"classifier": [RandomForestClassifier(n_estimators=100, class_weight="balanced")],
               "classifier__max_depth": [5, 10, None]},
              {"classifier": [KNeighborsClassifier(weights="distance")],
               "classifier__n_neighbors": [3, 7, 11]}]

CV = GridSearchCV(pipeline, parameters, cv=5, scoring="f1_weighted", refit=True, n_jobs=-1)
CV.fit(train_X, train_y)

Of course, I need the results with the best pipeline with the best parameters. However, when I request best estimators with CV.best_estimator_ I get only the winning components, not the hyperparameters:

Pipeline(steps=[('combiner', None), ('dimred', PCA()),
                ('classifier', RandomForestClassifier())])

When I print out the CV.best_params_, I get an even shorter info (only with the first element of the Pipeline, the combiner, no info about dimred, classifier whatsoever):

{'combiner': None}

How could I get the best pipeline combination with components and their hyperparameters?

hyperparameters data-science scikit-learn machine-learning python
2 Answers

Pipeline objects have a get_params() method which returns the parameters of the pipeline. This includes the parameters of the individual steps as well. Based on your example, the command

CV.best_estimator_.get_params()

will retrieve all pipeline parameters of the best estimator, including those you are looking for.

user_131828850

Since your param_grid is a list of dictionaries, each such dictionary gives a separate grid, and the search takes place over the disjoint union of those grids. So the best_estimator_ and best_params_ in your case correspond to the single-point grid with combiner=None and everything else as defined in the original pipeline. (And the search has never explored combiner=None with other hyperparameters.)

user_104958930
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