CODEWITHSUNDEEP
pythonpython-3.xmachine-learningscikit-learnpipeline
Miguel 2488
Miguel 2488

Reputation: 1440

How can i access to the best estimator params in a model that is contained in a pipeline?

I have the following sklearn pipeline:

Pipeline(memory=None,
     steps=[('feature_processor', DataProcessor()),
            ('classifier', GridSearchCV(cv=15,
                                        error_score='raise-deprecating',
                                        estimator=XGBClassifier(base_score=0.5,
                                                                booster='gbtree',
                                                                colsample_bylevel=1,
                                                                colsample_bynode=1, 
                                                                colsample_bytree=1,
                                                                gamma=0, learning_rate=0.1,
                                                                max_delta_step=0,..._dispatch='2*n_jobs',
                                                                refit=True,
                                                                return_train_score='warn',
                                                                scoring='accuracy', verbose=1))
            ])

There is a trained model inside, which parameters were optimized using GridSearchCV. The model with its pipeline were saved into a pickle. I'm using pickle.load() to read it back, but now i just don't know how to access the best params that were found by GridSearchCV.

Can please someone point me in the right direction?

If it was not possible to access this information via the pipe's info, would there be any other way to do this?

Thank you very much in advance

Upvotes: 0

Views: 1211

Answers (2)

Parthasarathy Subburaj
Parthasarathy Subburaj

Reputation: 4264

Try this:

pipeline.named_steps['classifier'].best_params_

Upvotes: 4

akilat90
akilat90

Reputation: 5696

From your example, you can get the best model by:

loaded_pipe = pickle.load(open("<your_pkl_file>", 'rb'))
loaded_pipe['classifer'].best_estimator_

Edit:

Only for best parameters:

loaded_pipe['classifer'].best_params_

Upvotes: 2

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