Extract best pipeline from GridSearchCV for cross_val_predict

Question

How can I extract the best pipeline from a fit GridSearchCV, so that I can pass it on to cross_val_predict?

Directly passing the fit GridSearchCV object causes cross_val_predict to run the whole grid search again, I just want to let the best pipeline be subject to the cross_val_predict evaluation.

My self-contained code is below:

from sklearn.datasets import fetch_20newsgroups
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import SVC
from sklearn.multiclass import OneVsRestClassifier
from sklearn.pipeline import Pipeline
from sklearn.grid_search import GridSearchCV
from sklearn.model_selection import cross_val_predict
from sklearn.model_selection import StratifiedKFold
from sklearn import metrics

# fetch data data
newsgroups = fetch_20newsgroups(remove=('headers', 'footers', 'quotes'), categories=['comp.graphics', 'rec.sport.baseball', 'sci.med'])
X = newsgroups.data
y = newsgroups.target

# setup and run GridSearchCV
wordvect = TfidfVectorizer(analyzer='word', lowercase=True)
classifier = OneVsRestClassifier(SVC(kernel='linear', class_weight='balanced'))
pipeline = Pipeline([('vect', wordvect), ('classifier', classifier)])
scoring = 'f1_weighted'
parameters = {
            'vect__min_df': [1, 2],
            'vect__max_df': [0.8, 0.9],
            'classifier__estimator__C': [0.1, 1, 10]
            }
gs_clf = GridSearchCV(pipeline, parameters, n_jobs=8, scoring=scoring, verbose=1)
gs_clf = gs_clf.fit(X, y)

### outputs: Fitting 3 folds for each of 12 candidates, totalling 36 fits

# manually extract the best models from the grid search to re-build the pipeline
best_clf = gs_clf.best_estimator_.named_steps['classifier']
best_vectorizer = gs_clf.best_estimator_.named_steps['vect']
best_pipeline = Pipeline([('best_vectorizer', best_vectorizer), ('classifier', best_clf)])

# passing gs_clf here would run the grind search again inside cross_val_predict
y_predicted = cross_val_predict(pipeline, X, y)
print(metrics.classification_report(y, y_predicted, digits=3))

What I am currently doing is manually re-building the pipeline from the best_estimator_. But my pipelines usually have more steps such as SVD or PCA, and sometimes I am adding or removing steps and re-running the grid-searches to explore the data. This step has to be then always repeated below when manually re-building the pipeline, which is error-prone.

Is there a way to directly extract the best pipeline from a fit GridSearchCV, so that I can pass it to cross_val_predict ?

Answer 1

y_predicted = cross_val_predict(gs_clf.best_estimator_, X, y)

works and returns:

Fitting 3 folds for each of 12 candidates, totalling 36 fits
[Parallel(n_jobs=4)]: Done  36 out of  36 | elapsed:   43.6s finished
             precision    recall  f1-score   support

          0      0.920     0.911     0.916       584
          1      0.894     0.943     0.918       597
          2      0.929     0.887     0.908       594

avg / total      0.914     0.914     0.914      1775

[Edit] When I tried the code again passing simply pipeline (the original pipeline), it returned the same output (as did passing best_pipeline). So it's possible you could just work with the Pipeline itself, but I'm not 100% on that.