[Python] Sklearn How to Save a Model Created From a Pipeline and GridSearchCV Using Joblib or...

After identifying the best parameters using a pipeline and GridSearchCV, how do I pickle/joblib this process to re-use later? I see how to do this when it's a single classifier...

import joblib
joblib.dump(clf, 'filename.pkl')


But how do I save this overall pipeline with the best parameters after performing and completing a gridsearch?

I tried:

• joblib.dump(grid, 'output.pkl') - But that dumped every gridsearch attempt (many files)
• joblib.dump(pipeline, 'output.pkl') - But I don't think that contains the best parameters

X_train = df['Keyword']
y_train = df['Ad Group']

pipeline = Pipeline([
('tfidf', TfidfVectorizer()),
('sgd', SGDClassifier())
])

parameters = {'tfidf__ngram_range': [(1, 1), (1, 2)],
'tfidf__use_idf': (True, False),
'tfidf__max_df': [0.25, 0.5, 0.75, 1.0],
'tfidf__max_features': [10, 50, 100, 250, 500, 1000, None],
'tfidf__stop_words': ('english', None),
'tfidf__smooth_idf': (True, False),
'tfidf__norm': ('l1', 'l2', None),
}

grid = GridSearchCV(pipeline, parameters, cv=2, verbose=1)
grid.fit(X_train, y_train)

#These were the best combination of tuning parameters discovered
##best_params = {'tfidf__max_features': None, 'tfidf__use_idf': False,
## 'tfidf__smooth_idf': False, 'tfidf__ngram_range': (1, 2),
## 'tfidf__max_df': 1.0, 'tfidf__stop_words': 'english',
## 'tfidf__norm': 'l2'}

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