Sklearn如何使用Joblib或Pickle保存从管道和GridSearchCV创建的模型？

Question

使用确定最佳参数后pipeline和GridSearchCV,我怎么pickle/ joblib后来这个过程中重新使用？当它是单个分类器时,我看到如何做到这一点......

from sklearn.externals import joblib
joblib.dump(clf, 'filename.pkl') 

但是,如何pipeline在执行和完成后用最佳参数保存整体gridsearch？

我试过了:

• joblib.dump(grid, 'output.pkl') - 但是转储了每次网格搜索尝试(许多文件)
• joblib.dump(pipeline, 'output.pkl') - 但我不认为它包含最好的参数

---

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'}

Answer 1

from sklearn.externals import joblib
joblib.dump(grid.best_estimator_, 'filename.pkl')

如果要将对象转储到一个文件中 - 请使用:

joblib.dump(grid.best_estimator_, 'filename.pkl', compress = 1)