Python Sklearn 如何使用 Joblib 或 Pickle 保存从管道和 GridSearchCV 创建的模型？
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Sklearn How to Save a Model Created From a Pipeline and GridSearchCV Using Joblib or Pickle?
提问by Jarad
After identifying the best parameters using a pipelineand GridSearchCV, how do I pickle/joblibthis process to re-use later? I see how to do this when it's a single classifier...
使用 apipeline和确定最佳参数后GridSearchCV，我pickle/joblib此过程如何在以后重新使用？当它是单个分类器时，我知道如何执行此操作...
from sklearn.externals import joblib
joblib.dump(clf, 'filename.pkl')
But how do I save this overall pipelinewith the best parameters after performing and completing a gridsearch?
但是，如何pipeline在执行和完成之后使用最佳参数保存整体gridsearch？
I tried:
我试过：
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
joblib.dump(grid, 'output.pkl')- 但是这放弃了每次 gridsearch 尝试（许多文件）joblib.dump(pipeline, 'output.pkl')- 但我不认为包含最好的参数
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'}
采纳答案by Ibraim Ganiev
from sklearn.externals import joblib
joblib.dump(grid.best_estimator_, 'filename.pkl')
If you want to dump your object into one file - use:
如果要将对象转储到一个文件中 - 使用：
joblib.dump(grid.best_estimator_, 'filename.pkl', compress = 1)
