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

问题描述

在使用pipeline 和GridSearchCV 确定最佳参数后，我如何pickle/joblib 这个过程以后再用?当它是单个分类器时，我知道如何执行此操作...

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...

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

但是如何在执行和完成 gridsearch 后使用最佳参数保存整个 pipeline ?

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

我试过了:

• joblib.dump(grid, 'output.pkl') - 但是这转储了每个 gridsearch尝试(许多文件)
• joblib.dump(pipeline, 'output.pkl') - 但我不要认为包含最好的参数

• joblib.dump(grid, 'output.pkl') - But that dumped every gridsearchattempt (many files)
• joblib.dump(pipeline, 'output.pkl') - But Idon'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'}

推荐答案

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

如果要将对象转储到一个文件中 - 使用:

If you want to dump your object into one file - use:

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

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