Use sklearn's GridSearchCV with a pipeline, preprocessing just once

Question

I’m using scickit-learn to tune a model hyper-parameters. I’m using a pipeline to have chain the preprocessing with the estimator. A simple version of my problem would look like this:

import numpy as np
from sklearn.model_selection import GridSearchCV
from sklearn.pipeline import make_pipeline
from sklearn.preprocessing import StandardScaler
from sklearn.linear_model import LogisticRegression


grid = GridSearchCV(make_pipeline(StandardScaler(), LogisticRegression()),
                    param_grid={'logisticregression__C': [0.1, 10.]},
                    cv=2,
                    refit=False)

_ = grid.fit(X=np.random.rand(10, 3),
             y=np.random.randint(2, size=(10,)))

In my case the preprocessing (what would be StandardScale() in the toy example) is time consuming, and I’m not tuning any parameter of it.

So, when I execute the example, the StandardScaler is executed 12 times. 2 fit/predict * 2 cv * 3 parameters. But every time StandardScaler is executed for a different value of the parameter C, it returns the same output, so it’d be much more efficient, to compute it once, and then just run the estimator part of the pipeline.

I can manually split the pipeline between the preprocessing (no hyper parameters tuned) and the estimator. But to apply the preprocessing to the data, I should provide the training set only. So, I would have to implement the splits manually, and not use GridSearchCV at all.

Is there a simple/standard way to avoid repeating the preprocessing while using GridSearchCV?

Asked By: Marc Garcia

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Source

Answer 1

Update:
Ideally, the answer below should not be used as it leads to data leakage as discussed in comments. In this answer, GridSearchCV will tune the hyperparameters on the data already preprocessed by StandardScaler, which is not correct. In most conditions that should not matter much, but algorithms which are too sensitive to scaling will give wrong results.

Essentially, GridSearchCV is also an estimator, implementing fit() and predict() methods, used by the pipeline.

So instead of:

grid = GridSearchCV(make_pipeline(StandardScaler(), LogisticRegression()),
                    param_grid={'logisticregression__C': [0.1, 10.]},
                    cv=2,
                    refit=False)

Do this:

clf = make_pipeline(StandardScaler(), 
                    GridSearchCV(LogisticRegression(),
                                 param_grid={'logisticregression__C': [0.1, 10.]},
                                 cv=2,
                                 refit=True))

clf.fit()
clf.predict()

What it will do is, call the StandardScalar() only once, for one call to clf.fit() instead of multiple calls as you described.

Edit:

Changed refit to True, when GridSearchCV is used inside a pipeline. As mentioned in documentation:

refit : boolean, default=True
Refit the best estimator with the entire dataset. If “False”, it is impossible to make predictions using this GridSearchCV instance
after fitting.

If refit=False, clf.fit() will have no effect because the GridSearchCV object inside the pipeline will be reinitialized after fit().
When refit=True, the GridSearchCV will be refitted with the best scoring parameter combination on the whole data that is passed in fit().

So if you want to make the pipeline, just to see the scores of the grid search, only then the refit=False is appropriate. If you want to call the clf.predict() method, refit=True must be used, else Not Fitted error will be thrown.

Answered By: Vivek Kumar

Answer 2

It is not possible to do this in the current version of scikit-learn (0.18.1). A fix has been proposed on the github project:

https://github.com/scikit-learn/scikit-learn/issues/8830

https://github.com/scikit-learn/scikit-learn/pull/8322

Answered By: Victor Deplasse

Answer 3

For those who stumbled upon a little bit different problem, that I had as well.

Suppose you have this pipeline:

classifier = Pipeline([
    ('vectorizer', CountVectorizer(max_features=100000, ngram_range=(1, 3))),
    ('clf', RandomForestClassifier(n_estimators=10, random_state=SEED, n_jobs=-1))])

Then, when specifying parameters you need to include this ‘clf_‘ name that you used for your estimator. So the parameters grid is going to be:

params={'clf__max_features':[0.3, 0.5, 0.7],
        'clf__min_samples_leaf':[1, 2, 3],
        'clf__max_depth':[None]
        }

Answered By: Ayan Omarov

Use sklearn's GridSearchCV with a pipeline, preprocessing just once

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