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?
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