Using sample_weight in GridSearchCV

Question

Is it possible to perform a GridSearchCV (to get the best SVM's C) and yet specify the sample_weight with scikit-learn?

Here's my code and the error I'm confronted to:

gs = GridSearchCV(
    svm.SVC(C=1),
    [{
        'kernel': ['linear'],
        'C': [.1, 1, 10],
        'probability': [True],
        'sample_weight': sw_train,
    }]
)

gs.fit(Xtrain, ytrain)

>> ValueError: Invalid parameter sample_weight for estimator SVC

---

Edit: I solved the issue by getting the latest scikit-learn version and using the following:

gs.fit(Xtrain, ytrain, fit_params={'sample_weight': sw_train})

Answer 1

Just trying to close out this long hanging question...

You needed to get the last version of SKL and use the following:

gs.fit(Xtrain, ytrain, fit_params={'sample_weight': sw_train})

However, it is more in line with the documentation to pass fit_params to the constructor:

gs = GridSearchCV(svm.SVC(C=1), [{'kernel': ['linear'], 'C': [.1, 1, 10], 'probability': [True], 'sample_weight': sw_train}], fit_params={'sample_weight': sw_train})

gs.fit(Xtrain, ytrain)

Answer 2

The previous answers are now obsolete. The dictionary fit_params should be passed to the fit method.

From the documentation for GridSearchCV:

fit_params : dict, optional

Parameters to pass to the fit method.

Deprecated since version 0.19: fit_params as a constructor argument was deprecated in version 0.19 and will be removed in version 0.21. Pass fit parameters to the fit method instead.