sample_weights array can't be used with GridSearchCV

[shenberg]

The internal cross-validation isn't aware of sample weights, so and exception is thrown if a sample_weights sequence is passed to the grid search, because fit_grid_point does not split the weights into training and test sets.

[ndawe]

I have added support for sample_weight in GridSearchCV in #1574. Hopefully I can get that merged soon.

[shenberg]

Thanks, that was extremely fast turn-around.

As a meta-question, is sampling weighted? e.g. Is it better for stratified folds to try maintain the ratio of total weight per class instead of the number of samples?

[amueller]

sample_weight support was merged long ago

[jnothman]

@amueller, sample_weight is still not supported as an argument to fit in *SearchCV

[amueller]

There is fit_params

[stephen-hoover]

@amueller , I'm running into a similar issue. It's possible to set a sample_weights array as an instance attribute via the GridSearchCV.fit_params dictionary, and then everything works fine. But this fails in nested cross-validation with model_selection.cross_val_predict, because the GridSearchCV.fit (and RandomizedSearchCV.fit) method doesn't accept fit parameters.

Is there a reason why the BaseSearchCV subclasses can't accept fit parameters? Having to set data-dependent fit parameters as instance attributes appears to contradict http://scikit-learn.org/stable/developers/contributing.html#fitting .

I would be willing to make that change (adding fit parameters) if you'd accept that PR.

[jnothman]

Yes, it's broken. One issue is that we need to be clear whether sample_weight is being passed only to scoring, only to fit, or both. Feel free to propose and champion a solution.