Initialization of hyperparameter search space fails when the first preprocessor cannot be used with given estimators

[teresaconc]

Initialization of hyperparameter search space fails when the first preprocessor method can not be used with none of the estimators.

Example: Using in the fit method:

 include_preprocessors = ['kernel_pca','select_rates']
 include_estimators = ['decision_tree','random_forest']

an error is raised: "Cannot find a legal default configuration." in _get_hyperparameter_search_space: line 216. This raises an error because kernel_pca can not be used with neither decision_tree nor random_forest.

However, if I reverse the include_preprocessors list: _include_preprocessors_ = ['select_rates', 'kernel_pca'] The program does not exit and the fit method is successful. It does not uses 'kernel_pca' but it does search for models with 'select_rates'

[mfeurer]

Indeed, this is an issue. Ideally, one would check every combination of preprocessor and classifier and then choose a legal one if possible. Would you like to work on a fix for this?

[teresaconc]

I haven't had time to look into this. But will hopefully do it soon

[mfeurer]

That would be great!

[github-actions[bot]]

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