Hi,
Sometimes when using GridSearchCV, I realize that in the grid there are
certain combinations of hyperparameters that are either incompatible or
redundant. For example, when using an MLP, if I specify the following grid:
grid = {'solver': ['sgd', 'adam'], 'learning_rate': ['constant',
'invscaling', 'adaptive']}
then it yields the following ParameterGrid:
[{'learning_rate': 'constant', 'solver': 'sgd'},
{'learning_rate': 'constant', 'solver': 'adam'},
{'learning_rate': 'invscaling', 'solver': 'sgd'},
{'learning_rate': 'invscaling', 'solver': 'adam'},
{'learning_rate': 'adaptive', 'solver': 'sgd'},
{'learning_rate': 'adaptive', 'solver': 'adam'}]
Now, three of these are redundant, since learning_rate is used only for the
sgd solver. Ideally I'd like to specify these cases upfront, and for that I
have a simple hack (
https://github.com/jaidevd/jarvis/blob/master/jarvis/cross_validation.py#L38).
Using that yields a ParameterGrid as follows:
[{'learning_rate': 'constant', 'solver': 'adam'},
{'learning_rate': 'invscaling', 'solver': 'adam'},
{'learning_rate': 'adaptive', 'solver': 'adam'}]
which is then simply removed from the original ParameterGrid.
I wonder if there's a simpler way of doing this. Would it help if we had an
additional parameter (something like "grid_exceptions") in GridSearchCV,
which would remove these dicts from the list of parameters?
Thanks
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