Hi Jaidev, well, `param_grid` in GridSearchCV can also be a list of dictionaries, so you could directly specify the cases you are interested in (instead of the full grid - exceptions), which might be simpler? On 23/11/16 11:15, Jaidev Deshpande wrote:
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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