Raghav's example of
[{'learning_rate': ['constant', 'invscaling', 'adaptive'], 'solver':
'sgd'}, {'solver': 'adam'}]
was not correct.
Should be
[{'learning_rate': ['constant', 'invscaling', 'adaptive'], 'solver':
['sgd']}, {'solver': ['adam']}]
(Note all values of dicts are lists)
On 23 November 2016 at 22:52, Jaidev Deshpande <deshpande.jai...@gmail.com>
wrote:
>
>
> On Wed, 23 Nov 2016 at 16:29 Raghav R V <rag...@gmail.com> wrote:
>
>> Hi!
>>
>> What you could do is specify lists of dicts to group the parameters which
>> apply together in one dict...
>>
>> [{'learning_rate': ['constant', 'invscaling', 'adaptive'], 'solver':
>> 'sgd'}, {'solver': 'adam'}]
>>
>> ```py
>> from sklearn.neural_network import MLPClassifier
>> from sklearn.model_selection import GridSearchCV
>> from sklearn.datasets import make_classification
>>
>> from pandas import DataFrame
>>
>> X, y = make_classification(random_state=42)
>>
>> gs = GridSearchCV(MLPClassifier(random_state=42),
>> param_grid=[{'learning_rate': ['constant',
>> 'invscaling', 'adaptive'],
>> 'solver': ['sgd',]},
>> {'solver': ['adam',]}])
>>
>> DataFrame(gs.fit(X, y).cv_results_)
>> ```
>>
>> Would give
>>
>> [image: image.png]
>>
>> HTH :)
>>
>
> Haha, this is perfect. I didn't know you could pass a list of dicts to
> param_grid.
>
> Thanks!
>
>
>>
>> On Wed, Nov 23, 2016 at 11:15 AM, Jaidev Deshpande <
>> deshpande.jai...@gmail.com> 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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>>
>>
>>
>>
>> --
>> Raghav RV
>> https://github.com/raghavrv
>>
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>>
>
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