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.jaidev@gmail.com> wrote:

On Wed, 23 Nov 2016 at 16:29 Raghav R V <ragvrv@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

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.jaidev@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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