[MRG] add support for lists of dictionaries to RandomizedSearchCV #14549
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Awesome! |
sklearn/model_selection/_search.py
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| Dictionary with parameters names (string) as keys and distributions | ||
| or lists of parameters to try. Distributions must provide a ``rvs`` | ||
| method for sampling (such as those from scipy.stats.distributions). | ||
| If a list is given, it is sampled uniformly. | ||
| If a list of dicts is given, for each parameter, one of the dicts |
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This is slightly unclear. It looks like, first dicts are sampled uniformly, then the parameters are sampled based on that dict.
sklearn/model_selection/_search.py
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| for _ in range(self.n_iter): | ||
| dist = self.param_distributions[ | ||
| rnd.randint(len(self.param_distributions))] |
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This is an awkwardly numpy way of expressing random.choose
| all_lists = all( | ||
| all(not hasattr(v, "rvs") for v in dist.values()) | ||
| for dist in self.param_distributions) | ||
| rng = check_random_state(self.random_state) |
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renamed this to be more consistent with the rest of the library
| @@ -210,6 +210,9 @@ Changelog | |||
| plot model scalability (see learning_curve example). | |||
| :pr:`13938` by :user:`Hadrien Reboul <H4dr1en>`. | |||
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| - |Enhancement| :class:`model_selection.RandomizedSearchCV` now accepts lists | |||
| of parameter distributions. :pr:`14549` by `Andreas Müller`_. | |||
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maybe
lists of dicts to sample from multiple parameter spaces
?
| for key in dist: | ||
| if (not isinstance(dist[key], Iterable) | ||
| and not hasattr(dist[key], 'rvs')): | ||
| raise TypeError('Parameter value is not iterable ' |
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... must be an iterable or a distribution?
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this is copy & pasted from ParameterGrid. Not sure if your version is any clearer and I think being semi-consistent between the two is good.
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OH YEAH! |
Follow up on #12759 with a slightly simplified interface.
This makes the API of RandomizedSearchCV a superset of GridSearchCV which makes it more convenient to use.