sklearn.model_selection.ParameterSampler¶
-
class
sklearn.model_selection.ParameterSampler(param_distributions, n_iter, *, random_state=None)[source]¶ Generator on parameters sampled from given distributions.
Non-deterministic iterable over random candidate combinations for hyper- parameter search. If all parameters are presented as a list, sampling without replacement is performed. If at least one parameter is given as a distribution, sampling with replacement is used. It is highly recommended to use continuous distributions for continuous parameters.
Read more in the User Guide.
- Parameters
- param_distributionsdict
Dictionary with parameters names (
str) as keys and distributions or lists of parameters to try. Distributions must provide arvsmethod 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, first a dict is sampled uniformly, and then a parameter is sampled using that dict as above.- n_iterinteger
Number of parameter settings that are produced.
- random_stateint or RandomState instance, default=None
Pseudo random number generator state used for random uniform sampling from lists of possible values instead of scipy.stats distributions. Pass an int for reproducible output across multiple function calls. See Glossary.
- Returns
- paramsdict of str to any
Yields dictionaries mapping each estimator parameter to as sampled value.
Examples
>>> from sklearn.model_selection import ParameterSampler >>> from scipy.stats.distributions import expon >>> import numpy as np >>> rng = np.random.RandomState(0) >>> param_grid = {'a':[1, 2], 'b': expon()} >>> param_list = list(ParameterSampler(param_grid, n_iter=4, ... random_state=rng)) >>> rounded_list = [dict((k, round(v, 6)) for (k, v) in d.items()) ... for d in param_list] >>> rounded_list == [{'b': 0.89856, 'a': 1}, ... {'b': 0.923223, 'a': 1}, ... {'b': 1.878964, 'a': 2}, ... {'b': 1.038159, 'a': 2}] True