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.


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, first a dict is sampled uniformly, and then a parameter is sampled using that dict as above.


Number of parameter settings that are produced.

random_stateint, RandomState instance or None, optional (default=None)

Pseudo random number generator state used for random uniform sampling from lists of possible values instead of scipy.stats distributions. If int, random_state is the seed used by the random number generator; If RandomState instance, random_state is the random number generator; If None, the random number generator is the RandomState instance used by np.random.

paramsdict of string to any

Yields dictionaries mapping each estimator parameter to as sampled value.


>>> 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}]
__init__(self, param_distributions, n_iter, random_state=None)[source]

Initialize self. See help(type(self)) for accurate signature.