model_selection.ParameterSampler()

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.

Note that before SciPy 0.16, the scipy.stats.distributions do not accept a custom RNG instance and always use the singleton RNG from numpy.random. Hence setting random_state will not guarantee a deterministic iteration whenever scipy.stats distributions are used to define the parameter search space. Deterministic behavior is however guaranteed from SciPy 0.16 onwards.

Read more in the User Guide.

Examples

>>> from sklearn.model_selection import ParameterSampler
>>> from scipy.stats.distributions import expon
>>> import numpy as np
>>> np.random.seed(0)
>>> param_grid = {'a':[1, 2], 'b': expon()}
>>> param_list = list(ParameterSampler(param_grid, n_iter=4))
>>> 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
.. automethod:: __init__