sklearn.grid_search.ParameterSampler¶
- class sklearn.grid_search.ParameterSampler(param_distributions, n_iter, random_state=None)¶
Generator on parameters sampled from given distributions.
Non-deterministic iterable over random candidate combinations for hyper- parameter search.
Note that as of SciPy 0.12, 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.
Parameters : param_distributions : dict
Dictionary where the keys are parameters and values are distributions from which a parameter is to be sampled. Distributions either have to provide a rvs function to sample from them, or can be given as a list of values, where a uniform distribution is assumed.
n_iter : integer
Number of parameter settings that are produced.
random_state : int or RandomState
Pseudo random number generator state used for random uniform sampling from lists of possible values instead of scipy.stats distributions.
Returns : params : dict of string to any
Yields dictionaries mapping each estimator parameter to as sampled value.
Examples
>>> from sklearn.grid_search 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
- __init__(param_distributions, n_iter, random_state=None)¶