deephyper.problem.CategoricalHyperparameter#

class deephyper.problem.CategoricalHyperparameter#

Bases: ConfigSpace.hyperparameters.Hyperparameter

Methods

allow_greater_less_comparison

check_default

compare

compare_vector

get_max_density

get_neighbors

get_num_neighbors

get_size

has_neighbors

is_legal

is_legal_vector

Check whether the given value is a legal value for the vector representation of this hyperparameter.

pdf

Computes the probability density function of the parameter in the original parameter space (the one specified by the user).

rvs

scipy compatibility wrapper for _sample, allowing the hyperparameter to be used in sklearn API hyperparameter searchers, eg.

sample

to_uniform

Creates a categorical parameter with equal weights for all choices This is used for the uniform configspace when sampling configurations in the local search in PiBO: https://openreview.net/forum?id=MMAeCXIa89

Attributes

choices

default_value

meta

name

normalized_default_value

num_choices

probabilities

weights

Check whether the given value is a legal value for the vector representation of this hyperparameter.

Parameters

value – the vector value to check

Returns

True if the given value is a legal vector value, otherwise False

Return type

bool

pdf()#

Computes the probability density function of the parameter in the original parameter space (the one specified by the user). For each parameter type, there is also a method _pdf which operates on the transformed (and possibly normalized) parameter space. Only legal values return a positive probability density, otherwise zero.

Parameters

vector (np.ndarray) – the (N, ) vector of inputs for which the probability density function is to be computed.

Returns

Probability density values of the input vector

Return type

np.ndarray(N, )

rvs()#

scipy compatibility wrapper for _sample, allowing the hyperparameter to be used in sklearn API hyperparameter searchers, eg. GridSearchCV.

to_uniform()#

Creates a categorical parameter with equal weights for all choices This is used for the uniform configspace when sampling configurations in the local search in PiBO: https://openreview.net/forum?id=MMAeCXIa89

Returns

An identical parameter as the original, except that all weights are uniform.

Return type

CategoricalHyperparameter