deephyper.problem.BetaIntegerHyperparameter#

class deephyper.problem.BetaIntegerHyperparameter#

Bases: ConfigSpace.hyperparameters.UniformIntegerHyperparameter

Methods

`allow_greater_less_comparison`
`check_default`
`check_int`
`compare`
`compare_vector`
`get_max_density`	Returns the maximal density on the pdf for the parameter (so not the mode, but the value of the pdf on the mode).
`get_neighbors`	Get the neighbors of a value
`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 hyperparameter in the hyperparameter 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`

Attributes

`alpha`
`beta`
`bfhp`
`default_value`
`log`
`lower`
`meta`
`name`
`normalized_default_value`
`q`
`upper`

get_max_density()#: Returns the maximal density on the pdf for the parameter (so not the mode, but the value of the pdf on the mode).

get_neighbors()#

Get the neighbors of a value

Note

This assumes the value is in the unit-hypercube [0, 1]

Parameters

value (float) – The value to get neighbors around. This assume the value has been converted to the [0, 1] range which can be done with _inverse_transform.
rs (RandomState) – The random state to use
number (int = 4) – How many neighbors to get
transform (bool = False) – Whether to transform this value from the unit cube, back to the hyperparameter’s specified range of values.
std (float = 0.2) – The std. dev. to use in the [0, 1] hypercube space while sampling for neighbors.

Returns

Some number of neighbors centered around value.

Return type

List[int]

is_legal_vector()#

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 hyperparameter in the hyperparameter space (the one specified by the user). For each hyperparameter type, there is also a method _pdf which operates on the transformed (and possibly normalized) hyperparameter 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.