# 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]

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.