deephyper.skopt.space.Integer#

class deephyper.skopt.space.Integer(low, high, prior='uniform', base=10, transform=None, name=None, dtype=<class 'numpy.int64'>, loc=None, scale=None)[source]#

Bases: Dimension

Search space dimension that can take on integer values.

Parameters:
  • low – int Lower bound (inclusive).

  • high – int Upper bound (inclusive).

  • prior

    “uniform” or “log-uniform”, default=”uniform” Distribution to use when sampling random integers for this dimension.

    • If “uniform”, integers are sampled uniformly between the lower

    and upper bounds. - If “log-uniform”, integers are sampled uniformly between log(lower, base) and log(upper, base) where log has base base.

  • base

    int The logarithmic base to use for a log-uniform prior.

    • Default 10, otherwise commonly 2.

  • transform

    “identity”, “normalize”, optional The following transformations are supported.

    • ”identity”, (default) the transformed space is the same as the

    original space. - “normalize”, the transformed space is scaled to be between 0 and 1.

  • name – str or None Name associated with dimension, e.g., “number of trees”.

  • dtype – str or dtype, default=np.int64 integer type which will be used in inverse_transform, can be int, np.int16, np.uint32, np.int32, np.int64 (default). When set to int, inverse_transform returns a list instead of a numpy array

Methods

distance

Compute distance between point a and b.

inverse_transform

Inverse transform samples from the warped space back into the original space.

rvs

Draw random samples.

set_transformer

Define _rvs and transformer spaces.

transform

Transform samples form the original space to a warped space.

Attributes

bounds

Bounds before transform/preprocessing.

is_constant

Test if the dimension is a constant.

name

Name of the dimension.

prior

size

Dimensionality of sampel from the dimension before the transform/preprocessing.

transformed_bounds

Bounds after the transform/preprocessing.

transformed_size

Dimensionality of samples from the dimension after the transform/preprocessing.

property bounds#

Bounds before transform/preprocessing.

distance(a, b)[source]#

Compute distance between point a and b.

Args: a : int

First point.

bint

Second point.

inverse_transform(Xt)[source]#

Inverse transform samples from the warped space back into the original space.

property is_constant#

Test if the dimension is a constant.

property name#

Name of the dimension.

rvs(n_samples=1, random_state=None)#

Draw random samples.

Args: n_samples : int or None

The number of samples to be drawn.

random_stateint, RandomState instance, or None (default)

Set random state to something other than None for reproducible results.

set_transformer(transform='identity')[source]#

Define _rvs and transformer spaces.

Args: transform : str

Can be ‘normalize’ or ‘identity’

property size#

Dimensionality of sampel from the dimension before the transform/preprocessing.

transform(X)#

Transform samples form the original space to a warped space.

property transformed_bounds#

Bounds after the transform/preprocessing.

property transformed_size#

Dimensionality of samples from the dimension after the transform/preprocessing.