skopt.space.space.Integer

class skopt.space.space.Integer(low, high, prior='uniform', base=10, transform=None, name=None, dtype=<class 'numpy.int64'>)

Search space dimension that can take on integer values.

Parameters

lowint

Lower bound (inclusive).

highint

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.

baseint

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.

namestr or None

Name associated with dimension, e.g., “number of trees”.

dtypestr 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

Attributes

bounds

is_constant

name

prior

size

transformed_bounds

transformed_size

Methods

distance(a, b)	Compute distance between point a and b.
inverse_transform(Xt)	Inverse transform samples from the warped space back into the original space.
rvs([n_samples, random_state])	Draw random samples.
set_transformer([transform])	Define _rvs and transformer spaces.
transform(X)	Transform samples form the original space to a warped space.

__init__(low, high, prior='uniform', base=10, transform=None, name=None, dtype=<class 'numpy.int64'>)

distance(a, b)

Compute distance between point a and b.

Parameters

aint

First point.

bint

Second point.

inverse_transform(Xt)

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

rvs(n_samples=1, random_state=None)

Draw random samples.

Parameters

n_samplesint 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')

Define _rvs and transformer spaces.

Parameters

transformstr

Can be ‘normalize’ or ‘identity’

transform(X)

Transform samples form the original space to a warped space.