skopt.space.space.Integer

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

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'>)[source][source]

Initialize self. See help(type(self)) for accurate signature.

distance(a, b)[source][source]

Compute distance between point a and b.

Parameters
aint

First point.

bint

Second point.

inverse_transform(Xt)[source][source]

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

rvs(n_samples=1, random_state=None)[source]

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='identitiy')[source][source]

Define _rvs and transformer spaces.

Parameters
transformstr

Can be ‘normalize’ or ‘identity’

transform(X)[source]

Transform samples form the original space to a warped space.