# skopt.space.space.Space¶

class skopt.space.space.Space(dimensions)[source][source]

Initialize a search space from given specifications.

Parameters
dimensionslist, shape=(n_dims,)

List of search space dimensions. Each search dimension can be defined either as

• a (lower_bound, upper_bound) tuple (for Real or Integer dimensions),

• a (lower_bound, upper_bound, "prior") tuple (for Real dimensions),

• as a list of categories (for Categorical dimensions), or

• an instance of a Dimension object (Real, Integer or Categorical).

Note

The upper and lower bounds are inclusive for Integer dimensions.

Attributes
bounds

The dimension bounds, in the original space.

dimension_names

Names of all the dimensions in the search-space.

is_categorical

Space contains exclusively categorical dimensions

is_partly_categorical

Space contains any categorical dimensions

is_real

Returns true if all dimensions are Real

n_constant_dimensions

Returns the number of constant dimensions which have zero degree of freedom, e.g.

n_dims

The dimensionality of the original space.

transformed_bounds

The dimension bounds, in the warped space.

transformed_n_dims

The dimensionality of the warped space.

Methods

 distance(point_a, point_b) Compute distance between two points in this space. from_yaml(yml_path[, namespace]) Create Space from yaml configuration file Returns all transformers as list Inverse transform samples from the warped space back to the rvs([n_samples, random_state]) Draw random samples. set_transformer(transform) Sets the transformer of all dimension objects to transform set_transformer_by_type(transform, dim_type) Sets the transformer of dim_type objects to transform Transform samples from the original space into a warped space.
__init__(dimensions)[source][source]

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

property bounds

The dimension bounds, in the original space.

property dimension_names

Names of all the dimensions in the search-space.

distance(point_a, point_b)[source][source]

Compute distance between two points in this space.

Parameters
point_aarray

First point.

point_barray

Second point.

classmethod from_yaml(yml_path, namespace=None)[source][source]

Create Space from yaml configuration file

Parameters
yml_pathstr

Full path to yaml configuration file, example YaML below: Space:

• Integer: low: -5 high: 5

• Categorical: categories: - a - b

• Real: low: 1.0 high: 5.0 prior: log-uniform

namespacestr, default=None

Namespace within configuration file to use, will use first namespace if not provided

Returns
spaceSpace

Instantiated Space object

get_transformer()[source][source]

Returns all transformers as list

inverse_transform(Xt)[source][source]
Inverse transform samples from the warped space back to the

original space.

Parameters
Xtarray of floats, shape=(n_samples, transformed_n_dims)

The samples to inverse transform.

Returns
Xlist of lists, shape=(n_samples, n_dims)

The original samples.

property is_categorical

Space contains exclusively categorical dimensions

property is_partly_categorical

Space contains any categorical dimensions

property is_real

Returns true if all dimensions are Real

property n_constant_dimensions

Returns the number of constant dimensions which have zero degree of freedom, e.g. an Integer dimensions with (0., 0.) as bounds.

property n_dims

The dimensionality of the original space.

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

Draw random samples.

The samples are in the original space. They need to be transformed before being passed to a model or minimizer by space.transform().

Parameters
n_samplesint, default=1

Number of samples to be drawn from the space.

random_stateint, RandomState instance, or None (default)

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

Returns
pointslist of lists, shape=(n_points, n_dims)

Points sampled from the space.

set_transformer(transform)[source][source]

Sets the transformer of all dimension objects to transform

Parameters
transformstr or list of str

Sets all transformer,, when transform is a string. Otherwise, transform must be a list with strings with the same length as dimensions

set_transformer_by_type(transform, dim_type)[source][source]

Sets the transformer of dim_type objects to transform

Parameters
transformstr

Sets all transformer of type dim_type to transform

dim_typetype
Can be skopt.space.Real, skopt.space.Integer or

skopt.space.Categorical

transform(X)[source][source]

Transform samples from the original space into a warped space.

Note: this transformation is expected to be used to project samples

into a suitable space for numerical optimization.

Parameters
Xlist of lists, shape=(n_samples, n_dims)

The samples to transform.

Returns
Xtarray of floats, shape=(n_samples, transformed_n_dims)

The transformed samples.

property transformed_bounds

The dimension bounds, in the warped space.

property transformed_n_dims

The dimensionality of the warped space.