skopt.space.space.Space

class skopt.space.space.Space(dimensions, constraint=None)

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.

constraintcallable or None, default: None

Constraint function. Should take a single list of parameters (i.e. a point in space) and return True if the point satisfies the constraints. If None, the space is not conditionally constrained.

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_df(df[, priors, bases, transforms])	Create Space from Pandas DataFrame object.
from_yaml(yml_path[, namespace])	Create Space from yaml configuration file.
get_transformer()	Returns all transformers as list.
inverse_transform(Xt)	Inverse transform samples from the warped space back to the original space.
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(X)	Transform samples from the original space into a warped space.

__init__(dimensions, constraint=None)

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)

Compute distance between two points in this space.

Parameters:

point_aarray

First point.

point_barray

Second point.

classmethod from_df(df, priors=None, bases=None, transforms=None)

Create Space from Pandas DataFrame object. Dimensions will be inferred from the column type in the Pandas DataFrame. Real and Integer dimensions will be set from the minimum and maximum of their corresponding columns. Category dimensions will be constructed from the unique values present in the column. Note: requires pandas installation. :param df: A Pandas DataFrame object :type df: pandas.DataFrame :param priors: A mapping of DataFrame column names to corresponding priors :type priors: dict, default=None :param bases: A mapping of DataFrame column names to corresponding bases :type bases: dict, default=None :param transforms: A mapping of DataFrame column names to corresponding transforms :type transforms: dict, default=None

Returns:

space – Instantiated Space object

Return type:

Space

classmethod from_yaml(yml_path, namespace=None)

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()

Returns all transformers as list.

inverse_transform(Xt)

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)

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)

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)

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)

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.