SubspaceContinuous

class baybe.searchspace.continuous.SubspaceContinuous

Bases: SerialMixin

Class for managing continuous subspaces.

Builds the subspace from parameter definitions, keeps track of search metadata, and provides access to candidate sets and different parameter views.

Public methods

__init__(parameters[, constraints_lin_eq, ...])	Method generated by attrs for class SubspaceContinuous.
empty()	Create an empty continuous subspace.
from_bounds(bounds)	Create a hyperrectangle-shaped continuous subspace with given bounds.
from_dataframe(df[, parameters])	Create a hyperrectangle-shaped continuous subspace from a dataframe.
from_dict(dictionary)	Create an object from its dictionary representation.
from_json(string)	Create an object from its JSON representation.
from_parameter(parameter)	Create a subspace from a single parameter.
from_product(parameters[, constraints])	See baybe.searchspace.core.SearchSpace.
get_parameters_by_name(names)	Return parameters with the specified names.
sample_from_full_factorial([batch_size])	Draw parameter configurations from the full factorial of the space.
sample_uniform([batch_size])	Draw uniform random parameter configurations from the continuous space.
samples_full_factorial([n_points])	Deprecated!
samples_random([n_points])	Deprecated!
to_dict()	Create an object's dictionary representation.
to_json()	Create an object's JSON representation.
to_searchspace()	Turn the subspace into a search space with no discrete part.
transform([df, allow_missing, allow_extra, data])	See baybe.searchspace.core.SearchSpace.transform().

Public attributes and properties

parameters	The parameters of the subspace.
constraints_lin_eq	Linear equality constraints.
constraints_lin_ineq	Linear inequality constraints.
constraints_nonlin	Nonlinear constraints.
comp_rep_bounds	The minimum and maximum values of the computational representation.
comp_rep_columns	The columns spanning the computational representation.
constraints_cardinality	Cardinality constraints.
full_factorial	Get the full factorial of the continuous space.
is_empty	Return whether this subspace is empty.
parameter_names	Return tuple of parameter names.

__init__(parameters: Collection[Parameter], constraints_lin_eq: Sequence = NOTHING, constraints_lin_ineq: Sequence = NOTHING, constraints_nonlin: Sequence = NOTHING)

Method generated by attrs for class SubspaceContinuous.

For details on the parameters, see Public attributes and properties.

classmethod empty()

Create an empty continuous subspace.

Return type:

SubspaceContinuous

classmethod from_bounds(bounds: DataFrame)

Create a hyperrectangle-shaped continuous subspace with given bounds.

Parameters:

bounds (DataFrame) – The bounds of the parameters.

Return type:

SubspaceContinuous

Returns:

The constructed subspace.

classmethod from_dataframe(df: DataFrame, parameters: Sequence[ContinuousParameter] | None = None)

Create a hyperrectangle-shaped continuous subspace from a dataframe.

More precisely, create the smallest axis-aligned hyperrectangle-shaped continuous subspace that contains the points specified in the given dataframe.

Parameters:

• df (DataFrame) – The dataframe specifying the points spanning the subspace.

• parameters (Optional[Sequence[ContinuousParameter]]) – Optional parameter objects corresponding to the columns in the given dataframe that can be provided to explicitly control parameter attributes. If a match between column name and parameter name is found, the corresponding parameter object is used. If a column has no match in the parameter list, a new baybe.parameters.numerical.NumericalContinuousParameter is created with default optional arguments. For more details, see baybe.parameters.utils.get_parameters_from_dataframe().

Raises:

ValueError – If parameter types other than baybe.parameters.numerical.NumericalContinuousParameter are provided.

Return type:

SubspaceContinuous

Returns:

The created continuous subspace.

classmethod from_dict(dictionary: dict)

Create an object from its dictionary representation.

Parameters:

dictionary (dict) – The dictionary representation.

Return type:

TypeVar(_T)

Returns:

The reconstructed object.

classmethod from_json(string: str)

Create an object from its JSON representation.

Parameters:

string (str) – The JSON representation of the object.

Return type:

TypeVar(_T)

Returns:

The reconstructed object.

classmethod from_parameter(parameter: ContinuousParameter)

Create a subspace from a single parameter.

Parameters:

parameter (ContinuousParameter) – The parameter to span the subspace.

Return type:

SubspaceContinuous

Returns:

The created subspace.

classmethod from_product(parameters: Sequence[ContinuousParameter], constraints: Sequence[ContinuousConstraint] | None = None)

See baybe.searchspace.core.SearchSpace.

Return type:

SubspaceContinuous

get_parameters_by_name(names: Sequence[str])

Return parameters with the specified names.

Parameters:

names (Sequence[str]) – Sequence of parameter names.

Return type:

tuple[NumericalContinuousParameter, ...]

Returns:

The named parameters.

sample_from_full_factorial(batch_size: int = 1)

Draw parameter configurations from the full factorial of the space.

Parameters:

batch_size (int) – The number of parameter configurations to be sampled.

Return type:

DataFrame

Returns:

A dataframe containing the parameter configurations as rows with columns corresponding to the parameter names.

Raises:

ValueError – If there are not enough points to sample from.

sample_uniform(batch_size: int = 1)

Draw uniform random parameter configurations from the continuous space.

Parameters:

batch_size (int) – The number of parameter configurations to be sampled.

Return type:

DataFrame

Returns:

A dataframe containing the parameter configurations as rows with columns corresponding to the parameter names.

Raises:

ValueError – If the subspace contains unsupported nonlinear constraints.

samples_full_factorial(n_points: int = 1)

Deprecated!

Return type:

DataFrame

samples_random(n_points: int = 1)

Deprecated!

Return type:

DataFrame

to_dict()

Create an object’s dictionary representation.

Return type:

dict

to_json()

Create an object’s JSON representation.

Return type:

str

Returns:

The JSON representation as a string.

to_searchspace()

Turn the subspace into a search space with no discrete part.

Return type:

SearchSpace

transform(df: DataFrame | None = None, /, *, allow_missing: bool = False, allow_extra: bool | None = None, data: DataFrame | None = None)

See baybe.searchspace.core.SearchSpace.transform().

Return type:

DataFrame

property comp_rep_bounds: DataFrame

The minimum and maximum values of the computational representation.

property comp_rep_columns: tuple[str, ...]

The columns spanning the computational representation.

property constraints_cardinality: tuple[ContinuousCardinalityConstraint, ...]

Cardinality constraints.

constraints_lin_eq: tuple[ContinuousLinearEqualityConstraint, ...]

Linear equality constraints.

constraints_lin_ineq: tuple[ContinuousLinearInequalityConstraint, ...]

Linear inequality constraints.

constraints_nonlin: tuple[ContinuousNonlinearConstraint, ...]

Nonlinear constraints.

property full_factorial: DataFrame

Get the full factorial of the continuous space.

property is_empty: bool

Return whether this subspace is empty.

property parameter_names: tuple[str, ...]

Return tuple of parameter names.

parameters: tuple[NumericalContinuousParameter, ...]

The parameters of the subspace.