ParamSpace#

class obsidian.parameters.param_space.ParamSpace(params: list[Parameter])[source]#

Bases: IParamSpace

Class designed to define the parameter space in which an optimization can be conducted.

params#

A tuple of Parameter types defining the parameter space.

Type:: tuple[Parameter]

X_names#

A tuple of the names of the parameters.

Type:: tuple[str]

X_cont#

A list of the names of the continuous parameters.

Type:: list[str]

X_obs#

A list of the names of the observational parameters.

Type:: list[str]

X_discrete#

A dictionary mapping the names of the discrete parameters to their categories.

Type:: dict[str, list[str]]

X_task#

A dictionary mapping the names of the task parameters to their categories.

Type:: dict[str, list[str]]

X_min#

A tensor containing the minimum values of the parameters.

Type:: torch.Tensor

X_max#

A tensor containing the maximum values of the parameters.

Type:: torch.Tensor

X_range#

A tensor containing the range of the parameters.

Type:: torch.Tensor

X_static#

A list of the names of the static parameters.

Type:: list[str]

X_vary#

A list of the names of the varying parameters.

Type:: list[str]

n_dim#

The number of dimensions in the parameter space.

Type:: int

n_tdim#

The total number of dimensions in the parameter space after transformation.

Type:: int

t_map#

A dictionary mapping parameter indices to transformed indices.

Type:: dict

tinv_map#

A dictionary mapping transformed indices to parameter indices.

Type:: dict

X_discrete_idx#

A list of the indices of the discrete parameters.

Type:: list[int]

X_t_discrete_idx#

A list of the indices of the transformed discrete parameters.

Type:: list[int]

X_t_cat_idx#

A list of the indices of the transformed categorical parameters.

Type:: list[int]

X_t_task_idx#

The index of the transformed task parameter.

Type:: int

search_space#

The allowable search space for future optimization.

Type:: pd.DataFrame

Raises:

ValueError – If the X_names are not unique.
UnsupportedError – If there is more than one task parameter.

__init__(params: list[Parameter])[source]#

Methods

`__init__`(params)
`clear_constraints`()	Clears all constraints from the input space.
`constrain_inputs`(constraint)	Constrains the input space based on the specified equality, inequality, or nonlinear constraint.
`decode`(X)	Decode parameter from transformed space
`encode`(X)	Encode parameter to a format that can be used for training
`load_state`(obj_dict)	Loads the state of the ParamSpace object from a dictionary.
`map_inv_transform`()	Maps the inverse of the transformed dictionary.
`map_transform`()	Maps the parameter indices to transformed indices based on the parameter types.
`mean`()	Calculates the mean values for each parameter in the parameter space.
`open_search`()	Set the search space to the parameter space
`save_state`()	Saves the state of the ParamSpace object.
`unit_demap`(X)	Map from 0,1 space to measured space
`unit_map`(X)	Map from measured to 0,1 space

Attributes

search_space

Returns the search space for the parameter space.

clear_constraints() → None[source]#: Clears all constraints from the input space.

constrain_inputs(constraint: Input_Constraint) → None[source]#

Constrains the input space based on the specified equality, inequality, or nonlinear constraint.

Parameters:: constraint (Input_Constraint) – The constraint to be applied to the input space.

decode(X)[source]#: Decode parameter from transformed space

encode(X)[source]#: Encode parameter to a format that can be used for training

classmethod load_state(obj_dict: dict)[source]#

Loads the state of the ParamSpace object from a dictionary.

Parameters:: obj_dict (dict) – A dictionary containing the state of the ParamSpace object.
Returns:: A new ParamSpace object with the loaded state.
Return type:: ParamSpace

map_inv_transform() → dict[source]#

Maps the inverse of the transformed dictionary.

Returns:: A dictionary where the keys are the original values and the values are the original keys.
Return type:: dict

map_transform() → dict[source]#

Maps the parameter indices to transformed indices based on the parameter types.

Returns:: A dictionary mapping parameter indices to transformed indices.
Return type:: dict

mean() → DataFrame[source]#

Calculates the mean values for each parameter in the parameter space.

Returns:: A DataFrame containing the mean values for each parameter.
Return type:: pd.DataFrame

open_search() → None[source]#: Set the search space to the parameter space

save_state() → dict[source]#

Saves the state of the ParamSpace object.

Returns:: A dictionary containing the state of the ParamSpace object.
Return type:: dict

property search_space: DataFrame#

Returns the search space for the parameter space.

Returns:: A dataframe containing the search space for the parameter space.
Return type:: pd.DataFrame

unit_demap(X)[source]#: Map from 0,1 space to measured space

unit_map(X)[source]#: Map from measured to 0,1 space