DependencySymmetry¶

class baybe.symmetries.DependencySymmetry[source]¶

Bases: Symmetry

Class for representing dependency symmetries.

A dependency symmetry expresses that certain parameters are dependent on another parameter having a specific value. For instance, the situation “The value of parameter y only matters if parameter x has the value ‘on’.”. In this scenario x is the causing parameter and y depends on x.

Public methods

`__init__`(parameter_name, condition, ...[, ...])	Method generated by attrs for class DependencySymmetry.
`augment_measurements`(df, parameters)	Augment the given measurements according to the symmetry.
`from_dict`(dictionary)	Create an object from its dictionary representation.
`from_json`(source, /)	Create an object from its JSON representation.
`summary`()	Return a custom summarization of the symmetry.
`to_dict`()	Create an object's dictionary representation.
`to_json`([sink, overwrite])	Create an object's JSON representation.
`validate_searchspace_context`(searchspace)	See base class.

Public attributes and properties

`condition`	The condition specifying the active range of the causing parameter.
`affected_parameter_names`	The parameters affected by the dependency.
`n_discretization_points`	Number of points used when subsampling continuous parameter ranges.
`parameter_names`	The names of the parameters affected by the symmetry.
`use_data_augmentation`	Flag indicating whether data augmentation would be used with surrogates that support this.

__init__(parameter_name: str, condition: Condition, affected_parameter_names: Sequence[str], *, use_data_augmentation: bool = True, n_discretization_points: int = 3)¶

Method generated by attrs for class DependencySymmetry.

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

augment_measurements(df: pd.DataFrame, parameters: Iterable[Parameter])[source]¶

Augment the given measurements according to the symmetry.

Parameters:

df (pd.DataFrame) – The dataframe containing the measurements to be augmented.
parameters (Iterable[Parameter]) – Parameter objects carrying additional information (might not be needed by all augmentation implementations).

Return type:

pd.DataFrame

Returns:

The augmented dataframe including the original measurements.

classmethod from_dict(dictionary: dict)¶

Create an object from its dictionary representation.

Parameters:: dictionary (dict) – The dictionary representation.
Return type:: TypeVar(_T, bound= SerialMixin)
Returns:: The reconstructed object.

classmethod from_json(source: str | Path | SupportsRead[str], /)¶

Create an object from its JSON representation.

Parameters:

source (str | Path | SupportsRead[str]) –

The JSON source. Can be:

A string containing JSON content.
A file path or Path object pointing to a JSON file.
A file-like object with a read() method.

Raises:

ValueError – If source is not one of the allowed types.

Return type:

Returns:

The reconstructed object.

summary()¶

Return a custom summarization of the symmetry.

Return type:: dict

to_dict()¶

Create an object’s dictionary representation.

Return type:: dict
Returns:: The dictionary representation of the object.

to_json(sink: str | Path | SupportsWrite[str] | None = None, /, *, overwrite: bool = False, **kwargs: Any)¶

Create an object’s JSON representation.

Parameters:

sink (str | Path | SupportsWrite[str] | None) –
The JSON sink. Can be:
- None (only returns the JSON string).
- A file path or Path object pointing to a location where to write the JSON content.
- A file-like object with a write() method.
overwrite (bool) – Boolean flag indicating if to overwrite the file if it already exists. Only relevant if sink is a file path or Path object.
**kwargs (Any) – Additional keyword arguments to pass to json.dumps().

Raises:

FileExistsError – If sink points to an already existing file but overwrite is False.

Return type:

str

Returns:

The JSON representation as a string.

validate_searchspace_context(searchspace: SearchSpace)[source]¶

See base class.

Parameters:

searchspace (SearchSpace) – The searchspace to validate against.

Raises:

ValueError – If any of the affected parameters is not present in the searchspace.
TypeError – If the causing parameter is not discrete.

Return type:

None

affected_parameter_names: tuple[str, ...]¶: The parameters affected by the dependency.

condition: Condition¶: The condition specifying the active range of the causing parameter.

n_discretization_points: int¶: Number of points used when subsampling continuous parameter ranges.

property parameter_names: tuple[str, ...]¶: The names of the parameters affected by the symmetry.

use_data_augmentation: bool¶: Flag indicating whether data augmentation would be used with surrogates that support this.