data_models.domain.domain.Domain

data_models.domain.domain.Domain()

Representation of the optimization problem/domain

Attributes

Name	Type	Description
inputs	List[Input]	List of input features. Defaults to [].
outputs	List[Output]	List of output features. Defaults to [].
constraints	List[Constraint]	List of constraints. Defaults to [].
context	str	Free-text context providing additional information about the optimization problem. Useful for agentic optimization where an LLM agent can leverage this description to better understand the overall problem, its goals, and any domain-specific knowledge.

Methods

Name	Description
aggregate_by_duplicates	Aggregate the dataframe by duplicate experiments
coerce_invalids	Coerces all invalid output measurements to np.nan
describe_experiments	Method to get a tabular overview of how many measurements and how many valid entries are included in the input data for each output feature
is_fulfilled	Check if all constraints are fulfilled on all rows of the provided dataframe
to_description	Render a human-readable description of the optimization problem.
validate_candidates	Method to check the validty of proposed candidates
validate_constraints	Validate that the constraints defined in the domain fit to the input features.
validate_experiments	Checks the experimental data on validity
validate_unique_feature_keys	Validates if provided input and output feature keys are unique

aggregate_by_duplicates

data_models.domain.domain.Domain.aggregate_by_duplicates(
    experiments,
    prec,
    delimiter='-',
    method='mean',
)

Aggregate the dataframe by duplicate experiments

Duplicates are identified based on the experiments with the same input features. Continuous input features are rounded before identifying the duplicates. Aggregation is performed by taking the average of the involved output features.

Parameters

Name	Type	Description	Default
experiments	pd.DataFrame	Dataframe containing experimental data	required
prec	int	Precision of the rounding of the continuous input features	required
delimiter	str	Delimiter used when combining the orig. labcodes to a new one. Defaults to “-”.	'-'
method	Literal['mean', 'median']	Which aggregation method to use. Defaults to “mean”.	'mean'

Returns

Name	Type	Description
	Tuple[pd.DataFrame, list]	Tuple[pd.DataFrame, list]: Dataframe holding the aggregated experiments, list of lists holding the labcodes of the duplicates

coerce_invalids

data_models.domain.domain.Domain.coerce_invalids(experiments)

Coerces all invalid output measurements to np.nan

Parameters

Name	Type	Description	Default
experiments	pd.DataFrame	Dataframe containing experimental data	required

Returns

Name	Type	Description
	pd.DataFrame	pd.DataFrame: coerced dataframe

describe_experiments

data_models.domain.domain.Domain.describe_experiments(experiments)

Method to get a tabular overview of how many measurements and how many valid entries are included in the input data for each output feature

Parameters

Name	Type	Description	Default
experiments	pd.DataFrame	Dataframe with experimental data	required

Returns

Name	Type	Description
	pd.DataFrame	pd.DataFrame: Dataframe with counts how many measurements and how many valid entries are included in the input data for each output feature

is_fulfilled

data_models.domain.domain.Domain.is_fulfilled(
    experiments,
    tol=1e-06,
    exlude_interpoint=True,
)

Check if all constraints are fulfilled on all rows of the provided dataframe both constraints and inputs are checked.

Parameters

Name	Type	Description	Default
experiments	pd.DataFrame	Dataframe with data, the constraint validity should be tested on	required
tol	float	Tolerance for checking the constraints. Defaults to 1e-6.	1e-06
exlude_interpoint	bool	If True, InterpointConstraints are excluded from the check. Defaults to True.	True

Returns

Name	Type	Description
	pd.Series	Boolean series indicating if all constraints are fulfilled for all rows.

to_description

data_models.domain.domain.Domain.to_description()

Render a human-readable description of the optimization problem.

Covers problem context, objectives, and constraints. Feature details are handled separately by Inputs.to_pydantic_model() which embeds bounds, types, and context into the dynamic output schema.

validate_candidates

data_models.domain.domain.Domain.validate_candidates(
    candidates,
    only_inputs=False,
    tol=1e-05,
    raise_validation_error=True,
)

Method to check the validty of proposed candidates

Parameters

Name	Type	Description	Default
candidates	pd.DataFrame	Dataframe with suggested new experiments (candidates)	required
only_inputs	(bool, optional)	If True, only the input columns are validated. Defaults to False.	False
tol	(float, optional)	tolerance parameter for constraints. A constraint is considered as not fulfilled if the violation is larger than tol. Defaults to 1e-6.	1e-05
raise_validation_error	bool	If true an error will be raised if candidates violate constraints, otherwise only a warning will be displayed. Defaults to True.	True

Raises

Name	Type	Description
	ValueError	when a column is missing for a defined input feature
	ValueError	when a column is missing for a defined output feature
	ValueError	when a non-numerical value is proposed
	ValueError	when an additional column is found
	ConstraintNotFulfilledError	when the constraints are not fulfilled and raise_validation_error = True

Returns

Name	Type	Description
	pd.DataFrame	pd.DataFrame: dataframe with suggested experiments (candidates)

validate_constraints

data_models.domain.domain.Domain.validate_constraints()

Validate that the constraints defined in the domain fit to the input features.

Parameters

Name	Type	Description	Default
v	List[Constraint]	List of constraints or empty if no constraints are defined	required
values	List[Input]	List of input features of the domain	required

Raises

Name	Type	Description
	ValueError	Feature key in constraint is unknown.

Returns

Name	Type	Description
		List[Constraint]: List of constraints defined for the domain

validate_experiments

data_models.domain.domain.Domain.validate_experiments(experiments, strict=False)

Checks the experimental data on validity

Parameters

Name	Type	Description	Default
experiments	pd.DataFrame	Dataframe with experimental data	required
strict	bool	Boolean to distinguish if the occurrence of fixed features in the dataset should be considered or not. Defaults to False.	False

Raises

Name	Type	Description
	ValueError	empty dataframe
	ValueError	the column for a specific feature is missing the provided data
	ValueError	there are labcodes with null value
	ValueError	there are labcodes with nan value
	ValueError	labcodes are not unique
	ValueError	the provided columns do no match to the defined domain
	ValueError	the provided columns do no match to the defined domain
	ValueError	Input with null values
	ValueError	Input with nan values

Returns

Name	Type	Description
	pd.DataFrame	pd.DataFrame: The provided dataframe with experimental data

validate_unique_feature_keys

data_models.domain.domain.Domain.validate_unique_feature_keys()

Validates if provided input and output feature keys are unique

Parameters

Name	Type	Description	Default
v	Outputs	List of all output features of the domain.	required
value	Dict[str, Inputs]	Dict containing a list of input features as single entry.	required

Raises

Name	Type	Description
	ValueError	Feature keys are not unique.

Returns

Name	Type	Description
Outputs		Keeps output features as given.