interaction.validators

Module Contents

Functions

validate_ref_point_with_ideal_and_nadir(...)

validate_ref_point_with_ideal(dimensions_data, ...)

validate_with_ref_point_nadir(dimensions_data, ...)

validate_ref_point_dimensions(dimensions_data, ...)

validate_ref_point_data_type(reference_point)

validate_specified_solutions(→ None)

Validate the Decision maker's choice of preferred/non-preferred solutions.

validate_bounds(→ None)

Validate the Decision maker's desired lower and upper bounds for objective values.
exception interaction.validators.ValidationError[source]

Bases: Exception

Raised when an error related to the validation is encountered.

Initialize self. See help(type(self)) for accurate signature.

interaction.validators.validate_ref_point_with_ideal_and_nadir(dimensions_data: pandas.DataFrame, reference_point: pandas.DataFrame)[source]
interaction.validators.validate_ref_point_with_ideal(dimensions_data: pandas.DataFrame, reference_point: pandas.DataFrame)[source]
interaction.validators.validate_with_ref_point_nadir(dimensions_data: pandas.DataFrame, reference_point: pandas.DataFrame)[source]
interaction.validators.validate_ref_point_dimensions(dimensions_data: pandas.DataFrame, reference_point: pandas.DataFrame)[source]
interaction.validators.validate_ref_point_data_type(reference_point: pandas.DataFrame)[source]
interaction.validators.validate_specified_solutions(indices: numpy.ndarray, n_solutions: int) → None[source]

Validate the Decision maker’s choice of preferred/non-preferred solutions.

Parameters:

  • indices (np.ndarray) – Index/indices of preferred solutions specified by the Decision maker.

  • n_solutions (int) – Number of solutions in total.

Returns:

Raises:

ValidationError – In case the preference is invalid.

interaction.validators.validate_bounds(dimensions_data: pandas.DataFrame, bounds: numpy.ndarray, n_objectives: int) → None[source]

Validate the Decision maker’s desired lower and upper bounds for objective values.

Parameters:

  • dimensions_data (pd.DataFrame) – DataFrame including information whether an objective is minimized or maximized, for each objective. In addition, includes ideal and nadir vectors.

  • bounds (np.ndarray) – Desired lower and upper bounds for each objective.

  • n_objectives (int) – Number of objectives in problem.

Returns:

Raises:

ValidationError – In case desired bounds are invalid.