"""Provides implementations that convert one type of preference information to another."""
import numpy as np
[docs]def classification_to_reference_point(
classification_preference: dict, ideal: np.ndarray, nadir: np.ndarray
) -> dict:
"""Convert classification type of preference (e.g. NIMBUS) to reference point
preference.
Args:
classification_preference (dict): A dict containing keys 'current solution',
'levels', and 'classifications'. Read the NIMBUS paper for more details.
ideal (np.ndarray): The ideal point of the problem.
nadir (np.ndarray): The nadir point of the problem.
Returns:
dict: The preference in the form of a reference point. Contains one key:
"reference point", which maps to the preference in a numpy array structure.
"""
z_bar = np.zeros_like(nadir, dtype=float)
improve_inds = np.where(
np.array(classification_preference["classifications"]) == "<"
)[0]
acceptable_inds = np.where(
np.array(classification_preference["classifications"]) == "="
)[0]
free_inds = np.where(np.array(classification_preference["classifications"]) == "0")[
0
]
improve_until_inds = np.where(
np.array(classification_preference["classifications"]) == "<="
)[0]
impaire_until_inds = np.where(
np.array(classification_preference["classifications"]) == ">="
)[0]
z_bar[improve_inds] = ideal[improve_inds]
z_bar[improve_until_inds] = classification_preference["levels"][improve_until_inds]
z_bar[acceptable_inds] = classification_preference["current solution"][
acceptable_inds
]
z_bar[impaire_until_inds] = classification_preference["levels"][impaire_until_inds]
z_bar[free_inds] = nadir[free_inds]
return {"reference point": z_bar}
