#!/usr/bin/env python
# -*- coding: utf-8 -*-
# License: BSD-3 (https://tldrlegal.com/license/bsd-3-clause-license-(revised))
# Copyright (c) 2016-2021, Cabral, Juan; Luczywo, Nadia
# Copyright (c) 2022, 2023, 2024 QuatroPe
# All rights reserved.
# =============================================================================
# DOCS
# =============================================================================
"""Implementation of functionalities for convert minimization criteria into \
maximization ones."""
# =============================================================================
# IMPORTS
# =============================================================================
from ..utils import hidden
with hidden():
import numpy as np
from ._preprocessing_base import SKCTransformerABC
from ..core import Objective
from ..utils import deprecated, doc_inherit
# =============================================================================
# Base Class
# =============================================================================
[docs]
class SKCObjectivesInverterABC(SKCTransformerABC):
"""Abstract class capable of invert objectives.
This abstract class require to redefine ``_invert``, instead of
``_transform_data``.
"""
_skcriteria_abstract_class = True
def _invert(self, matrix, minimize_mask):
"""Invert the minimization objectives.
Parameters
----------
matrix: :py:class:`numpy.ndarray`
The decision matrix to weights.
minimize_mask: :py:class:`numpy.ndarray`
Mask with the same size as the columns in the matrix. True values
indicate that this column is a criterion to be minimized.
Returns
-------
:py:class:`numpy.ndarray`
A new matrix with the minimization objectives inverted.
"""
raise NotImplementedError()
@doc_inherit(SKCTransformerABC._transform_data)
def _transform_data(self, matrix, objectives, dtypes, **kwargs):
# check where we need to transform
minimize_mask = np.equal(objectives, Objective.MIN.value)
# execute the transformation
inv_mtx = self._invert(matrix, minimize_mask)
# new objective array
inv_objectives = np.full(
len(objectives), Objective.MAX.value, dtype=int
)
# we are trying to preserve the original dtype as much as possible
# only the minimize criteria are changed.
inv_dtypes = np.where(minimize_mask, inv_mtx.dtype, dtypes)
kwargs.update(
matrix=inv_mtx, objectives=inv_objectives, dtypes=inv_dtypes
)
return kwargs
# =============================================================================
# -x
# =============================================================================
[docs]
class NegateMinimize(SKCObjectivesInverterABC):
r"""Transform all minimization criteria into maximization ones.
The transformations are made by calculating the inverse value of
the minimization criteria. :math:`\min{C} \equiv \max{-{C}}`.
"""
_skcriteria_parameters = []
@doc_inherit(SKCObjectivesInverterABC._invert)
def _invert(self, matrix, minimize_mask):
inv_mtx = np.array(matrix, dtype=float)
inverted_values = -inv_mtx[:, minimize_mask]
inv_mtx[:, minimize_mask] = inverted_values
return inv_mtx
# =============================================================================
# 1/x
# =============================================================================
[docs]
class InvertMinimize(SKCObjectivesInverterABC):
r"""Transform all minimization criteria into maximization ones.
The transformations are made by calculating the inverse value of
the minimization criteria. :math:`\min{C} \equiv \max{\frac{1}{C}}`
Notes
-----
All the dtypes of the decision matrix are preserved except the inverted
ones thar are converted to ``numpy.float64``.
"""
_skcriteria_parameters = []
@doc_inherit(SKCObjectivesInverterABC._invert)
def _invert(self, matrix, minimize_mask):
inv_mtx = np.array(matrix, dtype=float)
inverted_values = 1.0 / inv_mtx[:, minimize_mask]
inv_mtx[:, minimize_mask] = inverted_values
return inv_mtx
# =============================================================================
# DEPRECATED
# =============================================================================
[docs]
@deprecated(
reason=(
"Use ``skcriteria.preprocessing.invert_objectives.InvertMinimize`` "
"instead"
),
version="0.7",
)
class MinimizeToMaximize(InvertMinimize):
r"""Transform all minimization criteria into maximization ones.
The transformations are made by calculating the inverse value of
the minimization criteria. :math:`\min{C} \equiv \max{\frac{1}{C}}`
Notes
-----
All the dtypes of the decision matrix are preserved except the inverted
ones thar are converted to ``numpy.float64``.
"""