Source code for skcriteria.madm.similarity

#!/usr/bin/env python
# -*- coding: utf-8 -*-
# License: BSD-3 (
# Copyright (c) 2016-2021, Cabral, Juan; Luczywo, Nadia
# Copyright (c) 2022, QuatroPe
# All rights reserved.

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"""Methods based on a similarity between alternatives."""

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import warnings

import numpy as np

from scipy.spatial import distance

from ._base import RankResult, SKCDecisionMakerABC
from ..core import Objective
from ..utils import doc_inherit, rank

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[docs]def topsis(matrix, objectives, weights, metric="euclidean", **kwargs): """Execute TOPSIS without any validation.""" # apply weights wmtx = np.multiply(matrix, weights) # extract mins and maxes mins = np.min(wmtx, axis=0) maxs = np.max(wmtx, axis=0) # create the ideal and the anti ideal arrays ideal = np.where(objectives == Objective.MAX.value, maxs, mins) anti_ideal = np.where(objectives == Objective.MIN.value, maxs, mins) # calculate distances d_better = distance.cdist( wmtx, ideal[True], metric=metric, out=None, **kwargs ).flatten() d_worst = distance.cdist( wmtx, anti_ideal[True], metric=metric, out=None, **kwargs ).flatten() # relative closeness similarity = d_worst / (d_better + d_worst) # compute the rank and return the result return ( rank.rank_values(similarity, reverse=True), ideal, anti_ideal, similarity, )
[docs]class TOPSIS(SKCDecisionMakerABC): """The Technique for Order of Preference by Similarity to Ideal Solution. TOPSIS is based on the concept that the chosen alternative should have the shortest geometric distance from the ideal solution and the longest euclidean distance from the worst solution. An assumption of TOPSIS is that the criteria are monotonically increasing or decreasing, and also allow trade-offs between criteria, where a poor result in one criterion can be negated by a good result in another criterion. Parameters ---------- metric : str or callable, optional The distance metric to use. If a string, the distance function can be ``braycurtis``, ``canberra``, ``chebyshev``, ``cityblock``, ``correlation``, ``cosine``, ``dice``, ``euclidean``, ``hamming``, ``jaccard``, ``jensenshannon``, ``kulsinski``, ``mahalanobis``, ``matching``, ``minkowski``, ``rogerstanimoto``, ``russellrao``, ``seuclidean``, ``sokalmichener``, ``sokalsneath``, ``sqeuclidean``, ``wminkowski``, ``yule``. Warnings -------- UserWarning: If some objective is to minimize. References ---------- :cite:p:`hwang1981methods` :cite:p:`enwiki:1034743168` :cite:p:`tzeng2011multiple` """ _skcriteria_parameters = ["metric"] def __init__(self, *, metric="euclidean"): if not callable(metric) and metric not in _VALID_DISTANCES_METRICS: metrics = ", ".join(f"'{m}'" for m in _VALID_DISTANCES_METRICS) raise ValueError( f"Invalid metric '{metric}'. Plese choose from: {metrics}" ) self._metric = metric @property def metric(self): """Which distance metric will be used.""" return self._metric @doc_inherit(SKCDecisionMakerABC._evaluate_data) def _evaluate_data(self, matrix, objectives, weights, **kwargs): if Objective.MIN.value in objectives: warnings.warn( "Although TOPSIS can operate with minimization objectives, " "this is not recommended. Consider reversing the weights " "for these cases." ) rank, ideal, anti_ideal, similarity = topsis( matrix, objectives, weights, metric=self.metric, ) return rank, { "ideal": ideal, "anti_ideal": anti_ideal, "similarity": similarity, } @doc_inherit(SKCDecisionMakerABC._make_result) def _make_result(self, alternatives, values, extra): return RankResult( "TOPSIS", alternatives=alternatives, values=values, extra=extra )