The Python 3 library for Multi-Criteria Decision Analysis.
pip install pyrepo-mcda
pyrepo-mcda can be used to rank alternatives after providing their performance values in the two-dimensional decision matrix matrix
with alternatives in rows and criteria in columns, and criteria weights weights and types types in vectors.
All criteria weights must sum to 1. Criteria types are equal to 1 for profit criteria and -1 for cost criteria. The TOPSIS method returns a
vector with preference values pref assigned to alternatives. To rank alternatives according to TOPSIS preference values, we have to sort them
in descending order because, in the TOPSIS method, the best alternative has the highest preference value. The alternatives are ranked using
the rank_preferences method provided in the additions module of the pyrepo-mcda package. Parameter reverse = True means that alternatives
are sorted in descending order. Here is an example of using the TOPSIS method:
import numpy as np
from pyrepo_mcda.mcda_methods import TOPSIS
from pyrepo_mcda import distance_metrics as dists
from pyrepo_mcda import normalizations as norms
from pyrepo_mcda.additions import rank_preferences
matrix = np.array([[256, 8, 41, 1.6, 1.77, 7347.16],
[256, 8, 32, 1.0, 1.8, 6919.99],
[256, 8, 53, 1.6, 1.9, 8400],
[256, 8, 41, 1.0, 1.75, 6808.9],
[512, 8, 35, 1.6, 1.7, 8479.99],
[256, 4, 35, 1.6, 1.7, 7499.99]])
weights = np.array([0.405, 0.221, 0.134, 0.199, 0.007, 0.034])
types = np.array([1, 1, 1, 1, -1, -1])
topsis = TOPSIS(normalization_method=norms.vector_normalization, distance_metric=dists.euclidean)
pref = topsis(matrix, weights, types)
rank = rank_preferences(pref, reverse = True)
print(rank)pyrepo-mcda was created by Aleksandra Bączkiewicz. It is licensed under the terms of the MIT license.
Documentation of this library with instruction for installation and usage is provided here