Multiple-criteria decision-making (MCDM) with Electre, Promethee, Weighted Sum and Pareto

Last update: Nov 22, 2022

Overview

EasyMCDM - Quick Installation methods

Install with PyPI

Once you have created your Python environment (Python 3.6+) you can simply type:

pip3 install EasyMCDM

Install with GitHub

Once you have created your Python environment (Python 3.6+) you can simply type:

git clone https://github.com/qanastek/EasyMCDM.git
cd EasyMCDM
pip3 install -r requirements.txt
pip3 install --editable .

Any modification made to the EasyMCDM package will be automatically interpreted as we installed it with the --editable flag.

Setup with Anaconda

conda create --name EasyMCDM python=3.6 -y
conda activate EasyMCDM

More information on managing environments with Anaconda can be found in the conda cheat sheet.

Try It

Data in tests/data/donnees.csv :

alfa_156,23817,201,8,39.6,6,378,31.2
audi_a4,25771,195,5.7,35.8,7,440,33
cit_xantia,25496,195,7.9,37,2,480,34

Promethee

from EasyMCDM.models.Promethee import Promethee

data = pd.read_csv('tests/data/donnees.csv', header=None).to_numpy()
# or
data = {
  "alfa_156": [23817.0, 201.0, 8.0, 39.6, 6.0, 378.0, 31.2],
  "audi_a4": [25771.0, 195.0, 5.7, 35.8, 7.0, 440.0, 33.0],
  "cit_xantia": [25496.0, 195.0, 7.9, 37.0, 2.0, 480.0, 34.0]
}
weights = [0.14,0.14,0.14,0.14,0.14,0.14,0.14]
prefs = ["min","max","min","min","min","max","min"]

p = Promethee(data=data, verbose=False)
res = p.solve(weights=weights, prefs=prefs)
print(res)

Output :

{
  'phi_negative': [('rnlt_safrane', 2.381), ('vw_passat', 2.9404), ('bmw_320d', 3.3603), ('saab_tid', 3.921), ('audi_a4', 4.34), ('cit_xantia', 4.48), ('rnlt_laguna', 5.04), ('alfa_156', 5.32), ('peugeot_406', 5.461), ('cit_xsara', 5.741)],
  'phi_positive': [('rnlt_safrane', 6.301), ('vw_passat', 5.462), ('bmw_320d', 5.18), ('saab_tid', 4.76), ('audi_a4', 4.0605), ('cit_xantia', 3.921), ('rnlt_laguna', 3.6406), ('alfa_156', 3.501), ('peugeot_406', 3.08), ('cit_xsara', 3.08)],
  'phi': [('rnlt_safrane', 3.92), ('vw_passat', 2.5214), ('bmw_320d', 1.8194), ('saab_tid', 0.839), ('audi_a4', -0.27936), ('cit_xantia', -0.5596), ('rnlt_laguna', -1.3995), ('alfa_156', -1.8194), ('peugeot_406', -2.381), ('cit_xsara', -2.661)],
  'matrix': '...'
}

Electre Iv / Is

from EasyMCDM.models.Electre import Electre

data = {
    "A1" : [80, 90,  600, 5.4,  8,  5],
    "A2" : [65, 58,  200, 9.7,  1,  1],
    "A3" : [83, 60,  400, 7.2,  4,  7],
    "A4" : [40, 80, 1000, 7.5,  7, 10],
    "A5" : [52, 72,  600, 2.0,  3,  8],
    "A6" : [94, 96,  700, 3.6,  5,  6],
}
weights = [0.1, 0.2, 0.2, 0.1, 0.2, 0.2]
prefs = ["min", "max", "min", "min", "min", "max"]
vetoes = [45, 29, 550, 6, 4.5, 4.5]
indifference_threshold = 0.6
preference_thresholds = [20, 10, 200, 4, 2, 2] # or None for Electre Iv

e = Electre(data=data, verbose=False)

results = e.solve(weights, prefs, vetoes, indifference_threshold, preference_thresholds)

Output :

{'kernels': ['A4', 'A5']}

Pareto

from EasyMCDM.models.Pareto import Pareto

data = 'tests/data/donnees.csv'
# or
data = {
  "alfa_156": [23817.0, 201.0, 8.0, 39.6, 6.0, 378.0, 31.2],
  "audi_a4": [25771.0, 195.0, 5.7, 35.8, 7.0, 440.0, 33.0],
  "cit_xantia": [25496.0, 195.0, 7.9, 37.0, 2.0, 480.0, 34.0]
}

p = Pareto(data=data, verbose=False)
res = p.solve(indexes=[0,1,6], prefs=["min","max","min"])
print(res)

Output :

{
  'alfa_156': {'Weakly-dominated-by': [], 'Dominated-by': []},
  'audi_a4': {'Weakly-dominated-by': ['alfa_156'], 'Dominated-by': ['alfa_156']}, 
  'cit_xantia': {'Weakly-dominated-by': ['alfa_156', 'vw_passat'], 'Dominated-by': ['alfa_156']},
  'peugeot_406': {'Weakly-dominated-by': ['alfa_156', 'cit_xantia', 'rnlt_laguna', 'vw_passat'], 'Dominated-by': ['alfa_156', 'cit_xantia', 'rnlt_laguna', 'vw_passat']},
  'saab_tid': {'Weakly-dominated-by': ['alfa_156'], 'Dominated-by': ['alfa_156']}, 
  'rnlt_laguna': {'Weakly-dominated-by': ['vw_passat'], 'Dominated-by': ['vw_passat']}, 
  'vw_passat': {'Weakly-dominated-by': [], 'Dominated-by': []},
  'bmw_320d': {'Weakly-dominated-by': [], 'Dominated-by': []},
  'cit_xsara': {'Weakly-dominated-by': [], 'Dominated-by': []},
  'rnlt_safrane': {'Weakly-dominated-by': ['bmw_320d'], 'Dominated-by': ['bmw_320d']}
}

Weighted Sum

from EasyMCDM.models.WeightedSum import WeightedSum

data = 'tests/data/donnees.csv'
# or
data = {
  "alfa_156": [23817.0, 201.0, 8.0, 39.6, 6.0, 378.0, 31.2],
  "audi_a4": [25771.0, 195.0, 5.7, 35.8, 7.0, 440.0, 33.0],
  "cit_xantia": [25496.0, 195.0, 7.9, 37.0, 2.0, 480.0, 34.0]
}

p = WeightedSum(data=data, verbose=False)
res = p.solve(pref_indexes=[0,1,6],prefs=["min","max","min"], weights=[0.001,2,3], target='min')
print(res)

Output :

[(1, 'bmw_320d', -299.04), (2, 'alfa_156', -284.58299999999997), (3, 'rnlt_safrane', -280.84), (4, 'saab_tid', -275.817), (5, 'vw_passat', -265.856), (6, 'audi_a4', -265.229), (7, 'rnlt_laguna', -262.93600000000004), (8, 'cit_xantia', -262.504), (9, 'peugeot_406', -252.551), (10, 'cit_xsara', -244.416)]

Instant-Runoff Multicriteria Optimization (IRMO)

Short description : Eliminate the worst individual for each criteria, until we reach the last one and select the best one.

from EasyMCDM.models.Irmo import Irmo

p = Irmo(data="data/donnees.csv", verbose=False)
res = p.solve(
    indexes=[0,1,4,5], # price -> max_speed -> comfort -> trunk_space
    prefs=["min","max","min","max"]
)
print(res)

Output :

{'best': 'saab_tid'}

List of methods available

Promethee I
Promethee II
Electre Iv
Electre Is
Weighted Sum
Pareto
Instant-Runoff Multicriteria Optimization (IRMO)

Build PyPi package

Build: python setup.py sdist bdist_wheel

Upload: twine upload dist/*

Citation

If you want to cite the tool you can use this:

@misc{EasyMCDM,
  title={EasyMCDM},
  author={Yanis Labrak, Quentin Raymondaud, Philippe Turcotte},
  publisher={GitHub},
  journal={GitHub repository},
  howpublished={\url{https://github.com/qanastek/EasyMCDM}},
  year={2022}
}

Multiple-criteria decision-making (MCDM) with Electre, Promethee, Weighted Sum and Pareto

Related tags

Overview

EasyMCDM - Quick Installation methods

Install with PyPI

Install with GitHub

Setup with Anaconda

Try It

Promethee

Electre Iv / Is

Pareto

Weighted Sum

Instant-Runoff Multicriteria Optimization (IRMO)

List of methods available

Build PyPi package

Citation

Owner

Labrak Yanis

Official repository for Natural Image Matting via Guided Contextual Attention

ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator

A research toolkit for particle swarm optimization in Python

MT3: Multi-Task Multitrack Music Transcription

DenseCLIP: Language-Guided Dense Prediction with Context-Aware Prompting

Robust, modular and efficient implementation of advanced Hamiltonian Monte Carlo algorithms

Task-based end-to-end model learning in stochastic optimization

Canonical Capsules: Unsupervised Capsules in Canonical Pose (NeurIPS 2021)

Little Ball of Fur - A graph sampling extension library for NetworKit and NetworkX (CIKM 2020)

A PyTorch Implementation of Gated Graph Sequence Neural Networks (GGNN)

[NeurIPS '21] Adversarial Attacks on Graph Classification via Bayesian Optimisation (GRABNEL)

Official pytorch implementation of paper "Inception Convolution with Efficient Dilation Search" (CVPR 2021 Oral).

This is an example of object detection on Micro bacterium tuberculosis using Mask-RCNN

In this project we combine techniques from neural voice cloning and musical instrument synthesis to achieve good results from as little as 16 seconds of target data.

Cortex-compatible model server for Python and TensorFlow

Source code of article "Towards Toxic and Narcotic Medication Detection with Rotated Object Detector"

A Light in the Dark: Deep Learning Practices for Industrial Computer Vision

The source code of the paper "Understanding Graph Neural Networks from Graph Signal Denoising Perspectives"

Real-time multi-object tracker using YOLO v5 and deep sort

A PyTorch version of You Only Look at One-level Feature object detector