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

Attendance Monitoring with Face Recognition using Python

CondenseNet V2: Sparse Feature Reactivation for Deep Networks

天勤量化开发包, 期货量化, 实时行情/历史数据/实盘交易

Language Used: Python . Made in Jupyter(Anaconda) notebook.

Contains modeling practice materials and homework for the Computational Neuroscience course at Okinawa Institute of Science and Technology

RLHive: a framework designed to facilitate research in reinforcement learning.

A whale detector design for the Kaggle whale-detector challenge!

BuildingNet: Learning to Label 3D Buildings

Learning to Identify Top Elo Ratings with A Dueling Bandits Approach

High-fidelity 3D Model Compression based on Key Spheres

This code is an implementation for Singing TTS.

The repo contains the code to train and evaluate a system which extracts relations and explanations from dialogue.

Fashion Recommender System With Python

Official implementation of the paper WAV2CLIP: LEARNING ROBUST AUDIO REPRESENTATIONS FROM CLIP

Denoising Normalizing Flow

Library for machine learning stacking generalization.

An implementation of EWC with PyTorch

This code is 3d-CNN model that can predict environmental value

This is a simple framework to make object detection dataset very quickly

A Novel Incremental Learning Driven Instance Segmentation Framework to Recognize Highly Cluttered Instances of the Contraband Items