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

Overview

PyPI version GitHub Issues Contributions welcome License: MIT Downloads

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

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}
}
Owner
Labrak Yanis
👨🏻‍🎓 Student in Master of Science in Computer Science, Avignon University 🇫🇷 🏛 Research Scientist - Machine Learning in Healthcare
Labrak Yanis
OBBDetection: an oriented object detection toolbox modified from MMdetection

OBBDetection note: If you have questions or good suggestions, feel free to propose issues and contact me. introduction OBBDetection is an oriented obj

MIXIAOXIN_HO 3 Nov 11, 2022
Robust and Accurate Object Detection via Self-Knowledge Distillation

Robust and Accurate Object Detection via Self-Knowledge Distillation paper:https://arxiv.org/abs/2111.07239 Environments Python 3.7 Cuda 10.1 Prepare

Weipeng Xu 6 Jul 01, 2022
Mini Software that give reminder to drink water as per your weight.

Water Notification Desktop Python The Mini Software built in Python (tkinter) that will remind you to drink water on specific time span based on your

Om Jogani 5 Dec 16, 2022
Offical implementation of Shunted Self-Attention via Multi-Scale Token Aggregation

Shunted Transformer This is the offical implementation of Shunted Self-Attention via Multi-Scale Token Aggregation by Sucheng Ren, Daquan Zhou, Shengf

156 Dec 27, 2022
PyTorch implementation of "ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context" (INTERSPEECH 2020)

ContextNet ContextNet has CNN-RNN-transducer architecture and features a fully convolutional encoder that incorporates global context information into

Sangchun Ha 24 Nov 24, 2022
SpiroMask: Measuring Lung Function Using Consumer-Grade Masks

SpiroMask: Measuring Lung Function Using Consumer-Grade Masks Anonymised repository for paper submitted for peer review at ACM HEALTH (October 2021).

0 May 10, 2022
Official PyTorch implementation of "ArtFlow: Unbiased Image Style Transfer via Reversible Neural Flows"

ArtFlow Official PyTorch implementation of the paper: ArtFlow: Unbiased Image Style Transfer via Reversible Neural Flows Jie An*, Siyu Huang*, Yibing

123 Dec 27, 2022
Code release for "COTR: Correspondence Transformer for Matching Across Images"

COTR: Correspondence Transformer for Matching Across Images This repository contains the inference code for COTR. We plan to release the training code

UBC Computer Vision Group 360 Jan 06, 2023
Official pytorch implementation of "DSPoint: Dual-scale Point Cloud Recognition with High-frequency Fusion"

DSPoint Official implementation of "DSPoint: Dual-scale Point Cloud Recognition with High-frequency Fusion". Paper link: https://arxiv.org/abs/2111.10

Ziyao Zeng 14 Feb 26, 2022
Pytorch implementation code for [Neural Architecture Search for Spiking Neural Networks]

Neural Architecture Search for Spiking Neural Networks Pytorch implementation code for [Neural Architecture Search for Spiking Neural Networks] (https

Intelligent Computing Lab at Yale University 28 Nov 18, 2022
Code, Models and Datasets for OpenViDial Dataset

OpenViDial This repo contains downloading instructions for the OpenViDial dataset in 《OpenViDial: A Large-Scale, Open-Domain Dialogue Dataset with Vis

119 Dec 08, 2022
Py4fi2nd - Jupyter Notebooks and code for Python for Finance (2nd ed., O'Reilly) by Yves Hilpisch.

Python for Finance (2nd ed., O'Reilly) This repository provides all Python codes and Jupyter Notebooks of the book Python for Finance -- Mastering Dat

Yves Hilpisch 1k Jan 05, 2023
Implementation of a Transformer that Ponders, using the scheme from the PonderNet paper

Ponder(ing) Transformer Implementation of a Transformer that learns to adapt the number of computational steps it takes depending on the difficulty of

Phil Wang 65 Oct 04, 2022
Tensorflow Implementation for "Pre-trained Deep Convolution Neural Network Model With Attention for Speech Emotion Recognition"

Tensorflow Implementation for "Pre-trained Deep Convolution Neural Network Model With Attention for Speech Emotion Recognition" Pre-trained Deep Convo

Ankush Malaker 5 Nov 11, 2022
Learning Versatile Neural Architectures by Propagating Network Codes

Learning Versatile Neural Architectures by Propagating Network Codes Mingyu Ding, Yuqi Huo, Haoyu Lu, Linjie Yang, Zhe Wang, Zhiwu Lu, Jingdong Wang,

Mingyu Ding 36 Dec 06, 2022
PyTorch implementation of Lip to Speech Synthesis with Visual Context Attentional GAN (NeurIPS2021)

Lip to Speech Synthesis with Visual Context Attentional GAN This repository contains the PyTorch implementation of the following paper: Lip to Speech

6 Nov 02, 2022
Utility code for use with PyXLL

pyxll-utils There is no need to use this package as of PyXLL 5. All features from this package are now provided by PyXLL. If you were using this packa

PyXLL 10 Dec 18, 2021
BasicNeuralNetwork - This project looks over the basic structure of a neural network and how machine learning training algorithms work

BasicNeuralNetwork - This project looks over the basic structure of a neural network and how machine learning training algorithms work. For this project, I used the sigmoid function as an activation

Manas Bommakanti 1 Jan 22, 2022
PyTorch implementation of Off-policy Learning in Two-stage Recommender Systems

Off-Policy-2-Stage This repo provides a PyTorch implementation of the MovieLens experiments for the following paper: Off-policy Learning in Two-stage

Jiaqi Ma 25 Dec 12, 2022
Repository for the Bias Benchmark for QA dataset.

BBQ Repository for the Bias Benchmark for QA dataset. Authors: Alicia Parrish, Angelica Chen, Nikita Nangia, Vishakh Padmakumar, Jason Phang, Jana Tho

ML² AT CILVR 18 Nov 18, 2022