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
ByteTrack with ReID module following the paradigm of FairMOT, tracking strategy is borrowed from FairMOT/JDE.

ByteTrack_ReID ByteTrack is the SOTA tracker in MOT benchmarks with strong detector YOLOX and a simple association strategy only based on motion infor

Han GuangXin 46 Dec 29, 2022
Official code for CVPR2022 paper: Depth-Aware Generative Adversarial Network for Talking Head Video Generation

📖 Depth-Aware Generative Adversarial Network for Talking Head Video Generation (CVPR 2022) 🔥 If DaGAN is helpful in your photos/projects, please hel

Fa-Ting Hong 503 Jan 04, 2023
Empower Sequence Labeling with Task-Aware Language Model

LM-LSTM-CRF Check Our New NER Toolkit 🚀 🚀 🚀 Inference: LightNER: inference w. models pre-trained / trained w. any following tools, efficiently. Tra

Liyuan Liu 838 Jan 05, 2023
MIMO-UNet - Official Pytorch Implementation

MIMO-UNet - Official Pytorch Implementation This repository provides the official PyTorch implementation of the following paper: Rethinking Coarse-to-

Sungjin Cho 248 Jan 02, 2023
Magisk module to enable hidden features on Android 12 Developer Preview 1.

Android 12 Extensions This is a Magisk module that enables hidden features on Android 12 Developer Preview 1. Features Scrolling screenshots Wallpaper

Danny Lin 384 Jan 06, 2023
FairFuzz: AFL extension targeting rare branches

FairFuzz An AFL extension to increase code coverage by targeting rare branches. FairFuzz has a particular advantage on programs with highly nested str

Caroline Lemieux 222 Nov 16, 2022
Torch-based tool for quantizing high-dimensional vectors using additive codebooks

Trainable multi-codebook quantization This repository implements a utility for use with PyTorch, and ideally GPUs, for training an efficient quantizer

Daniel Povey 41 Jan 07, 2023
classification task on dataset-CIFAR10,by using Tensorflow/keras

CIFAR10-Tensorflow classification task on dataset-CIFAR10,by using Tensorflow/keras 在这一个库中,我使用Tensorflow与keras框架搭建了几个卷积神经网络模型,针对CIFAR10数据集进行了训练与测试。分别使

3 Oct 17, 2021
Code, final versions, and information on the Sparkfun Graphical Datasheets

Graphical Datasheets Code, final versions, and information on the SparkFun Graphical Datasheets. Generated Cells After Running Script Example Complete

SparkFun Electronics 102 Jan 05, 2023
This repo includes the CUB-GHA (Gaze-based Human Attention) dataset and code of the paper "Human Attention in Fine-grained Classification".

HA-in-Fine-Grained-Classification This repo includes the CUB-GHA (Gaze-based Human Attention) dataset and code of the paper "Human Attention in Fine-g

16 Oct 29, 2022
Code for ACL2021 long paper: Knowledgeable or Educated Guess? Revisiting Language Models as Knowledge Bases

LANKA This is the source code for paper: Knowledgeable or Educated Guess? Revisiting Language Models as Knowledge Bases (ACL 2021, long paper) Referen

Boxi Cao 30 Oct 24, 2022
Multiple Object Extraction from Aerial Imagery with Convolutional Neural Networks

This is an implementation of Volodymyr Mnih's dissertation methods on his Massachusetts road & building dataset and my original methods that are publi

Shunta Saito 255 Sep 07, 2022
Working demo of the Multi-class and Anomaly classification model using the CLIP feature space

👁️ Hindsight AI: Crime Classification With Clip About For Educational Purposes Only This is a recursive neural net trained to classify specific crime

Miles Tweed 2 Jun 05, 2022
An efficient and easy-to-use deep learning model compression framework

TinyNeuralNetwork 简体中文 TinyNeuralNetwork is an efficient and easy-to-use deep learning model compression framework, which contains features like neura

Alibaba 441 Dec 25, 2022
Deep Learning ❤️ OneFlow

Deep Learning with OneFlow made easy 🚀 ! Carefree? carefree-learn aims to provide CAREFREE usages for both users and developers. User Side Computer V

21 Oct 27, 2022
Very simple NCHW and NHWC conversion tool for ONNX. Change to the specified input order for each and every input OP. Also, change the channel order of RGB and BGR. Simple Channel Converter for ONNX.

scc4onnx Very simple NCHW and NHWC conversion tool for ONNX. Change to the specified input order for each and every input OP. Also, change the channel

Katsuya Hyodo 16 Dec 22, 2022
Example scripts for the detection of lanes using the ultra fast lane detection model in ONNX.

Example scripts for the detection of lanes using the ultra fast lane detection model in ONNX.

Ibai Gorordo 35 Sep 07, 2022
Implementation of hyperparameter optimization/tuning methods for machine learning & deep learning models

Hyperparameter Optimization of Machine Learning Algorithms This code provides a hyper-parameter optimization implementation for machine learning algor

Li Yang 1.1k Dec 19, 2022
PyTorch implementation for our paper Learning Character-Agnostic Motion for Motion Retargeting in 2D, SIGGRAPH 2019

Learning Character-Agnostic Motion for Motion Retargeting in 2D We provide PyTorch implementation for our paper Learning Character-Agnostic Motion for

Rundi Wu 367 Dec 22, 2022
Multi agent DDPG algorithm written in Python + Pytorch

Multi agent DDPG algorithm written in Python + Pytorch. It also includes a Jupyter notebook, Tennis.ipynb, as a showcase.

Rogier Wachters 2 Feb 26, 2022