Python package for Ranking ML models using TOPSIS algorithmic approach
Project description
topsis-python
Package Description :
Python package for TOPSIS (The Technique for Order of Preference by Similarity to Ideal Solution) ALGORITHM.
Motivation :
This is a part of project - I made for UCS633 - Data analytics and visualization at TIET.
@Author : Sourav Kumar
@Roll no. : 101883068
Algorithm :
STEP 1 :
Create an evaluation matrix consisting of m alternatives and n criteria, with the intersection of each alternative and criteria.
STEP 2 :
The matrix is then normalised using the norm.
STEP 3 :
Calculate the weighted normalised decision matrix.
STEP 4 :
Determine the worst alternative and the best alternative.
STEP 5 :
Calculate the L2-distance between the target alternative i and the worst condition.
STEP 6 :
Calculate the similarity to the worst condition.
STEP 7 :
Rank the alternatives according to final performance scores.
Getting started Locally :
Run On Terminal
python -m topsis.topsis <filename.csv> <weights> <impacts>
ex. python python -m topsis.topsis topsis.csv 0.25,0.25,0.25,0.25 -,+,+,+
Run In IDLE
from topsis import topsis
t = topsis.topsis('filepath', [list of weights], [list of impacts])
t.topsis_main()
Run on Jupyter
Open terminal (cmd)
jupyter notebook
Create a new python3 file.
If file <filename.csv> doesn't exists, then make sure to upload the file to jupyter env.
from topsis import topsis
t = topsis.topsis('filepath', [list of weights], [list of impacts])
t.topsis_main()
topsis_main()
has been specifically designed to inhibit leakeage of inbuilt functions.topsis_main(debug=True)
use this to display all the intermediate matrices.- Make sure that
filename.csv
is in same directory where package is installed.
PAPER :
Find the research paper at arxiv.
OUTPUT :
Prints out the best model / alternative.
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