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.
Apply any preprocessing if required.

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 overall ml models ranked and the best model / alternative.