semvec/pypi-packages huggingface dataset:
Parneet26-topsispkg 1.0.4
A package that calculates Topsis Score and Rank them accordingly
License: MIT

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.

output result on jupyter output result on idle output result on cmd

TESTING :
The package has been extensively tested on various datasets consisting varied types of expected and unexpected input data and any preprocessing , if required has been taken care of.
Topsis-pkg-Parneet26 1.0.4
A package that calculates Topsis Score and Rank them accordingly
License: MIT

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.

output result on jupyter output result on idle output result on cmd

TESTING :
The package has been extensively tested on various datasets consisting varied types of expected and unexpected input data and any preprocessing , if required has been taken care of.