Metadata-Version: 2.1
Name: Parneet26_topsispkg
Version: 1.0.4
Summary: A package that calculates Topsis Score and Rank them accordingly
Home-page: https://github.com/Parneet-26/Topsis-python
Author: Parneet Kaur Rakhra
Author-email: prakhra_be19@thapar.edu
License: MIT
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.
        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.
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Description-Content-Type: text/markdown
