Topsis-Aindri-102316039

A Python package implementing TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) for multi-criteria decision making.

What is TOPSIS?

TOPSIS is a multi-criteria decision analysis method that helps rank alternatives based on their similarity to the ideal solution. It calculates:

• The distance of each alternative from the ideal best solution
• The distance from the ideal worst solution
• A score that represents relative closeness to the ideal solution

The alternative with the highest TOPSIS score is ranked first.

Installation

pip install Topsis-Aindri-102316039

Usage

Command Line Interface

python -m topsis_aindri_102316039.topsis <InputDataFile> <Weights> <Impacts> <OutputResultFileName>

Or if installed via pip:

topsis <InputDataFile> <Weights> <Impacts> <OutputResultFileName>

Example

python -m topsis_aindri_102316039.topsis data.csv "1,1,1,2" "+,+,-,+" result.csv

Input File Format

The input CSV file must have:

• First column: Names/identifiers of alternatives
• Remaining columns: Numeric criteria values

Example (data.csv):

Model,Price,Storage,Camera,Looks
M1,250,16,12,5
M2,200,16,8,3
M3,300,32,16,4
M4,275,32,8,4
M5,225,16,16,2

Parameters

• Weights: Comma-separated numeric values representing the importance of each criterion (e.g., 1,1,1,2)
• Impacts: Comma-separated + or - signs indicating whether higher values are beneficial (+) or detrimental (-) for each criterion (e.g., +,+,-,+)

Output

The output CSV file will contain all original columns plus:

• Topsis Score: Calculated TOPSIS score (0-1, higher is better)
• Rank: Ranking based on TOPSIS score (1 is best)

Python API

You can also use TOPSIS programmatically:

from topsis_aindri_102316039 import topsis

topsis("data.csv", "1,1,1,2", "+,+,-,+", "result.csv")

Input Validation

The package validates:

• ✅ Correct number of parameters (4 required)
• ✅ Input file existence
• ✅ Minimum 3 columns in input file
• ✅ Numeric values in all criteria columns (2nd column onwards)
• ✅ Equal number of weights, impacts, and criteria
• ✅ Impacts are either '+' or '-'

Requirements

• Python >= 3.6
• pandas >= 1.0.0
• numpy >= 1.18.0

Author

Aindri Singh
Roll No: 102316039
Email: asingh2_be23@thapar.edu
GitHub: @aindrisingh

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contributing

Contributions, issues, and feature requests are welcome!

Acknowledgments

• Based on the TOPSIS method developed by Hwang and Yoon (1981)
• Created as part of UCS654 Assignment 2