Topsis-Sanyam-102303059 1.0.1

A Python package for TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) analysis.

TOPSIS Implementation in Python

Author: Sanyam Wadhwa

Roll Number: 102303059

Class: 3C12

Introduction

This project implements TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) as a Python package for multi-criteria decision analysis.

What is TOPSIS?

TOPSIS is a multi-criteria decision analysis method that ranks alternatives by measuring their geometric distance from ideal solutions. The best alternative has the shortest distance from the ideal best and the farthest distance from the ideal worst.

Methodology

Algorithm Steps:

1. Normalize Decision Matrix
   • r_ij = x_ij / √(Σ x_ij²)
2. Calculate Weighted Normalized Matrix
   • v_ij = w_j × r_ij
3. Determine Ideal Solutions
   • Ideal Best (A⁺): Max for beneficial (+), Min for non-beneficial (-)
   • Ideal Worst (A⁻): Min for beneficial (+), Max for non-beneficial (-)
4. Calculate Separation Measures
   • S_i⁺ = √(Σ (v_ij - v_j⁺)²) [Distance from ideal best]
   • S_i⁻ = √(Σ (v_ij - v_j⁻)²) [Distance from ideal worst]
5. Calculate TOPSIS Score
   • P_i = S_i⁻ / (S_i⁺ + S_i⁻) [Range: 0 to 1]
6. Rank Alternatives
   • Higher score = Better rank (Rank 1 is best)

Installation

pip install pandas numpy

Usage

As a Library

import pandas as pd
from Topsis_Sanyam_102303059.topsis import topsis

df = pd.read_csv('data.csv')
weights = [1,1,1,2,1]
impacts = ['+','+','-','+','+']
result = topsis(df, weights, impacts)
print(result)

As a Command-Line Tool

python topsis.py <InputFile> <Weights> <Impacts> <OutputFile>

Example:

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

Input Format

• CSV Structure:
  • First column: Alternative names/IDs
  • Remaining columns: Numerical criteria values

Sample (data.csv):

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

Output Format

Original columns + Topsis Score + Rank

Sample (result.csv):

Model,Price,Storage,Camera,Looks,Performance,Topsis Score,Rank
M3,300,32,16,4,4,0.6891,1
M4,275,32,8,4,4,0.6234,2
M1,250,16,12,5,5,0.5345,3
M5,225,16,16,2,2,0.4789,4
M2,200,16,8,3,3,0.4523,5

Error Handling

The program validates:

• Parameter Count: Exactly 4 arguments required
• File Existence
• Column Count: Minimum 3 columns required
• Numeric Values: Columns 2+ must be numeric
• Impact Validation: Only '+' or '-' allowed
• Parameter Matching: Weights, impacts, and criteria count must match

Example Demonstration

Test Case: Mobile Phone Selection

python topsis.py data.csv "1,1,1,1,1" "-,+,+,+,+" result.csv

Applications

• Product selection and comparison
• Supplier evaluation
• Project prioritization
• Investment decision making
• Technology selection
• Performance evaluation

File Structure

Topsis_Sanyam_102303059/
│
├── Topsis_Sanyam_102303059/
│   ├── __init__.py
│   └── topsis.py
├── README.md
├── setup.py
├── pyproject.toml
└── LICENSE

Quick Start

# 1. Install dependencies
pip install pandas numpy

# 2. Use as a library or run as a script

References

• Hwang, C.L.; Yoon, K. (1981). Multiple Attribute Decision Making
• Yoon, K. (1987). A reconciliation among discrete compromise situations

---

Developed by: Sanyam Wadhwa (102303059) - Class 3C12