topsis-harshit-kansal-102303554 1.0.1

TOPSIS (MCDM) implementation for ranking alternatives using multiple criteria.

TOPSIS Package

A Python package implementing TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) for Multi-Criteria Decision Making (MCDM) analysis. Rank alternatives based on multiple numeric criteria with a simple command-line interface.

Author: Harshit Kansal (Roll No: 102303554)

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What is TOPSIS?

TOPSIS is a multi-criteria decision analysis method that ranks alternatives based on their geometric distance from ideal solutions. The best alternative should have:

• The shortest distance from the positive ideal solution
• The longest distance from the negative ideal solution

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Installation

Install the package from PyPI:

pip install topsis-harshit-kansal-102303554

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Quick Start

CLI Usage

topsis-hk <input_csv> <weights> <impacts> <output_csv>

Example Command

topsis-hk sample.csv "0.25,0.25,0.25,0.25" "+,+,-,+" output.csv

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Input Format

CSV File Structure

Your input CSV must have:

• First column: Alternative names/IDs
• Remaining columns: Numeric criteria values only

Example (sample.csv):

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

Weights Vector

Comma-separated numeric values indicating criterion importance:

"0.25,0.25,0.25,0.25"

Impacts Vector

Comma-separated + or - values indicating if a criterion is beneficial (+) or non-beneficial (-):

"+,+,-,+"

• + : Higher is better (Storage, Camera, Rating)
• - : Lower is better (Price)

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Output

The output CSV contains all original columns plus two new columns:

Column Name	Description
Topsis Score (102303554)	TOPSIS score for each alternative
Rank (Harshit)	Rank of each alternative (1 = best)

Sample Output

Model	Storage	Camera	Price	Rating	Topsis Score (102303554)	Rank (Harshit)
M1	16	12	250	5	0.7234	2
M2	16	8	200	3	0.4521	5
M3	32	16	300	4	0.6892	3
M4	32	8	275	4	0.5643	4
M5	16	16	225	2	0.8123	1

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Validation Rules

The package enforces the following validations:

• Input file must be a valid CSV
• First column contains alternative names (non-numeric)
• All other columns contain numeric values only
• Number of weights must match number of criteria columns
• Number of impacts must match number of criteria columns
• Impact values must be either + or -
• Weights must be numeric and positive (> 0)

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Algorithm Steps

TOPSIS performs the following steps:

1. Normalize the decision matrix
2. Weight the normalized decision matrix
3. Calculate ideal best and ideal worst solutions
4. Compute Euclidean distances from each alternative to ideal solutions
5. Calculate TOPSIS score for each alternative
6. Rank alternatives in descending order of TOPSIS score

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Requirements

• Python >= 3.8
• numpy
• pandas

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Keywords

TOPSIS MCDM Multi-Criteria Decision Making Decision Analysis Optimization Ranking

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Author

Harshit Kansal
Roll No: 102303554

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License

This package is part of a college assignment project.