Topsis-Haryiank-102303088 1.0.2

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

Topsis-Haryiank-102303088

TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) implementation in Python for multi-criteria decision analysis.

Description

TOPSIS is a multi-criteria decision analysis method that ranks alternatives based on their similarity to the ideal solution. This package provides a simple command-line tool to perform TOPSIS analysis on CSV data files.

Installation

Install the package using pip:

pip install Topsis-Haryiank-102303088

Usage

After installation, you can use the topsis command from anywhere in your terminal:

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

Parameters

• InputDataFile: Path to the input CSV file
• Weights: Comma-separated weights for each criterion (e.g., "1,1,1,2")
• Impacts: Comma-separated impacts for each criterion ('+' for maximize, '-' for minimize)
• OutputResultFileName: Path for the output CSV file

Example

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

This command:

• Reads data from data.csv
• Applies weights: 1, 1, 1, 2 to the four criteria
• Maximizes criteria 1, 2, 4 and minimizes criterion 3
• Saves results to result.csv

Input File Format

The input CSV file must follow this structure:

• First column: Names of alternatives/options
• Remaining columns: Numeric values for each criterion
• Minimum: 3 columns (1 name column + at least 2 criteria)

Example Input (data.csv)

Model,Price,Storage,Camera,Battery
P1,250,64,12,4000
P2,200,32,8,3500
P3,300,128,16,4500
P4,275,64,12,4200
P5,225,32,16,3800

Output Format

The output CSV includes all original columns plus:

• Topsis Score: Score between 0 and 1 (higher is better)
• Rank: Ranking based on TOPSIS score (1 is best)

Example Output (result.csv)

Model,Price,Storage,Camera,Battery,Topsis Score,Rank
P3,300,128,16,4500,0.691,1
P4,275,64,12,4200,0.535,2
P1,250,64,12,4000,0.534,3
P5,225,32,16,3800,0.401,4
P2,200,32,8,3500,0.308,5

Weights and Impacts

Weights

Weights represent the relative importance of each criterion:

• Must be numeric values
• Comma-separated
• Number of weights must match number of criteria
• Example: "1,2,1,3" means criterion 2 is twice as important as criterion 1

Impacts

Impacts indicate whether a criterion should be maximized or minimized:

• '+': Higher values are better (e.g., performance, storage, battery)
• '-': Lower values are better (e.g., price, weight, power consumption)
• Comma-separated
• Number of impacts must match number of criteria
• Example: "+,+,-,+"

How TOPSIS Works

1. Normalize the decision matrix using vector normalization
2. Apply weights to the normalized matrix
3. Identify ideal best and ideal worst solutions for each criterion
4. Calculate Euclidean distance of each alternative from ideal best and ideal worst
5. Compute TOPSIS score: Score = Distance_to_worst / (Distance_to_best + Distance_to_worst)
6. Rank alternatives based on TOPSIS scores (higher score = better rank)

Error Handling

The package validates:

• ✓ Correct number of command-line arguments
• ✓ Input file existence
• ✓ Minimum 3 columns in input file
• ✓ All criteria columns contain numeric values only
• ✓ Number of weights matches number of criteria
• ✓ Number of impacts matches number of criteria
• ✓ Impacts are only '+' or '-'

Requirements

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

License

MIT License - see LICENSE file for details

Author

Haryiank Kumra

Version

1.0.2

Links

• PyPI: https://pypi.org/project/Topsis-Haryiank-102303088/
• GitHub: https://github.com/HaryiankKumra/TOPSIS

Support

For issues and questions, please open an issue on GitHub.