TOPSIS Package

This repository contains a Python implementation of the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method, a multi-criteria decision-making technique. The package supports command-line execution and enables users to calculate TOPSIS scores and rankings based on input data.

---

Features

• Calculates TOPSIS scores and rankings for multi-criteria decision-making.
• Handles both positive and negative impacts of criteria.
• Provides results in a CSV file with scores and rankings.
• Fully customizable through weights and impacts specified as command-line arguments.

---

Installation

To install the package from PyPI:

pip install topsis-Prince-3619

---

Usage

Command-Line Interface (CLI)

Run the TOPSIS program directly from the command line:

python3 -m Topsis <InputDataFile> <Weights> <Impacts> <ResultFileName>

Arguments

1. <InputDataFile>: Path to the input CSV file.

   • The first column must contain object names (e.g., M1, M2, M3).
   • The remaining columns must contain numeric values for criteria.

2. <Weights>: Comma-separated weights for the criteria (e.g., "1,1,1,2").

3. <Impacts>: Comma-separated impacts for the criteria (+ for positive impact, - for negative impact).

4. <ResultFileName>: Name of the output CSV file where results will be saved.

---

Example

Input Data

Input file (102203619-data.csv):

Model	Criterion 1	Criterion 2	Criterion 3	Criterion 4
M1	250	16	12	5
M2	200	18	8	3
M3	300	14	16	10
M4	275	17	10	8

Command

topsis 102203619-data.csv "1,1,1,2" "+,+,-,+" result.csv

Output File

Output file (result.csv):

Model	Criterion 1	Criterion 2	Criterion 3	Criterion 4	Topsis Score	Rank
M1	250	16	12	5	0.5346	2
M2	200	18	8	3	0.3084	4
M3	300	14	16	10	0.6912	1
M4	275	17	10	8	0.5340	3

---

How It Works

1. Normalization: The decision matrix is normalized using vector normalization:

   [ R_{ij} = \frac{a_{ij}}{\sqrt{\sum_{k=1}^{m} a_{kj}^2}} ]

2. Weighted Normalized Matrix: Each criterion is weighted according to user-defined weights:

   [ V_{ij} = R_{ij} \cdot w_j ]

3. Identify Ideal Solutions:

   • Positive Ideal Solution (PIS): Maximum values for positive impacts and minimum values for negative impacts.
   • Negative Ideal Solution (NIS): Minimum values for positive impacts and maximum values for negative impacts.

4. Calculate Separation Measures:

   • Separation from PIS (( S_i^+ )) and NIS (( S_i^- )) are computed using Euclidean distance.

5. Calculate TOPSIS Scores:

   • The score is calculated as: [ C_i = \frac{S_i^-}{S_i^+ + S_i^-} ]

6. Rank Alternatives:

   • Alternatives are ranked based on their scores, with higher scores indicating better performance.

---

Development

Project Structure

topsis-package/
├── topsis/
│   ├── __init__.py
│   ├── __main__.py
├── README.md
├── LICENSE
├── setup.py
├── pyproject.toml
└── MANIFEST.in

Dependencies

• numpy
• pandas

Setup for Development

1. Clone the repository:

   git clone https://github.com/Prince-05/TOPSIS
   

2. Navigate to the project directory:

   cd Topsis-Prince-3619
   

3. Install dependencies:

   pip install -r requirements.txt
   

---

Testing

Run the tests to ensure the package works correctly:

pytest

---

License

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

---

Contributing

Contributions are welcome! Please follow these steps:

1. Fork the repository.
2. Create a new branch for your feature or bug fix.
3. Submit a pull request with a detailed explanation of your changes.

---

Contact

For any questions or issues, please contact:

• Name: Prince
• Email: pprince_be22@thapar.edu
• GitHub: Prince-05