TOPSIS Implementation

Overview

TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) is a multi-criteria decision-making method. This Python package implements the TOPSIS method, allowing you to rank alternatives based on multiple criteria.

This implementation is specifically designed to work with a CSV input file and provides results with TOPSIS scores and ranks.

Features

• Handles multi-criteria decision-making problems
• Accepts CSV input files
• Outputs a CSV file with TOPSIS scores and ranks
• Validates input data and handles common errors
• Command-line interface for ease of use

Installation

You can install this package directly from PyPI using pip:

pip install 102218041-topsis

Usage

Command-Line Interface

After installing the package, you can use it from the command line. The basic usage is:

topsis <InputDataFile> <Weights> <Impacts> <ResultFileName>

Parameters

• InputDataFile: The path to the input CSV file containing the data.
• Weights: A string of comma-separated numerical weights (e.g., "1,1,1,2").
• Impacts: A string of comma-separated + or - signs indicating whether each criterion is beneficial (+) or non-beneficial (-) (e.g., "+,+,-,+").
• ResultFileName: The path to the output CSV file where results will be saved.

Example

Suppose you have an input file named 102218041-data.csv with the following content:

Alternative,Criterion1,Criterion2,Criterion3,Criterion4
M1,250,16,12,5
M2,200,6,8,3
M3,300,16,8,4
M4,275,10,10,4

You can run the following command:

topsis 102218041-data.csv "1,1,1,2" "+,+,-,+" 102218041-result.csv

This will generate a file 102218041-result.csv containing the input data along with two additional columns for the TOPSIS score and the rank:

Alternative,Criterion1,Criterion2,Criterion3,Criterion4,TOPSIS Score,Rank
M1,250,16,12,5,0.556,2
M2,200,6,8,3,0.222,4
M3,300,16,8,4,0.778,1
M4,275,10,10,4,0.444,3

Error Handling

The program includes several checks to handle common errors:

1. File Not Found: If the input file is not found, an appropriate error message will be displayed.
2. Invalid Number of Parameters: The program checks if the correct number of parameters is provided.
3. Invalid Data: The input file must contain at least three columns, with the first column being the name of alternatives, and the rest must contain numeric values.
4. Weights and Impacts Validation: The number of weights and impacts must match the number of criteria in the input file. Impacts must be either + or -.

Examples

Here are a few examples to demonstrate how the package can be used:

Example 1: Basic Usage

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

Example 2: Different Weights and Impacts

topsis data.csv "0.5,1,1.5,2" "+,-,+,-" result.csv

Example 3: Handling Errors

If the number of weights or impacts does not match the number of criteria:

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

This will result in an error message indicating the mismatch.

Testing

To ensure everything works correctly, you can run some tests. If you haven't already, you can clone the repository and run the tests locally.

# Clone the repository
git clone https://github.com/Eshitva/102218041_Eshitva_TOPSIS.git

# Navigate to the project directory
cd 102218041_Eshitva_TOPSIS

# Install the required dependencies
pip install -r requirements.txt

# Run the tests (assuming you've created test scripts)
python -m unittest discover tests

Development

If you'd like to contribute to this project, follow these steps:

1. Fork the repository on GitHub.
2. Clone your fork to your local machine.
3. Create a new branch for your feature or bugfix.
4. Make your changes and commit them with descriptive messages.
5. Push your changes to your fork.
6. Submit a pull request to the original repository.

License

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

Contact

If you have any questions, suggestions, or issues, feel free to contact me at goyaleshitva@gmail.com.

Acknowledgments

• Thanks to [Name or Organization] for their support.
• Inspired by the original TOPSIS method developed by C.L. Hwang and K. Yoon.

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