102217186-abhaijeet-topsis 1.1.0

A Python package for TOPSIS calculation

TOPSIS Package

A Python package for decision-making using the TOPSIS method.

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What is TOPSIS?

The Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is a multi-criteria decision analysis method.
It ranks alternatives based on their closeness to the ideal solution and farthest from the worst solution, making it an excellent tool for decision-making problems.

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Features

• Simple and efficient implementation of the TOPSIS algorithm.
• Accepts a decision matrix via a CSV file.
• Supports customizable weights and impacts for criteria.
• Outputs the TOPSIS scores and ranks into an easy-to-read CSV file.

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Installation

You can install the package from PyPI using:

pip install 102217186-abhaijeet-topsis==1.0.0

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How to Use

Run the Package

You can run the TOPSIS package directly from the command line using:

python -m 102217186_abhaijeet_topsis <input_file> <weights> <impacts> <output_file>

Parameters

• <input_file>: Path to the CSV file containing the decision matrix.
• <weights>: Comma-separated weights for each criterion (e.g., 0.2,0.1,0.43,0.3).
• <impacts>: Comma-separated impacts for each criterion (e.g., +,+,-,-, where + indicates a benefit criterion and - indicates a cost criterion).
• <output_file>: Path to the output CSV file where the results will be saved.

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Example Usage

Input CSV (input.csv)

Command

python -m 102217186_abhaijeet_topsis input.csv "0.2,0.1,0.4,0.3" "+,+,-,+"
output.csv

Output CSV (output.csv)

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How It Works

1. Normalization: The decision matrix is normalized to bring all criteria onto a comparable scale.
2. Weighting: Each criterion is multiplied by its corresponding weight.
3. Ideal Solutions: Calculates the ideal best and worst solutions based on the impacts.
4. Distance Calculation: Computes the distance of each alternative from the ideal best and worst solutions.
5. Ranking: Scores and ranks alternatives based on their relative closeness to the ideal solution.

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License

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

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Support

If you encounter any issues or have questions, feel free to open an issue on GitHub.

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