102217211-Ashmeet-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 102217211-Ashmeet-topsis==1.1.0

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How to Use

Run the Package

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

python -m 102217211_Ashmeet_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)

Fund Name,P1,P2,P3,P4,P5
M1,0.91,0.83,6,53,15.19
M2,0.88,0.77,4.1,61.1,16.71
M3,0.67,0.45,3.5,59.4,16.01
M4,0.83,0.69,4.8,44.9,12.81
M5,0.74,0.55,6.7,66.3,18.57
M6,0.6,0.36,4,37.8,10.69
M7,0.72,0.52,4.4,40.7,11.59
M8,0.73,0.53,4.4,66.8,18.12

Command

python -m 102217211_Ashmeet_topsis "input.csv" "0.2,0.1,0.4,0.3,0.1" "-,+,+,-,+" "output.csv"

Output CSV (output.csv)

Fund Name,P1,P2,P3,P4,P5,TOPSIS Score,Rank
M1,0.91,0.83,6,53,15.19,0.7521,2
M2,0.88,0.77,4.1,61.1,16.71,0.8256,1
M3,0.67,0.45,3.5,59.4,16.01,0.4823,6
M4,0.83,0.69,4.8,44.9,12.81,0.6332,4
M5,0.74,0.55,6.7,66.3,18.57,0.6789,3
M6,0.6,0.36,4,37.8,10.69,0.3145,8
M7,0.72,0.52,4.4,40.7,11.59,0.4012,7
M8,0.73,0.53,4.4,66.8,18.12,0.5798,5

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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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