Topsis-Pratham-102303052 1.0.2

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

Topsis-Pratham-102303052

A Python package for performing TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) analysis on multi-criteria decision-making problems.

Author: Pratham Garg
Roll Number: 102303052

What is TOPSIS?

TOPSIS is a multi-criteria decision analysis method that helps in selecting the best alternative from a set of alternatives based on multiple criteria. It works by finding the alternative that is closest to the ideal solution and farthest from the negative-ideal solution.

Installation

pip install Topsis-Pratham-102303052

Usage

Command Line Interface

After installation, you can use the topsis command directly from the command line:

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

Example

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

Parameters

• InputDataFile: Path to the CSV file containing the decision matrix

  • First column: Names of alternatives
  • Remaining columns: Criteria values (must be numeric)

• Weights: Comma-separated weights for each criterion

  • Example: "1,1,1,2"

• Impacts: Comma-separated impacts for each criterion

  • Use '+' for benefit criteria (higher is better)
  • Use '-' for cost criteria (lower is better)
  • Example: "+,+,-,+"

• OutputResultFileName: Path where the result file will be saved

Input File Format

The input CSV file should have the following structure:

Fund Name	P1	P2	P3	P4	P5
M1	0.67	0.45	6.5	42.6	12.56
M2	0.6	0.36	3.6	53.3	14.47
M3	0.79	0.61	6.4	63.1	17.84

Output File Format

The output file will contain all input columns plus two additional columns:

• Topsis Score: The calculated TOPSIS score for each alternative
• Rank: The rank of each alternative (1 = best)

Validation

The package performs the following validations:

• Correct number of parameters
• File existence check
• Minimum 3 columns in input file
• Numeric values in all columns except the first
• Matching number of weights, impacts, and criteria columns
• Valid impact values ('+' or '-')

Error Handling

The package provides clear error messages for:

• Missing or incorrect parameters
• File not found
• Non-numeric values in criteria columns
• Mismatch in number of weights/impacts/columns
• Invalid impact values

Algorithm

The TOPSIS algorithm follows these steps:

1. Normalization: Vector normalization of the decision matrix
2. Weighted Normalization: Multiply normalized values by weights
3. Ideal Solutions: Determine ideal best and ideal worst solutions
4. Distance Calculation: Calculate Euclidean distances from ideal solutions
5. TOPSIS Score: Calculate relative closeness to ideal solution
6. Ranking: Rank alternatives based on TOPSIS scores

License

MIT License

Author

Pratham Garg
Roll Number: 102303052
Email: [e-mail]

Links

• PyPI
• GitHub

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.

Support

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