Topsis-Parth-102203636 1.0.0

Multiple criteria decision making for selection of best model

TOPSIS Implementation in Python

About TOPSIS

TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) is a multi-criteria decision analysis method which was originally developed by Hwang and Yoon in 1981. TOPSIS chooses the alternative that is the closest to the positive ideal solution and farthest from the negative ideal solution.

Installation

pip install topsis

Usage

The program accepts the following arguments:

1. Input file (.csv)
2. Weights (comma-separated)
3. Impacts (comma-separated)
4. Output file (.csv)

python topsis.py input.csv "1,1,1,2,2" "+,-,-,+,-" output.csv

Input File Format

Input file must contain three or more columns:

• First column: Object/Variable name
• Second to last columns: Criteria values
• File must be a CSV file (.csv extension)

Sample input file:

Fund Name,P1,P2,P3,P4,P5
M1,0.68,0.48,13.07,0.74,0.87
M2,0.78,0.61,14.85,0.71,0.29
M3,0.61,0.37,14.01,0.71,0.95
M4,0.76,0.39,11.11,0.77,0.14
M5,0.59,0.41,11.32,0.82,0.55

Output Format

Output file contains all the columns of input file along with two additional columns having normalized scores and ranks.

Sample output file:

Fund Name,P1,P2,P3,P4,P5,Topsis Score,Rank
M1,0.68,0.48,13.07,0.74,0.87,0.7722,2
M2,0.78,0.61,14.85,0.71,0.29,0.6253,4
M3,0.61,0.37,14.01,0.71,0.95,0.8231,1
M4,0.76,0.39,11.11,0.77,0.14,0.5486,5
M5,0.59,0.41,11.32,0.82,0.55,0.6791,3

Parameters

Weights

Weights indicate the relative importance of each criterion. They should be provided as comma-separated values. Number of weights should be equal to number of criteria.

Example: "1,1,1,2,2"

Impacts

Impacts decide whether the criterion is to be maximized (+) or minimized (-). They should be provided as comma-separated values. Number of impacts should be equal to number of criteria.

Example: "+,-,-,+,-"

Error Handling

The program handles the following errors:

• Input file does not exist
• Input file is not a CSV file
• Input file has less than 3 columns
• Number of weights, impacts is not equal to number of criteria
• Impacts are not either '+' or '-'
• Non-numeric values in criteria columns

Example

Consider a real estate decision problem with the following criteria:

1. Cost (-)
2. Square footage (+)
3. Distance to city center (-)
4. Age of building (-)
5. Number of rooms (+)

python topsis.py houses.csv "0.25,0.25,0.15,0.15,0.20" "-,+,-,-,+" output.csv

Dependencies

• Python 3.x
• Pandas
• NumPy

License

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

Contributing

1. Fork the repository
2. Create your feature branch (git checkout -b feature/AmazingFeature)
3. Commit your changes (git commit -m 'Add some AmazingFeature')
4. Push to the branch (git push origin feature/AmazingFeature)
5. Open a Pull Request