Topsis-Saumil-102303862

A Python package implementing TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) for multi-criteria decision analysis (MCDM).

What is TOPSIS?

TOPSIS is a multi-criteria decision-making method that helps rank alternatives based on multiple criteria. It works by:

1. Calculating the distance of each alternative from the ideal best and ideal worst solutions
2. Ranking alternatives based on their relative closeness to the ideal solution

Installation

pip install Topsis-Saumil-102303862

Usage

Command Line Interface

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

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

Parameters

• InputDataFile: CSV file with the first column as object/alternative names and remaining columns as criteria
• Weights: Comma-separated weights for each criterion (e.g., "1,1,2,1")
• Impacts: Comma-separated impacts for each criterion, either '+' (benefit) or '-' (cost) (e.g., "+,+,-,+")
• OutputResultFileName: Name of the output CSV file with TOPSIS scores and ranks

Example

Input File (data.csv)

Fund Name,P1,P2,P3,P4,P5
M1,0.67,0.45,6.5,42.6,12.56
M2,0.60,0.36,3.6,53.3,14.47
M3,0.82,0.67,3.8,63.1,17.10
M4,0.60,0.36,3.5,69.2,18.42
M5,0.76,0.58,4.8,43.0,12.29
M6,0.69,0.48,6.6,48.7,14.12
M7,0.79,0.62,4.8,59.2,16.35
M8,0.84,0.71,6.5,34.5,10.64

Command

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

Output File (result.csv)

The output will include the original data plus two additional columns:

• Topsis Score: A score between 0-100 indicating closeness to the ideal solution
• Rank: Ranking based on TOPSIS score (1 = best)

Fund Name,P1,P2,P3,P4,P5,Topsis Score,Rank
M1,0.67,0.45,6.5,42.6,12.56,21.52,8
M2,0.60,0.36,3.6,53.3,14.47,46.80,4
M3,0.82,0.67,3.8,63.1,17.10,86.15,1
M4,0.60,0.36,3.5,69.2,18.42,58.85,3
M5,0.76,0.58,4.8,43.0,12.29,45.08,5
M6,0.69,0.48,6.6,48.7,14.12,32.66,7
M7,0.79,0.62,4.8,59.2,16.35,69.55,2
M8,0.84,0.71,6.5,34.5,10.64,41.42,6

Using in Python Code

You can also use the TOPSIS package programmatically:

from topsis.topsis import topsis
import pandas as pd

# Your data as a list of lists (excluding the first column with names)
matrix = [
    [0.67, 0.45, 6.5, 42.6, 12.56],
    [0.60, 0.36, 3.6, 53.3, 14.47],
    [0.82, 0.67, 3.8, 63.1, 17.10],
]

weights = [1, 1, 1, 1, 1]
impacts = ["+", "+", "-", "+", "+"]

scores, ranks = topsis(matrix, weights, impacts)
print("Scores:", scores)
print("Ranks:", ranks)

Input Validation

The package performs comprehensive input validation:

✅ Checks for correct number of command-line parameters
✅ Validates file existence
✅ Ensures minimum 3 columns (1 name + 2 criteria)
✅ Verifies all criteria values are numeric
✅ Confirms weights and impacts count matches criteria count
✅ Validates impacts are only '+' or '-'
✅ Ensures comma-separated format for weights and impacts

Error Messages

The package provides clear, actionable error messages:

# Wrong number of parameters
Error: Incorrect number of parameters.
Usage : topsis <InputFile> <Weights> <Impacts> <OutputFile>

# File not found
Error: Input file 'data.csv' not found.

# Non-numeric value
Error: Non-numeric value 'abc' in row 2, column 3.

# Mismatched weights
Error: Number of weights (3) does not match criteria columns (5).

# Invalid impact
Error: Invalid impact '0'. Must be '+' or '-'.

Algorithm Steps

1. Normalization: Convert the decision matrix using vector normalization
2. Weighted Normalization: Multiply normalized values by their weights
3. Ideal Solutions: Determine ideal best (A+) and ideal worst (A-) 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

Requirements

• Python >= 3.7
• pandas >= 1.0.0

License

MIT License

Author

Saumil Makkar
Email: saumilmakkar@example.com
GitHub: https://github.com/SaumilMakkar/UCS654

Contributing

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

Issues

If you encounter any problems, please file an issue at: https://github.com/SaumilMakkar/UCS654/issues

Changelog

Version 1.0.0 (2024)

• Initial release
• Command-line interface
• Comprehensive input validation
• Clear error messages
• Support for any number of criteria
• Handles both benefit (+) and cost (-) criteria

References

• Hwang, C.L.; Yoon, K. (1981). Multiple Attribute Decision Making: Methods and Applications. New York: Springer-Verlag.
• Yoon, K. (1987). "A reconciliation among discrete compromise situations". Journal of the Operational Research Society. 38 (3): 277–286.
• Original Implementation: https://github.com/SaumilMakkar/UCS654/blob/main/Assignment3/Assignment3UCS654.ipynb

Keywords

TOPSIS, MCDM, Multi-Criteria Decision Making, Decision Analysis, Optimization, Ranking, Python