A Python package for TOPSIS (Technique for Order Preference by Similarity to Ideal Solution)
Project description
TOPSIS – Python Package
Project Description
topsis-yash-102303796 is a Python package that implements
TOPSIS (Technique for Order Preference by Similarity to Ideal Solution),
a popular Multi-Criteria Decision Making (MCDM) technique.
This package ranks alternatives based on multiple numeric criteria, user-defined weights, and impacts.
This project is developed as part of an academic assignment.
Installation
Install the package using pip:
pip install topsis-yash-102303796==1.0.4
Usage
Enter csv filename followed by .csv extentsion, then enter the weights vector with vector values separated by commas, followed by the impacts vector with comma separated signs (+,-)
topsis <input_file.csv> <weights> <impacts> <output_file.csv>
Parameters
-
input_file.csv CSV file containing alternatives and criteria (first column = names, remaining columns = numeric values)
-
weights Comma-separated numeric values Example:
1,1,1,1,1
- impacts Comma-separated signs (+ for benefit, - for cost) Example:
+,+,+,+,+
- output_file.csv Output CSV file generated by the program
Example
Sample Input File (data.csv)
| Fund Name | P1 | P2 | P3 | P4 | P5 |
|---|---|---|---|---|---|
| M1 | 0.84 | 0.71 | 6.7 | 42.1 | 12.59 |
| M2 | 0.91 | 0.83 | 7.0 | 31.7 | 10.11 |
| M3 | 0.79 | 0.62 | 4.8 | 46.7 | 13.23 |
| M4 | 0.78 | 0.61 | 6.4 | 42.4 | 12.55 |
| M5 | 0.94 | 0.88 | 3.6 | 62.2 | 16.91 |
| M6 | 0.88 | 0.77 | 6.5 | 51.5 | 14.91 |
| M7 | 0.66 | 0.44 | 5.3 | 48.9 | 13.83 |
| M8 | 0.93 | 0.86 | 3.4 | 37.0 | 10.55 |
Command
topsis input.csv "1,1,1,1,1" "+,+,+,+,+" output.csv
Sample Output (output.csv)
| Fund Name | P1 | P2 | P3 | P4 | P5 | Topsis Score | Rank |
|---|---|---|---|---|---|---|---|
| M1 | 0.84 | 0.71 | 6.7 | 42.1 | 12.59 | 0.5636923298 | 3 |
| M2 | 0.91 | 0.83 | 7.0 | 31.7 | 10.11 | 0.5130321034 | 4 |
| M3 | 0.79 | 0.62 | 4.8 | 46.7 | 13.23 | 0.4391772826 | 6 |
| M4 | 0.78 | 0.61 | 6.4 | 42.4 | 12.55 | 0.4919560825 | 5 |
| M5 | 0.94 | 0.88 | 3.6 | 62.2 | 16.91 | 0.6418858152 | 2 |
| M6 | 0.88 | 0.77 | 6.5 | 51.5 | 14.91 | 0.7381481319 | 1 |
| M7 | 0.66 | 0.44 | 5.3 | 48.9 | 13.83 | 0.4073895318 | 8 |
| M8 | 0.93 | 0.86 | 3.4 | 37.0 | 10.55 | 0.4084986767 | 7 |
Notes
- Input CSV must contain numeric values only (except first column)
- Number of weights must match number of criteria columns
- Impacts must be either + or -
- Input and output files must be in the current working directory
- The package does not include sample CSV files
Author
Yash Raghuvanshi Roll Number: 102303796
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