TOPSIS Implementation in Python...

Overview

This Python script implements the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method for multi-criteria decision-making. It calculates the performance score and rank for given data based on user-defined weights and impacts.

Usage

Run the script with the following command:

python topsis.py <inputFileName> <weights> <impacts> <resultFileName>

• <inputFileName>: Path to the CSV file containing the dataset.
• <weights>: Comma-separated weights for the criteria (e.g., 1,2,3).
• <impacts>: Comma-separated impacts for the criteria (+ for benefit, - for cost).
• <resultFileName>: Name of the output file where results will be saved.

Input File Requirements

1. Must be a .csv file with at least 3 columns.
2. The first column can contain non-numeric values (e.g., IDs).
3. Columns from the 2nd to the last must contain numeric values only.

Features

• Validation:
  • Ensures correct number of inputs.
  • Validates weights, impacts, and column counts.
  • Handles missing or invalid files.
• Error Handling:
  • Displays user-friendly messages for issues like file not found, non-numeric data, or mismatched input parameters.
• Output:
  • Adds two new columns: Topsis Score and Rank.
  • Saves the results in the specified output file.

Dependencies

• Python 3.x
• Required libraries: pandas, math, sys

Install dependencies using:

pip install pandas

License

This project is open-source and free to use under the MIT License.