TOPSIS Analysis Package

A Python package for performing TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) analysis on numerical datasets. This package provides both a command-line interface and a importable module for performing TOPSIS analysis on datasets containing numerical data (int32, int64, float32, float64).

Overview

TOPSIS is a multi-criteria decision analysis method that helps identify the best alternative from a set of options based on multiple criteria. The package normalizes the input data, applies weights to different criteria, and considers whether each criterion should be maximized or minimized.

Installation

pip install topsis-102217133

Usage

As a Module

from topsis_analysis import run

# Perform TOPSIS analysis
result_df = run(
    input_df,           # pandas DataFrame with numerical values
    weights,            # List of weights for each criterion
    impacts,            # List of impacts ('+' or '-') for each criterion
)

Command Line Interface

python -m topsis_analysis <source_csv> <weights> <impacts> <output_csv>

Parameters

For Both Module and CLI:

1. Input Data:

   • Must contain only numerical values (int32, int64, float32, float64)
   • First column will be used as index
   • No missing values allowed

2. Weights:

   • Must sum to 1
   • Number of weights must match number of columns (excluding index)
   • Module: List of float values
   • CLI: Comma-separated values (e.g., "0.25,0.25,0.25,0.25")

3. Impacts:

   • Use '+' for criteria to be maximized
   • Use '-' for criteria to be minimized
   • Number of impacts must match number of columns (excluding index)
   • Module: List of strings
   • CLI: Comma-separated signs (e.g., "-,+,+,+")

CLI Only:

4. output_csv: Path where the result CSV will be stored

Example Usage

As a Module

import pandas as pd
from topsis_analysis import run

# Read input data
df = pd.read_csv('data.csv')

# Define weights and impacts
weights = [0.25, 0.25, 0.25, 0.25]
impacts = ['-', '+', '+', '+']

# Run TOPSIS analysis
result = run(df, weights, impacts)

# Save results if needed
result.to_csv('output.csv', index=False)

Command Line

python -m topsis-102217133 data.csv 0.25,0.25,0.25,0.25,0.25 -,+,+,+,+ output.csv

Input CSV Format

P1,P2,P3,P4,P5
0.62,0.38,5.7,47,13.43
0.8,0.64,5.4,38.6,11.36
0.83,0.69,3.3,57.9,15.68
0.84,0.71,5.5,52.3,14.84
0.72,0.52,4.6,44.8,12.66
0.61,0.37,7,41.2,12.3
0.61,0.37,3.9,46.7,12.9
0.93,0.86,6.5,35.4,10.92

• Weights not summing to 1

Output Format

P1,P2,P3,P4,P5,euclidean_distance_worst,euclidean_distance_best,performance_score,rank
0.29077553802544853,0.22636699320773462,0.37551232309446086,0.361269319952853,0.36248022622053017,0.18911380785500428,0.350388690096909,0.35053370201792894,7
0.375194242613482,0.3812496727709215,0.35574851661580503,0.296702037237875,0.30661022858266734,0.29757811013334073,0.18096741720614892,0.6218386613866165,2

Error Handling

The package provides comprehensive error handling for:

• Invalid number of weights or impacts
• Invalid data types
• Missing values in the dataset
• Invalid file paths (CLI only)
• Non-numeric data in columns
• Invalid impact symbols

Dependencies

• Python 3.7+
• pandas
• numpy

Contributing

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

License

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