topsis-vaibhav-102203381 1.0.3

A Python package to perform TOPSIS (Technique for Order Preference by Similarity to Ideal Solution)

TOPSIS Method Implementation - Vaibhav Garg (102203381)

This Python package implements the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method for multi-criteria decision analysis.

Installation

You can install the package using pip:

pip install topsis_vaibhav_102203381

Usage

Input File Format

The input CSV file should have the following structure:

• The first column should contain the names of the models.
• The subsequent columns should contain the criteria values for each alternative.
• The first row should contain the headers for each column.

Example:

Model	Criteria 1	Criteria 2	Criteria 3	Criteria 4	Criteria 5	Criteria 6
m1	250	16	12	30	80	7
m2	200	14	8	35	70	5
m3	300	18	15	25	90	6

Weights:

Comma-separated values that indicate the weight of each criterion.

Example:

0.2, 0.1, 0.15, 0.1, 0.25, 0.2

Impacts:

Comma-separated string of + or - that indicate the direction of optimization for each criterion.

Example:

+, -, +, -, +, +

Where:

• + indicates the criterion is to be maximized.
• - indicates the criterion is to be minimized.

Command-line Usage

After installation, you can use the topsis command in the terminal to process the data:

topsis <input_file> <weights> <impacts> <output_file>

• <input_file>: Path to the input CSV file.
• <weights>: Comma-separated string of weights for the criteria.
• <impacts>: Comma-separated string of '+' or '-' indicating the desirability of the criteria.
• <output_file>: Path to the output CSV file to save the results.

Example:

topsis data.csv "0.2,0.1,0.15,0.1,0.25,0.2" "+,-,+,-,+,+" result.csv

This command will process the data.csv file using the specified weights and impacts and output the results to result.csv.

Example Explanation

Input Data (CSV):

Model	Criteria 1	Criteria 2	Criteria 3	Criteria 4	Criteria 5	Criteria 6
m1	250	16	12	30	80	7
m2	200	14	8	35	70	5
m3	300	18	15	25	90	6

Weights:

0.2, 0.1, 0.15, 0.1, 0.25, 0.2

Impacts:

+, -, +, -, +, +

• Criteria 1 and Criteria 3 are to be maximized (+).
• Criteria 2 and Criteria 4 are to be minimized (-).
• Criteria 5 and Criteria 6 are to be maximized (+).

Command to Execute:

topsis data.csv "0.2,0.1,0.15,0.1,0.25,0.2" "+,-,+,-,+,+" result.csv

Output Example:

Model	Score	Rank
m1	0.78	2
m2	0.65	3
m3	0.89	1

Notes

• Ensure that the input file has at least three columns: one for alternatives and at least two for criteria.
• All criteria columns should contain numeric values.
• The number of weights and impacts must match the number of criteria.
• Impacts should only be + or -.

License

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