topsis-102203409 1.0

A Python package for implementing TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution).

TOPSIS 102203409

A Python package for implementing the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). This is a multi-criteria decision analysis method that helps in ranking alternatives based on multiple criteria.

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Installation

Install the package using pip:

pip install topsis-102203409

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Usage

Command-Line Interface (CLI)

You can run the TOPSIS method directly from the command line.

Syntax

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

Arguments

1. <input_file>: Path to the input CSV file containing the data.

   • The first column should contain the names of the alternatives.
   • The remaining columns should contain numerical values for the criteria.

2. <weights>: A comma-separated string of weights for each criterion (e.g., "1,1,1,1").

3. <impacts>: A comma-separated string of impacts (+ for positive, - for negative) corresponding to each criterion (e.g., "+,+,-,+").

4. <output_file>: Path to save the resulting output file, which will include the computed scores and ranks.

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Example

Input File (input.csv)

Alternative	Criterion1	Criterion2	Criterion3	Criterion4
Alt1	250	16	12	5
Alt2	200	32	8	3
Alt3	300	24	10	4
Alt4	275	20	11	4.5
Alt5	225	28	9	4

Command

topsis input.csv "1,1,1,1" "+,+,-,+" output.csv

Output File (output.csv)

Alternative	Criterion1	Criterion2	Criterion3	Criterion4	Score	Rank
Alt1	250	16	12	5	0.8729	1
Alt2	200	32	8	3	0.3243	4
Alt3	300	24	10	4	0.2985	5
Alt4	275	20	11	4.5	0.7315	3
Alt5	225	28	9	4	0.4532	2

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Library Usage

You can also use the package programmatically in Python.

from topsis_package.main import topsis

topsis("input.csv", "1,1,1,1", "+,+,-,+", "output.csv")

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Dependencies

• Python >= 3.7
• pandas
• numpy

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How TOPSIS Works

1. Normalize the Decision Matrix
   Convert each criterion to a unit vector.

2. Calculate the Weighted Normalized Decision Matrix
   Multiply each normalized value by its weight.

3. Determine the Ideal Best and Worst Solutions

   • For positive impacts: Max value.
   • For negative impacts: Min value.

4. Calculate Separation Measures

   • Distance from the ideal best.
   • Distance from the ideal worst.

5. Compute Relative Closeness to the Ideal Solution
   Rank the alternatives based on their closeness.

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License

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

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Author

Arin Goyal
Email: [e-mail]