Topsis-Akash-102317024 1.0.0

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

Topsis-Akash-102317024

By: Akash, 102317024

TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) is a multi-criteria decision analysis method. It is based on the principle that the best solution has the shortest distance from the positive-ideal solution and the longest distance from the negative-ideal solution.

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Installation

pip install Topsis-Akash-102317024

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Usage

Command Line

topsis <InputDataFile> <Weights> <Impacts> <ResultFileName>

Example:

topsis data.csv "1,1,2,1" "+,+,-,+" result.csv

Python API

from Topsis_Akash_102317024 import topsis

topsis("data.csv", "1,1,2,1", "+,+,-,+", "result.csv")

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Input File Format

The input CSV file must have:

• First column: Alternative names (e.g., M1, M2, …)
• Remaining columns: Numeric criteria values

Example data.csv:

Fund Name,P1,P2,P3,P4,P5
M1,0.84,0.71,6.7,42.1,12.97
M2,0.91,0.83,7.0,31.7,10.52
M3,0.79,0.62,4.8,46.7,13.23
M4,0.78,0.61,6.4,42.4,12.59
M5,0.94,0.88,3.6,62.2,10.11

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Parameters

Parameter	Description
InputDataFile	Path to input CSV file
Weights	Comma-separated numeric weights, e.g. "1,1,2,1"
Impacts	Comma-separated impacts (+ or -), e.g. "+,+,-,+"
ResultFileName	Path to output CSV file

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Output

The result CSV will contain all original columns plus two new columns:

• Topsis Score – Performance score (0 to 1, higher is better)
• Rank – Rank of each alternative (1 = best)

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Algorithm Steps

1. Normalise the decision matrix
2. Calculate weighted normalised matrix
3. Determine ideal best and ideal worst solutions
4. Compute Euclidean distances from ideal solutions
5. Calculate performance score (closeness coefficient)
6. Rank alternatives

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Dependencies

• numpy
• pandas

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License

MIT License — © 2024 Akash