Topsis-Shweta-2004 1.0.0

Implementation of TOPSIS - Technique for Order of Preference by Similarity to Ideal Solution

Topsis-Shweta-2004

TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) is a multi-criteria decision-making method. This package implements TOPSIS in Python — it takes a decision matrix CSV, weights, and impacts, then returns a ranked result.

Installation

pip install Topsis-Shweta-2004

Usage

In Python:

from Topsis_Shweta_2004.topsis import topsis

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

Via Command Line:

python -m Topsis_Shweta_2004.topsis input.csv "1,1,1,2" "+,+,-,+" output.csv

Parameters:

Parameter	Description
input	Path to input CSV file
weights	Comma-separated weights e.g. "1,1,2,1"
impacts	Comma-separated impacts e.g. "+,+,-,+"
result	Output CSV filename

Input Format

First column = object/alternative name. Remaining columns = numeric criteria.

Fund Name	P1	P2	P3	P4	P5
M1	0.62	0.38	6.7	58.7	16.60
M2	0.93	0.86	5.5	47.9	13.80
M3	0.62	0.38	3.2	65.9	17.53

Output Format

Same as input with two additional columns: Topsis Score and Rank.

Fund Name	P1	P2	P3	P4	P5	Topsis Score	Rank
M1	0.62	0.38	6.7	58.7	16.60	0.3876	8.0
M2	0.93	0.86	5.5	47.9	13.80	0.5213	4.0

How TOPSIS Works

1. Normalize the decision matrix
2. Apply weights to each criterion
3. Find Ideal Best & Worst solutions
4. Calculate distance from ideal best and worst
5. Rank alternatives by closeness to ideal solution

Error Handling

• File not found → clear error message
• Mismatched weights/impacts/columns → ValueError
• Non-numeric values in criteria columns → ValueError

Requirements

• Python 3
• pandas
• numpy

License

(c) 2024 Shweta — MIT License