Topsis-Naman-102116058 1.0.4

Implementation of Topsis

Project Description

• for: Assignment-1(UCS654)
• Submitted by: Naman Bhargava
• Roll no: 102116058
• Group: 3CS10

TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution)

This Python script implements the TOPSIS method for multi-criteria decision-making. It takes a CSV file containing a decision matrix, weights, and impacts as input, and produces a ranked result based on the TOPSIS score.

Installation

pip install Topsis-Naman-102103568

Usage

from Topsis_Naman_102116058.topsis import topsis 
input="data.csv"
weights="1,1,1,2,1"
impacts="+,+,-,+,-"
result="result.csv" 
topsis(input, weights, impacts, result)

OR

You can use this package via command line as:

python -m Topsis_Naman_102116058.topsis [Input as .csv] [Weights as a string] [Impacts as a string] [Result as .csv]

• Input: Path to the CSV file containing the input data.
• Weights: Comma-separated weights for each criterion.
• Impacts: Comma-separated impact direction for each criterion (+ for maximization, - for minimization).
• Result: Name of the file to save the TOPSIS results.

Requirements

• Python 3
• pandas
• numpy

Input File Format

The input data should be in a CSV format with the following structure:

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
M4	0.76	0.58	3.4	38.4	10.79
M5	0.74	0.55	3.1	56.4	15.20
M6	0.80	0.64	4.2	41.8	11.86
M7	0.95	0.90	4.1	50.3	14.06
M8	0.82	0.67	3.7	37.6	10.70

Output

The script generates a CSV file containing the TOPSIS score and rank for each object:

Fund Name	P1	P2	P3	P4	P5	Topsis Score	Rank
M1	0.62	0.38	6.7	58.7	16.6	0.3876	8.0
M2	0.93	0.86	5.5	47.9	13.8	0.5213	4.0
M3	0.62	0.38	3.2	65.9	17.53	0.5715	3.0
M4	0.76	0.58	3.4	38.4	10.79	0.4401	7.0
M5	0.74	0.55	3.1	56.4	15.2	0.5977	2.0
M6	0.8	0.64	4.2	41.8	11.86	0.4403	6.0
M7	0.95	0.9	4.1	50.3	14.06	0.6352	1.0
M8	0.82	0.67	3.7	37.6	10.7	0.4517	5.0

Error Handling

• Display an error message if the input file is not found.
• Raise a ValueError if the number of weights, impacts, or columns in the decision matrix is incorrect.
• Raise a ValueError if the columns from the 2nd to the last do not contain numeric values.
• Display any unexpected errors that occur during execution.

LICENSE

(c) 2024 Naman Bhargava

This project is licensed under the MIT License.