Topsis-Naman-102116058 1.0.5

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 is designed to implement the TOPSIS methodology for multi-criteria decision-making. It takes as input a CSV file that includes a decision matrix, user-defined weights, and impact specifications. The script then calculates TOPSIS scores and produces a ranked outcome, offering insights into the decision-making process.

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.