Topsis-Himanshu-102103568 1.0.3

Implementation of Topsis

Project Description

• for: Assignment-1(UCS654)
• Submitted by: Himanshu Bansal
• Roll no: 102103568
• Group: 3COE20

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-Himanshu-102103568

Usage

from Topsis_Himanshu_102103568.topsis import topsis 
inputFile="sample.csv"
weights="1,1,1,1"
impacts="-,+,+,+"
resultFile="result.csv" 
topsis(inputFile, weights, impacts, resultFile)

OR

You can use this package via command line as:

python -m Topsis_Himanshu_102103568.topsis [InputDataFile as .csv] [Weights as a string] [Impacts as a string] [ResultFileName as .csv]

• InputDataFile: 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).
• ResultFileName: 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	0.84	0.71	6.7	42.1	12.59
M2	0.91	0.83	7	31.7	10.11
M3	0.79	0.62	4.8	46.7	13.23
M4	0.78	0.61	6.4	42.4	12.55
M5	0.94	0.88	3.6	62.2	16.91
M6	0.88	0.77	6.5	51.5	14.91
M7	0.66	0.44	5.3	48.9	13.83
M8	0.93	0.86	3.4	37	10.55

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	0.84	0.71	6.7	42.1	12.59	0.41855328299643013	7.0
M2	0.91	0.83	7.0	31.7	10.11	0.4663977143091959	5.0
M3	0.79	0.62	4.8	46.7	13.23	0.5374784843237046	3.0
M4	0.78	0.61	6.4	42.4	12.55	0.4295182212044884	6.0
M5	0.94	0.88	3.6	62.2	16.91	0.5453066145383307	2.0
M6	0.88	0.77	6.5	51.5	14.91	0.39814192807166954	8.0
M7	0.66	0.44	5.3	48.9	13.83	0.4743648907682155	4.0
M8	0.93	0.86	3.4	37.0	10.55	0.6392872727749049	1.0

Error Handling

• If the input file is not found, an error message will be displayed.
• If the number of weights, impacts, or columns in the decision matrix is incorrect, a ValueError will be raised.
• If the columns from the 2nd to the last do not contain numeric values, a ValueError will be raised.
• Any unexpected errors during the execution will be displayed.

LICENSE

(c) 2024 Himanshu Bansal

This project is licensed under the MIT License.