topsis-101803128 1.4

A Python package implementing TOPSIS technique.

TOPSIS-Python

Assignment 6: UCS538

Submitted By: DIVYAM JAIN-101803128

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What is TOPSIS

Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) originated in the 1980s as a multi-criteria decision making method. TOPSIS chooses the alternative of shortest Euclidean distance from the ideal solution, and greatest distance from the negative-ideal solution. More details at wikipedia.

How to use this package:

TOPSIS-Divyam-101803128 can be run as in the following example:

Python Script

from topsis_101803128.topsis import CalcTopsisScore
filename = "input.csv"
weight = "1,1,1,2"
impact = "+,+,-,+"
CalcTopsisScore(filename, weight, impact )

Sample dataset

The decision matrix (a) should be constructed with each row representing a Model alternative, and each column representing a criterion like Accuracy, R², Root Mean Squared Error, Correlation, and many more.

Model	Correlation	R2	RMSE	Accuracy
M1	0.79	0.62	1.25	60.89
M2	0.66	0.44	2.89	63.07
M3	0.56	0.31	1.57	62.87
M4	0.82	0.67	2.68	70.19
M5	0.75	0.56	1.3	80.39

Weights (w) is not already normalised will be normalised later in the code.

Information of benefit positive(+) or negative(-) impact criteria should be provided in I.

Output

Model	Correlation	R2	RMSE	Accuracy	Score	Rank
M1	0.79	0.62	1.25	60.89	0.77221	2
M2	0.66	0.44	2.89	63.07	0.225599	5
M3	0.56	0.31	1.57	62.87	0.438897	4
M4	0.82	0.67	2.68	70.19	0.523878	3
M5	0.75	0.56	1.3	80.39	0.811389	1

The rankings are displayed in the form of a table using a package 'tabulate', with the 1st rank offering us the best decision, and last rank offering the worst decision making, according to TOPSIS method.

License

© 2020 Divyam Jain

This repository is licensed under the MIT license. See LICENSE for details.