TOPSIS-Python

Submitted By: Utkarsh Chaudhary

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pypi: https://pypi.org/project/topsis-102003083-Utkarsh/1.1.0/
git: https://github.com/UTK21/topsis-project.git

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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-102003083_Utkarsh can be run as in the following example:

In Command Prompt

>> topsis data.csv "1,1,1,1" "+,+,-,+" final.csv

In Python IDLE:

>>> import pandas as pd
>>> from topsis_python.topsis import topsis
>>> dataset = pd.read_csv('data.csv').values
>>> d = dataset[:,1:]
>>> f = dataset[:,1:]
>>> w = [1,1,1,1]
>>> im = ["+" , "+" , "-" , "+" ]
>>> topsis(d,w,im,f)

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   Score    Rank
-----  --------  ----
  1    0.77221     2
  2    0.225599    5
  3    0.438897    4
  4    0.523878    3
  5    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.