TOPSIS-YASHPAL-101803611 3.0.0

A Python package implementing TOPSIS technique.

TOPSIS-Python

Submitted By: Yashpal 101803611

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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-Yashpal-101803611 can be run as in the following example:

This package is used only on command line with keyword topsis followed by data file name, weights, impacts and file name in which result is stored.Output is stored in the file given in arguments along with data given.

In Command Prompt

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


## Sample dataset

The decision matrix (`a`) should be constructed with each row representing a Model alternative, and each column representing a criterion like Accuracy, R<sup>2</sup>, Root Mean Squared Error, Correlation, and many more.

Model | Correlation | R<sup>2</sup> | 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`.

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## Output

Model Score Rank

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1 0.77221 2 2 0.225599 5 3 0.438897 4 4 0.523878 3 5 0.811389 1

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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.