semvec/pypi-packages huggingface dataset:
TOPSIS-HIMANSHU-101983063 0.3
This is code to find topsis score.

Source code for TOPSIS optimization algorithm in python.

TOPSIS is an algorithm to determine the best choice out of many using Positive Ideal Solution and Negative Ideal 
Solution.

For sample solutions visit: http://www.jiem.org/index.php/jiem/article/view/573/498 WikiPedia: 
https://en.wikipedia.org/wiki/TOPSIS

TOPSIS is an acronym that stands for â€˜Technique of Order Preference Similarity to the Ideal Solutionâ€™ and is a pretty
straightforward MCDA method. As the name implies, the method is based on finding an ideal and an anti-ideal solution 
 
 
 
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`.

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.