semvec/pypi-packages huggingface dataset:
Topsis-hasika-102003542 0.1.0
A Python package for implementing TOPSIS technique.
License: MIT

# TOPSIS


Submitted By: **Hasika Tandon**

***

## What is TOPSIS?

**T**echnique for **O**rder **P**reference by **S**imilarity to **I**deal **S**olution 
(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. 

<br>

## How to install this package:
```
>> pip install TOPSIS-Hasika-102003542
```


### In Command Prompt
```
>> topsis data.csv "1,1,2,1" "+,+,-,+" result.csv
```

## Input file (data.csv)

The decision matrix 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 (`weights`) is not already normalised will be normalised later in the code.

Information of benefit positive(+) or negative(-) impact criteria should be provided in `impacts`.

<br>

## Output file (result.csv)


Model | Correlation | R<sup>2</sup> | RMSE | Accuracy | Topsis_score | Rank
------------ | ------------- | ------------ | ------------- | ------------ | ------------- | ------------- 
M1 |	0.79 | 0.62	| 1.25 | 60.89 | 0.849592 | 2
M2 |  0.66 | 0.44	| 2.89 | 63.07 | 0.143187 | 5
M3 |	0.56 | 0.31	| 1.57 | 62.87 | 0.597633 | 3
M4 |	0.82 | 0.67	| 2.68 | 70.19 | 0.364575 | 4
M5 |	0.75 | 0.56	| 1.3	 | 80.39 | 0.877204 | 1

<br>
The output file contains columns of input file along with two additional columns having **Topsis_score** and **Rank**