semvec/pypi-packages huggingface dataset:
TOPSIS-Rishab-101803522 0.01
A package for python implemetation of TOPSIS method for multiple criteria decision making
License: MIT
Keywords: SOME,MEANINGFULL,KEYWORDS

It is a method of compensatory aggregation that compares a set of alternatives by identifying weights for each criterion, normalising scores for each criterion and calculating the geometric distance between each alternative and the ideal alternative, which is the best score in each criterion
Topsis-Mansimar-101803292 0.0.1
This package can be used to calculate the topsis score of multiple component data and rank them accordingly
License: MIT

# TOPSIS Package in Python

UCS538  Data Science Fundamentals
Assignment06 - TOPSIS

Submitted by: Mansimar Anand

Roll no: 101803292

* * *

## Brief About TOPSIS

TOPSIS 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 and comparing the distance of each one of the alternatives to those.

* * *

## Installation
#### Before running, make sure you have pandas installed on your system
```
>> pip install TOPSIS-Mansimar-101803292
```

## Usage

```
>> python
>>> from topsis_analysis.topsispackage import topsis
>>> topsis("data.csv","1,1,1,2","+,+,-,+","output.csv")
```
## Sample Input

This input was used to test the module

<table><thead><tr><th>Model</th><th>Corr</th><th>Rseq</th><th>RMSE</th><th>Accuracy</th></tr></thead><tbody><tr><td>M1</td><td>0.79</td><td>0.62</td><td>1.25</td><td>60.89</td></tr><tr><td>M2</td><td>0.66</td><td>0.44</td><td>2.89</td><td>63.07</td></tr><tr><td>M3</td><td>0.56</td><td>0.31</td><td>1.57</td><td>62.87</td></tr><tr><td>M4</td><td>0.82</td><td>0.67</td><td>2.68</td><td>70.19</td></tr><tr><td>M5</td><td>0.75</td><td>0.56</td><td>1.3</td><td>80.39</td></tr></tbody></table>

## Output 


<table><thead><tr><th>Model</th><th align="right">Corr</th><th align="center">Rseq</th><th>RMSE</th><th>Accuracy</th><th>Topsis Score</th><th>Rank</th></tr></thead><tbody><tr><td>M1</td><td align="right">0.79</td><td align="center">0.62</td><td>1.25</td><td>60.89</td><td>0.639133</td><td>2.0</td></tr><tr><td>M2</td><td align="right">0.66</td><td align="center">0.44</td><td>2.89</td><td>63.07</td><td>0.212592</td><td>5.0</td></tr><tr><td>M3</td><td align="right">0.56</td><td align="center">0.31</td><td>1.57</td><td>62.87</td><td>0.407846</td><td>4.0</td></tr><tr><td>M4</td><td align="right">0.82</td><td align="center">0.67</td><td>2.68</td><td>70.19</td><td>0.519153</td><td>3.0</td></tr><tr><td>M5</td><td align="right">0.75</td><td align="center">0.56</td><td>1.3</td><td>80.39</td><td>0.828267</td><td>1.0</td></tr></tbody></table>


## License

Â© 2020 Mansimar Anand

This repository is licensed under the MIT license. See LICENSE for details.
Topsis-IshaanTakkar-101803025 1.0.7
A package -> Calculates Topsis Score and Rank them accordingly
License: MIT

# TOPSIS

Submitted By: **Ishaan Takkar - 101803025**.

Type: **Package**.

Title: **TOPSIS method for multiple-criteria decision making (MCDM)**.

Version: **1.0.3**.

Date: **2020-11-13**.

Author: **Ishaan Takkar**.

Maintainer: **Ishaan Takkar <ishaantakkar@gmail.com>**.

Description: **Evaluation of alternatives based on multiple criteria using TOPSIS method.**.

---

## 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_IshaanTakkar_101803025
```

### In Command Prompt

```
>> topsis data.csv "1,1,1,1" "+,+,-,+" 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    | 0.77         | 2    |
| M2    | 0.66        | 0.44          | 2.89 | 63.07    | 0.23         | 5    |
| M3    | 0.56        | 0.31          | 1.57 | 62.87    | 0.44         | 4    |
| M4    | 0.82        | 0.67          | 2.68 | 70.19    | 0.52         | 3    |
| M5    | 0.75        | 0.56          | 1.3  | 80.39    | 0.81         | 1    |

<br>
The output file contains columns of input file along with two additional columns having **Topsis_score** and **Rank**