semvec/pypi-packages huggingface dataset:
TOPSIS-Radhika-101803342 1.0.1
A Python package implementing TOPSIS technique.
License: MIT

# TOPSIS


Submitted By: **Harsh 101803327**

***

## 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-Harsh-101803327
```


### 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 | 0.7722 | 2
M2 |  0.66 | 0.44	| 2.89 | 63.07 | 0.2255 | 5
M3 |	0.56 | 0.31	| 1.57 | 62.87 | 0.4388 | 4
M4 |	0.82 | 0.67	| 2.68 | 70.19 | 0.5238 | 3
M5 |	0.75 | 0.56	| 1.3	 | 80.39 | 0.8113 | 1


<br>
The output file contains columns of input file along with two additional columns having **Topsis_score** and **Rank**
TOPSIS-Aditi-101803327 1.0.1
A Python package implementing TOPSIS technique.
License: MIT

# TOPSIS


Submitted By: **Aditi 101803029**

***

## 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-Aditi-101803029
```


### 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 | 0.7722 | 2
M2 |  0.66 | 0.44	| 2.89 | 63.07 | 0.2255 | 5
M3 |	0.56 | 0.31	| 1.57 | 62.87 | 0.4388 | 4
M4 |	0.82 | 0.67	| 2.68 | 70.19 | 0.5238 | 3
M5 |	0.75 | 0.56	| 1.3	 | 80.39 | 0.8113 | 1


<br>
The output file contains columns of input file along with two additional columns having **Topsis_score** and **Rank**
TOPSIS-ANMOL-101803669 0.2
THIS PACKAGE IS TO IMPLEMENT TOPSIS
License: MIT

# TOPSIS_ANMOL_101803669
With this you can calculate the TOPSIS score and RANK of the data provided in '.csv' format.
- Input file:
  - contain three or more columns
  - First column is the object/variable name.
  - From 2nd to last column contain numeric values only

# Overview
  - You can check intermediate steps as well as the final score i.e it provides functions to calculate normalized matrix, weight normalized decision matrix , ideal best , ideal worst lists and so on.

## Usage

In the following paragraphs, I am going to describe how you can get and use TOPSIS for your own projects.

### Getting it
To download TOPSIS, either fork this github repo or simply use Pypi via pip.

    $ pip install TOPSIS_ANMOL_101803669

### Using it
TOPSIS was programmed with ease-of-use in mind. Just, import topsis from TOPSIS-Aditi-101803650

    from TOPSIS_ANMOL_101803669.topsis1 import topsis
    topsis('inputfilename','Weights','Impacts','Outputfilename')

And you are ready to go! 

## topsis
There are 5 steps in this:
  - normalized_matrix
  - weight_normalized
  - ideal_best_worst
  - euclidean_distance
  - topsis_score
## License
[MIT](https://choosealicense.com/licenses/mit/)

## Pre-requisite
The data should be enclosed in the csv file. There must be more than 2 columns

## Result
the output(outputfilename)  is saved in the project folder with extra 2 columns with topsis score and rank.