semvec/pypi-packages huggingface dataset:
topsis-anubhav-102003049 1.0.2
A Python package implementing TOPSIS technique.
License: MIT

# TOPSIS


Submitted By: **Anubhav 102003049**

***

## 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-anubhav-102003049```


### 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**
Ekaspreet-topsis-102017078 1.0.1
A Python package implementing TOPSIS for MCDM
License: MIT
Keywords: python,TOPSIS,MCDM,MCDA,statistics,prescriptive analytics,cli

Ekaspreet-topsis-102017078


# Topsis in Python  
Author: **Ekaspreet 102017078**  
Maintainer: **Ekaspreet <ekaspreet0209@gmail.com>**.

TOPSIS: It is a for Multiple Criteria Decision Making,A Technique for Order Preference by Similarity to Ideal   
More details at [wikipedia](https://en.wikipedia.org/wiki/TOPSIS).  
<br>
<br>


### In Command Prompt

```
>> topsis data.csv "1,1,1,1" "+,+,-,+" result.csv
```

## Input file (data.csv)
| 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 positive(+) or negative(-) impact criteria should be provided in `impacts`.

<br>

## Output file (result.csv)

| Model | Correlation | R<sup>2</sup> | RMSE | Accuracy | 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 **Score** and **Rank**
Topsis-Abhilash-102053017 0.0.1
A Python package to find TOPSIS for multi-criteria decision analysis method
License: MIT
Keywords: topsis,Multicriteria

#Topsis-Abhilash-102053017
## Topsis Package



This package lets user enter an input file and then generated an output into a corresponding output file wherein the items are given a rank on the basis of weight and impact entered by the user

Used for multi-criteria decision analysis.

TOPSIS stands for Technique for Order of Preference by Similarity to Ideal Solution

Just provide the input attributes and it will gives the results

## Features
-checks for input and output file errors

## Installation

Install the dependencies and devDependencies and start the server.

```sh
$ pip install Topsis-Abhilash-102053017

$ python <package_name> <path to input_data_file_name> <weights as strings> <impacts as strings> <result_file_name>

E.g for input data file as data.csv:
$ python topsis.py data.csv "1,1,1,1" "+,+,-,+" output.csv

This will print all the output attribute values along with the rank column, in a tabular format
```
Topsis-Rohan-102003029 1.0.0
Topsis package for Multiple Criteria Decision Making problems(MCDM) problems
License: MIT
Requires Python: >=3.6

# Topsis Score Calculator

_**Project-1 (UCS654)**_<BR>
_Submitted By: **Rohan Grover**_
_Roll no: **102003029**_
_Group: **3COE2**_

Topsis-Rohan-102003029 is a Python library for dealing with Multiple Criteria Decision Making(MCDM) problems by using Technique for Order of Preference by Similarity to Ideal Solution(TOPSIS).

## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install Topsis-Rohan-102003029.

```bash
pip install Topsis-Rohan-102003029
```

## Usage
Enter csv filename followed by _.csv_ extentsion, then enter the _weights_ vector with vector values separated by commas, followed by the _impacts_ vector with comma separated signs _(+,-)_
```bash
topsis sample.csv "1,1,1,1" "+,-,+,+" output.csv
```
## Example

#### sample.csv

A csv file showing data for different mobile handsets having varying features.

| Model  | Storage space(in gb) | Camera(in MP)| Price(in $)  | Looks(out of 5) |
| :----: |:--------------------:|:------------:|:------------:|:---------------:|
| M1 | 16 | 12 | 250 | 5 |
| M2 | 16 | 8  | 200 | 3 |
| M3 | 32 | 16 | 300 | 4 |
| M4 | 32 | 8  | 275 | 4 |
| M5 | 16 | 16 | 225 | 2 |

weights vector = [ 0.25 , 0.25 , 0.25 , 0.25 ]

impacts vector = [ + , + , - , + ]

### input:

```python
topsis sample.csv "0.25,0.25,0.25,0.25" "+,+,-,+" output.csv
```

### output:

| Model  | Storage space(in gb) | Camera(in MP)| Price(in $)  | Looks(out of 5) | Topsis Score | Rank |
| :----: |:--------------------:|:------------:|:------------:|:---------------:| :---: | :---: |
| M1 | 16 | 12 | 250 | 5 | 0.534277 | 3
| M2 | 16 | 8  | 200 | 3 | 0.308368 | 5
| M3 | 32 | 16 | 300 | 4 | 0.691632 | 1
| M4 | 32 | 8  | 275 | 4 | 0.534737 | 2
| M5 | 16 | 16 | 225 | 2 | 0.401046 | 4


## Other notes

* The first column and first row are removed by the library before processing, in attempt to remove indices and headers. So make sure the csv follows the format as shown in sample.csv.
* Make sure the csv does not contain categorical values


## License
[MIT](https://choosealicense.com/licenses/mit/)
TOPSIS-Gurnoor-102003069 0.0.1
A Python package for implementing TOPSIS technique.
License: MIT

# TOPSIS


Submitted By: **Aseem Khullar**

***

## 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-Aseem-101803469
```


### 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**