semvec/pypi-packages huggingface dataset:
TOPSIS-Aditi-101803029 1.0.3
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-sunidhi 2.0.0
A python package to implement TOPSIS on a given dataset
License: MIT

# topsis_pckg
It is a Python package to find out the best value among the different data using the mathematical calculations.
## Installation

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install this package.

```bash
pip install topsis-sunidhi
```

## About
TOPSIS is an acronym that stands for 'Technique of Order Preference Similarity to the Ideal Solution' 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

To get started quickly, just use the following:

```bash
topsis filename weights impacts result
```
make ensure the weights and impacts should be in "" 

eg: "1,1,1,1" and "+,-,+,-"

## 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.
TOPSIS-Aryan-101803035 0.0.2
A Python package in which TOPSIS technique is implemented.
License: MIT

Package name = TOPSIS-Aryan-101803035

# About the project

As the name suggests, this package implements TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) which is a multi-criteria decision analysis method (MCDA or MCDM), which is a technique based on the concept that the chosen alternative should have the shortest geometric distance from the positive ideal solution (PIS) and the longest geometric distance from the negative ideal solution (NIS). 

This package hence provides with the TOPSIS score according to which a model's relative rank with  respect to other models with same number of entries is also provided.


### Installation

Install the package using pip as follows :

```
>> pip install TOPSIS-Aryan-101803035==0.0.2
```

### How to run in command prompt

First go to the directory (using cd following by your directory command) where it has been installed (prefreably the directory will be in main python folder -> Libs -> site-packages -> yourpackagename) then run the following commands : 
```
>> python TOPSIS.py <InputDataFile> <Weights> <Impacts> <ResultFileName>
```
Eg: 
```
>> python TOPSIS.py data.csv “1,1,1,2” “+,+,-,+” result.csv
```
TOPSIS-Akriti-101803608 1.0.3
A Python package to get best alternative available using TOPSIS method.
License: MIT

# TOPSIS-Akriti-101803608

A python package which implements TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) which is a multi-criteria decision analysis method.

Steps involved in TOPSIS Algorithm:
    
    1. Creating an evaluation matrix (m x n).
    2. Normalization of the matrix so formed such that each metric for a given model lies between 0 and 1.
    3. Creating a weighted normalized matrix i.e. multiplying the metrics with the weights of respective model.
    4. Determining the ideal best and ideal worst alternatives for each criterion.
    5. Calculating the euclidean distance between the target alternative and ideal best and idealworst alternatives.
    6. Calculate the TOPSIS score i.e. similarity to the ideal worst alternative.
    7. Rank the models on the basis of the TOPSIS score thus obtained.s

# Usage

Running the following query on the command-line interface will help you to rank the models i.e. choose the best alternative available from a given set of models based on multiple, usually conflicting criteria.
  
    TOPSIS-Akriti-101803608 <data_file_name> <weights> <impacts> <result_file_name>
    Example - TOPSIS-Akriti-101803608 data.csv "1,1,1,2" "+,+,-,+" result.csv

Note - Make sure that you run the command in the folder you have stored your data files , else provide the complete path of the file.
TOPSIS-Yash-101803064 1.0.1
A package -> Calculates Topsis Score and Rank them accordingly
License: MIT

## Topsis_Tewatia

# TOPSIS

Submitted By: **Yash Kumar Tewatia - 101803064**.

Type: **Package**.

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

Version: **1.0.4**.

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

Author: **Yash Kumar Tewatia**.

Maintainer: **Yash Kumar Tewatia <yashbsr3@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-Yash-101803064
```

### 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-Tushar-101803715 0.0.7
A Python package implementing TOPSIS technique.
License: MIT

#Topsis Package
It is a Python package to find out the best value among the different data using the mathematical calculations.
## Installation
Use the package manager [pip](https://pip.pypa.io/en/stable/) to install this package.
bash
pip install Topsis-Tushar-101803715
## About
TOPSIS is an acronym that stands for 'Technique of Order Preference Similarity to the Ideal Solution' 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

To get started quickly, just use the following:

bash
from topis_pckg.topsis import topsis
topsis('inputfilename','Weights','Impacts','Outputfilename')

make ensure the weights and impacts should be in "" 

eg: "1,1,1,1" and "+,-,+,-"

## 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.

## Contributing
Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change.
TOPSIS-aayushi-101803059 1.0.1
Topsis Calculator ---PARAMRTERS--- CSV file , Weight and Impact array
License: MIT

## 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-aayushi-101803059
```

### In Command Prompt

```
>> topsis_aayushi 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**

# Author Detail

Submitted By: **Aayushi Gupta (101803059)**.

Version: **1.0.0**.

---
TOPSIS-Lira-101803533 0.1
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-AmishaSachdeva-101983011 1.0
A Python package implementing TOPSIS technique.
License: MIT

# TOPSIS-Python

## What is TOPSIS

Technique for Order Preference by Similarity to Ideal Solution (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.


## How to use this package:



TOPSIS- AmishaSachdeva-101983011 can be run as in the following example:

### In command prompt:

```bash
>> TOPSIS data.csv "1,1,2,1" "+,-,+,-" result.csv
```




## Sample Dataset

```python



Model	Correlation	  R2	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

```

## Output

```python
Model   Score    Rank
-----  --------  ----
  1    0.77221     2
  2    0.225599    5
  3    0.438897    4
  4    0.523878    3
  5    0.811389    1

```
TOPSIS-Stuti-101853033 1.0.2
A Python package implementing TOPSIS technique.
License: MIT

# TOPSIS


Submitted By: **Stuti 101853033**

***

## 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-Stuti-101853033
```


### 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-RaghavSharma-101803688 1.0
A Python package implementing TOPSIS technique.
License: MIT

# TOPSIS-Python

## What is TOPSIS

Technique for Order Preference by Similarity to Ideal Solution (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.


## How to use this package:



TOPSIS-RaghavSharma-101803688 can be run as in the following example:

### In command prompt:

```bash
>> TOPSIS data.csv "1,1,2,1" "+,-,+,-" result.csv
```




## Sample Dataset

```python



Model	Correlation	  R2	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

```

## Output

```python
Model   Score    Rank
-----  --------  ----
  1    0.77221     2
  2    0.225599    5
  3    0.438897    4
  4    0.523878    3
  5    0.811389    1

```
TOPSIS-RadhikaModi-101803342 1.0.1
A Python package implementing TOPSIS technique.
License: MIT

# TOPSIS


Submitted By: **Radhika 101803342**

***

## 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_RadhikaModi-101803342
```


### 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**