semvec/pypi-packages huggingface dataset:
Topsis-Satyam-101803324 1.0.5
This package can be used to calculate the topsis score of multiple component data and rank them accordingly
License: MIT
Keywords: TOPSIS

# TOPSIS Package in Python

Submitted by: Satyam Verma

Roll no: 101803324

UCS538

---

## TOPSIS

TOPSIS is an acronym that 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.

---

## How to use

The package Topsis-Satyam-101803324 can be run though the command line as follows:

```
>> pip install Topsis-Satyam-101803324
```

```
>>topsis data.csv "1,1,1,1" "+,+,-,+" result.csv
```

## Sample Input

The decision matrix should be constructed with each row representing a Model alternative, and each column representing a criterion like Accuracy, R2, Root Mean Squared Error, Correlation, and many more.

<table><thead><tr><th>Model</th><th>Correlation</th><th>R2</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>

<br>
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 of this sample input

The output that will be generated from the following input data will be:

<table><thead><tr><th>Model</th><th align="right">Correlation</th><th align="center">R2</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>0.7722097345612788</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>0.22559875426413367</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>0.43889731728018605</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>0.5238778712729114</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>0.8113887082429979</td><td>1.0</td></tr></tbody></table>

<br>
The output file contains columns of input file along with two additional columns having **Topsis_score** and **Rank** .
Here the ranks are given as rank 1 is the best solution according to the weights and impacts given and rank 5 is the worst solution.

---
TOPSIS-Aashima-101803213 1.0.1
TOPSIS
License: MIT

# TOPSIS_Aashima_101803213
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. 

## Installation 
'''pip install TOPSIS_Aashima_101803213'''

## How to use it?
Open terminal and type PYTHON TOPSIS along with input file path whose topsis value and rank you wanted to find
i.e python TOPSIS "data.csv" 

Please make sure to update tests as appropriate.
https://github.com/aashima99/TOPSIS \#\# License
[MIT](https://choosealicense.com/licenses/mit/)

## License
Â©  2020 Aashima Tandon
This repository is licensed under the MIT license. 
See LICENSE for details
TOPSIS-ANUJ-101803638 0.5
THIS PACKAGE IS TO IMPLEMENT TOPSIS
License: MIT

# TOPSIS_Anuj_101803638
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_Anuj_101803638

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

    from TOPSIS_Anuj_101803638.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.
TOPSIS-Shruti-101803512 1
A Python pip package to apply topsis approach to rank the entries in a dataset
License: MIT

# TOPSIS\_Shruti\_101803512

## What is TOPSIS ?
The Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) is a multi-criteria decision analysis method

### How to use the package
TOPSIS_Shruti_101803512 package should be used as:
### In command prompt

>\>> pip install TOPSIS_Shruti_101803512

>\>> python 
>\>> import TOPSIS_Shruti_101803512 as t
>\>> t.topsis_evaluation(<InputDataFile>,<Weights>,<Impacts>,<ResultFileName>)
>\>> t.topsis_evaluation("data.csv","1,1,1,2","+,+,-,+","result.csv")

### Some instructions:
> â€¢ Input file must contain three or more columns.
> â€¢ From 2nd to last columns must contain numeric values only (Handling of non-numeric values)
> â€¢ Number of weights, number of impacts and number of columns (from 2nd to last columns) must
be same.
> â€¢ Impacts must be either +ve or -ve.
> â€¢ Impacts and weights must be separated by â€˜,â€™ (comma).

## Example:
The dataset upon which topsis is to be performed is taken as folows. It will be in the form of a csv file.
#### Sample Input

| Model | Corr | Rseq | 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    |

The following output with performance score and rank will be produced in a result(csv file) file and printed as output. 1st rank offering us the best decision, and last rank offering the worst decision making, according to TOPSIS method.
#### OUTPUT

| Model | Corr | Rseq | RMSE | Accuracy | Topsis Score        | Rank |
|-------|------|------|------|----------|---------------------|------|
| M1    | 0.79 | 0.62 | 1.25 | 60.89    | 0.6391330141342587  | 2    |
| M2    | 0.66 | 0.44 | 2.89 | 63.07    | 0.21259182969277918 | 5    |
| M3    | 0.56 | 0.31 | 1.57 | 62.87    | 0.4078456776130516  | 4    |
| M4    | 0.82 | 0.67 | 2.68 | 70.19    | 0.5191532395007472  | 3    |
| M5    | 0.75 | 0.56 | 1.3  | 80.39    | 0.8282665851935813  | 1    |
TOPSIS-Pranshu-101853037 0.0.1
This is a Python Package implementing TOPSIS used for multi-criteria decision analysis method
License: MIT
Keywords: topsis,becoe,UCS538,TIET

### TOPSIS
# 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.

Installation
>>pip install TOPSIS_Pranshu_101853037

How to run in command prompt

>>from TOPSIS_Pranshu_101853037.topsis import topsis

>>topsis("data.csv","1,1,1,2","+,+,-,+","result.csv")

Input File (data.csv)
Input file contain three or more columns
First column is the object/variable name (e.g. M1, M2, M3, M4,...)
From 2nd to last columns contain numeric values only
Output File (result.csv)
Result file contains all the columns of input file and two additional columns having TOPSIS SCORE and RANK

License
MIT
mlaccuracycalc 0.5
This is a package that houses the  functions which can produce accuracy results for each algorithm in the categories of  Clustering,Regression & Classification based on passing the arguments - independent and dependent variables/features
License: MIT
Keywords: Machiene,Machiene Learning,Learning,accuracy,class and object,Calculator,Test,Model,Accuracy Test,Accuracy Calculator

# ml-accuracy-calc/mlaccuracycalc
  This package consists of various algorithms  which can produce accuracy results for each algorithm in the categories of  Clustering,Regression & Classification based on given the arguments - independent and dependent variables/features
## Development Status:
Under Development (Early release)

### Developed by:

 [Vishal Balaji Sivaraman](https://github.com/TheSocialLion)

### Installation:

Use the package manager [pip](https://pip.pypa.io/en/stable/) to install ML-Accuracy-Calc

```bash
pip install mlaccuracycalc
```

### To import mlaccuracycalc:

```python
import mlaccuracycalc
```
### To use a particular function in mlaccuracycalc:
```python
from mlaccuracycalc import ml_model_accuracy
mma=ml_model_accuracy()
# Object for class declaration
mma.model_accuracy(X,y,i)
```

## License:
[MIT](https://choosealicense.com/licenses/mit/)
Topsis-shruti 1.0.0
A Python package to get the the topsis score and rank
License: MIT

# TOPSIS-Python

## Submitted By: Shruti agrawal 101803061
***
## 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.know more from [wikipedia]("https://en.wikipedia.org/wiki/TOPSIS")

## How to use this package:

TOPSIS-Shruti-101803061 can be run as in the following example:
## In Command Prompt :
>```python topsis.py data.csv "1,1,1,1" "+,+,-,+" result.csv```

## sample dataset:

|Model|Corr|Rseq|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

|Model|Corr|Rseq|RMSE|Accuracy|Topsis_score|Rank|
|-----|----|----|----|--------|------|----|
|M1	  |0.79	|0.62|	1.25|	60.89|0.772209735|2|
|M2|	0.66|	0.44|	2.89|	63.07|0.225598754|5|
|M3	|0.56|	0.31|	1.57|	62.87|0.438897317|4|
|M4	|0.82|	0.67|	2.68|	70.19|0.523877871|3|
|M5	|0.75|	0.56|	1.3	|80.39|0.811388708|1|