semvec/pypi-packages huggingface dataset:
srctt 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|
non-blocking-io-wrapper 0.7.0
Non blocking io wrapper over an input stream
License: MIT
Requires Python: >=3.6

# non_blocking_io_wrapper

## Description:

`non_blocking_io_wrapper.NonBlockingReader` is a wrapper class to
build a non blocking stream from any read stream. It can be used for
example to wrap the `stdout` pipe of a subprocess in a non blocking way.

It is a true `io.RawIOBase` subclass. Because of that, any method valid
on an input byte stream should work seamlessly. Yet, a line access
(readline, or iterator) will raise an OSError if no data is available at
call time (you can use the `select` method to test/wait for
that condition). If some data is available a line will be returned, or a
partial one if no newline is available in the buffer.

If no data is present, a read will return None instead of blocking.

### Additional methods

    drain(timeout=None)
    
Waits for the underlying stream to reach its end of file.

If timeout is given and is not None it is the maximum time in
seconds to wait.

Returns True if it succeeded or False on timeout.

It is intended to force a blocking wait after signaling an end of
communication to a peer.

    select(timeout=None)
    
Waits for data to become available.

If timeout is given and is not None it is the maximum time in
seconds to wait.

Returns True if it succeeded or False on timeout

It is intended to allow to wait for availability of some data.

## Installation

### From PyPI

Released version are normally available from PyPI. Just user `pip` to
install the last one:

    pip install non_blocking_io_wrapper
    
### From GitHUB

You can also clone the main repository from GitHUB:

    git clone https://github.com/s-ball/non_blocking_io_wrapper.git

## Contribution - development

A test package (97% coverage as of 0.5) is included in the GitHUB
repository and in the source packages on PyPI.

### Special processing for `version.py`

Versioning file is not included in the GitHUB repository because it is
generated with `setuptools_scm`. It is of course included in PyPI packages
so you should make sure to have a true git repository, or to get the sources
from PyPI. The symptom is that you get a version number of 0.0.0 ...

## Disclaimer: beta quality

It is fully functional and pass tests for Python 3.6 to 3.9 on Travis-CI.
Yet it could have a better documentation, and has not been extensively
tested.

## License

This work is licenced under a MIT Licence. See
[LICENSE.txt](https://raw.githubusercontent.com/s-ball/non_blocking_io_wrapper/master/LICENCE.txt)
TOPSIS-Mitul-101803084 0.0.2
Package for Multiple-criteria decision-making using TOPSIS.Requires input file,weights and impacts. Returns dataframe with score and rank of every label.This package can help improve decision-making.
License: MIT
Keywords: MCDA,TOPSIS,Data Science

# Topsis


## 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.

## Purpose

Package for Multiple-criteria decision-making using TOPSIS. Requires input file,weights and impacts. Returns a data frame which has score and rank of every label. This package can help improve decision-making.

### Use the package manager [pip](https://pip.pypa.io/en/stable/) to install Topsis-Mitul-101803084.

```bash
pip install Topsis-Mitul-101803084
```

## Usage

```python
from TOPSIS_Mitul_101803084 import Topsis_rank
Topsis_rank("input.csv","1,1,1,2","+,+,+,-")
# Outputs a dataframe with score and rank columns

```
```python
from TOPSIS_Mitul_101803084 import Topsis_rank
# if output file name is provided,output file is saved in your current directory
Topsis_rank("input.csv","1,1,1,2","+,+,+,-","output.csv")
# Dataframe named output.csv will be saved in your current directory.

```
## Things to take care

1) Weights and Impacts provided as arguments should be separated by comma's and equal to number of numerical columns.
2) Categorical columns are not supported yet. They should be dropped or feature engineered into numerical columns using techniques like One Hot encoding etc.
3) First column should be label column.

## License
[MIT](https://choosealicense.com/licenses/mit/)