semvec/pypi-packages huggingface dataset:
topsis-101703195-finalrevised-finalrevised 0.0.1
A Python package implementing TOPSIS technique.
License: MIT

#Topsis Package
A Python package to implement topsis on a given dataset.
##Usage
Following query on terminal will provide you the best and worst decisions for the dataset.
```
python topsis.py dataset_sample.csv 1,1,1,1 0,1,1,0
```
embedeval 1.0.4
NLP Embeddings Evaluation Tool
License: MIT
Requires Python: >=3.7.*
Keywords: machine-learning,natural-language-processing,nlp,embeddings,evaluation,report

# NLP Embeddings Evaluation Tool

[![PyPI License](https://img.shields.io/pypi/l/embedeval.svg)](https://github.com/timofurrer/embedeval/blob/master/LICENSE)
<br>
[![Actions Status](https://github.com/timofurrer/embedeval/workflows/CI/badge.svg)](https://github.com/timofurrer/embedeval/actions)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/ambv/black)
<br>
[![PyPI version](https://badge.fury.io/py/embedeval.svg)](https://badge.fury.io/py/embedeval)
[![PyPI](https://img.shields.io/pypi/pyversions/embedeval.svg)](https://pypi.python.org/pypi/embedeval)
[![PyPI](https://img.shields.io/pypi/wheel/embedeval.svg)](https://pypi.python.org/pypi/embedeval)

***

The *NLP Embeddings Evaluation Tool* is a command line tool to evaluate Natural Language Processing Embeddings
using custom intrinsic and extrinsic tasks.

# Installation

embedeval is available as `pip` package:

```bash
python -m pip install embedeval
```

NOTE: it might not be installable as of today using pip with PyPI.
However, installing from source will work. Use `.` instead of `embedeval` in the pip command.

# Getting started

Run the `word-analogy` Task on your Word Embedding:

```bash
embedeval embedding.vec -t word-analogy
```

Run the `word-analogy` and `word-similarity` Tasks on your Word Embedding:

```bash
embedeval embedding.vec -t word-analogy -t word-similarity
```

# Documentation

The whole documentation of embedeval is available on [Read The Docs](http://embedeval.readthedocs.org).

# Supported platforms

embedeval is supported on Windows, Mac and Linux

# Contribution

Yes, we are looking for some contributors and people who spread out a word about embedeval. Help us to improve these piece of software. You don't know what to do?
Just have a look at the Issues or create a new one.
Please have a look at the [Contributing Guidelines](https://github.com/timofurrer/embedeval/blob/master/.github/CONTRIBUTING.md), too.

# Project Information

embedeval is released under the MIT license, its documentation lives at [Read The Docs](http://embedeval.readthedocs.org),
the code on [GitHub](https://github.com/timofurrer/embedeval),
and the latest release on [PyPI](https://pypi.org/project/embedeval).
It’s rigorously tested on Python 3.5+.

If you'd like to contribute to embedeval you're most welcome and we've written a [little guide](https://github.com/timofurrer/embedeval/blob/master/.github/CONTRIBUTING.md) to get you started!

***

*<p align="center">This project is published under [MIT](LICENSE).<br>A [Timo Furrer](https://tuxtimo.me) project.<br>- :tada: -</p>*
topsis-101703192-girish-1.0.0 1.0.0
A Python package implementing TOPSIS technique.
License: MIT

# TOPSIS-Python

**Project 1 : UCS633**


Submitted By: **Girish Julka 101703192**

***

## 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. More details at [wikipedia](https://en.wikipedia.org/wiki/TOPSIS).

<br>

## How to use this package:

TOPSIS-Girish Julka 101703192  can be run as in the following example:



### In Command Prompt
```
>> topsis data.csv "1,1,1,1" "+,+,-,+"
```
<br>

### In Python IDLE:
```
>>> import pandas as pd
>>> from topsis_python.topsis import topsis
>>> dataset = pd.read_csv('data.csv').values
>>> d = dataset[:,1:]
>>> w = [1,1,1,1]
>>> im = ["+" , "+" , "-" , "+" ]
>>> topsis(d,w,im)
```

<br>

## Sample dataset

The decision matrix (`a`) 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 (`w`) is not already normalised will be normalised later in the code.

Information of benefit positive(+) or negative(-) impact criteria should be provided in `I`.

<br>

## Output

```
Model   Score    Rank
-----  --------  ----
  1    0.77221     2
  2    0.225599    5
  3    0.438897    4
  4    0.523878    3
  5    0.811389    1
```
<br>
The rankings are displayed in the form of a table using a package 'tabulate', with the 1st rank offering us the best decision, and last rank offering the worst decision making, according to TOPSIS method.