semvec/pypi-packages huggingface dataset:
Topsis-Pushpak-101903239 1.0
Topsis Calculation
License: MIT
Keywords: Topsis,TopsisPackage

# Topsis

## A Multi-Criteria Decision Analysis Method

**Submitted By** - Pushpak Kamboj
**Roll No.** - 101903239
**Group** - 3COE9

It is a Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) came in the 1980s as a multi-criteria-based decision-making method. TOPSIS chooses the alternative of shortest the Euclidean distance from the ideal solution and greatest distance from the negative ideal solution.

## Installation

This package requires pandas to be pre-installed and python to run.
Install the dependencies and devDependencies and start the server.
**To Install and run in cmd line:**

```sh
pip install Topsis-Pushpak-102083055
import Topsis_Pushpak_102083055
Topsis_Pushpak_102083055.Topsis(Input_File_Name.csv,"1,1,1,1 ...","+,-,+,- ...","resultfile.csv")
```
coral-ordinal 0.1.8
Tensorflow Keras implementation of CORAL ordinal regression output layer, loss, activation, and metrics
License: MIT
Requires Python: >=3.6

# Ordinal regression in Tensorflow Keras

[![PyPi version](https://badge.fury.io/py/coral-ordinal.svg)](https://pypi.org/project/coral-ordinal/)
[![PyPi downloads](https://img.shields.io/pypi/dm/coral-ordinal?style=flat)](https://pypi.org/project/coral-ordinal/)


Tensorflow Keras implementation of ordinal regression (aka ordinal classification) using consistent rank logits (CORAL) by Cao, Mirjalili, & Raschka (2019).

This package includes:

  * Ordinal output layer: `CoralOrdinal()`
  * Ordinal loss function: `OrdinalCrossEntropy()`
  * Ordinal error metric: `MeanAbsoluteErrorLabels()`
  * Ordinal activation function: `ordinal_softmax()`

This is a work in progress, so please post any issues to the [issue queue](https://github.com/ck37/coral-ordinal/issues). The package was developed as part of the Berkeley D-Lab's [hate speech measurement project](https://hatespeech.berkeley.edu) and paper (Kennedy et al. 2020).

**Acknowledgments**: Many thanks to [Sebastian Raschka](https://github.com/rasbt) for the help in porting from the [PyTorch source repository](https://github.com/Raschka-research-group/coral-cnn/).

Key pending items:

  * Function docstrings
  * Docs
  * Tests

## Installation

Install the stable version via pip:

```bash
pip install coral-ordinal
```

Install the most recent code on GitHub via pip:

```bash
pip install git+https://github.com/ck37/coral-ordinal/
```

## Dependencies

This package relies on Python 3.6+, Tensorflow 2.2+, and numpy.

## Example

This is a quick example to show a basic model implementation. With actual data one would also want to specify the input shape.

```python
import coral_ordinal as coral
NUM_CLASSES = 5
model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(32, activation = "relu"))
model.add(coral.CoralOrdinal(num_classes = NUM_CLASSES)) # Ordinal variable has 5 labels, 0 through 4.
model.compile(loss = coral.OrdinalCrossEntropy(),
              metrics = [coral.MeanAbsoluteErrorLabels()])
```

[See this colab notebook](https://colab.research.google.com/drive/1AQl4XeqRRhd7l30bmgLVObKt5RFPHttn) for extended examples of ordinal regression with MNIST (multilayer perceptron) and Amazon reviews (universal sentence encoder).

Note that the minimum value of the ordinal variable needs to be 0. If your labeled data ranges from 1 to 5, you will need to subtract 1 so that it is scaled to be 0 to 4.


## References

Cao, W., Mirjalili, V., & Raschka, S. (2019). [Rank-consistent ordinal regression for neural networks](https://arxiv.org/abs/1901.07884). arXiv preprint arXiv:1901.07884, 6. 

Kennedy, C. J., Bacon, G., Sahn, A., & von Vacano, C. (2020). [Constructing interval variables via faceted Rasch measurement and multitask deep learning: a hate speech application](https://arxiv.org/abs/2009.10277). arXiv preprint arXiv:2009.10277.
Topsis-RaghavNarula-101903795 1.0.0
It gives a csv file that includes the topsis result
License: MIT

# Topsis Result Calculator
It runs on terminal and takes a csv file , weights of column, impacts and name of the output file as input and gives an output csv file that contains topsis performance score and the rank of a particular entity.

## Installation
On your terminal run the command below:<br>
```pip install Topsis-RaghavNarula-101903795```

## Importing and using in source file
use the package in source file as follows:<br>

```
tbj=__import__("topsis")
tbj.get_topsis_result()
```

## How to get output?
output is taken by running the command on terminal as : python "sourcefile.py" "input.csv" "weights(separateed by ',')" "impact(separated by ','))" 
use impact as '+' for maximizing the feature and '-' for minimizing.
On your terminal run the command:<br>
```python source_file_name.py input_file.csv "1,1,0,1" "+,-,+,+" output_file_name.csv```
<br>
It will display the input arguments and the input data on terminal screen  and Output will be stored in your present working directory as a csv file.

## Constraints
### Number of columns in input file(other than the name of the entity) should be equal to length of impacts and length of weights.
### Input file should not have blank values
### Number of Columns in Input files should not be less than 3
### There should not be any csv file with same name as your output file in present directory
### Number of Arguments should be equal to the number as specified above