semvec/pypi-packages huggingface dataset:
Torchest 0.0.1
Toolbelt for pytorch framework
Keywords: python,ai,machine learning,neural network,pytorch,trainer

# Torchest
Pytorch tools and utilities (Trainers, data generators, functions, and more...)

# Trainers

One of the most common tasks you will do in pytorch is define training loops, which is a hassle because they are almost always the same. Torchest has trainers you can use for your projects

```python
from torchest.trainer import SimpleTrainer

# previous definition of model and data preparation
# train_dataloader = DataLoader(train_data)
# dev_dataloader = DataLoader(dev_data)
# test_dataloader = DataLoader(test_data)
# model = nn.Sequential(...)
...

"""
Prepare Trainer
"""
loss_fn = nn.CrossEntropyLoss()
learning_rate= 1e-2
optimizer = optim.Adam(model.parameters(), lr=learning_rate)

trainer = SimpleTrainer(model, loss_fn, optimizer)

"""
Train
"""
epochs = 500
trainer.train(data_train=train_dataloader, data_dev=dev_dataloader, data_test=test_dataloader, epochs=epochs)
```

Training loop progress

```
31%|██████████████████▍      | 156/500 [00:02<00:05, 58.01epoch/s, train_accuracy=76.2, train_cost=0.238290]
```

Trainer also saves the cost for the train, dev and test passes. You can simply call `trainer.plot_costs()` to display the graph with the costs

<img src="images/trainer_costs.png" alt="Trainer costs" style="width: 400px; display:block; margin-left:auto; margin-right:auto"/>


# Data generators

## Spiral data

This will create a 2d matrix with points in a dataplot. Ideal for testing non-linearity in your network

```python
from torchest.datagen import spiral_datagen

class_num =  3
X, Y = spiral_datagen(450, class_num) # 450 elements per class
```

<img src="images/spiral_2d.png" alt="Spiral data screenshot" style="width: 400px; display:block; margin-left:auto; margin-right:auto"/>
lestpy 0.0.11
Regression model with logical interactions
Keywords: python,modeling,regression

# lestpy

## Overview
### Why
Lestpy is aimed to model regression problems with a bunch of logical interactions. This approach is an alternative way to describe the relationship between the features and the targets.


## Getting Started

### Installation

You can install LestPy from [PyPI]():

```sh
    python -m pip install lestpy
```

### Last release
Lespy 0.0.11
```sh
    python -m pip install lestpy --upgrade
```

### Main classes, methods and attributes
* Interaction
Methods:
    1. compute
    2. get_interaction_list
    3. get_interaction_dict
    3. add_interaction_dict
    4. remove_interactions
* InteractionBuilder
* Transformer
Methods:
    1. fit
    2. transform
    3. fit_transform
    4. inverse_transform
* LBM_Regression
Methods:
    1. transform
    2. fit
    3. fit_transform
    4. predict
    5. optimize
    6. features_analysis
    7. print_model
    7. fitting_score
    8. extract_features
* Display
Methods:
    1. ternary_diagram
    2. response_surface
    3. pareto_frontier
    4. sensibility_analysis
    5. display_interaction
    6. residues
    7. fit
    8. metrics_curve
    9. describe
    10. corr_graph (in development)
* Outliers_Inspection
Methods:
    1. cooks_distance
    2. mahalanobis_distance
    3. z_score

### How to

## Use
Lestpy is designed to be used similarly to sklearn modelization classes and their methods (fit(), transform(), predict(), ...)

## Contributing
There are many ways to support the development of lestpy:

* **File an issue** on Github, if you encounter problems, have a proposal, etc.
* **Send an email with ideas** to the author.
* **Submit a pull request** on Github if you improved the code and know how to use git.


## Links
The source code and issue tracker of this package is to be found on **Github**: [pyonysos/lestpy].


[pyonysos/lestpy]: https://github.com/pyonysos/lestpy
asreview-wordcloud 1.0.1
Wordcloud tools for the ASReview project
Requires Python: >=3.7
Keywords: asreview wordcloud

# ASReview-wordcloud

[![PyPI version](https://badge.fury.io/py/asreview-wordcloud.svg)](https://badge.fury.io/py/asreview-wordcloud) [![Downloads](https://pepy.tech/badge/asreview-wordcloud)](https://pepy.tech/project/asreview-wordcloud) ![Deploy and release](https://github.com/asreview/asreview-wordcloud/workflows/Deploy%20and%20release/badge.svg) ![Build status](https://github.com/asreview/asreview-wordcloud/workflows/test-suite/badge.svg) [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.4672242.svg)](https://doi.org/10.5281/zenodo.4672242)


ASReview-wordcloud is an extension for the [ASReview
LAB](https://github.com/asreview/asreview) software. It offers an easy way to
create a visual impression of the contents of datasets.

## Installation

The easiest way to install the wordcloud extension is to install from PyPI:

``` bash
pip install asreview-wordcloud
```

After installation of the wordcloud extension, `asreview` should automatically
detect it. Test this by:

```bash
asreview --help
```

It should list the `wordcloud` subcommand.

## Basic usage

The dataset should contain a column containing titles and/or abstracts.
For specific requirements check the [ASReview documentation](https://asreview.readthedocs.io/en/latest/intro/datasets.html).
To use your data use:

```
asreview wordcloud MY_DATA.csv
```

The following shows the [Schoot et al. (2017) dataset:](https://asreview.readthedocs.io/en/latest/intro/datasets.html#benchmark-datasets)

![All texts](https://github.com/asreview/asreview-wordcloud/blob/main/figures/ptsd_all.png?raw=true)

To make a wordcloud on titles only, use the `title` flag.

```
asreview wordcloud MY_DATA.csv --title
```

![Titles only](https://github.com/asreview/asreview-wordcloud/blob/main/figures/ptsd_title.png?raw=true)

To make a wordcloud on abstracts only, use the `abstract` flag.

```
asreview wordcloud MY_DATA.csv --abstract
```

![Abstracts only](https://github.com/asreview/asreview-wordcloud/blob/main/figures/ptsd_abstract.png?raw=true)

To make a wordcloud on relevant (inclusions) only, use the `relevant` flag.

```
asreview wordcloud MY_DATA.csv --relevant
```

### Save figure

Save the wordcloud to a file with the `-o`  flag.

```
asreview wordcloud MY_DATA.csv -o MY_DATA_WORDCLOUD.png
```

## License

This extension is published under the [MIT license](/LICENSE).

## Contact

This extension is part of the ASReview project ([asreview.ai](https://asreview.ai)). It is maintained by the
maintainers of ASReview LAB. See [ASReview
LAB](https://github.com/asreview/asreview) for contact information and more
resources.