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Documentation #71

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doxygen files
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#46 insert box to user_guide and getting start
alvesisaque Jan 17, 2023
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(#57)(#58) Update sphinx path
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(#57)(#58) Remove /html and /xml from git monitoring
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#57 update config and insert files in .gitignore
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#57 Update hygia description and 'why hygia'
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(#57 #58) Increment _sources on .gitignore
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#68 documenting data_pipeline classes and functions
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#68 documenting feature_engineering classes and functions
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#68 documenting model classes and functions
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14 changes: 13 additions & 1 deletion .gitignore
Original file line number Diff line number Diff line change
Expand Up @@ -135,4 +135,16 @@ poetry.lock
.DS_Store

# Custom paths
data/tmp/*
data/tmp/*
# Sphinx
docs/html
docs/xml
docs/searchindex.js
docs/objects.inv
docs/.doctrees
docs/.buildinfo
docs/_sources
docs/_panels_static

genindex.html
search.html
52 changes: 16 additions & 36 deletions README.md
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Expand Up @@ -12,7 +12,11 @@

## What is it?

Hygia is a Python package that provides fast, flexible, and expressive data pipeline configuration through a YAML file to make working with Machine Learning data easy and intuitive. It consists of helping developers and users to register, organize, compare and share all their ML model metadata in a single place, facilitating the generation of requirements in the ETL (Extract, Transform and Load) process. Thus, the migration can be scaled, automated, and accelerated for similar contexts.
Hygia is a Python package that provides fast, flexible, and expressive data pipeline to make working with Machine Learning data easy and intuitive. The library is designed to make it easy for developers and data scientists to work with a wide range of data sources, perform data preprocessing, feature engineering, and train models with minimal effort.

One of the key features of Hygia is its support for configuration through YAML files. With the help of a configuration file, users can easily specify the steps they want to run in their data pipeline, including extracting data from various sources, transforming the data, and loading it into the pipeline for processing. This not only makes it easier to automate the pipeline, but also enables users to compare and share their results with others.

In addition, Hygia is designed to support the ETL (Extract, Transform and Load) process, making it an ideal solution for developers and data scientists looking to scale and automate their workflows. With its fast, flexible, and expressive data pipeline configuration, Hygia makes it easy to organize and manage all your ML model, saving time, effort and allowing you to focus on the most important aspects of your work.

## Main Features

Expand All @@ -22,50 +26,26 @@ Hygia is a Python package that provides fast, flexible, and expressive data pipe
- Visualize results in customized dashboards
- Test on different databases

## Where to get it

The source code is currently hosted on GitHub at: `https://github.com/hygia-org`

## Installation from sources

```
python -m venv env
source env/bin/activate
pip install -r requirements-dev.txt
```

### Boilerplate
## Check the documentation

```
examples/hygia_boilerplate.ipynb
```
If you're looking to use the Hygia library and get a better understanding of how it works, you can check out the comprehensive documentation available at [Hygia Documentation](https://hygia-org.github.io/hygia/). This website provides a wealth of information on the library's capabilities and how to use it.

### Testing
In addition to the documentation, we also have a number of boilerplates available at [Examples](https://github.com/hygia-org/hygia/tree/main/examples). These boilerplates provide hands-on examples and practical explanations of using the library and the .yaml file, making it easier for you to get started with using Hygia. Whether you're a seasoned data scientist or just starting out, these resources will help you get the most out of the library.

```
pytest --cov
```
## Where to get it

### Documentation
The source code is currently hosted on GitHub at: `https://github.com/hygia-org`

We used sphinx to write the documentation
## Become a part of our growing community

To run locally, you need to install sphinx:
Are you looking for a way to contribute to an open-source project and make a difference in the field of data science? Then consider joining the Hygia community! Hygia is a powerful and versatile Python library for data pipeline and experimentation, and we're always looking for new contributors to help us improve and expand it.

```
pip install sphinx
```
If you're interested in contributing, be sure to check out our community [Contribution Guide](https://github.com/hygia-org/hygia/blob/main/CONTRIBUTING.md) and [Code of Conduct](https://github.com/hygia-org/hygia/blob/main/CODE_OF_CONDUCT.md). These resources will give you an idea of what kind of contributions are welcome, as well as the standards we expect from our contributors.

Then install the theme used:
And if you have any questions or want to get started, don't hesitate to reach out! You can create an issue on our GitHub repository, or send an email to isaque.alves@ime.usp.br. We're always happy to hear from potential contributors, and we're looking forward to working with you!

```
pip install pydata-sphinx-theme
```

And Run the project

```
sphinx-build -b html source ./
```
## Installation from sources

And open the index.html
For experienced users of the library who are already a part of our community, we have put together a comprehensive [Installation Guide](https://github.com/hygia-org/hygia/blob/main/instalation_guide.md) to make the most of the features and functionalities offered by the Hygia library.
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