Anovos is an open source library for feature engineering at scale. Built by data scientists & ML Engineers for the data science community, it provides all the capabilities required for data ingestion, data analysis, data drift & data stability analysis, feature recommendation and feature composition. In addition, it automatically produces easily interpretable professional data reports that help users understand the nature of data at first sight and further enable data scientists to identify and engineer features.
Leveraging the power of Apache Spark behind the scenes, Anovos improves data scientists' productivity and helps them build more resilient and better performing models.
The easiest way to try out Anovos and explore its capabilities is through the provided examples that you can run via Docker without the need to install anything on your local machine.
# Launch an anovos-examples Docker container
sudo docker run -p 8888:8888 anovos/anovos-examples-3.2.2:latest
To reach the Jupyter environment, open the link to http://127.0.0.1:8888/?token...
generated by the Jupyter NotebookApp.
If you're not familiar with Anovos or feature engineering, the Getting Started with
Anovos guide is a good place to begin your journey.
You can find it in the /guides folder within the Jupyter environment.
For more detailed instructions on how to install Docker and how to troubleshoot potential issues, see the examples README.
To use Anovos, you need compatible versions of Apache Spark, Java and Python.
Currently, we officially support the following combinations:
To see what we're currently testing, see this configuration.
You can install the latest release of Anovos directly through PyPI:
pip install anovos
We provide a comprehensive documentation at docs.anovos.ai that includes user guides as well as a detailed API documentation.
For usage examples, see the provided interactive guides and Jupyter notebooks as well as the Spark demo.
Anovos has designed for to support any feature engineering tasks in a scalable form. To see what's planned for the upcoming releases, see our roadmap.
To try out the latest additions to Anovos, you can install it directly from GitHub:
pip install git+https://github.com/anovos/anovos.git
Please note that this version is frequently updated and might not be fully compatible with the documentation available at docs.anovos.ai.
We're always happy to discuss and accept improvements to Anovos. To get started, please refer to our Contributing to Anovos page in the documentation.
To start coding, clone this repository, install both the regular and development requirements, and set up the pre-commit hooks:
git clone https://github.com/anovos/anovos.git
cd anovos/
pip install -r requirements.txt
pip install -r dev_requirements.txt
pre-commit install
FAQs
An Open Source tool for Feature Engineering in Machine Learning
We found that anovos demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 1 open source maintainer collaborating on the project.
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