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https://github.com/feedzai/feedzai-openml-python

Python-based Feedzai OpenML Providers
https://github.com/feedzai/feedzai-openml-python

feedzai openml python scikit scikit-learn

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Python-based Feedzai OpenML Providers

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# Feedzai OpenML Providers for Python

[![Build Status](https://travis-ci.com/feedzai/feedzai-openml-python.svg?branch=master)](https://travis-ci.com/feedzai/feedzai-openml-python)

[![codecov](https://codecov.io/gh/feedzai/feedzai-openml-python/branch/master/graph/badge.svg)](https://codecov.io/gh/feedzai/feedzai-openml-python)

[![Codacy Badge](https://api.codacy.com/project/badge/Grade/4cb28e8bf4104cffa20812e207514b9b?branch=master)](https://www.codacy.com/app/feedzai/feedzai-openml-python?utm_source=github.com&utm_medium=referral&utm_content=feedzai/feedzai-openml-python&utm_campaign=Badge_Grade)



Implementations of the Feedzai OpenML API to allow support for machine

learning models in [Python](https://www.python.org/)

using [Java Embedded Python](https://github.com/ninia/jep). 



## Modules



### Generic Python

[![Maven metadata URI](https://img.shields.io/maven-metadata/v/http/central.maven.org/maven2/com/feedzai/openml-generic-python/maven-metadata.xml.svg)](https://mvnrepository.com/artifact/com.feedzai/openml-generic-python)



The `openml-generic-python` module contains a provider that allows

developers to load Python code that conforms to a simple API.

This is the most powerful approach (yet more cumbersome) since models

can actually hold state.



Pull the provider from Maven Central:

```xml



  com.feedzai

  openml-generic-python

  

  0.3.0



```



### Scikit-learn

[![Maven metadata URI](https://img.shields.io/maven-metadata/v/http/central.maven.org/maven2/com/feedzai/openml-scikit/maven-metadata.xml.svg)](https://mvnrepository.com/artifact/com.feedzai/openml-scikit)



The implementation in the `openml-scikit` module adds support for models built with

[scikit-learn](http://scikit-learn.org/stable/index.html).



Pull this module from Maven Central:

```xml



  com.feedzai

  openml-scikit

  

  0.3.0



```



## Building

This is a Maven project which you can build using the following command:

```bash

mvn clean install

```



## Environment



To use these providers you need to have Python 3.6 with the following packages installed in your environment:



    * numpy 

    * scipy

    * jep (this requires JAVA_HOME to be configured)

    * scikit-learn (for the scikit provider)

    

Note that this section only describes the known prerequisites that are common to any model generated in Python.

Before importing a model you need to ensure that the required packages for that model are also installed.



## Running the tests 



To actually run the tests, two other configurations may be necessary for Jep to work properly:



* The java.library.path property needs to point to the Jep library. An approach for this that typically works is setting the `LD_LIBRARY_PATH` environment variable: `export LD_LIBRARY_PATH=/...path to.../python3.6/site-packages/jep:$LD_LIBRARY_PATH`

    

* Depending on the environment and package manager it may also be necessary to set the `LD_PRELOAD` variable to include the Python library: `export LD_PRELOAD=/...path to.../lib/libpython3.6m.so`



Feedzai has built a helpful docker image for testing, [available on docker hub](https://hub.docker.com/r/feedzai/oracle-jep-miniconda/),

that is being used in this repository's continuous integration. See the [travis-ci configuration](.travis.yml) commands

on how to use it.

The image's [Dockerfile](https://github.com/feedzai/oracle-jep-miniconda/blob/master/Dockerfile) also provides an example 

of the environment installation.