https://github.com/indeedeng/vowpal-wabbit-java
Vowpal wabbit java wrapper and a friendly API for it
https://github.com/indeedeng/vowpal-wabbit-java
java-wrapper machine-learning vowpal-wabbit vw
Last synced: 5 months ago
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Vowpal wabbit java wrapper and a friendly API for it
- Host: GitHub
- URL: https://github.com/indeedeng/vowpal-wabbit-java
- Owner: indeedeng
- License: other
- Created: 2017-05-08T15:24:13.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2023-05-21T05:08:21.000Z (over 2 years ago)
- Last Synced: 2025-05-07T23:40:08.408Z (5 months ago)
- Topics: java-wrapper, machine-learning, vowpal-wabbit, vw
- Language: Java
- Homepage:
- Size: 99.8 MB
- Stars: 51
- Watchers: 5
- Forks: 13
- Open Issues: 6
-
Metadata Files:
- Readme: README.md
- License: LICENSE
- Code of conduct: CODE_OF_CONDUCT.md
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README
Vowpal Wabbit Wrapper
====================
A java wrapper and friendly API for the [Vowpal wabbit](https://github.com/JohnLangford/vowpal_wabbit/wiki) machine learning package.
Background
----------
The Vowpal Wabbit (VW) package is very production friendly and it implements the state of the art in modern machine learning research.
The existing java binding for VW has drawbacks:
- Because of a bug, saved models may have incorrect weights
- It requires the boost library to be installed on every machine where the wrapper is used, which is not always feasible
- its API is low-level, requiring you to operate with strings instead of providing a more convenient domain abstraction
This project addresses these drawbacks.
Building the library
-----
To build this library run the following command:
```
mvn clean install
```
Dependencies
------------
- guava
- log4j
Rebuilding C++ binaries
----------------
This distribution includes pre-built C++ binaries along with the code.
You can rebuild the binaries from source if necessary. Refer to [build-jni/README.md](build-jni/README.md) for instructions.
Using the library
------
Refer to the official [vowpal wabbit wiki](https://github.com/JohnLangford/vowpal_wabbit/wiki) for general instructions and advice on training the Vowpal Wabbit model.
Refer to [API javadocs](http://opensource.indeedeng.io/vowpal-wabbit-java) for instructions specific to this wrapper and java API.
The following integration tests provide references for using the API.
- [Twitter Sentiment analysis](src/test/java/com/indeed/vw/wrapper/integration/tests/TestOnTwitterSentimentDataset.java)
- [Movie lens 1M](src/test/java/com/indeed/vw/wrapper/integration/tests/TestOnMovieLensDataset.java)
Reporting issues
--------
Create an [issue](https://github.com/indeedeng/vowpal-wabbit-java/issues) in this project if you encounter issues or need help.
Tested platform
---------------
We have tested this wrapper on the following platforms:
- OS X Yosemite
- Ubuntu 14
- Enterprise Linux 5
- Enterprise Linux 6
- CentOS 5
- CentOS 6
- CentOS 7
Code of Conduct
-------
This project is governed by the [Contributor Covenant v 1.4.1](CODE_OF_CONDUCT.md)
License
-------
- This library is distributed under [The Apache Software License, Version 2.0](LICENSE).
- VW binaries are distributed under [BSD (revised) license](VW_LICENSE)