doddle-model

:cake: doddle-model: machine learning in Scala.

Description

doddle-model is an in-memory machine learning library that can be summed up with three main characteristics: it is built on top of Breeze, it provides immutable estimators that are a doddle to use in parallel code and it exposes its functionality through a scikit-learn-like API in idiomatic Scala using typeclasses.

Programming language: Scala

License: Apache License 2.0

Tags: Science And Data Analysis     Machine Learning     Statistics    

README

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doddle-model is an in-memory machine learning library that can be summed up with three main characteristics:

• it is built on top of Breeze
• it provides immutable estimators that are a doddle to use in parallel code
• it exposes its functionality through a scikit-learn-like API [2] in idiomatic Scala using typeclasses

How does it compare to existing solutions?

doddle-model takes the position of scikit-learn in Scala and as a consequence, it's much more lightweight than e.g. Spark ML. Fitted models can be deployed anywhere, from simple applications to concurrent, distributed systems built with Akka, Apache Beam or a framework of your choice. Training of estimators happens in-memory, which is advantageous unless you are dealing with enormous datasets that absolutely cannot fit into RAM.

Installation

The project is published for Scala versions 2.11, 2.12 and 2.13. Add the dependency to your SBT project definition:

libraryDependencies  ++= Seq(
  "io.github.picnicml" %% "doddle-model" % "<latest_version>",
  // add optionally to utilize native libraries for a significant performance boost
  "org.scalanlp" %% "breeze-natives" % "1.0"
)

Note that the latest version is displayed in the Latest Release badge above and that the v prefix should be removed from the SBT definition.

Getting Started

For a complete list of code examples see doddle-model-examples.

Contributing

Want to help us? :raised_hands: We have a document that will make deciding how to do that much easier.

Performance

Performance of implementations is described here. Also, take a peek at what's written in that document if you encounter java.lang.OutOfMemoryError: Java heap space.

Core Maintainers

This is a collaborative project which wouldn't be possible without all the awesome contributors. The core team currently consists of the following developers:

• @inejc
• @matejklemen

Resources

• [1] Pattern Recognition and Machine Learning, Christopher Bishop
• [2] API design for machine learning software: experiences from the scikit-learn project, L. Buitinck et al.
• [3] UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science, Dua, D. and Karra Taniskidou, E.