https://github.com/picnicml/doddle-model

:cake: doddle-model: machine learning in Scala.
https://github.com/picnicml/doddle-model

breeze data-science doddle-model machine-learning scala

Last synced: about 2 months ago
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:cake: doddle-model: machine learning in Scala.

• Host: GitHub
• URL: https://github.com/picnicml/doddle-model
• Owner: picnicml
• License: apache-2.0
• Created: 2018-02-09T13:54:54.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2024-07-29T21:13:26.000Z (6 months ago)
• Last Synced: 2024-08-04T00:06:28.770Z (5 months ago)
• Topics: breeze, data-science, doddle-model, machine-learning, scala
• Language: Scala
• Homepage: https://picnicml.github.io
• Size: 580 KB
• Stars: 137
• Watchers: 14
• Forks: 23
• Open Issues: 34
• Metadata Files:
  • Readme: README.md
  • Contributing: .github/CONTRIBUTING.md
  • License: LICENSE

Awesome Lists containing this project

• awesome-scala - doddle-model - model: machine learning in Scala. |   (Table of Contents / Science and Data Analysis)

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](https://github.com/scalanlp/breeze)

* it provides [immutable estimators](https://en.wikipedia.org/wiki/Immutable_object) that are a _doddle_ to use in parallel code

* it exposes its functionality through a [scikit-learn](https://github.com/scikit-learn/scikit-learn)-like API [2] in idiomatic Scala using [typeclasses](https://en.wikipedia.org/wiki/Type_class)

#### 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:

```scala

libraryDependencies  ++= Seq(

  "io.github.picnicml" %% "doddle-model" % "",

  // 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](https://github.com/picnicml/doddle-model-examples).

### Contributing

Want to help us? :raised_hands: We have a [document](https://github.com/picnicml/doddle-model/blob/master/.github/CONTRIBUTING.md) that will make deciding how to do that much easier.

### Performance

Performance of implementations is described [here](https://github.com/picnicml/doddle-model/wiki/Performance). 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](https://github.com/picnicml/doddle-model/graphs/contributors). The core team currently consists of the following developers:

- [@inejc](https://github.com/inejc)

- [@matejklemen](https://github.com/matejklemen)

### Resources

* [1] [Pattern Recognition and Machine Learning, Christopher Bishop](http://www.springer.com/gp/book/9780387310732)

* [2] [API design for machine learning software: experiences from the scikit-learn project, L. Buitinck et al.](https://arxiv.org/abs/1309.0238)

* [3] [UCI Machine Learning Repository. Irvine, CA: University of California, School of Information and Computer Science, Dua, D. and Karra Taniskidou, E.](http://archive.ics.uci.edu/ml)