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README.md

Nak

Nak is a Scala/Java library for machine learning and related tasks, with a focus on having an easy to use API for some standard algorithms. It is formed from Breeze, Liblinear Java, and Scalabha. It is currently undergoing a pretty massive evolution, so be prepared for quite big changes in the API for this and probably several future versions.

We'd love to have some more contributors: if you are interested in helping out, please see the #helpwanted issues or suggest your own ideas.

What's inside

Nak currently provides implementations for k-means clustering and supervised learning with logistic regression and support vector machines. Other algorithms will be added soon, especially due to the planned merger of breeze.learn with Nak. (See the ScalaNLP roadmap for details.)

See the Nak wiki for (some preliminary) documentation.

The latest stable release of Nak is 1.1.3. Changes from the previous release include:

  • Enabled the hashing trick to be used for linear models. See nak.data.HashedExampleIndex and nak.data.HashedFeatureMap.
  • PCA now supported directly in Nak for dimensionality reduction w/ kmeans.
  • All OpenNLP Maxent code purged from Nak.

See the CHANGELOG for changes in previous versions.

Using Nak

In SBT:

libraryDependencies += "org.scalanlp" % "nak" % "1.1.3"

In Maven:

<dependency>
   <groupId>org.scalanlp</groupId>
   <artifactId>nak</artifactId>
   <version>1.1.3</version>
</dependency>

Example

Here's an example of how easy it is to train and evaluate a text classifier using Nak. See TwentyNewsGroups.scala for more details.

def main(args: Array[String]) {
  val newsgroupsDir = new File(args(0))
  implicit val isoCodec = scala.io.Codec("ISO-8859-1")
  val stopwords = Set("the","a","an","of","in","for","by","on")

  val trainDir = new File(newsgroupsDir, "20news-bydate-train")
  val trainingExamples = fromLabeledDirs(trainDir).toList
  val config = LiblinearConfig(cost=5.0)
  val featurizer = new BowFeaturizer(stopwords)
  val classifier = trainClassifier(config, featurizer, trainingExamples)

  val evalDir = new File(newsgroupsDir, "20news-bydate-test")
  val maxLabelNews = maxLabel(classifier.labels) _
  val comparisons = for (ex <- fromLabeledDirs(evalDir).toList) yield 
    (ex.label, maxLabelNews(classifier.evalRaw(ex.features)), ex.features)
  val (goldLabels, predictions, inputs) = comparisons.unzip3
  println(ConfusionMatrix(goldLabels, predictions, inputs))
}

Questions or suggestions?

Post a message to the scalanlp-discuss mailing list or create an issue.

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