cc.factorie.tutorial.SimpleLDA.scala Maven / Gradle / Ivy

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FACTORIE is a toolkit for deployable probabilistic modeling, implemented as a software library in Scala. It provides its users with a succinct language for creating relational factor graphs, estimating parameters and performing inference.

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/* Copyright (C) 2008-2016 University of Massachusetts Amherst.
   This file is part of "FACTORIE" (Factor graphs, Imperative, Extensible)
   http://factorie.cs.umass.edu, http://github.com/factorie
   Licensed under the Apache License, Version 2.0 (the "License");
   you may not use this file except in compliance with the License.
   You may obtain a copy of the License at
    http://www.apache.org/licenses/LICENSE-2.0
   Unless required by applicable law or agreed to in writing, software
   distributed under the License is distributed on an "AS IS" BASIS,
   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
   See the License for the specific language governing permissions and
   limitations under the License. */


package cc.factorie.tutorial
import java.io.File

import cc.factorie.app.nlp.lexicon.StopWords
import cc.factorie.app.strings.alphaSegmenter
import cc.factorie.directed._
import cc.factorie.variable._

import scala.collection.mutable.ArrayBuffer

/**
 * LDA example using collapsed gibbs sampling; very flexible.
 */
object SimpleLDA {
  
  val numTopics = 10
  implicit val model = DirectedModel()
  object ZDomain extends DiscreteDomain(numTopics)
  object ZSeqDomain extends DiscreteSeqDomain { def elementDomain = ZDomain }
  class Zs(len:Int) extends DiscreteSeqVariable(len) { def domain = ZSeqDomain }
  object WordSeqDomain extends CategoricalSeqDomain[String]
  val WordDomain = WordSeqDomain.elementDomain
  class Words(strings:Seq[String]) extends CategoricalSeqVariable(strings) {
    def domain = WordSeqDomain
    def zs = model.parentFactor(this).asInstanceOf[PlatedCategoricalMixture.Factor]._3
  }
  class Document(val file:String, val theta:ProportionsVar, strings:Seq[String]) extends Words(strings)
  val beta = MassesVariable.growableUniform(WordDomain, 0.1)
  val alphas = MassesVariable.dense(numTopics, 0.1)

  def main(args: Array[String]): Unit = {
    implicit val random = new scala.util.Random(0)
    val directories = if (args.length > 0) args.toList else List("12", "11", "10", "09", "08").take(1).map("/Users/mccallum/research/data/text/nipstxt/nips"+_)
    val phis = Mixture(numTopics)(ProportionsVariable.growableDense(WordDomain) ~ Dirichlet(beta))
    val documents = new ArrayBuffer[Document]
    for (directory <- directories) {
      for (file <- new File(directory).listFiles; if file.isFile) {
        val theta = ProportionsVariable.dense(numTopics) ~ Dirichlet(alphas)
        val tokens = alphaSegmenter(file).map(_.toLowerCase).filter(!StopWords.contains(_)).toSeq
        val zs = new Zs(tokens.length) :~ PlatedDiscrete(theta)
        documents += new Document(file.toString, theta, tokens) ~ PlatedCategoricalMixture(phis, zs)
      }
    }

    val collapse = new ArrayBuffer[Var]
    collapse += phis
    collapse ++= documents.map(_.theta)
    val sampler = new CollapsedGibbsSampler(collapse, model)

    for (i <- 1 to 20) {
      for (doc <- documents) sampler.process(doc.zs)
   }
  }
}