org.apache.spark.ml.r.GaussianMixtureWrapper.scala Maven / Gradle / Ivy
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package org.apache.spark.ml.r
import org.apache.hadoop.fs.Path
import org.json4s._
import org.json4s.JsonDSL._
import org.json4s.jackson.JsonMethods._
import org.apache.spark.ml.{Pipeline, PipelineModel}
import org.apache.spark.ml.attribute.AttributeGroup
import org.apache.spark.ml.clustering.{GaussianMixture, GaussianMixtureModel}
import org.apache.spark.ml.feature.RFormula
import org.apache.spark.ml.linalg.Vector
import org.apache.spark.ml.util.{MLReadable, MLReader, MLWritable, MLWriter}
import org.apache.spark.sql.{DataFrame, Dataset}
import org.apache.spark.sql.functions._
private[r] class GaussianMixtureWrapper private (
val pipeline: PipelineModel,
val dim: Int,
val logLikelihood: Double,
val isLoaded: Boolean = false) extends MLWritable {
private val gmm: GaussianMixtureModel = pipeline.stages(1).asInstanceOf[GaussianMixtureModel]
lazy val k: Int = gmm.getK
lazy val lambda: Array[Double] = gmm.weights
lazy val mu: Array[Double] = gmm.gaussians.flatMap(_.mean.toArray)
lazy val sigma: Array[Double] = gmm.gaussians.flatMap(_.cov.toArray)
lazy val vectorToArray = udf { probability: Vector => probability.toArray }
lazy val posterior: DataFrame = gmm.summary.probability
.withColumn("posterior", vectorToArray(col(gmm.summary.probabilityCol)))
.drop(gmm.summary.probabilityCol)
def transform(dataset: Dataset[_]): DataFrame = {
pipeline.transform(dataset).drop(gmm.getFeaturesCol)
}
override def write: MLWriter = new GaussianMixtureWrapper.GaussianMixtureWrapperWriter(this)
}
private[r] object GaussianMixtureWrapper extends MLReadable[GaussianMixtureWrapper] {
def fit(
data: DataFrame,
formula: String,
k: Int,
maxIter: Int,
tol: Double): GaussianMixtureWrapper = {
val rFormula = new RFormula()
.setFormula(formula)
.setFeaturesCol("features")
RWrapperUtils.checkDataColumns(rFormula, data)
val rFormulaModel = rFormula.fit(data)
// get feature names from output schema
val schema = rFormulaModel.transform(data).schema
val featureAttrs = AttributeGroup.fromStructField(schema(rFormulaModel.getFeaturesCol))
.attributes.get
val features = featureAttrs.map(_.name.get)
val dim = features.length
val gm = new GaussianMixture()
.setK(k)
.setMaxIter(maxIter)
.setTol(tol)
.setFeaturesCol(rFormula.getFeaturesCol)
val pipeline = new Pipeline()
.setStages(Array(rFormulaModel, gm))
.fit(data)
val gmm: GaussianMixtureModel = pipeline.stages(1).asInstanceOf[GaussianMixtureModel]
val logLikelihood: Double = gmm.summary.logLikelihood
new GaussianMixtureWrapper(pipeline, dim, logLikelihood)
}
override def read: MLReader[GaussianMixtureWrapper] = new GaussianMixtureWrapperReader
override def load(path: String): GaussianMixtureWrapper = super.load(path)
class GaussianMixtureWrapperWriter(instance: GaussianMixtureWrapper) extends MLWriter {
override protected def saveImpl(path: String): Unit = {
val rMetadataPath = new Path(path, "rMetadata").toString
val pipelinePath = new Path(path, "pipeline").toString
val rMetadata = ("class" -> instance.getClass.getName) ~
("dim" -> instance.dim) ~
("logLikelihood" -> instance.logLikelihood)
val rMetadataJson: String = compact(render(rMetadata))
sc.parallelize(Seq(rMetadataJson), 1).saveAsTextFile(rMetadataPath)
instance.pipeline.save(pipelinePath)
}
}
class GaussianMixtureWrapperReader extends MLReader[GaussianMixtureWrapper] {
override def load(path: String): GaussianMixtureWrapper = {
implicit val format = DefaultFormats
val rMetadataPath = new Path(path, "rMetadata").toString
val pipelinePath = new Path(path, "pipeline").toString
val pipeline = PipelineModel.load(pipelinePath)
val rMetadataStr = sc.textFile(rMetadataPath, 1).first()
val rMetadata = parse(rMetadataStr)
val dim = (rMetadata \ "dim").extract[Int]
val logLikelihood = (rMetadata \ "logLikelihood").extract[Double]
new GaussianMixtureWrapper(pipeline, dim, logLikelihood, isLoaded = true)
}
}
}