org.apache.spark.ml.r.IsotonicRegressionWrapper.scala Maven / Gradle / Ivy
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* http://www.apache.org/licenses/LICENSE-2.0
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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.feature.RFormula
import org.apache.spark.ml.regression.{IsotonicRegression, IsotonicRegressionModel}
import org.apache.spark.ml.util._
import org.apache.spark.sql.{DataFrame, Dataset}
private[r] class IsotonicRegressionWrapper private (
val pipeline: PipelineModel,
val features: Array[String]) extends MLWritable {
private val isotonicRegressionModel: IsotonicRegressionModel =
pipeline.stages(1).asInstanceOf[IsotonicRegressionModel]
lazy val boundaries: Array[Double] = isotonicRegressionModel.boundaries.toArray
lazy val predictions: Array[Double] = isotonicRegressionModel.predictions.toArray
def transform(dataset: Dataset[_]): DataFrame = {
pipeline.transform(dataset).drop(isotonicRegressionModel.getFeaturesCol)
}
override def write: MLWriter = new IsotonicRegressionWrapper.IsotonicRegressionWrapperWriter(this)
}
private[r] object IsotonicRegressionWrapper
extends MLReadable[IsotonicRegressionWrapper] {
def fit(
data: DataFrame,
formula: String,
isotonic: Boolean,
featureIndex: Int,
weightCol: String): IsotonicRegressionWrapper = {
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)
require(features.size == 1)
// assemble and fit the pipeline
val isotonicRegression = new IsotonicRegression()
.setIsotonic(isotonic)
.setFeatureIndex(featureIndex)
.setFeaturesCol(rFormula.getFeaturesCol)
if (weightCol != null) isotonicRegression.setWeightCol(weightCol)
val pipeline = new Pipeline()
.setStages(Array(rFormulaModel, isotonicRegression))
.fit(data)
new IsotonicRegressionWrapper(pipeline, features)
}
override def read: MLReader[IsotonicRegressionWrapper] = new IsotonicRegressionWrapperReader
override def load(path: String): IsotonicRegressionWrapper = super.load(path)
class IsotonicRegressionWrapperWriter(instance: IsotonicRegressionWrapper) 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) ~
("features" -> instance.features.toSeq)
val rMetadataJson: String = compact(render(rMetadata))
sc.parallelize(Seq(rMetadataJson), 1).saveAsTextFile(rMetadataPath)
instance.pipeline.save(pipelinePath)
}
}
class IsotonicRegressionWrapperReader extends MLReader[IsotonicRegressionWrapper] {
override def load(path: String): IsotonicRegressionWrapper = {
implicit val format = DefaultFormats
val rMetadataPath = new Path(path, "rMetadata").toString
val pipelinePath = new Path(path, "pipeline").toString
val rMetadataStr = sc.textFile(rMetadataPath, 1).first()
val rMetadata = parse(rMetadataStr)
val features = (rMetadata \ "features").extract[Array[String]]
val pipeline = PipelineModel.load(pipelinePath)
new IsotonicRegressionWrapper(pipeline, features)
}
}
}