spark GeneralizedLinearRegressionWrapper 源码

spark GeneralizedLinearRegressionWrapper 代码

文件路径：/mllib/src/main/scala/org/apache/spark/ml/r/GeneralizedLinearRegressionWrapper.scala

/*
 * Licensed to the Apache Software Foundation (ASF) under one or more
 * contributor license agreements.  See the NOTICE file distributed with
 * this work for additional information regarding copyright ownership.
 * The ASF licenses this file to You 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 org.apache.spark.ml.r

import java.util.Locale

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.feature.RFormula
import org.apache.spark.ml.r.RWrapperUtils._
import org.apache.spark.ml.regression._
import org.apache.spark.ml.util._
import org.apache.spark.sql._

private[r] class GeneralizedLinearRegressionWrapper private (
    val pipeline: PipelineModel,
    val rFeatures: Array[String],
    val rCoefficients: Array[Double],
    val rDispersion: Double,
    val rNullDeviance: Double,
    val rDeviance: Double,
    val rResidualDegreeOfFreedomNull: Long,
    val rResidualDegreeOfFreedom: Long,
    val rAic: Double,
    val rNumIterations: Int,
    val isLoaded: Boolean = false) extends MLWritable {

  private val glm: GeneralizedLinearRegressionModel =
    pipeline.stages(1).asInstanceOf[GeneralizedLinearRegressionModel]

  lazy val rDevianceResiduals: DataFrame = glm.summary.residuals()

  lazy val rFamily: String = glm.getFamily

  def residuals(residualsType: String): DataFrame = glm.summary.residuals(residualsType)

  def transform(dataset: Dataset[_]): DataFrame = {
    pipeline.transform(dataset).drop(glm.getFeaturesCol)
  }

  override def write: MLWriter =
    new GeneralizedLinearRegressionWrapper.GeneralizedLinearRegressionWrapperWriter(this)
}

private[r] object GeneralizedLinearRegressionWrapper
  extends MLReadable[GeneralizedLinearRegressionWrapper] {

  // scalastyle:off
  def fit(
      formula: String,
      data: DataFrame,
      family: String,
      link: String,
      tol: Double,
      maxIter: Int,
      weightCol: String,
      regParam: Double,
      variancePower: Double,
      linkPower: Double,
      stringIndexerOrderType: String,
      offsetCol: String): GeneralizedLinearRegressionWrapper = {
  // scalastyle:on
    val rFormula = new RFormula().setFormula(formula)
      .setStringIndexerOrderType(stringIndexerOrderType)
    checkDataColumns(rFormula, data)
    val rFormulaModel = rFormula.fit(data)

    // assemble and fit the pipeline
    val glr = new GeneralizedLinearRegression()
      .setFamily(family)
      .setFitIntercept(rFormula.hasIntercept)
      .setTol(tol)
      .setMaxIter(maxIter)
      .setRegParam(regParam)
      .setFeaturesCol(rFormula.getFeaturesCol)
    // set variancePower and linkPower if family is tweedie; otherwise, set link function
    if (family.toLowerCase(Locale.ROOT) == "tweedie") {
      glr.setVariancePower(variancePower).setLinkPower(linkPower)
    } else {
      glr.setLink(link)
    }
    if (weightCol != null) glr.setWeightCol(weightCol)
    if (offsetCol != null) glr.setOffsetCol(offsetCol)

    val pipeline = new Pipeline()
      .setStages(Array(rFormulaModel, glr))
      .fit(data)

    val glm: GeneralizedLinearRegressionModel =
      pipeline.stages(1).asInstanceOf[GeneralizedLinearRegressionModel]
    val summary = glm.summary

    val rFeatures: Array[String] = if (glm.getFitIntercept) {
      Array("(Intercept)") ++ summary.featureNames
    } else {
      summary.featureNames
    }

    val rCoefficients: Array[Double] = if (summary.isNormalSolver) {
      summary.coefficientsWithStatistics.map(_._2) ++
        summary.coefficientsWithStatistics.map(_._3) ++
        summary.coefficientsWithStatistics.map(_._4) ++
        summary.coefficientsWithStatistics.map(_._5)
    } else {
      if (glm.getFitIntercept) {
        Array(glm.intercept) ++ glm.coefficients.toArray
      } else {
        glm.coefficients.toArray
      }
    }

    val rDispersion: Double = summary.dispersion
    val rNullDeviance: Double = summary.nullDeviance
    val rDeviance: Double = summary.deviance
    val rResidualDegreeOfFreedomNull: Long = summary.residualDegreeOfFreedomNull
    val rResidualDegreeOfFreedom: Long = summary.residualDegreeOfFreedom
    val rAic: Double = if (family.toLowerCase(Locale.ROOT) == "tweedie" &&
      !Array(0.0, 1.0, 2.0).exists(x => math.abs(x - variancePower) < 1e-8)) {
      0.0
    } else {
      summary.aic
    }
    val rNumIterations: Int = summary.numIterations

    new GeneralizedLinearRegressionWrapper(pipeline, rFeatures, rCoefficients, rDispersion,
      rNullDeviance, rDeviance, rResidualDegreeOfFreedomNull, rResidualDegreeOfFreedom,
      rAic, rNumIterations)
  }

  override def read: MLReader[GeneralizedLinearRegressionWrapper] =
    new GeneralizedLinearRegressionWrapperReader

  override def load(path: String): GeneralizedLinearRegressionWrapper = super.load(path)

  class GeneralizedLinearRegressionWrapperWriter(instance: GeneralizedLinearRegressionWrapper)
    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) ~
        ("rFeatures" -> instance.rFeatures.toSeq) ~
        ("rCoefficients" -> instance.rCoefficients.toSeq) ~
        ("rDispersion" -> instance.rDispersion) ~
        ("rNullDeviance" -> instance.rNullDeviance) ~
        ("rDeviance" -> instance.rDeviance) ~
        ("rResidualDegreeOfFreedomNull" -> instance.rResidualDegreeOfFreedomNull) ~
        ("rResidualDegreeOfFreedom" -> instance.rResidualDegreeOfFreedom) ~
        ("rAic" -> instance.rAic) ~
        ("rNumIterations" -> instance.rNumIterations)
      val rMetadataJson: String = compact(render(rMetadata))
      sc.parallelize(Seq(rMetadataJson), 1).saveAsTextFile(rMetadataPath)

      instance.pipeline.save(pipelinePath)
    }
  }

  class GeneralizedLinearRegressionWrapperReader
    extends MLReader[GeneralizedLinearRegressionWrapper] {

    override def load(path: String): GeneralizedLinearRegressionWrapper = {
      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 rFeatures = (rMetadata \ "rFeatures").extract[Array[String]]
      val rCoefficients = (rMetadata \ "rCoefficients").extract[Array[Double]]
      val rDispersion = (rMetadata \ "rDispersion").extract[Double]
      val rNullDeviance = (rMetadata \ "rNullDeviance").extract[Double]
      val rDeviance = (rMetadata \ "rDeviance").extract[Double]
      val rResidualDegreeOfFreedomNull = (rMetadata \ "rResidualDegreeOfFreedomNull").extract[Long]
      val rResidualDegreeOfFreedom = (rMetadata \ "rResidualDegreeOfFreedom").extract[Long]
      val rAic = (rMetadata \ "rAic").extract[Double]
      val rNumIterations = (rMetadata \ "rNumIterations").extract[Int]

      val pipeline = PipelineModel.load(pipelinePath)

      new GeneralizedLinearRegressionWrapper(pipeline, rFeatures, rCoefficients, rDispersion,
        rNullDeviance, rDeviance, rResidualDegreeOfFreedomNull, rResidualDegreeOfFreedom,
        rAic, rNumIterations, isLoaded = true)
    }
  }
}

相关信息

spark 源码目录

相关文章

spark AFTSurvivalRegressionWrapper 源码

spark ALSWrapper 源码

spark BisectingKMeansWrapper 源码

spark DecisionTreeClassifierWrapper 源码

spark DecisionTreeRegressorWrapper 源码

spark FMClassifierWrapper 源码

spark FMRegressorWrapper 源码

spark FPGrowthWrapper 源码

spark GBTClassifierWrapper 源码

spark GBTRegressorWrapper 源码