spark LogisticRegressionWrapper 源码

spark LogisticRegressionWrapper 代码

文件路径：/mllib/src/main/scala/org/apache/spark/ml/r/LogisticRegressionWrapper.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 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.classification.{LogisticRegression, LogisticRegressionModel}
import org.apache.spark.ml.feature.{IndexToString, RFormula}
import org.apache.spark.ml.linalg.{Matrices, Vector, Vectors}
import org.apache.spark.ml.r.RWrapperUtils._
import org.apache.spark.ml.util._
import org.apache.spark.sql.{DataFrame, Dataset}

private[r] class LogisticRegressionWrapper private (
    val pipeline: PipelineModel,
    val features: Array[String],
    val labels: Array[String]) extends MLWritable {

  import LogisticRegressionWrapper._

  private val lrModel: LogisticRegressionModel =
    pipeline.stages(1).asInstanceOf[LogisticRegressionModel]

  lazy val rFeatures: Array[String] = if (lrModel.getFitIntercept) {
    Array("(Intercept)") ++ features
  } else {
    features
  }

  lazy val rCoefficients: Array[Double] = {
    val numRows = lrModel.coefficientMatrix.numRows
    val numCols = lrModel.coefficientMatrix.numCols
    val numColsWithIntercept = if (lrModel.getFitIntercept) numCols + 1 else numCols
    val coefficients: Array[Double] = new Array[Double](numRows * numColsWithIntercept)
    val coefficientVectors: Seq[Vector] = lrModel.coefficientMatrix.rowIter.toSeq
    var i = 0
    if (lrModel.getFitIntercept) {
      while (i < numRows) {
        coefficients(i * numColsWithIntercept) = lrModel.interceptVector(i)
        System.arraycopy(coefficientVectors(i).toArray, 0,
          coefficients, i * numColsWithIntercept + 1, numCols)
        i += 1
      }
    } else {
      while (i < numRows) {
        System.arraycopy(coefficientVectors(i).toArray, 0,
          coefficients, i * numColsWithIntercept, numCols)
        i += 1
      }
    }
    coefficients
  }

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

  override def write: MLWriter = new LogisticRegressionWrapper.LogisticRegressionWrapperWriter(this)
}

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

  val PREDICTED_LABEL_INDEX_COL = "pred_label_idx"
  val PREDICTED_LABEL_COL = "prediction"

  def fit( // scalastyle:ignore
      data: DataFrame,
      formula: String,
      regParam: Double,
      elasticNetParam: Double,
      maxIter: Int,
      tol: Double,
      family: String,
      standardization: Boolean,
      thresholds: Array[Double],
      weightCol: String,
      aggregationDepth: Int,
      numRowsOfBoundsOnCoefficients: Int,
      numColsOfBoundsOnCoefficients: Int,
      lowerBoundsOnCoefficients: Array[Double],
      upperBoundsOnCoefficients: Array[Double],
      lowerBoundsOnIntercepts: Array[Double],
      upperBoundsOnIntercepts: Array[Double],
      handleInvalid: String
      ): LogisticRegressionWrapper = {

    val rFormula = new RFormula()
      .setFormula(formula)
      .setForceIndexLabel(true)
      .setHandleInvalid(handleInvalid)
    checkDataColumns(rFormula, data)
    val rFormulaModel = rFormula.fit(data)

    val fitIntercept = rFormula.hasIntercept

    // get labels and feature names from output schema
    val (features, labels) = getFeaturesAndLabels(rFormulaModel, data)

    // assemble and fit the pipeline
    val lr = new LogisticRegression()
      .setRegParam(regParam)
      .setElasticNetParam(elasticNetParam)
      .setMaxIter(maxIter)
      .setTol(tol)
      .setFitIntercept(fitIntercept)
      .setFamily(family)
      .setStandardization(standardization)
      .setFeaturesCol(rFormula.getFeaturesCol)
      .setLabelCol(rFormula.getLabelCol)
      .setPredictionCol(PREDICTED_LABEL_INDEX_COL)
      .setAggregationDepth(aggregationDepth)

    if (thresholds.length > 1) {
      lr.setThresholds(thresholds)
    } else {
      lr.setThreshold(thresholds(0))
    }

    if (weightCol != null) lr.setWeightCol(weightCol)

    if (numRowsOfBoundsOnCoefficients != 0 &&
      numColsOfBoundsOnCoefficients != 0 && lowerBoundsOnCoefficients != null) {
      val coef = Matrices.dense(numRowsOfBoundsOnCoefficients,
        numColsOfBoundsOnCoefficients, lowerBoundsOnCoefficients)
      lr.setLowerBoundsOnCoefficients(coef)
    }

    if (numRowsOfBoundsOnCoefficients != 0 &&
      numColsOfBoundsOnCoefficients != 0 && upperBoundsOnCoefficients != null) {
      val coef = Matrices.dense(numRowsOfBoundsOnCoefficients,
        numColsOfBoundsOnCoefficients, upperBoundsOnCoefficients)
      lr.setUpperBoundsOnCoefficients(coef)
    }

    if (lowerBoundsOnIntercepts != null) {
      val intercept = Vectors.dense(lowerBoundsOnIntercepts)
      lr.setLowerBoundsOnIntercepts(intercept)
    }

    if (upperBoundsOnIntercepts != null) {
      val intercept = Vectors.dense(upperBoundsOnIntercepts)
      lr.setUpperBoundsOnIntercepts(intercept)
    }

    val idxToStr = new IndexToString()
      .setInputCol(PREDICTED_LABEL_INDEX_COL)
      .setOutputCol(PREDICTED_LABEL_COL)
      .setLabels(labels)

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

    new LogisticRegressionWrapper(pipeline, features, labels)
  }

  override def read: MLReader[LogisticRegressionWrapper] = new LogisticRegressionWrapperReader

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

  class LogisticRegressionWrapperWriter(instance: LogisticRegressionWrapper) 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) ~
        ("labels" -> instance.labels.toSeq)
      val rMetadataJson: String = compact(render(rMetadata))
      sc.parallelize(Seq(rMetadataJson), 1).saveAsTextFile(rMetadataPath)

      instance.pipeline.save(pipelinePath)
    }
  }

  class LogisticRegressionWrapperReader extends MLReader[LogisticRegressionWrapper] {

    override def load(path: String): LogisticRegressionWrapper = {
      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 labels = (rMetadata \ "labels").extract[Array[String]]

      val pipeline = PipelineModel.load(pipelinePath)
      new LogisticRegressionWrapper(pipeline, features, labels)
    }
  }
}

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