spark FMClassifier 源码

spark FMClassifier 代码

文件路径：/mllib/src/main/scala/org/apache/spark/ml/classification/FMClassifier.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.classification

import org.apache.hadoop.fs.Path

import org.apache.spark.annotation.Since
import org.apache.spark.internal.Logging
import org.apache.spark.ml.linalg._
import org.apache.spark.ml.param._
import org.apache.spark.ml.regression.{FactorizationMachines, FactorizationMachinesParams}
import org.apache.spark.ml.regression.FactorizationMachines._
import org.apache.spark.ml.util._
import org.apache.spark.ml.util.DatasetUtils._
import org.apache.spark.ml.util.Instrumentation.instrumented
import org.apache.spark.mllib.linalg.{Vectors => OldVectors}
import org.apache.spark.sql._
import org.apache.spark.storage.StorageLevel

/**
 * Params for FMClassifier.
 */
private[classification] trait FMClassifierParams extends ProbabilisticClassifierParams
  with FactorizationMachinesParams {
}

/**
 * Factorization Machines learning algorithm for classification.
 * It supports normal gradient descent and AdamW solver.
 *
 * The implementation is based upon:
 * <a href="https://www.csie.ntu.edu.tw/~b97053/paper/Rendle2010FM.pdf">
 * S. Rendle. "Factorization machines" 2010</a>.
 *
 * FM is able to estimate interactions even in problems with huge sparsity
 * (like advertising and recommendation system).
 * FM formula is:
 * <blockquote>
 *   $$
 *   \begin{align}
 *   y = \sigma\left( w_0 + \sum\limits^n_{i-1} w_i x_i +
 *     \sum\limits^n_{i=1} \sum\limits^n_{j=i+1} \langle v_i, v_j \rangle x_i x_j \right)
 *   \end{align}
 *   $$
 * </blockquote>
 * First two terms denote global bias and linear term (as same as linear regression),
 * and last term denotes pairwise interactions term. v_i describes the i-th variable
 * with k factors.
 *
 * FM classification model uses logistic loss which can be solved by gradient descent method, and
 * regularization terms like L2 are usually added to the loss function to prevent overfitting.
 *
 * @note Multiclass labels are not currently supported.
 */
@Since("3.0.0")
class FMClassifier @Since("3.0.0") (
    @Since("3.0.0") override val uid: String)
  extends ProbabilisticClassifier[Vector, FMClassifier, FMClassificationModel]
  with FactorizationMachines with FMClassifierParams with DefaultParamsWritable with Logging {

  @Since("3.0.0")
  def this() = this(Identifiable.randomUID("fmc"))

  /**
   * Set the dimensionality of the factors.
   * Default is 8.
   *
   * @group setParam
   */
  @Since("3.0.0")
  def setFactorSize(value: Int): this.type = set(factorSize, value)

  /**
   * Set whether to fit intercept term.
   * Default is true.
   *
   * @group setParam
   */
  @Since("3.0.0")
  def setFitIntercept(value: Boolean): this.type = set(fitIntercept, value)

  /**
   * Set whether to fit linear term.
   * Default is true.
   *
   * @group setParam
   */
  @Since("3.0.0")
  def setFitLinear(value: Boolean): this.type = set(fitLinear, value)

  /**
   * Set the L2 regularization parameter.
   * Default is 0.0.
   *
   * @group setParam
   */
  @Since("3.0.0")
  def setRegParam(value: Double): this.type = set(regParam, value)

  /**
   * Set the mini-batch fraction parameter.
   * Default is 1.0.
   *
   * @group setParam
   */
  @Since("3.0.0")
  def setMiniBatchFraction(value: Double): this.type = set(miniBatchFraction, value)

  /**
   * Set the standard deviation of initial coefficients.
   * Default is 0.01.
   *
   * @group setParam
   */
  @Since("3.0.0")
  def setInitStd(value: Double): this.type = set(initStd, value)

  /**
   * Set the maximum number of iterations.
   * Default is 100.
   *
   * @group setParam
   */
  @Since("3.0.0")
  def setMaxIter(value: Int): this.type = set(maxIter, value)

  /**
   * Set the initial step size for the first step (like learning rate).
   * Default is 1.0.
   *
   * @group setParam
   */
  @Since("3.0.0")
  def setStepSize(value: Double): this.type = set(stepSize, value)

  /**
   * Set the convergence tolerance of iterations.
   * Default is 1E-6.
   *
   * @group setParam
   */
  @Since("3.0.0")
  def setTol(value: Double): this.type = set(tol, value)

  /**
   * Set the solver algorithm used for optimization.
   * Supported options: "gd", "adamW".
   * Default: "adamW"
   *
   * @group setParam
   */
  @Since("3.0.0")
  def setSolver(value: String): this.type = set(solver, value)

  /**
   * Set the random seed for weight initialization.
   *
   * @group setParam
   */
  @Since("3.0.0")
  def setSeed(value: Long): this.type = set(seed, value)

  override protected def train(
      dataset: Dataset[_]): FMClassificationModel = instrumented { instr =>
    val numClasses = 2
    if (isDefined(thresholds)) {
      require($(thresholds).length == numClasses, this.getClass.getSimpleName +
        ".train() called with non-matching numClasses and thresholds.length." +
        s" numClasses=$numClasses, but thresholds has length ${$(thresholds).length}")
    }

    instr.logPipelineStage(this)
    instr.logDataset(dataset)
    instr.logParams(this, factorSize, fitIntercept, fitLinear, regParam,
      miniBatchFraction, initStd, maxIter, stepSize, tol, solver, thresholds)
    instr.logNumClasses(numClasses)

    val numFeatures = getNumFeatures(dataset, $(featuresCol))
    instr.logNumFeatures(numFeatures)

    val handlePersistence = dataset.storageLevel == StorageLevel.NONE

    val data = dataset.select(
      checkClassificationLabels($(labelCol), Some(2)),
      checkNonNanVectors($(featuresCol))
    ).rdd.map { case Row(l: Double, v: Vector) => (l, OldVectors.fromML(v))
    }.setName("training instances")

    if (handlePersistence) data.persist(StorageLevel.MEMORY_AND_DISK)

    val (coefficients, objectiveHistory) = trainImpl(data, numFeatures, LogisticLoss)

    val (intercept, linear, factors) = splitCoefficients(
      coefficients, numFeatures, $(factorSize), $(fitIntercept), $(fitLinear))

    if (handlePersistence) data.unpersist()

    createModel(dataset, intercept, linear, factors, objectiveHistory)
  }

  private def createModel(
    dataset: Dataset[_],
    intercept: Double,
    linear: Vector,
    factors: Matrix,
    objectiveHistory: Array[Double]): FMClassificationModel = {
    val model = copyValues(new FMClassificationModel(uid, intercept, linear, factors))
    val weightColName = if (!isDefined(weightCol)) "weightCol" else $(weightCol)

    val (summaryModel, probabilityColName, predictionColName) = model.findSummaryModel()
    val summary = new FMClassificationTrainingSummaryImpl(
      summaryModel.transform(dataset),
      probabilityColName,
      predictionColName,
      $(labelCol),
      weightColName,
      objectiveHistory)
    model.setSummary(Some(summary))
  }

  @Since("3.0.0")
  override def copy(extra: ParamMap): FMClassifier = defaultCopy(extra)
}

@Since("3.0.0")
object FMClassifier extends DefaultParamsReadable[FMClassifier] {

  @Since("3.0.0")
  override def load(path: String): FMClassifier = super.load(path)
}

/**
 * Model produced by [[FMClassifier]]
 */
@Since("3.0.0")
class FMClassificationModel private[classification] (
  @Since("3.0.0") override val uid: String,
  @Since("3.0.0") val intercept: Double,
  @Since("3.0.0") val linear: Vector,
  @Since("3.0.0") val factors: Matrix)
  extends ProbabilisticClassificationModel[Vector, FMClassificationModel]
    with FMClassifierParams with MLWritable
    with HasTrainingSummary[FMClassificationTrainingSummary]{

  @Since("3.0.0")
  override val numClasses: Int = 2

  @Since("3.0.0")
  override val numFeatures: Int = linear.size

  /**
   * Gets summary of model on training set. An exception is thrown
   * if `hasSummary` is false.
   */
  @Since("3.1.0")
  override def summary: FMClassificationTrainingSummary = super.summary

  /**
   * Evaluates the model on a test dataset.
   *
   * @param dataset Test dataset to evaluate model on.
   */
  @Since("3.1.0")
  def evaluate(dataset: Dataset[_]): FMClassificationSummary = {
    val weightColName = if (!isDefined(weightCol)) "weightCol" else $(weightCol)
    // Handle possible missing or invalid probability or prediction columns
    val (summaryModel, probability, predictionColName) = findSummaryModel()
    new FMClassificationSummaryImpl(summaryModel.transform(dataset),
      probability, predictionColName, $(labelCol), weightColName)
  }

  @Since("3.0.0")
  override def predictRaw(features: Vector): Vector = {
    val rawPrediction = getRawPrediction(features, intercept, linear, factors)
    Vectors.dense(Array(-rawPrediction, rawPrediction))
  }

  override protected def raw2probabilityInPlace(rawPrediction: Vector): Vector = {
    rawPrediction match {
      case dv: DenseVector =>
        dv.values(1) = 1.0 / (1.0 + math.exp(-dv.values(1)))
        dv.values(0) = 1.0 - dv.values(1)
        dv
      case sv: SparseVector =>
        throw new RuntimeException("Unexpected error in FMClassificationModel:" +
          " raw2probabilityInPlace encountered SparseVector")
    }
  }

  @Since("3.0.0")
  override def copy(extra: ParamMap): FMClassificationModel = {
    copyValues(new FMClassificationModel(uid, intercept, linear, factors), extra)
  }

  @Since("3.0.0")
  override def write: MLWriter =
    new FMClassificationModel.FMClassificationModelWriter(this)

  override def toString: String = {
    s"FMClassificationModel: " +
      s"uid=${super.toString}, numClasses=$numClasses, numFeatures=$numFeatures, " +
      s"factorSize=${$(factorSize)}, fitLinear=${$(fitLinear)}, fitIntercept=${$(fitIntercept)}"
  }
}

@Since("3.0.0")
object FMClassificationModel extends MLReadable[FMClassificationModel] {

  @Since("3.0.0")
  override def read: MLReader[FMClassificationModel] = new FMClassificationModelReader

  @Since("3.0.0")
  override def load(path: String): FMClassificationModel = super.load(path)

  /** [[MLWriter]] instance for [[FMClassificationModel]] */
  private[FMClassificationModel] class FMClassificationModelWriter(
    instance: FMClassificationModel) extends MLWriter with Logging {

    private case class Data(
      intercept: Double,
      linear: Vector,
      factors: Matrix)

    override protected def saveImpl(path: String): Unit = {
      DefaultParamsWriter.saveMetadata(instance, path, sc)
      val data = Data(instance.intercept, instance.linear, instance.factors)
      val dataPath = new Path(path, "data").toString
      sparkSession.createDataFrame(Seq(data)).repartition(1).write.parquet(dataPath)
    }
  }

  private class FMClassificationModelReader extends MLReader[FMClassificationModel] {

    private val className = classOf[FMClassificationModel].getName

    override def load(path: String): FMClassificationModel = {
      val metadata = DefaultParamsReader.loadMetadata(path, sc, className)
      val dataPath = new Path(path, "data").toString
      val data = sparkSession.read.format("parquet").load(dataPath)

      val Row(intercept: Double, linear: Vector, factors: Matrix) =
        data.select("intercept", "linear", "factors").head()
      val model = new FMClassificationModel(metadata.uid, intercept, linear, factors)
      metadata.getAndSetParams(model)
      model
    }
  }
}

/**
 * Abstraction for FMClassifier results for a given model.
 */
sealed trait FMClassificationSummary extends BinaryClassificationSummary

/**
 * Abstraction for FMClassifier training results.
 */
sealed trait FMClassificationTrainingSummary extends FMClassificationSummary with TrainingSummary

/**
 * FMClassifier results for a given model.
 *
 * @param predictions dataframe output by the model's `transform` method.
 * @param scoreCol field in "predictions" which gives the probability of each instance.
 * @param predictionCol field in "predictions" which gives the prediction for a data instance as a
 *                      double.
 * @param labelCol field in "predictions" which gives the true label of each instance.
 * @param weightCol field in "predictions" which gives the weight of each instance.
 */
private class FMClassificationSummaryImpl(
    @transient override val predictions: DataFrame,
    override val scoreCol: String,
    override val predictionCol: String,
    override val labelCol: String,
    override val weightCol: String)
  extends FMClassificationSummary

/**
 * FMClassifier training results.
 *
 * @param predictions dataframe output by the model's `transform` method.
 * @param scoreCol field in "predictions" which gives the probability of each instance.
 * @param predictionCol field in "predictions" which gives the prediction for a data instance as a
 *                      double.
 * @param labelCol field in "predictions" which gives the true label of each instance.
 * @param weightCol field in "predictions" which gives the weight of each instance.
 * @param objectiveHistory objective function (scaled loss + regularization) at each iteration.
 */
private class FMClassificationTrainingSummaryImpl(
    predictions: DataFrame,
    scoreCol: String,
    predictionCol: String,
    labelCol: String,
    weightCol: String,
    override val objectiveHistory: Array[Double])
  extends FMClassificationSummaryImpl(
    predictions, scoreCol, predictionCol, labelCol, weightCol)
    with FMClassificationTrainingSummary

相关信息

spark 源码目录

相关文章

spark ClassificationSummary 源码

spark Classifier 源码

spark DecisionTreeClassifier 源码

spark GBTClassifier 源码

spark LinearSVC 源码

spark LogisticRegression 源码

spark MultilayerPerceptronClassifier 源码

spark NaiveBayes 源码

spark OneVsRest 源码

spark ProbabilisticClassifier 源码