path: root/mllib/src/test/scala/org/apache/spark/ml/classification/RandomForestClassifierSuite.scala

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 * 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
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 *
 *    http://www.apache.org/licenses/LICENSE-2.0
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 * Unless required by applicable law or agreed to in writing, software
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 * See the License for the specific language governing permissions and
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package org.apache.spark.ml.classification

import org.apache.spark.SparkFunSuite
import org.apache.spark.ml.param.ParamsSuite
import org.apache.spark.ml.tree.LeafNode
import org.apache.spark.ml.tree.impl.TreeTests
import org.apache.spark.ml.util.MLTestingUtils
import org.apache.spark.mllib.linalg.{Vector, Vectors}
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.mllib.tree.{EnsembleTestHelper, RandomForest => OldRandomForest}
import org.apache.spark.mllib.tree.configuration.{Algo => OldAlgo}
import org.apache.spark.mllib.util.MLlibTestSparkContext
import org.apache.spark.mllib.util.TestingUtils._
import org.apache.spark.rdd.RDD
import org.apache.spark.sql.{DataFrame, Row}

/**
 * Test suite for [[RandomForestClassifier]].
 */
class RandomForestClassifierSuite extends SparkFunSuite with MLlibTestSparkContext {

  import RandomForestClassifierSuite.compareAPIs

  private var orderedLabeledPoints50_1000: RDD[LabeledPoint] = _
  private var orderedLabeledPoints5_20: RDD[LabeledPoint] = _

  override def beforeAll() {
    super.beforeAll()
    orderedLabeledPoints50_1000 =
      sc.parallelize(EnsembleTestHelper.generateOrderedLabeledPoints(numFeatures = 50, 1000))
    orderedLabeledPoints5_20 =
      sc.parallelize(EnsembleTestHelper.generateOrderedLabeledPoints(numFeatures = 5, 20))
  }

  /////////////////////////////////////////////////////////////////////////////
  // Tests calling train()
  /////////////////////////////////////////////////////////////////////////////

  def binaryClassificationTestWithContinuousFeatures(rf: RandomForestClassifier) {
    val categoricalFeatures = Map.empty[Int, Int]
    val numClasses = 2
    val newRF = rf
      .setImpurity("Gini")
      .setMaxDepth(2)
      .setNumTrees(1)
      .setFeatureSubsetStrategy("auto")
      .setSeed(123)
    compareAPIs(orderedLabeledPoints50_1000, newRF, categoricalFeatures, numClasses)
  }

  test("params") {
    ParamsSuite.checkParams(new RandomForestClassifier)
    val model = new RandomForestClassificationModel("rfc",
      Array(new DecisionTreeClassificationModel("dtc", new LeafNode(0.0, 0.0, null), 1, 2)), 2, 2)
    ParamsSuite.checkParams(model)
  }

  test("Binary classification with continuous features:" +
    " comparing DecisionTree vs. RandomForest(numTrees = 1)") {
    val rf = new RandomForestClassifier()
    binaryClassificationTestWithContinuousFeatures(rf)
  }

  test("Binary classification with continuous features and node Id cache:" +
    " comparing DecisionTree vs. RandomForest(numTrees = 1)") {
    val rf = new RandomForestClassifier()
      .setCacheNodeIds(true)
    binaryClassificationTestWithContinuousFeatures(rf)
  }

  test("alternating categorical and continuous features with multiclass labels to test indexing") {
    val arr = Array(
      LabeledPoint(0.0, Vectors.dense(1.0, 0.0, 0.0, 3.0, 1.0)),
      LabeledPoint(1.0, Vectors.dense(0.0, 1.0, 1.0, 1.0, 2.0)),
      LabeledPoint(0.0, Vectors.dense(2.0, 0.0, 0.0, 6.0, 3.0)),
      LabeledPoint(2.0, Vectors.dense(0.0, 2.0, 1.0, 3.0, 2.0))
    )
    val rdd = sc.parallelize(arr)
    val categoricalFeatures = Map(0 -> 3, 2 -> 2, 4 -> 4)
    val numClasses = 3

    val rf = new RandomForestClassifier()
      .setImpurity("Gini")
      .setMaxDepth(5)
      .setNumTrees(2)
      .setFeatureSubsetStrategy("sqrt")
      .setSeed(12345)
    compareAPIs(rdd, rf, categoricalFeatures, numClasses)
  }

  test("subsampling rate in RandomForest") {
    val rdd = orderedLabeledPoints5_20
    val categoricalFeatures = Map.empty[Int, Int]
    val numClasses = 2

    val rf1 = new RandomForestClassifier()
      .setImpurity("Gini")
      .setMaxDepth(2)
      .setCacheNodeIds(true)
      .setNumTrees(3)
      .setFeatureSubsetStrategy("auto")
      .setSeed(123)
    compareAPIs(rdd, rf1, categoricalFeatures, numClasses)

    val rf2 = rf1.setSubsamplingRate(0.5)
    compareAPIs(rdd, rf2, categoricalFeatures, numClasses)
  }

  test("predictRaw and predictProbability") {
    val rdd = orderedLabeledPoints5_20
    val rf = new RandomForestClassifier()
      .setImpurity("Gini")
      .setMaxDepth(3)
      .setNumTrees(3)
      .setSeed(123)
    val categoricalFeatures = Map.empty[Int, Int]
    val numClasses = 2

    val df: DataFrame = TreeTests.setMetadata(rdd, categoricalFeatures, numClasses)
    val model = rf.fit(df)

    // copied model must have the same parent.
    MLTestingUtils.checkCopy(model)

    val predictions = model.transform(df)
      .select(rf.getPredictionCol, rf.getRawPredictionCol, rf.getProbabilityCol)
      .collect()

    predictions.foreach { case Row(pred: Double, rawPred: Vector, probPred: Vector) =>
      assert(pred === rawPred.argmax,
        s"Expected prediction $pred but calculated ${rawPred.argmax} from rawPrediction.")
      val sum = rawPred.toArray.sum
      assert(Vectors.dense(rawPred.toArray.map(_ / sum)) === probPred,
        "probability prediction mismatch")
      assert(probPred.toArray.sum ~== 1.0 relTol 1E-5)
    }
  }

  /////////////////////////////////////////////////////////////////////////////
  // Tests of feature importance
  /////////////////////////////////////////////////////////////////////////////
  test("Feature importance with toy data") {
    val numClasses = 2
    val rf = new RandomForestClassifier()
      .setImpurity("Gini")
      .setMaxDepth(3)
      .setNumTrees(3)
      .setFeatureSubsetStrategy("all")
      .setSubsamplingRate(1.0)
      .setSeed(123)

    // In this data, feature 1 is very important.
    val data: RDD[LabeledPoint] = TreeTests.featureImportanceData(sc)
    val categoricalFeatures = Map.empty[Int, Int]
    val df: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, numClasses)

    val importances = rf.fit(df).featureImportances
    val mostImportantFeature = importances.argmax
    assert(mostImportantFeature === 1)
    assert(importances.toArray.sum === 1.0)
    assert(importances.toArray.forall(_ >= 0.0))
  }

  /////////////////////////////////////////////////////////////////////////////
  // Tests of model save/load
  /////////////////////////////////////////////////////////////////////////////

  // TODO: Reinstate test once save/load are implemented  SPARK-6725
  /*
  test("model save/load") {
    val tempDir = Utils.createTempDir()
    val path = tempDir.toURI.toString

    val trees =
      Range(0, 3).map(_ => OldDecisionTreeSuite.createModel(OldAlgo.Classification)).toArray
    val oldModel = new OldRandomForestModel(OldAlgo.Classification, trees)
    val newModel = RandomForestClassificationModel.fromOld(oldModel)

    // Save model, load it back, and compare.
    try {
      newModel.save(sc, path)
      val sameNewModel = RandomForestClassificationModel.load(sc, path)
      TreeTests.checkEqual(newModel, sameNewModel)
    } finally {
      Utils.deleteRecursively(tempDir)
    }
  }
  */
}

private object RandomForestClassifierSuite extends SparkFunSuite {

  /**
   * Train 2 models on the given dataset, one using the old API and one using the new API.
   * Convert the old model to the new format, compare them, and fail if they are not exactly equal.
   */
  def compareAPIs(
      data: RDD[LabeledPoint],
      rf: RandomForestClassifier,
      categoricalFeatures: Map[Int, Int],
      numClasses: Int): Unit = {
    val numFeatures = data.first().features.size
    val oldStrategy =
      rf.getOldStrategy(categoricalFeatures, numClasses, OldAlgo.Classification, rf.getOldImpurity)
    val oldModel = OldRandomForest.trainClassifier(
      data, oldStrategy, rf.getNumTrees, rf.getFeatureSubsetStrategy, rf.getSeed.toInt)
    val newData: DataFrame = TreeTests.setMetadata(data, categoricalFeatures, numClasses)
    val newModel = rf.fit(newData)
    // Use parent from newTree since this is not checked anyways.
    val oldModelAsNew = RandomForestClassificationModel.fromOld(
      oldModel, newModel.parent.asInstanceOf[RandomForestClassifier], categoricalFeatures,
      numClasses)
    TreeTests.checkEqual(oldModelAsNew, newModel)
    assert(newModel.hasParent)
    assert(!newModel.trees.head.asInstanceOf[DecisionTreeClassificationModel].hasParent)
    assert(newModel.numClasses === numClasses)
    assert(newModel.numFeatures === numFeatures)
  }
}