[jvm-packages] test consistency of prediction functions with DMatrix and RDD (#1518)
* test consistency of prediction functions between DMatrix and RDD * fix the failed test cases
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Nan Zhu
GitHub
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@ -128,7 +128,7 @@ class XGBoostModel(_booster: Booster) extends Serializable {
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* @param testSet test set represented as DMatrix
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*/
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def predict(testSet: DMatrix): Array[Array[Float]] = {
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_booster.predict(testSet, true, 0)
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_booster.predict(testSet)
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}
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/**
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@ -128,18 +128,113 @@ class XGBoostSuite extends FunSuite with BeforeAndAfter {
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List("eta" -> "1", "max_depth" -> "2", "silent" -> "0",
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"objective" -> "binary:logistic").toMap,
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new scala.collection.mutable.HashMap[String, String],
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numWorkers = 2, round = 5, null, null, false)
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numWorkers = 2, round = 5, null, null, useExternalMemory = false)
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val boosterCount = boosterRDD.count()
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assert(boosterCount === 2)
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val boosters = boosterRDD.collect()
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val eval = new EvalError()
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for (booster <- boosters) {
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val predicts = booster.predict(testSetDMatrix, true)
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assert(new EvalError().eval(predicts, testSetDMatrix) < 0.17)
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val predicts = booster.predict(testSetDMatrix, outPutMargin = true)
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assert(eval.eval(predicts, testSetDMatrix) < 0.17)
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}
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}
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test("training with external memory cache") {
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sc.stop()
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sc = null
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val sparkConf = new SparkConf().setMaster("local[*]").setAppName("XGBoostSuite")
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val customSparkContext = new SparkContext(sparkConf)
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val eval = new EvalError()
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val trainingRDD = buildTrainingRDD(Some(customSparkContext))
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val testSet = readFile(getClass.getResource("/agaricus.txt.test").getFile).iterator
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import DataUtils._
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val testSetDMatrix = new DMatrix(new JDMatrix(testSet, null))
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val paramMap = List("eta" -> "1", "max_depth" -> "6", "silent" -> "0",
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"objective" -> "binary:logistic").toMap
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val xgBoostModel = XGBoost.train(trainingRDD, paramMap, round = 5,
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nWorkers = numWorkers, useExternalMemory = true)
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assert(eval.eval(xgBoostModel.booster.predict(testSetDMatrix, outPutMargin = true),
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testSetDMatrix) < 0.1)
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customSparkContext.stop()
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// clean
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val dir = new File(".")
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for (file <- dir.listFiles() if file.getName.startsWith("XGBoostSuite-0-dtrain_cache")) {
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file.delete()
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}
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}
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test("save and load model") {
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test("test with dense vectors containing missing value") {
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def buildDenseRDD(): RDD[LabeledPoint] = {
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val nrow = 100
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val ncol = 5
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val data0 = Array.ofDim[Double](nrow, ncol)
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// put random nums
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for (r <- 0 until nrow; c <- 0 until ncol) {
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data0(r)(c) = {
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if (c == ncol - 1) {
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-0.1
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} else {
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Random.nextDouble()
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}
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}
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}
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// create label
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val label0 = new Array[Double](nrow)
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for (i <- label0.indices) {
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label0(i) = Random.nextDouble()
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}
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val points = new ListBuffer[LabeledPoint]
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for (r <- 0 until nrow) {
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points += LabeledPoint(label0(r), Vectors.dense(data0(r)))
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}
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sc.parallelize(points)
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}
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val trainingRDD = buildDenseRDD().repartition(4)
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val testRDD = buildDenseRDD().repartition(4)
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val paramMap = List("eta" -> "1", "max_depth" -> "2", "silent" -> "0",
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"objective" -> "binary:logistic").toMap
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val xgBoostModel = XGBoost.train(trainingRDD, paramMap, 5, numWorkers)
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xgBoostModel.predict(testRDD.map(_.features.toDense), missingValue = -0.1f).collect()
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}
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test("test consistency of prediction functions with RDD") {
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val trainingRDD = buildTrainingRDD()
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val testSet = readFile(getClass.getResource("/agaricus.txt.test").getFile)
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val testRDD = sc.parallelize(testSet, numSlices = 1).map(_.features)
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val testCollection = testRDD.collect()
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for (i <- testSet.indices) {
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assert(testCollection(i).toDense.values.sameElements(testSet(i).features.toDense.values))
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}
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val paramMap = List("eta" -> "1", "max_depth" -> "2", "silent" -> "0",
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"objective" -> "binary:logistic").toMap
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val xgBoostModel = XGBoost.train(trainingRDD, paramMap, 5, numWorkers)
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val predRDD = xgBoostModel.predict(testRDD)
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val predResult1 = predRDD.collect()(0)
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import DataUtils._
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val predResult2 = xgBoostModel.predict(new DMatrix(testSet.iterator))
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for (i <- predResult1.indices; j <- predResult1(i).indices) {
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assert(predResult1(i)(j) === predResult2(i)(j))
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}
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}
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test("test prediction functionality with empty partition") {
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def buildEmptyRDD(sparkContext: Option[SparkContext] = None): RDD[SparkVector] = {
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val sampleList = new ListBuffer[SparkVector]
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sparkContext.getOrElse(sc).parallelize(sampleList, numWorkers)
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}
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val trainingRDD = buildTrainingRDD()
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val testRDD = buildEmptyRDD()
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import DataUtils._
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val tempDir = Files.createTempDirectory("xgboosttest-")
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val tempFile = Files.createTempFile(tempDir, "", "")
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val paramMap = List("eta" -> "1", "max_depth" -> "2", "silent" -> "0",
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"objective" -> "binary:logistic").toMap
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val xgBoostModel = XGBoost.train(trainingRDD, paramMap, 5, numWorkers)
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println(xgBoostModel.predict(testRDD).collect().length === 0)
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}
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test("test model consistency after save and load") {
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val eval = new EvalError()
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val trainingRDD = buildTrainingRDD()
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val testSet = readFile(getClass.getResource("/agaricus.txt.test").getFile).iterator
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@ -150,11 +245,12 @@ class XGBoostSuite extends FunSuite with BeforeAndAfter {
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val paramMap = List("eta" -> "1", "max_depth" -> "2", "silent" -> "0",
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"objective" -> "binary:logistic").toMap
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val xgBoostModel = XGBoost.train(trainingRDD, paramMap, 5, numWorkers)
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val evalResults = eval.eval(xgBoostModel.predict(testSetDMatrix), testSetDMatrix)
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val evalResults = eval.eval(xgBoostModel.booster.predict(testSetDMatrix, outPutMargin = true),
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testSetDMatrix)
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assert(evalResults < 0.1)
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xgBoostModel.saveModelAsHadoopFile(tempFile.toFile.getAbsolutePath)
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val loadedXGBooostModel = XGBoost.loadModelFromHadoopFile(tempFile.toFile.getAbsolutePath)
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val predicts = loadedXGBooostModel.predict(testSetDMatrix)
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val predicts = loadedXGBooostModel.booster.predict(testSetDMatrix, outPutMargin = true)
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val loadedEvalResults = eval.eval(predicts, testSetDMatrix)
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assert(loadedEvalResults == evalResults)
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}
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@ -190,83 +286,8 @@ class XGBoostSuite extends FunSuite with BeforeAndAfter {
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val paramMap = List("eta" -> "1", "max_depth" -> "2", "silent" -> "0",
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"objective" -> "binary:logistic").toMap
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val xgBoostModel = XGBoost.train(trainingRDD, paramMap, 5, numWorkers)
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assert(eval.eval(xgBoostModel.predict(testSetDMatrix), testSetDMatrix) < 0.1)
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assert(eval.eval(xgBoostModel.booster.predict(testSetDMatrix, outPutMargin = true),
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testSetDMatrix) < 0.1)
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customSparkContext.stop()
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}
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test("test with empty partition") {
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def buildEmptyRDD(sparkContext: Option[SparkContext] = None): RDD[SparkVector] = {
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val sampleList = new ListBuffer[SparkVector]
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sparkContext.getOrElse(sc).parallelize(sampleList, numWorkers)
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}
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val eval = new EvalError()
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val trainingRDD = buildTrainingRDD()
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val testRDD = buildEmptyRDD()
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import DataUtils._
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val tempDir = Files.createTempDirectory("xgboosttest-")
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val tempFile = Files.createTempFile(tempDir, "", "")
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val paramMap = List("eta" -> "1", "max_depth" -> "2", "silent" -> "0",
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"objective" -> "binary:logistic").toMap
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val xgBoostModel = XGBoost.train(trainingRDD, paramMap, 5, numWorkers)
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println(xgBoostModel.predict(testRDD).collect())
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}
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test("test with dense vectors containing missing value") {
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def buildDenseRDD(): RDD[LabeledPoint] = {
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val nrow = 100
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val ncol = 5
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val data0 = Array.ofDim[Double](nrow, ncol)
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// put random nums
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for (r <- 0 until nrow; c <- 0 until ncol) {
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data0(r)(c) = {
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if (c == ncol - 1) {
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-0.1
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} else {
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Random.nextDouble()
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}
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}
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}
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// create label
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val label0 = new Array[Double](nrow)
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for (i <- label0.indices) {
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label0(i) = Random.nextDouble()
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}
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val points = new ListBuffer[LabeledPoint]
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for (r <- 0 until nrow) {
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points += LabeledPoint(label0(r), Vectors.dense(data0(r)))
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}
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sc.parallelize(points)
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}
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val trainingRDD = buildDenseRDD().repartition(4)
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val testRDD = buildDenseRDD().repartition(4)
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val paramMap = List("eta" -> "1", "max_depth" -> "2", "silent" -> "0",
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"objective" -> "binary:logistic").toMap
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val xgBoostModel = XGBoost.train(trainingRDD, paramMap, 5, 4)
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xgBoostModel.predict(testRDD.map(_.features.toDense), missingValue = -0.1f).collect()
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}
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test("training with external memory cache") {
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sc.stop()
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sc = null
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val sparkConf = new SparkConf().setMaster("local[*]").setAppName("XGBoostSuite")
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val customSparkContext = new SparkContext(sparkConf)
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val eval = new EvalError()
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val trainingRDD = buildTrainingRDD(Some(customSparkContext))
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val testSet = readFile(getClass.getResource("/agaricus.txt.test").getFile).iterator
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import DataUtils._
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val testSetDMatrix = new DMatrix(new JDMatrix(testSet, null))
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val paramMap = List("eta" -> "1", "max_depth" -> "2", "silent" -> "0",
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"objective" -> "binary:logistic").toMap
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val xgBoostModel = XGBoost.train(trainingRDD, paramMap, 5, numWorkers, useExternalMemory = true)
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assert(eval.eval(xgBoostModel.predict(testSetDMatrix), testSetDMatrix) < 0.1)
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customSparkContext.stop()
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// clean
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val dir = new File(".")
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for (file <- dir.listFiles() if file.getName.startsWith("XGBoostSuite-dtrain_cache")) {
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file.delete()
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}
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}
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}
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