CheckpointManagerSuite.scala

/*
 Copyright (c) 2014 by Contributors

 Licensed 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 ml.dmlc.xgboost4j.scala.spark

import java.io.File

import ml.dmlc.xgboost4j.scala.{Classification, Booster, DMatrix, XGBoost => SXGBoost}
import org.scalatest.FunSuite
import org.apache.hadoop.fs.{FileSystem, Path}

class CheckpointManagerSuite extends FunSuite with TmpFolderPerSuite with PerTest {

  private lazy val (model4, model8) = {
    val training = buildDataFrame(Classification.train)
    val paramMap = Map("eta" -> "1", "max_depth" -> "2", "silent" -> "1",
      "objective" -> "binary:logistic", "num_workers" -> sc.defaultParallelism)
    (new XGBoostClassifier(paramMap ++ Seq("num_round" -> 2)).fit(training),
    new XGBoostClassifier(paramMap ++ Seq("num_round" -> 4)).fit(training))
  }

  test("test update/load models") {
    val tmpPath = createTmpFolder("test").toAbsolutePath.toString
    val manager = new CheckpointManager(sc, tmpPath)
    manager.updateCheckpoint(model4._booster)
    var files = FileSystem.get(sc.hadoopConfiguration).listStatus(new Path(tmpPath))
    assert(files.length == 1)
    assert(files.head.getPath.getName == "4.model")
    assert(manager.loadCheckpointAsBooster.booster.getVersion == 4)

    manager.updateCheckpoint(model8._booster)
    files = FileSystem.get(sc.hadoopConfiguration).listStatus(new Path(tmpPath))
    assert(files.length == 1)
    assert(files.head.getPath.getName == "8.model")
    assert(manager.loadCheckpointAsBooster.booster.getVersion == 8)
  }

  test("test cleanUpHigherVersions") {
    val tmpPath = createTmpFolder("test").toAbsolutePath.toString
    val manager = new CheckpointManager(sc, tmpPath)
    manager.updateCheckpoint(model8._booster)
    manager.cleanUpHigherVersions(round = 8)
    assert(new File(s"$tmpPath/8.model").exists())

    manager.cleanUpHigherVersions(round = 4)
    assert(!new File(s"$tmpPath/8.model").exists())
  }

  test("test checkpoint rounds") {
    val tmpPath = createTmpFolder("test").toAbsolutePath.toString
    val manager = new CheckpointManager(sc, tmpPath)
    assertResult(Seq(7))(manager.getCheckpointRounds(checkpointInterval = 0, round = 7))
    assertResult(Seq(2, 4, 6, 7))(manager.getCheckpointRounds(checkpointInterval = 2, round = 7))
    manager.updateCheckpoint(model4._booster)
    assertResult(Seq(4, 6, 7))(manager.getCheckpointRounds(2, 7))
  }


  private def trainingWithCheckpoint(cacheData: Boolean, skipCleanCheckpoint: Boolean): Unit = {
    val eval = new EvalError()
    val training = buildDataFrame(Classification.train)
    val testDM = new DMatrix(Classification.test.iterator)

    val tmpPath = createTmpFolder("model1").toAbsolutePath.toString
    val cacheDataMap = if (cacheData) Map("cacheTrainingSet" -> true) else Map()
    val skipCleanCheckpointMap =
      if (skipCleanCheckpoint) Map("skip_clean_checkpoint" -> true) else Map()
    val paramMap = Map("eta" -> "1", "max_depth" -> 2,
      "objective" -> "binary:logistic", "checkpoint_path" -> tmpPath,
      "checkpoint_interval" -> 2, "num_workers" -> numWorkers) ++ cacheDataMap ++
      skipCleanCheckpointMap

    val prevModel = new XGBoostClassifier(paramMap ++ Seq("num_round" -> 5)).fit(training)
    def error(model: Booster): Float = eval.eval(
      model.predict(testDM, outPutMargin = true), testDM)

    if (skipCleanCheckpoint) {
      // Check only one model is kept after training
      val files = FileSystem.get(sc.hadoopConfiguration).listStatus(new Path(tmpPath))
      assert(files.length == 1)
      assert(files.head.getPath.getName == "8.model")
      val tmpModel = SXGBoost.loadModel(s"$tmpPath/8.model")
      // Train next model based on prev model
      val nextModel = new XGBoostClassifier(paramMap ++ Seq("num_round" -> 8)).fit(training)
      assert(error(tmpModel) > error(prevModel._booster))
      assert(error(prevModel._booster) > error(nextModel._booster))
      assert(error(nextModel._booster) < 0.1)
    } else {
      assert(!FileSystem.get(sc.hadoopConfiguration).exists(new Path(tmpPath)))
    }
  }

  test("training with checkpoint boosters") {
    trainingWithCheckpoint(cacheData = false, skipCleanCheckpoint = true)
  }

  test("training with checkpoint boosters with cached training dataset") {
    trainingWithCheckpoint(cacheData = true, skipCleanCheckpoint = true)
  }

  test("the checkpoint file should be cleaned after a successful training") {
    trainingWithCheckpoint(cacheData = false, skipCleanCheckpoint = false)
  }
}