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/*
* 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.tree.impl
import org.apache.spark.SparkFunSuite
import org.apache.spark.internal.Logging
import org.apache.spark.mllib.regression.LabeledPoint
import org.apache.spark.mllib.tree.{GradientBoostedTreesSuite => OldGBTSuite}
import org.apache.spark.mllib.tree.configuration.{BoostingStrategy, Strategy}
import org.apache.spark.mllib.tree.configuration.Algo._
import org.apache.spark.mllib.tree.impurity.Variance
import org.apache.spark.mllib.tree.loss.{AbsoluteError, LogLoss, SquaredError}
import org.apache.spark.mllib.util.MLlibTestSparkContext
/**
* Test suite for [[GradientBoostedTrees]].
*/
class GradientBoostedTreesSuite extends SparkFunSuite with MLlibTestSparkContext with Logging {
test("runWithValidation stops early and performs better on a validation dataset") {
// Set numIterations large enough so that it stops early.
val numIterations = 20
val trainRdd = sc.parallelize(OldGBTSuite.trainData, 2)
val validateRdd = sc.parallelize(OldGBTSuite.validateData, 2)
val trainDF = sqlContext.createDataFrame(trainRdd)
val validateDF = sqlContext.createDataFrame(validateRdd)
val algos = Array(Regression, Regression, Classification)
val losses = Array(SquaredError, AbsoluteError, LogLoss)
algos.zip(losses).foreach { case (algo, loss) =>
val treeStrategy = new Strategy(algo = algo, impurity = Variance, maxDepth = 2,
categoricalFeaturesInfo = Map.empty)
val boostingStrategy =
new BoostingStrategy(treeStrategy, loss, numIterations, validationTol = 0.0)
val (validateTrees, validateTreeWeights) = GradientBoostedTrees
.runWithValidation(trainRdd, validateRdd, boostingStrategy, 42L)
val numTrees = validateTrees.length
assert(numTrees !== numIterations)
// Test that it performs better on the validation dataset.
val (trees, treeWeights) = GradientBoostedTrees.run(trainRdd, boostingStrategy, 42L)
val (errorWithoutValidation, errorWithValidation) = {
if (algo == Classification) {
val remappedRdd = validateRdd.map(x => new LabeledPoint(2 * x.label - 1, x.features))
(GradientBoostedTrees.computeError(remappedRdd, trees, treeWeights, loss),
GradientBoostedTrees.computeError(remappedRdd, validateTrees,
validateTreeWeights, loss))
} else {
(GradientBoostedTrees.computeError(validateRdd, trees, treeWeights, loss),
GradientBoostedTrees.computeError(validateRdd, validateTrees,
validateTreeWeights, loss))
}
}
assert(errorWithValidation <= errorWithoutValidation)
// Test that results from evaluateEachIteration comply with runWithValidation.
// Note that convergenceTol is set to 0.0
val evaluationArray = GradientBoostedTrees
.evaluateEachIteration(validateRdd, trees, treeWeights, loss, algo)
assert(evaluationArray.length === numIterations)
assert(evaluationArray(numTrees) > evaluationArray(numTrees - 1))
var i = 1
while (i < numTrees) {
assert(evaluationArray(i) <= evaluationArray(i - 1))
i += 1
}
}
}
}
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