Github user jkbradley commented on a diff in the pull request:
https://github.com/apache/spark/pull/2607#discussion_r19495241
--- Diff:
mllib/src/test/scala/org/apache/spark/mllib/tree/GradientBoostingSuite.scala ---
@@ -0,0 +1,208 @@
+/*
+ * 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.mllib.tree
+
+import org.scalatest.FunSuite
+
+import org.apache.spark.mllib.regression.LabeledPoint
+import org.apache.spark.mllib.tree.configuration.Algo._
+import org.apache.spark.mllib.tree.configuration.{BoostingStrategy,
Strategy}
+import org.apache.spark.mllib.tree.impurity.{Variance, Gini}
+import org.apache.spark.mllib.tree.loss.{SquaredError, LogLoss}
+import org.apache.spark.mllib.tree.model.{WeightedEnsembleModel,
DecisionTreeModel}
+
+import org.apache.spark.mllib.util.LocalSparkContext
+
+/**
+ * Test suite for [[GradientBoosting]].
+ */
+class GradientBoostingSuite extends FunSuite with LocalSparkContext {
+
+ test("Binary classification with continuous features:" +
+ " comparing DecisionTree vs. GradientBoosting (numEstimators = 1)") {
+
+ val arr = EnsembleTestHelper.generateOrderedLabeledPoints(numFeatures
= 50, 1000)
+ val rdd = sc.parallelize(arr)
+ val categoricalFeaturesInfo = Map.empty[Int, Int]
+ val numEstimators = 1
+
+ val remappedInput = rdd.map(x => new LabeledPoint((x.label * 2) - 1,
x.features))
+ val treeStrategy = new Strategy(algo = Regression, impurity =
Variance, maxDepth = 2,
+ numClassesForClassification = 2, categoricalFeaturesInfo =
categoricalFeaturesInfo)
+
+ val dt = DecisionTree.train(remappedInput, treeStrategy)
+
+ val boostingStrategy = new BoostingStrategy(algo = Classification,
+ numEstimators = numEstimators, loss = LogLoss, maxDepth = 2,
+ numClassesForClassification = 2, categoricalFeaturesInfo =
categoricalFeaturesInfo)
+
+ val gbt = GradientBoosting.trainClassifier(rdd, boostingStrategy)
+ assert(gbt.baseLearners.size === 1)
+ val gbtTree = gbt.baseLearners(0)
+
+
+ EnsembleTestHelper.validateClassifier(gbt, arr, 0.9)
+
+ // Make sure trees are the same.
+ assert(gbtTree.toString == dt.toString)
+ }
+
+ test("Binary classification with continuous features:" +
+ " comparing DecisionTree vs. GradientBoosting (numEstimators = 10)") {
+
+ val arr = EnsembleTestHelper.generateOrderedLabeledPoints(numFeatures
= 50, 1000)
+ val rdd = sc.parallelize(arr)
+ val categoricalFeaturesInfo = Map.empty[Int, Int]
+ val numEstimators = 10
+
+ val remappedInput = rdd.map(x => new LabeledPoint((x.label * 2) - 1,
x.features))
+ val treeStrategy = new Strategy(algo = Regression, impurity =
Variance, maxDepth = 2,
+ numClassesForClassification = 2, categoricalFeaturesInfo =
categoricalFeaturesInfo)
+
+ val dt = DecisionTree.train(remappedInput, treeStrategy)
+
+ val boostingStrategy = new BoostingStrategy(algo = Classification,
+ numEstimators = numEstimators, loss = LogLoss, maxDepth = 2,
+ numClassesForClassification = 2, categoricalFeaturesInfo =
categoricalFeaturesInfo)
+
+ val gbt = GradientBoosting.trainClassifier(rdd, boostingStrategy)
+ assert(gbt.baseLearners.size === 10)
+ val gbtTree = gbt.baseLearners(0)
+
+
+ EnsembleTestHelper.validateClassifier(gbt, arr, 0.9)
+
+ // Make sure trees are the same.
+ assert(gbtTree.toString == dt.toString)
+ }
+
+ test("Binary classification with continuous features:" +
+ " Stochastic GradientBoosting (numEstimators = 10, learning rate =
0.9, subsample = 0.75)") {
+
+ val arr = EnsembleTestHelper.generateOrderedLabeledPoints(numFeatures
= 50, 1000)
+ val rdd = sc.parallelize(arr)
+ val categoricalFeaturesInfo = Map.empty[Int, Int]
+ val numEstimators = 10
+
+ val boostingStrategy = new BoostingStrategy(algo = Classification,
+ numEstimators = numEstimators, loss = LogLoss, maxDepth = 2,
+ numClassesForClassification = 2, categoricalFeaturesInfo =
categoricalFeaturesInfo,
+ subsample = 0.75)
+
+ val gbt = GradientBoosting.trainClassifier(rdd, boostingStrategy)
+ assert(gbt.baseLearners.size === 10)
+
+ EnsembleTestHelper.validateClassifier(gbt, arr, 0.9)
+
+ }
+
+
--- End diff --
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