Combine regression and classification tests into a single one

Author: MechCoder

docs/mllib-ensembles.md

@@ -434,7 +434,7 @@ training. The method runWithValidation has been provided to make use of this opt
 first one being the training dataset and the second being the validation dataset.
 
 The training is stopped when the improvement in the validation error is not more than a certain tolerance
-(supplied by the validationTol argument in BoostingStrategy). In practice, the validation error
+(supplied by the `validationTol` argument in `BoostingStrategy`). In practice, the validation error
 decreases initially and later increases. There might be cases in which the validation error does not change monotonically,
 and the user is advised to set a large enough negative tolerance and examine the validation curve to to tune the number of
 iterations.

mllib/src/main/scala/org/apache/spark/mllib/tree/GradientBoostedTrees.scala

@@ -236,7 +236,7 @@ object GradientBoostedTrees extends Logging {
             boostingStrategy.treeStrategy.algo,
             baseLearners.slice(0, bestM),
             baseLearnerWeights.slice(0, bestM))
-        } else if (currentValidateError < bestValidateError){
+        } else if (currentValidateError < bestValidateError) {
             bestValidateError = currentValidateError
             bestM = m + 1
         }

mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala

@@ -38,7 +38,6 @@ import org.apache.spark.mllib.tree.loss.{LogLoss, SquaredError, Loss}
  *                      validation input between two iterations is less than the validationTol
  *                      then stop. Ignored when [[run]] is used.
  */
-
 @Experimental
 case class BoostingStrategy(
     // Required boosting parameters

mllib/src/test/scala/org/apache/spark/mllib/tree/GradientBoostedTreesSuite.scala

@@ -159,62 +159,39 @@ class GradientBoostedTreesSuite extends FunSuite with MLlibTestSparkContext {
     }
   }
 
-  test("runWithValidation performs better on a validation dataset (Regression)") {
+  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(GradientBoostedTreesSuite.trainData, 2)
     val validateRdd = sc.parallelize(GradientBoostedTreesSuite.validateData, 2)
 
-    val treeStrategy = new Strategy(algo = Regression, impurity = Variance, maxDepth = 2,
-      categoricalFeaturesInfo = Map.empty)
-    Array(SquaredError, AbsoluteError).foreach { error =>
-      val boostingStrategy =
-        new BoostingStrategy(treeStrategy, error, numIterations, validationTol = 0.0)
-
-      val gbtValidate = new GradientBoostedTrees(boostingStrategy).
-        runWithValidation(trainRdd, validateRdd)
-      assert(gbtValidate.numTrees !== numIterations)
-
-      val gbt = GradientBoostedTrees.train(trainRdd, boostingStrategy)
-      val errorWithoutValidation = error.computeError(gbt, validateRdd)
-      val errorWithValidation = error.computeError(gbtValidate, validateRdd)
-      assert(errorWithValidation < errorWithoutValidation)
+    val algos = Array(Regression, Regression, Classification)
+    val losses = Array(SquaredError, AbsoluteError, LogLoss)
+    (algos zip losses) map {
+      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 gbtValidate = new GradientBoostedTrees(boostingStrategy)
+          .runWithValidation(trainRdd, validateRdd)
+        assert(gbtValidate.numTrees !== numIterations)
+
+        // Test that it performs better on the validation dataset.
+        val gbt = GradientBoostedTrees.train(trainRdd, boostingStrategy)
+        val (errorWithoutValidation, errorWithValidation) = {
+          if (algo == Classification) {
+            val remappedRdd = validateRdd.map(x => new LabeledPoint(2 * x.label - 1, x.features))
+            (loss.computeError(gbt, remappedRdd), loss.computeError(gbtValidate, remappedRdd))
+          } else {
+            (loss.computeError(gbt, validateRdd), loss.computeError(gbtValidate, validateRdd))
+          }
+        }
+        assert(errorWithValidation <= errorWithoutValidation)
+      }
     }
   }
 
-  test("runWithValidation performs better on a validation dataset (Classification)") {
-    // Set numIterations large enough so that it stops early.
-    val numIterations = 20
-    val trainRdd = sc.parallelize(GradientBoostedTreesSuite.trainData, 2)
-    val validateRdd = sc.parallelize(GradientBoostedTreesSuite.validateData, 2)
-
-    val treeStrategy = new Strategy(algo = Classification, impurity = Variance, maxDepth = 2,
-      categoricalFeaturesInfo = Map.empty)
-    val boostingStrategy =
-      new BoostingStrategy(treeStrategy, LogLoss, numIterations, validationTol = 0.0)
-
-    // Test that it stops early.
-    val gbtValidate = new GradientBoostedTrees(boostingStrategy).
-      runWithValidation(trainRdd, validateRdd)
-    assert(gbtValidate.numTrees !== numIterations)
-
-    // Remap labels to {-1, 1}
-    val remappedInput = validateRdd.map(x => new LabeledPoint(2 * x.label - 1, x.features))
-
-    // The error checked for internally in the GradientBoostedTrees is based on Regression.
-    // Hence for the validation model, the Classification error need not be strictly less than
-    // that done with validation.
-    val gbtValidateRegressor = new GradientBoostedTreesModel(
-      Regression, gbtValidate.trees, gbtValidate.treeWeights)
-    val errorWithValidation = LogLoss.computeError(gbtValidateRegressor, remappedInput)
-
-    val gbt = GradientBoostedTrees.train(trainRdd, boostingStrategy)
-    val gbtRegressor = new GradientBoostedTreesModel(Regression, gbt.trees, gbt.treeWeights)
-    val errorWithoutValidation = LogLoss.computeError(gbtRegressor, remappedInput)
-
-    assert(errorWithValidation < errorWithoutValidation)
-  }
-
 }
 
 private object GradientBoostedTreesSuite {