[SPARK-4486][MLLIB] Improve GradientBoosting APIs and doc

There are some inconsistencies in the gradient boosting APIs. The target is a general boosting meta-algorithm, but the implementation is attached to trees. This was partially due to the delay of SPARK-1856. But for the 1.2 release, we should make the APIs consistent.

1. WeightedEnsembleModel -> private[tree] TreeEnsembleModel and renamed members accordingly.
1. GradientBoosting -> GradientBoostedTrees
1. Add RandomForestModel and GradientBoostedTreesModel and hide CombiningStrategy
1. Slightly refactored TreeEnsembleModel (Vote takes weights into consideration.)
1. Remove `trainClassifier` and `trainRegressor` from `GradientBoostedTrees` because they are the same as `train`
1. Rename class `train` method to `run` because it hides the static methods with the same name in Java. Deprecated `DecisionTree.train` class method.
1. Simplify BoostingStrategy and make sure the input strategy is not modified. Users should put algo and numClasses in treeStrategy. We create ensembleStrategy inside boosting.
1. Fix a bug in GradientBoostedTreesSuite with AbsoluteError
1. doc updates

manishamde jkbradley

Author: Xiangrui Meng <meng@databricks.com>

Closes #3374 from mengxr/SPARK-4486 and squashes the following commits:

7097251 [Xiangrui Meng] address joseph's comments
98dea09 [Xiangrui Meng] address manish's comments
4aae3b7 [Xiangrui Meng] add RandomForestModel and GradientBoostedTreesModel, hide CombiningStrategy
ea4c467 [Xiangrui Meng] fix unit tests
751da4e [Xiangrui Meng] rename class method train -> run
19030a5 [Xiangrui Meng] update boosting public APIs

1 files changed, 9 insertions, 9 deletions

diff --git a/examples/src/main/java/org/apache/spark/examples/mllib/JavaGradientBoostedTrees.java b/examples/src/main/java/org/apache/spark/examples/mllib/JavaGradientBoostedTreesRunner.java
index 1af2067b2b..4a5ac404ea 100644
--- a/examples/src/main/java/org/apache/spark/examples/mllib/JavaGradientBoostedTrees.java
+++ b/examples/src/main/java/org/apache/spark/examples/mllib/JavaGradientBoostedTreesRunner.java
@@ -27,18 +27,18 @@ import org.apache.spark.api.java.function.Function;
 import org.apache.spark.api.java.function.Function2;
 import org.apache.spark.api.java.function.PairFunction;
 import org.apache.spark.mllib.regression.LabeledPoint;
-import org.apache.spark.mllib.tree.GradientBoosting;
+import org.apache.spark.mllib.tree.GradientBoostedTrees;
 import org.apache.spark.mllib.tree.configuration.BoostingStrategy;
-import org.apache.spark.mllib.tree.model.WeightedEnsembleModel;
+import org.apache.spark.mllib.tree.model.GradientBoostedTreesModel;
 import org.apache.spark.mllib.util.MLUtils;
 
 /**
  * Classification and regression using gradient-boosted decision trees.
  */
-public final class JavaGradientBoostedTrees {
+public final class JavaGradientBoostedTreesRunner {
 
   private static void usage() {
-    System.err.println("Usage: JavaGradientBoostedTrees <libsvm format data file>" +
+    System.err.println("Usage: JavaGradientBoostedTreesRunner <libsvm format data file>" +
         " <Classification/Regression>");
     System.exit(-1);
   }
@@ -55,7 +55,7 @@ public final class JavaGradientBoostedTrees {
     if (args.length > 2) {
       usage();
     }
-    SparkConf sparkConf = new SparkConf().setAppName("JavaGradientBoostedTrees");
+    SparkConf sparkConf = new SparkConf().setAppName("JavaGradientBoostedTreesRunner");
     JavaSparkContext sc = new JavaSparkContext(sparkConf);
 
     JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(sc.sc(), datapath).toJavaRDD().cache();
@@ -64,7 +64,7 @@ public final class JavaGradientBoostedTrees {
     //  Note: All features are treated as continuous.
     BoostingStrategy boostingStrategy = BoostingStrategy.defaultParams(algo);
     boostingStrategy.setNumIterations(10);
-    boostingStrategy.weakLearnerParams().setMaxDepth(5);
+    boostingStrategy.treeStrategy().setMaxDepth(5);
 
     if (algo.equals("Classification")) {
       // Compute the number of classes from the data.
@@ -73,10 +73,10 @@ public final class JavaGradientBoostedTrees {
           return p.label();
         }
       }).countByValue().size();
-      boostingStrategy.setNumClassesForClassification(numClasses); // ignored for Regression
+      boostingStrategy.treeStrategy().setNumClassesForClassification(numClasses);
 
       // Train a GradientBoosting model for classification.
-      final WeightedEnsembleModel model = GradientBoosting.trainClassifier(data, boostingStrategy);
+      final GradientBoostedTreesModel model = GradientBoostedTrees.train(data, boostingStrategy);
 
       // Evaluate model on training instances and compute training error
       JavaPairRDD<Double, Double> predictionAndLabel =
@@ -95,7 +95,7 @@ public final class JavaGradientBoostedTrees {
       System.out.println("Learned classification tree model:\n" + model);
     } else if (algo.equals("Regression")) {
       // Train a GradientBoosting model for classification.
-      final WeightedEnsembleModel model = GradientBoosting.trainRegressor(data, boostingStrategy);
+      final GradientBoostedTreesModel model = GradientBoostedTrees.train(data, boostingStrategy);
 
       // Evaluate model on training instances and compute training error
       JavaPairRDD<Double, Double> predictionAndLabel =