[SPARK-5317]Set BoostingStrategy.defaultParams With Enumeration Algo.Classification or Algo.Regression

JIRA Issue: https://issues.apache.org/jira/browse/SPARK-5317
When setting the BoostingStrategy.defaultParams("Classification"), It's more straightforward to set it with the Enumeration Algo.Classification, just like BoostingStragety.defaultParams(Algo.Classification).
I overload the method BoostingStragety.defaultParams().

Author: Basin <jpsachilles@gmail.com>

Closes #4103 from Peishen-Jia/stragetyAlgo and squashes the following commits:

87bab1c [Basin] Docs and Code documentations updated.
3b72875 [Basin] defaultParams(algoStr: String) call defaultParams(algo: Algo).
7c1e6ee [Basin] Doc of Java updated. algo -> algoStr instead.
d5c8a2e [Basin] Merge branch 'stragetyAlgo' of github.com:Peishen-Jia/spark into stragetyAlgo
65f96ce [Basin] mllib-ensembles doc modified.
e04a5aa [Basin] boostingstrategy.defaultParam string algo to enumeration.
68cf544 [Basin] mllib-ensembles doc modified.
a4aea51 [Basin] boostingstrategy.defaultParam string algo to enumeration.

2 files changed, 28 insertions, 11 deletions

diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala
index cf51d041c6..ed8e6a796f 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/BoostingStrategy.scala
@@ -70,20 +70,29 @@ object BoostingStrategy {
 
   /**
    * Returns default configuration for the boosting algorithm
+   * @param algo Learning goal.  Supported: "Classification" or "Regression"
+   * @return Configuration for boosting algorithm
+   */
+  def defaultParams(algo: String): BoostingStrategy = {
+    defaultParams(Algo.fromString(algo))
+  }
+
+  /**
+   * Returns default configuration for the boosting algorithm
    * @param algo Learning goal.  Supported:
    *             [[org.apache.spark.mllib.tree.configuration.Algo.Classification]],
    *             [[org.apache.spark.mllib.tree.configuration.Algo.Regression]]
    * @return Configuration for boosting algorithm
    */
-  def defaultParams(algo: String): BoostingStrategy = {
-    val treeStrategy = Strategy.defaultStrategy(algo)
-    treeStrategy.maxDepth = 3
+  def defaultParams(algo: Algo): BoostingStrategy = {
+    val treeStragtegy = Strategy.defaultStategy(algo)
+    treeStragtegy.maxDepth = 3
     algo match {
-      case "Classification" =>
-        treeStrategy.numClasses = 2
-        new BoostingStrategy(treeStrategy, LogLoss)
-      case "Regression" =>
-        new BoostingStrategy(treeStrategy, SquaredError)
+      case Algo.Classification =>
+        treeStragtegy.numClasses = 2
+        new BoostingStrategy(treeStragtegy, LogLoss)
+      case Algo.Regression =>
+        new BoostingStrategy(treeStragtegy, SquaredError)
       case _ =>
         throw new IllegalArgumentException(s"$algo is not supported by boosting.")
     }
diff --git a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
index d5cd89ab94..972959885f 100644
--- a/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
+++ b/mllib/src/main/scala/org/apache/spark/mllib/tree/configuration/Strategy.scala
@@ -173,11 +173,19 @@ object Strategy {
    * Construct a default set of parameters for [[org.apache.spark.mllib.tree.DecisionTree]]
    * @param algo  "Classification" or "Regression"
    */
-  def defaultStrategy(algo: String): Strategy = algo match {
-    case "Classification" =>
+  def defaultStrategy(algo: String): Strategy = {
+    defaultStategy(Algo.fromString(algo))
+  }
+
+  /**
+   * Construct a default set of parameters for [[org.apache.spark.mllib.tree.DecisionTree]]
+   * @param algo Algo.Classification or Algo.Regression
+   */
+  def defaultStategy(algo: Algo): Strategy = algo match {
+    case Algo.Classification =>
       new Strategy(algo = Classification, impurity = Gini, maxDepth = 10,
         numClasses = 2)
-    case "Regression" =>
+    case Algo.Regression =>
       new Strategy(algo = Regression, impurity = Variance, maxDepth = 10,
         numClasses = 0)
   }