[jvm-packages] Implemented early stopping (#2710)

* Allowed subsampling test from the training data frame/RDD

The implementation requires storing 1 - trainTestRatio points in memory
to make the sampling work.

An alternative approach would be to construct the full DMatrix and then
slice it deterministically into train/test. The peak memory consumption
of such scenario, however, is twice the dataset size.

* Removed duplication from 'XGBoost.train'

Scala callers can (and should) use names to supply a subset of
parameters. Method overloading is not required.

* Reuse XGBoost seed parameter to stabilize train/test splitting

* Added early stopping support to non-distributed XGBoost

Closes #1544

* Added early-stopping to distributed XGBoost

* Moved construction of 'watches' into a separate method

This commit also fixes the handling of 'baseMargin' which previously
was not added to the validation matrix.

* Addressed review comments

jvm-packages/xgboost4j/src/main/scala/ml/dmlc/xgboost4j/scala/XGBoost.scala

@@ -36,6 +36,9 @@ object XGBoost {
     *                performance on the validation set.
     * @param metrics array containing the evaluation metrics for each matrix in watches for each
     *                iteration
+    * @param earlyStoppingRound if non-zero, training would be stopped
+    *                           after a specified number of consecutive
+    *                           increases in any evaluation metric.
     * @param obj     customized objective
     * @param eval    customized evaluation
     * @return The trained booster.
@@ -45,44 +48,20 @@ object XGBoost {
       dtrain: DMatrix,
       params: Map[String, Any],
       round: Int,
-      watches: Map[String, DMatrix],
-      metrics: Array[Array[Float]],
-      obj: ObjectiveTrait,
-      eval: EvalTrait): Booster = {
-    val jWatches = watches.map{case (name, matrix) => (name, matrix.jDMatrix)}
+      watches: Map[String, DMatrix] = Map(),
+      metrics: Array[Array[Float]] = null,
+      obj: ObjectiveTrait = null,
+      eval: EvalTrait = null,
+      earlyStoppingRound: Int = 0): Booster = {
+    val jWatches = watches.mapValues(_.jDMatrix).asJava
     val xgboostInJava = JXGBoost.train(
       dtrain.jDMatrix,
       // we have to filter null value for customized obj and eval
-      params.filter(_._2 != null).map{
-        case (key: String, value) => (key, value.toString)
-      }.toMap[String, AnyRef].asJava,
-      round, jWatches.asJava, metrics, obj, eval)
+      params.filter(_._2 != null).mapValues(_.toString.asInstanceOf[AnyRef]).asJava,
+      round, jWatches, metrics, obj, eval, earlyStoppingRound)
     new Booster(xgboostInJava)
   }
 
-  /**
-    * Train a booster given parameters.
-    *
-    * @param dtrain  Data to be trained.
-    * @param params  Parameters.
-    * @param round   Number of boosting iterations.
-    * @param watches a group of items to be evaluated during training, this allows user to watch
-    *                performance on the validation set.
-    * @param obj     customized objective
-    * @param eval    customized evaluation
-    * @return The trained booster.
-    */
-  @throws(classOf[XGBoostError])
-  def train(
-      dtrain: DMatrix,
-      params: Map[String, Any],
-      round: Int,
-      watches: Map[String, DMatrix] = Map[String, DMatrix](),
-      obj: ObjectiveTrait = null,
-      eval: EvalTrait = null): Booster = {
-    train(dtrain, params, round, watches, null, obj, eval)
-  }
-
   /**
     * Cross-validation with given parameters.
     *