[jvm-packages] Scala/Java interface for Fast Histogram Algorithm (#1966)

* add back train method but mark as deprecated

* fix scalastyle error

* first commit in scala binding for fast histo

* java test

* add missed scala tests

* spark training

* add back train method but mark as deprecated

* fix scalastyle error

* local change

* first commit in scala binding for fast histo

* local change

* fix df frame test

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

@@ -25,6 +25,41 @@ import scala.collection.JavaConverters._
   * XGBoost Scala Training function.
   */
 object XGBoost {
+
+  /**
+    * 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 metrics array containing the evaluation metrics for each matrix in watches for each
+    *                iteration
+    * @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],
+      metrics: Array[Array[Float]],
+      obj: ObjectiveTrait,
+      eval: EvalTrait): Booster = {
+    val jWatches = watches.map{case (name, matrix) => (name, matrix.jDMatrix)}
+    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)
+    new Booster(xgboostInJava)
+  }
+
   /**
     * Train a booster given parameters.
     *
@@ -45,16 +80,7 @@ object XGBoost {
       watches: Map[String, DMatrix] = Map[String, DMatrix](),
       obj: ObjectiveTrait = null,
       eval: EvalTrait = null): Booster = {
-    val jWatches = watches.map{case (name, matrix) => (name, matrix.jDMatrix)}
-    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,
-      obj, eval)
-    new Booster(xgboostInJava)
+    train(dtrain, params, round, watches, null, obj, eval)
   }
 
   /**