[jvm-packages] make XGBoostModel hold BoosterParams as well (#2214)

* add back train method but mark as deprecated

* fix scalastyle error

* make XGBoostModel hold BoosterParams as well

jvm-packages/xgboost4j-spark/src/test/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostDFSuite.scala

@@ -16,7 +16,7 @@
 
 package ml.dmlc.xgboost4j.scala.spark
 
-import java.io.{File, FileNotFoundException}
+import java.io.File
 
 import scala.collection.mutable.ListBuffer
 import scala.io.Source
@@ -25,11 +25,10 @@ import ml.dmlc.xgboost4j.java.{DMatrix => JDMatrix}
 import ml.dmlc.xgboost4j.scala.{DMatrix, XGBoost => ScalaXGBoost}
 
 import org.apache.spark.SparkContext
-import org.apache.spark.ml.evaluation.BinaryClassificationEvaluator
+import org.apache.spark.ml.Pipeline
 import org.apache.spark.ml.feature.LabeledPoint
 import org.apache.spark.ml.linalg.DenseVector
 import org.apache.spark.ml.param.ParamMap
-import org.apache.spark.ml.tuning.{CrossValidator, ParamGridBuilder}
 import org.apache.spark.sql._
 
 class XGBoostDFSuite extends SharedSparkContext with Utils {
@@ -272,4 +271,16 @@ class XGBoostDFSuite extends SharedSparkContext with Utils {
       collect().map(row => (row.getAs[Int]("id"), row.getAs[DenseVector]("features"))).toMap
     assert(testDF.count() === predResultsFromDF.size)
   }
+
+  test("params of estimator and produced model are coordinated correctly") {
+    val paramMap = Map("eta" -> "0.1", "max_depth" -> "6", "silent" -> "1",
+      "objective" -> "multi:softmax", "num_class" -> "6")
+    val trainingSet = loadCSVPoints(getClass.getResource("/dermatology.data").getFile)
+    val spark = SparkSession.builder().getOrCreate()
+    import spark.implicits._
+    val model =
+      XGBoost.trainWithDataFrame(trainingSet.toDF(), paramMap, round = 5, nWorkers = numWorkers)
+    assert(model.get[Double](model.eta).get == 0.1)
+    assert(model.get[Int](model.maxDepth).get == 6)
+  }
 }