[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/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostEstimator.scala

@@ -119,7 +119,7 @@ class XGBoostEstimator private[spark](
     val trainedModel = XGBoost.trainWithRDD(instances, derivedXGBoosterParamMap,
       $(round), $(nWorkers), $(customObj), $(customEval), $(useExternalMemory),
       $(missing)).setParent(this)
-    val returnedModel = copyValues(trainedModel)
+    val returnedModel = copyValues(trainedModel, extractParamMap())
     if (XGBoost.isClassificationTask(derivedXGBoosterParamMap)) {
       returnedModel.asInstanceOf[XGBoostClassificationModel].numOfClasses = $(numClasses)
     }

jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostModel.scala

@@ -17,10 +17,12 @@
 package ml.dmlc.xgboost4j.scala.spark
 
 import scala.collection.JavaConverters._
+
 import ml.dmlc.xgboost4j.java.{Rabit, DMatrix => JDMatrix}
-import ml.dmlc.xgboost4j.scala.spark.params.DefaultXGBoostParamsWriter
+import ml.dmlc.xgboost4j.scala.spark.params.{BoosterParams, DefaultXGBoostParamsWriter}
 import ml.dmlc.xgboost4j.scala.{Booster, DMatrix, EvalTrait}
 import org.apache.hadoop.fs.{FSDataOutputStream, Path}
+
 import org.apache.spark.ml.PredictionModel
 import org.apache.spark.ml.feature.{LabeledPoint => MLLabeledPoint}
 import org.apache.spark.ml.linalg.{DenseVector => MLDenseVector, Vector => MLVector}
@@ -36,13 +38,15 @@ import org.json4s.DefaultFormats
  * the base class of [[XGBoostClassificationModel]] and [[XGBoostRegressionModel]]
  */
 abstract class XGBoostModel(protected var _booster: Booster)
-  extends PredictionModel[MLVector, XGBoostModel] with Serializable with Params with MLWritable {
+  extends PredictionModel[MLVector, XGBoostModel] with BoosterParams with Serializable
+    with Params with MLWritable {
 
   def setLabelCol(name: String): XGBoostModel = set(labelCol, name)
 
   // scalastyle:off
 
-  final val useExternalMemory = new BooleanParam(this, "use_external_memory", "whether to use external memory for prediction")
+  final val useExternalMemory = new BooleanParam(this, "use_external_memory",
+    "whether to use external memory for prediction")
 
   setDefault(useExternalMemory, false)
 

jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/params/BoosterParams.scala

@@ -18,11 +18,9 @@ package ml.dmlc.xgboost4j.scala.spark.params
 
 import scala.collection.immutable.HashSet
 
-import ml.dmlc.xgboost4j.scala.spark.XGBoostEstimator
 import org.apache.spark.ml.param.{DoubleParam, IntParam, Param, Params}
 
 trait BoosterParams extends Params {
-  this: XGBoostEstimator =>
 
   /**
    * Booster to use, options: {'gbtree', 'gblinear', 'dart'}

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)
+  }
 }