Set feature_names and feature_types in jvm-packages (#9364)

* 1. Add parameters to set feature names and feature types
2. Save feature names and feature types to native json model

* Change serialization and deserialization format to ubj.

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

@@ -27,6 +27,8 @@ import org.apache.commons.io.IOUtils
 
 import org.apache.spark.Partitioner
 import org.apache.spark.ml.feature.VectorAssembler
+import org.json4s.{DefaultFormats, Formats}
+import org.json4s.jackson.parseJson
 
 class XGBoostClassifierSuite extends AnyFunSuite with PerTest with TmpFolderPerSuite {
 
@@ -453,4 +455,26 @@ class XGBoostClassifierSuite extends AnyFunSuite with PerTest with TmpFolderPerS
     assert(!compareTwoFiles(new File(modelJsonPath, "data/XGBoostClassificationModel").getPath,
       nativeUbjModelPath))
   }
+
+  test("native json model file should store feature_name and feature_type") {
+    val featureNames = (1 to 33).map(idx => s"feature_${idx}").toArray
+    val featureTypes = (1 to 33).map(idx => "q").toArray
+    val paramMap = Map("eta" -> "0.1", "max_depth" -> "6", "silent" -> "1",
+      "objective" -> "multi:softprob", "num_class" -> "6", "num_round" -> 5,
+      "num_workers" -> numWorkers, "tree_method" -> treeMethod
+    )
+    val trainingDF = buildDataFrame(MultiClassification.train)
+    val xgb = new XGBoostClassifier(paramMap)
+      .setFeatureNames(featureNames)
+      .setFeatureTypes(featureTypes)
+    val model = xgb.fit(trainingDF)
+    val modelStr = new String(model._booster.toByteArray("json"))
+    System.out.println(modelStr)
+    val jsonModel = parseJson(modelStr)
+    implicit val formats: Formats = DefaultFormats
+    val featureNamesInModel = (jsonModel \ "learner" \ "feature_names").extract[List[String]]
+    val featureTypesInModel = (jsonModel \ "learner" \ "feature_types").extract[List[String]]
+    assert(featureNamesInModel.length == 33)
+    assert(featureTypesInModel.length == 33)
+  }
 }