[JVM-packages] Support single instance prediction. (#3464)

* Support single instance prediction.

* Address comments.

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

@@ -66,6 +66,13 @@ class XGBoostClassifierSuite extends FunSuite with PerTest {
         assert(prediction3(i)(j) === prediction4(i)(j))
       }
     }
+
+    // check the equality of single instance prediction
+    val firstOfDM = testDM.slice(Array(0))
+    val firstOfDF = testDF.head().getAs[Vector]("features")
+    val prediction5 = math.round(model1.predict(firstOfDM)(0)(0))
+    val prediction6 = model2.predict(firstOfDF)
+    assert(prediction5 === prediction6)
   }
 
   test("Set params in XGBoost and MLlib way should produce same model") {

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

@@ -17,6 +17,7 @@
 package ml.dmlc.xgboost4j.scala.spark
 
 import ml.dmlc.xgboost4j.scala.{DMatrix, XGBoost => ScalaXGBoost}
+import org.apache.spark.ml.linalg.Vector
 import org.apache.spark.sql.functions._
 import org.apache.spark.sql.Row
 import org.apache.spark.sql.types._
@@ -49,6 +50,14 @@ class XGBoostRegressorSuite extends FunSuite with PerTest {
     assert(prediction1.indices.count { i =>
       math.abs(prediction1(i)(0) - prediction2(i)) > 0.01
     } < prediction1.length * 0.1)
+
+
+    // check the equality of single instance prediction
+    val firstOfDM = testDM.slice(Array(0))
+    val firstOfDF = testDF.head().getAs[Vector]("features")
+    val prediction3 = model1.predict(firstOfDM)(0)(0)
+    val prediction4 = model2.predict(firstOfDF)
+    assert(math.abs(prediction3 - prediction4) <= 0.01f)
   }
 
   test("Set params in XGBoost and MLlib way should produce same model") {