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

* Support single instance prediction.

* Address comments.

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

@@ -16,6 +16,7 @@
 
 package ml.dmlc.xgboost4j.scala.spark
 
+import scala.collection.Iterator
 import scala.collection.JavaConverters._
 import scala.collection.mutable
 
@@ -229,30 +230,28 @@ class XGBoostClassificationModel private[ml](
     this
   }
 
-  // TODO: Make it public after we resolve performance issue
+  /**
-  private def margin(features: Vector): Array[Float] = {
+   * Single instance prediction.
-    import DataUtils._
+   * Note: The performance is not ideal, use it carefully!
-    val dm = new DMatrix(scala.collection.Iterator(features.asXGB))
+   */
-    _booster.predict(data = dm, outPutMargin = true)(0)
-  }
-
-  private def probability(features: Vector): Array[Float] = {
-    import DataUtils._
-    val dm = new DMatrix(scala.collection.Iterator(features.asXGB))
-    _booster.predict(data = dm, outPutMargin = false)(0)
-  }
-
   override def predict(features: Vector): Double = {
-    throw new Exception("XGBoost-Spark does not support online prediction")
+    import DataUtils._
+    val dm = new DMatrix(Iterator(features.asXGB))
+    val probability = _booster.predict(data = dm)(0)
+    if (numClasses == 2) {
+      math.round(probability(0))
+    } else {
+      Vectors.dense(probability.map(_.toDouble)).argmax
+    }
   }
 
   // Actually we don't use this function at all, to make it pass compiler check.
-  override def predictRaw(features: Vector): Vector = {
+  override protected def predictRaw(features: Vector): Vector = {
     throw new Exception("XGBoost-Spark does not support \'predictRaw\'")
   }
 
   // Actually we don't use this function at all, to make it pass compiler check.
-  override def raw2probabilityInPlace(rawPrediction: Vector): Vector = {
+  override protected def raw2probabilityInPlace(rawPrediction: Vector): Vector = {
     throw new Exception("XGBoost-Spark does not support \'raw2probabilityInPlace\'")
   }
 

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

@@ -16,6 +16,7 @@
 
 package ml.dmlc.xgboost4j.scala.spark
 
+import scala.collection.Iterator
 import scala.collection.JavaConverters._
 
 import ml.dmlc.xgboost4j.java.Rabit
@@ -225,8 +226,14 @@ class XGBoostRegressionModel private[ml] (
     this
   }
 
+  /**
+   * Single instance prediction.
+   * Note: The performance is not ideal, use it carefully!
+   */
   override def predict(features: Vector): Double = {
-    throw new Exception("XGBoost-Spark does not support online prediction")
+    import DataUtils._
+    val dm = new DMatrix(Iterator(features.asXGB))
+    _booster.predict(data = dm)(0)(0)
   }
 
   private def transformInternal(dataset: Dataset[_]): DataFrame = {

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") {