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

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* Support single instance prediction.

* Address comments.
This commit is contained in:
Yanbo Liang
2018-07-12 14:17:53 -07:00
committed by Nan Zhu
parent 2200939416
commit 2f8764955c
4 changed files with 39 additions and 17 deletions
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31
jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostClassifier.scala
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@@ -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] = {
import DataUtils._
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)
}
/**
* 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))
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\'")
}

9
jvm-packages/xgboost4j-spark/src/main/scala/ml/dmlc/xgboost4j/scala/spark/XGBoostRegressor.scala
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@@ -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 = {