Github user BryanCutler commented on a diff in the pull request:
https://github.com/apache/spark/pull/6300#discussion_r30759972
--- Diff: mllib/src/main/scala/org/apache/spark/ml/Predictor.scala ---
@@ -184,9 +185,45 @@ abstract class PredictionModel[FeaturesType, M <:
PredictionModel[FeaturesType,
}
}
+ protected def transformImpl(dataset: DataFrame): DataFrame = {
+ dataset.withColumn($(predictionCol), callUDF(predict _, DoubleType,
col($(featuresCol))))
+ }
+
/**
* Predict label for the given features.
* This internal method is used to implement [[transform()]] and output
[[predictionCol]].
*/
protected def predict(features: FeaturesType): Double
}
+
+
+/**
+ * :: DeveloperApi ::
+ *
+ * Abstraction for a model for prediction tasks that will broadcast the
model used to predict.
+ *
+ * @tparam FeaturesType Type of features.
+ * E.g., [[org.apache.spark.mllib.linalg.VectorUDT]]
for vector features.
+ * @tparam M Specialization of [[PredictionModel]]. If you subclass this
type, use this type
+ * parameter to specify the concrete type for the corresponding
model.
+ */
+@DeveloperApi
+abstract class PredictionModelBroadcasting[
+ FeaturesType, M <: PredictionModelBroadcasting[FeaturesType, M]
+ ]
+ extends PredictionModel[FeaturesType, M] {
+
+ protected def transformImpl(dataset: DataFrame, bcastModel:
Broadcast[M]): DataFrame = {
--- End diff --
I'm not sure if using the Broadcast variable as a parameter is a good idea
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