Github user mengxr commented on a diff in the pull request:
https://github.com/apache/spark/pull/6993#discussion_r33198443
--- Diff:
mllib/src/main/scala/org/apache/spark/ml/classification/Classifier.scala ---
@@ -108,7 +108,7 @@ abstract class ClassificationModel[FeaturesType, M <:
ClassificationModel[Featur
}
if (getPredictionCol != "") {
val predUDF = if (getRawPredictionCol != "") {
- callUDF(raw2prediction _, DoubleType, col(getRawPredictionCol))
+ udf[Double, Vector](raw2prediction).apply(col(getRawPredictionCol))
--- End diff --
The type info is not necessary. Try
~~~scala
udf(raw2prediction _).apply(col(getRawPredictionCol))
~~~
The `callUDF` on line 106 needs some explicit type info since
`FeaturesType` doesn't have type tag. We can write this:
~~~scala
val predictRawUDF = udf { (features: Any) =>
predictRaw(features.asInstanceOf[FeaturesType])
}
outputData = outputData.withColumn(getRawPredictionCol,
predictRawUDF(col(getFeaturesCol)))
~~~
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