From fe16fd0b8b717f01151bc659ec3299dab091c97a Mon Sep 17 00:00:00 2001
From: Yanbo Liang <ybliang8@gmail.com>
Date: Mon, 31 Aug 2015 16:06:38 -0700
Subject: [PATCH] [SPARK-10349] [ML] OneVsRest use 'when ... otherwise' not UDF
to generate new label at binary reduction
Currently OneVsRest use UDF to generate new binary label during training.
Considering that [SPARK-7321](https://issues.apache.org/jira/browse/SPARK-7321) has been merged, we can use ```when ... otherwise``` which will be more efficiency.
Author: Yanbo Liang <ybliang8@gmail.com>
Closes #8519 from yanboliang/spark-10349.
---
.../org/apache/spark/ml/classification/OneVsRest.scala | 10 ++--------
1 file changed, 2 insertions(+), 8 deletions(-)
diff --git a/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala b/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala
index c62e132f5d..debc164bf2 100644
--- a/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala
+++ b/mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala
@@ -91,7 +91,6 @@ final class OneVsRestModel private[ml] (
// add an accumulator column to store predictions of all the models
val accColName = "mbc$acc" + UUID.randomUUID().toString
val initUDF = udf { () => Map[Int, Double]() }
- val mapType = MapType(IntegerType, DoubleType, valueContainsNull = false)
val newDataset = dataset.withColumn(accColName, initUDF())
// persist if underlying dataset is not persistent.
@@ -195,16 +194,11 @@ final class OneVsRest(override val uid: String)
// create k columns, one for each binary classifier.
val models = Range(0, numClasses).par.map { index =>
- val labelUDF = udf { (label: Double) =>
- if (label.toInt == index) 1.0 else 0.0
- }
-
// generate new label metadata for the binary problem.
- // TODO: use when ... otherwise after SPARK-7321 is merged
val newLabelMeta = BinaryAttribute.defaultAttr.withName("label").toMetadata()
val labelColName = "mc2b$" + index
- val trainingDataset =
- multiclassLabeled.withColumn(labelColName, labelUDF(col($(labelCol))), newLabelMeta)
+ val trainingDataset = multiclassLabeled.withColumn(
+ labelColName, when(col($(labelCol)) === index.toDouble, 1.0).otherwise(0.0), newLabelMeta)
val classifier = getClassifier
val paramMap = new ParamMap()
paramMap.put(classifier.labelCol -> labelColName)
--
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