zhengruifeng commented on a change in pull request #31472:
URL: https://github.com/apache/spark/pull/31472#discussion_r570108063
##########
File path:
mllib/src/test/scala/org/apache/spark/ml/classification/OneVsRestSuite.scala
##########
@@ -223,6 +223,13 @@ class OneVsRestSuite extends MLTest with
DefaultReadWriteTest {
assert(oldCols === newCols)
}
+ test("SPARK-SPARK-34356: OneVsRestModel.transform should avoid potential
column conflict") {
Review comment:
this test will fail in master and (maybe) all version of OVR.
but I think fix it in master maybe enough.
##########
File path:
mllib/src/test/scala/org/apache/spark/ml/classification/OneVsRestSuite.scala
##########
@@ -223,6 +223,13 @@ class OneVsRestSuite extends MLTest with
DefaultReadWriteTest {
assert(oldCols === newCols)
}
+ test("SPARK-SPARK-34356: OneVsRestModel.transform should avoid potential
column conflict") {
Review comment:
this test will fail in master and (maybe) all versions of OVR.
but I think fix it in master maybe enough.
##########
File path:
mllib/src/test/scala/org/apache/spark/ml/classification/OneVsRestSuite.scala
##########
@@ -223,6 +223,13 @@ class OneVsRestSuite extends MLTest with
DefaultReadWriteTest {
assert(oldCols === newCols)
}
+ test("SPARK-SPARK-34356: OneVsRestModel.transform should avoid potential
column conflict") {
Review comment:
this test will fail in master and (maybe) all versions of OVR.
but I think fix it in master maybe enough.
##########
File path:
mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala
##########
@@ -185,71 +185,56 @@ final class OneVsRestModel private[ml] (
return dataset.toDF
}
- // determine the input columns: these need to be passed through
- val origCols = dataset.schema.map(f => col(f.name))
-
// add an accumulator column to store predictions of all the models
val accColName = "mbc$acc" + UUID.randomUUID().toString
- val initUDF = udf { () => Map[Int, Double]() }
- val newDataset = dataset.withColumn(accColName, initUDF())
+ val newDataset = dataset.withColumn(accColName,
lit(Array.emptyDoubleArray))
// persist if underlying dataset is not persistent.
val handlePersistence = !dataset.isStreaming && dataset.storageLevel ==
StorageLevel.NONE
- if (handlePersistence) {
- newDataset.persist(StorageLevel.MEMORY_AND_DISK)
- }
+ if (handlePersistence) newDataset.persist(StorageLevel.MEMORY_AND_DISK)
Review comment:
ok, I will ignore similar case in the future.
##########
File path:
mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala
##########
@@ -185,71 +185,56 @@ final class OneVsRestModel private[ml] (
return dataset.toDF
}
- // determine the input columns: these need to be passed through
- val origCols = dataset.schema.map(f => col(f.name))
-
// add an accumulator column to store predictions of all the models
val accColName = "mbc$acc" + UUID.randomUUID().toString
- val initUDF = udf { () => Map[Int, Double]() }
- val newDataset = dataset.withColumn(accColName, initUDF())
+ val newDataset = dataset.withColumn(accColName,
lit(Array.emptyDoubleArray))
// persist if underlying dataset is not persistent.
val handlePersistence = !dataset.isStreaming && dataset.storageLevel ==
StorageLevel.NONE
- if (handlePersistence) {
- newDataset.persist(StorageLevel.MEMORY_AND_DISK)
- }
+ if (handlePersistence) newDataset.persist(StorageLevel.MEMORY_AND_DISK)
// update the accumulator column with the result of prediction of models
- val aggregatedDataset =
models.zipWithIndex.foldLeft[DataFrame](newDataset) {
- case (df, (model, index)) =>
+ val aggregatedDataset = models.foldLeft[DataFrame](newDataset) {
+ case (df, model) =>
// avoid calling directly setter of model
val tmpModel =
model.copy(ParamMap.empty).asInstanceOf[ClassificationModel[_, _]]
tmpModel.setFeaturesCol($(featuresCol))
- val rawPredictionCol = tmpModel.getRawPredictionCol
- val columns = origCols ++ List(col(rawPredictionCol), col(accColName))
-
- // add temporary column to store intermediate scores and update
- val tmpColName = "mbc$tmp" + UUID.randomUUID().toString
- val updateUDF = udf { (predictions: Map[Int, Double], prediction:
Vector) =>
- predictions + ((index, prediction(1)))
+ // use a temporary raw prediction column to avoid column conflict
+ val tmpRawPredName = "mbc$raw" + UUID.randomUUID().toString
+ tmpModel.setRawPredictionCol(tmpRawPredName)
+ tmpModel.setPredictionCol("")
Review comment:
no, `tmpModel` is jsut a temporary model
##########
File path:
mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala
##########
@@ -185,71 +185,56 @@ final class OneVsRestModel private[ml] (
return dataset.toDF
}
- // determine the input columns: these need to be passed through
- val origCols = dataset.schema.map(f => col(f.name))
-
// add an accumulator column to store predictions of all the models
val accColName = "mbc$acc" + UUID.randomUUID().toString
- val initUDF = udf { () => Map[Int, Double]() }
- val newDataset = dataset.withColumn(accColName, initUDF())
+ val newDataset = dataset.withColumn(accColName,
lit(Array.emptyDoubleArray))
// persist if underlying dataset is not persistent.
val handlePersistence = !dataset.isStreaming && dataset.storageLevel ==
StorageLevel.NONE
- if (handlePersistence) {
- newDataset.persist(StorageLevel.MEMORY_AND_DISK)
- }
+ if (handlePersistence) newDataset.persist(StorageLevel.MEMORY_AND_DISK)
// update the accumulator column with the result of prediction of models
- val aggregatedDataset =
models.zipWithIndex.foldLeft[DataFrame](newDataset) {
- case (df, (model, index)) =>
+ val aggregatedDataset = models.foldLeft[DataFrame](newDataset) {
+ case (df, model) =>
// avoid calling directly setter of model
val tmpModel =
model.copy(ParamMap.empty).asInstanceOf[ClassificationModel[_, _]]
tmpModel.setFeaturesCol($(featuresCol))
- val rawPredictionCol = tmpModel.getRawPredictionCol
- val columns = origCols ++ List(col(rawPredictionCol), col(accColName))
-
- // add temporary column to store intermediate scores and update
- val tmpColName = "mbc$tmp" + UUID.randomUUID().toString
- val updateUDF = udf { (predictions: Map[Int, Double], prediction:
Vector) =>
- predictions + ((index, prediction(1)))
+ // use a temporary raw prediction column to avoid column conflict
+ val tmpRawPredName = "mbc$raw" + UUID.randomUUID().toString
+ tmpModel.setRawPredictionCol(tmpRawPredName)
+ tmpModel.setPredictionCol("")
+ tmpModel match {
+ case m: ProbabilisticClassificationModel[_, _] =>
m.setProbabilityCol("")
+ case _ =>
Review comment:
ok
##########
File path:
mllib/src/main/scala/org/apache/spark/ml/classification/OneVsRest.scala
##########
@@ -185,71 +185,56 @@ final class OneVsRestModel private[ml] (
return dataset.toDF
}
- // determine the input columns: these need to be passed through
- val origCols = dataset.schema.map(f => col(f.name))
-
// add an accumulator column to store predictions of all the models
val accColName = "mbc$acc" + UUID.randomUUID().toString
- val initUDF = udf { () => Map[Int, Double]() }
- val newDataset = dataset.withColumn(accColName, initUDF())
+ val newDataset = dataset.withColumn(accColName,
lit(Array.emptyDoubleArray))
// persist if underlying dataset is not persistent.
val handlePersistence = !dataset.isStreaming && dataset.storageLevel ==
StorageLevel.NONE
- if (handlePersistence) {
- newDataset.persist(StorageLevel.MEMORY_AND_DISK)
- }
+ if (handlePersistence) newDataset.persist(StorageLevel.MEMORY_AND_DISK)
// update the accumulator column with the result of prediction of models
- val aggregatedDataset =
models.zipWithIndex.foldLeft[DataFrame](newDataset) {
- case (df, (model, index)) =>
+ val aggregatedDataset = models.foldLeft[DataFrame](newDataset) {
+ case (df, model) =>
// avoid calling directly setter of model
val tmpModel =
model.copy(ParamMap.empty).asInstanceOf[ClassificationModel[_, _]]
tmpModel.setFeaturesCol($(featuresCol))
- val rawPredictionCol = tmpModel.getRawPredictionCol
- val columns = origCols ++ List(col(rawPredictionCol), col(accColName))
-
- // add temporary column to store intermediate scores and update
- val tmpColName = "mbc$tmp" + UUID.randomUUID().toString
- val updateUDF = udf { (predictions: Map[Int, Double], prediction:
Vector) =>
- predictions + ((index, prediction(1)))
+ // use a temporary raw prediction column to avoid column conflict
+ val tmpRawPredName = "mbc$raw" + UUID.randomUUID().toString
+ tmpModel.setRawPredictionCol(tmpRawPredName)
+ tmpModel.setPredictionCol("")
Review comment:
`val tmpModel =
model.copy(ParamMap.empty).asInstanceOf[ClassificationModel[_, _]]`
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