Skip to content

[SPARK-22700][ML] Bucketizer.transform incorrectly drops row containing NaN #19894

New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Closed
wants to merge 2 commits into from
Closed

[SPARK-22700][ML] Bucketizer.transform incorrectly drops row containing NaN #19894

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
01604c7
create pr
zhengruifeng Dec 5, 2017
eaebedb
Update BucketizerSuite.scala
zhengruifeng Dec 5, 2017
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
14 changes: 8 additions & 6 deletions mllib/src/main/scala/org/apache/spark/ml/feature/Bucketizer.scala
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -155,10 +155,16 @@ final class Bucketizer @Since("1.4.0") (@Since("1.4.0") override val uid: String
override def transform(dataset: Dataset[_]): DataFrame = {
val transformedSchema = transformSchema(dataset.schema)

val (inputColumns, outputColumns) = if (isBucketizeMultipleColumns()) {
($(inputCols).toSeq, $(outputCols).toSeq)
} else {
(Seq($(inputCol)), Seq($(outputCol)))
}

val (filteredDataset, keepInvalid) = {
if (getHandleInvalid == Bucketizer.SKIP_INVALID) {
// "skip" NaN option is set, will filter out NaN values in the dataset
(dataset.na.drop().toDF(), false)
(dataset.na.drop(inputColumns).toDF(), false)
} else {
(dataset.toDF(), getHandleInvalid == Bucketizer.KEEP_INVALID)
}
Expand All @@ -176,11 +182,7 @@ final class Bucketizer @Since("1.4.0") (@Since("1.4.0") override val uid: String
}.withName(s"bucketizer_$idx")
}

val (inputColumns, outputColumns) = if (isBucketizeMultipleColumns()) {
($(inputCols).toSeq, $(outputCols).toSeq)
} else {
(Seq($(inputCol)), Seq($(outputCol)))
}

val newCols = inputColumns.zipWithIndex.map { case (inputCol, idx) =>
bucketizers(idx)(filteredDataset(inputCol).cast(DoubleType))
}
Expand Down
9 changes: 9 additions & 0 deletions mllib/src/test/scala/org/apache/spark/ml/feature/BucketizerSuite.scala
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -123,6 +123,15 @@ class BucketizerSuite extends SparkFunSuite with MLlibTestSparkContext with Defa
}
}

test("Bucketizer should only drop NaN in input columns, with handleInvalid=skip") {
val df = spark.createDataFrame(Seq((2.3, 3.0), (Double.NaN, 3.0), (6.7, Double.NaN)))
.toDF("a", "b")
val splits = Array(Double.NegativeInfinity, 3.0, Double.PositiveInfinity)
val bucketizer = new Bucketizer().setInputCol("a").setOutputCol("x").setSplits(splits)
bucketizer.setHandleInvalid("skip")
assert(bucketizer.transform(df).count() == 2)
}

test("Bucket continuous features, with NaN splits") {
val splits = Array(Double.NegativeInfinity, -0.5, 0.0, 0.5, Double.PositiveInfinity, Double.NaN)
withClue("Invalid NaN split was not caught during Bucketizer initialization") {
Expand Down