Github user imatiach-msft commented on a diff in the pull request:
https://github.com/apache/spark/pull/17123#discussion_r103949478
--- Diff: mllib/src/main/scala/org/apache/spark/ml/feature/Bucketizer.scala
---
@@ -171,23 +176,23 @@ object Bucketizer extends
DefaultParamsReadable[Bucketizer] {
* Binary searching in several buckets to place each data point.
* @param splits array of split points
* @param feature data point
- * @param keepInvalid NaN flag.
- * Set "true" to make an extra bucket for NaN values;
- * Set "false" to report an error for NaN values
+ * @param keepInvalid NaN/NULL flag.
+ * Set "true" to make an extra bucket for NaN/NULL
values;
+ * Set "false" to report an error for NaN/NULL values
* @return bucket for each data point
* @throws SparkException if a feature is < splits.head or > splits.last
*/
private[feature] def binarySearchForBuckets(
splits: Array[Double],
- feature: Double,
+ feature: java.lang.Double,
keepInvalid: Boolean): Double = {
- if (feature.isNaN) {
+ if (feature == null || feature.isNaN) {
if (keepInvalid) {
splits.length - 1
} else {
- throw new SparkException("Bucketizer encountered NaN value. To
handle or skip NaNs," +
- " try setting Bucketizer.handleInvalid.")
+ throw new SparkException("Bucketizer encountered NaN/NULL values.
" +
+ "To handle or skip NaNs/NULLs, try setting
Bucketizer.handleInvalid.")
}
} else if (feature == splits.last) {
--- End diff --
could you please add some tests to validate that NULL values can now be
handled in addition to NaN values by the bucketizer?
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