Github user MLnick commented on the issue:
https://github.com/apache/spark/pull/16011
I don't think this is correct. The idea is `QuantileDiscretizer` skips NaN
when creating the buckets, but there will still be an error thrown during
`transform` (rather than `fit`) if NaNs are in data. Hence the test cases
intercepting the error. I think the test cases show it works, no?
e.g.
`model.transform(df).show` throws:
```
...
Caused by: org.apache.spark.SparkException: Bucketizer encountered NaN
value. To handle or skip NaNs, try setting Bucketizer.handleInvalid.
at
org.apache.spark.ml.feature.Bucketizer$.binarySearchForBuckets(Bucketizer.scala:188)
at
org.apache.spark.ml.feature.Bucketizer$$anonfun$1.apply$mcDD$sp(Bucketizer.scala:115)
at
org.apache.spark.ml.feature.Bucketizer$$anonfun$1.apply(Bucketizer.scala:114)
at
org.apache.spark.ml.feature.Bucketizer$$anonfun$1.apply(Bucketizer.scala:114)
```
Setting the param works:
```
scala> model.setHandleInvalid("skip").transform(df).show
+-----+------+
|input|result|
+-----+------+
| -0.9| 0.0|
| -0.5| 0.0|
| -0.3| 1.0|
| 0.0| 1.0|
| 0.2| 2.0|
| 0.5| 2.0|
| 0.9| 2.0|
+-----+------+
scala> model.setHandleInvalid("keep").transform(df).show
+-----+------+
|input|result|
+-----+------+
| -0.9| 0.0|
| -0.5| 0.0|
| -0.3| 1.0|
| 0.0| 1.0|
| 0.2| 2.0|
| 0.5| 2.0|
| 0.9| 2.0|
| NaN| 3.0|
| NaN| 3.0|
| NaN| 3.0|
+-----+------+
```
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