Github user sethah commented on a diff in the pull request:
https://github.com/apache/spark/pull/15817#discussion_r87617849
--- Diff: python/pyspark/ml/feature.py ---
@@ -1163,9 +1184,11 @@ class QuantileDiscretizer(JavaEstimator,
HasInputCol, HasOutputCol, JavaMLReadab
>>> df = spark.createDataFrame([(0.1,), (0.4,), (1.2,), (1.5,)],
["values"])
>>> qds = QuantileDiscretizer(numBuckets=2,
- ... inputCol="values", outputCol="buckets", relativeError=0.01)
+ ... inputCol="values", outputCol="buckets", relativeError=0.01,
handleInvalid="error")
>>> qds.getRelativeError()
0.01
+ >>> qds.getHandleInvalid()
--- End diff --
We didn't add anything to the doctest of bucketizer. Actually, I think it
would be nice in both places to set `handleInvalid='skip'` and then add an
invalid value to the example data. That way we can show what we mean by invalid
and prove that it works.
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