Bill Chambers created SPARK-19714:
-------------------------------------
Summary: Bucketizer Bug Regarding Handling Unbucketed Inputs
Key: SPARK-19714
URL: https://issues.apache.org/jira/browse/SPARK-19714
Project: Spark
Issue Type: Bug
Components: ML, MLlib
Affects Versions: 2.1.0
Reporter: Bill Chambers
{code}
contDF = spark.range(500).selectExpr("cast(id as double) as id")
import org.apache.spark.ml.feature.Bucketizer
val splits = Array(5.0, 10.0, 250.0, 500.0)
val bucketer = new Bucketizer()
.setSplits(splits)
.setInputCol("id")
.setHandleInvalid("skip")
bucketer.transform(contDF).show()
{code}
You would expect that this would handle the invalid buckets. However it fails
{code}
Caused by: org.apache.spark.SparkException: Feature value 0.0 out of Bucketizer
bounds [5.0, 500.0]. Check your features, or loosen the lower/upper bound
constraints.
{code}
It seems strange that handleInvalud doesn't actually handleInvalid inputs.
Thoughts anyone?
--
This message was sent by Atlassian JIRA
(v6.3.15#6346)
---------------------------------------------------------------------
To unsubscribe, e-mail: [email protected]
For additional commands, e-mail: [email protected]
