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From "Bill Chambers (JIRA)" <j...@apache.org>
Subject [jira] [Created] (SPARK-19714) Bucketizer Bug Regarding Handling Unbucketed Inputs
Date Thu, 23 Feb 2017 18:27:44 GMT
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?



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