[ https://issues.apache.org/jira/browse/SPARK-19714?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Sean Owen resolved SPARK-19714. ------------------------------- Resolution: Fixed Fix Version/s: 3.0.0 Issue resolved by pull request 23003 [https://github.com/apache/spark/pull/23003] > Clarify Bucketizer handling of invalid input > -------------------------------------------- > > Key: SPARK-19714 > URL: https://issues.apache.org/jira/browse/SPARK-19714 > Project: Spark > Issue Type: Improvement > Components: ML, MLlib > Affects Versions: 2.1.0 > Reporter: Bill Chambers > Assignee: Wojciech Szymanski > Priority: Minor > Fix For: 3.0.0 > > > {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 (v7.6.3#76005) --------------------------------------------------------------------- To unsubscribe, e-mail: issues-unsubscr...@spark.apache.org For additional commands, e-mail: issues-h...@spark.apache.org