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Posted to issues@spark.apache.org by "Bago Amirbekian (JIRA)" <ji...@apache.org> on 2018/02/27 21:18:00 UTC
[jira] [Commented] (SPARK-19947) RFormulaModel always throws
Exception on transforming data with NULL or Unseen labels
[ https://issues.apache.org/jira/browse/SPARK-19947?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16379282#comment-16379282 ]
Bago Amirbekian commented on SPARK-19947:
-----------------------------------------
I think this was resolved by [https://github.com/apache/spark/pull/18496] & [https://github.com/apache/spark/pull/18613].
> RFormulaModel always throws Exception on transforming data with NULL or Unseen labels
> -------------------------------------------------------------------------------------
>
> Key: SPARK-19947
> URL: https://issues.apache.org/jira/browse/SPARK-19947
> Project: Spark
> Issue Type: Improvement
> Components: ML
> Affects Versions: 2.1.0
> Reporter: Andrey Yatsuk
> Priority: Major
>
> I have trained ML model and big data table in parquet. I want add new column to this table with predicted values. I can't lose any data, but can having null values in it.
> RFormulaModel.fit() method creates new StringIndexer with default (handleInvalid="error") parameter. Also VectorAssembler on NULL values throwing Exception. So I must call df.na.drop() to transform this DataFrame and I don't want to do this.
> Need add to RFormula new parameter like handleInvalid in StringIndexer.
> Or add transform(Seq<Column>): Vector method which user can use as UDF method in df.withColumn("predicted", functions.callUDF(rFormulaModel::transform, Seq<Column>))
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