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https://issues.apache.org/jira/browse/SPARK-19947?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16379282#comment-16379282
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Bago Amirbekian commented on SPARK-19947:
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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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