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Training fails if we have too many features (400+) #3

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nruemmele opened this issue Feb 2, 2017 · 1 comment
Open

Training fails if we have too many features (400+) #3

nruemmele opened this issue Feb 2, 2017 · 1 comment

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@nruemmele
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nruemmele commented Feb 2, 2017 •
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When using char-dist-features + header features for the domain "dbpedia", we get many features (400+). The training of RandomForestClassifier with Spark fails with the error:
Cause: org.codehaus.janino.JaninoRuntimeException: Code of method "compare(Lorg/apache/spark/sql/catalyst/InternalRow;Lorg/apache/spark/sql/catalyst/InternalRow;)I" of class "org.apache.spark.sql.catalyst.expressions.GeneratedClass$SpecificOrdering" grows beyond 64 KB

Apparently, there's a bug in Spark, but it's not clear if there is an easy fix for this problem:
https://issues.apache.org/jira/browse/SPARK-16845
http://stackoverflow.com/questions/40044779/find-mean-and-corr-of-10-000-columns-in-pyspark-dataframe
https://issues.apache.org/jira/browse/SPARK-17092

SparkTestSpec reproduces this error currently.
@nruemmele
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https://jira.csiro.au/browse/SERENE-202

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