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https://issues.apache.org/jira/browse/MADLIB-978?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel
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Frank McQuillan updated MADLIB-978:
-----------------------------------
Fix Version/s: (was: v1.9)
v1.9.1
> Implement skipping of arrays-with-NULL for elastic net training
> ---------------------------------------------------------------
>
> Key: MADLIB-978
> URL: https://issues.apache.org/jira/browse/MADLIB-978
> Project: Apache MADlib
> Issue Type: Improvement
> Components: Module: Regularized Regression
> Reporter: Frank McQuillan
> Assignee: Rahul Iyer
> Priority: Minor
> Fix For: v1.9.1
>
>
> Implement skipping of arrays-with-NULL for elastic net predict. Some context
> for this JIRA is below…
> (Q)
> Question came in this week from a MADlib user:
> Function "madlib.elastic_net_gaussian_predict(double precision[],double
> precision,double precision[])": Error converting an array w/ NULL value s
> to dense format. (UDF_impl.hpp:210)
> Is there a typical pattern for handling nulls in such a scenario, perhaps
> converting to 0.0 or something like this?
> (A)
> Answer:
> The skipping of arrays-with-NULL has not been implemented for elastic net
> predict yet.
> You can workaround it by creating the below function:
> http://stackoverflow.com/questions/7819021/replace-null-values-in-an-array-in-postgresql
> CREATE OR REPLACE FUNCTION f_array_replace_null (double precision[], double
> precision)
> RETURNS double precision[] AS
> $$
> SELECT ARRAY (
> SELECT COALESCE(x, $2)
> FROM unnest($1) x);
> $$ LANGUAGE SQL IMMUTABLE;
> They'll have to add the function before the feature array in the elastic_net
> statement:
> f_array_replace_null(array["pf_calc_fdy_position", ...], 0)
> This would replace each NULL with a 0. The downside is it could get slower
> since the unnest and nest would happen with each call. If performance is a
> concern, and if they're running over this data multiple times, I would create
> a new table with the NULLs replaced and execute elastic_net_xxx in the
> regular way.
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