[ 
https://issues.apache.org/jira/browse/MADLIB-1223?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16425929#comment-16425929
 ] 

Nikhil commented on MADLIB-1223:
--------------------------------

We decided that we will add a column called "dependent_var_type" in mlp's 
summary table. We will get the value of the column in the predict function and 
figure out if the dependent column is of type array. If the column doesn't 
exist (for models trained before 1.14), we will not unnest the array. This 
means that the output will look like an array of len 1 when the original 
dependent type is a scalar.

> MLP regression predict fails if input table does not exist
> ----------------------------------------------------------
>
>                 Key: MADLIB-1223
>                 URL: https://issues.apache.org/jira/browse/MADLIB-1223
>             Project: Apache MADlib
>          Issue Type: Bug
>          Components: Module: Neural Networks
>            Reporter: Nikhil
>            Priority: Major
>             Fix For: v1.14
>
>
> If a model is trained with mlp regression and then the input table is 
> dropped, mlp predict fails for that model.
> Ideally the predict function should not depend on the existence of the 
> training data. 
> The predict code for regression only needs to know if the dependent varname 
> type is an array or not. This information can be potentially stored in the 
> model's summary table.
> We also need to make sure that the predict function is backwards compatible. 



--
This message was sent by Atlassian JIRA
(v7.6.3#76005)

Reply via email to