[jira] [Commented] (MADLIB-1094) Elastic Net fails when used without normalization

["Rashmi Raghu (JIRA)" <ji...@apache.org>]

    [ https://issues.apache.org/jira/browse/MADLIB-1094?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15983299#comment-15983299 ] 

Rashmi Raghu commented on MADLIB-1094:
--------------------------------------

Trying the same problem with scikit-learn works fine. Resulting coefficients for the features are: {[   23.15135449,  7759.59888732,    59.03219813]}}. It is not yet clear what optimizer scikit-learn is using though.

> Elastic Net fails when used without normalization
> -------------------------------------------------
>
>                 Key: MADLIB-1094
>                 URL: https://issues.apache.org/jira/browse/MADLIB-1094
>             Project: Apache MADlib
>          Issue Type: Bug
>          Components: Module: Regularized Regression
>            Reporter: Nandish Jayaram
>             Fix For: v1.11
>
>
> Using Elastic Net with the normalization/standardize flag turned off (for Gaussian IGD) results in failure, with the following error:
> {code:sql}
> madlib-pg94=# SELECT madlib.elastic_net_train(
> 'houses1',
> 'houses_en',
> 'array[tax, bath, size]',
> 'gaussian',
> 0.5,
> 0.1, 
> FALSE,  -- Standardize 
> NULL,
> 'igd',
> '',
> NULL,
> 10000,1e-6);
> ERROR:  spiexceptions.NumericValueOutOfRange: value out of range: overflow
> CONTEXT:  Traceback (most recent call last):
>   PL/Python function "elastic_net_train", line 23, in <module>
>     return elastic_net.elastic_net_train(**globals())
>   PL/Python function "elastic_net_train", line 332, in elastic_net_train
>   PL/Python function "elastic_net_train", line 42, in __elastic_net_gaussian_igd_train
>   PL/Python function "elastic_net_train", line 268, in __elastic_net_igd_train
>   PL/Python function "elastic_net_train", line 373, in __elastic_net_igd_train_compute
>   PL/Python function "elastic_net_train", line 69, in __elastic_net_generate_result
>   PL/Python function "elastic_net_train", line 154, in __compute_log_likelihood
> PL/Python function "elastic_net_train"
> {code}



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