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Posted to issues@madlib.apache.org by "Nandish Jayaram (JIRA)" <ji...@apache.org> on 2017/04/24 18:12:04 UTC
[jira] [Updated] (MADLIB-1094) Elastic Net fails when used without
normalization
[ https://issues.apache.org/jira/browse/MADLIB-1094?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]
Nandish Jayaram updated MADLIB-1094:
------------------------------------
Description:
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}
was:
Using Elastic Net with the normalization/standardize flag turned off (for Gaussian IGD) results in failure, with the following error:
{code}
madlib-pg94=# SELECT madlib.elastic_net_train( 'houses1', -- Source table 'houses_en', -- Result table 'price', -- Dependent variable 'array[tax, bath, size]', -- Independent variable 'gaussian', -- Regression family 0.5, -- Alpha value 0.1, -- Lambda value FALSE, -- Standardize NULL, -- Grouping column(s) 'igd', -- Optimizer '', -- Optimizer parameters NULL, -- Excluded columns 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}
> 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
>
> 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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