[jira] [Assigned] (MADLIB-1087) Random Forest fails if features are INT or NUMERIC only and variable importance is TRUE

["Rahul Iyer (JIRA)" <ji...@apache.org>]

     [ https://issues.apache.org/jira/browse/MADLIB-1087?page=com.atlassian.jira.plugin.system.issuetabpanels:all-tabpanel ]

Rahul Iyer reassigned MADLIB-1087:
----------------------------------

    Assignee: Rahul Iyer

> Random Forest fails if features are INT or NUMERIC only and variable importance is TRUE
> ---------------------------------------------------------------------------------------
>
>                 Key: MADLIB-1087
>                 URL: https://issues.apache.org/jira/browse/MADLIB-1087
>             Project: Apache MADlib
>          Issue Type: Bug
>          Components: Module: Random Forest
>            Reporter: Paul Chang
>            Assignee: Rahul Iyer
>            Priority: Minor
>             Fix For: v1.12
>
>
> If we attempt to train on a dataset where all features are either INT or NUMERIC, and with variable importance TRUE, forest_train() fails with the following error:
> [2017-04-03 13:35:35] [XX000] ERROR: plpy.SPIError: invalid array length (plpython.c:4648)
> [2017-04-03 13:35:35] Detail: array_of_bigint: Size should be in [1, 1e7], 0 given
> [2017-04-03 13:35:35] Where: Traceback (most recent call last):
> [2017-04-03 13:35:35] PL/Python function "forest_train", line 42, in <module>
> [2017-04-03 13:35:35] sample_ratio
> [2017-04-03 13:35:35] PL/Python function "forest_train", line 591, in forest_train
> [2017-04-03 13:35:35] PL/Python function "forest_train", line 1038, in _calculate_oob_prediction
> [2017-04-03 13:35:35] PL/Python function "forest_train"
> However, if we add a single feature column that is FLOAT, REAL, or DOUBLE PRECISION, the trainer does not fail.



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