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https://issues.apache.org/jira/browse/MADLIB-1087?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=15993583#comment-15993583
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ASF GitHub Bot commented on MADLIB-1087:
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GitHub user iyerr3 opened a pull request:
https://github.com/apache/incubator-madlib/pull/129
DT/RF: Allow expressions in feature list
JIRA: MADLIB-1087
Changes:
- Add numeric as a continuous type
- Get data type of features from an expression instead of the table
column names
- Update to allow expressions in the feature list
You can merge this pull request into a Git repository by running:
$ git pull https://github.com/iyerr3/incubator-madlib
bugfix/rf_feature_input
Alternatively you can review and apply these changes as the patch at:
https://github.com/apache/incubator-madlib/pull/129.patch
To close this pull request, make a commit to your master/trunk branch
with (at least) the following in the commit message:
This closes #129
----
commit 4d18b07d69ae20475254245d65798b61edce1f31
Author: Rahul Iyer <[email protected]>
Date: 2017-05-02T19:39:52Z
DT/RF: Allow expressions in feature list
JIRA: MADLIB-1087
Changes:
- Add numeric as a continuous type
- Get data type of features from an expression instead of the table
column names
- Update to allow expressions in the feature list
----
> 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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