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https://issues.apache.org/jira/browse/MADLIB-1087?page=com.atlassian.jira.plugin.system.issuetabpanels:comment-tabpanel&focusedCommentId=16016058#comment-16016058
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Frank McQuillan commented on MADLIB-1087:
-----------------------------------------
If I run Paul's example from the description above, it does work now.
{code}
SELECT * FROM paul_badrftest2_train_summary;
{code}
produces
{code}
-[ RECORD 1 ]---------+----------------------------------------
method | forest_train
is_classification | t
source_table | paul_badrftest2
model_table | paul_badrftest2_train
id_col_name | id
dependent_varname | resp
independent_varnames | feat
cat_features |
con_features | feat
grouping_cols |
num_trees | 1
num_random_features | 1
max_tree_depth | 5
min_split | 3
min_bucket | 3
num_splits | 10
verbose | f
importance | t
num_permutations | 1
num_all_groups | 1
num_failed_groups | 0
total_rows_processed | 10
total_rows_skipped | 0
dependent_var_levels | "0","1","2","3","4","5","6","7","8","9"
dependent_var_type | integer
independent_var_types | numeric
{code}
> 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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