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Posted to issues@madlib.apache.org by "Rahul Iyer (JIRA)" <ji...@apache.org> on 2018/05/01 21:29:00 UTC
[jira] [Created] (MADLIB-1233) DT: install-check fails
intermittently
Rahul Iyer created MADLIB-1233:
----------------------------------
Summary: DT: install-check fails intermittently
Key: MADLIB-1233
URL: https://issues.apache.org/jira/browse/MADLIB-1233
Project: Apache MADlib
Issue Type: Task
Components: Module: Decision Tree
Reporter: Rahul Iyer
Decision tree install-check fails intermittently when run on a big, distributed GPDB server with below error:
{code}
DROP TABLE IF EXISTS train_output, train_output_summary;
psql:/tmp/madlib.xStCxm/recursive_partitioning/test/decision_tree.sql_in.tmp:273: NOTICE: table "train_output" does not exist, skipping
psql:/tmp/madlib.xStCxm/recursive_partitioning/test/decision_tree.sql_in.tmp:273: NOTICE: table "train_output_summary" does not exist, skipping
DROP TABLE
SELECT tree_train('dt_golf'::text, -- source table
'train_output'::text, -- output model table
'id'::text, -- id column
'temperature::double precision'::text, -- response
'humidity, windy, "Cont_features"'::text, -- features
NULL::text, -- exclude columns
'gini'::text, -- split criterion
NULL::text, -- no grouping
NULL::text, -- no weights
10::integer, -- max depth
6::integer, -- min split
2::integer, -- min bucket
8::integer, -- number of bins per continuous variable
'cp=0.01' -- cost-complexity pruning parameter
);
psql:/tmp/madlib.xStCxm/recursive_partitioning/test/decision_tree.sql_in.tmp:288: ERROR: plpy.SPIError: Function "_dst_compute_entropy_transition(integer[],integer,integer)": Invalid type conversion. Null where not expected. (seg62 slice1 sdw8.gphd.local:1031 pid=457374) (plpython.c:4656)
CONTEXT: Traceback (most recent call last):
PL/Python function "tree_train", line 28, in <module>
null_handling_params, verbose_mode)
PL/Python function "tree_train", line 484, in tree_train
PL/Python function "tree_train", line 264, in _get_tree_states
PL/Python function "tree_train", line 720, in _get_bins
PL/Python function "tree_train"
{code}
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