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Rahul Iyer resolved MADLIB-1258. -------------------------------- Resolution: Fixed Fixed by https://github.com/apache/madlib/commit/e2534e44ea36aedec843a3a7c48236d0e1104e2c > Individual group dropping a categorical variable can lead to incorrect results > ------------------------------------------------------------------------------ > > Key: MADLIB-1258 > URL: https://issues.apache.org/jira/browse/MADLIB-1258 > Project: Apache MADlib > Issue Type: Bug > Components: Module: Decision Tree, Module: Random Forest > Reporter: Rahul Iyer > Priority: Major > Fix For: v1.15 > > > In DT/RF, a categorical variable is dropped if it only has a single level. > This can lead to a situation in grouped models, where a particular group > drops a categorical variables which is retained by other groups (see example > below). > This is fine on its own, but will lead to issues with prediction, since the > predict functions assume a consistent list of categorical features across > groups. > There are two possible ways to fix the problem: > 1. Update `*predict` (and other downstream functions) to handle the varying > cat features across groups. > 2. Don't drop a categorical feature (This case would require ensuring that > our internal code does not assume that a categorical feature has at least 2 > levels). > Example: > Below example calls {{forest_train}} with three categorical features: > {{cat_features}} is an array with two values each and {{windy}} is a boolean > column. > {code:sql} > DROP TABLE IF EXISTS dt_golf CASCADE; > CREATE TABLE dt_golf ( > id integer NOT NULL, > "OUTLOOK" text, > temperature double precision, > humidity double precision, > "Cont_features" double precision[], > cat_features text[], > windy boolean, > class text > ) ; > INSERT INTO dt_golf > (id,"OUTLOOK",temperature,humidity,"Cont_features",cat_features, windy,class) > VALUES > (1, 'sunny', 85, 85,ARRAY[85, 85], ARRAY['a', 'b'], false, 'Don''t Play'), > (2, 'sunny', 80, 90, ARRAY[80, 90], ARRAY['a', 'b'], true, 'Don''t Play'), > (6, 'rain', NULL, 70, ARRAY[65, 70], ARRAY['a', 'b'], true, 'Don''t Play'), > (8, 'sunny', 72, 95, ARRAY[72, 95], ARRAY['a', 'b'], false, 'Don''t Play'), > (14, 'rain', 71, 80, ARRAY[71, 80], ARRAY['c', 'b'], true, 'Don''t Play'), > (3, 'overcast', 83, 78, ARRAY[83, 78], ARRAY['a', 'b'], false, 'Play'), > (4, 'rain', 70, NULL, ARRAY[70, 96], ARRAY['a', 'b'], false, 'Play'), > (5, 'rain', 68, 80, ARRAY[68, 80], ARRAY['a', 'b'], false, 'Play'), > (7, 'overcast', 64, 65, ARRAY[64, 65], ARRAY['c', 'b'], NULL , 'Play'), > (9, 'sunny', 69, 70, ARRAY[69, 70], ARRAY['a', 'b'], false, 'Play'), > (10, 'rain', 75, 80, ARRAY[75, 80], ARRAY['a', 'b'], false, 'Play'), > (11, 'sunny', 75, 70, ARRAY[75, 70], ARRAY['a', 'd'], true, 'Play'), > (12, 'overcast', 72, 90, ARRAY[72, 90], ARRAY['c', 'b'], NULL, 'Play'), > (13, 'overcast', 81, 75, ARRAY[81, 75], ARRAY['a', 'b'], false, 'Play'), > (15, NULL, 81, 75, ARRAY[81, 75], ARRAY['a', 'b'], false, 'Play'), > (16, 'overcast', NULL, 75, ARRAY[81, 75], ARRAY['a', 'd'], false, 'Play'); > DROP TABLE IF EXISTS train_output, train_output_summary, train_output_group, > train_output_poisson_count; > SELECT madlib.forest_train( > 'dt_golf', -- source table > 'train_output', -- output model table > 'id', -- id column > 'temperature::double precision', -- response > 'cat_features, windy', -- features > NULL, -- exclude columns > 'class', -- grouping > 5, -- num of trees > NULL, -- num of random features > TRUE, -- importance > 20, -- num_permutations > 10, -- max depth > 1, -- min split > 1, -- min bucket > 3, -- number of bins per continuous variable > 'max_surrogates = 2 ', > FALSE > ); > \x on > SELECT * from train_output_group; > {code} > Result (note that group 1 has just 2 values in {{cat_n_levels}} indicating > just two categorical features and group 2 has 3 values): > {code} > -[ RECORD 1 ]-----------+------------------------------------------------- > gid | 1 > class | Don't Play > success | t > cat_n_levels | {2,2} > cat_levels_in_text | {c,a,True,False} > oob_error | 78.2893518518518 > oob_var_importance | {2.368475785867e-15,2.368475785867e-15} > impurity_var_importance | {2.296944444444,0} > -[ RECORD 2 ]-----------+------------------------------------------------- > gid | 2 > class | Play > success | t > cat_n_levels | {2,2,2} > cat_levels_in_text | {c,a,b,d,False,True} > oob_error | 38.1958872778793 > oob_var_importance | {10.9137514172336,0,0} > impurity_var_importance | {8.1044222372,0.25723053952258,0.25723053952258} > {code} -- This message was sent by Atlassian JIRA (v7.6.3#76005)