This example explains how to run ensemble learning in Hivemall.
Two heads are better than one? Let's verify it by ensemble learning.
delete jar /home/myui/tmp/hivemall.jar; add jar /home/myui/tmp/hivemall.jar; source /home/myui/tmp/define-all.hive;
SET hive.exec.parallel=true; SET hive.exec.parallel.thread.number=8; SET mapred.reduce.tasks=4; drop table news20mc_ensemble_model1; create table news20mc_ensemble_model1 as select label, -- cast(feature as int) as feature, -- hivemall v0.1 argmin_kld(feature, covar) as feature, -- hivemall v0.2 or later voted_avg(weight) as weight from (select -- train_multiclass_cw(add_bias(features),label) as (label,feature,weight) -- hivemall v0.1 train_multiclass_cw(add_bias(features),label) as (label,feature,weight,covar) -- hivemall v0.2 or later from news20mc_train_x3 union all select -- train_multiclass_arow(add_bias(features),label) as (label,feature,weight) -- hivemall v0.1 train_multiclass_arow(add_bias(features),label) as (label,feature,weight,covar) -- hivemall v0.2 or later from news20mc_train_x3 union all select -- train_multiclass_scw(add_bias(features),label) as (label,feature,weight) -- hivemall v0.1 train_multiclass_scw(add_bias(features),label) as (label,feature,weight,covar) -- hivemall v0.2 or later from news20mc_train_x3 ) t group by label, feature; -- reset to the default SET hive.exec.parallel=false; SET mapred.reduce.tasks=-1;
create or replace view news20mc_ensemble_predict1 as select rowid, m.col0 as score, m.col1 as label from ( select rowid, maxrow(score, label) as m from ( select t.rowid, m.label, sum(m.weight * t.value) as score from news20mc_test_exploded t LEFT OUTER JOIN news20mc_ensemble_model1 m ON (t.feature = m.feature) group by t.rowid, m.label ) t1 group by rowid ) t2;
create or replace view news20mc_ensemble_submit1 as select t.label as actual, pd.label as predicted from news20mc_test t JOIN news20mc_ensemble_predict1 pd on (t.rowid = pd.rowid);
select count(1)/3993 from news20mc_ensemble_submit1 where actual == predicted;
0.8494866015527173
drop table news20mc_ensemble_model1; drop view news20mc_ensemble_predict1; drop view news20mc_ensemble_submit1;
Unfortunately, too many cooks spoil the broth in this case :-(
Algorithm | Accuracy |
---|---|
AROW | 0.8474830954169797 |
SCW2 | 0.8482344102178813 |
Ensemble(model) | 0.8494866015527173 |
CW | 0.850488354620586 |
create or replace view news20mc_pred_ensemble_predict1 as select rowid, m.col1 as label from ( select rowid, maxrow(cnt, label) as m from ( select rowid, label, count(1) as cnt from ( select * from news20mc_arow_predict1 union all select * from news20mc_scw2_predict1 union all select * from news20mc_cw_predict1 ) t1 group by rowid, label ) t2 group by rowid ) t3;
create or replace view news20mc_pred_ensemble_submit1 as select t.label as actual, pd.label as predicted from news20mc_test t JOIN news20mc_pred_ensemble_predict1 pd on (t.rowid = pd.rowid);
select count(1)/3993 from news20mc_pred_ensemble_submit1 where actual == predicted;
0.8499874780866516
Unfortunately, too many cooks spoil the broth in this case too :-(
Algorithm | Accuracy |
---|---|
AROW | 0.8474830954169797 |
SCW2 | 0.8482344102178813 |
Ensemble(model) | 0.8494866015527173 |
Ensemble(prediction) | 0.8499874780866516 |
CW | 0.850488354620586 |