-- SET mapred.reduce.tasks=32; drop table kdd10a_pa1_model1; create table kdd10a_pa1_model1 as select feature, voted_avg(weight) as weight from (select train_pa1(add_bias(features),label) as (feature,weight) from kdd10a_train_x3 ) t group by feature;
create or replace view kdd10a_pa1_predict1 as select t.rowid, sum(m.weight * t.value) as total_weight, case when sum(m.weight * t.value) > 0.0 then 1 else -1 end as label from kdd10a_test_exploded t LEFT OUTER JOIN kdd10a_pa1_model1 m ON (t.feature = m.feature) group by t.rowid;
create or replace view kdd10a_pa1_submit1 as select t.rowid, t.label as actual, pd.label as predicted from kdd10a_test t JOIN kdd10a_pa1_predict1 pd on (t.rowid = pd.rowid); select count(1)/510302 from kdd10a_pa1_submit1 where actual = predicted;
0.8677782959894337
-- SET mapred.reduce.tasks=32; drop table kdd10a_cw_model1; create table kdd10a_cw_model1 as select feature, argmin_kld(weight, covar) as weight from (select train_cw(add_bias(features),label) as (feature,weight,covar) from kdd10a_train_x3 ) t group by feature; create or replace view kdd10a_cw_predict1 as select t.rowid, sum(m.weight * t.value) as total_weight, case when sum(m.weight * t.value) > 0.0 then 1 else -1 end as label from kdd10a_test_exploded t LEFT OUTER JOIN kdd10a_cw_model1 m ON (t.feature = m.feature) group by t.rowid; create or replace view kdd10a_cw_submit1 as select t.rowid, t.label as actual, pd.label as predicted from kdd10a_test t JOIN kdd10a_cw_predict1 pd on (t.rowid = pd.rowid); select count(1)/510302 from kdd10a_cw_submit1 where actual = predicted;
0.8678037711002504
-- SET mapred.reduce.tasks=32; drop table kdd10a_arow_model1; create table kdd10a_arow_model1 as select feature, -- voted_avg(weight) as weight argmin_kld(weight, covar) as weight -- [hivemall v0.2alpha3 or later] from (select -- train_arow(add_bias(features),label) as (feature,weight) -- [hivemall v0.1] train_arow(add_bias(features),label) as (feature,weight,covar) -- [hivemall v0.2 or later] from kdd10a_train_x3 ) t group by feature; create or replace view kdd10a_arow_predict1 as select t.rowid, sum(m.weight * t.value) as total_weight, case when sum(m.weight * t.value) > 0.0 then 1 else -1 end as label from kdd10a_test_exploded t LEFT OUTER JOIN kdd10a_arow_model1 m ON (t.feature = m.feature) group by t.rowid; create or replace view kdd10a_arow_submit1 as select t.rowid, t.label as actual, pd.label as predicted from kdd10a_test t JOIN kdd10a_arow_predict1 pd on (t.rowid = pd.rowid); select count(1)/510302 from kdd10a_arow_submit1 where actual = predicted;
0.8676038894615345
-- SET mapred.reduce.tasks=32; drop table kdd10a_scw_model1; create table kdd10a_scw_model1 as select feature, argmin_kld(weight, covar) as weight from (select train_scw(add_bias(features),label) as (feature,weight,covar) from kdd10a_train_x3 ) t group by feature; create or replace view kdd10a_scw_predict1 as select t.rowid, sum(m.weight * t.value) as total_weight, case when sum(m.weight * t.value) > 0.0 then 1 else -1 end as label from kdd10a_test_exploded t LEFT OUTER JOIN kdd10a_scw_model1 m ON (t.feature = m.feature) group by t.rowid; create or replace view kdd10a_scw_submit1 as select t.rowid, t.label as actual, pd.label as predicted from kdd10a_test t JOIN kdd10a_scw_predict1 pd on (t.rowid = pd.rowid); select count(1)/510302 from kdd10a_scw_submit1 where actual = predicted;
0.8678096499719774
Algorithm | Accuracy |
---|---|
AROW | 0.8676038894615345 |
PA1 | 0.8677782959894337 |
CW | 0.8678037711002504 |
SCW1 | 0.8678096499719774 |