use news20; delete jar /home/myui/tmp/hivemall.jar; add jar /home/myui/tmp/hivemall.jar; source /home/myui/tmp/define-all.hive;
drop table news20b_cw_model1; create table news20b_cw_model1 as select feature, -- voted_avg(weight) as weight -- [hivemall v0.1] argmin_kld(weight, covar) as weight -- [hivemall v0.2 or later] from (select -- train_cw(add_bias(features), label) as (feature, weight) -- [hivemall v0.1] train_cw(add_bias(features), label) as (feature, weight, covar) -- [hivemall v0.2 or later] from news20b_train_x3 ) t group by feature;
create or replace view news20b_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 news20b_test_exploded t LEFT OUTER JOIN news20b_cw_model1 m ON (t.feature = m.feature) group by t.rowid;
create or replace view news20b_cw_submit1 as select t.rowid, t.label as actual, pd.label as predicted from news20b_test t JOIN news20b_cw_predict1 pd on (t.rowid = pd.rowid);
select count(1)/4996 from news20b_cw_submit1 where actual = predicted;
0.9655724579663731
drop table news20b_cw_model1; drop view news20b_cw_predict1; drop view news20b_cw_submit1;
drop table news20b_arow_model1; create table news20b_arow_model1 as select feature, -- voted_avg(weight) as weight -- [hivemall v0.1] argmin_kld(weight, covar) as weight -- [hivemall v0.2 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 news20b_train_x3 ) t group by feature;
create or replace view news20b_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 news20b_test_exploded t LEFT OUTER JOIN news20b_arow_model1 m ON (t.feature = m.feature) group by t.rowid;
create or replace view news20b_arow_submit1 as select t.rowid, t.label as actual, pd.label as predicted from news20b_test t JOIN news20b_arow_predict1 pd on (t.rowid = pd.rowid);
select count(1)/4996 from news20b_arow_submit1 where actual = predicted;
0.9659727782225781
drop table news20b_arow_model1; drop view news20b_arow_predict1; drop view news20b_arow_submit1;
drop table news20b_scw_model1; create table news20b_scw_model1 as select feature, -- voted_avg(weight) as weight -- [hivemall v0.1] argmin_kld(weight, covar) as weight -- [hivemall v0.2 or later] from (select -- train_scw(add_bias(features),label) as (feature,weight) -- [hivemall v0.1] train_scw(add_bias(features),label) as (feature,weight,covar) -- [hivemall v0.2 or later] from news20b_train_x3 ) t group by feature;
create or replace view news20b_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 news20b_test_exploded t LEFT OUTER JOIN news20b_scw_model1 m ON (t.feature = m.feature) group by t.rowid;
create or replace view news20b_scw_submit1 as select t.rowid, t.label as actual, pd.label as predicted from news20b_test t JOIN news20b_scw_predict1 pd on (t.rowid = pd.rowid);
select count(1)/4996 from news20b_scw_submit1 where actual = predicted;
0.9661729383506805
drop table news20b_scw_model1; drop view news20b_scw_predict1; drop view news20b_scw_submit1;
drop table news20b_scw2_model1; create table news20b_scw2_model1 as select feature, -- voted_avg(weight) as weight -- [hivemall v0.1] argmin_kld(weight, covar) as weight -- [hivemall v0.2 or later] from (select -- train_scw2(add_bias(features),label) as (feature,weight) -- [hivemall v0.1] train_scw2(add_bias(features),label) as (feature,weight,covar) -- [hivemall v0.2 or later] from news20b_train_x3 ) t group by feature;
create or replace view news20b_scw2_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 news20b_test_exploded t LEFT OUTER JOIN news20b_scw2_model1 m ON (t.feature = m.feature) group by t.rowid;
create or replace view news20b_scw2_submit1 as select t.rowid, t.label as actual, pd.label as predicted from news20b_test t JOIN news20b_scw2_predict1 pd on (t.rowid = pd.rowid);
select count(1)/4996 from news20b_scw2_submit1 where actual = predicted;
0.9579663730984788
drop table news20b_scw2_model1; drop view news20b_scw2_predict1; drop view news20b_scw2_submit1;
Algorithm | Accuracy |
---|---|
Perceptron | 0.9459567654123299 |
SCW2 | 0.9579663730984788 |
PA2 | 0.9597678142514011 |
PA1 | 0.9601681345076061 |
PA | 0.9603682946357086 |
CW | 0.9655724579663731 |
AROW | 0.9659727782225781 |
SCW1 | 0.9661729383506805 |
My recommendation is AROW for classification.