Algorithm	Accuracy
PA2	0.8204357625845229
SCW1	0.8314550463310794
AROW	0.8474830954169797
SCW2	0.8482344102178813
CW	0.850488354620586

---

Preparation

UDF preparation

delete jar /home/myui/tmp/hivemall.jar;
add jar /home/myui/tmp/hivemall.jar;

source /home/myui/tmp/define-all.hive;

---

#[CW]

training

drop table news20mc_cw_model1;
create table news20mc_cw_model1 as
select 
 label, 
 cast(feature as int) as feature,
 -- voted_avg(weight) as weight -- [hivemall v0.1]
 argmin_kld(weight, covar) as weight -- [hivemall v0.2 or later]
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
 ) t 
group by label, feature;

prediction

create or replace view news20mc_cw_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_cw_model1 m ON (t.feature = m.feature)
  group by
    t.rowid, m.label
) t1
group by rowid
) t2;

evaluation

create or replace view news20mc_cw_submit1 as
select 
  t.label as actual, 
  pd.label as predicted
from 
  news20mc_test t JOIN news20mc_cw_predict1 pd 
    on (t.rowid = pd.rowid);

select count(1)/3993 from news20mc_cw_submit1 
where actual == predicted;

0.850488354620586

Cleaning

drop table news20mc_cw_model1;
drop table news20mc_cw_predict1;
drop view news20mc_cw_submit1;

---

#[AROW]

training

drop table news20mc_arow_model1;
create table news20mc_arow_model1 as
select 
 label, 
 cast(feature as int) as feature,
 -- voted_avg(weight) as weight -- [hivemall v0.1]
 argmin_kld(weight, covar) as weight -- [hivemall v0.2 or later]
from 
 (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
 ) t 
group by label, feature;

prediction

create or replace view news20mc_arow_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_arow_model1 m ON (t.feature = m.feature)
  group by
    t.rowid, m.label
) t1
group by rowid
) t2;

evaluation

create or replace view news20mc_arow_submit1 as
select 
  t.label as actual, 
  pd.label as predicted
from 
  news20mc_test t JOIN news20mc_arow_predict1 pd 
    on (t.rowid = pd.rowid);

select count(1)/3993 from news20mc_arow_submit1 
where actual == predicted;

0.8474830954169797

Cleaning

drop table news20mc_arow_model1;
drop table news20mc_arow_predict1;
drop view news20mc_arow_submit1;

---

#[SCW1]

training

drop table news20mc_scw_model1;
create table news20mc_scw_model1 as
select 
 label, 
 cast(feature as int) as feature,
 -- voted_avg(weight) as weight -- [hivemall v0.1]
 argmin_kld(weight, covar) as weight -- [hivemall v0.2 or later]
from 
 (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;

prediction

create or replace view news20mc_scw_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_scw_model1 m ON (t.feature = m.feature)
  group by
    t.rowid, m.label
) t1
group by rowid
) t2;

evaluation

create or replace view news20mc_scw_submit1 as
select 
  t.label as actual, 
  pd.label as predicted
from 
  news20mc_test t JOIN news20mc_scw_predict1 pd 
    on (t.rowid = pd.rowid);

select count(1)/3993 from news20mc_scw_submit1 
where actual == predicted;

0.8314550463310794

Cleaning

drop table news20mc_scw_model1;
drop table news20mc_scw_predict1;
drop view news20mc_scw_submit1;

---

#[SCW2]

training

drop table news20mc_scw2_model1;
create table news20mc_scw2_model1 as
select 
 label, 
 cast(feature as int) as feature,
 -- voted_avg(weight) as weight -- [hivemall v0.1]
 argmin_kld(weight, covar) as weight -- [hivemall v0.2 or later]
from 
 (select 
     -- train_multiclass_scw2(add_bias(features),label) as (label,feature,weight) -- [hivemall v0.1]
     train_multiclass_scw2(add_bias(features),label) as (label,feature,weight,covar) -- [hivemall v0.2 or later]
  from 
     news20mc_train_x3
 ) t 
group by label, feature;

prediction

create or replace view news20mc_scw2_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_scw2_model1 m ON (t.feature = m.feature)
  group by
    t.rowid, m.label
) t1
group by rowid
) t2;

evaluation

create or replace view news20mc_scw2_submit1 as
select 
  t.label as actual, 
  pd.label as predicted
from 
  news20mc_test t JOIN news20mc_scw2_predict1 pd 
    on (t.rowid = pd.rowid);

select count(1)/3993 from news20mc_scw2_submit1 
where actual == predicted;

0.8482344102178813

Cleaning

drop table news20mc_scw2_model1;
drop table news20mc_scw2_predict1;
drop view news20mc_scw2_submit1;