docs/gitbook/binaryclass/webspam_scw.md

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# Preparation

```
use webspam;

delete jar ./tmp/hivemall.jar;
add jar ./tmp/hivemall.jar;
source ./tmp/define-all.hive;
```

# PA1

```sql
drop table webspam_pa1_model1;
create table webspam_pa1_model1 as
select 
 feature,
 cast(voted_avg(weight) as float) as weight
from 
 (select 
     train_pa1(features,label) as (feature,weight) -- sparse model
     -- train_pa1(features,label,"-dense -dims 33554432") as (feature,weight)
  from 
     webspam_train_x3
 ) t 
group by feature;

create or replace view webspam_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 
  webspam_test_exploded t LEFT OUTER JOIN
  webspam_pa1_model1 m ON (t.feature = m.feature)
group by
  t.rowid;

create or replace view webspam_pa1_submit1 as
select 
  t.rowid, 
  t.label as actual, 
  pd.label as predicted
from 
  webspam_test t JOIN webspam_pa1_predict1 pd 
    on (t.rowid = pd.rowid);

select count(1)/70000 from webspam_pa1_submit1 
where actual = predicted;
```
> Prediction accuracy: 0.9628428571428571

# AROW

```sql
drop table webspam_arow_model1;
create table webspam_arow_model1 as
select 
 feature,
 argmin_kld(weight,covar)as weight
from 
 (select 
     train_arow(features,label) as (feature,weight,covar)
  from 
     webspam_train_x3
 ) t 
group by feature;

create or replace view webspam_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 
  webspam_test_exploded t LEFT OUTER JOIN
  webspam_arow_model1 m ON (t.feature = m.feature)
group by
  t.rowid;

create or replace view webspam_arow_submit1 as
select 
  t.rowid, 
  t.label as actual, 
  pd.label as predicted
from 
  webspam_test t JOIN webspam_arow_predict1 pd 
    on (t.rowid = pd.rowid);

select count(1)/70000 from webspam_arow_submit1 
where actual = predicted;
```
> Prediction accuracy: 0.9747428571428571

# SCW1

```sql
drop table webspam_scw_model1;
create table webspam_scw_model1 as
select 
 feature,
 argmin_kld(weight,covar)as weight
from 
 (select 
     train_scw(features,label) as (feature,weight,covar)
  from 
     webspam_train_x3
 ) t 
group by feature;

create or replace view webspam_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 
  webspam_test_exploded t LEFT OUTER JOIN
  webspam_scw_model1 m ON (t.feature = m.feature)
group by
  t.rowid;

create or replace view webspam_scw_submit1 as
select 
  t.rowid, 
  t.label as actual, 
  pd.label as predicted
from 
  webspam_test t JOIN webspam_scw_predict1 pd 
    on (t.rowid = pd.rowid);

select count(1)/70000 from webspam_scw_submit1 
where actual = predicted;
```
> Prediction accuracy: 0.9778714285714286