docs/gitbook/tips/ensemble_learning.md

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This example explains how to run ensemble learning in Hivemall.   
Two heads are better than one? Let's verify it by ensemble learning.

<!-- toc -->

---

## UDF preparation
```sql
delete jar /home/myui/tmp/hivemall.jar;
add jar /home/myui/tmp/hivemall.jar;

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

# [Case1] Model ensemble/mixing

## training
```sql
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;
```

## prediction
```sql
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;
```

## evaluation
```sql
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

## Cleaning

```sql
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 |


---

# [Case2] Prediction ensemble

## prediction
```sql
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;
```

## evaluation
```sql
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 |