Github user takuti commented on the issue:
https://github.com/apache/incubator-hivemall/pull/79
I tested generic classifier and regressor on EMR by using the a9a data.
### Classifier
```
set hivevar:n_samples=16281;
set hivevar:total_steps=32562;
```
#### `logress`
```sql
drop table if exists logress_model;
create table logress_model as
select
feature,
avg(weight) as weight
from
(
select
logress(add_bias(features), label, '-total_steps ${total_steps}') as
(feature, weight)
-- logress(add_bias(features), label, '-total_steps ${total_steps}
-mini_batch 10') as (feature, weight)
from
train_x3
) t
group by feature;
```
```sql
WITH test_exploded as (
select
rowid,
label,
extract_feature(feature) as feature,
extract_weight(feature) as value
from
test LATERAL VIEW explode(add_bias(features)) t AS feature
),
predict as (
select
t.rowid,
sigmoid(sum(m.weight * t.value)) as prob,
CAST((case when sigmoid(sum(m.weight * t.value)) >= 0.5 then 1.0 else
0.0 end) as FLOAT) as label
from
test_exploded t LEFT OUTER JOIN
logress_model m ON (t.feature = m.feature)
group by
t.rowid
),
submit as (
select
t.label as actual,
pd.label as predicted,
pd.prob as probability
from
test t JOIN predict pd
on (t.rowid = pd.rowid)
)
select count(1) / ${n_samples} from submit
where actual = predicted;
```
#### `train_classifier`
```sql
train_classifier(add_bias(features), label, '-loss logloss -opt SGD -reg no
-eta simple -total_steps ${total_steps}') as (feature, weight)
-- train_classifier(add_bias(features), label, '-loss logloss -opt SGD -reg
no -eta simple -total_steps ${total_steps} -mini_batch 10') as (feature, weight)
```
Results were completely same:
| | online | mini-batch |
|:--|:--:|:--:|
|`logress`| 0.8414716540753026 | 0.848965051286776 |
|`train_classifier`| 0.8414716540753026 | 0.848965051286776 |
### Regression
Solved the a9a label prediction as a regression problem.
// Since non-generic Adagrad was designed for logistic loss (i.e.
classification), we cannot compare it with generic regressor under the exactly
same condition.
#### `train_adagrad_regr` (internally uses logistic loss)
```sql
drop table if exists adagrad_model;
create table adagrad_model as
select
feature,
avg(weight) as weight
from
(
select
train_adagrad_regr(features, label) as (feature, weight)
from
train_x3
) t
group by feature;
```
```sql
WITH test_exploded as (
select
rowid,
label,
extract_feature(feature) as feature,
extract_weight(feature) as value
from
test LATERAL VIEW explode(add_bias(features)) t AS feature
),
predict as (
select
t.rowid,
sigmoid(sum(m.weight * t.value)) as prob
from
test_exploded t LEFT OUTER JOIN
adagrad_model m ON (t.feature = m.feature)
group by
t.rowid
),
submit as (
select
t.label as actual,
pd.prob as probability
from
test t JOIN predict pd
on (t.rowid = pd.rowid)
)
select rmse(probability, actual) from submit;
```
### `train_regression`
```sql
train_regression(features, label, '-loss squaredloss -opt AdaGrad -reg no')
as (feature, weight)
-- train_regression(features, label, '-loss squaredloss -opt AdaGrad -reg
no -mini_batch 10') as (feature, weight)
```
| | online | mini-batch |
|:--|:--:|:--:|
|`train_adagrad_regr` (logistic loss) | 0.3254586866367811 | -- |
|`train_regression` (squared loss) | 0.3356422627079689 |
0.3348889704327727 |
As I mentioned in the last comment, I'm afraid whether the `-mini_batch`
option works correctly for Adagrad. Fortunately, this example showed that the
option slightly improved the accuracy of prediction in terms of RMSE.
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