Feature Vectorization

array<string> vectorize_feature(array<string> featureNames, ...) is useful to generate a feature vector for each row, from a table.

select vectorize_features(array("a","b"),"0.2","0.3") from dual;
>["a:0.2","b:0.3"]

-- avoid zero weight
select vectorize_features(array("a","b"),"0.2",0) from dual;
> ["a:0.2"]

-- true boolean value is treated as 1.0 (a categorical value w/ its column name)
select vectorize_features(array("a","b","bool"),0.2,0.3,true) from dual;
> ["a:0.2","b:0.3","bool:1.0"]

-- an example to generate feature vectors from table
select * from dual;
> 1                                         
select vectorize_features(array("a"),*) from dual;
> ["a:1.0"]

-- has categorical feature
select vectorize_features(array("a","b","wheather"),"0.2","0.3","sunny") from dual;
> ["a:0.2","b:0.3","whether#sunny"]

select
  id,
  vectorize_features(
    array("age","job","marital","education","default","balance","housing","loan","contact","day","month","duration","campaign","pdays","previous","poutcome"), 
    age,job,marital,education,default,balance,housing,loan,contact,day,month,duration,campaign,pdays,previous,poutcome
  ) as features,
  y
from
  train
limit 2;

1 [“age:39.0”,“job#blue-collar”,“marital#married”,“education#secondary”,“default#no”,“balance:1756.0”,“housing#yes”,“loan#no”,“contact#cellular”,“day:3.0”,“month#apr”,“duration:939.0”,“campaign:1.0”,“pdays:-1.0”,“poutcome#unknown”] 1 2 [“age:51.0”,“job#entrepreneur”,“marital#married”,“education#primary”,“default#no”,“balance:1443.0”,“housing#no”,“loan#no”,“contact#cellular”,“day:18.0”,“month#feb”,“duration:172.0”,“campaign:10.0”,“pdays:-1.0”,“poutcome#unknown”] 1