Introduction

Expanding numeric labels to actual count of samples can contribute to accuracy improvement in some cases. binarize_label explode a record that keeps the count of positive/negative labeled samples into corresponding actual count of samples. For example,

positive	negative	features
2	3	“[a:1, b:2]”

is converted into

features	label
“[a:1, b:2]”	0
“[a:1, b:2]”	0
“[a:1, b:2]”	1
“[a:1, b:2]”	1
“[a:1, b:2]”	1

Function signature

binarize_label(int/long positive, int/long negative, ANY arg1, ANY arg2, ..., ANY argN) returns (ANY arg1, ANY arg2, ..., ANY argN, int label) where label is 0 or 1.

Usage

WITH input as (
  select 2 as positive, 3 as negative, array('a:1','b:2') as features
  UNION ALL
  select 2 as positive, 1 as negative, array('c:3','d:4') as features
)
SELECT
  binarize_label(positive, negative, features)
from 
  input;

features	label
[“a:1”,“b:2”]	1
[“a:1”,“b:2”]	1
[“a:1”,“b:2”]	0
[“a:1”,“b:2”]	0
[“a:1”,“b:2”]	0
[“c:3”,“d:4”]	1
[“c:3”,“d:4”]	1
[“c:3”,“d:4”]	0

Caution

Don't forget to shuffle converted training instances in a random order, e.g., by CLUSTER BY rand().