docs/gitbook/ft_engineering/binning.md

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Feature binning is a method of dividing quantitative variables into categorical values.
It groups quantitative values into a pre-defined number of bins.

*Note: This feature is supported from Hivemall v0.5-rc.1 or later.*

<!-- toc -->

# Usage

Prepare sample data (*users* table) first as follows:

``` sql
CREATE TABLE users (
  name string, age int, gender string
);

INSERT INTO users VALUES
  ('Jacob', 20, 'Male'),
  ('Mason', 22, 'Male'),
  ('Sophia', 35, 'Female'),
  ('Ethan', 55, 'Male'),
  ('Emma', 15, 'Female'),
  ('Noah', 46, 'Male'),
  ('Isabella', 20, 'Female');
```

## A. Feature Vector trasformation by applying Feature Binning

``` sql
WITH t AS (
  SELECT
    array_concat(
      categorical_features(
        array('name', 'gender'),
	name, gender
      ),
      quantitative_features(
	array('age'),
	age
      )
    ) AS features
  FROM
    users
),
bins AS (
  SELECT
    map('age', build_bins(age, 3)) AS quantiles_map
  FROM
    users
)
SELECT
  feature_binning(features, quantiles_map) AS features
FROM
  t CROSS JOIN bins;
```

*Result*

| features: `array<features::string>` |
| :-: |
| ["name#Jacob","gender#Male","age:1"] |
| ["name#Mason","gender#Male","age:1"] |
| ["name#Sophia","gender#Female","age:2"] |
| ["name#Ethan","gender#Male","age:2"] |
| ["name#Emma","gender#Female","age:0"] |
| ["name#Noah","gender#Male","age:2"] |
| ["name#Isabella","gender#Female","age:1"] |


## B. Get a mapping table by Feature Binning

```sql
WITH bins AS (
  SELECT build_bins(age, 3) AS quantiles
  FROM users
)
SELECT
  age, feature_binning(age, quantiles) AS bin
FROM
  users CROSS JOIN bins;
```

*Result*

| age:` int` | bin: `int` |
|:-:|:-:|
| 20 | 1 |
| 22 | 1 |
| 35 | 2 |
| 55 | 2 |
| 15 | 0 |
| 46 | 2 |
| 20 | 1 |

# Function Signature

## [UDAF] `build_bins(weight, num_of_bins[, auto_shrink])`

### Input

| weight: int&#124;bigint&#124;float&#124;double | num\_of\_bins: `int` | [auto\_shrink: `boolean` = false] |
| :-: | :-: | :-: |
| weight | 2 <= | behavior when separations are repeated: T=\>skip, F=\>exception |

### Output

| quantiles: `array<double>` |
| :-: |
| array of separation value |

> #### Note
> There is the possibility quantiles are repeated because of too many `num_of_bins` or too few data.
> If `auto_shrink` is true, skip duplicated quantiles. If not, throw an exception.

## [UDF] `feature_binning(features, quantiles_map)/(weight, quantiles)`

### Variation: A

#### Input 

| features: `array<features::string>` | quantiles\_map: `map<string, array<double>>` |
| :-: | :-: |
| serialized feature | entry:: key: col name, val: quantiles |

#### Output

| features: `array<feature::string>` |
| :-: |
| serialized and binned features |

### Variation: B

#### Input

| weight: int&#124;bigint&#124;float&#124;double | quantiles: `array<double>` |
| :-: | :-: |
| weight | array of separation value |

#### Output

| bin: `int` |
| :-: |
| categorical value (bin ID) |