Design SQLFlow syntax extension for data transform.

[brightcoder01]

The root of the discussion series is #1670

The following transform functions are common used. We can support these in the first stage.

Name	Transformation	Statitical Parameter	Input Type	Output Type
NORMALIZE(x)	Scale the inputs to the range [0, 1]. out = x - x_min / (x_max - x_min)	x_min, x_max	number	float64
STANDARDIZE(x)	Scale the inputs to z-score subtracts out the mean and divides by standard deviation. out = x - x_mean / x_stddev	x_mean, x_stddev	number	float64
BUCKETIZE(x, num_buckets, boundaries)	Transform the numeric features into categorical ids using a set of thresholds.	boundaries	Number	int64
HASH_BUCKET(x, hash_bucket_size)	Map the inputs into a finite number of buckets by hashing. out_id = Hash(input_feature) % bucket_size	hash_bucket_size	string, int32, int64	int64
VOCABULARIZE(x)	Map the inputs to integer ids by looking up the vocabulary	vocabulary_list	string, int32, int64	int64
EMBEDDING(x, dimension)	Map the inputs to embedding vectors	N/A	int32, int64	float32
CROSS(x1, x2, ..., xn, hash_bucket_size)	Hash(cartesian product of features) % hash_bucket_size	N/A	string, number	int64
CONCAT(x1, x2, ..., xn)	Concatenate multiple tensors representing categorical ids into one tensor.	N/A	int32, int64	int64

There are three options for the style of the generated transform code:

1. Feature Column API. Integrate it with model definition using tf.keras.layers.DenseFeatures;
2. Customized Keras Layer provided from ElasticDL. The functionality should cover all the common used feature engineering operations above;
3. Keras Preprocess Layer. This will be ready in TF2.2;

[brightcoder01]

Let's take the simple DNN model of the census income dataset from ElasticDL model zoo.
The SQLFlow expression proposal is

SELECT *
FROM census_income
TO TRAIN DNNClassifier
WITH model.hidden_units = [10, 20]
COLUMN (
    age, 
    capital_gain, 
    capital_loss, 
    hours_per_week, 
    EMBEDDING(HASH(workclass, 64), 16),
    EMBEDDING(HASH(education, 64), 16),
    EMBEDDING(HASH(martial_status, 64), 16),
    EMBEDDING(HASH(occupation, 64), 16),
    EMBEDDING(HASH(relationship, 64), 16),
    EMBEDDING(HASH(race, 64), 16),
    EMBEDDING(HASH(sex, 64), 16),
    EMBEDDING(HASH(native_country, 64), 16)
)
LABEL label

[brightcoder01]

Let's take the wide and deep model of the census income dataset in the PR #1671 for example.
The SQLFlow expression proposal is

SELECT *
FROM census_income
TO TRAIN WideAndDeepClassifier
COLUMN (
    SET GROUP(APPLY_VOCAB(workclass), BUCKETIZE(capital_gain, bucket_num=5), BUCKETIZE(capital_loss, bucket_num=5), BUCKTIZE(hours_per_week, bucket_num=6)) AS group_1,
    SET GROUP(HASH(education), HASH(occupation), APPLY_VOCAB(martial_status), APPLY_VOCAB(relationship)) AS group_2,
    SET GROUP(BUCKETIZE(age, bucket_num=5), HASH(native_country), APPLY_VOCAB(race), APPLY_VOCAB(sex)) AS group_3,

    [EMBEDDING(group1, 1), EMBEDDING(group2, 1)] AS wide_embeddings
    [EMBEDDING(group1, 8), EMBEDDING(group2, 8), EMBEDDING(group3, 8)] AS deep_embeddings
)
LABEL label

[workingloong]

SELECT *
FROM census_income
TO TRAIN WideAndDeepClassifier
COLUMNS (
SET GROUP(APPLY_VOCAB(workclass), BUCKETIZE(capital_gain, bucket_num=5), BUCKETIZE(capital_loss, bucket_num=5), BUCKTIZE(hours_per_week, bucket_num=6)) AS group_1,
SET GROUP(HASH(education), HASH(occupation), APPLY_VOCAB(martial_status), APPLY_VOCAB(relationship)) AS group_2,
SET GROUP(BUCKETIZE(age, bucket_num=5), HASH(native_country), APPLY_VOCAB(race), APPLY_VOCAB(sex)) AS group_3,

[EMBEDDING(group1, 1), EMBEDDING(group2, 1)] AS wide
[EMBEDDING(group1, 8), EMBEDDING(group2, 8), EMBEDDING(group3, 8)] AS deep
)
LABEL label

Maybe a nested expression?

SELECT *
FROM census_income
TO TRAIN WideAndDeepClassifier
COLUMNS (
    [EMBEDDING(group1, 1), EMBEDDING(group2, 1)] AS wide
    [EMBEDDING(group1, 8), EMBEDDING(group2, 8), EMBEDDING(group3, 8)] AS deep
    FROM(
        GROUP(APPLY_VOCAB(workclass), BUCKETIZE(capital_gain, bucket_num=5), BUCKETIZE(capital_loss, bucket_num=5), BUCKTIZE(hours_per_week, bucket_num=6)) AS group_1,
        GROUP(HASH(education), HASH(occupation), APPLY_VOCAB(martial_status), APPLY_VOCAB(relationship)) AS group_2,
        GROUP(BUCKETIZE(age, bucket_num=5), HASH(native_country), APPLY_VOCAB(race), APPLY_VOCAB(sex)) AS group_3,
    )
)
LABEL label

[workingloong]

We can implement LOG_ROUND using BUCKETIZE(x, bucket_boundaries), so it can be removed. And why do we use HASH, not HASH_BUCKET? HASH may be confusing.

[brightcoder01]

Let's take the wide and deep model of the census income dataset in the PR #1671 for example.
The SQLFlow expression proposal is

SELECT *
FROM census_income
TO TRAIN WideAndDeepClassifier
COLUMN (
    SET GROUP(APPLY_VOCAB(workclass), BUCKETIZE(capital_gain, bucket_num=5), BUCKETIZE(capital_loss, bucket_num=5), BUCKTIZE(hours_per_week, bucket_num=6)) AS group_1,
    SET GROUP(HASH(education), HASH(occupation), APPLY_VOCAB(martial_status), APPLY_VOCAB(relationship)) AS group_2,
    SET GROUP(BUCKETIZE(age, bucket_num=5), HASH(native_country), APPLY_VOCAB(race), APPLY_VOCAB(sex)) AS group_3,

    [EMBEDDING(group1, 1), EMBEDDING(group2, 1)] AS wide_embeddings
    [EMBEDDING(group1, 8), EMBEDDING(group2, 8), EMBEDDING(group3, 8)] AS deep_embeddings
)
LABEL label

After discussion in the link, we will choose the following syntax design:

SELECT *
FROM census_income
TO TRAIN WideAndDeepClassifier
COLUMN
    EMBEDDING(CONCAT(APPLY_VOCAB(workclass), BUCKETIZE(capital_gain, bucket_num=5), BUCKETIZE(capital_loss, bucket_num=5), BUCKTIZE(hours_per_week, bucket_num=6)) AS group_1, 8),
    EMBEDDING(CONCAT(HASH(education), HASH(occupation), APPLY_VOCAB(martial_status), APPLY_VOCAB(relationship)) AS group_2, 8),
    EMBEDDING(CONCAT(BUCKETIZE(age, bucket_num=5), HASH(native_country), APPLY_VOCAB(race), APPLY_VOCAB(sex)) AS group_3, 8)
    FOR deep_embeddings
COLUMN
    EMBEDDING(group1, 1),
    EMBEDDING(group2, 1)
    FOR wide_embeddings
LABEL label