Machine Learning Connector in Presto

This is quick tutorial for presto-ml connector. The connector isn't maintenanced actively and the supported model is only SVM.

You can see below sample query in the test directory. As the same as Teradata Aster and BigQuery ML, there're two kinds of functions.

learn_classifier receives training data and generates the model
classify receives the model & test data and returns the prediction

SELECT classify(features(1, 2), model)
FROM (
SELECT learn_classifier(labels, features) AS model
FROM (
VALUES (1, features(1, 2))) t(labels, features)
) t2
-- 1

SELECT classify(features(1, 2), model)
FROM (
SELECT learn_classifier(labels, features) AS model
FROM (
VALUES ('cat', features(1, 2))) t(labels, features)
) t2
-- 'cat'

Let's try using Iris data sets.

CREATE TABLE iris(
 id int
,sepal_length double
,sepal_width double
,petal_length double
,petal_width double
,species varchar
);

INSERT INTO iris VALUES
(1, 5.1, 3.5, 1.4, 0.2, 'Iris-setosa'),
(2, 4.9, 3, 1.4, 0.2, 'Iris-setosa'),
(3, 4.7, 3.2, 1.3, 0.2, 'Iris-setosa'),
(4, 4.6, 3.1, 1.5, 0.2, 'Iris-setosa'),
(5, 5, 3.6, 1.4, 0.2, 'Iris-setosa'),
(6, 5.4, 3.9, 1.7, 0.4, 'Iris-setosa'),
(7, 4.6, 3.4, 1.4, 0.3, 'Iris-setosa'),
(8, 5, 3.4, 1.5, 0.2, 'Iris-setosa'),
(9, 4.4, 2.9, 1.4, 0.2, 'Iris-setosa'),
(10, 4.9, 3.1, 1.5, 0.1, 'Iris-setosa'),
(11, 5.4, 3.7, 1.5, 0.2, 'Iris-setosa'),
(12, 4.8, 3.4, 1.6, 0.2, 'Iris-setosa'),
(13, 4.8, 3, 1.4, 0.1, 'Iris-setosa'),
(14, 4.3, 3, 1.1, 0.1, 'Iris-setosa'),
(15, 5.8, 4, 1.2, 0.2, 'Iris-setosa'),
(16, 5.7, 4.4, 1.5, 0.4, 'Iris-setosa'),
(17, 5.4, 3.9, 1.3, 0.4, 'Iris-setosa'),
(18, 5.1, 3.5, 1.4, 0.3, 'Iris-setosa'),
(19, 5.7, 3.8, 1.7, 0.3, 'Iris-setosa'),
(20, 5.1, 3.8, 1.5, 0.3, 'Iris-setosa'),
(21, 5.4, 3.4, 1.7, 0.2, 'Iris-setosa'),
(22, 5.1, 3.7, 1.5, 0.4, 'Iris-setosa'),
(23, 4.6, 3.6, 1, 0.2, 'Iris-setosa'),
(24, 5.1, 3.3, 1.7, 0.5, 'Iris-setosa'),
(25, 4.8, 3.4, 1.9, 0.2, 'Iris-setosa'),
(26, 5, 3, 1.6, 0.2, 'Iris-setosa'),
(27, 5, 3.4, 1.6, 0.4, 'Iris-setosa'),
(28, 5.2, 3.5, 1.5, 0.2, 'Iris-setosa'),
(29, 5.2, 3.4, 1.4, 0.2, 'Iris-setosa'),
(30, 4.7, 3.2, 1.6, 0.2, 'Iris-setosa'),
(31, 4.8, 3.1, 1.6, 0.2, 'Iris-setosa'),
(32, 5.4, 3.4, 1.5, 0.4, 'Iris-setosa'),
(33, 5.2, 4.1, 1.5, 0.1, 'Iris-setosa'),
(34, 5.5, 4.2, 1.4, 0.2, 'Iris-setosa'),
(35, 4.9, 3.1, 1.5, 0.1, 'Iris-setosa'),
(36, 5, 3.2, 1.2, 0.2, 'Iris-setosa'),
(37, 5.5, 3.5, 1.3, 0.2, 'Iris-setosa'),
(38, 4.9, 3.1, 1.5, 0.1, 'Iris-setosa'),
(39, 4.4, 3, 1.3, 0.2, 'Iris-setosa'),
(40, 5.1, 3.4, 1.5, 0.2, 'Iris-setosa'),
(41, 5, 3.5, 1.3, 0.3, 'Iris-setosa'),
(42, 4.5, 2.3, 1.3, 0.3, 'Iris-setosa'),
(43, 4.4, 3.2, 1.3, 0.2, 'Iris-setosa'),
(44, 5, 3.5, 1.6, 0.6, 'Iris-setosa'),
(45, 5.1, 3.8, 1.9, 0.4, 'Iris-setosa'),
(46, 4.8, 3, 1.4, 0.3, 'Iris-setosa'),
(47, 5.1, 3.8, 1.6, 0.2, 'Iris-setosa'),
(48, 4.6, 3.2, 1.4, 0.2, 'Iris-setosa'),
(49, 5.3, 3.7, 1.5, 0.2, 'Iris-setosa'),
(50, 5, 3.3, 1.4, 0.2, 'Iris-setosa'),
(51, 7, 3.2, 4.7, 1.4, 'Iris-versicolor'),
(52, 6.4, 3.2, 4.5, 1.5, 'Iris-versicolor'),
(53, 6.9, 3.1, 4.9, 1.5, 'Iris-versicolor'),
(54, 5.5, 2.3, 4, 1.3, 'Iris-versicolor'),
(55, 6.5, 2.8, 4.6, 1.5, 'Iris-versicolor'),
(56, 5.7, 2.8, 4.5, 1.3, 'Iris-versicolor'),
(57, 6.3, 3.3, 4.7, 1.6, 'Iris-versicolor'),
(58, 4.9, 2.4, 3.3, 1, 'Iris-versicolor'),
(59, 6.6, 2.9, 4.6, 1.3, 'Iris-versicolor'),
(60, 5.2, 2.7, 3.9, 1.4, 'Iris-versicolor'),
(61, 5, 2, 3.5, 1, 'Iris-versicolor'),
(62, 5.9, 3, 4.2, 1.5, 'Iris-versicolor'),
(63, 6, 2.2, 4, 1, 'Iris-versicolor'),
(64, 6.1, 2.9, 4.7, 1.4, 'Iris-versicolor'),
(65, 5.6, 2.9, 3.6, 1.3, 'Iris-versicolor'),
(66, 6.7, 3.1, 4.4, 1.4, 'Iris-versicolor'),
(67, 5.6, 3, 4.5, 1.5, 'Iris-versicolor'),
(68, 5.8, 2.7, 4.1, 1, 'Iris-versicolor'),
(69, 6.2, 2.2, 4.5, 1.5, 'Iris-versicolor'),
(70, 5.6, 2.5, 3.9, 1.1, 'Iris-versicolor'),
(71, 5.9, 3.2, 4.8, 1.8, 'Iris-versicolor'),
(72, 6.1, 2.8, 4, 1.3, 'Iris-versicolor'),
(73, 6.3, 2.5, 4.9, 1.5, 'Iris-versicolor'),
(74, 6.1, 2.8, 4.7, 1.2, 'Iris-versicolor'),
(75, 6.4, 2.9, 4.3, 1.3, 'Iris-versicolor'),
(76, 6.6, 3, 4.4, 1.4, 'Iris-versicolor'),
(77, 6.8, 2.8, 4.8, 1.4, 'Iris-versicolor'),
(78, 6.7, 3, 5, 1.7, 'Iris-versicolor'),
(79, 6, 2.9, 4.5, 1.5, 'Iris-versicolor'),
(80, 5.7, 2.6, 3.5, 1, 'Iris-versicolor'),
(81, 5.5, 2.4, 3.8, 1.1, 'Iris-versicolor'),
(82, 5.5, 2.4, 3.7, 1, 'Iris-versicolor'),
(83, 5.8, 2.7, 3.9, 1.2, 'Iris-versicolor'),
(84, 6, 2.7, 5.1, 1.6, 'Iris-versicolor'),
(85, 5.4, 3, 4.5, 1.5, 'Iris-versicolor'),
(86, 6, 3.4, 4.5, 1.6, 'Iris-versicolor'),
(87, 6.7, 3.1, 4.7, 1.5, 'Iris-versicolor'),
(88, 6.3, 2.3, 4.4, 1.3, 'Iris-versicolor'),
(89, 5.6, 3, 4.1, 1.3, 'Iris-versicolor'),
(90, 5.5, 2.5, 4, 1.3, 'Iris-versicolor'),
(91, 5.5, 2.6, 4.4, 1.2, 'Iris-versicolor'),
(92, 6.1, 3, 4.6, 1.4, 'Iris-versicolor'),
(93, 5.8, 2.6, 4, 1.2, 'Iris-versicolor'),
(94, 5, 2.3, 3.3, 1, 'Iris-versicolor'),
(95, 5.6, 2.7, 4.2, 1.3, 'Iris-versicolor'),
(96, 5.7, 3, 4.2, 1.2, 'Iris-versicolor'),
(97, 5.7, 2.9, 4.2, 1.3, 'Iris-versicolor'),
(98, 6.2, 2.9, 4.3, 1.3, 'Iris-versicolor'),
(99, 5.1, 2.5, 3, 1.1, 'Iris-versicolor'),
(100, 5.7, 2.8, 4.1, 1.3, 'Iris-versicolor'),
(101, 6.3, 3.3, 6, 2.5, 'Iris-virginica'),
(102, 5.8, 2.7, 5.1, 1.9, 'Iris-virginica'),
(103, 7.1, 3, 5.9, 2.1, 'Iris-virginica'),
(104, 6.3, 2.9, 5.6, 1.8, 'Iris-virginica'),
(105, 6.5, 3, 5.8, 2.2, 'Iris-virginica'),
(106, 7.6, 3, 6.6, 2.1, 'Iris-virginica'),
(107, 4.9, 2.5, 4.5, 1.7, 'Iris-virginica'),
(108, 7.3, 2.9, 6.3, 1.8, 'Iris-virginica'),
(109, 6.7, 2.5, 5.8, 1.8, 'Iris-virginica'),
(110, 7.2, 3.6, 6.1, 2.5, 'Iris-virginica'),
(111, 6.5, 3.2, 5.1, 2, 'Iris-virginica'),
(112, 6.4, 2.7, 5.3, 1.9, 'Iris-virginica'),
(113, 6.8, 3, 5.5, 2.1, 'Iris-virginica'),
(114, 5.7, 2.5, 5, 2, 'Iris-virginica'),
(115, 5.8, 2.8, 5.1, 2.4, 'Iris-virginica'),
(116, 6.4, 3.2, 5.3, 2.3, 'Iris-virginica'),
(117, 6.5, 3, 5.5, 1.8, 'Iris-virginica'),
(118, 7.7, 3.8, 6.7, 2.2, 'Iris-virginica'),
(119, 7.7, 2.6, 6.9, 2.3, 'Iris-virginica'),
(120, 6, 2.2, 5, 1.5, 'Iris-virginica'),
(121, 6.9, 3.2, 5.7, 2.3, 'Iris-virginica'),
(122, 5.6, 2.8, 4.9, 2, 'Iris-virginica'),
(123, 7.7, 2.8, 6.7, 2, 'Iris-virginica'),
(124, 6.3, 2.7, 4.9, 1.8, 'Iris-virginica'),
(125, 6.7, 3.3, 5.7, 2.1, 'Iris-virginica'),
(126, 7.2, 3.2, 6, 1.8, 'Iris-virginica'),
(127, 6.2, 2.8, 4.8, 1.8, 'Iris-virginica'),
(128, 6.1, 3, 4.9, 1.8, 'Iris-virginica'),
(129, 6.4, 2.8, 5.6, 2.1, 'Iris-virginica'),
(130, 7.2, 3, 5.8, 1.6, 'Iris-virginica'),
(131, 7.4, 2.8, 6.1, 1.9, 'Iris-virginica'),
(132, 7.9, 3.8, 6.4, 2, 'Iris-virginica'),
(133, 6.4, 2.8, 5.6, 2.2, 'Iris-virginica'),
(134, 6.3, 2.8, 5.1, 1.5, 'Iris-virginica'),
(135, 6.1, 2.6, 5.6, 1.4, 'Iris-virginica'),
(136, 7.7, 3, 6.1, 2.3, 'Iris-virginica'),
(137, 6.3, 3.4, 5.6, 2.4, 'Iris-virginica'),
(138, 6.4, 3.1, 5.5, 1.8, 'Iris-virginica'),
(139, 6, 3, 4.8, 1.8, 'Iris-virginica'),
(140, 6.9, 3.1, 5.4, 2.1, 'Iris-virginica'),
(141, 6.7, 3.1, 5.6, 2.4, 'Iris-virginica'),
(142, 6.9, 3.1, 5.1, 2.3, 'Iris-virginica'),
(143, 5.8, 2.7, 5.1, 1.9, 'Iris-virginica'),
(144, 6.8, 3.2, 5.9, 2.3, 'Iris-virginica'),
(145, 6.7, 3.3, 5.7, 2.5, 'Iris-virginica'),
(146, 6.7, 3, 5.2, 2.3, 'Iris-virginica'),
(147, 6.3, 2.5, 5, 1.9, 'Iris-virginica'),
(148, 6.5, 3, 5.2, 2, 'Iris-virginica'),
(149, 6.2, 3.4, 5.4, 2.3, 'Iris-virginica'),
(150, 5.9, 3, 5.1, 1.8, 'Iris-virginica');

In this data sets, species is a label and sepal_length, sepal_width and so on are features.

SELECT learn_classifier(species, features(sepal_length, sepal_width, petal_length, petal_width)) AS modelFROM iris

   model 
------------------------------------------------- 
3c 43 6c 61 73 73 69 66 69 65 72 28 76 61 72 63 68 61 72 29 3e

This features function gives the number (0~9) to each arguments and returns it as map types. It returns unexpected parameters if the arguments number is over 10.

SELECT features(-100, 100);

_col0     
--------------------- 
{0=-100.0, 1=100.0}

SELECT features(1,2,3,4,5,6,7,8,9,10,11);
-- Unexpected parameters

Next, let's try to get the prediction using classify function. features(5.9, 3, 5.1, 1.8) is final rows above the insert statements. I used it for clarify. We can get expected result 'Iris-virginica', the same as inserted data.

SELECT classify(features(5.9, 3, 5.1, 1.8), model)
FROM (
SELECT
 learn_classifier(species, features(sepal_length, sepal_width, petal_length, petal_width)) AS model
FROM iris
) t

_col0
---------------
Iris-virginica

Though we want to store the machine learning model in a storage, current learn_classifier doesn't allow it. It uses original type in the connector.

CREATE TABLE learn_classifier_model AS
SELECT learn_classifier(labels, features) AS model
FROM (
VALUES ('cat', features(1, 2))) t(labels, features);
-- Unsupported type: Classifier(varchar)