Machine learning functions

The machine learning plugin provides machine learning functionality as an aggregation function. It enables you to train Support Vector Machine (SVM) based classifiers and regressors for the supervised learning problems.

Note: The machine learning functions are not optimized for distributed processing.The capability to train large data sets is limited by the execution of the final training on a single instance.

Feature vector

features()

Function	Description	Return Type
features(values...)	Constructs a feature vector from the given numerical values. Each value corresponds to a feature at the respective index in the map.	map

Example:

SELECT features(1.0, 2.0, 3.0) AS features;
    ---features
-----------------------
 {0=1.0, 1=2.0, 2=3.0}

Classification

The function to train a classification model looks like as follows:

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

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

The trained model can not be saved natively, and needs to be passed in the format of a nested query:

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

#output:
 predicted_label
-----------------
 Iris-virginica

Regression

The following code shows the creation of the model predicting sepal_length from the other 3 features:

SELECT
  learn_regressor(sepal_length, features(sepal_width, petal_length, petal_width)) AS model
FROM
  iris

#or

SELECT
  regress(features(3, 5.1, 1.8), model) AS predicted_target
FROM (
  SELECT
    learn_regressor(sepal_length, features(sepal_width, petal_length, petal_width)) AS model
  FROM iris
) t;
#output:
 predicted_target
-------------------
 6.407376822560477

Machine learning functions

features()

Function	Description	Return Type
features(double, ...)	Constructs a feature vector as a map of indices to values.	feature vector (map)

learn_classifier()

Function	Description	Return Type
learn_classifier(label, features)	Trains an SVM-based classifier model with the given label and feature datasets.	Classifier

learn_libsvm_classifier()

Function	Description	Return Type
learn_libsvm_classifier(label, features, params)	Trains an SVM-based classifier model with the given label and feature datasets, controlling the training process by libsvm parameters.	Classifier

classify()

Function	Description	Return Type
classify(features, model)	Predicts a label using the given classifier SVM model.	label

learn_regressor()

Function	Description	Return Type
learn_regressor(target, features)	Trains an SVM-based regressor model with the given target and feature datasets.	Regressor

leearn_libsvm_regressor()

Function	Description	Return Type
learn_libsvm_regressor(target, features, params)	Trains an SVM-based regressor model with the given target and feature datasets, controlling the training process by libsvm parameters.	Regressor

regress()

Function	Description	Return Type
regress(features, model)	Predicts a target value using the given regressor SVM model.	target