updateBayesianRegression (Stream Processor)

This extension builds/updates a linear Bayesian regression model. This extension uses an improved version of stochastic variational inference.

Syntax

streamingml:updateBayesianRegression(<STRING> model.name, <INT|DOUBLE|LONG|FLOAT> model.target, <DOUBLE|FLOAT|INT|LONG> model.feature, <DOUBLE|FLOAT|INT|LONG> ...)
streamingml:updateBayesianRegression(<STRING> model.name, <INT|DOUBLE|LONG|FLOAT> model.target, <INT> model.samples, <DOUBLE|FLOAT|INT|LONG> model.feature, <DOUBLE|FLOAT|INT|LONG> ...)
streamingml:updateBayesianRegression(<STRING> model.name, <INT|DOUBLE|LONG|FLOAT> model.target, <STRING> model.optimizer, <DOUBLE|FLOAT|INT|LONG> model.feature, <DOUBLE|FLOAT|INT|LONG> ...)
streamingml:updateBayesianRegression(<STRING> model.name, <INT|DOUBLE|LONG|FLOAT> model.target, <DOUBLE> learning.rate, <DOUBLE|FLOAT|INT|LONG> model.feature, <DOUBLE|FLOAT|INT|LONG> ...)
streamingml:updateBayesianRegression(<STRING> model.name, <INT|DOUBLE|LONG|FLOAT> model.target, <INT> model.samples, <STRING> model.optimizer, <DOUBLE|FLOAT|INT|LONG> model.feature, <DOUBLE|FLOAT|INT|LONG> ...)
streamingml:updateBayesianRegression(<STRING> model.name, <INT|DOUBLE|LONG|FLOAT> model.target, <INT> model.samples, <DOUBLE> learning.rate, <DOUBLE|FLOAT|INT|LONG> model.feature, <DOUBLE|FLOAT|INT|LONG> ...)
streamingml:updateBayesianRegression(<STRING> model.name, <INT|DOUBLE|LONG|FLOAT> model.target, <STRING> model.optimizer, <DOUBLE> learning.rate, <DOUBLE|FLOAT|INT|LONG> model.feature, <DOUBLE|FLOAT|INT|LONG> ...)
streamingml:updateBayesianRegression(<STRING> model.name, <INT|DOUBLE|LONG|FLOAT> model.target, <INT> model.samples, <STRING> model.optimizer, <DOUBLE> learning.rate, <DOUBLE|FLOAT|INT|LONG> model.feature, <DOUBLE|FLOAT|INT|LONG> ...)

Query Parameters

Name	Description	Default Value	Possible Data Types	Optional	Dynamic
model.name	The name of the model to be built.		STRING	No	No
model.target	The target attribute (dependant variable) of the input stream.		INT DOUBLE LONG FLOAT	No	Yes
model.samples	Number of samples used to construct the gradients.	1	INT	Yes	No
model.optimizer	The type of optimization used	ADAM	STRING	Yes	No
learning.rate	The learning rate of the updater	0.05	DOUBLE	Yes	No
model.feature	Features of the model that need to be attributes of the stream.		DOUBLE FLOAT INT LONG	No	Yes

Extra Return Attributes

Name	Description	Possible Types
loss	Loss of the model.	DOUBLE

Example 1

CREATE STREAM StreamA (attribute_0 double, attribute_1 double, attribute_2 double, attribute_3 double, attribute_4 double );

insert all events into outputStream
from StreamA#streamingml:updateBayesianRegression('model1', attribute_4, attribute_0, attribute_1, attribute_2, attribute_3);

This query builds/updates a Bayesian Linear regression model named model1 using attribute_0, attribute_1, attribute_2, and attribute_3 as features, and attribute_4 as the label. Updated weights of the model are emitted to the OutputStream stream.

Example 2

CREATE STREAM StreamA (attribute_0 double, attribute_1 double, attribute_2 double, attribute_3 double, attribute_4 double );

insert all events into outputStream
from StreamA#streamingml:updateBayesianRegression('model1', attribute_4, 2, 'NADAM', 0.01, attribute_0, attribute_1, attribute_2, attribute_3);

This query builds/updates a Bayesian Linear regression model named model1 with a 0.01 learning rate using attribute_0, attribute_1, attribute_2, and attribute_3 as features, and attribute_4 as the label. Updated weights of the model are emitted to the OutputStream stream. This model draws two samples during monte-carlo integration and uses NADAM optimizer.