updateBayesianRegression (Stream Processor)
This function builds or updates a linear Bayesian regression model using an improved version of stochastic variational inference. Bayesian regression models allow you to quantify the uncertainty in the model's predictions, providing a more comprehensive understanding of the data.
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);
@info(name = 'updateBayesianRegressionQuery')
INSERT ALL EVENTS INTO OutputStream
FROM StreamA#streamingml:updateBayesianRegression('model1', attribute_4, attribute_0, attribute_1, attribute_2, attribute_3);
This query builds or 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);
@info(name = 'updateBayesianRegressionQuery')
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 or 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. This model draws two samples during Monte Carlo integration and uses the NADAM optimizer. Updated weights of the model are emitted to the OutputStream stream.
Example 3
CREATE STREAM StreamA (attribute_0 double, attribute_1 double, attribute_2 double, attribute_3 double, attribute_4 double, attribute_5 double);
@info(name = 'updateBayesianRegressionQuery')
INSERT ALL EVENTS INTO OutputStream
FROM StreamA#streamingml:updateBayesianRegression('model1', attribute_5, 3, 'RMSPROP', 0.01, attribute_0, attribute_1, attribute_2, attribute_3, attribute_4);
This query builds or updates a Bayesian Linear regression model named model1 with a 0.01 learning rate, using attribute_0, attribute_1, attribute_2, attribute_3, and attribute_4 as features, and attribute_5 as the label. The model uses the RMSPROP optimizer, draws three samples during Monte Carlo integration, and emits the updated weights of the model to the OutputStream stream.