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API Docs - v2.0.5

Tested Siddhi Core version: 5.1.21

It could also support other Siddhi Core minor versions.

Streamingml

bayesianRegression (Stream Processor)

This extension predicts using a Bayesian linear regression model.Bayesian linear regression allows determining the uncertainty of each prediction by estimating the full-predictive distribution

Syntax

streamingml:bayesianRegression(<STRING> model.name, <DOUBLE|FLOAT|INT|LONG> model.feature, <DOUBLE|FLOAT|INT|LONG> ...)
streamingml:bayesianRegression(<STRING> model.name, <INT> prediction.samples, <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 used		STRING	No	No
prediction.samples	The number of samples to be drawn to estimate the prediction	1000	INT	Yes	No
model.feature	The 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
prediction	The predicted value (double)	DOUBLE
confidence	Inverse of the standard deviation of the predictive distribution	DOUBLE

Examples EXAMPLE 1

define stream StreamA (attribute_0 double, attribute_1 double, attribute_2 double, attribute_3 double);

from StreamA#streamingml:bayesianRegression('model1', attribute_0, attribute_1, attribute_2, attribute_3) 
insert all events into OutputStream;

This query uses a Bayesian regression model named model1 to predict the label of the feature vector represented by attribute_0, attribute_1, attribute_2, and attribute_3. The predicted value is emitted to the OutputStream streamalong with the prediction confidence (std of predictive distribution) and the feature vector. As a result, the OutputStream stream is defined as follows: (attribute_0 double, attribute_1 double, attribute_2 double, attribute_3 double, prediction double, confidence double).

kMeansIncremental (Stream Processor)

Performs K-Means clustering on a streaming data set. Data points can be of any dimension and the dimensionality is calculated from number of parameters. All data points to be processed by a query should be of the same dimensionality. The Euclidean distance is taken as the distance metric. The algorithm resembles Sequential K-Means Clustering at https://www.cs.princeton.edu/courses/archive/fall08/cos436/Duda/C/sk_means.htm

Syntax

streamingml:kMeansIncremental(<INT> no.of.clusters, <DOUBLE|FLOAT|INT|LONG> model.feature, <DOUBLE|FLOAT|INT|LONG> ...)
streamingml:kMeansIncremental(<INT> no.of.clusters, <DOUBLE> decay.rate, <DOUBLE|FLOAT|INT|LONG> model.feature, <DOUBLE|FLOAT|INT|LONG> ...)

QUERY PARAMETERS

Name	Description	Default Value	Possible Data Types	Optional	Dynamic
no.of.clusters	The assumed number of natural clusters in the data set.		INT	No	No
decay.rate	this is the decay rate of old data compared to new data. Value of this will be in [0,1]. 0 means only old data used and1 will mean that only new data is used	0.01	DOUBLE	Yes	No
model.feature	This is a variable length argument. Depending on the dimensionality of data points we will receive coordinates as features along each axis.		DOUBLE FLOAT INT LONG	No	Yes

Extra Return Attributes

Name	Description	Possible Types
euclideanDistanceToClosestCentroid	Represents the Euclidean distance between the current data point and the closest centroid.	DOUBLE
closestCentroidCoordinate	This is a variable length attribute. Depending on the dimensionality(D) we will return closestCentroidCoordinate1, closestCentroidCoordinate2,... closestCentroidCoordinateD which are the d dimensional coordinates of the closest centroid from the model to the current event. This is the prediction result and this represents the cluster to which the current event belongs to.	DOUBLE

Examples EXAMPLE 1

define stream InputStream (x double, y double);
@info(name = 'query1')
from InputStream#streamingml:kMeansIncremental(2, 0.2, x, y)
select closestCentroidCoordinate1, closestCentroidCoordinate2, x, y
insert into OutputStream;

This is an example where user provides the decay rate. First two events will be used to initiate the model since the required number of clusters is specified as 2. After the first event itself prediction would start.

EXAMPLE 2

define stream InputStream (x double, y double);
@info(name = 'query1')
from InputStream#streamingml:kMeansIncremental(2, x, y)
select closestCentroidCoordinate1, closestCentroidCoordinate2, x, y
insert into OutputStream;

This is an example where user doesnt give the decay rate so the default value will be used

kMeansMiniBatch (Stream Processor)

Performs K-Means clustering on a streaming data set. Data points can be of any dimension and the dimensionality is calculated from number of parameters. All data points to be processed in a single query should be of the same dimensionality. The Euclidean distance is taken as the distance metric. The algorithm resembles mini-batch K-Means. (refer Web-Scale K-Means Clustering by D.Sculley, Google, Inc.).

Syntax

streamingml:kMeansMiniBatch(<INT> no.of.clusters, <DOUBLE|FLOAT|INT|LONG> model.feature, <DOUBLE|FLOAT|INT|LONG> ...)
streamingml:kMeansMiniBatch(<INT> no.of.clusters, <DOUBLE> decay.rate, <DOUBLE|FLOAT|INT|LONG> model.feature, <DOUBLE|FLOAT|INT|LONG> ...)
streamingml:kMeansMiniBatch(<INT> no.of.clusters, <INT> maximum.iterations, <DOUBLE|FLOAT|INT|LONG> model.feature, <DOUBLE|FLOAT|INT|LONG> ...)
streamingml:kMeansMiniBatch(<INT> no.of.clusters, <INT> no.of.events.to.retrain, <DOUBLE|FLOAT|INT|LONG> model.feature, <DOUBLE|FLOAT|INT|LONG> ...)
streamingml:kMeansMiniBatch(<INT> no.of.clusters, <DOUBLE> decay.rate, <INT> maximum.iterations, <DOUBLE|FLOAT|INT|LONG> model.feature, <DOUBLE|FLOAT|INT|LONG> ...)
streamingml:kMeansMiniBatch(<INT> no.of.clusters, <DOUBLE> decay.rate, <INT> no.of.events.to.retrain, <DOUBLE|FLOAT|INT|LONG> model.feature, <DOUBLE|FLOAT|INT|LONG> ...)
streamingml:kMeansMiniBatch(<INT> no.of.clusters, <INT> maximum.iterations, <INT> no.of.events.to.retrain, <DOUBLE|FLOAT|INT|LONG> model.feature, <DOUBLE|FLOAT|INT|LONG> ...)
streamingml:kMeansMiniBatch(<INT> no.of.clusters, <DOUBLE> decay.rate, <INT> maximum.iterations, <INT> no.of.events.to.retrain, <DOUBLE|FLOAT|INT|LONG> model.feature, <DOUBLE|FLOAT|INT|LONG> ...)

QUERY PARAMETERS

Name	Description	Default Value	Possible Data Types	Optional	Dynamic
no.of.clusters	The assumed number of natural clusters in the data set.		INT	No	No
decay.rate	this is the decay rate of old data compared to new data. Value of this will be in [0,1]. 0 means only old data used and1 will mean that only new data is used	0.01	DOUBLE	Yes	No
maximum.iterations	Number of iterations, the process iterates until the number of maximum iterations is reached or the centroids do not change	50	INT	Yes	No
no.of.events.to.retrain	number of events to recalculate cluster centers.	20	INT	Yes	No
model.feature	This is a variable length argument. Depending on the dimensionality of data points we will receive coordinates as features along each axis.		DOUBLE FLOAT INT LONG	No	Yes

Extra Return Attributes

Name	Description	Possible Types
euclideanDistanceToClosestCentroid	Represents the Euclidean distance between the current data point and the closest centroid.	DOUBLE
closestCentroidCoordinate	This is a variable length attribute. Depending on the dimensionality(d) we will return closestCentroidCoordinate1 to closestCentroidCoordinated which are the d dimensional coordinates of the closest centroid from the model to the current event. This is the prediction result and this represents the cluster towhich the current event belongs to.	DOUBLE

Examples EXAMPLE 1

define stream InputStream (x double, y double);
@info(name = 'query1')
from InputStream#streamingml:kMeansMiniBatch(2, 0.2, 10, 20, x, y)
select closestCentroidCoordinate1, closestCentroidCoordinate2, x, y
insert into OutputStream;

This is an example where user gives all three hyper parameters. first 20 events will be consumed to build the model and from the 21st event prediction would start

EXAMPLE 2

define stream InputStream (x double, y double);
@info(name = 'query1')
from InputStream#streamingml:kMeansMiniBatch(2, x, y)
select closestCentroidCoordinate1, closestCentroidCoordinate2, x, y
insert into OutputStream;

This is an example where user has not specified hyper params. So default values will be used.

perceptronClassifier (Stream Processor)

This extension predicts using a linear binary classification Perceptron model.

Syntax

streamingml:perceptronClassifier(<STRING> model.name, <DOUBLE|FLOAT|INT|LONG> model.feature, <DOUBLE|FLOAT|INT|LONG> ...)
streamingml:perceptronClassifier(<STRING> model.name, <DOUBLE> model.bias, <DOUBLE|FLOAT|INT|LONG> model.feature, <DOUBLE|FLOAT|INT|LONG> ...)
streamingml:perceptronClassifier(<STRING> model.name, <DOUBLE> model.threshold, <DOUBLE|FLOAT|INT|LONG> model.feature, <DOUBLE|FLOAT|INT|LONG> ...)
streamingml:perceptronClassifier(<STRING> model.name, <DOUBLE> model.bias, <DOUBLE> model.threshold, <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 used.		STRING	No	No
model.bias	The bias of the Perceptron algorithm.	0.0	DOUBLE	Yes	No
model.threshold	The threshold that separates the two classes. The value specified must be between zero and one.	0.5	DOUBLE	Yes	No
model.feature	The 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
prediction	The predicted value (`true/false`)	BOOL
confidenceLevel	The probability of the prediction	DOUBLE

Examples EXAMPLE 1

define stream StreamA (attribute_0 double, attribute_1 double, attribute_2 double, attribute_3 double);

from StreamA#streamingml:perceptronClassifier('model1',0.0,0.5, attribute_0, attribute_1, attribute_2, attribute_3) 
insert all events into OutputStream;

This query uses a Perceptron model named model1 with a 0.0 bias and a 0.5 threshold learning rate to predict the label of the feature vector represented by attribute_0, attribute_1, attribute_2, and attribute_3. The predicted label (true/false) is emitted to the OutputStream streamalong with the prediction confidence level(probability) and the feature vector. As a result, the OutputStream stream is defined as follows: (attribute_0 double, attribute_1 double, attribute_2 double, attribute_3 double, prediction bool, confidenceLevel double).

EXAMPLE 2

define stream StreamA (attribute_0 double, attribute_1 double, attribute_2 double, attribute_3 double);

from StreamA#streamingml:perceptronClassifier('model1',0.0, attribute_0, attribute_1, attribute_2, attribute_3) 
insert all events into OutputStream;

This query uses a Perceptron model named model1 with a 0.0 bias to predict the label of the feature vector represented by attribute_0, attribute_1, attribute_2, and attribute_3. The prediction(true/false) is emitted to the OutputStreamstream along with the prediction confidence level(probability) and the feature. As a result, the OutputStream stream is defined as follows: (attribute_0 double, attribute_1 double, attribute_2 double, attribute_3 double, prediction bool, confidenceLevel double).

EXAMPLE 3

define stream StreamA (attribute_0 double, attribute_1 double, attribute_2 double, attribute_3 double);

from StreamA#streamingml:perceptronClassifier(`model1`, attribute_0, attribute_1, attribute_2) 
insert all events into OutputStream;

This query uses a Perceptron model named model1 with a default 0.0 bias to predict the label of the feature vector represented by attribute_0, attribute_1, and attribute_2. The predicted probability is emitted to the OutputStream stream along with the feature vector. As a result, the OutputStream is defined as follows: (attribute_0 double, attribute_1 double, attribute_2 double, attribute_3 double, prediction bool, confidenceLevel double).

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

Examples EXAMPLE 1

define stream StreamA (attribute_0 double, attribute_1 double, attribute_2 double, attribute_3 double, attribute_4 double );

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

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

define stream StreamA (attribute_0 double, attribute_1 double, attribute_2 double, attribute_3 double, attribute_4 double );

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

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.

updatePerceptronClassifier (Stream Processor)

This extension builds/updates a linear binary classification Perceptron model.

Syntax

streamingml:updatePerceptronClassifier(<STRING> model.name, <BOOL|STRING> model.label, <DOUBLE|FLOAT|INT|LONG> model.feature, <DOUBLE|FLOAT|INT|LONG> ...)
streamingml:updatePerceptronClassifier(<STRING> model.name, <BOOL|STRING> model.label, <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/updated.		STRING	No	No
model.label	The attribute of the label or the class of the dataset.		BOOL STRING	No	Yes
learning.rate	The learning rate of the Perceptron algorithm.	0.1	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
featureWeight	Weight of the <feature.name> of the model.	DOUBLE

Examples EXAMPLE 1

define stream StreamA (attribute_0 double, attribute_1 double, attribute_2 double, attribute_3 double, attribute_4 string );

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

This query builds/updates a Perceptron 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.

EXAMPLE 2

define stream StreamA (attribute_0 double, attribute_1 double, attribute_2 double,attribute_3 double, attribute_4 string );

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

This query builds/updates a Perceptron model named model1 with a default 0.1 learning rate using attribute_0, attribute_1, attribute_2, and attribute_3 as features, and attribute_4 as the label. The updated weights of the model are appended to the outputStream.