API Docs - v1.0.10
Streamingml
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> decay.rate, <DOUBLE|FLOAT|INT|LONG> model.features)
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.features | 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 | No |
| 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> decay.rate, <INT> maximum.iterations, <INT> no.of.events.to.retrain, <DOUBLE|FLOAT|INT|LONG> model.features)
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.features | 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 | No |
| 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> model.bias, <DOUBLE> model.threshold, <DOUBLE> model.features)
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 | No | No |
| model.threshold | The threshold that separates the two classes. The value specified must be between zero and one. | The output is a probability. | DOUBLE | No | No |
| model.features | The features of the model that need to be attributes of the stream. | DOUBLE | No | No |
| 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).
updatePerceptronClassifier (Stream Processor)
This extension builds/updates a linear binary classification Perceptron model.
Syntax
streamingml:updatePerceptronClassifier(<STRING> model.name, <BOOL|STRING> model.label, <DOUBLE> learning.rate, <DOUBLE|INT> model.features)
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 | No | |
| learning.rate | The learning rate of the Perceptron algorithm. | 0.1 | DOUBLE | Yes | No |
| model.features | Features of the model that need to be attributes of the stream. | DOUBLE INT |
No | No |
| 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.