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This extension facilitates you to use the machine learning models which you generate using WSO2 ML within WSO2 Complex Event Processor (CEP) for making predictions. Thereby, it integrates WSO2 ML with WSO2 CEP, to perform realtime predictions on an event stream by applying a model generated by WSO2 ML. An input event stream which is received by an event receiver of WSO2 CEP is processed by executing an execution plan within WSO2 CEP. This execution plan is written using Siddhi language. It processes the input event stream by applying the model generated using WSO2 ML. The output of this processing which includes the prediction will be published to an output stream through an event publisher of WSO2 CEP. For more information on WSO2 CEP, go to WSO2 CEP Documentation.
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Machine Learner models are not backward compatible. e.g., models generated using WSO2 ML 1.0.0 cannot be used with WSO2 ML 1.1.0 functionality. |
Anchor Siddhi syntax for the extension Siddhi syntax for the extension
Siddhi syntax for the extension
| Siddhi syntax for the extension | |
| Siddhi syntax for the extension |
There are two possible Siddhi query syntaxes to use the extension in an execution plan as follows.
<double|float|long|int|string|boolean>predict(<string>pathToMLModel, <string> dataType)- Extension Type: StreamProcessor
- Description: Returns an output event with the additional attribute with the response variable name of the model, set with the predicted value, using the feature values extracted from the input event.
- Parameter: pathToMLModel: The file path or the registry path where ML model is located. If the model storage location is registry, the value of this this parameter should have the prefix “
registry:” Parameter: dataType: Data type of the predicted value (double, float, long, integer/int, string, boolean/bool).
Parameter: percentileValue: Percentile value for the prediction. It should be a double value between 0 - 100. This parameter is only relevant when the algorithm of the model used for prediction is of the
Anomaly Detectiontype.Example:
predict(‘registry:/_system/governance/mlmodels/indian-diabetes-model’,'double')
<double|float|long|int|string|boolean>predict(<string>pathToMLModel, <string> dataType,<double>input)- Extension Type: StreamProcessor
- Description: Returns an output event with the additional attribute with the response variable name of the model, set with the predicted value, using the feature values extracted from the input event.
- Parameter: pathToMLModel: The file path or the registry path where ML model is located. If the model storage location is registry, the value of this parameter should have the prefix “
registry:” Parameter: dataType: Data type of the predicted value (double, float, long, integer/int, string, boolean/bool).
Parameter: input: A variable attribute value of the input stream which is sent to the ML model as feature values for predictions. Function does not accept any constant values as input parameters. You can have multiple input parameters.
Parameter: percentileValue: Percentile value for the prediction. It should be a double value between 0 - 100. This parameter is only relevant when the algorithm of the model used for prediction is of the
Anomaly Detectiontype.Example:
predict(‘registry:/_system/governance/mlmodels/indian-diabetes-model’,'double', NumPregnancies, TSFT, DPF, BMI, DBP, PG2, Age, SI2)
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@Import('InputStream:1.0.0')
define stream InputStream (NumPregnancies double, TSFT double, DPF double, BMI double, DBP double, PG2 double, Age double, SI2 double);
@Export('PredictionStream:1.0.0')
define stream PredictionStream (NumPregnancies double, TSFT double, DPF double, BMI double, DBP double, PG2 double, Age double, SI2 double, prediction string);
from InputStream#ml:predict('registry:/_system/governance/ml/indian-diabetes-model', 'string', 95.0)
select *
insert into PredictionStream; |
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Set up the following prerequisites before starting the configurations.
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In the above examples, the path to the |
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Set up the following prerequisites before starting the configurations.
- Download WSO2 ML, and start the server. For instructions, see see Getting Started.
Installing required features in WSO2 CEP
- Generate a model using WSO2 ML which you will use to make the predictions. For instructions on generating a model in WSO2 ML, see Generating Models.
- Download WSO2 CEP, and start the server. For instructions, see Getting Started.
Installing required features in WSO2 CEP
Follow the steps below to install the required features in WSO2 CEP.
- Log in to the WSO2 CEP management console using admin/admin credentials and the following URL: https://<CEP_HOME>:<CEP_PORT>/carbon/
- Click Configure, and then click Features.
- Click Repository Management, and then click Add Repository.
Enter the details as shown below to add the Carbon P2 repository.
- Click Add.
- Click the Available Features tab, and select the repository added in the previous step.
Clear the Group features by category check box.
Click Find Features. It can take a while to list out all the available features in the feature repository. Once they are listed, select the following features.
Machine Learner Core
Machine Learner Commons
Machine Learner Database Service
ML Siddhi Extension
Tip If you cannot see this feature:
- Try adding a more recent P2 repository. The repository you added could be deprecated.
- Check the Installed Features tab to see whether the feature is already installed.
Once the features are selected, click Install to proceed with the installation.
- Click Next, and then select I accept the terms of the license agreement.
Once the installation is completed, click Restart Now, and click Yes in the message which pops up.
Info When you are installing ML features to the CEP extension within WSO2 DAS, click Restart Later. Then stop the server and start it again using one of the following commands.
On Windows:
<PRODUCT_HOME>\bin\wso2server.bat --run -DdisableMLSparkCtx=true
On Linux/Solaris/Mac OS:sh <PRODUCT_HOME>/bin/wso2server.sh -DdisableMLSparkCtx=true
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When you run WSO2 CEP in a distributed mode, the following needs to be carried out
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