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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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- 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.
Parameter Value Name Carbon P2 repositoryURL http://product-dist.wso2.com/p2/carbon/releases/wilkes/
- 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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