SAP Edge Services, cloud edition Edge Services Predictive Analytics Service Guide Version 1803

Transcription

1 SAP Edge Services, cloud edition Edge Services Predictive Analytics Service Guide Version 1803

2 Table of Contents MACHINE LEARNING AND PREDICTIVE ANALYTICS... 3 Model Trained with R and Exported as PMML... 3 Export the Model in PMML... 4 Syntax... 4 Arguments... 4 Import the Model using JPMML... 5 Package Model into a Deployment Bundle... 7 Model Trained with SAP Predictive Analytics... 7 Export a Model from SAP Predictive Analytics... 7 Package Model into a Deployment Bundle... 7 DEVELOPMENT ENVIRONMENT CONFIGURATION... 8 Internet of Things Service SDK... 8 Package OSGi bundle... 8 BUNDLE DEPLOYMENT... 8 APPENDIX - OSGI OPEN SERVICE GATEWAY INITIATIVE... 9 Summary... 9 Bundles... 9 Event Admin Declarative Services

3 MACHINE LEARNING AND PREDICTIVE ANALYTICS There are many tools in the market that provide machine learning and predictive analytics capabilities. This document provides two examples that are most relevant to SAP customers and SAP Edge Services, and provides general guidance along with development details. All approaches to machine learning and predictive analytics are separated into two parts: the training of a model; and the application of the trained model. While there may be specific scenarios where a model could be trained on the edge, the more common situation is to train models in the cloud or in a customer data center, and then apply the resulting model at the edge. At a high level, the general approach to support Machine Learning and Predictive Analytics in SAP Edge Services is as follows: 1. Train the model in the cloud or in the customer data center 2. Export the model 3. Package the model for use with SAP Edge Services (as an OSGi bundle). Integrate the data flow with SAP Edge Services API s as necessary. 4. Deploy the model to SAP Edge Services The two examples shown below in more detail take fundamentally different approaches to exporting models: 1. Model trained with R and exported as PMML The predictive analytics workbench R can be used to export a model in PMML (Predictive Model Mark-up Language), a specific XML dialect for describing predictive models. There is a corresponding open-source Java program called JPMML that can read and execute PMML specifications. See PMML Specification and JPMML API for more information. The deployment bundle needs to include the JPMML code and the exported PMML file. The input vector for the model needs to be provided, for example the values of temperature, pressure, humidity, and so on, and a way to process the result. 2. Model trained with SAP Predictive Analytics SAP Predictive Analytics can be used to train a predictive model through a convenient user interface. This model can then be exported in a variety of different formats, including Java. The deployment bundle needs to include the exported Java code and integration with SAP Edge Services to provide the appropriate input for the predictor variables and the processing of the output of the model. Note: Refer to for additional information on SAP Predictive Analytics. In both cases, there are various options to produce the input vector: Configure the streaming services to pre-process the raw values and output the input vector for the model. This would allow processing of raw data over a time window, and aggregated values (ie minimum, maximum, average) as discreet input variables for the model. Query the data stored in the persistence services at an interval or based on an external trigger. A useful approach for processing the output of the model is to take the result and send it to the Streaming Service, where the user interface can be used to define rules for driving action. For example, if likelihood_of_failure >.80 then create_service_ticket( device-id, likelihood_of_failure ). Model Trained with R and Exported as PMML How to load the training data into R and to train the model is beyond the scope of this document. See the R manuals or specific SAP solutions like Predictive Maintenance Services, which uses R for details. 3

4 Export the Model in PMML Once the model has been trained, export the model in PMML. PMML is an XML based language that provides a way for applications to define statistical and data mining models and to share models between PMML compliant applications. To export the model as PMML, file libraries pmml and xml need to be imported. After importing the libraries, use pmml function to convert the R model into PMML. If data used for an R model contains features of type character, these must be converted to factors before the model is trained and converted with pmml. Syntax pmml(model=null, model.name= XYZ", app.name=" xyz", description=null, copyright=null, transforms=null,...) Arguments model model.name app.name an object to be converted to PMML. a name to be given to the model in the PMML code. the name of the application that generated the PMML code. description a descriptive text for the Header element of the PMML code. copyright transforms the copyright notice for the model. data transformations represented in PMML via pmmltransformations.... further arguments passed to or from other methods The resulting PMML file can then be imported into other systems that accept PMML. 4

5 Import the Model using JPMML JPMML is the java library used to import the PMML files and execute them. The JPMML operator allows the deployment of an arbitrary number of models to be executed against incoming events. It is the reposnsibility of the event flow designer to convert the incoming data into the model s expected features. It frequently requires event enrichment, such as cross-referencing, attribute lookup, or prior events related to the same context. Following is an example of a commonly used and openly available dataset about abalone to predict the number of rings an abalone would have, based on a set of chosen features. The predictive model is a linear regression. The method below represents how a PMML file is loaded into memory using the JPMML library. Once the PMML has been loaded, prediction can occur using streaming data. In this example, the stream is a test csv file, but in a production environment this would more likely be real-time data streams. /** * Load a PMML model from the file system. * file PMML Exception */ public final static PMML loadmodel(final String file) throws Exception { PMML pmml = null; File inputfilepath = new File( file ); try( InputStream in = new FileInputStream( inputfilepath ) ){ Source source = ImportFilter.apply(new InputSource(in)); pmml = JAXBUtil.unmarshalPMML(source); } } catch( Exception e) { logger.error( e.tostring() ); throw e; } return pmml; Now that the linear regression model has been loaded as a PMML object, the next step is setting up the prediction processing. The next code section shows how to load, gain a predictive evaluator and get the list of input feature fields needed for the evaluator. // Load the file using our simple util function. PMML pmml = JPMMLUtils.loadModel( pmmlfilepath ); // Create prediction evaluator for the loaded model PMMLManager mgr = new PMMLManager( pmml ); ModelEvaluator modelevaluator = (ModelEvaluator) mgr.getmodelmanager(modelname, ModelEvaluatorFactory.getInstance()); Evaluator evaluator = modelevaluator; 5

6 // Get the list of required feature set model needs to predict. List requiredmodelfeatures = evaluator.getactivefields(); Below is the main body of the prediction routine. The code performs the following steps: 1. Parse the passed csv line into tokens and split into required variables. 2. Get the required feature variable field name. 3. Prepare the field to value mapping and assign to feature hashmap. 4. Perform the prediction (evaluate function). 5. Convert values back to original state and print out result. // For each CSV line perform a predict. while ((line = br.readline())!= null) { String[] tokens = line.split( cvssplitby ); double d = Double.valueOf( tokens[2] ); double e = Double.valueOf( tokens[3] ); FieldName fieldname = requiredmodelfeatures.get(0); // In this instance there is only one feature // For a production system this would be performed in a //..transformation stage and may collect data externally. FieldValue value = evaluator.prepare(fieldname, Double.valueOf(d)); features.put( fieldname, value ); Map results = evaluator.evaluate( features ); // Convert back to original ring value so the prediction becomes //meaningful. double y = (Double)results.get( evaluator.gettargetfield()); int predictedrings = (int) Math.abs( Math.pow( 10, y)); int expectedrings = (int) Math.abs( Math.pow( 10, e)); } double diameter = Math.pow( 10, d); System.out.println(String.format("Diameter %f Expected rings %d : Predicted rings: %d", diameter, expectedrings, predictedrings)); 6

7 The following section presents the predictions performed using the JPMML linear regression evaluator using the passed offline R model. Diameter Expected rings 7 : Predicted rings: 6 Diameter Expected rings 11 : Predicted rings: 11 Diameter Expected rings 11 : Predicted rings: 11 Diameter Expected rings 8 : Predicted rings: 10 Diameter Expected rings 7 : Predicted rings: 7 Diameter Expected rings 11 : Predicted rings: 11 Diameter Expected rings 10 : Predicted rings: 7 Diameter Expected rings 9 : Predicted rings: 10 Diameter Expected rings 7 : Predicted rings: 8 Diameter Expected rings 6 : Predicted rings: Package Model into a Deployment Bundle The JPMML code is now ready to be packaged into an OSGi deployment bundle. Model Trained with SAP Predictive Analytics How to load the training data into SAP Predictive Analytics and how to train a model is beyond the scope of this document. See the SAP Predictive Analytics documentation and training materials for details. Export a Model from SAP Predictive Analytics As mentioned above, SAP Predictive Analytics provides a number of formats for exporting models. To run the model in SAP Edge Services, select the Java export option. The following steps are necessary to do this: a. Export the model under the Save/Export tab. b. Generate Source Code and select java code as code type. Package Model into a Deployment Bundle As the result of exporting the model, a jar file is created on the file system. The Java code can now be packaged into an OSGi deployment bundle. 7

8 DEVELOPMENT ENVIRONMENT CONFIGURATION SAP Edge Services is built on top of SAP Cloud Platform IoT Services ( SAP IoT Services ). Predictive Analytics Services are deployed onto SAP Cloud Platform IoT Services Gateway Edge and leverage services provided by SAP Edge Services. Therefore, to package the models developed above, you are required to setup a development environment for bundle development for SAP IoT Services Gateway Edge. Internet of Things Service SDK The SAP Internet of Things Service SDK is necessary to access the base and utility classes for interacting with SAP IoT Services OSGi bundles and functionality. It must be included with each bundle project that needs to reference or utilize these classes. The SDK can be obtained from SAP. Instructions are located under the Internet of Things Service SDK link provided here: Package OSGi bundle The OSGi bundle must be packaged before it can be installed and used on a Gateway. This document uses Eclipse to export the OSGi bundle, but a packaging manager could be used for packaging and managing dependencies as well. An exported OSGi bundle will be a single JAR file that can then be installed and started on a Gateway. 1. File > Export 2. Choose "Deployable Plugins and Fragments" Select desired project/bundle and export. 3. Enable Remote Debugging (OPTIONAL) Eclipse can be used to enable Remote Debugging, which can be used to significantly simplify the development experience. 1. Run > Debug Configurations 2. Create a new "Remote Java Application" configuration. a. Select the project and specify the Connection Properties: Host : [GATEWAY_IP] Post : Test by setting an accessible breakpoint and verify that the Gateway can trigger the breakpoint in Eclipse. BUNDLE DEPLOYMENT Bundles are deployed using the SAP IoT Services Cloud Cockpit. Using the Cloud Cockpit, a gateway can be selected and the bundle can be deployed. Please refere to SAP IoT Services documention for further details. 8

9 APPENDIX - OSGI OPEN SERVICE GATEWAY INITIATIVE OSGi is a Java framework for developing and deploying modular software programs and libraries. i. Specification for modular components called bundles, commonly referred to as plug ins. The specification defines an infrastructure for a bundle's life cycle and determines how bundles will interact. ii. The second part of OSGi is a Java Virtual Machine (JVM) level service registry that bundles can use to publish, discover and bind to services in a service oriented architecture (SOA). OSGi provides a way that bundles can communicate with each other without being coupled together (interfaces, etc.). Summary Note: Bundle and plug in can be used interchangeably; plug in is preferred by Eclipse, bundle is preferred by OSGi. Most applications consist of several different parts, called modules or components, which interact with each other through an Application Programming Interface, or API. The API is considered to be the classes/methods which are accessible to other components. A component also has a set of classes/methods that are considered internal. Using an API from another component creates a dependency on the component, as it now requires the other component to be present in order to work correctly. Currently, Java doesn't provide a structured way to describe the various dependencies of a component; rather, every public class can be called from another component. OSGi is a specification framework that defines a component and service model for creating loosely coupled modular bundles. Each bundle can define its API through a set of exported packages and also specify its own required dependencies. Bundles A bundle can define exports or dependencies on other bundles in its manifest file. OSGi prevents access to classes without a defined dependency (except for packages from the Java runtime). Dependencies can be defined as either bundle or package dependencies. Bundle dependencies enable accessing all exported packages from the bundle while package dependencies enable accessing a specific package without worrying what bundle exports it enabling a later change. The OSGi runtime ensures that all dependencies are present before starting a plugin, activating them if necessary. Bundle Lifecycle # Status Description 1 Installed Bundle has been installed 2 Resolved All bundle dependencies have been resolved 3 Starting Bundle is starting 4 Active Bundle has started and is running 5 Stopping Bundle is stopping 9

10 6 Resolved N/A 7 Uninstalled Bundle has been uninstalled NOTE: Bundles must all contain an Activator class that is responsible for managing the bundle's life cycle, including a start() and stop() set of methods. These methods are responsible for handling any necessary preparation or clean up during the beginning and end of the bundle's life cycle. Event Admin OSGi Event Admin defines a publish/subscribe communication mechanism for bundle communication. The Event Publisher sends events or messages related to a specific topic, while the Event Handler receives all messages that are published to a specific topic. The published events are composed of two attributes, a topic and a set of descriptive properties. The topic defines the nature of the event and is typically denoted with slashes to separate hierarchical levels (i.e. com/sap/iotservices/eventreceived). Declarative Services can be used to simplify registering and consuming Event Admin handlers in OSGi bundles. Rather than programmatically registering the subscription topic, Declarative Services enable using an external file (manually or automatically generated) to register the subscription, which results in a cleaner and easier to understand relationship. Declarative Services A service in OSGi is defined by a standard Java class or interface, composed of the class/interface for which you want to provide a service and the implementation class for the service interface. This enables switching out the service implementation but still using the existing service interface. Services can be dynamically stopped/started, so bundles that use these services must be able to handle this behavior, typically done by the ServiceListener handlers. It is good practice to define a service with a bundle that contains only the service interface definition, with another bundle providing the service implementation. This way, the implementation of the service can be changed at a later time. Since dependent bundles only communicate with the service through the service interface, updating the service implementation should not break existing dependent bundles. For example, if an existing implementation used a SQL database to persist data but the developers wanted to switch to another database vendor, the only requirement would be that the new implementation correctly supports the existing service interface. Declarative Services remove the need for registering and consuming services programmatically. Instead, Declarative Services can be declared by a Component Definition file, which is either manually configured or rendered automatically by Eclipse annotations (which will also update the MANIFEST). NOTE: A Service Component Runtime bundle must be installed and activated on the Gateway if using Declarative Services. However, this bundle will likely already be installed as part of the SAP environment. 10

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