Chapter 369. Weka Component Since Camel 3.1 Only producer is supported The Weka component provides access to the (Weka Data Mining) toolset. Weka is tried and tested open source machine learning software that can be accessed through a graphical user interface, standard terminal applications, or a Java API. It is widely used for teaching, research, and industrial applications, contains a plethora of built-in tools for standard machine learning tasks, and additionally gives transparent access to well-known toolboxes such as scikit-learn, R, and Deeplearning4j. Maven users will need to add the following dependency to their pom.xml for this component: <dependency> <groupId>org.apache.camel</groupId> <artifactId>camel-weka</artifactId> <version>x.x.x</version> <!-- use the same version as your Camel core version --> </dependency> 369.1. URI format weka://cmd 369.2. Options The Weka component supports 2 options, which are listed below. Name Description Default Type lazyStartProducer (producer) Whether the producer should be started lazy (on the first message). By starting lazy you can use this to allow CamelContext and routes to startup in situations where a producer may otherwise fail during starting and cause the route to fail being started. By deferring this startup to be lazy then the startup failure can be handled during routing messages via Camel's routing error handlers. Beware that when the first message is processed then creating and starting the producer may take a little time and prolong the total processing time of the processing. false boolean basicPropertyBinding (advanced) Whether the component should use basic property binding (Camel 2.x) or the newer property binding with additional capabilities false boolean The Weka endpoint is configured using URI syntax: with the following path and query parameters: 369.2.1. Path Parameters (1 parameters): Name Description Default Type command Required The command to use. The value can be one of: filter, model, read, write, push, pop, version Command 369.2.2. Query Parameters (12 parameters): Name Description Default Type lazyStartProducer (producer) Whether the producer should be started lazy (on the first message). By starting lazy you can use this to allow CamelContext and routes to startup in situations where a producer may otherwise fail during starting and cause the route to fail being started. By deferring this startup to be lazy then the startup failure can be handled during routing messages via Camel's routing error handlers. Beware that when the first message is processed then creating and starting the producer may take a little time and prolong the total processing time of the processing. false boolean basicPropertyBinding (advanced) Whether the endpoint should use basic property binding (Camel 2.x) or the newer property binding with additional capabilities false boolean synchronous (advanced) Sets whether synchronous processing should be strictly used, or Camel is allowed to use asynchronous processing (if supported). false boolean apply (filter) The filter spec (i.e. Name Options) String build (model) The classifier spec (i.e. Name Options) String dsname (model) The named dataset to train the classifier with String folds (model) Number of folds to use for cross-validation 10 int loadFrom (model) Path to load the model from String saveTo (model) Path to save the model to String seed (model) An optional seed for the randomizer 1 int xval (model) Flag on whether to use cross-validation with the current dataset false boolean path (write) An in/out path for the read/write commands String 369.3. Karaf support This component is not supported in Karaf 369.4. Message Headers 369.5. Samples 369.5.1. Read + Filter + Write This first example shows how to read a CSV file with the file component and then pass it on to Weka. In Weka we apply a few filters to the data set and then pass it on to the file component for writing. @Override public void configure() throws Exception { // Use the file component to read the CSV file from("file:src/test/resources/data?fileName=sfny.csv") // Convert the 'in_sf' attribute to nominal .to("weka:filter?apply=NumericToNominal -R first") // Move the 'in_sf' attribute to the end .to("weka:filter?apply=Reorder -R 2-last,1") // Rename the relation .to("weka:filter?apply=RenameRelation -modify sfny") // Use the file component to write the Arff file .to("file:target/data?fileName=sfny.arff") } Here we do the same as above without use of the file component. @Override public void configure() throws Exception { // Initiate the route from somewhere .from("...") // Use Weka to read the CSV file .to("weka:read?path=src/test/resources/data/sfny.csv") // Convert the 'in_sf' attribute to nominal .to("weka:filter?apply=NumericToNominal -R first") // Move the 'in_sf' attribute to the end .to("weka:filter?apply=Reorder -R 2-last,1") // Rename the relation .to("weka:filter?apply=RenameRelation -modify sfny") // Use Weka to write the Arff file .to("weka:write?path=target/data/sfny.arff"); } In this example, the client would provide the input path or some other supported type. Have a look at the WekaTypeConverters for the set of supported input types. @Override public void configure() throws Exception { // Initiate the route from somewhere .from("...") // Convert the 'in_sf' attribute to nominal .to("weka:filter?apply=NumericToNominal -R first") // Move the 'in_sf' attribute to the end .to("weka:filter?apply=Reorder -R 2-last,1") // Rename the relation .to("weka:filter?apply=RenameRelation -modify sfny") // Use Weka to write the Arff file .to("weka:write?path=target/data/sfny.arff"); } 369.5.2. Building a Model When building a model, we first choose the classification algorithm to use and then train it with some data. The result is the trained model that we can later use to classify unseen data. Here we train J48 with 10 fold cross-validation. try (CamelContext camelctx = new DefaultCamelContext()) { camelctx.addRoutes(new RouteBuilder() { @Override public void configure() throws Exception { // Use the file component to read the training data from("file:src/test/resources/data?fileName=sfny-train.arff") // Build a J48 classifier using cross-validation with 10 folds .to("weka:model?build=J48&xval=true&folds=10&seed=1") // Persist the J48 model .to("weka:model?saveTo=src/test/resources/data/sfny-j48.model") } }); camelctx.start(); } 369.5.3. Predicting a Class Here we use a Processor to access functionality that is not directly available from endpoint URIs. In case you come here directly and this syntax looks a bit overwhelming, you might want to have a brief look at the section about Nessus API Concepts . try (CamelContext camelctx = new DefaultCamelContext()) { camelctx.addRoutes(new RouteBuilder() { @Override public void configure() throws Exception { // Use the file component to read the test data from("file:src/test/resources/data?fileName=sfny-test.arff") // Remove the class attribute .to("weka:filter?apply=Remove -R last") // Add the 'prediction' placeholder attribute .to("weka:filter?apply=Add -N predicted -T NOM -L 0,1") // Rename the relation .to("weka:filter?apply=RenameRelation -modify sfny-predicted") // Load an already existing model .to("weka:model?loadFrom=src/test/resources/data/sfny-j48.model") // Use a processor to do the prediction .process(new Processor() { public void process(Exchange exchange) throws Exception { Dataset dataset = exchange.getMessage().getBody(Dataset.class); dataset.applyToInstances(new NominalPredictor()); } }) // Write the data file .to("weka:write?path=src/test/resources/data/sfny-predicted.arff") } }); camelctx.start(); } 369.6. Resources Practical Machine Learning Tools and Techniques Machine Learning Courses Weka Documentation Nessus-Weka

Commands:
<dependency> <groupId>org.apache.camel</groupId> <artifactId>camel-weka</artifactId> <version>x.x.x</version> <!-- use the same version as your Camel core version --> </dependency>
weka://cmd
weka:command
@Override public void configure() throws Exception { // Use the file component to read the CSV file from("file:src/test/resources/data?fileName=sfny.csv") // Convert the 'in_sf' attribute to nominal .to("weka:filter?apply=NumericToNominal -R first") // Move the 'in_sf' attribute to the end .to("weka:filter?apply=Reorder -R 2-last,1") // Rename the relation .to("weka:filter?apply=RenameRelation -modify sfny") // Use the file component to write the Arff file .to("file:target/data?fileName=sfny.arff") }
@Override public void configure() throws Exception { // Initiate the route from somewhere .from("...") // Use Weka to read the CSV file .to("weka:read?path=src/test/resources/data/sfny.csv") // Convert the 'in_sf' attribute to nominal .to("weka:filter?apply=NumericToNominal -R first") // Move the 'in_sf' attribute to the end .to("weka:filter?apply=Reorder -R 2-last,1") // Rename the relation .to("weka:filter?apply=RenameRelation -modify sfny") // Use Weka to write the Arff file .to("weka:write?path=target/data/sfny.arff"); }
@Override public void configure() throws Exception { // Initiate the route from somewhere .from("...") // Convert the 'in_sf' attribute to nominal .to("weka:filter?apply=NumericToNominal -R first") // Move the 'in_sf' attribute to the end .to("weka:filter?apply=Reorder -R 2-last,1") // Rename the relation .to("weka:filter?apply=RenameRelation -modify sfny") // Use Weka to write the Arff file .to("weka:write?path=target/data/sfny.arff"); }
try (CamelContext camelctx = new DefaultCamelContext()) { camelctx.addRoutes(new RouteBuilder() { @Override public void configure() throws Exception { // Use the file component to read the training data from("file:src/test/resources/data?fileName=sfny-train.arff") // Build a J48 classifier using cross-validation with 10 folds .to("weka:model?build=J48&xval=true&folds=10&seed=1") // Persist the J48 model .to("weka:model?saveTo=src/test/resources/data/sfny-j48.model") } }); camelctx.start(); }
try (CamelContext camelctx = new DefaultCamelContext()) { camelctx.addRoutes(new RouteBuilder() { @Override public void configure() throws Exception { // Use the file component to read the test data from("file:src/test/resources/data?fileName=sfny-test.arff") // Remove the class attribute .to("weka:filter?apply=Remove -R last") // Add the 'prediction' placeholder attribute .to("weka:filter?apply=Add -N predicted -T NOM -L 0,1") // Rename the relation .to("weka:filter?apply=RenameRelation -modify sfny-predicted") // Load an already existing model .to("weka:model?loadFrom=src/test/resources/data/sfny-j48.model") // Use a processor to do the prediction .process(new Processor() { public void process(Exchange exchange) throws Exception { Dataset dataset = exchange.getMessage().getBody(Dataset.class); dataset.applyToInstances(new NominalPredictor()); } }) // Write the data file .to("weka:write?path=src/test/resources/data/sfny-predicted.arff") } }); camelctx.start(); }