Chapter 369. Weka Component

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");
    }

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();
}

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();
}