Red Hat Summit Red Hat SummitSupportConsoleDevelopersStart a trialContact

Chapter 368. 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>
Copy to Clipboard Toggle word wrap

368.1. URI format
Copy link

weka://cmd
Copy to Clipboard Toggle word wrap

368.2. Options
Copy link

The Weka component supports 2 options, which are listed below.

Expand
NameDescriptionDefaultType

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: 

weka:command
Copy to Clipboard Toggle word wrap

with the following path and query parameters:

368.2.1. Path Parameters (1 parameters):
Copy link

Expand
NameDescriptionDefaultType

command

Required The command to use. The value can be one of: filter, model, read, write, push, pop, version

 

Command

368.2.2. Query Parameters (12 parameters):
Copy link

Expand
NameDescriptionDefaultType

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

368.3. Karaf support
Copy link

This component is not supported in Karaf

368.4. Message Headers
Copy link

368.5. Samples
Copy link

368.5.1. Read + Filter + Write
Copy link

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")
    }
Copy to Clipboard Toggle word wrap

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");
    }
Copy to Clipboard Toggle word wrap

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");
    }
Copy to Clipboard Toggle word wrap

368.5.2. Building a Model
Copy link

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();
}
Copy to Clipboard Toggle word wrap

368.5.3. Predicting a Class
Copy link

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();
}
Copy to Clipboard Toggle word wrap

368.6. Resources
Copy link

Back to top
Red Hat logoGithubredditYoutubeTwitter

Learn

Try, buy, & sell

Communities

About Red Hat Documentation

We help Red Hat users innovate and achieve their goals with our products and services with content they can trust. Explore our recent updates.

Making open source more inclusive

Red Hat is committed to replacing problematic language in our code, documentation, and web properties. For more details, see the Red Hat Blog.

About Red Hat

We deliver hardened solutions that make it easier for enterprises to work across platforms and environments, from the core datacenter to the network edge.

Theme

© 2025 Red Hat