Weka

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>

URI format

weka://cmd

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

autowiredEnabled (advanced)

Whether autowiring is enabled. This is used for automatic autowiring options (the option must be marked as autowired) by looking up in the registry to find if there is a single instance of matching type, which then gets configured on the component. This can be used for automatic configuring JDBC data sources, JMS connection factories, AWS Clients, etc.

true

boolean

The Weka endpoint is configured using URI syntax:

weka:command

with the following path and query parameters:

Path Parameters (1 parameters):

Name Description Default Type

command

Required The command to use. There are 7 enums and the value can be one of: filter, model, read, write, push, pop, version

Command

Query Parameters (11 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

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

Karaf support

This component is not supported in Karaf

Message Headers

Samples

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, would the client 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");
    }

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();
}

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();
}

Resources

Spring Boot Auto-Configuration

When using weka with Spring Boot make sure to use the following Maven dependency to have support for auto configuration:

<dependency>
  <groupId>org.apache.camel.springboot</groupId>
  <artifactId>camel-weka-starter</artifactId>
  <version>x.x.x</version>
  <!-- use the same version as your Camel core version -->
</dependency>

The component supports 3 options, which are listed below.

Name Description Default Type

camel.component.weka.autowired-enabled

Whether autowiring is enabled. This is used for automatic autowiring options (the option must be marked as autowired) by looking up in the registry to find if there is a single instance of matching type, which then gets configured on the component. This can be used for automatic configuring JDBC data sources, JMS connection factories, AWS Clients, etc.

true

Boolean

camel.component.weka.enabled

Whether to enable auto configuration of the weka component. This is enabled by default.

Boolean

camel.component.weka.lazy-start-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
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