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Download WSO2 ML, and start the server. For instructions, see Getting Started.
Generate a model using WSO2 ML. This model will be used to make the predictions. For instructions on generating a model, see Generating Models. To build a model, see ML UI Workflow.
Download the generated model.

Info
You can stop the WSO2 ML server after generating the required model, since this process of using the model in a Java client does not require a running ML server.

Building the sample

To build this sample, a Java project should be created as follows.

Create a Maven project.

Include the following dependencies in the pom.xml file of the project.

Code Block

language	xml

<dependency>
	<groupId>org.wso2.carbon.ml</groupId>
	<artifactId>org.wso2.carbon.ml.core</artifactId>
	<version>1.0.1-SNAPSHOT</version>
</dependency>
<dependency>
	<groupId>org.wso2.carbon.ml</groupId>
	<artifactId>org.wso2.carbon.ml.commons</artifactId>
	<version>1.0.1-SNAPSHOT</version>
</dependency>

Runtime dependencies

Code Block

language	xml

<!--Dependencies required for MLModel deserialization-->
<dependency>
	<groupId>org.wso2.orbit.org.apache.spark</groupId>
	<artifactId>spark-core_2.10</artifactId>
	<version>1.4.1.wso2v1</version>
</dependency>
<dependency>
	<groupId>org.wso2.orbit.org.apache.spark</groupId>
	<artifactId>spark-mllib_2.10</artifactId>
	<version>1.4.1.wso2v1</version>
</dependency>
<dependency>
	<groupId>org.scala-lang</groupId>
	<artifactId>scala-library</artifactId>
	<version>2.10.4</version>
</dependency>
<dependency>
	<groupId>org.wso2.orbit.org.scalanlp</groupId>
	<artifactId>breeze_2.10</artifactId>
	<version>0.11.1.wso2v1</version>
</dependency>
<dependency>
	<groupId>org.wso2.orbit.github.fommil.netlib</groupId>
	<artifactId>core</artifactId>
	<version>1.1.2.wso2v1</version>
</dependency>
<!--Dependencies required for predictions-->
<dependency>
	<groupId>org.wso2.orbit.sourceforge.f2j</groupId>
	<artifactId>arpack_combined</artifactId>
	<version>0.1.wso2v1</version>
</dependency>

The downloaded model contains a serialized MLModel object. Deserialize the MLModel as follows.

Code Block

language	java

private MLModel deserializeMLModel() throws IOException, ClassNotFoundException, URISyntaxException {
  // Path to downloaded-ml-model
  String pathToDownloadedModel = “/tmp/downloaded-ml-model”;
  FileInputStream fileInputStream = new FileInputStream(pathToDownloadedModel);
  ObjectInputStream in = new ObjectInputStream(fileInputStream);
  MLModel mlModel = (MLModel) in.readObject();

  logger.info("Algorithm Type : " + mlModel.getAlgorithmClass());
  logger.info("Algorithm Name : " + mlModel.getAlgorithmName());
  logger.info("Response Variable : " + mlModel.getResponseVariable());
  logger.info("Features : " + mlModel.getFeatures());
  return mlModel;
}

Use org.wso2.carbon.ml.core.impl.Predictor to make predictions using the downloaded ML Model. Predictor accepts a list of string arrays as feature values. The feature values should be in the order of the feature index inside the array as shown below.

Code Block

language	java

MLModelUsageSample modelUsageSample = new MLModelUsageSample();
// Deserialize
MLModel mlModel = modelUsageSample.deserializeMLModel();
// Predict
String[] featureValueArray1 = new String[] { "6", "148", "72", "35", "0", "33.6", "0.627", "50" };
String[] featureValueArray2 = new String[] { "0", "101", "80", "40", "0", "26", "0.5", "33" };
ArrayList<String[]> list = new ArrayList<String[]>();
list.add(featureValueArray1);
list.add(featureValueArray2);
modelUsageSample.predict(list, mlModel);
public void predict(List<String[]> featureValueLists, MLModel mlModel) throws MLModelHandlerException {
	Predictor predictor = new Predictor(0, mlModel, featureValueLists);
	List<?> predictions = predictor.predict();
	logger.info("Predictions : " + predictions);
}

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Versions Compared

Old Version 14

New Version 15

Key

Building the sample

Runtime dependencies