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This guide demonstrates how to use a downloaded ML model generated using WSO2 ML in plain Java code. This guide uses the <ML_HOME>/samples/ml-model-usage/ sample Java client implementation.

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Create a Maven project.

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

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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

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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.

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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.

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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);
}

The complete project created above can be found in the <ML_HOME>/samples/ml-model-usage directory. To build this, execute the following command from this directory.

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To execute this sample, execute the following command from the <ML_HOME>/samples/ml-model-usage directory.

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mvn exec:java

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Runtime dependencies