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Introduction

This sample 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.

Prerequisites

Before you run this sample, do the following.

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1. Create a Maven project.

2. 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

   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>

3. 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; }

4. 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); }

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

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Executing the sample

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

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Analysing the output

The following output while be is displayed in the ML console once you execute the sample.

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