Using ML Models in a Java Client

Introduction

This guide demonstrates how to use a downloaded ML model generated using WSO2 ML in plain Java code. This guide uses the https://github.com/wso2/carbon-ml/tree/master/samples/model-usage sample Java client implementation.

Prerequisites

Before you run this sample, do the following.

1. Download WSO2 ML, and start the server. For instructions, see Getting Started.
2. 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.
3. Download the generated model.
4. Replace the downloaded-ml-model in {CARBON_ML_PATH}/samples/model-usage/src/main/resources with the model you have downloaded.

Building the sample

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

1. Create a Maven project.

2. Include the following compile time dependencies in the pom.xml file of the project.

   <dependency>
   	<groupId>org.wso2.carbon.ml</groupId>
   	<artifactId>org.wso2.carbon.ml.core</artifactId>
   </dependency>
   <dependency>
   	<groupId>org.wso2.carbon.ml</groupId>
   	<artifactId>org.wso2.carbon.ml.commons</artifactId>
   </dependency>

3. Include the following runtime dependencies also in the pom.xml file of the project.

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

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

   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;
   }

5. 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.

   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 at https://github.com/wso2/carbon-ml/tree/master/samples/model-usage.  To build this, execute the following command from the model-usage directory.

mvn clean install

Executing the sample

To execute this sample, execute the following command from the model-usage directory.

mvn exec:java

Analysing the output

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

INFO  [MLModelUsageSample] - Algorithm Type : Classification
INFO  [MLModelUsageSample] - Algorithm Name : LOGISTIC_REGRESSION
INFO  [MLModelUsageSample] - Response Variable : Class
INFO  [MLModelUsageSample] - Features : [Feature [name=Age, index=7, type=NUMERICAL, imputeOption=DISCARD, include=true], Feature [name=BMI, index=5, type=NUMERICAL, imputeOption=DISCARD, include=true], Feature [name=DBP, index=2, type=NUMERICAL, imputeOption=DISCARD, include=true], Feature [name=DPF, index=6, type=NUMERICAL, imputeOption=DISCARD, include=true], Feature [name=NumPregnancies, index=0, type=NUMERICAL, imputeOption=DISCARD, include=true], Feature [name=PG2, index=1, type=NUMERICAL, imputeOption=DISCARD, include=true], Feature [name=SI2, index=4, type=NUMERICAL, imputeOption=DISCARD, include=true], Feature [name=TSFT, index=3, type=NUMERICAL, imputeOption=DISCARD, include=true]]
WARN  [org.wso2.carbon.ml.core.impl.Predictor] - Metric Service is not available!
Sep 24, 2015 3:06:28 PM com.github.fommil.netlib.BLAS <clinit>
WARNING: Failed to load implementation from: com.github.fommil.netlib.NativeSystemBLAS
Sep 24, 2015 3:06:28 PM com.github.fommil.netlib.BLAS <clinit>
WARNING: Failed to load implementation from: com.github.fommil.netlib.NativeRefBLAS
INFO  [MLModelUsageSample] - Feature values list 1 : [2, 84, 0, 0, 0, 0.0, 0.304, 21]
INFO  [MLModelUsageSample] - Prediction : 1
INFO  [MLModelUsageSample] - Feature values list 2 : [0, 101, 80, 40, 0, 26, 0.5, 33]
INFO  [MLModelUsageSample] - Prediction : 0