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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.

   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 

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

   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>

4. 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; }

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.

   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); }

...

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]]
Sep 03, 2015 9:35:20 AM com.github.fommil.netlib.BLAS <clinit>
WARNING: Failed to load implementation from: com.github.fommil.netlib.NativeSystemBLAS
Sep 03, 2015 9:35:20 AM com.github.fommil.netlib.BLAS <clinit>
WARNING: Failed to load implementation from: com.github.fommil.netlib.NativeRefBLAS
INFO  [MLModelUsageSample] - Predictions : [0, 0]