Test exception/retrain

Author: muencseb

src/main/java/recommendation/model/RecommendationMlModel.java

@@ -34,7 +34,7 @@ public class RecommendationMlModel {
     private final RDDHelper rddHelper = new RDDHelper(javaSparkContext);
     private MatrixFactorizationModel model;
 
-
+    private volatile Integer modelNumber = 0;
     private volatile boolean modelIsReady = false;
 
     /**
@@ -117,6 +117,7 @@ private void trainModel(Collection<InputRating> ratings) {
         mutex.writeLock().lock();
         model = als.setRank(10).setIterations(10).run(ratingRDD);
         mutex.writeLock().unlock();
+        modelNumber++;
         modelIsReady = true;
 
     }
@@ -128,4 +129,8 @@ private List<Rating> createSparkRating(Collection<InputRating> inputRatings) {
                 .map(ir -> new Rating(ir.getUserId(), ir.getProductId(), ir.getRating()))
                 .collect(Collectors.toList());
     }
+
+    public Integer getModelNumber() {
+        return modelNumber;
+    }
 }

src/main/java/recommendation/service/RecommendationService.java

@@ -31,5 +31,10 @@ public void close() {
         recommendationMlModel.close();
     }
 
-
+    /**
+     * returns the current version of the model (+1 for each new model)
+     */
+    public Integer getModelNumber() {
+        return recommendationMlModel.getModelNumber();
+    }
 }

src/test/java/service/RecommendationServiceModelNotReadyTest.java

@@ -0,0 +1,34 @@
+package service;
+
+import org.junit.AfterClass;
+import org.junit.BeforeClass;
+import org.junit.Test;
+import recommendation.data.InputRating;
+import recommendation.exceptions.ModelNotReadyException;
+import recommendation.service.RecommendationService;
+
+import java.util.ArrayList;
+
+public class RecommendationServiceModelNotReadyTest {
+
+
+    private static RecommendationService recommendationService;
+
+    @BeforeClass
+    public static void initModel() {
+        ArrayList<InputRating> inputRatings = new ArrayList<>();
+        inputRatings.add(new InputRating(1, 1, 1));
+        recommendationService = new RecommendationService(inputRatings);
+    }
+
+    @AfterClass
+    public static void close() {
+        recommendationService.close();
+    }
+
+    @Test(expected = ModelNotReadyException.class)
+    public void shouldNotForgetKnownRating() throws ModelNotReadyException {
+        recommendationService.getPrediction(1, 1);
+    }
+
+}

src/test/java/service/RecommendationServiceRetrainTest.java

@@ -0,0 +1,57 @@
+package service;
+
+import org.junit.AfterClass;
+import org.junit.BeforeClass;
+import org.junit.Test;
+import recommendation.data.InputRating;
+import recommendation.exceptions.ModelNotReadyException;
+import recommendation.service.RecommendationService;
+
+import java.util.ArrayList;
+
+import static org.hamcrest.MatcherAssert.assertThat;
+import static org.hamcrest.Matchers.lessThan;
+import static org.junit.Assert.assertNotEquals;
+
+public class RecommendationServiceRetrainTest {
+
+
+    private static RecommendationService recommendationService;
+
+    static boolean firstCall = true;
+
+    private static ArrayList<InputRating> dataProvider() {
+        ArrayList<InputRating> inputRatings = new ArrayList<>();
+        inputRatings.add(new InputRating(1, 1, 1));
+        inputRatings.add(new InputRating(1, 2, 0));
+        inputRatings.add(new InputRating(1, 3, 2));
+        inputRatings.add(new InputRating(2, 1, 1));
+        if (firstCall) {
+            inputRatings.add(new InputRating(1, 3, 0));
+            firstCall = false;
+        }
+        return inputRatings;
+    }
+
+    @BeforeClass
+    public static void initModel() {
+        recommendationService = new RecommendationService(() -> dataProvider(), 10, 0);
+        while (!recommendationService.isModelReady()) {
+        }
+    }
+
+    @AfterClass
+    public static void close() {
+        recommendationService.close();
+    }
+
+    @Test
+    public void shouldDeliverANewPredictionAfterTraining() throws ModelNotReadyException {
+        Double firstPrediction = recommendationService.getPrediction(2, 3);
+        while (recommendationService.getModelNumber() == 1) {
+        }
+        Double secondPrediction = recommendationService.getPrediction(2, 3);
+        assertNotEquals(firstPrediction, secondPrediction);
+        assertThat(firstPrediction, lessThan(secondPrediction));
+    }
+}