Handle unknown user/product

Author: muencseb

pom.xml

@@ -36,7 +36,7 @@
                     </execution>
                 </executions>
                 <configuration>
-                    <jvmTarget>1.8</jvmTarget>
+                    <jvmTarget>1.11</jvmTarget>
                 </configuration>
             </plugin>
             <plugin>
@@ -59,8 +59,8 @@
                     </execution>
                 </executions>
                 <configuration>
-                    <source>8</source>
-                    <target>8</target>
+                    <source>11</source>
+                    <target>11</target>
                 </configuration>
             </plugin>
         </plugins>

src/main/java/ch/sebastianmue/javarank/recommendation/model/RecommendationMlModel.java

@@ -12,6 +12,7 @@
 
 import java.util.Collection;
 import java.util.List;
+import java.util.Optional;
 import java.util.concurrent.Callable;
 import java.util.concurrent.Executors;
 import java.util.concurrent.ScheduledExecutorService;
@@ -71,15 +72,22 @@ public boolean isModelReady() {
      *
      * @param userId
      * @param eventId
-     * @return the prediction, which rating the used is likely to give
+     * @return the prediction, which rating the used is likely to give. If either the user or the product is unknown, it will return empty
      * @throws ModelNotReadyException
      */
-    public Double getInterestPrediction(Integer userId, Integer eventId) throws ModelNotReadyException {
+    public  Optional<Double> getInterestPrediction(Integer userId, Integer eventId) throws ModelNotReadyException {
         if (!modelIsReady)
             throw new ModelNotReadyException();
         mutex.readLock().lock();
-        Double prediction = model.predict(userId, eventId);
-        mutex.readLock().unlock();
+        Optional<Double> prediction;
+        try {
+            prediction = Optional.of(model.predict(userId, eventId));
+        } catch (IllegalArgumentException e) {
+            prediction = Optional.empty();
+        }
+        finally {
+            mutex.readLock().unlock();
+        }
         return prediction;
     }
 

src/main/java/ch/sebastianmue/javarank/recommendation/service/RecommendationService.java

@@ -1,10 +1,12 @@
 package ch.sebastianmue.javarank.recommendation.service;
 
+import breeze.util.Opt;
 import ch.sebastianmue.javarank.recommendation.data.InputRating;
 import ch.sebastianmue.javarank.recommendation.exceptions.ModelNotReadyException;
 import ch.sebastianmue.javarank.recommendation.model.RecommendationMlModel;
 
 import java.util.Collection;
+import java.util.Optional;
 import java.util.concurrent.Callable;
 
 public class RecommendationService {
@@ -23,7 +25,7 @@ public boolean isModelReady() {
         return recommendationMlModel.isModelReady();
     }
 
-    public Double getPrediction(Integer userId, Integer productId) throws ModelNotReadyException {
+    public Optional<Double> getPrediction(Integer userId, Integer productId) throws ModelNotReadyException {
         return recommendationMlModel.getInterestPrediction(userId, productId);
     }
 

src/test/java/ch/sebastianmue/javarank/service/RecommendationServiceRetrainTest.java

@@ -8,6 +8,7 @@
 import ch.sebastianmue.javarank.recommendation.service.RecommendationService;
 
 import java.util.ArrayList;
+import java.util.Optional;
 
 import static org.hamcrest.MatcherAssert.assertThat;
 import static org.hamcrest.Matchers.lessThan;
@@ -47,11 +48,11 @@ public static void close() {
 
     @Test
     public void shouldDeliverANewPredictionAfterTraining() throws ModelNotReadyException {
-        Double firstPrediction = recommendationService.getPrediction(2, 3);
+        Optional<Double> firstPrediction = recommendationService.getPrediction(2, 3);
         while (recommendationService.getModelNumber() == 1) {
         }
-        Double secondPrediction = recommendationService.getPrediction(2, 3);
-        assertNotEquals(firstPrediction, secondPrediction);
-        assertThat(firstPrediction, lessThan(secondPrediction));
+        Optional<Double> secondPrediction = recommendationService.getPrediction(2, 3);
+        assertNotEquals(firstPrediction.get(), secondPrediction.get());
+        assertThat(firstPrediction.get(), lessThan(secondPrediction.get()));
     }
 }

src/test/java/ch/sebastianmue/javarank/service/RecommendationServiceTest.java

@@ -9,11 +9,13 @@
 import java.util.ArrayList;
 
 import static org.hamcrest.MatcherAssert.assertThat;
+import static org.hamcrest.Matchers.is;
 import static org.hamcrest.Matchers.lessThan;
 import static org.junit.Assert.assertNotEquals;
+import static org.junit.Assert.assertTrue;
 
 /**
- * The testing was keepen simple, as sparkml is already tested. The testing focus is on the new features.
+ * The testing was keepn simple, as sparkml is already tested. The testing focus is on the new features.
  * This test just verifies, that sparkml was correctly called
  */
 public class RecommendationServiceTest {
@@ -36,13 +38,22 @@ public static void initModel() {
 
     @Test
     public void shouldNotForgetKnownRating() throws ModelNotReadyException {
-        assertThat(recommendationService.getPrediction(1, 2), lessThan(recommendationService.getPrediction(1, 1)));
+        assertThat(recommendationService.getPrediction(1, 2).get(), lessThan(recommendationService.getPrediction(1, 1).get()));
         assertNotEquals(0.0, recommendationService.getPrediction(1, 1));
     }
 
     @Test
     public void shouldPredictFromOtherUser() throws ModelNotReadyException {
-        assertThat(recommendationService.getPrediction(2, 2), lessThan(recommendationService.getPrediction(1, 3)));
+        assertThat(recommendationService.getPrediction(2, 2).get(), lessThan(recommendationService.getPrediction(1, 3).get()));
+    }
+    @Test
+    public void shouldHandleUnknownUser() throws ModelNotReadyException {
+        assertTrue(recommendationService.getPrediction(3,2).isEmpty());
+    }
+
+    @Test
+    public void shouldHandleUnknownProduct() throws ModelNotReadyException {
+        assertTrue(recommendationService.getPrediction(2,4).isEmpty());
     }
 
 }