Big Data-EmilioCastilloW

pom.xml

@@ -12,6 +12,8 @@
 
   <properties>
     <project.build.sourceEncoding>UTF-8</project.build.sourceEncoding>
+    <maven.compiler.source>7</maven.compiler.source>
+    <maven.compiler.target>7</maven.compiler.target>
   </properties>
 
   <dependencies>

src/test/java/nearsoft/academy/bigdata/recommendation/MovieRecommender.java

@@ -0,0 +1,116 @@
+package nearsoft.academy.bigdata.recommendation;
+import java.io.BufferedReader;
+import java.io.BufferedWriter;
+import java.io.File;
+import java.io.FileReader;
+import java.io.FileWriter;
+import java.io.IOException;
+import java.util.ArrayList;
+import java.util.HashMap;
+import java.util.List;
+import org.apache.mahout.cf.taste.common.TasteException;
+import org.apache.mahout.cf.taste.impl.model.file.FileDataModel;
+import org.apache.mahout.cf.taste.impl.neighborhood.ThresholdUserNeighborhood;
+import org.apache.mahout.cf.taste.impl.recommender.GenericUserBasedRecommender;
+import org.apache.mahout.cf.taste.impl.similarity.PearsonCorrelationSimilarity;
+import org.apache.mahout.cf.taste.model.DataModel;
+import org.apache.mahout.cf.taste.neighborhood.UserNeighborhood;
+import org.apache.mahout.cf.taste.recommender.RecommendedItem;
+import org.apache.mahout.cf.taste.recommender.UserBasedRecommender;
+import org.apache.mahout.cf.taste.similarity.UserSimilarity;
+
+
+public class MovieRecommender {
+
+
+    private int totalReviews = 0;
+    private int totalUsers = 0;
+    private int totalProducts = 0;
+
+    private int ProductNumb=0;
+    private int UserNumb=0;
+
+    private final HashMap<String, Integer> usersHash = new HashMap();
+    private final HashMap<String, Integer> productsHash = new HashMap();
+    private final HashMap<Integer, String> InverseProductsHash = new HashMap();
+
+
+    // Constructor
+    public MovieRecommender(String path) throws IOException, TasteException {
+       File file = new File(path);
+        BufferedReader br = new BufferedReader(new FileReader(file));
+        File reviews = new File("RMovies.csv");
+        FileWriter fw = new FileWriter(reviews);
+        BufferedWriter wr = new BufferedWriter(fw);
+
+// Ocupamos le hashmap
+
+        String userId = "", productId = "", score;
+         String line;
+        while ((line = br.readLine()) != null) {
+          switch (line.split(" ")[0]) {
+              case "product/productId:":
+                  productId = line.split(" ")[1];
+                  if (!productsHash.containsKey(productId)) {
+                    totalProducts++;
+                    productsHash.put(productId, totalProducts);
+                    InverseProductsHash.put(totalProducts, productId);
+                    ProductNumb = totalProducts;
+                  }
+                  else{
+                      ProductNumb = productsHash.get(productId); 
+                  }
+                  break;
+              case "review/userId:":
+                  userId = line.split(" ")[1];
+                  if (!usersHash.containsKey(userId)) {
+                    totalUsers++;
+                    usersHash.put(userId,totalUsers);
+                    UserNumb = totalUsers;
+                  }
+                  else{
+                      UserNumb = usersHash.get(userId); 
+                  }
+                  break;
+              case "review/score:":
+                  score = line.split(" ")[1];
+                  wr.write(UserNumb+","+ProductNumb+","+score+"\n");
+                  totalReviews++;
+                  break;
+          }
+        }
+        br.close();
+        wr.close();
+
+    }
+
+    public int getTotalReviews() {
+        return totalReviews;
+    }
+
+    public int getTotalProducts() {
+        return totalProducts;
+    }
+
+    public int getTotalUsers() {
+        return totalUsers;
+    }
+
+    public List<String> getRecommendationsForUser(String userId) throws TasteException, IOException  {
+        DataModel model = new FileDataModel(new File("RMovies.csv"));
+        UserSimilarity similarity = new PearsonCorrelationSimilarity(model);
+        UserNeighborhood neighborhood = new ThresholdUserNeighborhood(0.1, similarity, model);
+        UserBasedRecommender recommender = new GenericUserBasedRecommender(model, neighborhood, similarity);
+
+        List <RecommendedItem> recommendations = recommender.recommend(usersHash.get(userId), 3);
+
+        List<String> Response = new ArrayList <String>();
+
+        for (RecommendedItem recommendation : recommendations) {
+            Response.add(InverseProductsHash.get((int)recommendation.getItemID()));
+        }
+
+        return Response;
+    }
+
+}

src/test/java/nearsoft/academy/bigdata/recommendation/MovieRecommenderTest.java

@@ -15,7 +15,7 @@ public class MovieRecommenderTest {
     public void testDataInfo() throws IOException, TasteException {
         //download movies.txt.gz from 
         //    http://snap.stanford.edu/data/web-Movies.html
-        MovieRecommender recommender = new MovieRecommender("/path/to/movies.txt.gz");
+        MovieRecommender recommender = new MovieRecommender("D:\\Emilio\\Documents\\Nearsoft\\Academy\\Code\\HwCode\\big-data-exercises-master\\movies.txt");
         assertEquals(7911684, recommender.getTotalReviews());
         assertEquals(253059, recommender.getTotalProducts());
         assertEquals(889176, recommender.getTotalUsers());