test for evaluator #3

commons/pom.xml

@@ -1,15 +1,16 @@
 <?xml version="1.0"?>
-<project xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd" xmlns="http://maven.apache.org/POM/4.0.0"
-    xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
-  <modelVersion>4.0.0</modelVersion>
-  <parent>
-    <groupId>de.hpi.mmds.wiki</groupId>
-    <artifactId>wiki</artifactId>
-    <version>0.0.1-SNAPSHOT</version>
-  </parent>
-  <artifactId>commons</artifactId>
-  <name>mmds-wiki-commons</name>
-  <url>http://maven.apache.org</url>
-  <dependencies>
-  </dependencies>
+<project xsi:schemaLocation="http://maven.apache.org/POM/4.0.0 http://maven.apache.org/xsd/maven-4.0.0.xsd"
+         xmlns="http://maven.apache.org/POM/4.0.0"
+         xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
+    <modelVersion>4.0.0</modelVersion>
+    <parent>
+        <groupId>de.hpi.mmds.wiki</groupId>
+        <artifactId>wiki</artifactId>
+        <version>0.0.1-SNAPSHOT</version>
+    </parent>
+    <artifactId>commons</artifactId>
+    <name>mmds-wiki-commons</name>
+    <url>http://maven.apache.org</url>
+    <dependencies>
+    </dependencies>
 </project>

commons/src/main/java/de/hpi/mmds/wiki/Edits.java

@@ -1,14 +1,13 @@
 package de.hpi.mmds.wiki;
 
-import java.io.Serializable;
-
+import de.hpi.mmds.wiki.spark.KeyFilter;
 import org.apache.spark.api.java.JavaPairRDD;
 import org.apache.spark.api.java.JavaRDD;
 import org.apache.spark.api.java.JavaSparkContext;
 import org.apache.spark.api.java.function.PairFunction;
-
 import scala.Tuple2;
-import de.hpi.mmds.wiki.spark.KeyFilter;
+
+import java.io.Serializable;
 
 public class Edits implements Serializable {
 

commons/src/main/java/de/hpi/mmds/wiki/Evaluator.java

@@ -2,7 +2,6 @@
 
 import org.apache.commons.io.FileUtils;
 import org.apache.spark.api.java.JavaRDD;
-import org.apache.spark.api.java.JavaSparkContext;
 
 import java.io.BufferedWriter;
 import java.io.File;
@@ -19,25 +18,60 @@ public class Evaluator {
 	private final File out;
 	private final Recommender recommender;
 
+	public static class Result<T> {
+
+		private final Set<T> recommendations;
+		private final Set<T> groundTruth;
+		private final Set<T> intersect;
+
+		public Result(Set<T> recommendations, Set<T> groundTruth) {
+			this.recommendations = recommendations;
+			this.groundTruth = groundTruth;
+			this.intersect = new HashSet<>(groundTruth);
+			this.intersect.retainAll(recommendations);
+		}
+
+		public double precision() {
+			return recommendations.isEmpty() ? 0 : (double) intersect.size() / recommendations.size();
+		}
+
+		public double recall() {
+			return (double) intersect.size() / groundTruth.size();
+		}
+
+		public String printResult() {
+			StringBuilder sb = new StringBuilder();
+			sb.append("Recommendations: " + recommendations + "\n");
+			sb.append("Gold standard: " + groundTruth + "\n");
+			sb.append("Matches: " + intersect + "\n");
+			sb.append("Precision: " + precision() + "\n");
+			sb.append("Recall: " + recall());
+			return sb.toString();
+		}
+	}
+
 	public Evaluator(Recommender recommender, Edits test, Edits training, File out) {
 		this.recommender = recommender;
 		this.training = training;
 		this.test = test;
 		this.out = out;
 	}
 
-	public void evaluate(int num, long seed) {
+	public Map<Integer, Result> evaluate(int num, long seed) {
+		Map<Integer, Result> results = new HashMap<>();
 		List<Integer> uids = new ArrayList<>(test.getUsers().intersection(training.getUsers()).collect());
 		int i = 0;
 		double totalPrecision = 0.0;
 		double totalRecall = 0.0;
-		try {
-			FileUtils.forceDelete(out);
-		} catch (IOException e) {
-			throw new RuntimeException("Could not delete output file " + out.getPath(), e);
+		if (out.exists()) {
+			try {
+				FileUtils.forceDelete(out);
+			} catch (IOException e) {
+				throw new RuntimeException("Could not delete output file " + out.getPath(), e);
+			}
 		}
 		Collections.shuffle(uids, new Random(seed));
-		try (BufferedWriter writer = new BufferedWriter(new FileWriter(this.out))) {
+		try (BufferedWriter writer = new BufferedWriter(new FileWriter(out))) {
 			for (int user : uids) {
 				if (i >= num) {
 					break;
@@ -48,27 +82,21 @@ public void evaluate(int num, long seed) {
 				Set<Integer> groundTruth = new HashSet<>(a);
 				groundTruth.removeAll(p);
 				if (!groundTruth.isEmpty()) {
-					List<Integer> recommendations = recommender.recommend(user, articles, NUM_RECOMMENDATIONS).stream()
-							.map(Recommendation::getArticle).collect(Collectors.toList());
-					Set<Integer> intersect = new HashSet<>(groundTruth);
-					intersect.retainAll(recommendations);
-					double precision = recommendations.isEmpty() ?
-							0 :
-							(double) intersect.size() / recommendations.size();
-					double recall = (double) intersect.size() / groundTruth.size();
-					totalPrecision += precision;
-					totalRecall += recall;
+					Set<Integer> recommendations = recommender.recommend(user, articles, NUM_RECOMMENDATIONS).stream()
+							.map(Recommendation::getArticle).collect(Collectors.toSet());
+					Result<Integer> result = new Result<>(recommendations, groundTruth);
+					results.put(user, result);
+					totalPrecision += result.precision();
+					totalRecall += result.recall();
 					i++;
 					writer.write("User: " + user + "\n");
-					writer.write("Recommendations: " + recommendations + "\n");
-					writer.write("Gold standard: " + groundTruth + "\n");
-					writer.write("Matches: " + intersect + "\n");
-					writer.write("Precision: " + precision + "\n");
+					writer.write(result.printResult());
+					writer.newLine();
 					writer.write("AVG Precision: " + totalPrecision / i + "\n");
-					writer.write("Recall: " + recall + "\n");
 					writer.write("AVG Recall: " + totalRecall / i + "\n");
 					writer.write("Processed: " + i + "\n");
-					writer.write("---\n");
+					writer.write("---");
+					writer.newLine();
 					writer.flush();
 				}
 			}
@@ -79,9 +107,10 @@ public void evaluate(int num, long seed) {
 			System.out.println("AVG Precision: " + totalPrecision / i);
 			System.out.println("AVG Recall: " + totalRecall / i);
 		}
+		return results;
 	}
 
-	public void evaluate(int num) {
-		evaluate(num, 1L);
+	public Map<Integer, Result> evaluate(int num) {
+		return evaluate(num, 1L);
 	}
 }

commons/src/main/java/de/hpi/mmds/wiki/MultiRecommender.java

@@ -3,7 +3,10 @@
 import org.apache.spark.api.java.JavaRDD;
 import scala.Tuple2;
 
-import java.util.*;
+import java.util.ArrayList;
+import java.util.HashMap;
+import java.util.List;
+import java.util.Map;
 import java.util.stream.Collectors;
 
 public class MultiRecommender implements Recommender {

commons/src/main/java/de/hpi/mmds/wiki/Recommender.java

@@ -1,9 +1,9 @@
 package de.hpi.mmds.wiki;
 
-import java.util.List;
-
 import org.apache.spark.api.java.JavaRDD;
 
+import java.util.List;
+
 public interface Recommender {
 
 	default List<Recommendation> recommend(int userId, JavaRDD<Integer> articles) {

commons/src/main/java/de/hpi/mmds/wiki/SparkUtil.java

@@ -2,7 +2,6 @@
 
 import org.apache.spark.SparkConf;
 import org.apache.spark.api.java.JavaSparkContext;
-
 import scala.Tuple2;
 
 public class SparkUtil {

commons/src/main/java/de/hpi/mmds/wiki/spark/KeyFilter.java

@@ -1,7 +1,6 @@
 package de.hpi.mmds.wiki.spark;
 
 import org.apache.spark.api.java.function.Function;
-
 import scala.Tuple2;
 
 public final class KeyFilter<T> implements Function<Tuple2<T, T>, Boolean> {

commons/src/test/java/de/hpi/mmds/wiki/EditsTest.java

@@ -1,18 +1,17 @@
 package de.hpi.mmds.wiki;
 
-import static org.junit.Assert.assertEquals;
-import static org.junit.Assert.assertTrue;
-
-import java.util.List;
-import java.util.Map;
-
 import org.apache.spark.api.java.JavaSparkContext;
 import org.junit.AfterClass;
 import org.junit.BeforeClass;
 import org.junit.Test;
-
 import scala.Tuple2;
 
+import java.util.List;
+import java.util.Map;
+
+import static org.junit.Assert.assertEquals;
+import static org.junit.Assert.assertTrue;
+
 public class EditsTest {
 
 	private static Edits edits;
@@ -21,7 +20,8 @@ public class EditsTest {
 	@BeforeClass
 	public static void setup() {
 		jsc = SparkUtil.getContext();
-		edits = new Edits(jsc, Thread.currentThread().getContextClassLoader().getResource("test_data.txt").getPath());
+		edits = new Edits(jsc,
+				Thread.currentThread().getContextClassLoader().getResource("training_data.txt").getPath());
 	}
 
 	@AfterClass

commons/src/test/java/de/hpi/mmds/wiki/EvaluatorTest.java

@@ -0,0 +1,60 @@
+package de.hpi.mmds.wiki;
+
+import de.hpi.mmds.wiki.Evaluator.Result;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.junit.*;
+
+import java.io.File;
+import java.util.Arrays;
+import java.util.Map;
+
+import static org.junit.Assert.assertEquals;
+
+public class EvaluatorTest {
+
+	public static final double DOUBLE_TOLERANCE = 0.001;
+	private static Edits test;
+	private static Edits training;
+	private static JavaSparkContext jsc;
+	private static Recommender recommender;
+	private File out;
+
+	@BeforeClass
+	public static void setupClass() {
+		jsc = SparkUtil.getContext();
+		test = new Edits(jsc, Thread.currentThread().getContextClassLoader().getResource("test_data.txt").getPath());
+		training = new Edits(jsc,
+				Thread.currentThread().getContextClassLoader().getResource("training_data.txt").getPath());
+		recommender = (userId, articles, howMany) -> Arrays
+				.asList(new Recommendation(1.0, 10), new Recommendation(1.0, 11));
+	}
+
+	@AfterClass
+	public static void tearDownClass() {
+		jsc.close();
+	}
+
+	@Before
+	public void setup() {
+		out = new File("out.txt");
+	}
+
+	@After
+	public void tearDown() {
+		out.delete();
+	}
+
+	@Test
+	public void test() {
+		Evaluator eval = new Evaluator(recommender, test, training, out);
+		Map<Integer, Result> results = eval.evaluate(3);
+		assertEquals(3, results.size());
+		assertEquals(1.0, results.get(1).precision(), DOUBLE_TOLERANCE);
+		assertEquals(1.0, results.get(1).recall(), DOUBLE_TOLERANCE);
+		assertEquals(0.5, results.get(2).precision(), DOUBLE_TOLERANCE);
+		assertEquals(1.0 / 3, results.get(2).recall(), DOUBLE_TOLERANCE);
+		assertEquals(0.0, results.get(3).precision(), DOUBLE_TOLERANCE);
+		assertEquals(0.0, results.get(3).recall(), DOUBLE_TOLERANCE);
+	}
+
+}

commons/src/test/java/de/hpi/mmds/wiki/MultiRecommenderTest.java

@@ -5,12 +5,13 @@
 import org.junit.AfterClass;
 import org.junit.BeforeClass;
 import org.junit.Test;
-import static org.junit.Assert.*;
 
 import java.util.Arrays;
 import java.util.Collections;
 import java.util.List;
 
+import static org.junit.Assert.assertEquals;
+
 public class MultiRecommenderTest {
 
 	public static final double DOUBLE_TOLERANCE = 0.001;
@@ -20,7 +21,8 @@ public class MultiRecommenderTest {
 	@BeforeClass
 	public static void setup() {
 		jsc = SparkUtil.getContext();
-		edits = new Edits(jsc, Thread.currentThread().getContextClassLoader().getResource("test_data.txt").getPath());
+		edits = new Edits(jsc,
+				Thread.currentThread().getContextClassLoader().getResource("training_data.txt").getPath());
 	}
 
 	@AfterClass

commons/src/test/resources/test_data.txt

@@ -1,7 +1,6 @@
-1,1
-1,2
-2,1
-2,3
-2,4
-3,2
-3,5
+1,10
+1,11
+2,10
+2,12
+2,13
+3,13

commons/src/test/resources/training_data.txt

@@ -0,0 +1,7 @@
+1,1
+1,2
+2,1
+2,3
+2,4
+3,2
+3,5