Added Java unit test, data, and main method for Naive Bayes

Also fixes mains of a few other algorithms to print the final model

1 files changed, 72 insertions, 0 deletions

diff --git a/mllib/src/test/java/org/apache/spark/mllib/classification/JavaNaiveBayesSuite.java b/mllib/src/test/java/org/apache/spark/mllib/classification/JavaNaiveBayesSuite.java
new file mode 100644
index 0000000000..23ea3548b9
--- /dev/null
+++ b/mllib/src/test/java/org/apache/spark/mllib/classification/JavaNaiveBayesSuite.java
@@ -0,0 +1,72 @@
+package org.apache.spark.mllib.classification;
+
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import org.junit.After;
+import org.junit.Assert;
+import org.junit.Before;
+import org.junit.Test;
+
+import java.io.Serializable;
+import java.util.Arrays;
+import java.util.List;
+
+public class JavaNaiveBayesSuite implements Serializable {
+  private transient JavaSparkContext sc;
+
+  @Before
+  public void setUp() {
+    sc = new JavaSparkContext("local", "JavaNaiveBayesSuite");
+  }
+
+  @After
+  public void tearDown() {
+    sc.stop();
+    sc = null;
+    System.clearProperty("spark.driver.port");
+  }
+
+  private static final List<LabeledPoint> POINTS = Arrays.asList(
+    new LabeledPoint(0, new double[] {1.0, 0.0, 0.0}),
+    new LabeledPoint(0, new double[] {2.0, 0.0, 0.0}),
+    new LabeledPoint(1, new double[] {0.0, 1.0, 0.0}),
+    new LabeledPoint(1, new double[] {0.0, 2.0, 0.0}),
+    new LabeledPoint(2, new double[] {0.0, 0.0, 1.0}),
+    new LabeledPoint(2, new double[] {0.0, 0.0, 2.0})
+  );
+
+  private int validatePrediction(List<LabeledPoint> points, NaiveBayesModel model) {
+    int correct = 0;
+    for (LabeledPoint p: points) {
+      if (model.predict(p.features()) == p.label()) {
+        correct += 1;
+      }
+    }
+    return correct;
+  }
+
+  @Test
+  public void runUsingConstructor() {
+    JavaRDD<LabeledPoint> testRDD = sc.parallelize(POINTS, 2).cache();
+
+    NaiveBayes nb = new NaiveBayes().setLambda(1.0);
+    NaiveBayesModel model = nb.run(testRDD.rdd());
+
+    int numAccurate = validatePrediction(POINTS, model);
+    Assert.assertEquals(POINTS.size(), numAccurate);
+  }
+
+  @Test
+  public void runUsingStaticMethods() {
+    JavaRDD<LabeledPoint> testRDD = sc.parallelize(POINTS, 2).cache();
+
+    NaiveBayesModel model1 = NaiveBayes.train(testRDD.rdd());
+    int numAccurate1 = validatePrediction(POINTS, model1);
+    Assert.assertEquals(POINTS.size(), numAccurate1);
+
+    NaiveBayesModel model2 = NaiveBayes.train(testRDD.rdd(), 0.5);
+    int numAccurate2 = validatePrediction(POINTS, model2);
+    Assert.assertEquals(POINTS.size(), numAccurate2);
+  }
+}