1 files changed, 7 insertions, 19 deletions
diff --git a/examples/src/main/java/org/apache/spark/examples/mllib/JavaDecisionTreeClassificationExample.java b/examples/src/main/java/org/apache/spark/examples/mllib/JavaDecisionTreeClassificationExample.java
index 66387b9df5..032c168b94 100644
--- a/examples/src/main/java/org/apache/spark/examples/mllib/JavaDecisionTreeClassificationExample.java
+++ b/examples/src/main/java/org/apache/spark/examples/mllib/JavaDecisionTreeClassificationExample.java
@@ -27,8 +27,6 @@ import org.apache.spark.SparkConf;
 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.Function;
-import org.apache.spark.api.java.function.PairFunction;
 import org.apache.spark.mllib.regression.LabeledPoint;
 import org.apache.spark.mllib.tree.DecisionTree;
 import org.apache.spark.mllib.tree.model.DecisionTreeModel;
@@ -53,31 +51,21 @@ class JavaDecisionTreeClassificationExample {
 
     // Set parameters.
     //  Empty categoricalFeaturesInfo indicates all features are continuous.
-    Integer numClasses = 2;
+    int numClasses = 2;
     Map<Integer, Integer> categoricalFeaturesInfo = new HashMap<>();
     String impurity = "gini";
-    Integer maxDepth = 5;
-    Integer maxBins = 32;
+    int maxDepth = 5;
+    int maxBins = 32;
 
     // Train a DecisionTree model for classification.
-    final DecisionTreeModel model = DecisionTree.trainClassifier(trainingData, numClasses,
+    DecisionTreeModel model = DecisionTree.trainClassifier(trainingData, numClasses,
       categoricalFeaturesInfo, impurity, maxDepth, maxBins);
 
     // Evaluate model on test instances and compute test error
     JavaPairRDD<Double, Double> predictionAndLabel =
-      testData.mapToPair(new PairFunction<LabeledPoint, Double, Double>() {
-        @Override
-        public Tuple2<Double, Double> call(LabeledPoint p) {
-          return new Tuple2<>(model.predict(p.features()), p.label());
-        }
-      });
-    Double testErr =
-      1.0 * predictionAndLabel.filter(new Function<Tuple2<Double, Double>, Boolean>() {
-        @Override
-        public Boolean call(Tuple2<Double, Double> pl) {
-          return !pl._1().equals(pl._2());
-        }
-      }).count() / testData.count();
+      testData.mapToPair(p -> new Tuple2<>(model.predict(p.features()), p.label()));
+    double testErr =
+      predictionAndLabel.filter(pl -> !pl._1().equals(pl._2())).count() / (double) testData.count();
 
     System.out.println("Test Error: " + testErr);
     System.out.println("Learned classification tree model:\n" + model.toDebugString());