Created JavaDecisionTree example from example in docs, and corrected doc example as needed.

Author: jkbradley

docs/mllib-decision-tree.md

@@ -167,14 +167,14 @@ println("Training Error = " + trainErr)
 
 <div data-lang="java">
 {% highlight java %}
+import scala.Tuple2;
 import org.apache.spark.api.java.JavaPairRDD;
 import org.apache.spark.api.java.JavaRDD;
 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;
-import scala.Tuple2;
 
 JavaRDD<LabeledPoint> data = ... // data set
 
@@ -186,7 +186,7 @@ String impurity = "gini";
 Integer maxDepth = 5;
 Integer maxBins = 100;
 
-final DecisionTreeModel model = DecisionTree.trainClassifier(data.rdd(), numClasses,
+final DecisionTreeModel model = DecisionTree.trainClassifier(data, numClasses,
   categoricalFeaturesInfo, impurity, maxDepth, maxBins);
 
 // Evaluate model on training instances and compute training error
@@ -198,9 +198,11 @@ JavaPairRDD<Double, Double> predictionAndLabel =
   });
 Double trainErr = 1.0 * predictionAndLabel.filter(new Function<Tuple2<Double, Double>, Boolean>() {
     @Override public Boolean call(Tuple2<Double, Double> pl) {
-      return pl._1() != pl._2();
+      return !pl._1().equals(pl._2());
     }
   }).count() / data.count();
+System.out.print("Training error: " + trainErr);
+System.out.print("Learned model:\n" + model);
 {% endhighlight %}
 </div>
 
@@ -289,7 +291,7 @@ String impurity = "variance";
 Integer maxDepth = 5;
 Integer maxBins = 100;
 
-final DecisionTreeModel model = DecisionTree.trainRegressor(data.rdd(),
+final DecisionTreeModel model = DecisionTree.trainRegressor(data,
   categoricalFeaturesInfo, impurity, maxDepth, maxBins);
 
 // Evaluate model on training instances and compute training error
@@ -305,6 +307,8 @@ Double trainMSE = predictionAndLabel.map(new Function<Tuple2<Double, Double>, Do
       return diff * diff;
     }
   }).sum() / data.count();
+System.out.print("Training Mean Squared Error: " + trainMSE);
+System.out.print("Learned model:\n" + model);
 {% endhighlight %}
 </div>
 

examples/src/main/java/org/apache/spark/examples/mllib/JavaDecisionTree.java

@@ -0,0 +1,115 @@
+/*
+ * Licensed to the Apache Software Foundation (ASF) under one or more
+ * contributor license agreements.  See the NOTICE file distributed with
+ * this work for additional information regarding copyright ownership.
+ * The ASF licenses this file to You under the Apache License, Version 2.0
+ * (the "License"); you may not use this file except in compliance with
+ * the License.  You may obtain a copy of the License at
+ *
+ *    http://www.apache.org/licenses/LICENSE-2.0
+ *
+ * Unless required by applicable law or agreed to in writing, software
+ * distributed under the License is distributed on an "AS IS" BASIS,
+ * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+ * See the License for the specific language governing permissions and
+ * limitations under the License.
+ */
+
+package org.apache.spark.examples.mllib;
+
+import java.util.HashMap;
+
+import scala.reflect.ClassTag;
+import scala.Tuple2;
+
+import org.apache.spark.api.java.function.Function2;
+      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;
+import org.apache.spark.mllib.util.MLUtils;
+import org.apache.spark.SparkConf;
+
+
+/**
+ * Classification and regression using decision trees.
+ */
+public final class JavaDecisionTree {
+
+  public static void main(String[] args) {
+    if (args.length != 1) {
+      System.err.println("Usage: JavaDecisionTree <libsvm format data file>");
+      System.exit(1);
+    }
+    SparkConf sparkConf = new SparkConf().setAppName("JavaDecisionTree");
+    JavaSparkContext sc = new JavaSparkContext(sparkConf);
+    String datapath = args[0];
+
+    JavaRDD<LabeledPoint> data = JavaRDD.fromRDD(MLUtils.loadLibSVMFile(sc.sc(), datapath));
+
+    // Compute the number of classes from the data.
+    Integer numClasses = data.map(new Function<LabeledPoint, Double>() {
+      @Override public Double call(LabeledPoint p) {
+        return p.label();
+      }
+    }).countByValue().size();
+    // Empty categoricalFeaturesInfo indicates all features are continuous.
+    HashMap<Integer, Integer> categoricalFeaturesInfo = new HashMap<Integer, Integer>();
+    String impurity = "gini";
+    Integer maxDepth = 5;
+    Integer maxBins = 100;
+
+    // Train a DecisionTree model for classification.
+    final DecisionTreeModel model = DecisionTree.trainClassifier(data, numClasses,
+      categoricalFeaturesInfo, impurity, maxDepth, maxBins);
+
+    // Evaluate model on training instances and compute training error
+    JavaPairRDD<Double, Double> predictionAndLabel =
+      data.mapToPair(new PairFunction<LabeledPoint, Double, Double>() {
+        @Override public Tuple2<Double, Double> call(LabeledPoint p) {
+          return new Tuple2<Double, Double>(model.predict(p.features()), p.label());
+        }
+      });
+    Double trainErr =
+      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() / data.count();
+    System.out.print("Training error: " + trainErr);
+    System.out.print("Learned classification tree model:\n" + model);
+
+    // Train a DecisionTree model for regression.
+    impurity = "variance";
+
+    final DecisionTreeModel regressionModel = DecisionTree.trainRegressor(data,
+        categoricalFeaturesInfo, impurity, maxDepth, maxBins);
+
+    // Evaluate model on training instances and compute training error
+    JavaPairRDD<Double, Double> regressorPredictionAndLabel =
+      data.mapToPair(new PairFunction<LabeledPoint, Double, Double>() {
+        @Override public Tuple2<Double, Double> call(LabeledPoint p) {
+          return new Tuple2<Double, Double>(regressionModel.predict(p.features()), p.label());
+        }
+      });
+    Double trainMSE =
+      regressorPredictionAndLabel.map(new Function<Tuple2<Double, Double>, Double>() {
+        @Override public Double call(Tuple2<Double, Double> pl) {
+          Double diff = pl._1() - pl._2();
+          return diff * diff;
+        }
+      }).reduce(new Function2<Double, Double, Double>() {
+        @Override public Double call(Double a, Double b) {
+          return a + b;
+        }
+      }) / data.count();
+    System.out.print("Training Mean Squared Error: " + trainMSE);
+    System.out.print("Learned regression tree model:\n" + regressionModel);
+
+    sc.stop();
+  }
+}