[SPARK-11382] Replace example code in mllib-decision-tree.md using include_example

https://issues.apache.org/jira/browse/SPARK-11382

B.T.W. I fix an error in naive_bayes_example.py.

Author: Xusen Yin <yinxusen@gmail.com>

Closes #9596 from yinxusen/SPARK-11382.

2 files changed, 187 insertions, 0 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
new file mode 100644
index 0000000000..5839b0cf8a
--- /dev/null
+++ b/examples/src/main/java/org/apache/spark/examples/mllib/JavaDecisionTreeClassificationExample.java
@@ -0,0 +1,91 @@
+/*
+ * 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;
+
+// $example on$
+import java.util.HashMap;
+import java.util.Map;
+
+import scala.Tuple2;
+
+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;
+import org.apache.spark.mllib.util.MLUtils;
+// $example off$
+
+class JavaDecisionTreeClassificationExample {
+
+  public static void main(String[] args) {
+
+    // $example on$
+    SparkConf sparkConf = new SparkConf().setAppName("JavaDecisionTreeClassificationExample");
+    JavaSparkContext jsc = new JavaSparkContext(sparkConf);
+
+    // Load and parse the data file.
+    String datapath = "data/mllib/sample_libsvm_data.txt";
+    JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(jsc.sc(), datapath).toJavaRDD();
+    // Split the data into training and test sets (30% held out for testing)
+    JavaRDD<LabeledPoint>[] splits = data.randomSplit(new double[]{0.7, 0.3});
+    JavaRDD<LabeledPoint> trainingData = splits[0];
+    JavaRDD<LabeledPoint> testData = splits[1];
+
+    // Set parameters.
+    //  Empty categoricalFeaturesInfo indicates all features are continuous.
+    Integer numClasses = 2;
+    Map<Integer, Integer> categoricalFeaturesInfo = new HashMap<Integer, Integer>();
+    String impurity = "gini";
+    Integer maxDepth = 5;
+    Integer maxBins = 32;
+
+    // Train a DecisionTree model for classification.
+    final 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<Double, Double>(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();
+
+    System.out.println("Test Error: " + testErr);
+    System.out.println("Learned classification tree model:\n" + model.toDebugString());
+
+    // Save and load model
+    model.save(jsc.sc(), "target/tmp/myDecisionTreeClassificationModel");
+    DecisionTreeModel sameModel = DecisionTreeModel
+      .load(jsc.sc(), "target/tmp/myDecisionTreeClassificationModel");
+    // $example off$
+  }
+}
diff --git a/examples/src/main/java/org/apache/spark/examples/mllib/JavaDecisionTreeRegressionExample.java b/examples/src/main/java/org/apache/spark/examples/mllib/JavaDecisionTreeRegressionExample.java
new file mode 100644
index 0000000000..ccde578249
--- /dev/null
+++ b/examples/src/main/java/org/apache/spark/examples/mllib/JavaDecisionTreeRegressionExample.java
@@ -0,0 +1,96 @@
+/*
+ * 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;
+
+// $example on$
+import java.util.HashMap;
+import java.util.Map;
+
+import scala.Tuple2;
+
+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.Function2;
+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;
+// $example off$
+
+class JavaDecisionTreeRegressionExample {
+
+  public static void main(String[] args) {
+
+    // $example on$
+    SparkConf sparkConf = new SparkConf().setAppName("JavaDecisionTreeRegressionExample");
+    JavaSparkContext jsc = new JavaSparkContext(sparkConf);
+
+    // Load and parse the data file.
+    String datapath = "data/mllib/sample_libsvm_data.txt";
+    JavaRDD<LabeledPoint> data = MLUtils.loadLibSVMFile(jsc.sc(), datapath).toJavaRDD();
+    // Split the data into training and test sets (30% held out for testing)
+    JavaRDD<LabeledPoint>[] splits = data.randomSplit(new double[]{0.7, 0.3});
+    JavaRDD<LabeledPoint> trainingData = splits[0];
+    JavaRDD<LabeledPoint> testData = splits[1];
+
+    // Set parameters.
+    // Empty categoricalFeaturesInfo indicates all features are continuous.
+    Map<Integer, Integer> categoricalFeaturesInfo = new HashMap<Integer, Integer>();
+    String impurity = "variance";
+    Integer maxDepth = 5;
+    Integer maxBins = 32;
+
+    // Train a DecisionTree model.
+    final DecisionTreeModel model = DecisionTree.trainRegressor(trainingData,
+      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<Double, Double>(model.predict(p.features()), p.label());
+      }
+    });
+    Double testMSE =
+      predictionAndLabel.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.println("Test Mean Squared Error: " + testMSE);
+    System.out.println("Learned regression tree model:\n" + model.toDebugString());
+
+    // Save and load model
+    model.save(jsc.sc(), "target/tmp/myDecisionTreeRegressionModel");
+    DecisionTreeModel sameModel = DecisionTreeModel
+      .load(jsc.sc(), "target/tmp/myDecisionTreeRegressionModel");
+    // $example off$
+  }
+}