[SPARK-11445][DOCS] Replaced example code in mllib-ensembles.md using…

… include_example

I have made the required changes and tested.
Kindly review the changes.

Author: Rishabh Bhardwaj <rbnext29@gmail.com>

Closes #9407 from rishabhbhardwaj/SPARK-11445.

(cherry picked from commit 61a2848)
Signed-off-by: Xiangrui Meng <meng@databricks.com>

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

@@ -0,0 +1,92 @@
+/*
+ * 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.GradientBoostedTrees;
+import org.apache.spark.mllib.tree.configuration.BoostingStrategy;
+import org.apache.spark.mllib.tree.model.GradientBoostedTreesModel;
+import org.apache.spark.mllib.util.MLUtils;
+// $example off$
+
+public class JavaGradientBoostingClassificationExample {
+  public static void main(String[] args) {
+    // $example on$
+    SparkConf sparkConf = new SparkConf()
+      .setAppName("JavaGradientBoostedTreesClassificationExample");
+    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];
+
+    // Train a GradientBoostedTrees model.
+    // The defaultParams for Classification use LogLoss by default.
+    BoostingStrategy boostingStrategy = BoostingStrategy.defaultParams("Classification");
+    boostingStrategy.setNumIterations(3); // Note: Use more iterations in practice.
+    boostingStrategy.getTreeStrategy().setNumClasses(2);
+    boostingStrategy.getTreeStrategy().setMaxDepth(5);
+    // Empty categoricalFeaturesInfo indicates all features are continuous.
+    Map<Integer, Integer> categoricalFeaturesInfo = new HashMap<Integer, Integer>();
+    boostingStrategy.treeStrategy().setCategoricalFeaturesInfo(categoricalFeaturesInfo);
+
+    final GradientBoostedTreesModel model =
+      GradientBoostedTrees.train(trainingData, boostingStrategy);
+
+    // 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 GBT model:\n" + model.toDebugString());
+
+    // Save and load model
+    model.save(jsc.sc(), "target/tmp/myGradientBoostingClassificationModel");
+    GradientBoostedTreesModel sameModel = GradientBoostedTreesModel.load(jsc.sc(),
+      "target/tmp/myGradientBoostingClassificationModel");
+    // $example off$
+  }
+
+}

examples/src/main/java/org/apache/spark/examples/mllib/JavaGradientBoostingRegressionExample.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.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.GradientBoostedTrees;
+import org.apache.spark.mllib.tree.configuration.BoostingStrategy;
+import org.apache.spark.mllib.tree.model.GradientBoostedTreesModel;
+import org.apache.spark.mllib.util.MLUtils;
+// $example off$
+
+public class JavaGradientBoostingRegressionExample {
+  public static void main(String[] args) {
+    // $example on$
+    SparkConf sparkConf = new SparkConf()
+      .setAppName("JavaGradientBoostedTreesRegressionExample");
+    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];
+
+    // Train a GradientBoostedTrees model.
+    // The defaultParams for Regression use SquaredError by default.
+    BoostingStrategy boostingStrategy = BoostingStrategy.defaultParams("Regression");
+    boostingStrategy.setNumIterations(3); // Note: Use more iterations in practice.
+    boostingStrategy.getTreeStrategy().setMaxDepth(5);
+    // Empty categoricalFeaturesInfo indicates all features are continuous.
+    Map<Integer, Integer> categoricalFeaturesInfo = new HashMap<Integer, Integer>();
+    boostingStrategy.treeStrategy().setCategoricalFeaturesInfo(categoricalFeaturesInfo);
+
+    final GradientBoostedTreesModel model =
+      GradientBoostedTrees.train(trainingData, boostingStrategy);
+
+    // 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 GBT model:\n" + model.toDebugString());
+
+    // Save and load model
+    model.save(jsc.sc(), "target/tmp/myGradientBoostingRegressionModel");
+    GradientBoostedTreesModel sameModel = GradientBoostedTreesModel.load(jsc.sc(),
+      "target/tmp/myGradientBoostingRegressionModel");
+    // $example off$
+  }
+}

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

@@ -0,0 +1,89 @@
+/*
+ * 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 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.RandomForest;
+import org.apache.spark.mllib.tree.model.RandomForestModel;
+import org.apache.spark.mllib.util.MLUtils;
+// $example off$
+
+public class JavaRandomForestClassificationExample {
+  public static void main(String[] args) {
+    // $example on$
+    SparkConf sparkConf = new SparkConf().setAppName("JavaRandomForestClassificationExample");
+    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];
+
+    // Train a RandomForest model.
+    // Empty categoricalFeaturesInfo indicates all features are continuous.
+    Integer numClasses = 2;
+    HashMap<Integer, Integer> categoricalFeaturesInfo = new HashMap<Integer, Integer>();
+    Integer numTrees = 3; // Use more in practice.
+    String featureSubsetStrategy = "auto"; // Let the algorithm choose.
+    String impurity = "gini";
+    Integer maxDepth = 5;
+    Integer maxBins = 32;
+    Integer seed = 12345;
+
+    final RandomForestModel model = RandomForest.trainClassifier(trainingData, numClasses,
+      categoricalFeaturesInfo, numTrees, featureSubsetStrategy, impurity, maxDepth, maxBins,
+      seed);
+
+    // 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 forest model:\n" + model.toDebugString());
+
+    // Save and load model
+    model.save(jsc.sc(), "target/tmp/myRandomForestClassificationModel");
+    RandomForestModel sameModel = RandomForestModel.load(jsc.sc(),
+      "target/tmp/myRandomForestClassificationModel");
+    // $example off$
+  }
+}

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

@@ -0,0 +1,95 @@
+/*
+ * 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.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.RandomForest;
+import org.apache.spark.mllib.tree.model.RandomForestModel;
+import org.apache.spark.mllib.util.MLUtils;
+import org.apache.spark.SparkConf;
+// $example off$
+
+public class JavaRandomForestRegressionExample {
+  public static void main(String[] args) {
+    // $example on$
+    SparkConf sparkConf = new SparkConf().setAppName("JavaRandomForestRegressionExample");
+    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>();
+    Integer numTrees = 3; // Use more in practice.
+    String featureSubsetStrategy = "auto"; // Let the algorithm choose.
+    String impurity = "variance";
+    Integer maxDepth = 4;
+    Integer maxBins = 32;
+    Integer seed = 12345;
+    // Train a RandomForest model.
+    final RandomForestModel model = RandomForest.trainRegressor(trainingData,
+      categoricalFeaturesInfo, numTrees, featureSubsetStrategy, impurity, maxDepth, maxBins, seed);
+
+    // 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;
+        }
+      }) / testData.count();
+    System.out.println("Test Mean Squared Error: " + testMSE);
+    System.out.println("Learned regression forest model:\n" + model.toDebugString());
+
+    // Save and load model
+    model.save(jsc.sc(), "target/tmp/myRandomForestRegressionModel");
+    RandomForestModel sameModel = RandomForestModel.load(jsc.sc(),
+      "target/tmp/myRandomForestRegressionModel");
+    // $example off$
+  }
+}