[SPARK-11383][DOCS] Replaced example code in mllib-naive-bayes.md/mllib-isotonic-regression.md using include_example

I have made the required changes in mllib-naive-bayes.md/mllib-isotonic-regression.md and also verified them.
Kindle Review it.

Author: Rishabh Bhardwaj <rbnext29@gmail.com>

Closes #9353 from rishabhbhardwaj/SPARK-11383.

6 files changed, 385 insertions, 0 deletions

diff --git a/examples/src/main/java/org/apache/spark/examples/mllib/JavaIsotonicRegressionExample.java b/examples/src/main/java/org/apache/spark/examples/mllib/JavaIsotonicRegressionExample.java
new file mode 100644
index 0000000000..37e709b4cb
--- /dev/null
+++ b/examples/src/main/java/org/apache/spark/examples/mllib/JavaIsotonicRegressionExample.java
@@ -0,0 +1,86 @@
+/*
+ * 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 scala.Tuple2;
+import scala.Tuple3;
+import org.apache.spark.api.java.function.Function;
+import org.apache.spark.api.java.function.PairFunction;
+import org.apache.spark.api.java.JavaDoubleRDD;
+import org.apache.spark.api.java.JavaPairRDD;
+import org.apache.spark.api.java.JavaSparkContext;
+import org.apache.spark.api.java.JavaRDD;
+import org.apache.spark.mllib.regression.IsotonicRegression;
+import org.apache.spark.mllib.regression.IsotonicRegressionModel;
+// $example off$
+import org.apache.spark.SparkConf;
+
+public class JavaIsotonicRegressionExample {
+  public static void main(String[] args) {
+    SparkConf sparkConf = new SparkConf().setAppName("JavaIsotonicRegressionExample");
+    JavaSparkContext jsc = new JavaSparkContext(sparkConf);
+    // $example on$
+    JavaRDD<String> data = jsc.textFile("data/mllib/sample_isotonic_regression_data.txt");
+
+    // Create label, feature, weight tuples from input data with weight set to default value 1.0.
+    JavaRDD<Tuple3<Double, Double, Double>> parsedData = data.map(
+      new Function<String, Tuple3<Double, Double, Double>>() {
+        public Tuple3<Double, Double, Double> call(String line) {
+          String[] parts = line.split(",");
+          return new Tuple3<>(new Double(parts[0]), new Double(parts[1]), 1.0);
+        }
+      }
+    );
+
+    // Split data into training (60%) and test (40%) sets.
+    JavaRDD<Tuple3<Double, Double, Double>>[] splits = parsedData.randomSplit(new double[]{0.6, 0.4}, 11L);
+    JavaRDD<Tuple3<Double, Double, Double>> training = splits[0];
+    JavaRDD<Tuple3<Double, Double, Double>> test = splits[1];
+
+    // Create isotonic regression model from training data.
+    // Isotonic parameter defaults to true so it is only shown for demonstration
+    final IsotonicRegressionModel model = new IsotonicRegression().setIsotonic(true).run(training);
+
+    // Create tuples of predicted and real labels.
+    JavaPairRDD<Double, Double> predictionAndLabel = test.mapToPair(
+      new PairFunction<Tuple3<Double, Double, Double>, Double, Double>() {
+        @Override
+        public Tuple2<Double, Double> call(Tuple3<Double, Double, Double> point) {
+          Double predictedLabel = model.predict(point._2());
+          return new Tuple2<Double, Double>(predictedLabel, point._1());
+        }
+      }
+    );
+
+    // Calculate mean squared error between predicted and real labels.
+    Double meanSquaredError = new JavaDoubleRDD(predictionAndLabel.map(
+      new Function<Tuple2<Double, Double>, Object>() {
+        @Override
+        public Object call(Tuple2<Double, Double> pl) {
+          return Math.pow(pl._1() - pl._2(), 2);
+        }
+      }
+    ).rdd()).mean();
+    System.out.println("Mean Squared Error = " + meanSquaredError);
+
+    // Save and load model
+    model.save(jsc.sc(), "target/tmp/myIsotonicRegressionModel");
+    IsotonicRegressionModel sameModel = IsotonicRegressionModel.load(jsc.sc(), "target/tmp/myIsotonicRegressionModel");
+    // $example off$
+  }
+}
diff --git a/examples/src/main/java/org/apache/spark/examples/mllib/JavaNaiveBayesExample.java b/examples/src/main/java/org/apache/spark/examples/mllib/JavaNaiveBayesExample.java
new file mode 100644
index 0000000000..e6a5904bd7
--- /dev/null
+++ b/examples/src/main/java/org/apache/spark/examples/mllib/JavaNaiveBayesExample.java
@@ -0,0 +1,64 @@
+/*
+ * 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 scala.Tuple2;
+import org.apache.spark.api.java.function.Function;
+import org.apache.spark.api.java.function.PairFunction;
+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.mllib.classification.NaiveBayes;
+import org.apache.spark.mllib.classification.NaiveBayesModel;
+import org.apache.spark.mllib.regression.LabeledPoint;
+import org.apache.spark.mllib.util.MLUtils;
+// $example off$
+import org.apache.spark.SparkConf;
+
+public class JavaNaiveBayesExample {
+  public static void main(String[] args) {
+    SparkConf sparkConf = new SparkConf().setAppName("JavaNaiveBayesExample");
+    JavaSparkContext jsc = new JavaSparkContext(sparkConf);
+    // $example on$
+    String path = "data/mllib/sample_naive_bayes_data.txt";
+    JavaRDD<LabeledPoint> inputData = MLUtils.loadLibSVMFile(jsc.sc(), path).toJavaRDD();
+    JavaRDD<LabeledPoint>[] tmp = inputData.randomSplit(new double[]{0.6, 0.4}, 12345);
+    JavaRDD<LabeledPoint> training = tmp[0]; // training set
+    JavaRDD<LabeledPoint> test = tmp[1]; // test set
+    final NaiveBayesModel model = NaiveBayes.train(training.rdd(), 1.0);
+    JavaPairRDD<Double, Double> predictionAndLabel =
+      test.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 accuracy = predictionAndLabel.filter(new Function<Tuple2<Double, Double>, Boolean>() {
+      @Override
+      public Boolean call(Tuple2<Double, Double> pl) {
+        return pl._1().equals(pl._2());
+      }
+    }).count() / (double) test.count();
+
+    // Save and load model
+    model.save(jsc.sc(), "target/tmp/myNaiveBayesModel");
+    NaiveBayesModel sameModel = NaiveBayesModel.load(jsc.sc(), "target/tmp/myNaiveBayesModel");
+    // $example off$
+  }
+}
diff --git a/examples/src/main/python/mllib/isotonic_regression_example.py b/examples/src/main/python/mllib/isotonic_regression_example.py
new file mode 100644
index 0000000000..89dc9f4b66
--- /dev/null
+++ b/examples/src/main/python/mllib/isotonic_regression_example.py
@@ -0,0 +1,56 @@
+#
+# 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.
+#
+
+"""
+Isotonic Regression Example.
+"""
+from __future__ import print_function
+
+from pyspark import SparkContext
+# $example on$
+import math
+from pyspark.mllib.regression import IsotonicRegression, IsotonicRegressionModel
+# $example off$
+
+if __name__ == "__main__":
+
+    sc = SparkContext(appName="PythonIsotonicRegressionExample")
+
+    # $example on$
+    data = sc.textFile("data/mllib/sample_isotonic_regression_data.txt")
+
+    # Create label, feature, weight tuples from input data with weight set to default value 1.0.
+    parsedData = data.map(lambda line: tuple([float(x) for x in line.split(',')]) + (1.0,))
+
+    # Split data into training (60%) and test (40%) sets.
+    training, test = parsedData.randomSplit([0.6, 0.4], 11)
+
+    # Create isotonic regression model from training data.
+    # Isotonic parameter defaults to true so it is only shown for demonstration
+    model = IsotonicRegression.train(training)
+
+    # Create tuples of predicted and real labels.
+    predictionAndLabel = test.map(lambda p: (model.predict(p[1]), p[0]))
+
+    # Calculate mean squared error between predicted and real labels.
+    meanSquaredError = predictionAndLabel.map(lambda pl: math.pow((pl[0] - pl[1]), 2)).mean()
+    print("Mean Squared Error = " + str(meanSquaredError))
+
+    # Save and load model
+    model.save(sc, "target/tmp/myIsotonicRegressionModel")
+    sameModel = IsotonicRegressionModel.load(sc, "target/tmp/myIsotonicRegressionModel")
+    # $example off$
diff --git a/examples/src/main/python/mllib/naive_bayes_example.py b/examples/src/main/python/mllib/naive_bayes_example.py
new file mode 100644
index 0000000000..a2e7dacf25
--- /dev/null
+++ b/examples/src/main/python/mllib/naive_bayes_example.py
@@ -0,0 +1,56 @@
+#
+# 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.
+#
+
+"""
+NaiveBayes Example.
+"""
+from __future__ import print_function
+
+# $example on$
+from pyspark.mllib.classification import NaiveBayes, NaiveBayesModel
+from pyspark.mllib.linalg import Vectors
+from pyspark.mllib.regression import LabeledPoint
+
+
+def parseLine(line):
+    parts = line.split(',')
+    label = float(parts[0])
+    features = Vectors.dense([float(x) for x in parts[1].split(' ')])
+    return LabeledPoint(label, features)
+# $example off$
+
+if __name__ == "__main__":
+
+    sc = SparkContext(appName="PythonNaiveBayesExample")
+
+    # $example on$
+    data = sc.textFile('data/mllib/sample_naive_bayes_data.txt').map(parseLine)
+
+    # Split data aproximately into training (60%) and test (40%)
+    training, test = data.randomSplit([0.6, 0.4], seed=0)
+
+    # Train a naive Bayes model.
+    model = NaiveBayes.train(training, 1.0)
+
+    # Make prediction and test accuracy.
+    predictionAndLabel = test.map(lambda p: (model.predict(p.features), p.label))
+    accuracy = 1.0 * predictionAndLabel.filter(lambda (x, v): x == v).count() / test.count()
+
+    # Save and load model
+    model.save(sc, "target/tmp/myNaiveBayesModel")
+    sameModel = NaiveBayesModel.load(sc, "target/tmp/myNaiveBayesModel")
+    # $example off$
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/IsotonicRegressionExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/IsotonicRegressionExample.scala
new file mode 100644
index 0000000000..52ac9ae7dd
--- /dev/null
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/IsotonicRegressionExample.scala
@@ -0,0 +1,66 @@
+/*
+ * 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.
+ */
+
+// scalastyle:off println
+package org.apache.spark.examples.mllib
+
+// $example on$
+import org.apache.spark.mllib.regression.{IsotonicRegression, IsotonicRegressionModel}
+// $example off$
+import org.apache.spark.{SparkConf, SparkContext}
+
+object IsotonicRegressionExample {
+
+  def main(args: Array[String]) : Unit = {
+
+    val conf = new SparkConf().setAppName("IsotonicRegressionExample")
+    val sc = new SparkContext(conf)
+    // $example on$
+    val data = sc.textFile("data/mllib/sample_isotonic_regression_data.txt")
+
+    // Create label, feature, weight tuples from input data with weight set to default value 1.0.
+    val parsedData = data.map { line =>
+      val parts = line.split(',').map(_.toDouble)
+      (parts(0), parts(1), 1.0)
+    }
+
+    // Split data into training (60%) and test (40%) sets.
+    val splits = parsedData.randomSplit(Array(0.6, 0.4), seed = 11L)
+    val training = splits(0)
+    val test = splits(1)
+
+    // Create isotonic regression model from training data.
+    // Isotonic parameter defaults to true so it is only shown for demonstration
+    val model = new IsotonicRegression().setIsotonic(true).run(training)
+
+    // Create tuples of predicted and real labels.
+    val predictionAndLabel = test.map { point =>
+      val predictedLabel = model.predict(point._2)
+      (predictedLabel, point._1)
+    }
+
+    // Calculate mean squared error between predicted and real labels.
+    val meanSquaredError = predictionAndLabel.map { case (p, l) => math.pow((p - l), 2) }.mean()
+    println("Mean Squared Error = " + meanSquaredError)
+
+    // Save and load model
+    model.save(sc, "target/tmp/myIsotonicRegressionModel")
+    val sameModel = IsotonicRegressionModel.load(sc, "target/tmp/myIsotonicRegressionModel")
+    // $example off$
+  }
+}
+// scalastyle:on println
diff --git a/examples/src/main/scala/org/apache/spark/examples/mllib/NaiveBayesExample.scala b/examples/src/main/scala/org/apache/spark/examples/mllib/NaiveBayesExample.scala
new file mode 100644
index 0000000000..a7a47c2a35
--- /dev/null
+++ b/examples/src/main/scala/org/apache/spark/examples/mllib/NaiveBayesExample.scala
@@ -0,0 +1,57 @@
+/*
+ * 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.
+ */
+
+// scalastyle:off println
+package org.apache.spark.examples.mllib
+
+// $example on$
+import org.apache.spark.mllib.classification.{NaiveBayes, NaiveBayesModel}
+import org.apache.spark.mllib.linalg.Vectors
+import org.apache.spark.mllib.regression.LabeledPoint
+// $example off$
+import org.apache.spark.{SparkConf, SparkContext}
+
+object NaiveBayesExample {
+
+  def main(args: Array[String]) : Unit = {
+    val conf = new SparkConf().setAppName("NaiveBayesExample")
+    val sc = new SparkContext(conf)
+    // $example on$
+    val data = sc.textFile("data/mllib/sample_naive_bayes_data.txt")
+    val parsedData = data.map { line =>
+      val parts = line.split(',')
+      LabeledPoint(parts(0).toDouble, Vectors.dense(parts(1).split(' ').map(_.toDouble)))
+    }
+
+    // Split data into training (60%) and test (40%).
+    val splits = parsedData.randomSplit(Array(0.6, 0.4), seed = 11L)
+    val training = splits(0)
+    val test = splits(1)
+
+    val model = NaiveBayes.train(training, lambda = 1.0, modelType = "multinomial")
+
+    val predictionAndLabel = test.map(p => (model.predict(p.features), p.label))
+    val accuracy = 1.0 * predictionAndLabel.filter(x => x._1 == x._2).count() / test.count()
+
+    // Save and load model
+    model.save(sc, "target/tmp/myNaiveBayesModel")
+    val sameModel = NaiveBayesModel.load(sc, "target/tmp/myNaiveBayesModel")
+    // $example off$
+  }
+}
+
+// scalastyle:on println