[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.

2 files changed, 6 insertions, 207 deletions

diff --git a/docs/mllib-isotonic-regression.md b/docs/mllib-isotonic-regression.md
index f91a697b31..85f9226b43 100644
--- a/docs/mllib-isotonic-regression.md
+++ b/docs/mllib-isotonic-regression.md
@@ -61,42 +61,8 @@ labels and real labels in the test set.
 
 Refer to the [`IsotonicRegression` Scala docs](api/scala/index.html#org.apache.spark.mllib.regression.IsotonicRegression) and [`IsotonicRegressionModel` Scala docs](api/scala/index.html#org.apache.spark.mllib.regression.IsotonicRegressionModel) for details on the API.
 
-{% highlight scala %}
-import org.apache.spark.mllib.regression.{IsotonicRegression, IsotonicRegressionModel}
-
-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, "myModelPath")
-val sameModel = IsotonicRegressionModel.load(sc, "myModelPath")
-{% endhighlight %}
+{% include_example scala/org/apache/spark/examples/mllib/IsotonicRegressionExample.scala %}
 </div>
-
 <div data-lang="java" markdown="1">
 Data are read from a file where each line has a format label,feature
 i.e. 4710.28,500.00. The data are split to training and testing set.
@@ -105,66 +71,8 @@ labels and real labels in the test set.
 
 Refer to the [`IsotonicRegression` Java docs](api/java/org/apache/spark/mllib/regression/IsotonicRegression.html) and [`IsotonicRegressionModel` Java docs](api/java/org/apache/spark/mllib/regression/IsotonicRegressionModel.html) for details on the API.
 
-{% highlight java %}
-import org.apache.spark.SparkConf;
-import org.apache.spark.api.java.JavaDoubleRDD;
-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.IsotonicRegressionModel;
-import scala.Tuple2;
-import scala.Tuple3;
-
-JavaRDD<String> 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.
-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
-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(sc.sc(), "myModelPath");
-IsotonicRegressionModel sameModel = IsotonicRegressionModel.load(sc.sc(), "myModelPath");
-{% endhighlight %}
+{% include_example java/org/apache/spark/examples/mllib/JavaIsotonicRegressionExample.java %}
 </div>
-
 <div data-lang="python" markdown="1">
 Data are read from a file where each line has a format label,feature
 i.e. 4710.28,500.00. The data are split to training and testing set.
@@ -173,32 +81,6 @@ labels and real labels in the test set.
 
 Refer to the [`IsotonicRegression` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.regression.IsotonicRegression) and [`IsotonicRegressionModel` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.regression.IsotonicRegressionModel) for more details on the API.
 
-{% highlight python %}
-import math
-from pyspark.mllib.regression import IsotonicRegression, IsotonicRegressionModel
-
-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, "myModelPath")
-sameModel = IsotonicRegressionModel.load(sc, "myModelPath")
-{% endhighlight %}
+{% include_example python/mllib/isotonic_regression_example.py %}
 </div>
 </div>
diff --git a/docs/mllib-naive-bayes.md b/docs/mllib-naive-bayes.md
index f4f6a10c82..60ac6c7e5b 100644
--- a/docs/mllib-naive-bayes.md
+++ b/docs/mllib-naive-bayes.md
@@ -40,32 +40,8 @@ can be used for evaluation and prediction.
 
 Refer to the [`NaiveBayes` Scala docs](api/scala/index.html#org.apache.spark.mllib.classification.NaiveBayes) and [`NaiveBayesModel` Scala docs](api/scala/index.html#org.apache.spark.mllib.classification.NaiveBayesModel) for details on the API.
 
-{% highlight scala %}
-import org.apache.spark.mllib.classification.{NaiveBayes, NaiveBayesModel}
-import org.apache.spark.mllib.linalg.Vectors
-import org.apache.spark.mllib.regression.LabeledPoint
-
-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, "myModelPath")
-val sameModel = NaiveBayesModel.load(sc, "myModelPath")
-{% endhighlight %}
+{% include_example scala/org/apache/spark/examples/mllib/NaiveBayesExample.scala %}
 </div>
-
 <div data-lang="java" markdown="1">
 
 [NaiveBayes](api/java/org/apache/spark/mllib/classification/NaiveBayes.html) implements
@@ -77,40 +53,8 @@ can be used for evaluation and prediction.
 
 Refer to the [`NaiveBayes` Java docs](api/java/org/apache/spark/mllib/classification/NaiveBayes.html) and [`NaiveBayesModel` Java docs](api/java/org/apache/spark/mllib/classification/NaiveBayesModel.html) for details on the API.
 
-{% 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.classification.NaiveBayes;
-import org.apache.spark.mllib.classification.NaiveBayesModel;
-import org.apache.spark.mllib.regression.LabeledPoint;
-
-JavaRDD<LabeledPoint> training = ... // training set
-JavaRDD<LabeledPoint> test = ... // 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(sc.sc(), "myModelPath");
-NaiveBayesModel sameModel = NaiveBayesModel.load(sc.sc(), "myModelPath");
-{% endhighlight %}
+{% include_example java/org/apache/spark/examples/mllib/JavaNaiveBayesExample.java %}
 </div>
-
 <div data-lang="python" markdown="1">
 
 [NaiveBayes](api/python/pyspark.mllib.html#pyspark.mllib.classification.NaiveBayes) implements multinomial
@@ -124,33 +68,6 @@ Note that the Python API does not yet support model save/load but will in the fu
 
 Refer to the [`NaiveBayes` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.classification.NaiveBayes) and [`NaiveBayesModel` Python docs](api/python/pyspark.mllib.html#pyspark.mllib.classification.NaiveBayesModel) for more details on the API.
 
-{% highlight python %}
-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)
-
-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, "myModelPath")
-sameModel = NaiveBayesModel.load(sc, "myModelPath")
-{% endhighlight %}
-
+{% include_example python/mllib/naive_bayes_example.py %}
 </div>
 </div>