[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.

1 files changed, 56 insertions, 0 deletions

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")
+    # $example off$