[SPARK-11548][DOCS] Replaced example code in mllib-collaborative-filtering.md using include_example

Kindly review the changes.

Author: Rishabh Bhardwaj <rbnext29@gmail.com>

Closes #9519 from rishabhbhardwaj/SPARK-11337.

1 files changed, 54 insertions, 0 deletions

diff --git a/examples/src/main/python/mllib/recommendation_example.py b/examples/src/main/python/mllib/recommendation_example.py
new file mode 100644
index 0000000000..615db0749b
--- /dev/null
+++ b/examples/src/main/python/mllib/recommendation_example.py
@@ -0,0 +1,54 @@
+#
+# 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.
+#
+
+"""
+Collaborative Filtering Classification Example.
+"""
+from __future__ import print_function
+
+import sys
+
+from pyspark import SparkContext
+
+# $example on$
+from pyspark.mllib.recommendation import ALS, MatrixFactorizationModel, Rating
+# $example off$
+
+if __name__ == "__main__":
+    sc = SparkContext(appName="PythonCollaborativeFilteringExample")
+    # $example on$
+    # Load and parse the data
+    data = sc.textFile("data/mllib/als/test.data")
+    ratings = data.map(lambda l: l.split(','))\
+        .map(lambda l: Rating(int(l[0]), int(l[1]), float(l[2])))
+
+    # Build the recommendation model using Alternating Least Squares
+    rank = 10
+    numIterations = 10
+    model = ALS.train(ratings, rank, numIterations)
+
+    # Evaluate the model on training data
+    testdata = ratings.map(lambda p: (p[0], p[1]))
+    predictions = model.predictAll(testdata).map(lambda r: ((r[0], r[1]), r[2]))
+    ratesAndPreds = ratings.map(lambda r: ((r[0], r[1]), r[2])).join(predictions)
+    MSE = ratesAndPreds.map(lambda r: (r[1][0] - r[1][1])**2).mean()
+    print("Mean Squared Error = " + str(MSE))
+
+    # Save and load model
+    model.save(sc, "target/tmp/myCollaborativeFilter")
+    sameModel = MatrixFactorizationModel.load(sc, "target/tmp/myCollaborativeFilter")
+    # $example off$