merge changes

Author: bob7783

recommenders/spark.py

@@ -10,9 +10,14 @@
 # print(tmp)
 
 from pyspark.mllib.recommendation import ALS, MatrixFactorizationModel, Rating
+import os
 
 # load in the data
+<<<<<<< HEAD
 data = sc.textFile("../large_files/movielens-20m-dataset/small_rating.csv")
+=======
+data = sc.textFile(os.path.expanduser('~') + "/Code/machine_learning_examples/large_files/movielens-20m-dataset/small_rating.csv")
+>>>>>>> f6f97af5e368bb1343243049d44f0a12635cfa38
 
 # filter out header
 header = data.first() #extract header
@@ -42,12 +47,12 @@
 # joins on first item: (user_id, movie_id)
 # each row of result is: ((user_id, movie_id), (rating, prediction))
 mse = ratesAndPreds.map(lambda r: (r[1][0] - r[1][1])**2).mean()
-print("train mse:", mse)
+print("train mse: %s" % mse)
 
 
 # test
 x = test.map(lambda p: (p[0], p[1]))
 p = model.predictAll(x).map(lambda r: ((r[0], r[1]), r[2]))
 ratesAndPreds = test.map(lambda r: ((r[0], r[1]), r[2])).join(p)
 mse = ratesAndPreds.map(lambda r: (r[1][0] - r[1][1])**2).mean()
-print("test mse:", mse)
+print("test mse: %s" % mse)