spark

Author: bob7783

recommenders/spark.py

@@ -0,0 +1,53 @@
+# https://udemy.com/recommender-systems
+# https://deeplearningcourses.com/recommender-systems
+
+### meant to be pasted into console ###
+
+# notes:
+# you may have trouble with full dataset on just your local machine
+# if you want to know what's in an RDD, use .take(n), ex:
+# tmp = p.take(5)
+# print(tmp)
+
+from pyspark.mllib.recommendation import ALS, MatrixFactorizationModel, Rating
+
+# load in the data
+data = sc.textFile("/Users/macuser/Code/machine_learning_examples/large_files/movielens-20m-dataset/small_rating.csv")
+
+# filter out header
+header = data.first() #extract header
+data = data.filter(lambda row: row != header)
+
+# convert into a sequence of Rating objects
+ratings = data.map(
+  lambda l: l.split(',')
+).map(
+  lambda l: Rating(int(l[0]), int(l[1]), float(l[2]))
+)
+
+# split into train and test
+train, test = ratings.randomSplit([0.8, 0.2])
+
+# train the model
+K = 10
+epochs = 10
+model = ALS.train(train, K, epochs)
+
+# evaluate the model
+
+# train
+x = train.map(lambda p: (p[0], p[1]))
+p = model.predictAll(x).map(lambda r: ((r[0], r[1]), r[2]))
+ratesAndPreds = train.map(lambda r: ((r[0], r[1]), r[2])).join(p)
+# 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)
+
+
+# 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)