python/mllib/ranking_metrics_example.py

0001 #
0002 # Licensed to the Apache Software Foundation (ASF) under one or more
0003 # contributor license agreements.  See the NOTICE file distributed with
0004 # this work for additional information regarding copyright ownership.
0005 # The ASF licenses this file to You under the Apache License, Version 2.0
0006 # (the "License"); you may not use this file except in compliance with
0007 # the License.  You may obtain a copy of the License at
0008 #
0009 #    http://www.apache.org/licenses/LICENSE-2.0
0010 #
0011 # Unless required by applicable law or agreed to in writing, software
0012 # distributed under the License is distributed on an "AS IS" BASIS,
0013 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
0014 # See the License for the specific language governing permissions and
0015 # limitations under the License.
0016 #
0017
0018 # $example on$
0019 from pyspark.mllib.recommendation import ALS, Rating
0020 from pyspark.mllib.evaluation import RegressionMetrics
0021 # $example off$
0022 from pyspark import SparkContext
0023
0024 if __name__ == "__main__":
0025     sc = SparkContext(appName="Ranking Metrics Example")
0026
0027     # Several of the methods available in scala are currently missing from pyspark
0028     # $example on$
0029     # Read in the ratings data
0030     lines = sc.textFile("data/mllib/sample_movielens_data.txt")
0031
0032     def parseLine(line):
0033         fields = line.split("::")
0034         return Rating(int(fields[0]), int(fields[1]), float(fields[2]) - 2.5)
0035     ratings = lines.map(lambda r: parseLine(r))
0036
0037     # Train a model on to predict user-product ratings
0038     model = ALS.train(ratings, 10, 10, 0.01)
0039
0040     # Get predicted ratings on all existing user-product pairs
0041     testData = ratings.map(lambda p: (p.user, p.product))
0042     predictions = model.predictAll(testData).map(lambda r: ((r.user, r.product), r.rating))
0043
0044     ratingsTuple = ratings.map(lambda r: ((r.user, r.product), r.rating))
0045     scoreAndLabels = predictions.join(ratingsTuple).map(lambda tup: tup[1])
0046
0047     # Instantiate regression metrics to compare predicted and actual ratings
0048     metrics = RegressionMetrics(scoreAndLabels)
0049
0050     # Root mean squared error
0051     print("RMSE = %s" % metrics.rootMeanSquaredError)
0052
0053     # R-squared
0054     print("R-squared = %s" % metrics.r2)
0055     # $example off$