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#
# 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.
#
# $example on$
from pyspark.mllib.recommendation import ALS, Rating
from pyspark.mllib.evaluation import RegressionMetrics, RankingMetrics
# $example off$
from pyspark import SparkContext
if __name__ == "__main__":
sc = SparkContext(appName="Ranking Metrics Example")
# Several of the methods available in scala are currently missing from pyspark
# $example on$
# Read in the ratings data
lines = sc.textFile("data/mllib/sample_movielens_data.txt")
def parseLine(line):
fields = line.split("::")
return Rating(int(fields[0]), int(fields[1]), float(fields[2]) - 2.5)
ratings = lines.map(lambda r: parseLine(r))
# Train a model on to predict user-product ratings
model = ALS.train(ratings, 10, 10, 0.01)
# Get predicted ratings on all existing user-product pairs
testData = ratings.map(lambda p: (p.user, p.product))
predictions = model.predictAll(testData).map(lambda r: ((r.user, r.product), r.rating))
ratingsTuple = ratings.map(lambda r: ((r.user, r.product), r.rating))
scoreAndLabels = predictions.join(ratingsTuple).map(lambda tup: tup[1])
# Instantiate regression metrics to compare predicted and actual ratings
metrics = RegressionMetrics(scoreAndLabels)
# Root mean sqaured error
print("RMSE = %s" % metrics.rootMeanSquaredError)
# R-squared
print("R-squared = %s" % metrics.r2)
# $example off$
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