#
# 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.
#
from pyspark import SparkContext
from pyspark.serializers import PickleSerializer, AutoBatchedSerializer
from pyspark.rdd import RDD
from pyspark.mllib.linalg import _to_java_object_rdd
__all__ = ['MatrixFactorizationModel', 'ALS']
class Rating(object):
def __init__(self, user, product, rating):
self.user = int(user)
self.product = int(product)
self.rating = float(rating)
def __reduce__(self):
return Rating, (self.user, self.product, self.rating)
def __repr__(self):
return "Rating(%d, %d, %d)" % (self.user, self.product, self.rating)
class MatrixFactorizationModel(object):
"""A matrix factorisation model trained by regularized alternating
least-squares.
>>> r1 = (1, 1, 1.0)
>>> r2 = (1, 2, 2.0)
>>> r3 = (2, 1, 2.0)
>>> ratings = sc.parallelize([r1, r2, r3])
>>> model = ALS.trainImplicit(ratings, 1)
>>> model.predict(2,2) is not None
True
>>> testset = sc.parallelize([(1, 2), (1, 1)])
>>> model = ALS.train(ratings, 1)
>>> model.predictAll(testset).count() == 2
True
"""
def __init__(self, sc, java_model):
self._context = sc
self._java_model = java_model
def __del__(self):
self._context._gateway.detach(self._java_model)
def predict(self, user, product):
return self._java_model.predict(user, product)
def predictAll(self, user_product):
assert isinstance(user_product, RDD), "user_product should be RDD of (user, product)"
first = user_product.first()
if isinstance(first, list):
user_product = user_product.map(tuple)
first = tuple(first)
assert type(first) is tuple and len(first) == 2, \
"user_product should be RDD of (user, product)"
if any(isinstance(x, str) for x in first):
user_product = user_product.map(lambda (u, p): (int(x), int(p)))
first = tuple(map(int, first))
assert all(type(x) is int for x in first), "user and product in user_product shoul be int"
sc = self._context
tuplerdd = sc._jvm.SerDe.asTupleRDD(_to_java_object_rdd(user_product).rdd())
jresult = self._java_model.predict(tuplerdd).toJavaRDD()
return RDD(sc._jvm.SerDe.javaToPython(jresult), sc,
AutoBatchedSerializer(PickleSerializer()))
class ALS(object):
@classmethod
def _prepare(cls, ratings):
assert isinstance(ratings, RDD), "ratings should be RDD"
first = ratings.first()
if not isinstance(first, Rating):
if isinstance(first, (tuple, list)):
ratings = ratings.map(lambda x: Rating(*x))
else:
raise ValueError("rating should be RDD of Rating or tuple/list")
# serialize them by AutoBatchedSerializer before cache to reduce the
# objects overhead in JVM
cached = ratings._reserialize(AutoBatchedSerializer(PickleSerializer())).cache()
return _to_java_object_rdd(cached)
@classmethod
def train(cls, ratings, rank, iterations=5, lambda_=0.01, blocks=-1):
sc = ratings.context
jrating = cls._prepare(ratings)
mod = sc._jvm.PythonMLLibAPI().trainALSModel(jrating, rank, iterations, lambda_, blocks)
return MatrixFactorizationModel(sc, mod)
@classmethod
def trainImplicit(cls, ratings, rank, iterations=5, lambda_=0.01, blocks=-1, alpha=0.01):
sc = ratings.context
jrating = cls._prepare(ratings)
mod = sc._jvm.PythonMLLibAPI().trainImplicitALSModel(
jrating, rank, iterations, lambda_, blocks, alpha)
return MatrixFactorizationModel(sc, mod)
def _test():
import doctest
import pyspark.mllib.recommendation
globs = pyspark.mllib.recommendation.__dict__.copy()
globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2)
(failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
globs['sc'].stop()
if failure_count:
exit(-1)
if __name__ == "__main__":
_test()
