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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.
#
from pyspark import SparkContext
from pyspark.mllib._common import \
_get_unmangled_rdd, _get_unmangled_double_vector_rdd, \
_serialize_double_matrix, _deserialize_double_matrix, \
_serialize_double_vector, _deserialize_double_vector, \
_get_initial_weights, _serialize_rating, _regression_train_wrapper, \
_serialize_tuple, _deserialize_rating
from pyspark.serializers import BatchedSerializer
from pyspark.rdd import RDD
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(sc, ratings, 1)
>>> model.predict(2,2) is not None
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, usersProducts):
usersProductsJRDD = _get_unmangled_rdd(usersProducts, _serialize_tuple)
return RDD(self._java_model.predictJavaRDD(usersProductsJRDD._jrdd),
self._context, BatchedSerializer(_deserialize_rating, self._context._batchSize))
class ALS(object):
@classmethod
def train(cls, sc, ratings, rank, iterations=5, lambda_=0.01, blocks=-1):
ratingBytes = _get_unmangled_rdd(ratings, _serialize_rating)
mod = sc._jvm.PythonMLLibAPI().trainALSModel(ratingBytes._jrdd,
rank, iterations, lambda_, blocks)
return MatrixFactorizationModel(sc, mod)
@classmethod
def trainImplicit(cls, sc, ratings, rank, iterations=5, lambda_=0.01, blocks=-1, alpha=0.01):
ratingBytes = _get_unmangled_rdd(ratings, _serialize_rating)
mod = sc._jvm.PythonMLLibAPI().trainImplicitALSModel(ratingBytes._jrdd,
rank, iterations, lambda_, blocks, alpha)
return MatrixFactorizationModel(sc, mod)
def _test():
import doctest
globs = globals().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()
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