75 lines
2.7 KiB
Python
75 lines
2.7 KiB
Python
|
#
|
||
|
|
# 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
|
||
|
|
|
||
|
|
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)
|
||
|
|
|
||
|
|
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()
|