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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 collections import namedtuple
from pyspark import SparkContext
from pyspark.rdd import RDD
from pyspark.mllib.common import JavaModelWrapper, callMLlibFunc, inherit_doc
from pyspark.mllib.util import JavaLoader, JavaSaveable
__all__ = ['MatrixFactorizationModel', 'ALS', 'Rating']
class Rating(namedtuple("Rating", ["user", "product", "rating"])):
"""
Represents a (user, product, rating) tuple.
>>> r = Rating(1, 2, 5.0)
>>> (r.user, r.product, r.rating)
(1, 2, 5.0)
>>> (r[0], r[1], r[2])
(1, 2, 5.0)
"""
def __reduce__(self):
return Rating, (int(self.user), int(self.product), float(self.rating))
@inherit_doc
class MatrixFactorizationModel(JavaModelWrapper, JavaSaveable, JavaLoader):
"""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, seed=10)
>>> model.predict(2, 2)
0.4...
>>> testset = sc.parallelize([(1, 2), (1, 1)])
>>> model = ALS.train(ratings, 2, seed=0)
>>> model.predictAll(testset).collect()
[Rating(user=1, product=1, rating=1.0...), Rating(user=1, product=2, rating=1.9...)]
>>> model = ALS.train(ratings, 4, seed=10)
>>> model.userFeatures().collect()
[(1, array('d', [...])), (2, array('d', [...]))]
>>> first_user = model.userFeatures().take(1)[0]
>>> latents = first_user[1]
>>> len(latents) == 4
True
>>> model.productFeatures().collect()
[(1, array('d', [...])), (2, array('d', [...]))]
>>> first_product = model.productFeatures().take(1)[0]
>>> latents = first_product[1]
>>> len(latents) == 4
True
>>> model = ALS.train(ratings, 1, nonnegative=True, seed=10)
>>> model.predict(2,2)
3.8...
>>> model = ALS.trainImplicit(ratings, 1, nonnegative=True, seed=10)
>>> model.predict(2,2)
0.4...
>>> import os, tempfile
>>> path = tempfile.mkdtemp()
>>> model.save(sc, path)
>>> sameModel = MatrixFactorizationModel.load(sc, path)
>>> sameModel.predict(2,2)
0.4...
>>> sameModel.predictAll(testset).collect()
[Rating(...
>>> try:
... os.removedirs(path)
... except OSError:
... pass
"""
def predict(self, user, product):
return self._java_model.predict(int(user), int(product))
def predictAll(self, user_product):
assert isinstance(user_product, RDD), "user_product should be RDD of (user, product)"
first = user_product.first()
assert len(first) == 2, "user_product should be RDD of (user, product)"
user_product = user_product.map(lambda (u, p): (int(u), int(p)))
return self.call("predict", user_product)
def userFeatures(self):
return self.call("getUserFeatures")
def productFeatures(self):
return self.call("getProductFeatures")
@classmethod
def load(cls, sc, path):
model = cls._load_java(sc, path)
wrapper = sc._jvm.MatrixFactorizationModelWrapper(model)
return MatrixFactorizationModel(wrapper)
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")
return ratings
@classmethod
def train(cls, ratings, rank, iterations=5, lambda_=0.01, blocks=-1, nonnegative=False,
seed=None):
model = callMLlibFunc("trainALSModel", cls._prepare(ratings), rank, iterations,
lambda_, blocks, nonnegative, seed)
return MatrixFactorizationModel(model)
@classmethod
def trainImplicit(cls, ratings, rank, iterations=5, lambda_=0.01, blocks=-1, alpha=0.01,
nonnegative=False, seed=None):
model = callMLlibFunc("trainImplicitALSModel", cls._prepare(ratings), rank,
iterations, lambda_, blocks, alpha, nonnegative, seed)
return MatrixFactorizationModel(model)
def _test():
import doctest
import pyspark.mllib.recommendation
globs = pyspark.mllib.recommendation.__dict__.copy()
globs['sc'] = SparkContext('local[4]', 'PythonTest')
(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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