# Source code for pyspark.mllib.recommendation

```
#
# 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.
#
import array
from collections import namedtuple
from pyspark import SparkContext, since
from pyspark.rdd import RDD
from pyspark.mllib.common import JavaModelWrapper, callMLlibFunc, inherit_doc
from pyspark.mllib.util import JavaLoader, JavaSaveable
from pyspark.sql import DataFrame
__all__ = ['MatrixFactorizationModel', 'ALS', 'Rating']
[docs]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)
.. versionadded:: 1.2.0
"""
def __reduce__(self):
return Rating, (int(self.user), int(self.product), float(self.rating))
[docs]@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', [...]))]
>>> model.recommendUsers(1, 2)
[Rating(user=2, product=1, rating=1.9...), Rating(user=1, product=1, rating=1.0...)]
>>> model.recommendProducts(1, 2)
[Rating(user=1, product=2, rating=1.9...), Rating(user=1, product=1, rating=1.0...)]
>>> model.rank
4
>>> first_user = model.userFeatures().take(1)[0]
>>> latents = first_user[1]
>>> len(latents)
4
>>> model.productFeatures().collect()
[(1, array('d', [...])), (2, array('d', [...]))]
>>> first_product = model.productFeatures().take(1)[0]
>>> latents = first_product[1]
>>> len(latents)
4
>>> products_for_users = model.recommendProductsForUsers(1).collect()
>>> len(products_for_users)
2
>>> products_for_users[0]
(1, (Rating(user=1, product=2, rating=...),))
>>> users_for_products = model.recommendUsersForProducts(1).collect()
>>> len(users_for_products)
2
>>> users_for_products[0]
(1, (Rating(user=2, product=1, rating=...),))
>>> model = ALS.train(ratings, 1, nonnegative=True, seed=10)
>>> model.predict(2, 2)
3.73...
>>> df = sqlContext.createDataFrame([Rating(1, 1, 1.0), Rating(1, 2, 2.0), Rating(2, 1, 2.0)])
>>> model = ALS.train(df, 1, nonnegative=True, seed=10)
>>> model.predict(2, 2)
3.73...
>>> 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(...
>>> from shutil import rmtree
>>> try:
... rmtree(path)
... except OSError:
... pass
.. versionadded:: 0.9.0
"""
[docs] @since("0.9.0")
def predict(self, user, product):
"""
Predicts rating for the given user and product.
"""
return self._java_model.predict(int(user), int(product))
[docs] @since("0.9.0")
def predictAll(self, user_product):
"""
Returns a list of predicted ratings for input user and product
pairs.
"""
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_p[0]), int(u_p[1])))
return self.call("predict", user_product)
[docs] @since("1.2.0")
def userFeatures(self):
"""
Returns a paired RDD, where the first element is the user and the
second is an array of features corresponding to that user.
"""
return self.call("getUserFeatures").mapValues(lambda v: array.array('d', v))
[docs] @since("1.2.0")
def productFeatures(self):
"""
Returns a paired RDD, where the first element is the product and the
second is an array of features corresponding to that product.
"""
return self.call("getProductFeatures").mapValues(lambda v: array.array('d', v))
[docs] @since("1.4.0")
def recommendUsers(self, product, num):
"""
Recommends the top "num" number of users for a given product and
returns a list of Rating objects sorted by the predicted rating in
descending order.
"""
return list(self.call("recommendUsers", product, num))
[docs] @since("1.4.0")
def recommendProducts(self, user, num):
"""
Recommends the top "num" number of products for a given user and
returns a list of Rating objects sorted by the predicted rating in
descending order.
"""
return list(self.call("recommendProducts", user, num))
[docs] def recommendProductsForUsers(self, num):
"""
Recommends the top "num" number of products for all users. The
number of recommendations returned per user may be less than "num".
"""
return self.call("wrappedRecommendProductsForUsers", num)
[docs] def recommendUsersForProducts(self, num):
"""
Recommends the top "num" number of users for all products. The
number of recommendations returned per product may be less than
"num".
"""
return self.call("wrappedRecommendUsersForProducts", num)
@property
@since("1.4.0")
def rank(self):
"""Rank for the features in this model"""
return self.call("rank")
[docs] @classmethod
@since("1.3.1")
def load(cls, sc, path):
"""Load a model from the given path"""
model = cls._load_java(sc, path)
wrapper = sc._jvm.org.apache.spark.mllib.api.python.MatrixFactorizationModelWrapper(model)
return MatrixFactorizationModel(wrapper)
[docs]class ALS(object):
"""Alternating Least Squares matrix factorization
.. versionadded:: 0.9.0
"""
@classmethod
def _prepare(cls, ratings):
if isinstance(ratings, RDD):
pass
elif isinstance(ratings, DataFrame):
ratings = ratings.rdd
else:
raise TypeError("Ratings should be represented by either an RDD or a DataFrame, "
"but got %s." % type(ratings))
first = ratings.first()
if isinstance(first, Rating):
pass
elif isinstance(first, (tuple, list)):
ratings = ratings.map(lambda x: Rating(*x))
else:
raise TypeError("Expect a Rating or a tuple/list, but got %s." % type(first))
return ratings
[docs] @classmethod
@since("0.9.0")
def train(cls, ratings, rank, iterations=5, lambda_=0.01, blocks=-1, nonnegative=False,
seed=None):
"""
Train a matrix factorization model given an RDD of ratings by users
for a subset of products. The ratings matrix is approximated as the
product of two lower-rank matrices of a given rank (number of
features). To solve for these features, ALS is run iteratively with
a configurable level of parallelism.
:param ratings:
RDD of `Rating` or (userID, productID, rating) tuple.
:param rank:
Number of features to use (also referred to as the number of latent factors).
:param iterations:
Number of iterations of ALS.
(default: 5)
:param lambda_:
Regularization parameter.
(default: 0.01)
:param blocks:
Number of blocks used to parallelize the computation. A value
of -1 will use an auto-configured number of blocks.
(default: -1)
:param nonnegative:
A value of True will solve least-squares with nonnegativity
constraints.
(default: False)
:param seed:
Random seed for initial matrix factorization model. A value
of None will use system time as the seed.
(default: None)
"""
model = callMLlibFunc("trainALSModel", cls._prepare(ratings), rank, iterations,
lambda_, blocks, nonnegative, seed)
return MatrixFactorizationModel(model)
[docs] @classmethod
@since("0.9.0")
def trainImplicit(cls, ratings, rank, iterations=5, lambda_=0.01, blocks=-1, alpha=0.01,
nonnegative=False, seed=None):
"""
Train a matrix factorization model given an RDD of 'implicit
preferences' of users for a subset of products. The ratings matrix
is approximated as the product of two lower-rank matrices of a
given rank (number of features). To solve for these features, ALS
is run iteratively with a configurable level of parallelism.
:param ratings:
RDD of `Rating` or (userID, productID, rating) tuple.
:param rank:
Number of features to use (also referred to as the number of latent factors).
:param iterations:
Number of iterations of ALS.
(default: 5)
:param lambda_:
Regularization parameter.
(default: 0.01)
:param blocks:
Number of blocks used to parallelize the computation. A value
of -1 will use an auto-configured number of blocks.
(default: -1)
:param alpha:
A constant used in computing confidence.
(default: 0.01)
:param nonnegative:
A value of True will solve least-squares with nonnegativity
constraints.
(default: False)
:param seed:
Random seed for initial matrix factorization model. A value
of None will use system time as the seed.
(default: 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
from pyspark.sql import SQLContext
globs = pyspark.mllib.recommendation.__dict__.copy()
sc = SparkContext('local[4]', 'PythonTest')
globs['sc'] = sc
globs['sqlContext'] = SQLContext(sc)
(failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
globs['sc'].stop()
if failure_count:
exit(-1)
if __name__ == "__main__":
_test()
```