Source Code for Module pyspark.mllib.recommendation

 1  # 
 2  # Licensed to the Apache Software Foundation (ASF) under one or more 
 3  # contributor license agreements.  See the NOTICE file distributed with 
 4  # this work for additional information regarding copyright ownership. 
 5  # The ASF licenses this file to You under the Apache License, Version 2.0 
 6  # (the "License"); you may not use this file except in compliance with 
 7  # the License.  You may obtain a copy of the License at 
 8  # 
 9  #    http://www.apache.org/licenses/LICENSE-2.0 
10  # 
11  # Unless required by applicable law or agreed to in writing, software 
12  # distributed under the License is distributed on an "AS IS" BASIS, 
13  # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 
14  # See the License for the specific language governing permissions and 
15  # limitations under the License. 
16  # 
17   
18  from pyspark import SparkContext 
19  from pyspark.mllib._common import \ 
20      _get_unmangled_rdd, _get_unmangled_double_vector_rdd, \ 
21      _serialize_double_matrix, _deserialize_double_matrix, \ 
22      _serialize_double_vector, _deserialize_double_vector, \ 
23      _get_initial_weights, _serialize_rating, _regression_train_wrapper, \ 
24      _serialize_tuple, RatingDeserializer 
25  from pyspark.rdd import RDD 
26   
27 -class MatrixFactorizationModel(object): 
28      """A matrix factorisation model trained by regularized alternating 
29      least-squares. 
30   
31      >>> r1 = (1, 1, 1.0) 
32      >>> r2 = (1, 2, 2.0) 
33      >>> r3 = (2, 1, 2.0) 
34      >>> ratings = sc.parallelize([r1, r2, r3]) 
35      >>> model = ALS.trainImplicit(ratings, 1) 
36      >>> model.predict(2,2) is not None 
37      True 
38      >>> testset = sc.parallelize([(1, 2), (1, 1)]) 
39      >>> model.predictAll(testset).count() == 2 
40      True 
41      """ 
42   
43 -    def __init__(self, sc, java_model): 
44          self._context = sc 
45          self._java_model = java_model 
46   
47 -    def __del__(self): 
48          self._context._gateway.detach(self._java_model) 
49   
50 -    def predict(self, user, product): 
51          return self._java_model.predict(user, product) 
52   
53 -    def predictAll(self, usersProducts): 
54          usersProductsJRDD = _get_unmangled_rdd(usersProducts, _serialize_tuple) 
55          return RDD(self._java_model.predict(usersProductsJRDD._jrdd), 
56                     self._context, RatingDeserializer()) 
57   
58 -class ALS(object): 
59      @classmethod 
60 -    def train(cls, ratings, rank, iterations=5, lambda_=0.01, blocks=-1): 
61          sc = ratings.context 
62          ratingBytes = _get_unmangled_rdd(ratings, _serialize_rating) 
63          mod = sc._jvm.PythonMLLibAPI().trainALSModel(ratingBytes._jrdd, 
64                  rank, iterations, lambda_, blocks) 
65          return MatrixFactorizationModel(sc, mod) 
66   
67      @classmethod 
68 -    def trainImplicit(cls, ratings, rank, iterations=5, lambda_=0.01, blocks=-1, alpha=0.01): 
69          sc = ratings.context 
70          ratingBytes = _get_unmangled_rdd(ratings, _serialize_rating) 
71          mod = sc._jvm.PythonMLLibAPI().trainImplicitALSModel(ratingBytes._jrdd, 
72                  rank, iterations, lambda_, blocks, alpha) 
73          return MatrixFactorizationModel(sc, mod) 
74   
75 -def _test(): 
76      import doctest 
77      globs = globals().copy() 
78      globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2) 
79      (failure_count, test_count) = doctest.testmod(globs=globs, 
80              optionflags=doctest.ELLIPSIS) 
81      globs['sc'].stop() 
82      if failure_count: 
83          exit(-1) 
84   
85  if __name__ == "__main__": 
86      _test() 
87