Source Code for Module pyspark.mllib.recommendation

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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   
28 -class MatrixFactorizationModel(object): 
29      """A matrix factorisation model trained by regularized alternating 
30      least-squares. 
31   
32      >>> r1 = (1, 1, 1.0) 
33      >>> r2 = (1, 2, 2.0) 
34      >>> r3 = (2, 1, 2.0) 
35      >>> ratings = sc.parallelize([r1, r2, r3]) 
36      >>> model = ALS.trainImplicit(ratings, 1) 
37      >>> model.predict(2,2) is not None 
38      True 
39      >>> testset = sc.parallelize([(1, 2), (1, 1)]) 
40      >>> model.predictAll(testset).count() == 2 
41      True 
42      """ 
43   
44 -    def __init__(self, sc, java_model): 
45          self._context = sc 
46          self._java_model = java_model 
47   
48 -    def __del__(self): 
49          self._context._gateway.detach(self._java_model) 
50   
51 -    def predict(self, user, product): 
52          return self._java_model.predict(user, product) 
53   
54 -    def predictAll(self, usersProducts): 
55          usersProductsJRDD = _get_unmangled_rdd(usersProducts, _serialize_tuple) 
56          return RDD(self._java_model.predict(usersProductsJRDD._jrdd), 
57                     self._context, RatingDeserializer()) 
58   
59   
60 -class ALS(object): 
61      @classmethod 
62 -    def train(cls, ratings, rank, iterations=5, lambda_=0.01, blocks=-1): 
63          sc = ratings.context 
64          ratingBytes = _get_unmangled_rdd(ratings, _serialize_rating) 
65          mod = sc._jvm.PythonMLLibAPI().trainALSModel( 
66              ratingBytes._jrdd, rank, iterations, lambda_, blocks) 
67          return MatrixFactorizationModel(sc, mod) 
68   
69      @classmethod 
70 -    def trainImplicit(cls, ratings, rank, iterations=5, lambda_=0.01, blocks=-1, alpha=0.01): 
71          sc = ratings.context 
72          ratingBytes = _get_unmangled_rdd(ratings, _serialize_rating) 
73          mod = sc._jvm.PythonMLLibAPI().trainImplicitALSModel( 
74              ratingBytes._jrdd, rank, iterations, lambda_, blocks, alpha) 
75          return MatrixFactorizationModel(sc, mod) 
76   
77   
78 -def _test(): 
79      import doctest 
80      globs = globals().copy() 
81      globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2) 
82      (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS) 
83      globs['sc'].stop() 
84      if failure_count: 
85          exit(-1) 
86   
87   
88  if __name__ == "__main__": 
89      _test() 
90