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   
30      """A matrix factorisation model trained by regularized alternating 
31      least-squares. 
32   
33      >>> r1 = (1, 1, 1.0) 
34      >>> r2 = (1, 2, 2.0) 
35      >>> r3 = (2, 1, 2.0) 
36      >>> ratings = sc.parallelize([r1, r2, r3]) 
37      >>> model = ALS.trainImplicit(ratings, 1) 
38      >>> model.predict(2,2) is not None 
39      True 
40      >>> testset = sc.parallelize([(1, 2), (1, 1)]) 
41      >>> model.predictAll(testset).count() == 2 
42      True 
43      """ 
44   
45 -    def __init__(self, sc, java_model): 
46          self._context = sc 
47          self._java_model = java_model 
48   
49 -    def __del__(self): 
50          self._context._gateway.detach(self._java_model) 
51   
52 -    def predict(self, user, product): 
53          return self._java_model.predict(user, product) 
54   
55 -    def predictAll(self, usersProducts): 
56          usersProductsJRDD = _get_unmangled_rdd(usersProducts, _serialize_tuple) 
57          return RDD(self._java_model.predict(usersProductsJRDD._jrdd), 
58                     self._context, RatingDeserializer()) 
59   
60   
61 -class ALS(object): 
62   
63      @classmethod 
64 -    def train(cls, ratings, rank, iterations=5, lambda_=0.01, blocks=-1): 
65          sc = ratings.context 
66          ratingBytes = _get_unmangled_rdd(ratings, _serialize_rating) 
67          mod = sc._jvm.PythonMLLibAPI().trainALSModel( 
68              ratingBytes._jrdd, rank, iterations, lambda_, blocks) 
69          return MatrixFactorizationModel(sc, mod) 
70   
71      @classmethod 
72 -    def trainImplicit(cls, ratings, rank, iterations=5, lambda_=0.01, blocks=-1, alpha=0.01): 
73          sc = ratings.context 
74          ratingBytes = _get_unmangled_rdd(ratings, _serialize_rating) 
75          mod = sc._jvm.PythonMLLibAPI().trainImplicitALSModel( 
76              ratingBytes._jrdd, rank, iterations, lambda_, blocks, alpha) 
77          return MatrixFactorizationModel(sc, mod) 
78   
79   
80 -def _test(): 
81      import doctest 
82      globs = globals().copy() 
83      globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2) 
84      (failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS) 
85      globs['sc'].stop() 
86      if failure_count: 
87          exit(-1) 
88   
89   
90  if __name__ == "__main__": 
91      _test() 
92