6481d27425
Put all public API in __all__, also put them all in pyspark.__init__.py, then we can got all the documents for public API by `pydoc pyspark`. It also can be used by other programs (such as Sphinx or Epydoc) to generate only documents for public APIs. Author: Davies Liu <davies.liu@gmail.com> Closes #2205 from davies/public and squashes the following commits: c6c5567 [Davies Liu] fix message f7b35be [Davies Liu] put SchemeRDD, Row in pyspark.sql module 7e3016a [Davies Liu] add __all__ in mllib 6281b48 [Davies Liu] fix doc for SchemaRDD 6caab21 [Davies Liu] add public interfaces into pyspark.__init__.py
94 lines
3.2 KiB
Python
94 lines
3.2 KiB
Python
#
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# Licensed to the Apache Software Foundation (ASF) under one or more
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# contributor license agreements. See the NOTICE file distributed with
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# this work for additional information regarding copyright ownership.
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# The ASF licenses this file to You under the Apache License, Version 2.0
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# (the "License"); you may not use this file except in compliance with
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# the License. You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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#
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from pyspark import SparkContext
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from pyspark.mllib._common import \
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_get_unmangled_rdd, _get_unmangled_double_vector_rdd, \
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_serialize_double_matrix, _deserialize_double_matrix, \
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_serialize_double_vector, _deserialize_double_vector, \
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_get_initial_weights, _serialize_rating, _regression_train_wrapper, \
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_serialize_tuple, RatingDeserializer
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from pyspark.rdd import RDD
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__all__ = ['MatrixFactorizationModel', 'ALS']
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class MatrixFactorizationModel(object):
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"""A matrix factorisation model trained by regularized alternating
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least-squares.
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>>> r1 = (1, 1, 1.0)
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>>> r2 = (1, 2, 2.0)
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>>> r3 = (2, 1, 2.0)
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>>> ratings = sc.parallelize([r1, r2, r3])
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>>> model = ALS.trainImplicit(ratings, 1)
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>>> model.predict(2,2) is not None
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True
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>>> testset = sc.parallelize([(1, 2), (1, 1)])
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>>> model.predictAll(testset).count() == 2
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True
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"""
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def __init__(self, sc, java_model):
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self._context = sc
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self._java_model = java_model
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def __del__(self):
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self._context._gateway.detach(self._java_model)
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def predict(self, user, product):
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return self._java_model.predict(user, product)
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def predictAll(self, usersProducts):
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usersProductsJRDD = _get_unmangled_rdd(usersProducts, _serialize_tuple)
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return RDD(self._java_model.predict(usersProductsJRDD._jrdd),
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self._context, RatingDeserializer())
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class ALS(object):
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@classmethod
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def train(cls, ratings, rank, iterations=5, lambda_=0.01, blocks=-1):
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sc = ratings.context
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ratingBytes = _get_unmangled_rdd(ratings, _serialize_rating)
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mod = sc._jvm.PythonMLLibAPI().trainALSModel(
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ratingBytes._jrdd, rank, iterations, lambda_, blocks)
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return MatrixFactorizationModel(sc, mod)
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@classmethod
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def trainImplicit(cls, ratings, rank, iterations=5, lambda_=0.01, blocks=-1, alpha=0.01):
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sc = ratings.context
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ratingBytes = _get_unmangled_rdd(ratings, _serialize_rating)
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mod = sc._jvm.PythonMLLibAPI().trainImplicitALSModel(
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ratingBytes._jrdd, rank, iterations, lambda_, blocks, alpha)
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return MatrixFactorizationModel(sc, mod)
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def _test():
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import doctest
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globs = globals().copy()
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globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2)
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(failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
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globs['sc'].stop()
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if failure_count:
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exit(-1)
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if __name__ == "__main__":
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_test()
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