9a0dfdf868
- Added a Python wrapper for Naive Bayes - Updated the Scala Naive Bayes to match the style of our other algorithms better and in particular make it easier to call from Java (added builder pattern, removed default value in train method) - Updated Python MLlib functions to not require a SparkContext; we can get that from the RDD the user gives - Added a toString method in LabeledPoint - Made the Python MLlib tests run as part of run-tests as well (before they could only be run individually through each file)
87 lines
3.2 KiB
Python
87 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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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(ratingBytes._jrdd,
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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(ratingBytes._jrdd,
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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,
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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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