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#
# Licensed to the Apache Software Foundation (ASF) under one or more
# contributor license agreements. See the NOTICE file distributed with
# this work for additional information regarding copyright ownership.
# The ASF licenses this file to You under the Apache License, Version 2.0
# (the "License"); you may not use this file except in compliance with
# the License. You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
"""
Python package for feature in MLlib.
"""
from pyspark.serializers import PickleSerializer, AutoBatchedSerializer
from pyspark.mllib.linalg import _convert_to_vector, _to_java_object_rdd
__all__ = ['Word2Vec', 'Word2VecModel']
class Word2VecModel(object):
"""
class for Word2Vec model
"""
def __init__(self, sc, java_model):
"""
:param sc: Spark context
:param java_model: Handle to Java model object
"""
self._sc = sc
self._java_model = java_model
def __del__(self):
self._sc._gateway.detach(self._java_model)
def transform(self, word):
"""
:param word: a word
:return: vector representation of word
Transforms a word to its vector representation
Note: local use only
"""
# TODO: make transform usable in RDD operations from python side
result = self._java_model.transform(word)
return PickleSerializer().loads(str(self._sc._jvm.SerDe.dumps(result)))
def findSynonyms(self, x, num):
"""
:param x: a word or a vector representation of word
:param num: number of synonyms to find
:return: array of (word, cosineSimilarity)
Find synonyms of a word
Note: local use only
"""
# TODO: make findSynonyms usable in RDD operations from python side
ser = PickleSerializer()
if type(x) == str:
jlist = self._java_model.findSynonyms(x, num)
else:
bytes = bytearray(ser.dumps(_convert_to_vector(x)))
vec = self._sc._jvm.SerDe.loads(bytes)
jlist = self._java_model.findSynonyms(vec, num)
words, similarity = ser.loads(str(self._sc._jvm.SerDe.dumps(jlist)))
return zip(words, similarity)
class Word2Vec(object):
"""
Word2Vec creates vector representation of words in a text corpus.
The algorithm first constructs a vocabulary from the corpus
and then learns vector representation of words in the vocabulary.
The vector representation can be used as features in
natural language processing and machine learning algorithms.
We used skip-gram model in our implementation and hierarchical softmax
method to train the model. The variable names in the implementation
matches the original C implementation.
For original C implementation, see https://code.google.com/p/word2vec/
For research papers, see
Efficient Estimation of Word Representations in Vector Space
and
Distributed Representations of Words and Phrases and their Compositionality.
>>> sentence = "a b " * 100 + "a c " * 10
>>> localDoc = [sentence, sentence]
>>> doc = sc.parallelize(localDoc).map(lambda line: line.split(" "))
>>> model = Word2Vec().setVectorSize(10).setSeed(42L).fit(doc)
>>> syms = model.findSynonyms("a", 2)
>>> str(syms[0][0])
'b'
>>> str(syms[1][0])
'c'
>>> len(syms)
2
>>> vec = model.transform("a")
>>> len(vec)
10
>>> syms = model.findSynonyms(vec, 2)
>>> str(syms[0][0])
'b'
>>> str(syms[1][0])
'c'
>>> len(syms)
2
"""
def __init__(self):
"""
Construct Word2Vec instance
"""
self.vectorSize = 100
self.learningRate = 0.025
self.numPartitions = 1
self.numIterations = 1
self.seed = 42L
def setVectorSize(self, vectorSize):
"""
Sets vector size (default: 100).
"""
self.vectorSize = vectorSize
return self
def setLearningRate(self, learningRate):
"""
Sets initial learning rate (default: 0.025).
"""
self.learningRate = learningRate
return self
def setNumPartitions(self, numPartitions):
"""
Sets number of partitions (default: 1). Use a small number for accuracy.
"""
self.numPartitions = numPartitions
return self
def setNumIterations(self, numIterations):
"""
Sets number of iterations (default: 1), which should be smaller than or equal to number of
partitions.
"""
self.numIterations = numIterations
return self
def setSeed(self, seed):
"""
Sets random seed.
"""
self.seed = seed
return self
def fit(self, data):
"""
Computes the vector representation of each word in vocabulary.
:param data: training data. RDD of subtype of Iterable[String]
:return: python Word2VecModel instance
"""
sc = data.context
ser = PickleSerializer()
vectorSize = self.vectorSize
learningRate = self.learningRate
numPartitions = self.numPartitions
numIterations = self.numIterations
seed = self.seed
model = sc._jvm.PythonMLLibAPI().trainWord2Vec(
_to_java_object_rdd(data), vectorSize,
learningRate, numPartitions, numIterations, seed)
return Word2VecModel(sc, model)
def _test():
import doctest
from pyspark import SparkContext
globs = globals().copy()
globs['sc'] = SparkContext('local[4]', 'PythonTest', batchSize=2)
(failure_count, test_count) = doctest.testmod(globs=globs, optionflags=doctest.ELLIPSIS)
globs['sc'].stop()
if failure_count:
exit(-1)
if __name__ == "__main__":
_test()
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