def trainTextVectors(connector, w2vEmbeddingsPath, wordIndexMapPath, wordFrequencyMapPath, wordEmbeddingsPath, contextsPath,
sample, minCount, windowSize, negative, strict, contextsPerText, superBatchSize, fileEmbeddingSize,
epochs, learningRate, fileEmbeddingsPath):
if exists(wordIndexMapPath) and exists(wordFrequencyMapPath) and exists(wordEmbeddingsPath) \
and exists(contextsPath) and exists(pathTo.textIndexMap):
wordIndexMap = parameters.loadMap(wordIndexMapPath)
wordFrequencyMap = parameters.loadMap(wordFrequencyMapPath)
wordEmbeddings = parameters.loadEmbeddings(wordEmbeddingsPath)
textIndexMap = parameters.loadMap(pathTo.textIndexMap)
else:
w2vWordIndexMap, w2vWordEmbeddings = parameters.loadW2VParameters(w2vEmbeddingsPath)
names, texts = extract(connector)
wordIndexMap, wordFrequencyMap, wordEmbeddings = buildWordMaps(texts, w2vWordIndexMap, w2vWordEmbeddings)
parameters.dumpWordMap(wordIndexMap, wordIndexMapPath)
del w2vWordIndexMap
del w2vWordEmbeddings
gc.collect()
parameters.dumpWordMap(wordFrequencyMap, wordFrequencyMapPath)
log.progress('Dumping contexts...')
parameters.dumpEmbeddings(wordEmbeddings, wordEmbeddingsPath)
log.info('Dumped indices, frequencies and embeddings')
texts = subsampleAndPrune(texts, wordFrequencyMap, sample, minCount)
textIndexMap = inferContexts(contextsPath, names, texts, wordIndexMap, windowSize, negative, strict, contextsPerText)
parameters.dumpWordMap(textIndexMap, pathTo.textIndexMap)
with h5py.File(contextsPath, 'r') as contextsFile:
contexts = contextsFile['contexts']
log.info('Loaded {0} contexts. Shape: {1}', len(contexts), contexts.shape)
fileEmbeddings = numpy.random.rand(len(contexts), fileEmbeddingSize).astype('float32')
trainingBatch = numpy.zeros((superBatchSize, contextsPerText, 1+windowSize+negative)).astype('int32')
superBatchesCount = len(contexts) / superBatchSize
for superBatchIndex in xrange(0, superBatchesCount):
log.info('Text batch: {0}/{1}.', superBatchIndex + 1, superBatchesCount)
# TODO: this only works if superBatchSize == textsCount; otherwise text indices do not match
contexts.read_direct(trainingBatch, source_sel=numpy.s_[superBatchIndex*superBatchSize:(superBatchIndex+1)*superBatchSize])
trainingBatchReshaped = trainingBatch.reshape((superBatchSize*contextsPerText, 1+windowSize+negative))
fileEmbeddingsBatch = fileEmbeddings[superBatchIndex*superBatchSize:(superBatchIndex+1)*superBatchSize]
model = traininig.Model(fileEmbeddingsBatch, wordEmbeddings, contextSize=windowSize-2, negative=negative)
traininig.train(model, textIndexMap, wordIndexMap, wordEmbeddings, trainingBatchReshaped, epochs, 1, learningRate)
fileEmbeddings[superBatchIndex*superBatchSize:(superBatchIndex+1)*superBatchSize] = model.fileEmbeddings.get_value()
contextsFile.flush()
log.progress('Dumping text embeddings...')
binary.dumpTensor(fileEmbeddingsPath, fileEmbeddings)
log.info('Dumping text embeddings complete')
def dump(self, fileEmbeddingsPath, weightsPath):
fileEmbeddings = self.fileEmbeddings.get_value()
binary.dumpTensor(fileEmbeddingsPath, fileEmbeddings)
weights = self.weights.get_value()
binary.dumpTensor(weightsPath, weights)
