def showProgress(currentStep, totalSteps, epoch):
perc = (float(currentStep) / float(totalSteps)) * 100.0
temp = perc / 10
sys.stdout.write('\r[{0}] {1}% - {2}/{3} - Epoch {4}'.format(
'#' * int(temp), (perc), currentStep, totalSteps, epoch))
sys.stdout.flush()
print 'loading configuration'
config, modelConfig = readConfig()
print 'configuration loaded'
print 'loading word embeddings : {} - embedding size : {}'.format(
modelConfig.embeddingType, modelConfig.embeddingSize)
sentenceLoader, predicateLoader = getEmbeddings()
print 'sentenceLoader shape {}'.format(sentenceLoader.weights.shape)
nnUtils = NNUtils.Instance()
nnUtils.setWordUtils(sentenceLoader.word2idx, sentenceLoader.idx2word)
print 'loaded'
print 'loading corpus'
csvFiles = [
config.convertedCorpusDir + '/propbank_training.csv',
config.convertedCorpusDir + '/propbank_test.csv'
]
converter = CorpusConverter(csvFiles, sentenceLoader, predicateLoader)
fold = 1
foldComplement = '_1'
def showProgress(currentStep, totalSteps, epoch):
perc = (float(currentStep) / float(totalSteps)) * 100.0
temp = perc / 10
sys.stdout.write('\r[{0}] {1}% - {2}/{3} - Epoch {4}'.format(
'#' * int(temp), (perc), currentStep, totalSteps, epoch))
sys.stdout.flush()
print 'loading configuration'
config, modelConfig = readConfig()
print 'configuration loaded'
print 'loading word embeddings : {} - embedding size : {}'.format(
modelConfig.embeddingType, modelConfig.embeddingSize)
sentenceLoader, predicateLoader = getEmbeddings()
print 'sentenceLoader shape {}'.format(sentenceLoader.weights.shape)
nnUtils = NNUtils.Instance()
nnUtils.setWordUtils(sentenceLoader.word2idx, sentenceLoader.idx2word)
print 'loaded'
print 'loading corpus'
csvFiles = [
config.convertedCorpusDir + '/propbank_training.csv',
config.convertedCorpusDir + '/propbank_test.csv'
]
converter = CorpusConverter(csvFiles, sentenceLoader, predicateLoader)
data = converter.load(config.resourceDir + '/feature_file.npy')
tagMap = converter.tagMap