class CorpusIteratorFuncHead_V():
def __init__(self,
language,
partition="train",
storeMorph=False,
splitLemmas=False,
shuffleDataSeed=None):
self.basis = CorpusIterator_V(language,
partition=partition,
storeMorph=storeMorph,
splitLemmas=splitLemmas,
shuffleDataSeed=shuffleDataSeed)
def permute(self):
self.basis.permute()
def length(self):
return self.basis.length()
def iterator(self, rejectShortSentences=False):
iterator = self.basis.iterator(
rejectShortSentences=rejectShortSentences)
for sentence in iterator:
reverse_content_head(sentence)
yield sentence
def getSentence(self, index):
return reverse_content_head(self.basis.getSentence(index))
class CorpusIteratorFuncHeadFraction_V():
def __init__(self, language, partition="train", fraction=1.0, storeMorph=False, splitLemmas=False):
self.basis = CorpusIterator_V(language, partition=partition, storeMorph=storeMorph, splitLemmas=splitLemmas, shuffleDataSeed=4)
self.basis.data = self.basis.data[:int(fraction*len(self.basis.data))]
self.permute()
self.fraction = fraction
def permute(self):
self.basis.permute()
def length(self):
return self.basis.length()
def iterator(self, rejectShortSentences = False):
iterator = self.basis.iterator(rejectShortSentences=rejectShortSentences)
counter = 0
print("Actual length", self.length())
for sentence in iterator:
# if counter > self.fraction * self.length():
# break
# counter += 1
reverse_content_head(sentence)
yield sentence
def getSentence(self, index):
return reverse_content_head(self.basis.getSentence(index))
devWords += newWords
if printHere:
print "Dev examples " + str(devCounter)
devCounterTimesBatchSize += devBatchSize
devSurprisalTableHere = [
surp / (devCounterTimesBatchSize) for surp in surprisalTable
]
return devLoss / devWords, devSurprisalTableHere
DEV_PERIOD = 5000
epochCount = 0
corpusBase = CorpusIterator_V(language, storeMorph=True)
while failedDevRuns == 0:
epochCount += 1
print "Starting new epoch, permuting corpus"
corpusBase.permute()
# corpus = getNextSentence("train")
corpus = corpusBase.iterator(rejectShortSentences=False)
stream = createStream(corpus)
if counter > 5:
# if counter % DEV_PERIOD == 0:
newDevLoss, devSurprisalTableHere = computeDevLoss()
# devLosses.append(
devLosses.append(newDevLoss)
print "New dev loss " + str(newDevLoss) + ". previous was: " + str(
lastDevLoss)
if newDevLoss > 15 or len(devLosses) > 99:
print "Abort, training too slow?"
devLosses.append(newDevLoss + 0.001)
optim.step()
print(docs)
times = {'Graal_1225_prose': 1225, 'Aucassin_early13_verse-prose': 1210, 'QuatreLivresReis_late12_prose': 1180, 'TroyesYvain_1180_verse': 1180, 'Roland_1100_verse': 1100, 'BeroulTristan_late12_verse': 1180, 'StLegier_1000_verse': 1000, 'StAlexis_1050_verse': 1050, 'Strasbourg_842_prose': 842, 'Lapidaire_mid12_prose': 1150}
timesVector = (torch.FloatTensor([times[itos_docs[i]] for i in range(len(docs))])/100.0)
#covarianceMatrix =
print(timesVector)
print(times)
quit()
for epoch in range(100):
corpus.permute()
for q in range(corpus.length()):
loss = forward(corpus, q)
backward(loss)
if epoch % 1 == 0:
print("Saving")
save_path = "../raw-results/"
#save_path = "/afs/cs.stanford.edu/u/mhahn/scr/deps/"
with open(save_path+"/manual_output_ground_coarse/"+args.language+"_"+__file__+"_model_"+str(myID)+".tsv", "w") as outFile:
print("\t".join(list(map(str,["Epochs", "DH_Mean_NoPunct", "DH_Sigma_NoPunct", "Distance_Mean_NoPunct", "Distance_Sigma_NoPunct", "Dependency"]))), file=outFile)
dh_numpy = pyro.get_param_store().get_param("mu_DH").data.numpy()
dh_sigma_numpy = pyro.get_param_store().get_param("sigma_DH").data.numpy()
dist_numpy = pyro.get_param_store().get_param("mu_Dist").data.numpy()
dist_sigma_numpy = pyro.get_param_store().get_param("sigma_Dist").data.numpy()
