def Predict(self, conll_path):
with open(conll_path, 'r') as conllFP:
for iSentence, sentence in enumerate(read_conll(conllFP, False)):
self.Init()
forest = ParseForest(sentence)
self.getWordEmbeddings(forest, False)
for root in forest.roots:
root.lstms = [self.builders[0].initial_state().add_input(root.vec),
self.builders[1].initial_state().add_input(root.vec)]
while len(forest.roots) > 1:
self.__evaluate(forest, False)
bestParent, bestChild, bestScore = None, None, float("-inf")
bestIndex, bestOp = None, None
roots = forest.roots
for i in xrange(len(forest.roots) - 1):
for irel, rel in enumerate(self.irels):
for op in xrange(2):
if bestScore < roots[i].scores[irel][op] and (i + (1 - op)) > 0:
bestParent, bestChild = i + op, i + (1 - op)
bestScore = roots[i].scores[irel][op]
bestIndex, bestOp = i, op
bestRelation, bestIRelation = rel, irel
for j in xrange(max(0, bestIndex - self.k - 1), min(len(forest.roots), bestIndex + self.k + 2)):
roots[j].scores = None
roots[bestChild].pred_parent_id = forest.roots[bestParent].id
roots[bestChild].pred_relation = bestRelation
roots[bestParent].lstms[bestOp] = roots[bestParent].lstms[bestOp].add_input((self.activation(self.lstm2lstmbias + self.lstm2lstm *
concatenate([roots[bestChild].lstms[0].output(), lookup(self.model["rels-lookup"], bestIRelation), roots[bestChild].lstms[1].output()]))))
forest.Attach(bestParent, bestChild)
renew_cg()
yield sentence
def predict(self, sentences):
self.getWordEmbeddings(sentences, False)
for sentence in sentences:
stack = ParseForest([])
buf = ParseForest(sentence)
for root in sentence:
root.lstms = [root.vec for _ in range(self.nnvecs)]
hoffset = 1 if self.headFlag else 0
while not (len(buf) == 1 and len(stack) == 0):
scores = self.__evaluate(stack, buf, False)
best = max(chain(*scores), key=itemgetter(2))
if best[1] == 2:
stack.roots.append(buf.roots[0])
del buf.roots[0]
elif best[1] == 0:
child = stack.roots.pop()
parent = buf.roots[0]
child.pred_parent_id = parent.id
child.pred_relation = best[0]
bestOp = 0
if self.rlMostFlag:
parent.lstms[bestOp + hoffset] = child.lstms[bestOp + hoffset]
if self.rlFlag:
parent.lstms[bestOp + hoffset] = child.vec
elif best[1] == 1:
child = stack.roots.pop()
parent = stack.roots[-1]
child.pred_parent_id = parent.id
child.pred_relation = best[0]
bestOp = 1
if self.rlMostFlag:
parent.lstms[bestOp + hoffset] = child.lstms[bestOp + hoffset]
if self.rlFlag:
parent.lstms[bestOp + hoffset] = child.vec
def Train(self, trainData, options):
mloss = 0.0
eloss = 0.0
eerrors = 0
lerrors = 0
etotal = 0
ninf = -float('inf')
ts = time()
start = ts
random.shuffle(trainData) # in certain cases the data will already have been shuffled after being read from file or while creating dev data
print "Length of training data: ", len(trainData)
errs = []
self.feature_extractor.Init(options)
for iSentence, sentence in enumerate(trainData,1):
if iSentence % 100 == 0:
loss_message = 'Processing sentence number: %d'%iSentence + \
' Loss: %.3f'%(eloss / etotal)+ \
' Errors: %.3f'%((float(eerrors)) / etotal)+\
' Labeled Errors: %.3f'%(float(lerrors) / etotal)+\
' Time: %.2gs'%(time()-start)
print loss_message
start = time()
eerrors = 0
eloss = 0.0
etotal = 0
lerrors = 0
sentence = deepcopy(sentence) # ensures we are working with a clean copy of sentence and allows memory to be recycled each time round the loop
conll_sentence = [entry for entry in sentence if isinstance(entry, utils.ConllEntry)]
conll_sentence = conll_sentence[1:] + [conll_sentence[0]]
self.feature_extractor.getWordEmbeddings(conll_sentence, True, options)
stack = ParseForest([])
buf = ParseForest(conll_sentence)
hoffset = 1 if self.headFlag else 0
for root in conll_sentence:
root.lstms = [root.vec] if self.headFlag else []
#root.lstms += [self.feature_extractor.paddingVec for _ in range(self.nnvecs - hoffset)]
if not self.recursive_composition:
root.lstms += [self.feature_extractor.paddingVec for _ in range(self.nnvecs - hoffset)]
else:
root.lstms += [root.vec]
root.lstm = None
while not (len(buf) == 1 and len(stack) == 0):
scores = self.__evaluate(stack, buf, True)
#to ensure that we have at least one wrong operation
scores.append([(None, 4, ninf ,None)])
stack_ids = [sitem.id for sitem in stack.roots]
s1 = [stack.roots[-2]] if len(stack) > 1 else []
s0 = [stack.roots[-1]] if len(stack) > 0 else []
b = [buf.roots[0]] if len(buf) > 0 else []
beta = buf.roots[1:] if len(buf) > 1 else []
costs, shift_case = self.calculate_cost(scores,s0,s1,b,beta,stack_ids)
bestValid = list(( s for s in chain(*scores) if costs[s[1]] == 0 and ( s[1] == SHIFT or s[1] == SWAP or s[0] == s0[0].relation ) ))
bestValid = max(bestValid, key=itemgetter(2))
bestWrong = max(( s for s in chain(*scores) if costs[s[1]] != 0 or ( s[1] != SHIFT and s[1] != SWAP and s[0] != s0[0].relation ) ), key=itemgetter(2))
#force swap
if costs[SWAP]== 0:
best = bestValid
else:
#select a transition to follow
# + aggresive exploration
#1: might want to experiment with that parameter
if bestWrong[1] == SWAP:
best = bestValid
else:
best = bestValid if ( (not self.oracle) or (bestValid[2] - bestWrong[2] > 1.0) or (bestValid[2] > bestWrong[2] and random.random() > 0.1) ) else bestWrong
if best[1] == LEFT_ARC or best[1] ==RIGHT_ARC:
child = s0[0]
#updates for the dynamic oracle
if self.oracle:
self.oracle_updates(best,b,s0,stack_ids,shift_case)
self.apply_transition(best,stack,buf,hoffset)
if bestValid[2] < bestWrong[2] + 1.0:
loss = bestWrong[3] - bestValid[3]
mloss += 1.0 + bestWrong[2] - bestValid[2]
eloss += 1.0 + bestWrong[2] - bestValid[2]
errs.append(loss)
#labeled errors
if best[1] == LEFT_ARC or best[1] ==RIGHT_ARC:
if (child.pred_parent_id != child.parent_id or child.pred_relation != child.relation):
lerrors += 1
#attachment error
if child.pred_parent_id != child.parent_id:
eerrors += 1
#??? when did this happen and why?
if best[1] == 0 or best[1] == 2:
etotal += 1
#footnote 8 in Eli's original paper
if len(errs) > 50: # or True:
eerrs = dy.esum(errs)
scalar_loss = eerrs.scalar_value() #forward
eerrs.backward()
self.trainer.update()
errs = []
lerrs = []
dy.renew_cg()
self.feature_extractor.Init(options)
if len(errs) > 0:
eerrs = (dy.esum(errs))
eerrs.scalar_value()
eerrs.backward()
self.trainer.update()
errs = []
lerrs = []
dy.renew_cg()
self.trainer.update()
print "Loss: ", mloss/iSentence
print "Total training time: %.2fs"%(time()-ts)
def Predict(self, treebanks, datasplit, options):
reached_max_swap = 0
char_map = {}
if options.char_map_file:
char_map_fh = codecs.open(options.char_map_file,encoding='utf-8')
char_map = json.loads(char_map_fh.read())
# should probably use a namedtuple in get_vocab to make this prettier
print "Collecting test data vocab"
_, test_words, test_chars, _, _, _, test_treebanks, test_langs = utils.get_vocab(treebanks,datasplit,char_map)
# get external embeddings for the set of words and chars in the test vocab but not in the training vocab
test_embeddings = defaultdict(lambda:{})
if options.word_emb_size > 0:
new_test_words = set(test_words) - self.feature_extractor.words.viewkeys()
print "Number of OOV word types at test time: %i (out of %i)"%(len(new_test_words),len(test_words))
if len(new_test_words) > 0: # no point loading embeddings if there are no words to look for
for lang in test_langs:
test_embeddings["words"].update(utils.get_external_embeddings(options,lang,new_test_words))
if len(test_langs) > 1 and test_embeddings["words"]:
print "External embeddings found for %i words (out of %i)"%(len(test_embeddings["words"]),len(new_test_words))
if options.char_emb_size > 0:
new_test_chars = set(test_chars) - self.feature_extractor.chars.viewkeys()
print "Number of OOV char types at test time: %i (out of %i)"%(len(new_test_chars),len(test_chars))
if len(new_test_chars) > 0:
for lang in test_langs:
test_embeddings["chars"].update(utils.get_external_embeddings(options,lang,new_test_chars,chars=True))
if len(test_langs) > 1 and test_embeddings["chars"]:
print "External embeddings found for %i chars (out of %i)"%(len(test_embeddings["chars"]),len(new_test_chars))
ts = time()
data = utils.read_conll_dir(treebanks,datasplit,char_map=char_map)
for iSentence, osentence in enumerate(data,1):
sentence = deepcopy(osentence)
reached_swap_for_i_sentence = False
max_swap = 2*len(sentence)
iSwap = 0
self.feature_extractor.Init(options)
conll_sentence = [entry for entry in sentence if isinstance(entry, utils.ConllEntry)]
conll_sentence = conll_sentence[1:] + [conll_sentence[0]]
self.feature_extractor.getWordEmbeddings(conll_sentence, False, options, test_embeddings)
stack = ParseForest([])
buf = ParseForest(conll_sentence)
hoffset = 1 if self.headFlag else 0
for root in conll_sentence:
root.lstms = [root.vec] if self.headFlag else []
if not self.recursive_composition:
root.lstms += [self.feature_extractor.paddingVec for _ in range(self.nnvecs - hoffset)]
else:
root.lstms += [root.vec]
root.lstm = None #only necessary for treeLSTM case
root.composed_rep = root.vec.value()
while not (len(buf) == 1 and len(stack) == 0):
scores = self.__evaluate(stack, buf, False)
best = max(chain(*(scores if iSwap < max_swap else scores[:3] )), key = itemgetter(2) )
if iSwap == max_swap and not reached_swap_for_i_sentence:
reached_max_swap += 1
reached_swap_for_i_sentence = True
print "reached max swap in %d out of %d sentences"%(reached_max_swap, iSentence)
self.apply_transition(best,stack,buf,hoffset)
if best[1] == SWAP:
iSwap += 1
#keep in memory the information we need, not all the vectors
oconll_sentence = [entry for entry in osentence if isinstance(entry, utils.ConllEntry)]
oconll_sentence = oconll_sentence[1:] + [oconll_sentence[0]]
for tok_o, tok in zip(oconll_sentence, conll_sentence):
tok_o.pred_relation = tok.pred_relation
tok_o.pred_parent_id = tok.pred_parent_id
if self.recursive_composition:
tok_o.composed_rep = tok.composed_rep
yield osentence
dy.renew_cg()
print "Total prediction time: %.2fs"%(time()-ts)
def Train(self, conll_path):
mloss = 0.0
errors = 0
batch = 0
eloss = 0.0
eerrors = 0
lerrors = 0
etotal = 0
ltotal = 0
ninf = -float('inf')
hoffset = 1 if self.headFlag else 0
start = time.time()
with open(conll_path, 'r') as conllFP:
shuffledData = list(read_conll(conllFP, True))
random.shuffle(shuffledData)
errs = []
eeloss = 0.0
self.Init()
for iSentence, sentence in enumerate(shuffledData):
# 每处理100个句子，输出信息
if iSentence % 100 == 0 and iSentence != 0:
print '处理第 ', iSentence, ' 个句子，Loss:', eloss / etotal, 'Errors:', (
float(eerrors)) / etotal, 'Labeled Errors:', (
float(lerrors) / etotal), '用时', time.time() - start
# logger.debug('处理第%s个句子，Loss:%s，Errors:%s，Labeled Errors:%s，用时:%s', iSentence, eloss / etotal, (float(eerrors)) / etotal, (float(lerrors) / etotal), time.time()-start)
start = time.time()
eerrors = 0
eloss = 0.0
etotal = 0
lerrors = 0
ltotal = 0
conll_sentence = [
entry for entry in sentence
if isinstance(entry, utils.ConllEntry)
]
conll_sentence = conll_sentence[1:] + [conll_sentence[0]]
self.getWordEmbeddings(conll_sentence, True)
# 初始化stack为空
stack = ParseForest([])
# 将句子放入buf中
buf = ParseForest(conll_sentence)
for root in conll_sentence:
# 词的LSTM输入为self.nnvecs个输入向量串联
root.lstms = [root.vec for _ in xrange(self.nnvecs)]
hoffset = 1 if self.headFlag else 0
while not (len(buf) == 1 and len(stack) == 0):
scores = self.__evaluate(stack, buf, True)
scores.append([(None, 3, ninf, None)])
# alpha是栈中其他元素
alpha = stack.roots[:-2] if len(stack) > 2 else []
# s1为栈顶第二个元素
s1 = [stack.roots[-2]] if len(stack) > 1 else []
# s0为栈顶第一个元素
s0 = [stack.roots[-1]] if len(stack) > 0 else []
# b为buffer第一个元素
b = [buf.roots[0]] if len(buf) > 0 else []
# beta是buffer中其他元素
beta = buf.roots[1:] if len(buf) > 1 else []
left_cost = (
len([h
for h in s1 + beta if h.id == s0[0].parent_id]) +
len([d for d in b + beta if d.parent_id == s0[0].id])
) if len(scores[0]) > 0 else 1
right_cost = (
len([h for h in b + beta if h.id == s0[0].parent_id]) +
len([d for d in b + beta if d.parent_id == s0[0].id])
) if len(scores[1]) > 0 else 1
shift_cost = (
len([h
for h in s1 + alpha if h.id == b[0].parent_id]) +
len([
d
for d in s0 + s1 + alpha if d.parent_id == b[0].id
])) if len(scores[2]) > 0 else 1
costs = (left_cost, right_cost, shift_cost, 1)
bestValid = max(
(s for s in chain(*scores) if costs[s[1]] == 0 and (
s[1] == 2 or s[0] == stack.roots[-1].relation)),
key=itemgetter(2))
bestWrong = max(
(s for s in chain(*scores) if costs[s[1]] != 0 or (
s[1] != 2 and s[0] != stack.roots[-1].relation)),
key=itemgetter(2))
best = bestValid if (
(not self.oracle) or
(bestValid[2] - bestWrong[2] > 1.0) or
(bestValid[2] > bestWrong[2]
and random.random() > 0.1)) else bestWrong
# shift，未得到relation
if best[1] == 2:
stack.roots.append(buf.roots[0])
del buf.roots[0]
# left，head词是b0
elif best[1] == 0:
child = stack.roots.pop()
# head词是b0
parent = buf.roots[0]
child.pred_parent_id = parent.id
child.pred_relation = best[0]
bestOp = 0
if self.rlMostFlag:
parent.lstms[bestOp +
hoffset] = child.lstms[bestOp +
hoffset]
if self.rlFlag:
parent.lstms[bestOp + hoffset] = child.vec
# right，head词是s0
elif best[1] == 1:
child = stack.roots.pop()
# head词是s0
parent = stack.roots[-1]
child.pred_parent_id = parent.id
child.pred_relation = best[0]
bestOp = 1
if self.rlMostFlag:
parent.lstms[bestOp +
hoffset] = child.lstms[bestOp +
hoffset]
if self.rlFlag:
parent.lstms[bestOp + hoffset] = child.vec
if bestValid[2] < bestWrong[2] + 1.0:
# 损失函数
loss = bestWrong[3] - bestValid[3]
mloss += 1.0 + bestWrong[2] - bestValid[2]
eloss += 1.0 + bestWrong[2] - bestValid[2]
errs.append(loss)
if best[1] != 2 and (
child.pred_parent_id != child.parent_id
or child.pred_relation != child.relation):
# id或者relation估计不准确，labelederror加1
lerrors += 1
if child.pred_parent_id != child.parent_id:
# id估计不准确，unlabelederror加1
errors += 1
eerrors += 1
etotal += 1
if len(errs) > 50: # or True:
#eerrs = ((esum(errs)) * (1.0/(float(len(errs)))))
eerrs = esum(errs)
scalar_loss = eerrs.scalar_value()
eerrs.backward()
self.trainer.update()
errs = []
lerrs = []
renew_cg()
self.Init()
if len(errs) > 0:
eerrs = (esum(errs)) # * (1.0/(float(len(errs))))
eerrs.scalar_value()
# 根据损失函数求梯度
eerrs.backward()
# 参数更新
self.trainer.update()
errs = []
lerrs = []
renew_cg()
self.trainer.update()
print "Loss: ", mloss / iSentence
def Predict(self, conll_path):
with open(conll_path, 'r') as conllFP:
for iSentence, sentence in enumerate(read_conll(conllFP, False)):
self.Init()
# 中文中pos为'X'的词为非语素词，需要过滤掉，否则会导致程序崩溃，英文无
# 中文宾州树库中训练集和发展集中无这种例子，但是测试集中包含个位数的例子
conll_sentence = [
entry for entry in sentence if
(isinstance(entry, utils.ConllEntry) and entry.pos != 'X')
]
conll_sentence = conll_sentence[1:] + [conll_sentence[0]]
self.getWordEmbeddings(conll_sentence, False)
# 初始stack为空
stack = ParseForest([])
# 初始buf为整个句子
buf = ParseForest(conll_sentence)
for root in conll_sentence:
root.lstms = [root.vec for _ in xrange(self.nnvecs)]
hoffset = 1 if self.headFlag else 0
# 循环直到句子中的所有依存关系都找到
while not (len(buf) == 1 and len(stack) == 0):
scores = self.__evaluate(stack, buf, False)
best = max(chain(*scores), key=itemgetter(2))
if best[1] == 2:
stack.roots.append(buf.roots[0])
del buf.roots[0]
elif best[1] == 0:
child = stack.roots.pop()
parent = buf.roots[0]
child.pred_parent_id = parent.id
child.pred_relation = best[0]
bestOp = 0
if self.rlMostFlag:
parent.lstms[bestOp +
hoffset] = child.lstms[bestOp +
hoffset]
if self.rlFlag:
parent.lstms[bestOp + hoffset] = child.vec
elif best[1] == 1:
child = stack.roots.pop()
parent = stack.roots[-1]
child.pred_parent_id = parent.id
child.pred_relation = best[0]
bestOp = 1
if self.rlMostFlag:
parent.lstms[bestOp +
hoffset] = child.lstms[bestOp +
hoffset]
if self.rlFlag:
parent.lstms[bestOp + hoffset] = child.vec
renew_cg()
yield sentence
def Train(self, conll_path, epoch):
mloss = 0.0
errors = 0
batch = 0
eloss = 0.0
eerrors = 0
lerrors = 0
etotal = 0
ltotal = 0
ninf = -float('inf')
hoffset = 1 if self.model.headFlag else 0
start = time.time()
fout = open('loss_coco_0001_epoch_%d.log' % epoch, 'w')
with open(conll_path, 'r') as conllFP:
shuffledData = list(read_conll(conllFP, True))
random.shuffle(shuffledData)
errs = []
eeloss = 0.0
self.model.Init()
non_proj = 0
for iSentence, sentence in enumerate(shuffledData):
isProj = True
if iSentence % 100 == 0 and iSentence != 0:
print 'Processing sentence number:', iSentence, 'Loss:', eloss / etotal, 'Errors:', (
float(eerrors)) / etotal, 'Labeled Errors:', (
float(lerrors) /
etotal), 'Time', time.time() - start
#print "check"
fout.write(str(eloss / etotal) + '\n')
start = time.time()
del eerrors, eloss, etotal, lerrors, ltotal
eerrors = 0
eloss = 0.0
etotal = 0
lerrors = 0
ltotal = 0
conll_sentence = [
entry for entry in sentence
if isinstance(entry, utils.ConllEntry)
]
conll_sentence = conll_sentence[1:] + [conll_sentence[0]]
if iSentence != 0:
del sent_vec, lstms
sent_vec = self.model.getWordEmbeddings(conll_sentence, True)
lstms = []
stack = ParseForest([])
buf = ParseForest(conll_sentence)
for i in range(len(sent_vec)):
buf.roots[i].lstms = i
lstms.append(
[sent_vec[i] for _ in xrange(self.model.nnvecs)])
hoffset = 1 if self.model.headFlag else 0
while not (len(buf) == 1 and len(stack) == 0):
scores = self.model.evaluate(stack, buf, True, lstms)
scores.append([(None, self.model.num_transitioins, ninf,
None)])
alpha = stack.roots[:-2] if len(stack) > 2 else []
s1 = [stack.roots[-2]] if len(stack) > 1 else []
s0 = [stack.roots[-1]] if len(stack) > 0 else []
b = [buf.roots[0]] if len(buf) > 0 else []
beta = buf.roots[1:] if len(buf) > 1 else []
left_cost = (
len([h
for h in s1 + beta if h.id == s0[0].parent_id]) +
len([d for d in b + beta if d.parent_id == s0[0].id])
) if len(scores[0]) > 0 else 1
right_cost = (
len([h for h in b + beta if h.id == s0[0].parent_id]) +
len([d for d in b + beta if d.parent_id == s0[0].id])
) if len(scores[1]) > 0 else 1
shift_cost = (
len([h
for h in s1 + alpha if h.id == b[0].parent_id]) +
len([
d
for d in s0 + s1 + alpha if d.parent_id == b[0].id
])) if len(scores[2]) > 0 else 1
reduce_cost = (len([
h for h in s1 + b + beta if h.id == s0[0].parent_id
]) + len([d for d in b + beta if d.parent_id == s0[0].id])
) if len(scores[3]) > 0 else 1
#print "\nWord in Buff: "
#for word in b+beta:
# print "buf parent id: ", word.parent_id
# print "word id: ", word.id
# print "word: ", word.form
if len(stack) > 0:
#print "parent id: ", s0[0].parent_id
if s0[0].parent_id == -1 and reduce_cost == 0:
left_cost += 1
right_cost += 1
shift_cost += 1
costs = (left_cost, right_cost, shift_cost, reduce_cost, 1)
#print "CCCOST ", costs
#costs = (left_cost, right_cost, shift_cost, reduce_cost, isObj_cost, isPred_cost, 1)
try:
bestValid = max(
(s for s in chain(*scores) if costs[s[1]] == 0 and
(s[1] == 2 or s[0] == stack.roots[-1].relation
or s[0] == None)),
key=itemgetter(2))
except:
print "length of stack: ", len(stack.roots)
#for roots in stack.roots:
# print roots.parent
# print roots.children
print "This is non projective"
exit()
non_proj += 1
isProj = False
break
bestWrong = max(
(s for s in chain(*scores) if costs[s[1]] != 0 or (
s[1] != 2 and s[0] != stack.roots[-1].relation)),
key=itemgetter(2))
#best = bestValid if ( (not self.model.oracle) or (bestValid[2] - bestWrong[2] > 1.0) or (bestValid[2] > bestWrong[2] and random.random() > 0.1) ) else bestWrong
best = bestValid
#print "No. %d sentences, best[1] = %d" % (iSentence, best[1])
if best[1] == 2:
stack.roots.append(buf.roots[0])
del buf.roots[0]
elif best[1] == 0:
child = stack.roots.pop()
parent = buf.roots[0]
child.pred_parent_id = parent.id
child.pred_relation = best[0]
bestOp = 0
if self.model.rlMostFlag:
lstms[parent.lstms][bestOp + hoffset] = lstms[
child.lstms][bestOp + hoffset]
if self.model.rlFlag:
lstms[parent.lstms][bestOp + hoffset] = sent_vec[
child.lstms]
elif best[1] == 1:
child = stack.roots.pop()
parent = stack.roots[-1]
child.pred_parent_id = parent.id
child.pred_relation = best[0]
bestOp = 1
if self.model.rlMostFlag:
lstms[parent.lstms][bestOp + hoffset] = lstms[
child.lstms][bestOp + hoffset]
if self.model.rlFlag:
lstms[parent.lstms][bestOp + hoffset] = sent_vec[
child.lstms]
elif best[1] == 3:
child = stack.roots.pop()
child.pred_parent_id = -1
child.pred_relation = '_'
bestOp = 3
if bestValid[2] < bestWrong[2] + 1.0:
loss = bestWrong[3] - bestValid[3]
mloss += 1.0 + bestWrong[2] - bestValid[2]
eloss += 1.0 + bestWrong[2] - bestValid[2]
errs.append(loss)
del loss
if best[1] != 2 and (
child.pred_parent_id != child.parent_id
or child.pred_relation != child.relation):
lerrors += 1
if child.pred_parent_id != child.parent_id:
errors += 1
eerrors += 1
etotal += 1
del scores
#print "Finish %d sentences" %(iSentence)
#print os.system('nvidia-smi')
#exit()
if len(errs) > 50: # or True:
eerrs = torch.sum(cat(errs))
scalar_loss = get_data(eerrs).numpy()[0]
eerrs.backward()
self.trainer.step()
del eerrs
errs = []
lerrs = []
self.model.Init()
self.trainer.zero_grad()
if len(errs) > 0:
eerrs = torch.sum(cat(errs)) # * (1.0/(float(len(errs))))
get_data(eerrs).numpy()[0]
eerrs.backward()
self.trainer.step()
del eerrs
errs = []
lerrs = []
self.trainer.zero_grad()
print "Loss: ", mloss / iSentence
def Train(self, shuffledData):
mloss = 0.0
eloss = 0.0
eerrors = 0
lerrors = 0
etotal = 0
ninf = -float('inf')
start = time.time()
random.shuffle(shuffledData)
print "Length of training data: ", len(shuffledData)
errs = []
self.Init()
trainData = shuffledData
if self.debug:
trainData = shuffledData[:200]
for iSentence, sentence in enumerate(trainData):
if iSentence % 100 == 0 and iSentence != 0:
loss_message = 'Processing sentence number: %d'%iSentence + \
' Loss: %.3f'%(eloss / etotal)+ \
' Errors: %.3f'%((float(eerrors)) / etotal)+\
' Labeled Errors: %.3f'%(float(lerrors) / etotal)+\
' Time: %.2gs'%(time.time()-start)
print loss_message
start = time.time()
eerrors = 0
eloss = 0.0
etotal = 0
lerrors = 0
conll_sentence = [
entry for entry in sentence
if isinstance(entry, utils.ConllEntry)
]
conll_sentence = conll_sentence[1:] + [conll_sentence[0]]
self.getWordEmbeddings(conll_sentence, True)
stack = ParseForest([])
buf = ParseForest(conll_sentence)
hoffset = 1 if self.headFlag else 0
for root in conll_sentence:
root.lstms = [root.vec] if self.headFlag else []
root.lstms += [
self.paddingVec for _ in range(self.nnvecs - hoffset)
]
root.relation = root.relation if root.relation in self.rels else 'runk'
while not (len(buf) == 1 and len(stack) == 0):
scores = self.__evaluate(stack, buf, True)
#to ensure that we have at least one wrong operation
scores.append([(None, 4, ninf, None)])
stack_ids = [sitem.id for sitem in stack.roots]
s1 = [stack.roots[-2]] if len(stack) > 1 else []
s0 = [stack.roots[-1]] if len(stack) > 0 else []
b = [buf.roots[0]] if len(buf) > 0 else []
beta = buf.roots[1:] if len(buf) > 1 else []
costs, shift_case = self.calculate_cost(
scores, s0, s1, b, beta, stack_ids)
bestValid = list(
(s for s in chain(*scores) if costs[s[1]] == 0 and (
s[1] == 2 or s[1] == 3 or s[0] == s0[0].relation)))
if len(bestValid) < 1:
print "===============dropping a sentence==============="
break
bestValid = max(bestValid, key=itemgetter(2))
bestWrong = max(
(s for s in chain(*scores) if costs[s[1]] != 0 or (
s[1] != 2 and s[1] != 3 and s[0] != s0[0].relation)),
key=itemgetter(2))
#force swap
if costs[3] == 0:
best = bestValid
else:
#select a transition to follow
# + aggresive exploration
#1: might want to experiment with that parameter
if bestWrong[1] == 3:
best = bestValid
else:
best = bestValid if (
(not self.oracle) or
(bestValid[2] - bestWrong[2] > 1.0) or
(bestValid[2] > bestWrong[2]
and random.random() > 0.1)) else bestWrong
if best[1] == 2:
#SHIFT
if shift_case == 2:
if b[0].parent_entry.id in stack_ids[:-1] and b[
0].id in b[0].parent_entry.rdeps:
b[0].parent_entry.rdeps.remove(b[0].id)
blocked_deps = [
d for d in b[0].rdeps if d in stack_ids
]
for d in blocked_deps:
b[0].rdeps.remove(d)
stack.roots.append(buf.roots[0])
del buf.roots[0]
elif best[1] == 3:
#SWAP
child = stack.roots.pop()
buf.roots.insert(1, child)
elif best[1] == 0:
#LEFT-ARC
s0[0].rdeps = []
if s0[0].id in s0[0].parent_entry.rdeps:
s0[0].parent_entry.rdeps.remove(s0[0].id)
child = stack.roots.pop()
parent = buf.roots[0]
child.pred_parent_id = parent.id
child.pred_relation = best[0]
elif best[1] == 1:
#RIGHT-ARC
s0[0].rdeps = []
if s0[0].id in s0[0].parent_entry.rdeps:
s0[0].parent_entry.rdeps.remove(s0[0].id)
child = stack.roots.pop()
parent = stack.roots[-1]
child.pred_parent_id = parent.id
child.pred_relation = best[0]
#update the representation of head for attaching transitions
if best[1] == 0 or best[1] == 1:
#linear order
if self.rlMostFlag:
parent.lstms[best[1] + hoffset] = child.lstms[best[1] +
hoffset]
#actual children
if self.rlFlag:
parent.lstms[best[1] + hoffset] = child.vec
if bestValid[2] < bestWrong[2] + 1.0:
loss = bestWrong[3] - bestValid[3]
mloss += 1.0 + bestWrong[2] - bestValid[2]
eloss += 1.0 + bestWrong[2] - bestValid[2]
errs.append(loss)
#labeled errors
if best[1] != 2 and best[1] != 3 and (
child.pred_parent_id != child.parent_id
or child.pred_relation != child.relation):
lerrors += 1
#attachment error
if child.pred_parent_id != child.parent_id:
eerrors += 1
if best[1] == 0 or best[1] == 2:
etotal += 1
#footnote 8 in Eli's original paper
if len(errs) > 50: # or True:
eerrs = dy.esum(errs)
scalar_loss = eerrs.scalar_value() #forward
eerrs.backward()
self.trainer.update()
errs = []
lerrs = []
dy.renew_cg()
self.Init()
if len(errs) > 0:
eerrs = (dy.esum(errs))
eerrs.scalar_value()
eerrs.backward()
self.trainer.update()
errs = []
lerrs = []
dy.renew_cg()
self.trainer.update()
print "Loss: ", mloss / iSentence
def forward(self, sentences, errs):
tmp = time.time()
self.getWordEmbeddings(sentences, True)
self.ebd += time.time() - tmp
dloss, deerrors, detotal = 0, 0, 0
for sentence in sentences:
stack = ParseForest([])
buf = ParseForest(sentence)
for root in sentence:
root.lstms = [root.vec for _ in range(self.nnvecs)]
hoffset = 1 if self.headFlag else 0
while not (len(buf) == 1 and len(stack) == 0):
tmp = time.time()
scores = self.__evaluate(stack, buf, True)
self.evl += time.time() - tmp
scores.append([(None, 3, -np.inf ,None)])
alpha = stack.roots[:-2] if len(stack) > 2 else []
s1 = [stack.roots[-2]] if len(stack) > 1 else []
s0 = [stack.roots[-1]] if len(stack) > 0 else []
b = [buf.roots[0]] if len(buf) > 0 else []
beta = buf.roots[1:] if len(buf) > 1 else []
left_cost = (len([h for h in s1 + beta if h.id == s0[0].parent_id]) +
len([d for d in b + beta if d.parent_id == s0[0].id])) if len(scores[0]) > 0 else 1
right_cost = (len([h for h in b + beta if h.id == s0[0].parent_id]) +
len([d for d in b + beta if d.parent_id == s0[0].id])) if len(scores[1]) > 0 else 1
shift_cost = (len([h for h in s1 + alpha if h.id == b[0].parent_id]) +
len([d for d in s0 + s1 + alpha if d.parent_id == b[0].id])) if len(scores[2]) > 0 else 1
costs = (left_cost, right_cost, shift_cost, 1)
bestValid = max((s for s in chain(*scores) if costs[s[1]] == 0 and ( s[1] == 2 or s[0] == stack.roots[-1].relation)), key=itemgetter(2))
bestWrong = max((s for s in chain(*scores) if costs[s[1]] != 0 or ( s[1] != 2 and s[0] != stack.roots[-1].relation)), key=itemgetter(2))
best = bestValid if ((not self.oracle) or (bestValid[2] - bestWrong[2] > 1.0) or (bestValid[2] > bestWrong[2] and random.random() > 0.1)) else bestWrong
if best[1] == 2:
stack.roots.append(buf.roots[0])
del buf.roots[0]
elif best[1] == 0:
child = stack.roots.pop()
parent = buf.roots[0]
child.pred_parent_id = parent.id
child.pred_relation = best[0]
bestOp = 0
if self.rlMostFlag:
parent.lstms[bestOp + hoffset] = child.lstms[bestOp + hoffset]
if self.rlFlag:
parent.lstms[bestOp + hoffset] = child.vec
elif best[1] == 1:
child = stack.roots.pop()
parent = stack.roots[-1]
child.pred_parent_id = parent.id
child.pred_relation = best[0]
bestOp = 1
if self.rlMostFlag:
parent.lstms[bestOp + hoffset] = child.lstms[bestOp + hoffset]
if self.rlFlag:
parent.lstms[bestOp + hoffset] = child.vec
if bestValid[2] < bestWrong[2] + 1.0:
loss = bestWrong[3] - bestValid[3]
dloss += 1.0 + bestWrong[2] - bestValid[2]
errs.append(loss)
if best[1] != 2 and (child.pred_parent_id != child.parent_id or child.pred_relation != child.relation):
if child.pred_parent_id != child.parent_id:
deerrors += 1
detotal += 1
return dloss, deerrors, detotal
def Train(self, conll_path, options):
mloss = 0.0
errors = 0
batch = 0
eloss = 0.0
#eerrors = 0
#lerrors = 0
etotal = 0
#ltotal = 0
max_quotient = float("-inf")
min_quotient = float("inf")
NUM_SAMPLES = options.num_samples #default 10
start = time.time()
with open(conll_path, 'r') as conllFP:
shuffledData = list(read_conll(conllFP, True))
random.shuffle(shuffledData)
errs = []
#eeloss = 0.0
batch_errs = []
self.Init()
for iSentence, sentence in enumerate(shuffledData):
if iSentence % 100 == 0 and iSentence != 0:
print 'Processing sentence number:', iSentence, 'Loss:', eloss / etotal, 'Time', time.time(
) - start
#print 'Processing sentence number:', iSentence, 'Loss:', eloss / etotal, 'Errors:', (float(eerrors)) / etotal, 'Labeled Errors:', (float(lerrors) / etotal) , 'Time', time.time()-start
start = time.time()
#eerrors = 0
eloss = 0.0
etotal = 0
#lerrors = 0
#ltotal = 0
sample_errs = []
sample_quotients = []
#print('Sentence: {}'.format(sentence))
DEBUG = random.random() < 0.0001
if DEBUG:
print("Train sentence: {}".format(
[e.form for e in sentence]))
for _ in xrange(NUM_SAMPLES):
forest = ParseForest(sentence)
self.getWordEmbeddings(forest, True)
for root in forest.roots:
root.lstms = [
self.builders[0].initial_state().add_input(
root.vec),
self.builders[1].initial_state().add_input(
root.vec)
]
unassigned = {
entry.id: sum([
1 for pentry in sentence
if pentry.parent_id == entry.id
])
for entry in sentence
}
#loss = 0
log_q_total = 0.0
log_p_total = 0.0
while len(forest.roots) > 1:
self.__evaluate(
forest, True) #NOTE(prkriley): this updates scores
roots = forest.roots
rootsIds = set([root.id for root in roots])
def _isValid(i):
return (unassigned[roots[i].id] == 0) and (
(i > 0
and roots[i].parent_id == roots[i - 1].id) or
(i < len(roots) - 1
and roots[i].parent_id == roots[i + 1].id))
valid_zs = [
j for j in xrange(1, len(roots)) if _isValid(j)
]
z_scores = concatenate([r.zexpr for r in roots[1:]])
valid_z_scores = concatenate(
[roots[j].zexpr for j in valid_zs])
p_zs = softmax(z_scores)
#print("P(z): {}".format(p_zs.npvalue()))
q_temperature = 16.0
q_zs = softmax(valid_z_scores * 1.0 / q_temperature)
q_zs_numpy = q_zs.npvalue()
q_zs_numpy /= np.sum(q_zs_numpy)
if DEBUG:
print("Valid z indices: {}".format(valid_zs))
print("Q(z): {}".format(q_zs_numpy))
valid_i = np.random.choice(len(valid_zs), p=q_zs_numpy)
q_z = pick(q_zs, valid_i)
i = valid_zs[valid_i]
log_q_total += log(q_z).scalar_value()
p_z = pick(p_zs, i - 1)
log_p_total += log(p_z).scalar_value()
irel = list(self.irels).index(roots[i].relation)
op = 0 if roots[i].parent_id == roots[i - 1].id else 1
#TODO(prkriley): verify correctness of this index math
presoftmax_p_y = [
val for tup in roots[i].exprs for val in tup
]
if i < len(roots) - 1:
neglog_p_y = pickneglogsoftmax(
concatenate(presoftmax_p_y), irel * 2 + op)
else:
assert op == 0
presoftmax_p_y = presoftmax_p_y[::2]
neglog_p_y = pickneglogsoftmax(
concatenate(presoftmax_p_y), irel)
neglog_p_z = pickneglogsoftmax(z_scores, i - 1)
errs.append(neglog_p_y + neglog_p_z)
log_p_total -= neglog_p_y.scalar_value()
mloss += neglog_p_y.scalar_value()
mloss += neglog_p_z.scalar_value()
etotal += 1
selectedChild = i
selectedIndex = i
selectedOp = op
selectedParent = i + [-1, 1][op]
selectedIRel = irel
for j in xrange(
max(0, selectedIndex - self.k - 2),
min(len(forest.roots),
selectedIndex + self.k + 2)):
roots[j].scores = None
#NOTE(prkriley): counts number of real children that are still gettable
unassigned[roots[selectedChild].parent_id] -= 1
#NOTE(prkriley): I think lstms[0] is the right one, [1] is the left...
roots[selectedParent].lstms[selectedOp] = roots[
selectedParent].lstms[selectedOp].add_input(
self.activation(self.lstm2lstm * noise(
concatenate([
roots[selectedChild].lstms[0].output(),
lookup(self.model["rels-lookup"],
selectedIRel),
roots[selectedChild].lstms[1].output()
]), 0.0) + self.lstm2lstmbias))
forest.Attach(selectedParent, selectedChild)
#END OF SINGLE SAMPLE
#TODO(prkriley): finalize loss, do update, etc
eerrs = (
(esum(errs)) * (1.0 / (float(len(errs))))
) #TODO(prkriley): consider removing this division
#eerrs = esum(errs)
#TODO(prkriley): scale by p/q which is exp(logp-logq)
#print("logp: {}; logq: {}".format(log_p_total, log_q_total))
pq_quotient = np.exp(log_p_total - log_q_total)
scaled_pq_quotient = pq_quotient * 1e3
#scaled_pq_quotient = min(scaled_pq_quotient, 1.5e-5)
#scaled_pq_quotient = max(scaled_pq_quotient, 1.5e-8)
#eerrs *= scaled_pq_quotient
#print("P/Q: {}".format(pq_quotient))
max_quotient = max(scaled_pq_quotient, max_quotient)
min_quotient = min(scaled_pq_quotient, min_quotient)
eloss += eerrs.scalar_value()
sample_errs.append(eerrs)
sample_quotients.append(scaled_pq_quotient)
errs = []
DEBUG = False
#END OF SAMPLING
#upper_clip = 5e-6
#lower_clip = 2e-8
#scale = 1.0
#if max_quotient < lower_clip:
# scale = lower_clip / max_quotient
###
#SCALING QUOTIENTS
#max_sample_quotient = max(sample_quotients)
#if max_sample_quotient > upper_clip:
# scale = upper_clip / max_sample_quotient
sum_quotients = sum(sample_quotients)
PQ_NORMALIZE_SUM = options.pq_norm
scale = PQ_NORMALIZE_SUM / sum_quotients
sample_quotients = [e * scale for e in sample_quotients]
#for q in sample_quotients:
# assert q <= upper_clip * 1.1, "Large quotient: {}".format(q)
###
if options.use_pq:
sample_errs = [
e * q for (e, q) in zip(sample_errs, sample_quotients)
]
final_error = esum(sample_errs)
if not options.use_pq:
assert len(sample_errs) == NUM_SAMPLES
final_error *= (1.0 / (float(len(sample_errs))))
#TODO(prkriley): put final_error somewhere and update once we have N of them
batch_errs.append(final_error)
if len(batch_errs) >= options.batch_size:
total_error = esum(batch_errs)
total_error.backward()
self.trainer.update()
batch_errs = []
renew_cg()
self.Init()
#final_error.backward()
#self.trainer.update()
#renew_cg()
#self.Init()
#END OF EPOCH
#FILE CLOSE
if options.use_pq:
print("Max Quotient: {}; Min Quotient: {}".format(
max_quotient, min_quotient))
#self.trainer.update_epoch() #TODO(prkriley): verify that AdamTrainer handles everything this did before
print "Loss: ", mloss / (iSentence * NUM_SAMPLES)
def Predict(self, conll_path):
with open(conll_path, 'r') as conllFP:
for iSentence, sentence in enumerate(read_conll(conllFP, False)):
print("Sentence: {}".format([e.form for e in sentence]))
self.Init()
forest = ParseForest(sentence)
self.getWordEmbeddings(forest, False)
for root in forest.roots:
root.lstms = [
self.builders[0].initial_state().add_input(root.vec),
self.builders[1].initial_state().add_input(root.vec)
]
###
#NOTE(prkriley): looking at truth here, but ONLY for reporting
unassigned = {
entry.id: sum([
1 for pentry in sentence
if pentry.parent_id == entry.id
])
for entry in sentence
}
###
while len(forest.roots) > 1:
self.__evaluate(forest, False)
#bestParent, bestChild, bestScore = None, None, float("-inf")
#bestIndex, bestOp = None, None
roots = forest.roots
###
z_scores = concatenate([r.zexpr for r in roots[1:]])
p_z = softmax(z_scores).npvalue()
bestIndex = np.argmax(p_z) + 1
print('P(z): {}'.format(p_z))
print('Best index: {} ({})'.format(bestIndex,
roots[bestIndex].form))
valid_exprs = [
val for tup in roots[bestIndex].exprs for val in tup
]
if bestIndex == len(roots) - 1:
valid_exprs = valid_exprs[::2]
p_y = softmax(concatenate(valid_exprs))
max_y_index = np.argmax(
p_y.npvalue()
) #NOTE(prkriley): don't need to actually do softmax just to pick max
if bestIndex < len(roots) - 1:
bestOp = max_y_index % 2
bestIRelation = (max_y_index - bestOp) / 2
else:
bestOp = 0
bestIRelation = max_y_index
#TODO(prkriley): make sure op is valid
bestChild = bestIndex
bestParent = bestIndex + [-1, 1][bestOp]
bestRelation = self.irels[bestIRelation]
###
###
#NOTE(prkriley): again, using truth but only for reporting
def _isValid(i):
return (unassigned[roots[i].id] == 0) and (
(i > 0 and roots[i].parent_id == roots[i - 1].id)
or (i < len(roots) - 1
and roots[i].parent_id == roots[i + 1].id))
valid_zs = [
j for j in xrange(1, len(roots)) if _isValid(j)
]
valid_probs = [p_z[j - 1] for j in valid_zs]
invalid_probs = [
p_z[j - 1] for j in xrange(1, len(roots))
if j not in valid_zs
]
avg_valid_prob = sum(valid_probs) * 1.0 / len(
valid_probs) if valid_probs else -1
avg_invalid_prob = sum(invalid_probs) * 1.0 / len(
invalid_probs) if invalid_probs else -1
print("Avg valid prob: {}/{} = {}".format(
sum(valid_probs), len(valid_probs), avg_valid_prob))
print("Avg invalid prob: {}/{} = {}".format(
sum(invalid_probs), len(invalid_probs),
avg_invalid_prob))
###
#for j in xrange(max(0, bestIndex - self.k - 1), min(len(forest.roots), bestIndex + self.k + 2)):
for j in xrange(
max(0, bestIndex - self.k - 2),
min(len(forest.roots), bestIndex + self.k + 2)):
roots[j].scores = None
roots[bestChild].pred_parent_id = forest.roots[
bestParent].id
roots[bestChild].pred_relation = bestRelation
roots[bestParent].lstms[bestOp] = roots[bestParent].lstms[
bestOp].add_input((self.activation(
self.lstm2lstmbias + self.lstm2lstm * concatenate([
roots[bestChild].lstms[0].output(),
lookup(self.model["rels-lookup"], bestIRelation
), roots[bestChild].lstms[1].output()
]))))
unassigned[roots[bestChild].parent_id] -= 1
forest.Attach(bestParent, bestChild)
renew_cg()
yield sentence
def Predict(self, data, prefix=None):
reached_max_swap = 0
get_vectors = False
if prefix:
pref_idx = 0
if type(prefix) == list:
pf = prefix[pref_idx]
else:
pf = prefix
fcemb = codecs.open(pf + '-char-emb.vec', 'w', encoding='utf-8')
fwemb = codecs.open(pf + '-word-emb.vec', 'w', encoding='utf-8')
fenc = codecs.open(pf + '-encoder.vec', 'w', encoding='utf-8')
get_vectors = True
lang_name = ''
for iSentence, osentence in enumerate(data, 1):
if type(prefix) == list:
if iSentence == 1:
lang_name = osentence[0].language_id
print 'Extract feature:', pf, lang_name
else:
if lang_name != osentence[0].language_id:
fcemb.close()
fwemb.close()
fenc.close()
pref_idx += 1
pf = prefix[pref_idx]
fcemb = codecs.open(pf + '-char-emb.vec',
'w',
encoding='utf-8')
fwemb = codecs.open(pf + '-word-emb.vec',
'w',
encoding='utf-8')
fenc = codecs.open(pf + '-encoder.vec',
'w',
encoding='utf-8')
lang_name = osentence[0].language_id
print 'Extract feature:', pf, lang_name
sentence = deepcopy(osentence)
reached_swap_for_i_sentence = False
max_swap = 2 * len(sentence)
iSwap = 0
self.feature_extractor.Init()
conll_sentence = [
entry for entry in sentence
if isinstance(entry, utils.ConllEntry)
]
conll_sentence = conll_sentence[1:] + [conll_sentence[0]]
data_vec = self.feature_extractor.getWordEmbeddings(
conll_sentence, False, get_vectors=get_vectors)
if get_vectors:
for dat in data_vec:
# flabel.write(str(dat[0]) + '\t' + dat[1] + '\t' + dat[2] + '\t' + dat[3] + '\n')
fcemb.write(','.join([str(x) for x in dat[4]]) + '\n')
fwemb.write(','.join([str(x) for x in dat[5]]) + '\n')
fenc.write(','.join([str(x) for x in dat[6]]) + '\n')
stack = ParseForest([])
buf = ParseForest(conll_sentence)
hoffset = 1 if self.headFlag else 0
lang = conll_sentence[1].language_id
for root in conll_sentence:
root.lstms = [root.vec] if self.headFlag else []
if not self.feature_extractor.multiling or self.feature_extractor.shareWordLookup:
root.lstms += [
self.feature_extractor.paddingVec
for _ in range(self.nnvecs - hoffset)
]
else:
root.lstms += [
self.feature_extractor.paddingVecs[lang]
for _ in range(self.nnvecs - hoffset)
]
while not (len(buf) == 1 and len(stack) == 0):
scores = self.__evaluate(stack, buf, False)
best = max(
chain(*(scores if iSwap < max_swap else scores[:3])),
key=itemgetter(2))
if iSwap == max_swap and not reached_swap_for_i_sentence:
reached_max_swap += 1
reached_swap_for_i_sentence = True
print "reached max swap in %d out of %d sentences" % (
reached_max_swap, iSentence)
self.apply_transition(best, stack, buf, hoffset)
if best[1] == 3:
iSwap += 1
dy.renew_cg()
#keep in memory the information we need, not all the vectors
oconll_sentence = [
entry for entry in osentence
if isinstance(entry, utils.ConllEntry)
]
oconll_sentence = oconll_sentence[1:] + [oconll_sentence[0]]
for tok_o, tok in zip(oconll_sentence, conll_sentence):
tok_o.pred_relation = tok.pred_relation
tok_o.pred_parent_id = tok.pred_parent_id
yield osentence
if prefix:
fcemb.close()
fwemb.close()
fenc.close()
def Train(self, conll_path, epoch):
mloss = 0.0
errors = 0
batch = 0
eloss = 0.0
eerrors = 0
lerrors = 0
etotal = 0
ltotal = 0
ninf = -float('inf')
hoffset = 1 if self.headFlag else 0
start1 = start = time.time()
onlyNonProjectives = True
with open(conll_path, 'r') as conllFP:
shuffledData = list(read_conll(conllFP, onlyNonProjectives))
random.shuffle(shuffledData)
errs = []
eeloss = 0.0
self.Init()
numOfSent = len(shuffledData)
displayFreq = 500
if numOfSent < 2000: displayFreq = 200
for iSentence, sentence in enumerate(shuffledData):
if iSentence % displayFreq == 0 and iSentence != 0:
#print 'Processing sentence number:', iSentence, 'Loss:', eloss / etotal, 'Errors:', (float(eerrors)) / etotal, 'Labeled Errors:', (float(lerrors) / etotal) , 'Time', time.time()-start
timeSpent = time.time()-start
totalTimeSpent = time.time()-start1
timeToGo = totalTimeSpent*(numOfSent-iSentence) / iSentence
print 'Epoch: %2d sentence number: %6d/%d Loss: %.5f Errors: %.5f Labeled Errors: %.5f Time: %.1f s, total: %.1f s ETA: %.1f s' \
% (epoch+1,
iSentence,
numOfSent,
(eloss / etotal),
(float(eerrors) / etotal),
(float(lerrors) / etotal),
timeSpent, totalTimeSpent,
timeToGo
)
start = time.time()
eerrors = 0
eloss = 0.0
etotal = 0
lerrors = 0
ltotal = 0
if self.use_root:
# garder le noeud "*root*" rajoute par read_conll (peut causer plusieurs racines)
sentence = sentence[1:] + [sentence[0]]
else:
sentence = sentence[1:]
self.getWordEmbeddings(sentence, True)
stack = ParseForest([])
buf = ParseForest(sentence)
for root in sentence:
root.lstms = [root.vec for _ in xrange(self.nnvecs)]
hoffset = 1 if self.headFlag else 0
while len(buf) > 0 or len(stack) > 1 :
scores = self.__evaluate(stack, buf, True)
scores.append([(None, 3, ninf ,None)])
alpha = stack.roots[:-2] if len(stack) > 2 else []
s1 = [stack.roots[-2]] if len(stack) > 1 else []
s0 = [stack.roots[-1]] if len(stack) > 0 else []
b = [buf.roots[0]] if len(buf) > 0 else []
beta = buf.roots[1:] if len(buf) > 1 else []
left_cost = ( len([h for h in s1 + beta if h.id == s0[0].parent_id]) +
len([d for d in b + beta if d.parent_id == s0[0].id]) ) if len(scores[0]) > 0 else 1
right_cost = ( len([h for h in b + beta if h.id == s0[0].parent_id]) +
len([d for d in b + beta if d.parent_id == s0[0].id]) ) if len(scores[1]) > 0 else 1
shift_cost = ( len([h for h in s1 + alpha if h.id == b[0].parent_id]) +
len([d for d in s0 + s1 + alpha if d.parent_id == b[0].id]) ) if len(scores[2]) > 0 else 1
costs = (left_cost, right_cost, shift_cost, 1)
bestOK = True
try:
bestValid = max(( s for s in chain(*scores) if costs[s[1]] == 0 and ( s[1] == 2 or s[0] == stack.roots[-1].relation ) ), key=itemgetter(2))
# print "best",bestValid
except:
bestOK = False
try:
bestWrong = max(( s for s in chain(*scores) if costs[s[1]] != 0 or ( s[1] != 2 and s[0] != stack.roots[-1].relation ) ), key=itemgetter(2))
except:
print "wrong", bestWrong
bestOK = False
# bestValid or bastWrong may fail when chain(*scores) gives an empty list
# in this (rare) case we keep the last best
# Will crash if the first word has an empty list
if bestOK:
best = bestValid if ( (not self.oracle) or (bestValid[2] - bestWrong[2] > 1.0) or (bestValid[2] > bestWrong[2] and random.random() > 0.1) ) else bestWrong
if best[1] == 2:
# we learned a SHIFT
stack.roots.append(buf.roots[0])
del buf.roots[0]
elif best[1] == 0:
# we learnded a LEFT ARC
child = stack.roots.pop()
parent = buf.roots[0]
child.pred_parent_id = parent.id
child.pred_relation = best[0]
bestOp = 0
if self.rlMostFlag:
parent.lstms[bestOp + hoffset] = child.lstms[bestOp + hoffset]
if self.rlFlag:
parent.lstms[bestOp + hoffset] = child.vec
elif best[1] == 1:
# RIGHT ARC
child = stack.roots.pop()
parent = stack.roots[-1]
child.pred_parent_id = parent.id
child.pred_relation = best[0]
bestOp = 1
if self.rlMostFlag:
parent.lstms[bestOp + hoffset] = child.lstms[bestOp + hoffset]
if self.rlFlag:
parent.lstms[bestOp + hoffset] = child.vec
if bestValid[2] < bestWrong[2] + 1.0:
loss = bestWrong[3] - bestValid[3]
mloss += 1.0 + bestWrong[2] - bestValid[2]
eloss += 1.0 + bestWrong[2] - bestValid[2]
errs.append(loss)
if best[1] != 2 and (child.pred_parent_id != child.parent_id or child.pred_relation != child.relation):
lerrors += 1
if child.pred_parent_id != child.parent_id:
errors += 1
eerrors += 1
etotal += 1
if len(errs) > 50: # or True:
#print "too many errors"
#eerrs = ((esum(errs)) * (1.0/(float(len(errs)))))
eerrs = esum(errs)
scalar_loss = eerrs.scalar_value()
eerrs.backward()
self.trainer.update()
errs = []
lerrs = []
renew_cg()
self.Init()
if len(errs) > 0:
eerrs = (esum(errs)) # * (1.0/(float(len(errs))))
eerrs.scalar_value()
eerrs.backward()
self.trainer.update()
errs = []
lerrs = []
renew_cg()
self.trainer.update_epoch()
print "Loss: %.4f time spent in epoch %.1f min" % (mloss/iSentence, (time.time()-start1)/60)
def Predict(self, conll_path, is_string=False):
conllFP = None
#OLD=False
#self.use_root=False
if is_string:
conllFP = StringIO.StringIO(conll_path)
else:
conllFP = open(conll_path, 'r')
#with open(conll_path, 'r') as conllFP:
if conllFP:
for iSentence, sentence in enumerate(read_conll(conllFP, False)):
self.Init()
if self.use_root:
# garder le noeud "*root*" (peut causer plusieurs racines)
sentence = sentence[1:] + [sentence[0]]
else:
sentence = sentence[1:]
#print "aaaa ", sentence[0], type(sentence)
self.getWordEmbeddings(sentence, False)
stack = ParseForest([])
buf = ParseForest(sentence)
for root in sentence:
root.lstms = [root.vec for _ in xrange(self.nnvecs)]
hoffset = 1 if self.headFlag else 0
cttrans = 0
#print "\n====="
while len(buf) > 0 or len(stack) > 1 :
scores = self.__evaluate(stack, buf, False)
best = max(chain(*scores), key = itemgetter(2) )
#print "\nBUFFER: ", buf
#print "STACK: ", stack
#print scores
cttrans += 1
# transitions
if best[1] == 2:
# SHIFT
stack.roots.append(buf.roots[0])
del buf.roots[0]
#print cttrans, "SHIFT"
elif best[1] == 0:
# LEFT (?) ARC
#print cttrans, "LEFT"
child = stack.roots.pop()
parent = buf.roots[0]
child.pred_parent_id = parent.id
child.pred_relation = best[0]
#print child, child.form, child.pred_parent_id
bestOp = 0
if self.rlMostFlag:
parent.lstms[bestOp + hoffset] = child.lstms[bestOp + hoffset]
if self.rlFlag:
parent.lstms[bestOp + hoffset] = child.vec
elif best[1] == 1:
# RIGHT ARC
#print cttrans, "RIGHT"
child = stack.roots.pop()
parent = stack.roots[-1]
child.pred_parent_id = parent.id
child.pred_relation = best[0]
bestOp = 1
if self.rlMostFlag:
parent.lstms[bestOp + hoffset] = child.lstms[bestOp + hoffset]
if self.rlFlag:
parent.lstms[bestOp + hoffset] = child.vec
renew_cg()
if self.use_root:
yield [sentence[-1]] + sentence[:-1]
else:
# write_conll coupe le premier mot, il faut mettre qq chose ici
yield [sentence[-1]] + sentence
def Train(self, conll_path):
mloss = 0.0
errors = 0
batch = 0
eloss = 0.0
eerrors = 0
lerrors = 0
etotal = 0
ltotal = 0
start = time.time()
with open(conll_path, 'r') as conllFP:
shuffledData = list(read_conll(conllFP, True))
random.shuffle(shuffledData)
errs = []
eeloss = 0.0
self.Init()
for iSentence, sentence in enumerate(shuffledData):
if iSentence % 100 == 0 and iSentence != 0:
print 'Processing sentence number:', iSentence, 'Loss:', eloss / etotal, 'Errors:', (
float(eerrors)) / etotal, 'Labeled Errors:', (
float(lerrors) /
etotal), 'Time', time.time() - start
start = time.time()
eerrors = 0
eloss = 0.0
etotal = 0
lerrors = 0
ltotal = 0
forest = ParseForest(sentence)
self.getWordEmbeddings(forest, True)
for root in forest.roots:
root.lstms = [
self.builders[0].initial_state().add_input(root.vec),
self.builders[1].initial_state().add_input(root.vec)
]
unassigned = {
entry.id: sum([
1 for pentry in sentence
if pentry.parent_id == entry.id
])
for entry in sentence
}
while len(forest.roots) > 1:
self.__evaluate(forest, True)
bestValidOp, bestValidScore = None, float("-inf")
bestWrongOp, bestWrongScore = None, float("-inf")
bestValidParent, bestValidChild = None, None
bestValidIndex, bestWrongIndex = None, None
roots = forest.roots
rootsIds = set([root.id for root in roots])
for i in xrange(len(forest.roots) - 1):
for irel, rel in enumerate(self.irels):
for op in xrange(2):
child = i + (1 - op)
parent = i + op
oracleCost = unassigned[roots[child].id] + (
0 if roots[child].parent_id not in rootsIds
or roots[child].parent_id
== roots[parent].id else 1)
if oracleCost == 0 and (
roots[child].parent_id !=
roots[parent].id
or roots[child].relation == rel):
if bestValidScore < forest.roots[i].scores[
irel][op]:
bestValidScore = forest.roots[
i].scores[irel][op]
bestValidOp = op
bestValidParent, bestValidChild = parent, child
bestValidIndex = i
bestValidIRel, bestValidRel = irel, rel
bestValidExpr = roots[
bestValidIndex].exprs[
bestValidIRel][bestValidOp]
elif bestWrongScore < forest.roots[i].scores[
irel][op]:
bestWrongScore = forest.roots[i].scores[
irel][op]
bestWrongParent, bestWrongChild = parent, child
bestWrongOp = op
bestWrongIndex = i
bestWrongIRel, bestWrongRel = irel, rel
bestWrongExpr = roots[
bestWrongIndex].exprs[bestWrongIRel][
bestWrongOp]
if bestValidScore < bestWrongScore + 1.0:
loss = bestWrongExpr - bestValidExpr
mloss += 1.0 + bestWrongScore - bestValidScore
eloss += 1.0 + bestWrongScore - bestValidScore
errs.append(loss)
if not self.oracle or bestValidScore - bestWrongScore > 1.0 or (
bestValidScore > bestWrongScore
and random.random() > 0.1):
selectedOp = bestValidOp
selectedParent = bestValidParent
selectedChild = bestValidChild
selectedIndex = bestValidIndex
selectedIRel, selectedRel = bestValidIRel, bestValidRel
else:
selectedOp = bestWrongOp
selectedParent = bestWrongParent
selectedChild = bestWrongChild
selectedIndex = bestWrongIndex
selectedIRel, selectedRel = bestWrongIRel, bestWrongRel
if roots[selectedChild].parent_id != roots[
selectedParent].id or selectedRel != roots[
selectedChild].relation:
lerrors += 1
if roots[selectedChild].parent_id != roots[
selectedParent].id:
errors += 1
eerrors += 1
etotal += 1
for j in xrange(
max(0, selectedIndex - self.k - 1),
min(len(forest.roots),
selectedIndex + self.k + 2)):
roots[j].scores = None
unassigned[roots[selectedChild].parent_id] -= 1
roots[selectedParent].lstms[selectedOp] = roots[
selectedParent].lstms[selectedOp].add_input(
self.activation(self.lstm2lstm * noise(
concatenate([
roots[selectedChild].lstms[0].output(),
lookup(self.model["rels-lookup"],
selectedIRel),
roots[selectedChild].lstms[1].output()
]), 0.0) + self.lstm2lstmbias))
forest.Attach(selectedParent, selectedChild)
if len(errs) > 50.0:
eerrs = ((esum(errs)) * (1.0 / (float(len(errs)))))
scalar_loss = eerrs.scalar_value()
eerrs.backward()
self.trainer.update()
errs = []
lerrs = []
renew_cg()
self.Init()
if len(errs) > 0:
eerrs = (esum(errs)) * (1.0 / (float(len(errs))))
eerrs.scalar_value()
eerrs.backward()
self.trainer.update()
errs = []
lerrs = []
renew_cg()
self.trainer.update_epoch()
print "Loss: ", mloss / iSentence
def Train(self, trainData):
mloss = 0.0
eloss = 0.0
eerrors = 0
lerrors = 0
etotal = 0
ninf = -float('inf')
beg = time.time()
start = time.time()
random.shuffle(
trainData
) # in certain cases the data will already have been shuffled after being read from file or while creating dev data
print "Length of training data: ", len(trainData)
errs = []
self.feature_extractor.Init()
for iSentence, sentence in enumerate(trainData, 1):
if iSentence % 100 == 0:
loss_message = 'Processing sentence number: %d'%iSentence + \
' Loss: %.3f'%(eloss / etotal)+ \
' Errors: %.3f'%((float(eerrors)) / etotal)+\
' Labeled Errors: %.3f'%(float(lerrors) / etotal)+\
' Time: %.2gs'%(time.time()-start)
print loss_message
start = time.time()
eerrors = 0
eloss = 0.0
etotal = 0
lerrors = 0
sentence = deepcopy(
sentence
) # ensures we are working with a clean copy of sentence and allows memory to be recycled each time round the loop
conll_sentence = [
entry for entry in sentence
if isinstance(entry, utils.ConllEntry)
]
conll_sentence = conll_sentence[1:] + [conll_sentence[0]]
self.feature_extractor.getWordEmbeddings(conll_sentence, True)
stack = ParseForest([])
buf = ParseForest(conll_sentence)
hoffset = 1 if self.headFlag else 0
lang = conll_sentence[1].language_id
for root in conll_sentence:
root.lstms = [root.vec] if self.headFlag else []
if not self.feature_extractor.multiling or self.feature_extractor.shareWordLookup:
root.lstms += [
self.feature_extractor.paddingVec
for _ in range(self.nnvecs - hoffset)
]
else:
root.lstms += [
self.feature_extractor.paddingVecs[lang]
for _ in range(self.nnvecs - hoffset)
]
while not (len(buf) == 1 and len(stack) == 0):
scores = self.__evaluate(stack, buf, True)
#to ensure that we have at least one wrong operation
scores.append([(None, 4, ninf, None)])
stack_ids = [sitem.id for sitem in stack.roots]
s1 = [stack.roots[-2]] if len(stack) > 1 else []
s0 = [stack.roots[-1]] if len(stack) > 0 else []
b = [buf.roots[0]] if len(buf) > 0 else []
beta = buf.roots[1:] if len(buf) > 1 else []
costs, shift_case = self.calculate_cost(
scores, s0, s1, b, beta, stack_ids)
bestValid = list(
(s for s in chain(*scores) if costs[s[1]] == 0 and (
s[1] == 2 or s[1] == 3 or s[0] == s0[0].relation)))
if len(bestValid) < 1:
print "===============dropping a sentence==============="
break
bestValid = max(bestValid, key=itemgetter(2))
bestWrong = max(
(s for s in chain(*scores) if costs[s[1]] != 0 or (
s[1] != 2 and s[1] != 3 and s[0] != s0[0].relation)),
key=itemgetter(2))
#force swap
if costs[3] == 0:
best = bestValid
else:
#select a transition to follow
# + aggresive exploration
if bestWrong[1] == 3:
best = bestValid
else:
best = bestValid if (
(not self.oracle) or
(bestValid[2] - bestWrong[2] > 1.0) or
(bestValid[2] > bestWrong[2]
and random.random() > 0.1)) else bestWrong
#updates for the dynamic oracle
if best[1] == 2:
#SHIFT
if shift_case == 2:
if b[0].parent_entry.id in stack_ids[:-1] and b[
0].id in b[0].parent_entry.rdeps:
b[0].parent_entry.rdeps.remove(b[0].id)
blocked_deps = [
d for d in b[0].rdeps if d in stack_ids
]
for d in blocked_deps:
b[0].rdeps.remove(d)
elif best[1] == 0 or best[1] == 1:
#LA or RA
child = s0[0]
s0[0].rdeps = []
if s0[0].id in s0[0].parent_entry.rdeps:
s0[0].parent_entry.rdeps.remove(s0[0].id)
self.apply_transition(best, stack, buf, hoffset)
if bestValid[2] < bestWrong[2] + 1.0:
loss = bestWrong[3] - bestValid[3]
mloss += 1.0 + bestWrong[2] - bestValid[2]
eloss += 1.0 + bestWrong[2] - bestValid[2]
errs.append(loss)
#labeled errors
if best[1] != 2 and best[1] != 3 and (
child.pred_parent_id != child.parent_id
or child.pred_relation != child.relation):
lerrors += 1
#attachment error
if child.pred_parent_id != child.parent_id:
eerrors += 1
if best[1] == 0 or best[1] == 2:
etotal += 1
#footnote 8 in Eli's original paper
if len(errs) > 50: # or True:
eerrs = dy.esum(errs)
scalar_loss = eerrs.scalar_value() #forward
eerrs.backward()
self.trainer.update()
errs = []
lerrs = []
dy.renew_cg()
self.feature_extractor.Init()
if len(errs) > 0:
eerrs = (dy.esum(errs))
eerrs.scalar_value()
eerrs.backward()
self.trainer.update()
errs = []
lerrs = []
dy.renew_cg()
self.trainer.update()
print "Loss: ", mloss / iSentence
print "Total Training Time: %.2gs" % (time.time() - beg)
def Predict(self, treebanks, datasplit, options):
reached_max_swap = 0
char_map = {}
if options.char_map_file:
char_map_fh = open(options.char_map_file, encoding='utf-8')
char_map = json.loads(char_map_fh.read())
# should probably use a namedtuple in get_vocab to make this prettier
_, test_words, test_chars, _, _, _, test_treebanks, test_langs = utils.get_vocab(
treebanks, datasplit, char_map)
# get external embeddings for the set of words and chars in the
# test vocab but not in the training vocab
test_embeddings = defaultdict(lambda: {})
if options.word_emb_size > 0 and options.ext_word_emb_file:
new_test_words = \
set(test_words) - self.feature_extractor.words.keys()
print("Number of OOV word types at test time: %i (out of %i)" %
(len(new_test_words), len(test_words)))
if len(new_test_words) > 0:
# no point loading embeddings if there are no words to look for
for lang in test_langs:
embeddings = utils.get_external_embeddings(
options,
emb_file=options.ext_word_emb_file,
lang=lang,
words=new_test_words)
test_embeddings["words"].update(embeddings)
if len(test_langs) > 1 and test_embeddings["words"]:
print("External embeddings found for %i words "\
"(out of %i)" % \
(len(test_embeddings["words"]), len(new_test_words)))
if options.char_emb_size > 0:
new_test_chars = \
set(test_chars) - self.feature_extractor.chars.keys()
print("Number of OOV char types at test time: %i (out of %i)" %
(len(new_test_chars), len(test_chars)))
if len(new_test_chars) > 0:
for lang in test_langs:
embeddings = utils.get_external_embeddings(
options,
emb_file=options.ext_char_emb_file,
lang=lang,
words=new_test_chars,
chars=True)
test_embeddings["chars"].update(embeddings)
if len(test_langs) > 1 and test_embeddings["chars"]:
print("External embeddings found for %i chars "\
"(out of %i)" % \
(len(test_embeddings["chars"]), len(new_test_chars)))
data = utils.read_conll_dir(treebanks, datasplit, char_map=char_map)
for iSentence, osentence in enumerate(data, 1):
sentence = deepcopy(osentence)
reached_swap_for_i_sentence = False
max_swap = 2 * len(sentence)
iSwap = 0
self.feature_extractor.Init(options)
conll_sentence = [
entry for entry in sentence
if isinstance(entry, utils.ConllEntry)
]
conll_sentence = conll_sentence[1:] + [conll_sentence[0]]
self.feature_extractor.getWordEmbeddings(conll_sentence, False,
options, test_embeddings)
stack = ParseForest([])
buf = ParseForest(conll_sentence)
hoffset = 1 if self.headFlag else 0
for root in conll_sentence:
#empty = dy.zeros(2*options.lstm_output_size)
root.lstms = [root.vec] if self.headFlag else []
root.lstms += [root.vec for _ in range(self.nnvecs - hoffset)]
root.relation = root.relation if root.relation in self.irels else 'runk'
while not (len(buf) == 1 and len(stack) == 0):
scores = self.__evaluate(stack, buf, False)
best = max(
chain(*(scores if iSwap < max_swap else scores[:3])),
key=itemgetter(2))
if iSwap == max_swap and not reached_swap_for_i_sentence:
reached_max_swap += 1
reached_swap_for_i_sentence = True
print("reached max swap in %d out of %d sentences" %
(reached_max_swap, iSentence))
self.apply_transition(best, stack, buf, hoffset)
if best[1] == SWAP:
iSwap += 1
dy.renew_cg()
#keep in memory the information we need, not all the vectors
oconll_sentence = [
entry for entry in osentence
if isinstance(entry, utils.ConllEntry)
]
oconll_sentence = oconll_sentence[1:] + [oconll_sentence[0]]
for tok_o, tok in zip(oconll_sentence, conll_sentence):
tok_o.pred_relation = tok.pred_relation
tok_o.pred_parent_id = tok.pred_parent_id
yield osentence
def Train(self, conll_path):
mloss = 0.0
errors = 0
batch = 0
eloss = 0.0
eerrors = 0
lerrors = 0
etotal = 0
ltotal = 0
ninf = -float('inf')
hoffset = 1 if self.headFlag else 0
start = time.time()
with open(conll_path, 'r') as conllFP:
shuffledData = list(read_conll(conllFP, True))
random.shuffle(shuffledData)
errs = []
eeloss = 0.0
self.Init()
for iSentence, sentence in enumerate(shuffledData):
if iSentence % 100 == 0 and iSentence != 0:
print 'Processing sentence number:', iSentence, 'Loss:', eloss / etotal, 'Errors:', (
float(eerrors)) / etotal, 'Labeled Errors:', (
float(lerrors) /
etotal), 'Time', time.time() - start
start = time.time()
eerrors = 0
eloss = 0.0
etotal = 0
lerrors = 0
ltotal = 0
conll_sentence = [
entry for entry in sentence
if isinstance(entry, utils.ConllEntry)
]
conll_sentence = conll_sentence[1:] + [conll_sentence[0]]
self.getWordEmbeddings(conll_sentence, True)
stack = ParseForest([])
buf = ParseForest(conll_sentence)
for root in conll_sentence:
root.lstms = [root.vec for _ in xrange(self.nnvecs)]
hoffset = 1 if self.headFlag else 0
while not (len(buf) == 1 and len(stack) == 0):
scores = self.__evaluate(stack, buf, True)
scores.append([(None, 3, ninf, None)])
alpha = stack.roots[:-2] if len(stack) > 2 else []
s1 = [stack.roots[-2]] if len(stack) > 1 else []
s0 = [stack.roots[-1]] if len(stack) > 0 else []
b = [buf.roots[0]] if len(buf) > 0 else []
beta = buf.roots[1:] if len(buf) > 1 else []
left_cost = (
len([h
for h in s1 + beta if h.id == s0[0].parent_id]) +
len([d for d in b + beta if d.parent_id == s0[0].id])
) if len(scores[0]) > 0 else 1
right_cost = (
len([h for h in b + beta if h.id == s0[0].parent_id]) +
len([d for d in b + beta if d.parent_id == s0[0].id])
) if len(scores[1]) > 0 else 1
shift_cost = (
len([h
for h in s1 + alpha if h.id == b[0].parent_id]) +
len([
d
for d in s0 + s1 + alpha if d.parent_id == b[0].id
])) if len(scores[2]) > 0 else 1
costs = (left_cost, right_cost, shift_cost, 1)
bestValid = max(
(s for s in chain(*scores) if costs[s[1]] == 0 and (
s[1] == 2 or s[0] == stack.roots[-1].relation)),
key=itemgetter(2))
bestWrong = max(
(s for s in chain(*scores) if costs[s[1]] != 0 or (
s[1] != 2 and s[0] != stack.roots[-1].relation)),
key=itemgetter(2))
best = bestValid if (
(not self.oracle) or
(bestValid[2] - bestWrong[2] > 1.0) or
(bestValid[2] > bestWrong[2]
and random.random() > 0.1)) else bestWrong
if best[1] == 2:
stack.roots.append(buf.roots[0])
del buf.roots[0]
elif best[1] == 0:
child = stack.roots.pop()
parent = buf.roots[0]
child.pred_parent_id = parent.id
child.pred_relation = best[0]
bestOp = 0
if self.rlMostFlag:
parent.lstms[bestOp +
hoffset] = child.lstms[bestOp +
hoffset]
if self.rlFlag:
parent.lstms[bestOp + hoffset] = child.vec
elif best[1] == 1:
child = stack.roots.pop()
parent = stack.roots[-1]
child.pred_parent_id = parent.id
child.pred_relation = best[0]
bestOp = 1
if self.rlMostFlag:
parent.lstms[bestOp +
hoffset] = child.lstms[bestOp +
hoffset]
if self.rlFlag:
parent.lstms[bestOp + hoffset] = child.vec
if bestValid[2] < bestWrong[2] + 1.0:
loss = bestWrong[3] - bestValid[3]
mloss += 1.0 + bestWrong[2] - bestValid[2]
eloss += 1.0 + bestWrong[2] - bestValid[2]
errs.append(loss)
if best[1] != 2 and (
child.pred_parent_id != child.parent_id
or child.pred_relation != child.relation):
lerrors += 1
if child.pred_parent_id != child.parent_id:
errors += 1
eerrors += 1
etotal += 1
if len(errs) > 50: # or True:
#eerrs = ((esum(errs)) * (1.0/(float(len(errs)))))
eerrs = esum(errs)
scalar_loss = eerrs.scalar_value()
eerrs.backward()
self.trainer.update()
errs = []
lerrs = []
renew_cg()
self.Init()
if len(errs) > 0:
eerrs = (esum(errs)) # * (1.0/(float(len(errs))))
eerrs.scalar_value()
eerrs.backward()
self.trainer.update()
errs = []
lerrs = []
renew_cg()
# self.trainer.update_epoch() # hanwj 6.20 . there is no any decay, so just remove it.
# self.trainer.learning_rate /= (1 - rate_decay)
print "Loss: ", mloss / iSentence
def Predict(self, data):
reached_max_swap = 0
for iSentence, sentence in data:
reached_swap_for_i_sentence = False
max_swap = 2 * len(sentence)
iSwap = 0
self.Init()
conll_sentence = [
entry for entry in sentence
if isinstance(entry, utils.ConllEntry)
]
conll_sentence = conll_sentence[1:] + [conll_sentence[0]]
self.getWordEmbeddings(conll_sentence, False)
stack = ParseForest([])
buf = ParseForest(conll_sentence)
hoffset = 1 if self.headFlag else 0
for root in conll_sentence:
root.lstms = [root.vec] if self.headFlag else []
root.lstms += [
self.paddingVec for _ in range(self.nnvecs - hoffset)
]
root.relation = root.relation if root.relation in self.rels else 'runk'
while not (len(buf) == 1 and len(stack) == 0):
scores = self.__evaluate(stack, buf, False)
best = max(
chain(*(scores if iSwap < max_swap else scores[:3])),
key=itemgetter(2))
if iSwap == max_swap and not reached_swap_for_i_sentence:
reached_max_swap += 1
reached_swap_for_i_sentence = True
print "reached max swap in %d out of %d sentences" % (
reached_max_swap, iSentence)
if best[1] == 2:
#SHIFT
stack.roots.append(buf.roots[0])
del buf.roots[0]
elif best[1] == 3:
#SWAP
iSwap += 1
child = stack.roots.pop()
buf.roots.insert(1, child)
elif best[1] == 0:
#LEFT-ARC
child = stack.roots.pop()
parent = buf.roots[0]
#predict rel and label
child.pred_parent_id = parent.id
child.pred_relation = best[0]
elif best[1] == 1:
#RIGHT-ARC
child = stack.roots.pop()
parent = stack.roots[-1]
child.pred_parent_id = parent.id
child.pred_relation = best[0]
#update the representation of head for attaching transitions
if best[1] == 0 or best[1] == 1:
#linear order
if self.rlMostFlag:
parent.lstms[best[1] + hoffset] = child.lstms[best[1] +
hoffset]
#actual children
if self.rlFlag:
parent.lstms[best[1] + hoffset] = child.vec
dy.renew_cg()
yield sentence
