def eval_avg(self, setting=1):
"""
sentence representation = average of word vectors
:return: final acc.
"""
LogInfo.begin_track(
"Eval on Copa using average word representations using setting %d...",
setting)
correct = 0
for i in range(500, 1000):
ask4 = self.copa_ground[i][0]
sentence, option1, option2 = self.copa_data[i]
sent_vec = self.get_repr(sentence, ask4, setting, 'q')
opt1_vec = self.get_repr(option1, ask4, setting, 'o')
opt2_vec = self.get_repr(option2, ask4, setting, 'o')
score1 = self.get_similarity(sent_vec, opt1_vec)
score2 = self.get_similarity(sent_vec, opt2_vec)
truth = self.copa_ground[i][1]
if score1 > score2:
if truth == 1:
# LogInfo.logs("[%d] ret: %d(%.2f>%.2f), truth: %d. [T]", i+1, 1, score1, score2, truth)
correct += 1
# else:
# LogInfo.logs("[%d] ret: %d(%.2f>%.2f), truth: %d. [F]", i+1, 1, score1, score2, truth)
else:
if truth == 2:
# LogInfo.logs("[%d] ret: %d(%.2f<%.2f), truth: %d. [T]", i+1, 2, score1, score2, truth)
correct += 1
# else:
# LogInfo.logs("[%d] ret: %d(%.2f<%.2f), truth: %d. [F]", i+1, 2, score1, score2, truth)
LogInfo.logs("[summary] accuracy: %.4f(%d/%d).",
float(correct) / 500, correct, 500)
LogInfo.end_track()
def load_pinlei(self):
LogInfo.begin_track("Load pinlei names...")
with codecs.open("/u01/xusheng/word2vec/vec/yyh_pinlei.txt",
'r',
encoding='utf-8') as fin:
for line in fin:
name = line.strip().split()[0]
if name.startswith("[["):
self.pinlei.add(name)
LogInfo.end_track("Pinlei name loaded. Size: %d.", len(self.pinlei))
def add_pinlei_tag_yyh(self):
LogInfo.begin_track("Begin adding tags for pinleis...")
fin = codecs.open(self.root_fp + "/yyh_w2v_train.txt",
'r',
encoding='utf-8')
fout = codecs.open(self.root_fp + "/yyh_w2v_train.txt.pinlei_tag",
'w',
encoding='utf-8')
cnt = 0
for line in fin:
spt = line.strip().split()
new_line = ""
i = 0
while i < len(spt):
if i + 3 < len(spt):
str4 = spt[i] + spt[i + 1] + spt[i + 2] + spt[3]
if str4 in self.pinlei_set:
LogInfo.logs("Found 4-term pinlei [%s|%s|%s|%s]",
spt[i], spt[i + 1], spt[i + 2],
spt[i + 3])
new_line += "[[" + str4 + "]] "
i += 4
continue
if i + 2 < len(spt):
str3 = spt[i] + spt[i + 1] + spt[i + 2]
if str3 in self.pinlei_set:
# LogInfo.logs("Found 3-term pinlei [%s|%s|%s]",
# spt[i], spt[i+1], spt[i+2])
new_line += "[[" + str3 + "]] "
i += 3
continue
if i + 1 < len(spt):
str2 = spt[i] + spt[i + 1]
if str2 in self.pinlei_set:
# LogInfo.logs("Found 2-term pinlei [%s|%s]",
# spt[i], spt[i+1])
new_line += "[[" + str2 + "]] "
i += 2
continue
if spt[i] in self.pinlei_set:
# LogInfo.logs("Found pinlei [%s]", spt[i])
new_line += "[[" + spt[i] + "]] "
i += 1
continue
new_line += spt[i] + " "
i += 1
fout.write(new_line + "\n")
cnt += 1
if cnt < 5:
LogInfo.logs("res ==> (%s)", new_line)
LogInfo.show_line(cnt, 100000)
fin.close()
fout.close()
LogInfo.end_track("Pinlei tags added.")
def eval_pair(self, setting=1, strategy=1):
"""
evaluation based on word pairs
:param setting:
:param strategy: 1: sum, 2: /T1+T2, 3: /T1*T2
:return: final acc.
"""
LogInfo.begin_track(
"Eval on ROC using word pairs using setting %d and strategy %d...",
setting, strategy)
correct = 0
for i in range(0, 1871):
sentence, option1, option2 = self.copa_data[i]
ask4 = self.copa_ground[i][0]
q_vec_map = self.get_vec_map(ask4=ask4, setting=setting, role='q')
o_vec_map = self.get_vec_map(ask4=ask4, setting=setting, role='o')
score1 = 0.0
score2 = 0.0
for word1 in sentence:
for word2 in option1:
if word1 in q_vec_map and word2 in o_vec_map:
score1 += self.get_similarity(q_vec_map[word1],
o_vec_map[word2])
for word1 in sentence:
for word2 in option2:
if word1 in q_vec_map and word2 in o_vec_map:
score2 += self.get_similarity(q_vec_map[word1],
o_vec_map[word2])
if strategy == 2:
score1 /= (len(sentence) + len(option1))
score2 /= (len(sentence) + len(option2))
elif strategy == 3:
score1 /= (len(sentence) * len(option1))
score2 /= (len(sentence) * len(option2))
truth = self.copa_ground[i][1]
if score1 > score2:
if truth == 1:
# LogInfo.logs("[%d] ret: %d(%.2f>%.2f), truth: %d. [T]", i+1, 1, score1, score2, truth)
correct += 1
# else:
# LogInfo.logs("[%d] ret: %d(%.2f>%.2f), truth: %d. [F]", i+1, 1, score1, score2, truth)
else:
if truth == 2:
# LogInfo.logs("[%d] ret: %d(%.2f<%.2f), truth: %d. [T]", i+1, 2, score1, score2, truth)
correct += 1
# else:
# LogInfo.logs("[%d] ret: %d(%.2f<%.2f), truth: %d. [F]", i+1, 2, score1, score2, truth)
LogInfo.logs("[summary] accuracy: %.4f(%d/%d).",
float(correct) / 1871, correct, 1871)
LogInfo.end_track()
def load_pinlei(self):
LogInfo.begin_track("Load pinlei names...")
with codecs.open(self.root_fp + "/raw/kg_pinlei_id",
'r',
encoding='utf-8') as fin:
for line in fin:
spt = line.strip().split("\t")
if len(spt) < 2:
continue
pinlei = spt[0]
self.pinlei_set.add(pinlei)
LogInfo.end_track("%d names loaded.", len(self.pinlei_set))
def load_vocab_embedding(self, embedding_file, encoding):
LogInfo.begin_track("Loading embeddings from %s...", embedding_file)
vocab_embedding = len(self.vocab_index_dict) * [None]
with codecs.open(embedding_file, 'r', encoding=encoding) as fin:
count = 0
for line in fin:
strs = line.split()
embedding = [float(strs[i].strip()) for i in range(1, len(strs))]
vocab_embedding[self.vocab_index_dict[strs[0].strip()]] = embedding
count += 1
LogInfo.show_line(count, 50000)
assert count == len(vocab_embedding)
self.vocab_embedding = np.asarray(vocab_embedding)
LogInfo.end_track("Vocab loaded. Size: %d.", self.vocab_size)
def load(self, data_file, encoding):
LogInfo.begin_track("Loading data from %s...", data_file)
context_idxs, context_seqs, pinlei_idxs = list(), list(), list()
cnt = 0
with codecs.open(data_file, 'r', encoding=encoding) as fin:
for line in fin:
context_idx, context_seq, pinlei_idx = self.decode_line(line)
context_idxs.append(context_idx)
context_seqs.append(context_seq)
pinlei_idxs.append(pinlei_idx)
cnt += 1
LogInfo.show_line(cnt, 10000)
self.data = list(zip(context_idxs, context_seqs, pinlei_idxs))
self.data_size = len(self.data)
LogInfo.end_track()
def load_vocab_name(self, vocab_file, encoding):
LogInfo.begin_track("Loading vocab from %s...", vocab_file)
self.vocab_size = 0
self.index_vocab_dict.clear()
self.vocab_index_dict.clear()
with codecs.open(vocab_file, 'r', encoding=encoding) as fin:
index = 0
for line in fin:
self.vocab_index_dict[line.strip()] = index
self.index_vocab_dict.append(line.strip())
index += 1
LogInfo.show_line(index, 50000)
self.vocab_size = index
LogInfo.end_track("Vocab loaded. Size: %d.", self.vocab_size)
def main():
copa, worddic = readcopa()
label = readlabel()
cdic = readvec(worddic, "/home/yuchen/CppFiles/Causal/sync_wdFin_iter200.txt")
enegdic = readvec(worddic, "/home/yuchen/CppFiles/Causal/syneneg_wdFin_iter200.txt")
cnegdic = readvec(worddic, "/home/yuchen/CppFiles/Causal/syne_wdFin_iter200.txt")
edic = readvec(worddic, "/home/yuchen/CppFiles/Causal/syncneg_wdFin_iter200.txt")
verbose = False
import sys
mode = sys.argv[1]
if mode == 'full':
for ratio in range(21):
for lamd in range(11):
acc = word_word1(copa, label, cdic, enegdic, cnegdic, edic, lamd*0.1, 500, 4, True, ratio*0.1, verbose)
print ratio*0.1, lamd*0.1, acc
# print "word pair with norm:"
# for setting in range(3):
# for lamd in range(11):
# acc = word_word1(copa, label, cdic, enegdic, cnegdic, edic, lamd*0.1, 500, setting, True, verbose)
# print lamd*0.1, setting, acc
# print "\nword pair without norm:"
# for setting in range(3):
# for lamd in range(11):
# acc = word_word1(copa, label, cdic, enegdic, cnegdic, edic, lamd*0.1, 500, setting, False, verbose)
# print lamd*0.1, setting, acc
#
# print "\nsentence level with norm:"
# for lamd in range(11):
# acc = sen_sen(copa, label, cdic, enegdic, cnegdic, edic, lamd*0.1, 500, True)
# print lamd*0.1, acc
#
# print "\nsentence level without norm:"
# for lamd in range(11):
# acc = sen_sen(copa, label, cdic, enegdic, cnegdic, edic, lamd*0.1, 500, False)
# print lamd*0.1, acc
elif mode == 'case':
para1 = float(sys.argv[2])
para2 = int(sys.argv[3])
LogInfo.begin_track("case tracing for word-pair & lambda=%.1f, setting=%d:", para1, para2)
verbose = True
acc = word_word1(copa, label, cdic, enegdic, cnegdic, edic, para1, 500, para2, True, verbose)
LogInfo.logs("[Accuracy] %.4f", acc)
LogInfo.end_track()
def eval_avg_lambda(self, lamb=1.0):
"""
sentence representation = average of word vectors
:return: final acc.
"""
LogInfo.begin_track(
"Eval on Copa using average word representations using lambda %.2f...",
lamb)
correct = 0
for i in range(500, 1000):
ask4 = self.copa_ground[i][0]
sentence, option1, option2 = self.copa_data[i]
sent_vec = self.get_repr(sentence, ask4, 1, 'q')
opt1_vec = self.get_repr(option1, ask4, 1, 'o')
opt2_vec = self.get_repr(option2, ask4, 1, 'o')
score1a = self.get_similarity(sent_vec, opt1_vec)
score2a = self.get_similarity(sent_vec, opt2_vec)
sent_vec = self.get_repr(sentence, ask4, 2, 'q')
opt1_vec = self.get_repr(option1, ask4, 2, 'o')
opt2_vec = self.get_repr(option2, ask4, 2, 'o')
score1b = self.get_similarity(sent_vec, opt1_vec)
score2b = self.get_similarity(sent_vec, opt2_vec)
score1 = (score1a * lamb) + (score1b * (1 - lamb))
score2 = (score2a * lamb) + (score2b * (1 - lamb))
# LogInfo.logs("[log] %.4f(%.2f^%.2f*%.2f^%.2f) ||| %.4f(%.2f^%.2f*%.2f^%.2f)",
# score1, score1a, lamb, score1b, 1-lamb,
# score2, score2a, lamb, score2b, 1-lamb)
truth = self.copa_ground[i][1]
if score1 > score2:
if truth == 1:
# LogInfo.logs("[%d] ret: %d(%.2f>%.2f), truth: %d. [T]", i+1, 1, score1, score2, truth)
correct += 1
# else:
# LogInfo.logs("[%d] ret: %d(%.2f>%.2f), truth: %d. [F]", i+1, 1, score1, score2, truth)
else:
if truth == 2:
# LogInfo.logs("[%d] ret: %d(%.2f<%.2f), truth: %d. [T]", i+1, 2, score1, score2, truth)
correct += 1
# else:
# LogInfo.logs("[%d] ret: %d(%.2f<%.2f), truth: %d. [F]", i+1, 2, score1, score2, truth)
LogInfo.logs("[summary] accuracy: %.4f(%d/%d).",
float(correct) / 500, correct, 500)
LogInfo.end_track()
def prepare_model_data(self, pinlei_num):
self.pinlei_num = pinlei_num
LogInfo.begin_track("Generate Multi-Pinlei Data for evaluation...")
fin = codecs.open(self.root_fp + "/query_label.txt." +
str(self.pinlei_num),
'r',
encoding='utf-8')
fout = codecs.open(self.root_fp + "/model_data_test." +
str(self.pinlei_num) + ".name",
'w',
encoding='utf-8')
fsho = codecs.open(self.root_fp + "/model_data_test." +
str(self.pinlei_num) + ".check",
'w',
encoding='utf-8')
cnt = 0
not_cover = set()
for line in fin:
cnt += 1
if cnt % 100000 == 0:
LogInfo.logs("%d lines processed.", cnt)
fout.flush()
spt = line.strip().split("\t")
context = spt[1]
is_cover = True
for i in range(2, 2 + self.pinlei_num):
pinlei = "[[" + spt[i] + "]]"
if pinlei not in self.pinlei:
# LogInfo.logs("%s not cover.", pinlei)
is_cover = False
not_cover.add(pinlei)
if not is_cover:
continue
if len(spt[1].split(" ")) < 6 or len(spt[1].split(" ")) > 15:
continue
for i in range(2, 2 + self.pinlei_num):
pinlei = "[[" + spt[i] + "]]"
fout.write(context + "\t" + pinlei + "\n")
fsho.write(spt[0] + "\n")
fin.close()
fout.close()
fsho.close()
LogInfo.end_track("%d pinlei not cover.", len(not_cover))
def load_configs(fp):
LogInfo.begin_track('Loading config from %s: ', fp)
config_dict = {}
with open(fp, 'r') as br:
for line in br.readlines():
line = line.strip()
if line.startswith('#') or line == '':
continue
if line.find('\t') == -1:
continue
spt = line.split('\t')
if len(spt) < 3:
LogInfo.logs("[%s] is invalid, pls add type!", line)
continue
k = spt[0]
v_str = spt[1]
t = spt[2]
if t == "d" or t == "int":
config_dict[k] = int(v_str)
elif t == "f" or t == "float" or t == "double":
config_dict[k] = float(v_str)
elif t == "b" or t == "bool":
if v_str == "true" or v_str == "True" \
or v_str == "TRUE" or v_str == "1":
config_dict[k] = True
else:
config_dict[k] = False
elif t == "tf" or t == "tensorflow":
if v_str == 'relu':
config_dict[k] = tf.nn.relu
elif v_str == 'sigmoid':
config_dict[k] = tf.nn.sigmoid
elif v_str == 'tanh':
config_dict[k] = tf.nn.tanh
elif t == "None" or v_str == "None":
config_dict[k] = None
else:
config_dict[k] = v_str
LogInfo.logs('%s = %s', k, v_str)
LogInfo.end_track()
return config_dict
def prepare_model_data(self):
LogInfo.begin_track("Generate model data...")
# .1 means single pinlei
fin = codecs.open(self.root_fp + "/query_label.txt.1",
'r',
encoding='utf-8')
fout = codecs.open(self.root_fp + "/model_data_train.name",
'w',
encoding='utf-8')
not_cover = 0
not_context = 0
cnt = 0
for line in fin:
cnt += 1
if cnt % 100000 == 0:
LogInfo.logs("%d lines processed.", cnt)
fout.flush()
spt = line.strip().split("\t")
context = spt[1]
pinlei = "[[" + spt[2] + "]]"
if pinlei not in self.pinlei:
not_cover += 1
continue
if len(spt[1].split(" ")) < 6 or len(spt[1].split(" ")) > 15:
not_context += 1
continue
fout.write(context + "\t" + pinlei + "\n")
negs = self.neg_sample_random(pinlei, 19)
for neg in negs:
fout.write(context + "\t" + neg + "\n")
fin.close()
fout.close()
LogInfo.end_track("Model data prepared. Size: %d. (%d, %d).",
cnt - not_context - not_cover, not_cover,
not_context)
def load_vocab(self, vocab_file, embedding_dim, encoding):
LogInfo.begin_track("Loading vocab from %s...", vocab_file)
self.vocab_size = 0
self.index_vocab_dict.clear()
self.vocab_index_dict.clear()
self.vocab_embedding.clear()
with codecs.open(vocab_file, 'r', encoding=encoding) as fin:
index = 0
# 0 embedding for not-found query term
self.vocab_index_dict["[[NULL]]"] = index
self.index_vocab_dict.append("[[NULL]]")
self.vocab_embedding.append([0.0 for _ in range(embedding_dim)])
index += 1
for line in fin:
spt = line.strip().split()
self.vocab_index_dict[spt[0]] = index
self.index_vocab_dict.append(spt[0])
embedding = [float(spt[i].strip()) for i in range(1, len(spt))]
self.vocab_embedding.append(embedding)
index += 1
LogInfo.show_line(index, 50000)
self.vocab_size = len(self.vocab_embedding)
self.vocab_embedding = np.array(self.vocab_embedding)
LogInfo.end_track("Vocab loaded. Size: %d.", self.vocab_size)
def load(self, data_file, encoding):
LogInfo.begin_track("Loading data from %s...", data_file)
if os.path.isfile(data_file):
LogInfo.begin_track("[Exist] Loading from %s...", data_file)
query_idxs, query_lens, labels, intents, link_masks, entity_idxs \
= list(), list(), list(), list(), list(), list()
cnt = 0
with codecs.open(data_file, 'r', encoding=encoding) as fin:
for line in fin:
spt = line.strip().split("\t")
query_idxs.append([int(idx) for idx in spt[0].split(" ")])
query_lens.append(int(spt[1]))
labels.append([int(idx) for idx in spt[2].split(" ")])
intents.append(int(spt[3]))
link_masks.append([int(idx) for idx in spt[4].split(" ")])
entity_idxs.append([int(idx) for idx in spt[5].split(" ")])
cnt += 1
LogInfo.show_line(cnt, 1000000)
LogInfo.end_track("Max_seq_len = %d.", self.max_seq_len)
else:
txt_data_file = data_file + ".name"
LogInfo.begin_track("[Not Exist] Loading from %s...",
txt_data_file)
query_idxs, query_lens, labels, intents, link_masks, entity_idxs \
= list(), list(), list(), list(), list(), list()
cnt = 0
fout = codecs.open(data_file, 'w', encoding=encoding)
with codecs.open(txt_data_file, 'r', encoding=encoding) as fin:
for line in fin:
query_idx, query_len, label, intent, link_mask, entity_idx\
= self.decode_line(line)
fout.write(" ".join([str(x) for x in query_idx]) + "\t" +
str(query_len) + "\t" +
" ".join([str(x) for x in label]) + "\t" +
str(intent) + "\t" +
" ".join([str(x) for x in link_mask]) + "\t" +
" ".join([str(x) for x in entity_idx]) + "\n")
query_idxs.append(query_idx)
query_lens.append(query_len)
labels.append(label)
intents.append(intent)
link_masks.append(link_mask)
entity_idxs.append(entity_idx)
cnt += 1
LogInfo.show_line(cnt, 1000000)
fout.close()
LogInfo.logs("Write into %s.", data_file)
LogInfo.end_track("Max_seq_len = %d.", self.max)
self.data = list(
zip(query_idxs, query_lens, labels, intents, link_masks,
entity_idxs))
self.data_size = len(self.data)
LogInfo.end_track("Loaded. Size: %d.", self.data_size)
def eval_seq_crf_with_o_atis(y_pred_, y_true_, method='precision'):
"""
Evaluation for ATIS dataset, including "Outside"
under specific conditions (3-class)
:param y_pred_: [B, T, ]
:param y_true_: [B, T, ]
:param method: precision/ recall
:return: f1 score
"""
# LogInfo.logs("y_pred: %s", '\n'.join([str(x) for x in y_pred_]))
# LogInfo.logs("y_true: %s", '\n'.join([str(x) for x in y_true_]))
tag_dict = [
'O', 'B-day_number', 'B-stoploc.state_code', 'B-toloc.state_code',
'B-time_relative', 'B-fromloc.state_code', 'B-stoploc.airport_code',
'B-airline_code', 'B-connect', 'B-depart_time.period_mod', 'B-flight',
'B-arrive_time.period_mod', 'B-booking_class', 'B-month_name',
'B-return_date.day_name', 'B-depart_date.month_name',
'B-arrive_date.today_relative', 'B-return_time.period_of_day',
'B-aircraft_code', 'B-arrive_date.date_relative', 'B-state_code',
'B-days_code', 'B-airport_code', 'B-period_of_day',
'B-arrive_date.day_name', 'B-flight_days', 'B-return_time.period_mod',
'B-fromloc.airport_code', 'B-arrive_date.month_name', 'B-mod',
'B-stoploc.airport_name', 'B-compartment', 'B-toloc.airport_code',
'B-depart_date.date_relative', 'B-day_name', 'B-or',
'B-depart_date.year', 'B-depart_date.day_name', 'B-toloc.country_name',
'B-return_date.month_name', 'B-meal', 'B-stoploc.city_name',
'I-stoploc.city_name', 'B-round_trip', 'I-round_trip', 'B-state_name',
'I-state_name', 'B-fromloc.city_name', 'I-fromloc.city_name',
'B-airline_name', 'I-airline_name', 'B-flight_stop', 'I-flight_stop',
'B-fromloc.airport_name', 'I-fromloc.airport_name',
'B-arrive_time.start_time', 'I-arrive_time.start_time',
'B-cost_relative', 'I-cost_relative', 'B-city_name', 'I-city_name',
'B-arrive_time.end_time', 'I-arrive_time.end_time', 'B-meal_code',
'I-meal_code', 'B-depart_date.day_number', 'I-depart_date.day_number',
'B-meal_description', 'I-meal_description', 'B-arrive_time.time',
'I-arrive_time.time', 'B-depart_date.today_relative',
'I-depart_date.today_relative', 'B-fare_amount', 'I-fare_amount',
'B-airport_name', 'I-airport_name', 'B-flight_time', 'I-flight_time',
'B-flight_number', 'I-flight_number', 'B-toloc.airport_name',
'I-toloc.airport_name', 'B-flight_mod', 'I-flight_mod',
'B-depart_time.time_relative', 'I-depart_time.time_relative',
'B-return_date.date_relative', 'I-return_date.date_relative',
'B-economy', 'I-economy', 'B-class_type', 'I-class_type',
'B-toloc.state_name', 'I-toloc.state_name',
'B-arrive_time.period_of_day', 'I-arrive_time.period_of_day',
'B-toloc.city_name', 'I-toloc.city_name', 'B-depart_time.start_time',
'I-depart_time.start_time', 'B-return_date.day_number',
'I-return_date.day_number', 'B-today_relative', 'I-today_relative',
'B-depart_time.end_time', 'I-depart_time.end_time',
'B-fromloc.state_name', 'I-fromloc.state_name', 'B-depart_time.time',
'I-depart_time.time', 'B-return_date.today_relative',
'I-return_date.today_relative', 'B-fare_basis_code',
'I-fare_basis_code', 'B-arrive_date.day_number',
'I-arrive_date.day_number', 'B-restriction_code', 'I-restriction_code',
'B-transport_type', 'I-transport_type', 'B-time', 'I-time',
'B-arrive_time.time_relative', 'I-arrive_time.time_relative',
'B-depart_time.period_of_day', 'I-depart_time.period_of_day'
]
LogInfo.begin_track("Eval seq %s on %d tags...", method, len(tag_dict))
if method == 'precision':
y_pred = np.array(y_pred_)
y_true = np.array(y_true_)
elif method == 'recall':
y_pred = np.array(y_true_)
y_true = np.array(y_pred_)
names = set()
for tag in tag_dict:
if tag == 'O':
names.add('O')
else:
names.add(tag[2:])
LogInfo.logs("%d different terms", len(names))
correct = dict()
act_cnt = dict()
for name in names:
correct[name] = 0
act_cnt[name] = 0
for line_pred, line_true in zip(y_pred, y_true):
i = 0
cnt = len(line_pred)
while i < cnt:
tag_num = line_pred[i]
tag = tag_dict[tag_num]
if tag_num <= 40:
# tags with "B" without "I", including "O"
if tag_num == 0:
kind = 'O'
else:
kind = tag[2:]
act_cnt[kind] += 1
if line_true[i] == line_pred[i]:
correct[kind] += 1
i += 1
continue
else:
kind = tag[2:]
sign = tag[0]
if sign == 'B':
j = i + 1
while j < cnt:
next_tag = tag_dict[line_pred[j]]
if next_tag[2:] == kind and next_tag[0] == 'I':
j += 1
else:
break
else:
i += 1
continue
act_cnt[kind] += 1
act_label = ' '.join([str(x) for x in line_true[i:j]])
proposed_label = ' '.join([str(x) for x in line_pred[i:j]])
if act_label == proposed_label and (
j == cnt or line_true[j] != line_true[i] + 1):
correct[kind] += 1
i = j
ret = dict()
keys = act_cnt.keys()
correct_total = 0
cnt_total = 0
for key in keys:
if act_cnt[key] == 0:
ret[key] = 0.0
else:
ret[key] = correct[key] * 1.0 / act_cnt[key]
LogInfo.logs("%s : %.4f(%d/%d)", key, ret[key], correct[key],
act_cnt[key])
correct_total += correct[key]
cnt_total += act_cnt[key]
if cnt_total == 0:
overall = 0.0
else:
overall = correct_total * 1.0 / cnt_total
LogInfo.logs("Over-all %s: %.4f(%d/%d)", method, overall, correct_total,
cnt_total)
LogInfo.end_track()
return overall
def process_query(self):
LogInfo.begin_track("Begin adding tags for queries...")
fin = codecs.open(self.root_fp + "/query.txt", 'r', encoding='utf-8')
fout = codecs.open(self.root_fp + "/query_label.txt",
'w',
encoding='utf-8')
cnt = 0
for line in fin:
spt = line.strip().split()
new_line = ""
context = ""
label = set()
i = 0
while i < len(spt):
if i + 4 < len(spt):
str5 = spt[i] + spt[i + 1] + spt[i + 2] + spt[i +
3] + spt[i +
4]
if str5 in self.pinlei_set:
LogInfo.logs("Found 5-term pinlei [%s|%s|%s|%s|%s]",
spt[i], spt[i + 1], spt[i + 2],
spt[i + 3], spt[i + 4])
label.add(str5)
new_line += "[[" + str5 + "]] "
i += 5
continue
if i + 3 < len(spt):
str4 = spt[i] + spt[i + 1] + spt[i + 2] + spt[i + 3]
if str4 in self.pinlei_set:
LogInfo.logs("Found 4-term pinlei [%s|%s|%s|%s]",
spt[i], spt[i + 1], spt[i + 2],
spt[i + 3])
label.add(str4)
new_line += "[[" + str4 + "]] "
i += 4
continue
if i + 2 < len(spt):
str3 = spt[i] + spt[i + 1] + spt[i + 2]
if str3 in self.pinlei_set:
LogInfo.logs("Found 3-term pinlei [%s|%s|%s]", spt[i],
spt[i + 1], spt[i + 2])
label.add(str3)
new_line += "[[" + str3 + "]] "
i += 3
continue
if i + 1 < len(spt):
str2 = spt[i] + spt[i + 1]
if str2 in self.pinlei_set:
# LogInfo.logs("Found 2-term pinlei [%s|%s]",
# spt[i], spt[i+1])
label.add(str2)
new_line += "[[" + str2 + "]] "
i += 2
continue
if spt[i] in self.pinlei_set:
# LogInfo.logs("Found pinlei [%s]", spt[i])
label.add(spt[i])
new_line += "[[" + spt[i] + "]] "
i += 1
continue
context += spt[i] + " "
new_line += spt[i] + " "
i += 1
if len(label) != 0:
ret = new_line.strip() + "\t" + \
context.strip() + "\t" + \
"\t".join(label) + "\n"
else:
ret = new_line.strip() + "\n"
fout.write(ret)
cnt += 1
if cnt < 5:
LogInfo.logs("res ==> (%s)", ret.strip())
LogInfo.show_line(cnt, 100000)
fin.close()
fout.close()
LogInfo.end_track("Query processed.")
def eval_pair_lambda(self, lamb=1.0, strategy=1):
"""
evaluation based on word pairs
:param lamb:
:param strategy: 1: sum, 2: /T1+T2, 3: /T1*T2
:return: final acc.
"""
LogInfo.begin_track(
"Eval on Copa using word pairs using lambda %.2f and strategy %d...",
lamb, strategy)
correct = 0
cause = 0
effect = 0
cause_correct = 0
effect_correct = 0
for i in range(500, 1000):
sentence, option1, option2 = self.copa_data[i]
ask4 = self.copa_ground[i][0]
if ask4 == 'cause':
cause += 1
else:
effect += 1
# left
q_vec_map = self.get_vec_map(ask4=ask4, setting=1, role='q')
o_vec_map = self.get_vec_map(ask4=ask4, setting=1, role='o')
score1a = 0.0
score2a = 0.0
for word1 in sentence:
for word2 in option1:
if word1 in q_vec_map and word2 in o_vec_map:
score1a += self.get_similarity(q_vec_map[word1],
o_vec_map[word2])
for word1 in sentence:
for word2 in option2:
if word1 in q_vec_map and word2 in o_vec_map:
score2a += self.get_similarity(q_vec_map[word1],
o_vec_map[word2])
# right
q_vec_map = self.get_vec_map(ask4=ask4, setting=2, role='q')
o_vec_map = self.get_vec_map(ask4=ask4, setting=2, role='o')
score1b = 0.0
score2b = 0.0
for word1 in sentence:
for word2 in option1:
if word1 in q_vec_map and word2 in o_vec_map:
score1b += self.get_similarity(q_vec_map[word1],
o_vec_map[word2])
for word1 in sentence:
for word2 in option2:
if word1 in q_vec_map and word2 in o_vec_map:
score2b += self.get_similarity(q_vec_map[word1],
o_vec_map[word2])
score1 = (score1a * lamb) + (score1b * (1 - lamb))
score2 = (score2a * lamb) + (score2b * (1 - lamb))
if strategy == 2:
score1 /= (len(sentence) + len(option1))
score2 /= (len(sentence) + len(option2))
elif strategy == 3:
score1 /= (len(sentence) * len(option1))
score2 /= (len(sentence) * len(option2))
truth = self.copa_ground[i][1]
if score1 > score2:
if truth == 1:
# LogInfo.logs("[%d] ret: %d(%.2f>%.2f), truth: %d. [T]", i+1, 1, score1, score2, truth)
correct += 1
if ask4 == 'cause':
cause_correct += 1
else:
effect_correct += 1
# else:
# LogInfo.logs("[%d] ret: %d(%.2f>%.2f), truth: %d. [F]", i+1, 1, score1, score2, truth)
else:
if truth == 2:
# LogInfo.logs("[%d] ret: %d(%.2f<%.2f), truth: %d. [T]", i+1, 2, score1, score2, truth)
correct += 1
if ask4 == 'cause':
cause_correct += 1
else:
effect_correct += 1
# else:
# LogInfo.logs("[%d] ret: %d(%.2f<%.2f), truth: %d. [F]", i+1, 2, score1, score2, truth)
LogInfo.logs("[summary] accuracy: %.4f(%d/%d).",
float(correct) / 500, correct, 500)
LogInfo.logs("[summary] cause/effect acc.: %.4f(%d/%d)/%.4f(%d/%d)",
float(cause_correct) / cause, cause_correct, cause,
float(effect_correct) / effect, effect_correct, effect)
LogInfo.end_track()
def eval_seq_softmax(raw_score, y_true, method='precision'):
"""
Evaluation for sequence labeling
under specific conditions (3-class)
:param raw_score: [B, T, class_dim]
:param y_true: [B, T, ]
:param method: precision/ recall
:return: f1 score
"""
tag_dict = ['O', 'PL_B', 'PL_I', 'PK_B', 'PK_I', 'PV_B', 'PV_I']
LogInfo.begin_track("Eval seq %s...", method)
if method == 'precision':
y_pred = np.argmax(raw_score, axis=1).reshape((-1))
y_true = np.array(y_true).reshape((-1))
elif method == 'recall':
y_pred = np.array(y_true).reshape((-1))
y_true = np.argmax(raw_score, axis=1).reshape((-1))
# LogInfo.logs("y_pred: [%s]", ' '.join([str(x) for x in y_pred]))
# LogInfo.logs("y_true: [%s]", ' '.join([str(x) for x in y_true]))
# LogInfo.logs("y_pred: [%s]", y_pred)
# LogInfo.logs("y_true: [%s]", y_true)
cnt = len(y_pred)
i = 0
correct = {'PL': 0, 'PK': 0, 'PV': 0}
act_cnt = {'PL': 0, 'PK': 0, 'PV': 0}
while i < cnt:
tag_num = y_pred[i]
tag = tag_dict[tag_num]
if tag == 'O':
i += 1
continue
else:
kind = tag[:2]
sign = tag[3]
if sign == 'B':
j = i + 1
while j < cnt:
next_tag = tag_dict[y_pred[j]]
if next_tag[:2] == kind and next_tag[3] == 'I':
j += 1
else:
break
else:
i += 1
continue
act_cnt[kind] += 1
act_label = ' '.join([str(x) for x in y_true[i:j]])
proposed_label = ' '.join([str(x) for x in y_pred[i:j]])
if act_label == proposed_label and (j == cnt
or y_true[j] != y_true[i] + 1):
correct[kind] += 1
i = j
ret = dict()
keys = act_cnt.keys()
correct_total = 0
cnt_total = 0
for key in keys:
if act_cnt[key] == 0:
ret[key] = 0.0
else:
ret[key] = correct[key] * 1.0 / act_cnt[key]
LogInfo.logs("%s : %.4f(%d/%d)", key, ret[key], correct[key],
act_cnt[key])
correct_total += correct[key]
cnt_total += act_cnt[key]
if cnt_total == 0:
overall = 0.0
else:
overall = correct_total * 1.0 / cnt_total
LogInfo.logs("Over-all %s: %.4f(%d/%d)", method, overall, correct_total,
cnt_total)
LogInfo.end_track()
return overall
def eval_pair(self, setting=1, strategy=1):
"""
evaluation based on word pairs
:param setting:
:param strategy: 1: sum, 2: /T1+T2, 3: /T1*T2
:return: final acc.
"""
LogInfo.begin_track(
"Eval on Copa using word pairs using setting %d and strategy %d...",
setting, strategy)
correct = 0
cause = 0
effect = 0
cause_correct = 0
effect_correct = 0
for i in range(500, 1000):
sentence, option1, option2 = self.copa_data[i]
ask4 = self.copa_ground[i][0]
if ask4 == 'cause':
cause += 1
else:
effect += 1
q_vec_map = self.get_vec_map(ask4=ask4, setting=setting, role='q')
o_vec_map = self.get_vec_map(ask4=ask4, setting=setting, role='o')
score1 = 0.0
score2 = 0.0
show_list1 = list()
show_list2 = list()
for word1 in sentence:
for word2 in option1:
if word1 in q_vec_map and word2 in o_vec_map:
tmp = self.get_similarity(q_vec_map[word1],
o_vec_map[word2])
score1 += tmp
show_list1.append("(%s, %s)-->%.2f" %
(word1, word2, tmp))
for word1 in sentence:
for word2 in option2:
if word1 in q_vec_map and word2 in o_vec_map:
tmp = self.get_similarity(q_vec_map[word1],
o_vec_map[word2])
score2 += tmp
show_list2.append("(%s, %s)-->%.2f" %
(word1, word2, tmp))
# LogInfo.logs("[%d] Q: %s", i+1, ' '.join(sentence))
# LogInfo.logs("[%d] O1: %s", i+1, ' '.join(option1))
# LogInfo.logs("[%d] O2: %s", i+1, ' '.join(option2))
# LogInfo.logs("[%d] ask4: [%s].", i+1, ask4)
#
# LogInfo.logs("[%d] %s.", i+1, " | ".join(show_list1))
# LogInfo.logs("[%d] %s.", i+1, " | ".join(show_list2))
if strategy == 2:
score1 /= (len(sentence) + len(option1))
score2 /= (len(sentence) + len(option2))
elif strategy == 3:
score1 /= (len(sentence) * len(option1))
score2 /= (len(sentence) * len(option2))
truth = self.copa_ground[i][1]
if score1 > score2:
if truth == 1:
# LogInfo.logs("[%d] ret: %d(%.4f>%.4f), truth: %d. [T]", i+1, 1, score1, score2, truth)
correct += 1
if setting == 3:
if ask4 == 'cause':
cause_correct += 1
else:
effect_correct += 1
# else:
# LogInfo.logs("[%d] ret: %d(%.4f>%.4f), truth: %d. [F]", i+1, 1, score1, score2, truth)
else:
if truth == 2:
# LogInfo.logs("[%d] ret: %d(%.4f<%.4f), truth: %d. [T]", i+1, 2, score1, score2, truth)
correct += 1
if setting == 3:
if ask4 == 'cause':
cause_correct += 1
else:
effect_correct += 1
# else:
# LogInfo.logs("[%d] ret: %d(%.4f<%.4f), truth: %d. [F]", i+1, 2, score1, score2, truth)
LogInfo.logs("[summary] accuracy: %.4f(%d/%d).",
float(correct) / 500, correct, 500)
if setting == 3:
LogInfo.logs(
"[summary] cause/effect acc.: %.4f(%d/%d)/%.4f(%d/%d)",
float(cause_correct) / cause, cause_correct, cause,
float(effect_correct) / effect, effect_correct, effect)
LogInfo.end_track()
def eval_seq_crf(y_pred_, y_true_, method='precision'):
"""
Evaluation for sequence labeling, without "Outside"
under specific conditions (3-class)
:param y_pred_: [B, T, ]
:param y_true_: [B, T, ]
:param method: precision/ recall
:return: f1 score
"""
# LogInfo.logs("y_pred: %s", '\n'.join([str(x) for x in y_pred_]))
# LogInfo.logs("y_true: %s", '\n'.join([str(x) for x in y_true_]))
tag_dict = ['O', 'PL_B', 'PL_I', 'PK_B', 'PK_I', 'PV_B', 'PV_I']
LogInfo.begin_track("Eval seq %s...", method)
if method == 'precision':
y_pred = np.array(y_pred_)
y_true = np.array(y_true_)
elif method == 'recall':
y_pred = np.array(y_true_)
y_true = np.array(y_pred_)
correct = {'PL': 0, 'PK': 0, 'PV': 0}
act_cnt = {'PL': 0, 'PK': 0, 'PV': 0}
for line_pred, line_true in zip(y_pred, y_true):
i = 0
cnt = len(line_pred)
while i < cnt:
tag_num = line_pred[i]
tag = tag_dict[tag_num]
if tag == 'O':
i += 1
continue
else:
kind = tag[:2]
sign = tag[3]
if sign == 'B':
j = i + 1
while j < cnt:
next_tag = tag_dict[line_pred[j]]
if next_tag[:2] == kind and next_tag[3] == 'I':
j += 1
else:
break
else:
i += 1
continue
act_cnt[kind] += 1
act_label = ' '.join([str(x) for x in line_true[i:j]])
proposed_label = ' '.join([str(x) for x in line_pred[i:j]])
if act_label == proposed_label and (
j == cnt or line_true[j] != line_true[i] + 1):
correct[kind] += 1
i = j
ret = dict()
keys = act_cnt.keys()
correct_total = 0
cnt_total = 0
for key in keys:
if act_cnt[key] == 0:
ret[key] = 0.0
else:
ret[key] = correct[key] * 1.0 / act_cnt[key]
LogInfo.logs("%s : %.4f(%d/%d)", key, ret[key], correct[key],
act_cnt[key])
correct_total += correct[key]
cnt_total += act_cnt[key]
if cnt_total == 0:
overall = 0.0
else:
overall = correct_total * 1.0 / cnt_total
LogInfo.logs("Over-all %s: %.4f(%d/%d)", method, overall, correct_total,
cnt_total)
LogInfo.end_track()
return overall
def load_data(self):
"""
load data from files
:return:
"""
LogInfo.begin_track("Loading data...")
# with open("/home/yuchen/CppFiles/Causal/syn0_w2v_200.txt") as finc, \
# open("/home/yuchen/CppFiles/Causal/syn0_w2v_200.txt") as fine:
with open("/home/yuchen/CppFiles/Causal/sync_wdFin_iter200.txt") as finc, \
open("/home/yuchen/CppFiles/Causal/syneneg_wdFin_iter200.txt") as fine:
cnt = 0
for linec, linee in zip(finc, fine):
cnt += 1
LogInfo.show_line(cnt, 100000)
sptc = linec.strip().split()
spte = linee.strip().split()
wordc = sptc[0]
worde = spte[0]
try:
vecc = map(lambda x: float(x), sptc[1:])
vece = map(lambda x: float(x), spte[1:])
self.sync[wordc] = vecc
self.syne_neg[worde] = vece
except ValueError:
LogInfo.logs("[error] %s | %s", sptc[0:3], spte[0:3])
continue
LogInfo.logs("[log] sync/syneneg cause/effect vectors loaded (%d/%d).",
len(self.sync), len(self.syne_neg))
with open("/home/yuchen/CppFiles/Causal/syncneg_wdFin_iter200.txt") as finc, \
open("/home/yuchen/CppFiles/Causal/syne_wdFin_iter200.txt") as fine:
# with open("/home/yuchen/CppFiles/Causal/syn0_w2v_200.txt") as finc, \
# open("/home/yuchen/CppFiles/Causal/syn0_w2v_200.txt") as fine:
cnt = 0
for linec, linee in zip(finc, fine):
cnt += 1
LogInfo.show_line(cnt, 100000)
sptc = linec.strip().split()
spte = linee.strip().split()
wordc = sptc[0]
worde = spte[0]
try:
vecc = map(lambda x: float(x), sptc[1:])
vece = map(lambda x: float(x), spte[1:])
self.sync_neg[wordc] = vecc
self.syne[worde] = vece
except ValueError:
LogInfo.logs("[error] %s | %s", sptc[0:3], spte[0:3])
continue
LogInfo.logs("[log] syncneg/syne cause/effect vectors loaded (%d/%d).",
len(self.sync_neg), len(self.syne))
# NN, JJ, VB
with open("/home/yuchen/data/copa_phr.txt") as fin:
for i in range(1000):
raw_sentence = fin.readline()
raw_option1 = fin.readline()
raw_option2 = fin.readline()
sentence = map(lambda x: x.split(':')[1],
raw_sentence.strip().split())
option1 = map(lambda x: x.split(':')[1],
raw_option1.strip().split())
option2 = map(lambda x: x.split(':')[1],
raw_option2.strip().split())
self.copa_data.append([sentence, option1, option2])
LogInfo.logs("[log] copa dataset loaded (%d).", len(self.copa_data))
with open("/home/yuchen/data/copa_label.txt") as fin:
for line in fin:
spt = line.strip().split('\t')
self.copa_ground.append([spt[1], int(spt[2])])
LogInfo.logs("[log] copa ground truth loaded (%d).",
len(self.copa_ground))
LogInfo.end_track()
config.add("vocab_size", vocab_loader.vocab_size)
LogInfo.logs("Embedding shape: %s.", vocab_loader.vocab_embedding.shape)
data_loader = DataLoader(config.get("max_seq_len"),
vocab_loader.vocab_index_dict)
data_loader.load(config.get("test_data_fp"), 'utf-8')
LogInfo.begin_track("Create models...")
graph = tf.Graph()
with graph.as_default():
test_model = IntentionIdentifier(
config=config,
mode=tf.contrib.learn.ModeKeys.EVAL,
embedding_vocab=vocab_loader.vocab_embedding)
LogInfo.logs("Test model created.")
LogInfo.end_track()
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
LogInfo.begin_track("Start testing...")
with tf.Session(graph=graph, config=tf_config) as session:
test_model.load(session, root_path + "/model/best_model")
batch_size = config.get("test_batch_size")
batch_num = int(data_loader.data_size / batch_size)
score_list = list()
for i in range(batch_num + 1):
LogInfo.logs("Testing batch %d...", i + 1)
if (i + 1) * batch_size > data_loader.data_size:
test_data_batch = data_loader.data[i * batch_size:data_loader.
data_size]
LogInfo.logs("%d pinlei pairs loaded.", len(pinlei_pairs))
# data transformer
data_feeder = DataLoader(config.get("max_seq_len"),
vocab_loader.vocab_index_dict)
# create model
LogInfo.begin_track("Create models...")
graph = tf.Graph()
with graph.as_default():
test_model = IntentionIdentifier(
config=config,
mode=tf.contrib.learn.ModeKeys.TRAIN,
embedding_vocab=vocab_loader.vocab_embedding)
LogInfo.logs("Test model created.")
LogInfo.end_track()
# tensorflow configuration
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
# testing started
LogInfo.begin_track("Start testing...")
with tf.Session(graph=graph, config=tf_config) as session:
test_model.load(session, root_path + "/model/best_model")
cnt = 0
while True:
LogInfo.logs("Please input your segmented (split by \' \') query:")
query = input()
LogInfo.begin_track("\nQuery: %s", query)
if query == "exit" or query == "q":
def word_word1(copa, label, cdic, enegdic, cnegdic, edic, lamd, num, setting, norm, ratio, verbose=False):
acc = 0
wrong = 0
for i in range(num, 1000):
hyp, alt1, alt2 = copa[i]
ask, labl = label[i]
if verbose:
LogInfo.begin_track("step into copa #%d", i+1)
LogInfo.logs("q: %s", hyp)
LogInfo.logs("o1: %s", alt1)
LogInfo.logs("o2: %s", alt2)
LogInfo.logs("answer: o%d", labl)
# ask for cause
if ask == 0:
if verbose:
LogInfo.begin_track("[ask for cause] o1/o2 -> q")
cause, effect = alt1, hyp
if verbose:
LogInfo.begin_track("o1->q: [%s]->[%s]", cause, effect)
score1 = cal_score(cause, effect, cdic, enegdic, cnegdic, edic, lamd, setting, norm, ratio, verbose)
if verbose:
LogInfo.logs("final score: %.4f", score1)
LogInfo.end_track()
cause, effect = alt2, hyp
if verbose:
LogInfo.begin_track("o2->q: [%s]->[%s]", cause, effect)
score2 = cal_score(cause, effect, cdic, enegdic, cnegdic, edic, lamd, setting, norm, ratio, verbose)
if verbose:
LogInfo.logs("final score: %.4f", score2)
LogInfo.end_track()
if score1 > score2 and labl == 1:
acc += 1
if verbose:
LogInfo.logs("[[correct]]")
if score1 < score2 and labl == 2:
acc += 1
if verbose:
LogInfo.logs("[[correct]]")
if verbose:
LogInfo.end_track()
# ask for effect
elif ask == 1:
if verbose:
LogInfo.begin_track("[ask for effect] q -> o1/o2")
cause, effect = hyp, alt1
if verbose:
LogInfo.begin_track("q->o1: [%s]->[%s]", cause, effect)
score1 = cal_score(cause, effect, cdic, enegdic, cnegdic, edic, lamd, setting, norm, ratio, verbose)
if verbose:
LogInfo.logs("final score: %.4f", score1)
LogInfo.end_track()
cause, effect = hyp, alt2
if verbose:
LogInfo.begin_track("q->o2: [%s]->[%s]", cause, effect)
score2 = cal_score(cause, effect, cdic, enegdic, cnegdic, edic, lamd, setting, norm, ratio, verbose)
if verbose:
LogInfo.logs("final score: %.4f", score2)
LogInfo.end_track()
if score1 > score2 and labl == 1:
acc += 1
if verbose:
LogInfo.logs(">>correct<<")
elif score1 < score2 and labl == 2:
acc += 1
if verbose:
LogInfo.logs(">>correct<<")
else:
wrong += 1
if verbose:
LogInfo.logs(">>wrong<<")
if verbose:
LogInfo.end_track()
else:
print ask
if verbose:
LogInfo.logs("[error] ask=%d", ask)
if verbose:
LogInfo.end_track("end for #%d", i+1)
LogInfo.logs("===========")
if verbose:
LogInfo.logs("status: %dY-%dW/%d", acc, wrong, 1000-num)
return acc*1.0/(1000-num)
with graph.as_default():
train_model = IntentionIdentifier(
config=config,
mode=tf.contrib.learn.ModeKeys.TRAIN,
embedding_vocab=vocab_loader.vocab_embedding)
LogInfo.logs("Train model created.")
# all get_variable parameters will be reused in eval
tf.get_variable_scope().reuse_variables()
eval_model = IntentionIdentifier(
config=config,
mode=tf.contrib.learn.ModeKeys.EVAL,
embedding_vocab=vocab_loader.vocab_embedding)
LogInfo.logs("Eval model created.")
LogInfo.end_track()
tf_config = tf.ConfigProto()
tf_config.gpu_options.allow_growth = True
best_valid_acc = 0.0
waiting = 0
LogInfo.begin_track("Start training...")
with tf.Session(graph=graph, config=tf_config) as session:
session.run(tf.global_variables_initializer())
for epoch in range(config.get("epoch")):
LogInfo.begin_track("Epoch %d/%d...", epoch, config.get("epoch"))
batch_generator.reset_batch_pointer()
for batch in range(batch_generator.num_batches):
batch_data = batch_generator.next_batch()
def load_data(self):
"""
load data from files
:return:
"""
LogInfo.begin_track("Loading data...")
# with open("/home/yuchen/CppFiles/Causal/syn0_w2v_200.txt") as finc, \
# open("/home/yuchen/CppFiles/Causal/syn0_w2v_200.txt") as fine:
with open("/home/yuchen/CppFiles/Causal/copy_sync_half_200_iter100.txt") as finc, \
open("/home/yuchen/CppFiles/Causal/copy_syneneg_half_200_iter100.txt") as fine:
cnt = 0
for linec, linee in zip(finc, fine):
cnt += 1
LogInfo.show_line(cnt, 100000)
sptc = linec.strip().split()
spte = linee.strip().split()
wordc = sptc[0]
worde = spte[0]
vecc = map(lambda x: float(x), sptc[1:])
vece = map(lambda x: float(x), spte[1:])
self.sync[wordc] = vecc
self.syne_neg[worde] = vece
LogInfo.logs("[log] sync/syneneg cause/effect vectors loaded (%d/%d).",
len(self.sync), len(self.syne_neg))
with open("/home/yuchen/CppFiles/Causal/copy_syncneg_half_200_iter100.txt") as finc, \
open("/home/yuchen/CppFiles/Causal/copy_syne_half_200_iter100.txt") as fine:
# with open("/home/yuchen/CppFiles/Causal/syn0_w2v_200.txt") as finc, \
# open("/home/yuchen/CppFiles/Causal/syn0_w2v_200.txt") as fine:
cnt = 0
for linec, linee in zip(finc, fine):
cnt += 1
LogInfo.show_line(cnt, 100000)
sptc = linec.strip().split()
spte = linee.strip().split()
wordc = sptc[0]
worde = spte[0]
vecc = map(lambda x: float(x), sptc[1:])
vece = map(lambda x: float(x), spte[1:])
self.sync_neg[wordc] = vecc
self.syne[worde] = vece
LogInfo.logs("[log] syncneg/syne cause/effect vectors loaded (%d/%d).",
len(self.sync_neg), len(self.syne))
# NN, JJ, VB
with open("/home/yuchen/data/copa_lem.txt") as fin:
for i in range(1000):
raw_sentence = fin.readline()
raw_option1 = fin.readline()
raw_option2 = fin.readline()
sentence = list()
option1 = list()
option2 = list()
for word in raw_sentence.strip().split():
if word.startswith('NN') or word.startswith(
'JJ') or word.startswith('VB'):
sentence.append(word.split(':')[1])
for word in raw_option1.strip().split():
if word.startswith('NN') or word.startswith(
'JJ') or word.startswith('VB'):
option1.append(word.split(':')[1])
for word in raw_option2.strip().split():
if word.startswith('NN') or word.startswith(
'JJ') or word.startswith('VB'):
option2.append(word.split(':')[1])
self.copa_data.append([sentence, option1, option2])
LogInfo.logs("[log] copa dataset loaded (%d).", len(self.copa_data))
with open("/home/yuchen/data/copa_label.txt") as fin:
for line in fin:
spt = line.strip().split('\t')
self.copa_ground.append([spt[1], int(spt[2])])
LogInfo.logs("[log] copa ground truth loaded (%d).",
len(self.copa_ground))
LogInfo.end_track()
LogInfo.begin_track("Loading wiki-fb entity map...")
wiki_fb_map = dict()
cnt = 0
with open(fb_path + "/GS-cleanWiki-triple.txt") as fin:
for line in fin:
spt = line.strip().split('\t')
if len(spt) < 3:
continue
fb_ent = spt[0]
wiki_ent = spt[2].split('/wiki/')[1][:-1]
wiki_ent = wiki_ent.lower().replace('_', ' ')
wiki_fb_map[wiki_ent] = fb_ent
cnt += 1
LogInfo.show_line(cnt, 500000)
LogInfo.end_track("%d pairs in total", cnt)
LogInfo.begin_track("Loading fb entity pop...")
fb_ent_pop_map = dict()
cnt = 0
with open("/home/xusheng/freebase/top5m.mid") as fin:
for line in fin:
spt = line.strip().split('\t')
if len(spt) < 2:
continue
ent = spt[0]
pop = int(spt[1])
fb_ent_pop_map[ent] = pop
cnt += 1
LogInfo.show_line(cnt, 500000)
LogInfo.end_track("%d entities in total", cnt)