class Data:
def __init__(self):
self.MAX_SENTENCE_LENGTH = 250
self.MAX_WORD_LENGTH = -1
self.number_normalized = True
self.norm_word_emb = False
self.norm_char_emb = False
self.norm_trans_emb = False
self.word_alphabet = Alphabet('word')
self.char_alphabet = Alphabet('character')
self.translation_alphabet = Alphabet('translation')
self.translation_id_format = {}
self.feature_name = []
self.feature_alphabets = []
self.feature_num = len(self.feature_alphabets)
self.feat_config = None
self.label_alphabet = Alphabet('label', True)
self.tagScheme = "NoSeg" ## BMES/BIO
self.seg = True
### I/O
self.train_dir = None
self.dev_dir = None
self.test_dir = None
self.raw_dir = None
self.trans_dir = None
self.decode_dir = None
self.dset_dir = None ## data vocabulary related file
self.model_dir = None ## model save file
self.load_model_dir = None ## model load file
self.word_emb_dir = None
self.char_emb_dir = None
self.trans_embed_dir = None
self.feature_emb_dirs = []
self.train_texts = []
self.dev_texts = []
self.test_texts = []
self.raw_texts = []
self.train_Ids = []
self.dev_Ids = []
self.test_Ids = []
self.raw_Ids = []
self.pretrain_word_embedding = None
self.pretrain_char_embedding = None
self.pretrain_trans_embedding = None
self.pretrain_feature_embeddings = []
self.label_size = 0
self.word_alphabet_size = 0
self.char_alphabet_size = 0
self.label_alphabet_size = 0
self.trans_alphabet_size = 0
self.feature_alphabet_sizes = []
self.feature_emb_dims = []
self.norm_feature_embs = []
self.word_emb_dim = 50
self.char_emb_dim = 30
self.trans_emb_dim = 100
###Networks
self.word_feature_extractor = "LSTM" ## "LSTM"/"CNN"/"GRU"/
self.use_char = True
self.char_seq_feature = "CNN" ## "LSTM"/"CNN"/"GRU"/None
self.use_trans = True
self.use_crf = True
self.nbest = None
## Training
self.average_batch_loss = False
self.optimizer = "SGD" ## "SGD"/"AdaGrad"/"AdaDelta"/"RMSProp"/"Adam"
self.status = "train"
### Hyperparameters
self.HP_cnn_layer = 4
self.HP_iteration = 100
self.HP_batch_size = 10
self.HP_char_hidden_dim = 50
self.HP_trans_hidden_dim = 50
self.HP_hidden_dim = 200
self.HP_dropout = 0.5
self.HP_lstm_layer = 1
self.HP_bilstm = True
self.HP_gpu = False
self.HP_lr = 0.015
self.HP_lr_decay = 0.05
self.HP_clip = None
self.HP_momentum = 0
self.HP_l2 = 1e-8
def show_data_summary(self):
print("++" * 50)
print("DATA SUMMARY START:")
print(" I/O:")
print(" Tag scheme: %s" % (self.tagScheme))
print(" MAX SENTENCE LENGTH: %s" % (self.MAX_SENTENCE_LENGTH))
print(" MAX WORD LENGTH: %s" % (self.MAX_WORD_LENGTH))
print(" Number normalized: %s" % (self.number_normalized))
print(" Word alphabet size: %s" % (self.word_alphabet_size))
print(" Char alphabet size: %s" % (self.char_alphabet_size))
print(" Label alphabet size: %s" % (self.label_alphabet_size))
print(" Trans alphabet size: %s" % (self.trans_alphabet_size))
print(" Word embedding dir: %s" % (self.word_emb_dir))
print(" Char embedding dir: %s" % (self.char_emb_dir))
print(" Tran embedding dir: %s" % (self.trans_embed_dir))
print(" Word embedding size: %s" % (self.word_emb_dim))
print(" Char embedding size: %s" % (self.char_emb_dim))
print(" Tran embedding size: %s" % (self.trans_emb_dim))
print(" Norm word emb: %s" % (self.norm_word_emb))
print(" Norm char emb: %s" % (self.norm_char_emb))
print(" Norm tran emb: %s" % (self.norm_trans_emb))
print(" Train file directory: %s" % (self.train_dir))
print(" Dev file directory: %s" % (self.dev_dir))
print(" Test file directory: %s" % (self.test_dir))
print(" Raw file directory: %s" % (self.raw_dir))
print(" Dset file directory: %s" % (self.dset_dir))
print(" Model file directory: %s" % (self.model_dir))
print(" Loadmodel directory: %s" % (self.load_model_dir))
print(" Decode file directory: %s" % (self.decode_dir))
print(" Train instance number: %s" % (len(self.train_texts)))
print(" Dev instance number: %s" % (len(self.dev_texts)))
print(" Test instance number: %s" % (len(self.test_texts)))
print(" Raw instance number: %s" % (len(self.raw_texts)))
print(" FEATURE num: %s" % (self.feature_num))
for idx in range(self.feature_num):
print(" Fe: %s alphabet size: %s" %
(self.feature_alphabets[idx].name,
self.feature_alphabet_sizes[idx]))
print(
" Fe: %s embedding dir: %s" %
(self.feature_alphabets[idx].name, self.feature_emb_dirs[idx]))
print(
" Fe: %s embedding size: %s" %
(self.feature_alphabets[idx].name, self.feature_emb_dims[idx]))
print(" Fe: %s norm emb: %s" %
(self.feature_alphabets[idx].name,
self.norm_feature_embs[idx]))
print(" " + "++" * 20)
print(" Model Network:")
print(" Model use_crf: %s" % (self.use_crf))
print(" Model word extractor: %s" % (self.word_feature_extractor))
print(" Model use_char: %s" % (self.use_char))
if self.use_char:
print(" Model char_seq_feature: %s" % (self.char_seq_feature))
print(" Model char_hidden_dim: %s" % (self.HP_char_hidden_dim))
if self.use_trans:
print(" Model trans_hidden_dim: %s" %
(self.HP_trans_hidden_dim))
print(" " + "++" * 20)
print(" Training:")
print(" Optimizer: %s" % (self.optimizer))
print(" Iteration: %s" % (self.HP_iteration))
print(" BatchSize: %s" % (self.HP_batch_size))
print(" Average batch loss: %s" % (self.average_batch_loss))
print(" " + "++" * 20)
print(" Hyperparameters:")
print(" Hyper lr: %s" % (self.HP_lr))
print(" Hyper lr_decay: %s" % (self.HP_lr_decay))
print(" Hyper HP_clip: %s" % (self.HP_clip))
print(" Hyper momentum: %s" % (self.HP_momentum))
print(" Hyper l2: %s" % (self.HP_l2))
print(" Hyper hidden_dim: %s" % (self.HP_hidden_dim))
print(" Hyper dropout: %s" % (self.HP_dropout))
print(" Hyper lstm_layer: %s" % (self.HP_lstm_layer))
print(" Hyper bilstm: %s" % (self.HP_bilstm))
print(" Hyper GPU: %s" % (self.HP_gpu))
print("DATA SUMMARY END.")
print("++" * 50)
sys.stdout.flush()
def initial_feature_alphabets(self):
items = open(self.train_dir, 'r').readline().strip('\n').split()
print(items)
total_column = len(items)
if total_column > 2:
for idx in range(1, total_column - 1):
feature_prefix = items[idx].split(']', 1)[0] + "]"
print("feature_prefix:{}".format(feature_prefix))
self.feature_alphabets.append(Alphabet(feature_prefix))
self.feature_name.append(feature_prefix)
print("Find feature: ", feature_prefix)
self.feature_num = len(self.feature_alphabets)
self.pretrain_feature_embeddings = [None] * self.feature_num
self.feature_emb_dims = [20] * self.feature_num
self.feature_emb_dirs = [None] * self.feature_num
self.norm_feature_embs = [False] * self.feature_num
self.feature_alphabet_sizes = [0] * self.feature_num
if self.feat_config:
for idx in range(self.feature_num):
if self.feature_name[idx] in self.feat_config:
self.feature_emb_dims[idx] = self.feat_config[
self.feature_name[idx]]['emb_size']
self.feature_emb_dirs[idx] = self.feat_config[
self.feature_name[idx]]['emb_dir']
self.norm_feature_embs[idx] = self.feat_config[
self.feature_name[idx]]['emb_norm']
# exit(0)
def build_alphabet(self, input_file):
print("Build alphabet......")
in_lines = open(input_file, 'r').readlines()
for line in in_lines:
if len(line) > 2:
pairs = line.strip().split()
word = pairs[0].decode('utf-8')
if self.number_normalized:
word = normalize_word(word)
label = pairs[-1]
self.label_alphabet.add(label)
self.word_alphabet.add(word)
## build feature alphabet
for idx in range(self.feature_num):
feat_idx = pairs[idx + 1].split(']', 1)[-1]
self.feature_alphabets[idx].add(feat_idx)
for char in word:
self.char_alphabet.add(char)
self.word_alphabet_size = self.word_alphabet.size()
self.char_alphabet_size = self.char_alphabet.size()
self.label_alphabet_size = self.label_alphabet.size()
for idx in range(self.feature_num):
self.feature_alphabet_sizes[idx] = self.feature_alphabets[
idx].size()
startS = False
startB = False
for label, _ in self.label_alphabet.iteritems():
if "S-" in label.upper():
startS = True
elif "B-" in label.upper():
startB = True
if startB:
if startS:
self.tagScheme = "BMES"
else:
self.tagScheme = "BIO"
def fix_alphabet(self):
self.word_alphabet.close()
self.char_alphabet.close()
self.label_alphabet.close()
self.translation_alphabet.close()
for idx in range(self.feature_num):
self.feature_alphabets[idx].close()
def build_pretrain_emb(self):
if self.word_emb_dir:
print("Load pretrained word embedding, norm: %s, dir: %s" %
(self.norm_word_emb, self.word_emb_dir))
self.pretrain_word_embedding, self.word_emb_dim = build_pretrain_embedding(
self.word_emb_dir, self.word_alphabet, self.word_emb_dim,
self.norm_word_emb)
if self.char_emb_dir:
print("Load pretrained char embedding, norm: %s, dir: %s" %
(self.norm_char_emb, self.char_emb_dir))
self.pretrain_char_embedding, self.char_emb_dim = build_pretrain_embedding(
self.char_emb_dir, self.char_alphabet, self.char_emb_dim,
self.norm_char_emb)
if self.trans_embed_dir:
print("Load pretrained trans embedding, norm: %s, dir: %s" %
(self.norm_trans_emb, self.trans_embed_dir))
self.pretrain_trans_embedding, self.trans_emb_dim = build_pretrain_embedding(
self.trans_embed_dir, self.translation_alphabet,
self.trans_emb_dim, self.norm_trans_emb)
for idx in range(self.feature_num):
if self.feature_emb_dirs[idx]:
print(
"Load pretrained feature %s embedding:, norm: %s, dir: %s"
% (self.feature_name[idx], self.norm_feature_embs[idx],
self.feature_emb_dirs[idx]))
self.pretrain_feature_embeddings[idx], self.feature_emb_dims[
idx] = build_pretrain_embedding(
self.feature_emb_dirs[idx],
self.feature_alphabets[idx],
self.feature_emb_dims[idx],
self.norm_feature_embs[idx])
def generate_instance(self, name):
self.fix_alphabet()
if name == "train":
self.train_texts, self.train_Ids = read_instance(
self.train_dir, self.word_alphabet, self.char_alphabet,
self.feature_alphabets, self.label_alphabet,
self.number_normalized, self.MAX_SENTENCE_LENGTH,
self.translation_id_format)
elif name == "dev":
self.dev_texts, self.dev_Ids = read_instance(
self.dev_dir, self.word_alphabet, self.char_alphabet,
self.feature_alphabets, self.label_alphabet,
self.number_normalized, self.MAX_SENTENCE_LENGTH,
self.translation_id_format)
elif name == "test":
self.test_texts, self.test_Ids = read_instance(
self.test_dir, self.word_alphabet, self.char_alphabet,
self.feature_alphabets, self.label_alphabet,
self.number_normalized, self.MAX_SENTENCE_LENGTH,
self.translation_id_format)
elif name == "raw":
self.raw_texts, self.raw_Ids = read_instance(
self.raw_dir, self.word_alphabet, self.char_alphabet,
self.feature_alphabets, self.label_alphabet,
self.number_normalized, self.MAX_SENTENCE_LENGTH,
self.translation_id_format)
else:
print(
"Error: you can only generate train/dev/test instance! Illegal input:%s"
% (name))
def write_decoded_results(self, predict_results, name):
fout = open(self.decode_dir, 'w')
sent_num = len(predict_results)
content_list = []
if name == 'raw':
content_list = self.raw_texts
elif name == 'test':
content_list = self.test_texts
elif name == 'dev':
content_list = self.dev_texts
elif name == 'train':
content_list = self.train_texts
else:
print(
"Error: illegal name during writing predict result, name should be within train/dev/test/raw !"
)
assert (sent_num == len(content_list))
for idx in range(sent_num):
sent_length = len(predict_results[idx])
for idy in range(sent_length):
## content_list[idx] is a list with [word, char, label]
fout.write(content_list[idx][0][idy].encode('utf-8') + " " +
predict_results[idx][idy] + '\n')
fout.write('\n')
fout.close()
print("Predict %s result has been written into file. %s" %
(name, self.decode_dir))
def load(self, data_file):
f = open(data_file, 'rb')
tmp_dict = pickle.load(f)
f.close()
self.__dict__.update(tmp_dict)
def save(self, save_file):
f = open(save_file, 'wb')
pickle.dump(self.__dict__, f, 2)
f.close()
def write_nbest_decoded_results(self, predict_results, pred_scores, name):
## predict_results : [whole_sent_num, nbest, each_sent_length]
## pred_scores: [whole_sent_num, nbest]
fout = open(self.decode_dir, 'w')
sent_num = len(predict_results)
content_list = []
if name == 'raw':
content_list = self.raw_texts
elif name == 'test':
content_list = self.test_texts
elif name == 'dev':
content_list = self.dev_texts
elif name == 'train':
content_list = self.train_texts
else:
print(
"Error: illegal name during writing predict result, name should be within train/dev/test/raw !"
)
assert (sent_num == len(content_list))
assert (sent_num == len(pred_scores))
for idx in range(sent_num):
sent_length = len(predict_results[idx][0])
nbest = len(predict_results[idx])
score_string = "# "
for idz in range(nbest):
score_string += format(pred_scores[idx][idz], '.4f') + " "
fout.write(score_string.strip() + "\n")
for idy in range(sent_length):
label_string = content_list[idx][0][idy].encode('utf-8') + " "
for idz in range(nbest):
label_string += predict_results[idx][idz][idy] + " "
label_string = label_string.strip() + "\n"
fout.write(label_string)
fout.write('\n')
fout.close()
print("Predict %s %s-best result has been written into file. %s" %
(name, nbest, self.decode_dir))
def read_config(self, config_file):
config = config_file_to_dict(config_file)
## read data:
the_item = 'train_dir'
if the_item in config:
self.train_dir = config[the_item]
the_item = 'dev_dir'
if the_item in config:
self.dev_dir = config[the_item]
the_item = 'test_dir'
if the_item in config:
self.test_dir = config[the_item]
the_item = 'trans_dir'
if the_item in config:
self.trans_dir = config[the_item]
the_item = 'raw_dir'
if the_item in config:
self.raw_dir = config[the_item]
the_item = 'decode_dir'
if the_item in config:
self.decode_dir = config[the_item]
the_item = 'dset_dir'
if the_item in config:
self.dset_dir = config[the_item]
the_item = 'model_dir'
if the_item in config:
self.model_dir = config[the_item]
the_item = 'load_model_dir'
if the_item in config:
self.load_model_dir = config[the_item]
the_item = 'word_emb_dir'
if the_item in config:
self.word_emb_dir = config[the_item]
the_item = 'char_emb_dir'
if the_item in config:
self.char_emb_dir = config[the_item]
the_item = 'trans_embed_dir'
if the_item in config:
self.trans_embed_dir = config[the_item]
the_item = 'MAX_SENTENCE_LENGTH'
if the_item in config:
self.MAX_SENTENCE_LENGTH = int(config[the_item])
the_item = 'MAX_WORD_LENGTH'
if the_item in config:
self.MAX_WORD_LENGTH = int(config[the_item])
the_item = 'norm_word_emb'
if the_item in config:
self.norm_word_emb = str2bool(config[the_item])
the_item = 'norm_char_emb'
if the_item in config:
self.norm_char_emb = str2bool(config[the_item])
the_item = 'number_normalized'
if the_item in config:
self.number_normalized = str2bool(config[the_item])
the_item = 'seg'
if the_item in config:
self.seg = str2bool(config[the_item])
the_item = 'word_emb_dim'
if the_item in config:
self.word_emb_dim = int(config[the_item])
the_item = 'char_emb_dim'
if the_item in config:
self.char_emb_dim = int(config[the_item])
the_item = 'trans_emb_dim'
if the_item in config:
self.trans_emb_dim = int(config[the_item])
## read network:
the_item = 'use_crf'
if the_item in config:
self.use_crf = str2bool(config[the_item])
the_item = 'use_char'
if the_item in config:
self.use_char = str2bool(config[the_item])
the_item = 'use_trans'
if the_item in config:
self.use_trans = str2bool(config[the_item])
the_item = 'word_seq_feature'
if the_item in config:
self.word_feature_extractor = config[the_item]
the_item = 'char_seq_feature'
if the_item in config:
self.char_seq_feature = config[the_item]
the_item = 'nbest'
if the_item in config:
self.nbest = int(config[the_item])
the_item = 'feature'
if the_item in config:
self.feat_config = config[the_item] ## feat_config is a dict
## read training setting:
the_item = 'optimizer'
if the_item in config:
self.optimizer = config[the_item]
the_item = 'ave_batch_loss'
if the_item in config:
self.average_batch_loss = str2bool(config[the_item])
the_item = 'status'
if the_item in config:
self.status = config[the_item]
## read Hyperparameters:
the_item = 'cnn_layer'
if the_item in config:
self.HP_cnn_layer = int(config[the_item])
the_item = 'iteration'
if the_item in config:
self.HP_iteration = int(config[the_item])
the_item = 'batch_size'
if the_item in config:
self.HP_batch_size = int(config[the_item])
the_item = 'char_hidden_dim'
if the_item in config:
self.HP_char_hidden_dim = int(config[the_item])
the_item = 'trans_hidden_dim'
if the_item in config:
self.HP_trans_hidden_dim = int(config[the_item])
the_item = 'hidden_dim'
if the_item in config:
self.HP_hidden_dim = int(config[the_item])
the_item = 'dropout'
if the_item in config:
self.HP_dropout = float(config[the_item])
the_item = 'lstm_layer'
if the_item in config:
self.HP_lstm_layer = int(config[the_item])
the_item = 'bilstm'
if the_item in config:
self.HP_bilstm = str2bool(config[the_item])
the_item = 'gpu'
if the_item in config:
self.HP_gpu = str2bool(config[the_item])
the_item = 'learning_rate'
if the_item in config:
self.HP_lr = float(config[the_item])
the_item = 'lr_decay'
if the_item in config:
self.HP_lr_decay = float(config[the_item])
the_item = 'clip'
if the_item in config:
self.HP_clip = float(config[the_item])
the_item = 'momentum'
if the_item in config:
self.HP_momentum = float(config[the_item])
the_item = 'l2'
if the_item in config:
self.HP_l2 = float(config[the_item])
def build_translation_alphabet(self, trans_path):
print("Creating translation alphabet......")
with codecs.open(trans_path, 'r') as f:
lines = f.readlines()
for line in lines:
if len(line.strip().split(":")) == 2:
temp = line.strip().split(":", 1)
words = temp[1].split()
for word in words:
self.translation_alphabet.add(word.strip())
self.trans_alphabet_size = self.translation_alphabet.size()
def build_translation_dict(self, trans_path):
print("Creating Id to Id translation dictionary......")
translation_id_format_temp = {}
with codecs.open(trans_path, 'r') as f:
lines = f.readlines()
for line in lines:
ids = []
if len(line.strip().split(":")) == 2:
temp = line.strip().split(":")
word_id = self.word_alphabet.get_index(temp[0].strip())
translations = temp[1].split()
for translation in translations:
ids.append(
self.translation_alphabet.get_index(
translation.strip()))
translation_id_format_temp[word_id] = ids
for word in self.word_alphabet.instances:
if self.word_alphabet.get_index(
word) in translation_id_format_temp.keys():
self.translation_id_format[self.word_alphabet.get_index(
word)] = translation_id_format_temp[
self.word_alphabet.get_index(word)]
else:
self.translation_id_format[self.word_alphabet.get_index(
word)] = [0]
class Data:
def __init__(self):
self.MAX_SENTENCE_LENGTH = 250
self.MAX_WORD_LENGTH = -1
self.number_normalized = True
self.norm_word_emb = True
self.norm_biword_emb = True
self.norm_gaz_emb = False
self.word_alphabet = Alphabet('word')
self.biword_alphabet = Alphabet('biword')
self.char_alphabet = Alphabet('character')
self.label_alphabet = Alphabet('label', True)
#self.simi_alphabet = Alphabet('simi') #添加计算相似度词语的信息
self.gaz_lower = False
self.gaz = Gazetteer(self.gaz_lower)
self.gaz_alphabet = Alphabet('gaz')
self.gaz_count = {}
self.gaz_split = {}
self.biword_count = {}
self.HP_fix_gaz_emb = False
self.HP_use_gaz = True
self.HP_use_count = False
self.tagScheme = "NoSeg"
self.char_features = "LSTM"
self.train_texts = []
self.dev_texts = []
self.test_texts = []
self.raw_texts = []
self.train_Ids = []
self.dev_Ids = []
self.test_Ids = []
self.raw_Ids = []
self.train_split_index = []
self.dev_split_index = []
self.use_bigram = True
self.word_emb_dim = 200
self.biword_emb_dim = 200
self.char_emb_dim = 30
self.gaz_emb_dim = 200
self.gaz_dropout = 0.5
self.pretrain_word_embedding = None
self.pretrain_biword_embedding = None
self.pretrain_gaz_embedding = None
self.label_size = 0
self.word_alphabet_size = 0
self.biword_alphabet_size = 0
self.char_alphabet_size = 0
self.label_alphabet_size = 0
#设置词典相似度相关的参数
self.simi_dic_emb = None #设置相似度的嵌入值
self.simi_dic_dim = 10 #设置相似度向量的纬度
self.use_dictionary = False # 设置当前是否使用词典
self.simi_list = [] #存储当前的每个字对应的相似度值
# self.use_gazcount = 'True'
### hyperparameters
self.HP_iteration = 60
self.HP_batch_size = 10
self.HP_char_hidden_dim = 50
self.HP_hidden_dim = 128
self.HP_dropout = 0.5
self.HP_lstm_layer = 1
self.HP_bilstm = True
self.HP_use_char = False
self.HP_gpu = True
self.HP_lr = 0.015
self.HP_lr_decay = 0.05
self.HP_clip = 5.0
self.HP_momentum = 0
self.HP_num_layer = 4
def show_data_summary(self):
print("DATA SUMMARY START:")
print(" Tag scheme: %s" % (self.tagScheme))
print(" MAX SENTENCE LENGTH: %s" % (self.MAX_SENTENCE_LENGTH))
print(" MAX WORD LENGTH: %s" % (self.MAX_WORD_LENGTH))
print(" Number normalized: %s" % (self.number_normalized))
print(" Use bigram: %s" % (self.use_bigram))
print(" Word alphabet size: %s" % (self.word_alphabet_size))
print(" Biword alphabet size: %s" % (self.biword_alphabet_size))
print(" Char alphabet size: %s" % (self.char_alphabet_size))
print(" Gaz alphabet size: %s" % (self.gaz_alphabet.size()))
print(" Label alphabet size: %s" % (self.label_alphabet_size))
print(" Word embedding size: %s" % (self.word_emb_dim))
print(" Biword embedding size: %s" % (self.biword_emb_dim))
print(" Char embedding size: %s" % (self.char_emb_dim))
print(" Gaz embedding size: %s" % (self.gaz_emb_dim))
print(" Norm word emb: %s" % (self.norm_word_emb))
print(" Norm biword emb: %s" % (self.norm_biword_emb))
print(" Norm gaz emb: %s" % (self.norm_gaz_emb))
print(" Norm gaz dropout: %s" % (self.gaz_dropout))
print(" Train instance number: %s" % (len(self.train_texts)))
print(" Dev instance number: %s" % (len(self.dev_texts)))
print(" Test instance number: %s" % (len(self.test_texts)))
print(" Raw instance number: %s" % (len(self.raw_texts)))
print(" Hyperpara iteration: %s" % (self.HP_iteration))
print(" Hyperpara batch size: %s" % (self.HP_batch_size))
print(" Hyperpara lr: %s" % (self.HP_lr))
print(" Hyperpara lr_decay: %s" % (self.HP_lr_decay))
print(" Hyperpara HP_clip: %s" % (self.HP_clip))
print(" Hyperpara momentum: %s" % (self.HP_momentum))
print(" Hyperpara hidden_dim: %s" % (self.HP_hidden_dim))
print(" Hyperpara dropout: %s" % (self.HP_dropout))
print(" Hyperpara lstm_layer: %s" % (self.HP_lstm_layer))
print(" Hyperpara bilstm: %s" % (self.HP_bilstm))
print(" Hyperpara GPU: %s" % (self.HP_gpu))
print(" Hyperpara use_gaz: %s" % (self.HP_use_gaz))
print(" Hyperpara fix gaz emb: %s" % (self.HP_fix_gaz_emb))
print(" Hyperpara use_char: %s" % (self.HP_use_char))
if self.HP_use_char:
print(" Char_features: %s" % (self.char_features))
print("DATA SUMMARY END.")
sys.stdout.flush()
def refresh_label_alphabet(self, input_file):
old_size = self.label_alphabet_size
self.label_alphabet.clear(True)
in_lines = open(input_file, 'r', encoding="utf-8").readlines()
for line in in_lines:
if len(line) > 2:
pairs = line.strip().split()
label = pairs[-1]
self.label_alphabet.add(label)
self.label_alphabet_size = self.label_alphabet.size()
startS = False
startB = False
for label, _ in self.label_alphabet.iteritems():
if "S-" in label.upper():
startS = True
elif "B-" in label.upper():
startB = True
if startB:
if startS:
self.tagScheme = "BMES"
else:
self.tagScheme = "BIO"
self.fix_alphabet()
print("Refresh label alphabet finished: old:%s -> new:%s" %
(old_size, self.label_alphabet_size))
def build_alphabet(self, input_file):
in_lines = open(input_file, 'r', encoding="utf-8").readlines()
seqlen = 0
for idx in range(len(in_lines)):
line = in_lines[idx]
if len(line) > 2:
pairs = line.strip().split()
word = pairs[0]
if self.number_normalized:
word = normalize_word(word)
label = pairs[-1]
self.label_alphabet.add(label)
self.word_alphabet.add(word)
if idx < len(in_lines) - 1 and len(in_lines[idx + 1]) > 2:
biword = word + in_lines[idx + 1].strip().split()[0]
else:
biword = word + NULLKEY
self.biword_alphabet.add(biword)
# biword_index = self.biword_alphabet.get_index(biword)
self.biword_count[biword] = self.biword_count.get(biword,
0) + 1
for char in word:
self.char_alphabet.add(char)
#当前句子的长度
seqlen += 1
else:
#出现空行则清零
seqlen = 0
#计算各个字表的长度
self.word_alphabet_size = self.word_alphabet.size()
self.biword_alphabet_size = self.biword_alphabet.size()
self.char_alphabet_size = self.char_alphabet.size()
self.label_alphabet_size = self.label_alphabet.size()
startS = False
startB = False
for label, _ in self.label_alphabet.iteritems():
if "S-" in label.upper():
startS = True
elif "B-" in label.upper():
startB = True
if startB:
if startS:
self.tagScheme = "BMES"
else:
self.tagScheme = "BIO"
def build_gaz_file(self, gaz_file):
## build gaz file,initial read gaz embedding file
if gaz_file:
fins = open(gaz_file, 'r', encoding="utf-8").readlines()
for fin in fins:
fin = fin.strip().split()[0]
if fin:
self.gaz.insert(fin, "one_source")
print("Load gaz file: ", gaz_file, " total size:", self.gaz.size())
else:
print("Gaz file is None, load nothing")
#def build_dict_alphabet(
def build_gaz_alphabet(self, input_file, count=False):
in_lines = open(input_file, 'r', encoding="utf-8").readlines()
word_list = []
for line in in_lines:
if len(line) > 3:
word = line.split()[0]
if self.number_normalized:
word = normalize_word(word)
word_list.append(word)
else:
#word_list为当前这个句子的所有字
w_length = len(word_list)
entitys = []
#获取到了句子
for idx in range(w_length):
matched_entity = self.gaz.enumerateMatchList(
word_list[idx:])
entitys += matched_entity
for entity in matched_entity:
# print entity, self.gaz.searchId(entity),self.gaz.searchType(entity)
self.gaz_alphabet.add(entity)
index = self.gaz_alphabet.get_index(entity)
self.gaz_count[index] = self.gaz_count.get(
index, 0) ## initialize gaz count
#0表示若无想要的关键词则返回0，没有index这一个键值
if count:
entitys.sort(key=lambda x: -len(x))
while entitys:
longest = entitys[0]
longest_index = self.gaz_alphabet.get_index(longest)
#最长词的index加1
self.gaz_count[longest_index] = self.gaz_count.get(
longest_index, 0) + 1
#把一个词语覆盖的词全部删掉
gazlen = len(longest)
for i in range(gazlen):
for j in range(i + 1, gazlen + 1):
covering_gaz = longest[i:j]
if covering_gaz in entitys:
entitys.remove(covering_gaz)
# print('remove:',covering_gaz)
word_list = []
print("gaz alphabet size:", self.gaz_alphabet.size())
def fix_alphabet(self):
self.word_alphabet.close()
self.biword_alphabet.close()
self.char_alphabet.close()
self.label_alphabet.close()
self.gaz_alphabet.close()
def build_word_pretrain_emb(self, emb_path):
print("build word pretrain emb...")
self.pretrain_word_embedding, self.word_emb_dim = build_pretrain_embedding(
emb_path, self.word_alphabet, self.word_emb_dim,
self.norm_word_emb)
def build_biword_pretrain_emb(self, emb_path):
print("build biword pretrain emb...")
self.pretrain_biword_embedding, self.biword_emb_dim = build_pretrain_embedding(
emb_path, self.biword_alphabet, self.biword_emb_dim,
self.norm_biword_emb)
def build_gaz_pretrain_emb(self, emb_path):
print("build gaz pretrain emb...")
self.pretrain_gaz_embedding, self.gaz_emb_dim = build_pretrain_embedding(
emb_path, self.gaz_alphabet, self.gaz_emb_dim, self.norm_gaz_emb)
def generate_instance_with_gaz(self, input_file, name):
self.fix_alphabet()
if name == "train":
self.train_texts, self.train_Ids = read_instance_with_gaz(
self.HP_num_layer, input_file, self.gaz, self.word_alphabet,
self.biword_alphabet, self.biword_count, self.char_alphabet,
self.gaz_alphabet, self.gaz_count, self.gaz_split,
self.label_alphabet, self.number_normalized,
self.MAX_SENTENCE_LENGTH)
elif name == "dev":
self.dev_texts, self.dev_Ids = read_instance_with_gaz(
self.HP_num_layer, input_file, self.gaz, self.word_alphabet,
self.biword_alphabet, self.biword_count, self.char_alphabet,
self.gaz_alphabet, self.gaz_count, self.gaz_split,
self.label_alphabet, self.number_normalized,
self.MAX_SENTENCE_LENGTH)
elif name == "test":
self.test_texts, self.test_Ids = read_instance_with_gaz(
self.HP_num_layer, input_file, self.gaz, self.word_alphabet,
self.biword_alphabet, self.biword_count, self.char_alphabet,
self.gaz_alphabet, self.gaz_count, self.gaz_split,
self.label_alphabet, self.number_normalized,
self.MAX_SENTENCE_LENGTH)
elif name == "raw":
self.raw_texts, self.raw_Ids = read_instance_with_gaz(
self.HP_num_layer, input_file, self.gaz, self.word_alphabet,
self.biword_alphabet, self.biword_count, self.char_alphabet,
self.gaz_alphabet, self.gaz_count, self.gaz_split,
self.label_alphabet, self.number_normalized,
self.MAX_SENTENCE_LENGTH)
else:
print(
"Error: you can only generate train/dev/test instance! Illegal input:%s"
% (name))
def write_decoded_results(self, output_file, predict_results, name):
fout = open(output_file, 'w')
sent_num = len(predict_results)
content_list = []
if name == 'raw':
content_list = self.raw_texts
elif name == 'test':
content_list = self.test_texts
elif name == 'dev':
content_list = self.dev_texts
elif name == 'train':
content_list = self.train_texts
else:
print(
"Error: illegal name during writing predict result, name should be within train/dev/test/raw !"
)
assert (sent_num == len(content_list))
for idx in range(sent_num):
sent_length = len(predict_results[idx])
for idy in range(sent_length):
## content_list[idx] is a list with [word, char, label]
fout.write(content_list[idx][0][idy].encode('utf-8') + " " +
predict_results[idx][idy] + '\n')
fout.write('\n')
fout.close()
print("Predict %s result has been written into file. %s" %
(name, output_file))
class Data:
def __init__(self):
self.MAX_SENTENCE_LENGTH = 250
self.MAX_WORD_LENGTH = -1
self.number_normalized = False
self.norm_word_emb = True
self.norm_biword_emb = True
self.word_alphabet = Alphabet('word')
self.biword_alphabet = Alphabet('biword')
self.pos_alphabet = Alphabet('pos')
self.label_alphabet = Alphabet('label', True)
self.tagScheme = "NoSeg"
self.char_features = "LSTM"
self.train_texts = []
self.dev_texts = []
self.test_texts = []
self.raw_texts = []
self.train_Ids = []
self.dev_Ids = []
self.test_Ids = []
self.raw_Ids = []
self.use_bigram = False
self.word_emb_dim = 50
self.biword_emb_dim = 50
self.pretrain_word_embedding = None
self.pretrain_biword_embedding = None
self.label_size = 0
self.word_alphabet_size = 0
self.biword_alphabet_size = 0
self.label_alphabet_size = 0
# hyperparameters
self.HP_iteration = 100
self.HP_batch_size = 16
self.HP_char_hidden_dim = 50
self.HP_hidden_dim = 200
self.HP_dropout = 0.2
self.HP_lstmdropout = 0
self.HP_lstm_layer = 1
self.HP_bilstm = True
self.HP_gpu = False
self.HP_lr = 0.015
self.HP_lr_decay = 0.05
self.HP_clip = 5.0
self.HP_momentum = 0
# attention
self.tencent_word_embed_dim = 200
self.pos_embed_dim = 200
self.cross_domain = False
self.cross_test = False
self.use_san = False
self.use_cnn = False
self.use_attention = True
self.pos_to_idx = {}
self.external_pos = {}
self.token_replace_prob = {}
self.use_adam = False
self.use_bert = False
self.use_warmup_adam = False
self.use_sgd = False
self.use_adadelta = False
self.use_window = True
self.mode = 'train'
self.use_tencent_dic = False
# cross domain file
self.computer_file = ""
self.finance_file = ""
self.medicine_file = ""
self.literature_file = ""
def show_data_summary(self):
print("DATA SUMMARY START:")
print(" Tag scheme: %s" % (self.tagScheme))
print(" MAX SENTENCE LENGTH: %s" % (self.MAX_SENTENCE_LENGTH))
print(" MAX WORD LENGTH: %s" % (self.MAX_WORD_LENGTH))
print(" Number normalized: %s" % (self.number_normalized))
print(" Use bigram: %s" % (self.use_bigram))
print(" Char alphabet size: %s" % (self.word_alphabet_size))
print(" BiChar alphabet size: %s" % (self.biword_alphabet_size))
print(" Label alphabet size: %s" % (self.label_alphabet_size))
print(" Char embedding size: %s" % (self.word_emb_dim))
print(" BiChar embedding size: %s" % (self.biword_emb_dim))
print(" Norm char emb: %s" % (self.norm_word_emb))
print(" Norm bichar emb: %s" % (self.norm_biword_emb))
print(" Train instance number: %s" % (len(self.train_texts)))
print(" Dev instance number: %s" % (len(self.dev_texts)))
print(" Test instance number: %s" % (len(self.test_texts)))
print(" Raw instance number: %s" % (len(self.raw_texts)))
print(" Hyperpara iteration: %s" % (self.HP_iteration))
print(" Hyperpara batch size: %s" % (self.HP_batch_size))
print(" Hyperpara lr: %s" % (self.HP_lr))
print(" Hyperpara lr_decay: %s" % (self.HP_lr_decay))
print(" Hyperpara HP_clip: %s" % (self.HP_clip))
print(" Hyperpara momentum: %s" % (self.HP_momentum))
print(" Hyperpara hidden_dim: %s" % (self.HP_hidden_dim))
print(" Hyperpara dropout: %s" % (self.HP_dropout))
print(" Hyperpara lstm_layer: %s" % (self.HP_lstm_layer))
print(" Hyperpara bilstm: %s" % (self.HP_bilstm))
print(" Hyperpara GPU: %s" % (self.HP_gpu))
print(" Cross domain: %s" % self.cross_domain)
print(" Hyperpara use window: %s" % self.use_window)
print("DATA SUMMARY END.")
sys.stdout.flush()
def build_alphabet(self, input_file):
in_lines = open(input_file, 'r').readlines()
for idx in range(len(in_lines)):
line = in_lines[idx]
if len(line) > 2:
pairs = line.strip().split('\t')
# word = pairs[0].decode('utf-8')
word = pairs[0]
if self.number_normalized:
word = normalize_word(word)
label = pairs[-1][0] + '-SEG'
self.label_alphabet.add(label)
self.word_alphabet.add(word)
if idx < len(in_lines) - 1 and len(in_lines[idx + 1]) > 2:
# biword = word + in_lines[idx + 1].strip('\t').split()[0].decode('utf-8')
biword = word + in_lines[idx + 1].strip('\t').split()[0]
else:
biword = word + NULLKEY
self.biword_alphabet.add(biword)
self.word_alphabet_size = self.word_alphabet.size()
self.biword_alphabet_size = self.biword_alphabet.size()
self.label_alphabet_size = self.label_alphabet.size()
startS = False
startB = False
for label, _ in self.label_alphabet.iteritems():
if "S-" in label.upper():
startS = True
elif "B-" in label.upper():
startB = True
if startB:
if startS:
self.tagScheme = "BMES"
else:
self.tagScheme = "BIO"
def fix_alphabet(self):
self.word_alphabet.close()
self.biword_alphabet.close()
self.label_alphabet.close()
def build_word_pretrain_emb(self, emb_path):
print("build word pretrain emb...")
self.pretrain_word_embedding, self.word_emb_dim = build_pretrain_embedding(
emb_path, self.word_alphabet, self.word_emb_dim,
self.norm_word_emb)
def build_biword_pretrain_emb(self, emb_path):
print("build biword pretrain emb...")
self.pretrain_biword_embedding, self.biword_emb_dim = build_pretrain_embedding(
emb_path, self.biword_alphabet, self.biword_emb_dim,
self.norm_biword_emb)
def build_word_vec_100(self):
self.pretrain_word_embedding, self.pretrain_biword_embedding = self.get_embedding(
)
self.word_emb_dim, self.biword_emb_dim = 100, 100
# get pre-trained embeddings
def get_embedding(self, size=100):
fname = 'data/wordvec_' + str(size)
print("build pretrain word embedding from: ", fname)
word_init_embedding = np.zeros(shape=[self.word_alphabet.size(), size])
bi_word_init_embedding = np.zeros(
shape=[self.biword_alphabet.size(), size])
pre_trained = gensim.models.KeyedVectors.load(fname, mmap='r')
# pre_trained_vocab = set([unicode(w.decode('utf8')) for w in pre_trained.vocab.keys()])
pre_trained_vocab = set([w for w in pre_trained.vocab.keys()])
c = 0
for word, index in self.word_alphabet.iteritems():
if word in pre_trained_vocab:
word_init_embedding[index] = pre_trained[word]
else:
word_init_embedding[index] = np.random.uniform(-0.5, 0.5, size)
c += 1
for word, index in self.biword_alphabet.iteritems():
bi_word_init_embedding[index] = (
word_init_embedding[self.word_alphabet.get_index(word[0])] +
word_init_embedding[self.word_alphabet.get_index(word[1])]) / 2
# word_init_embedding[word2id[PAD]] = np.zeros(shape=size)
# bi_word_init_embedding[]
print('oov character rate %f' % (float(c) / self.word_alphabet.size()))
return word_init_embedding, bi_word_init_embedding
def generate_instance(self, input_file, name):
self.fix_alphabet()
if name == "train":
self.train_texts, self.train_Ids = read_instance(
input_file, self.word_alphabet, self.biword_alphabet,
self.label_alphabet, self.number_normalized,
self.MAX_SENTENCE_LENGTH)
elif name == "dev":
self.dev_texts, self.dev_Ids = read_instance(
input_file, self.word_alphabet, self.biword_alphabet,
self.label_alphabet, self.number_normalized,
self.MAX_SENTENCE_LENGTH)
elif name == "test":
self.test_texts, self.test_Ids = read_instance(
input_file, self.word_alphabet, self.biword_alphabet,
self.label_alphabet, self.number_normalized,
self.MAX_SENTENCE_LENGTH)
elif name == "raw":
self.raw_texts, self.raw_Ids = read_instance(
input_file, self.word_alphabet, self.biword_alphabet,
self.label_alphabet, self.number_normalized,
self.MAX_SENTENCE_LENGTH)
else:
print(
"Error: you can only generate train/dev/test instance! Illegal input:%s"
% (name))
def write_decoded_results(self, output_file, predict_results, name):
fout = open(output_file, 'w')
sent_num = len(predict_results)
content_list = []
if name == 'raw':
content_list = self.raw_texts
elif name == 'test':
content_list = self.test_texts
elif name == 'dev':
content_list = self.dev_texts
elif name == 'train':
content_list = self.train_texts
else:
print(
"Error: illegal name during writing predict result, name should be within train/dev/test/raw !"
)
assert (sent_num == len(content_list))
for idx in range(sent_num):
sent_length = len(predict_results[idx])
for idy in range(sent_length):
## content_list[idx] is a list with [word, char, label]
fout.write(content_list[idx][0][idy] + "\t" +
predict_results[idx][idy][0] + '\n')
fout.write('\n')
fout.close()
print("Predict %s result has been written into file. %s" %
(name, output_file))
class Data(object):
def __init__(self, args):
super(Data, self).__init__()
self.args = args
self.data_dir = args.data_dir # './data/gene_term_format_by_sentence.json'
self.data_ratio = (0.9, 0.05, 0.05) # total 2000
self.model_save_dir = args.savemodel # './saves/model/'
self.output_dir = args.output # './saves/output/'
self.data_save_file = args.savedset # './saves/data/dat.pkl'
self.pos_as_feature = args.use_pos
self.use_elmo = args.use_elmo
self.elmodim = args.elmodim
self.pos_emb_dim = args.posdim
self.useSpanLen = args.use_len
self.use_sentence_att = args.use_sent_att
self.use_char = True
self.ranking = 1
self.word_alphabet = Alphabet('word')
self.char_alphabet = Alphabet('character')
self.ptag_alphabet = Alphabet('tag')
self.label_alphabet = Alphabet('label', label=True)
self.seqlabel_alphabet = Alphabet('span_label', label=True)
self.word_alphabet_size = 0
self.char_alphabet_size = 0
self.ptag_alphabet_size = 0
self.label_alphabet_size = 0
self.seqlabel_alphabet_size = 0
self.max_sentence_length = 500
self.term_truples = []
self.sent_texts = []
self.chars = []
self.lengths = []
self.ptags = []
self.seq_labels = []
self.word_ids_sent = []
self.char_id_sent = []
self.tag_ids_sent = []
self.label_ids_sent = []
self.seq_labels_ids = []
self.longSpan = True
self.shortSpan = True
self.termratio = args.term_ratio
self.term_span = args.max_length
self.word_feature_extractor = "LSTM" ## "LSTM"/"CNN"/"GRU"/
self.char_feature_extractor = "CNN" ## "LSTM"/"CNN"/"GRU"/None
# training
self.optimizer = 'Adam' # "SGD"/"AdaGrad"/"AdaDelta"/"RMSProp"/"Adam"
self.training = True
self.average_batch_loss = True
self.evaluate_every = args.evaluate_every # 10 # evaluate every n batches
self.print_every = args.print_every
self.silence = True
self.earlystop = args.early_stop
# Embeddings
self.word_emb_dir = args.wordemb # './data/glove.6B.100d.txt' # None #'../data/glove.6b.100d.txt'
self.char_emb_dir = args.charemb
self.word_emb_dim = 50
self.char_emb_dim = 30
self.spamEm_dim = 30
self.norm_word_emb = False
self.norm_char_emb = False
self.pretrain_word_embedding = None
self.pretrain_char_embedding = None
# HP
self.HP_char_hidden_dim = 50
self.HP_hidden_dim = 100
self.HP_cnn_layer = 2
self.HP_batch_size = 100
self.HP_epoch = 100
self.HP_lr = args.lr
self.HP_lr_decay = 0.05
self.HP_clip = None
self.HP_l2 = 1e-8
self.HP_dropout = args.dropout
self.HP_lstm_layer = 2
self.HP_bilstm = True
self.HP_gpu = args.use_gpu # False#True
self.HP_term_span = 6
self.HP_momentum = 0
# init data
self.build_vocabs()
self.all_instances = self.load_data()
self.load_pretrain_emb()
def build_vocabs(self):
''''''
with open(self.data_dir, 'r') as filin:
filelines = filin.readlines()
for lin_id, lin_cnt in enumerate(filelines):
lin_cnt = lin_cnt.strip()
line = json.loads(lin_cnt)
words = line['words']
tags = line['tags']
terms = line['terms']
for word in words:
self.word_alphabet.add(word)
for char in word:
self.char_alphabet.add(char)
for tag in tags:
self.ptag_alphabet.add(tag)
self.sent_texts.append(words)
self.ptags.append(tags)
assert len(words) == len(tags)
self.lengths.append(len(words))
seq_label, termple = self.reformat_label(words, terms)
self.seq_labels.append(seq_label)
if len(terms) > 0:
tmp_terms = []
for itm in termple:
tmp_terms.append([itm[0], itm[1], itm[2]])
self.label_alphabet.add(itm[2])
self.term_truples.append(tmp_terms)
else:
self.term_truples.append([[-1, -1, 'None']])
self.label_alphabet.add('None')
for ter in self.seq_labels:
for ater in ter:
for ate in ater:
self.seqlabel_alphabet.add(ate)
self.word_alphabet_size = self.word_alphabet.size()
self.char_alphabet_size = self.char_alphabet.size()
self.ptag_alphabet_size = self.ptag_alphabet.size()
self.seqlabel_alphabet_size = self.seqlabel_alphabet.size()
self.label_alphabet_size = self.label_alphabet.size()
self.close_alphabet()
def load_pretrain_emb(self):
''''''
if self.word_emb_dir:
print('Loading pretrained Word Embedding from {}'.format(
self.word_emb_dir))
self.pretrain_word_embedding, self.word_emb_dim = build_pretrain_embedding(
self.word_emb_dir, self.word_alphabet, self.word_emb_dim,
self.norm_word_emb)
if self.char_emb_dir:
print('Loading pretrained Char Embedding from {}'.format(
self.char_emb_dir))
self.pretrain_word_embedding, self.char_emb_dim = build_pretrain_embedding(
self.char_emb_dir, self.char_alphabet, self.char_emb_dim,
self.norm_char_emb)
def load_data(self):
''''''
all_instances = []
assert len(self.sent_texts) == len(self.term_truples) == len(
self.ptags)
for sent_text, ptag, term_truple, seqlabel in zip(
self.sent_texts, self.ptags, self.term_truples,
self.seq_labels):
self.word_ids_sent.append(self.word_alphabet.get_indexs(sent_text))
sent_char = []
sent_char_ids = []
for word in sent_text:
char_list = list(word)
sent_char.append(char_list)
char_ids = self.char_alphabet.get_indexs(char_list)
sent_char_ids.append(char_ids)
seqLabel_ids = [
self.seqlabel_alphabet.get_indexs(seqlab)
for seqlab in seqlabel
]
self.seq_labels_ids.append(seqLabel_ids)
self.chars.append(sent_char)
self.char_id_sent.append(sent_char_ids)
self.tag_ids_sent.append(self.ptag_alphabet.get_indexs(ptag))
term_truple = [[
term[0], term[1],
self.label_alphabet.get_index(term[2])
] for term in term_truple]
self.label_ids_sent.append(term_truple)
all_instances.append([
self.word_ids_sent[-1], sent_char_ids, self.tag_ids_sent[-1],
[term_truple, seqLabel_ids], sent_text
])
return all_instances
def reformat_label(self, words, terms):
label = [[] for i in range(len(words))]
termtruple = []
if len(terms) > 0:
for term in terms:
beg = term[0]
end = term[1]
lab_ = term[2]
termtruple.append((beg, end, lab_))
if beg == end:
label[beg].append('S')
continue
label[beg].append('B')
label[end].append('E')
if end - beg > 1:
for itm in range(beg + 1, end):
label[itm].append('I')
for slab in label:
if slab == []:
slab.append('O')
label = [list(set(lab)) for lab in label]
return label, termtruple
def restore(self, data_file):
print('Loading data from %s' % data_file)
with open(data_file, 'rb') as filin:
obj_dict = pkl.load(filin)
self.__dict__.update(obj_dict)
def save(self, save_file):
print('Saving data to %s' % save_file)
with open(save_file, 'wb') as filout:
pkl.dump(self.__dict__, filout, 2)
def close_alphabet(self):
self.word_alphabet.close()
self.ptag_alphabet.close()
self.label_alphabet.close()
self.seqlabel_alphabet.close()
self.char_alphabet.close()
return
class Data(object):
def __init__(self, data_config_file, alphabet_path, if_train=True):
if if_train:
with open(data_config_file, 'r') as rf:
self.data_config = yaml.load(rf, Loader=yaml.FullLoader)
# init data file
mode = self.data_config['mode']
self.data_file = os.path.join(ROOT_PATH,
self.data_config['data'][mode])
# init ac tree
specific_words_file = os.path.join(
ROOT_PATH, self.data_config['specific_words_file'])
self.trees = Trees.build_trees(specific_words_file)
# init alphabet
self.char_alphabet = Alphabet('char')
self.intent_alphabet = Alphabet('intent')
self.label_alphabet = Alphabet('label', label=True)
self.char_alphabet_size, self.intent_alphabet_size, self.label_alphabet_size = -1, -1, -1
# pad length
self.char_max_length = self.data_config['char_max_length']
# read data file
with open(self.data_file, 'r') as rf:
self.corpus = rf.readlines()
self.build_alphabet(alphabet_path)
self.texts, self.ids = self.read_instance()
self.train_texts, self.train_ids, self.dev_texts, self.dev_ids, self.test_texts, self.test_ids = self.sample_split(
)
else: # inference use
self.char_alphabet = Alphabet('char', keep_growing=False)
self.intent_alphabet = Alphabet('intent', keep_growing=False)
self.label_alphabet = Alphabet('label',
label=True,
keep_growing=False)
def build_alphabet(self, alphabet_path):
for line in self.corpus:
line = ast.literal_eval(line)
char, char_label, seg_list, intent = line['char'], line[
'char_label'], line['word'], line['intent']
for word in seg_list:
# lexicon
lexi_feat = []
for lexi_type, lb in self.trees.lexi_trees.items():
lexi_feat.append(lb.search(word))
for n in range(len(lexi_feat)):
if lexi_feat[n] is None or lexi_feat[n] == '_STEM_':
lexi_feat[n] = 0
else:
lexi_feat[n] = 1
lexi_feat = ''.join([str(i) for i in lexi_feat])
# 抽象成一个字符
self.char_alphabet.add(lexi_feat)
# char
for c in char:
self.char_alphabet.add(normalize_word(c))
# intent
self.intent_alphabet.add(intent)
# label
for label in char_label:
self.label_alphabet.add(label)
# alphabet_size
self.char_alphabet_size = self.char_alphabet.size()
self.intent_alphabet_size = self.intent_alphabet.size()
self.label_alphabet_size = self.label_alphabet.size()
# close alphabet
self.fix_alphabet()
# write alphabet:
if not os.path.exists(alphabet_path):
with open(alphabet_path, 'wb') as wbf:
pickle.dump(self.char_alphabet.instance2index, wbf)
pickle.dump(self.intent_alphabet.instance2index, wbf)
pickle.dump(self.label_alphabet.instance2index, wbf)
pickle.dump(self.label_alphabet.instances, wbf)
pickle.dump(self.char_alphabet_size, wbf)
pickle.dump(self.intent_alphabet_size, wbf)
pickle.dump(self.label_alphabet_size, wbf)
def read_instance(self):
"""
这里读取完整读数据，不做截断，functions.py中做截断
:return:
"""
texts, ids = [], []
for idx, line in enumerate(self.corpus):
line = ast.literal_eval(line)
intent_id_list = []
# word：'0010000' -> 合并成一个标签
seq_char, seq_char_id_list, seq_label, seq_label_id_list = [], [], [], []
char, char_label, seg_list, intent = line['char'], line[
'char_label'], line['word'], line['intent']
# 存储one-hot形式的属性特征
lexicons = []
# 记录字符的index
word_indices = []
start = 0
flag = True # 判断跳至上一循环
for word in seg_list:
if flag is True:
end = start + len(word)
lexi_feat = []
for lexi_type, lb in self.trees.lexi_trees.items():
lexi_feat.append(lb.search(word))
for n in range(len(lexi_feat)):
if lexi_feat[n] is None or lexi_feat[n] == '_STEM_':
lexi_feat[n] = 0
else:
lexi_feat[n] = 1
lexi_feat = ''.join([str(i) for i in lexi_feat])
lexicons.append(lexi_feat)
word_indices.append([start, end])
# char
# '0010000'
if '1' in lexi_feat:
seq_char.append(lexi_feat)
seq_char_id_list.append(
self.char_alphabet.get_index(lexi_feat))
# ["B-room", "I-room", "I-room"]
specific_word_label = char_label[start:end]
tmp_label = [
swl.split('-')[-1] for swl in specific_word_label
]
if len(set(tmp_label)) > 1:
# 判断是否过滤该条数据
print('Be filtered: %s' % line['text'], word,
tmp_label)
flag = False
else:
assert len(set(tmp_label)) == 1
if tmp_label[0] == 'O':
tmp_label = 'O'
else:
tmp_label = 'B' + '-' + tmp_label[0]
seq_label += [tmp_label]
seq_label_id_list += [
self.label_alphabet.get_index(tmp_label)
]
# '0000000'
else:
for c in word:
seq_char.append(c)
seq_char_id_list.append(
self.char_alphabet.get_index(
normalize_word(c)))
seq_label += char_label[start:end]
seq_label_id_list += [
self.label_alphabet.get_index(cl)
for cl in char_label[start:end]
]
start = end
else:
break # 跳至下一个corpus
intent_id_list.append(self.intent_alphabet.get_index(intent))
if idx % 10000 == 0:
logger.info('read instance : %s' % idx)
if flag is True:
# text, char, intent, sequence_label
texts.append([line['text'], seq_char, intent, seq_label])
ids.append(
[seq_char_id_list, intent_id_list, seq_label_id_list])
# 新形式的corpus的保存下来,方便查bug
output_path = self.data_config['data']['output']
with open(output_path, 'w') as wf:
for text in texts:
line_data = dict()
line_data['text'] = text[0]
line_data['char'] = text[1]
line_data['intent'] = text[2]
line_data['char_label'] = text[-1]
wf.write(json.dumps(line_data, ensure_ascii=False) + '\n')
return texts, ids
def fix_alphabet(self):
self.char_alphabet.close()
self.intent_alphabet.close()
self.label_alphabet.close()
# data sampling
def sample_split(self):
sampling_rate = self.data_config['sampling_rate']
indexes = list(range(len(self.ids)))
random.shuffle(indexes)
shuffled_texts = [self.texts[i] for i in indexes]
shuffled_ids = [self.ids[i] for i in indexes]
logger.info('Top 10 shuffled indexes: %s' % indexes[:10])
n = int(len(shuffled_ids) * sampling_rate)
dev_texts, dev_ids = shuffled_texts[:n], shuffled_ids[:n]
test_texts, test_ids = shuffled_texts[n:2 * n], shuffled_ids[n:2 * n]
train_texts, train_ids = shuffled_texts[2 * n:], shuffled_ids[2 * n:]
return train_texts, train_ids, dev_texts, dev_ids, test_texts, test_ids