def load_dataset(self, dataset_filepaths, parameters):
'''
dataset_filepaths : dictionary with keys 'train', 'valid', 'test'
'''
start_time = time.time()
pprint('Load dataset... ')
# Load pretraining dataset to ensure that index to label is compatible to the pretrained model,
# and that token embeddings that are learned in the pretrained model are loaded properly.
all_tokens_in_pretraining_dataset = []
if parameters['use_pretrained_model']:
pretrained_model_folder = os.path.dirname(
parameters['pretrained_model_checkpoint_filepath'])
pretraining_dataset = pickle.load(
open(os.path.join(pretrained_model_folder, 'dataset.pickle'),
'rb'))
all_tokens_in_pretraining_dataset = pretraining_dataset.index_to_token.values(
)
self.vocab_embeddings = all_tokens_in_pretraining_dataset
remap_to_unk_count_threshold = 1
self.PADDING_CHARACTER_INDEX = 1
self.PADDING_TOKEN_INDEX = 1
self.UNK_TOKEN_INDEX = 0
self.UNK_CHARACTER_INDEX = 0
self.tokens_mapped_to_unk = []
self.UNK = '<UNK>'
self.PAD = '<PAD>'
self.unique_labels = []
labels = {}
tokens = {}
characters = {}
token_lengths = {}
sequence_lengths = {}
longest_token_length_in_sequence = {}
label_count = {}
token_count = {}
character_count = {}
for dataset_type in ['train', 'valid', 'test']:
labels[dataset_type], tokens[dataset_type], token_count[dataset_type], label_count[dataset_type], character_count[dataset_type] \
= self._parse_dataset(dataset_filepaths.get(dataset_type, None), parameters['language'], parameters['data_to_use'] if 'data_to_use' in parameters else None)
if self.verbose: print("dataset_type: {0}".format(dataset_type))
if self.verbose:
print("len(token_count[dataset_type]): {0}".format(
len(token_count[dataset_type])))
token_count['all'] = {}
for token in list(token_count['train'].keys()) + list(
token_count['valid'].keys()) + list(
token_count['test'].keys()):
token_count['all'][
token] = token_count['train'][token] + token_count['valid'][
token] + token_count['test'][token]
for dataset_type in dataset_filepaths.keys():
if self.verbose: print("dataset_type: {0}".format(dataset_type))
if self.verbose:
print("len(token_count[dataset_type]): {0}".format(
len(token_count[dataset_type])))
character_count['all'] = {}
for character in list(character_count['train'].keys()) + list(
character_count['valid'].keys()) + list(
character_count['test'].keys()):
character_count['all'][character] = character_count['train'][
character] + character_count['valid'][
character] + character_count['test'][character]
label_count['all'] = {}
for character in list(label_count['train'].keys()) + list(
label_count['valid'].keys()) + list(
label_count['test'].keys()):
label_count['all'][
character] = label_count['train'][character] + label_count[
'valid'][character] + label_count['test'][character]
token_count['all'] = utils.order_dictionary(token_count['all'],
'value_key',
reverse=True)
label_count['all'] = utils.order_dictionary(label_count['all'],
'key',
reverse=False)
character_count['all'] = utils.order_dictionary(character_count['all'],
'value',
reverse=True)
if self.verbose:
print('character_count[\'all\']: {0}'.format(
character_count['all']))
token_to_index = {}
token_to_index[self.UNK] = self.UNK_TOKEN_INDEX
token_to_index[self.PAD] = self.PADDING_TOKEN_INDEX
iteration_number = 0
number_of_unknown_tokens = 0
if self.verbose:
print("parameters['remap_unknown_tokens_to_unk']: {0}".format(
parameters['remap_unknown_tokens_to_unk']))
if self.verbose:
print("len(token_count['train'].keys()): {0}".format(
len(token_count['train'].keys())))
for token, count in token_count['all'].items():
if iteration_number == self.UNK_TOKEN_INDEX: iteration_number += 1
if iteration_number == self.PADDING_TOKEN_INDEX:
iteration_number += 1
if parameters['remap_unknown_tokens_to_unk'] == 1 and \
(token_count['train'][token] == 0 or \
parameters['load_only_pretrained_token_embeddings']) and \
not utils_nlp.is_token_in_pretrained_embeddings(token, self.vocab_embeddings, parameters) and \
token not in all_tokens_in_pretraining_dataset:
if self.verbose: print("token: {0}".format(token))
if self.verbose:
print("token.lower(): {0}".format(token.lower()))
if self.verbose:
print("re.sub('\d', '0', token.lower()): {0}".format(
re.sub('\d', '0', token.lower())))
token_to_index[token] = iteration_number
iteration_number += 1
#if parameters['embedding_type'] == 'fasttext':
# token_to_index[token] = iteration_number
# iteration_number += 1
#else:
# token_to_index[token] = self.UNK_TOKEN_INDEX
# number_of_unknown_tokens += 1
# self.tokens_mapped_to_unk.append(token)
else:
token_to_index[token] = iteration_number
iteration_number += 1
if self.verbose:
print("number_of_unknown_tokens: {0}".format(
number_of_unknown_tokens))
infrequent_token_indices = []
for token, count in token_count['train'].items():
if 0 < count <= remap_to_unk_count_threshold:
infrequent_token_indices.append(token_to_index[token])
if self.verbose:
print("len(token_count['train']): {0}".format(
len(token_count['train'])))
if self.verbose:
print("len(infrequent_token_indices): {0}".format(
len(infrequent_token_indices)))
label_count['all'] = utils.order_dictionary(label_count['all'],
'key',
reverse=False)
if parameters['use_pretrained_model']:
self.unique_labels = sorted(
list(pretraining_dataset.label_to_index.keys()))
# Make sure labels are compatible with the pretraining dataset.
for label in label_count['all']:
if label not in pretraining_dataset.label_to_index:
raise AssertionError(
"The label {0} does not exist in the pretraining dataset. "
.format(label) +
"Please ensure that only the following labels exist in the dataset: {0}"
.format(', '.join(self.unique_labels)))
label_to_index = pretraining_dataset.label_to_index.copy()
else:
label_to_index = {}
iteration_number = 0
for label, count in label_count['all'].items():
label_to_index[label] = iteration_number
iteration_number += 1
self.unique_labels.append(label)
self.PADDING_LABEL_INDEX = label_to_index['O']
if self.verbose:
print('self.unique_labels: {0}'.format(self.unique_labels))
character_to_index = {}
character_to_index[self.UNK] = self.UNK_CHARACTER_INDEX
if parameters['use_pretrained_model']:
# TODO: initialize character_to_index from saved pickle
character_to_index = pretraining_dataset.character_to_index.copy()
else:
character_to_index[self.PAD] = self.PADDING_CHARACTER_INDEX
iteration_number = 0
for character, count in character_count['all'].items():
if iteration_number == self.UNK_CHARACTER_INDEX:
iteration_number += 1
if iteration_number == self.PADDING_CHARACTER_INDEX:
iteration_number += 1
character_to_index[character] = iteration_number
iteration_number += 1
if self.verbose:
print('token_count[\'train\'][0:10]: {0}'.format(
list(token_count['train'].items())[0:10]))
token_to_index = utils.order_dictionary(token_to_index,
'value',
reverse=False)
if self.verbose: print('token_to_index: {0}'.format(token_to_index))
index_to_token = utils.reverse_dictionary(token_to_index)
if parameters['remap_unknown_tokens_to_unk'] == 1:
index_to_token[self.UNK_TOKEN_INDEX] = self.UNK
if self.verbose: print('index_to_token: {0}'.format(index_to_token))
if self.verbose:
print('label_count[\'train\']: {0}'.format(label_count['train']))
label_to_index = utils.order_dictionary(label_to_index,
'value',
reverse=False)
if self.verbose: print('label_to_index: {0}'.format(label_to_index))
index_to_label = utils.reverse_dictionary(label_to_index)
if self.verbose: print('index_to_label: {0}'.format(index_to_label))
character_to_index = utils.order_dictionary(character_to_index,
'value',
reverse=False)
index_to_character = utils.reverse_dictionary(character_to_index)
if self.verbose:
print('character_to_index: {0}'.format(character_to_index))
if self.verbose:
print('index_to_character: {0}'.format(index_to_character))
if self.verbose:
print('labels[\'train\'][0:10]: {0}'.format(labels['train'][0:10]))
if self.verbose:
print('tokens[\'train\'][0:10]: {0}'.format(tokens['train'][0:10]))
if self.verbose:
# Print sequences of length 1 in train set
for token_sequence, label_sequence in zip(tokens['train'],
labels['train']):
if len(label_sequence) == 1 and label_sequence[0] != 'O':
print("{0}\t{1}".format(token_sequence[0],
label_sequence[0]))
# Map tokens and labels to their indices
token_indices = {}
label_indices = {}
character_indices = {}
#character_indices_padded = {}
for dataset_type in dataset_filepaths.keys():
token_indices[dataset_type] = []
characters[dataset_type] = []
character_indices[dataset_type] = []
token_lengths[dataset_type] = []
sequence_lengths[dataset_type] = []
longest_token_length_in_sequence[dataset_type] = []
#character_indices_padded[dataset_type] = []
for token_sequence in tokens[dataset_type]:
token_indices[dataset_type].append(
[token_to_index[token] for token in token_sequence])
characters[dataset_type].append(
[list(token) for token in token_sequence])
character_indices[dataset_type].append(
[[character_to_index[character] for character in token]
for token in token_sequence])
token_lengths[dataset_type].append(
[len(token) for token in token_sequence])
sequence_lengths[dataset_type].append(len(token_sequence))
longest_token_length_in_sequence[dataset_type].append(
max(token_lengths[dataset_type][-1]))
#character_indices_padded[dataset_type].append([ utils.pad_list(temp_token_indices, longest_token_length_in_sequence, self.PADDING_CHARACTER_INDEX)
# for temp_token_indices in character_indices[dataset_type][-1]])
label_indices[dataset_type] = []
for label_sequence in labels[dataset_type]:
label_indices[dataset_type].append(
[label_to_index[label] for label in label_sequence])
if self.verbose:
print('token_lengths[\'train\'][0][0:10]: {0}'.format(
token_lengths['train'][0][0:10]))
if self.verbose:
print('characters[\'train\'][0][0:10]: {0}'.format(
characters['train'][0][0:10]))
if self.verbose:
print('token_indices[\'train\'][0:10]: {0}'.format(
token_indices['train'][0:10]))
if self.verbose:
print('label_indices[\'train\'][0:10]: {0}'.format(
label_indices['train'][0:10]))
if self.verbose:
print('character_indices[\'train\'][0][0:10]: {0}'.format(
character_indices['train'][0][0:10]))
#if self.verbose: print('character_indices_padded[\'train\'][0][0:10]: {0}'.format(character_indices_padded['train'][0][0:10]))
label_vector_indices = {}
tmp_vector = [0] * len(self.unique_labels)
tmp_vector[label_to_index["O"]] = 1
self.PADDING_LABEL_VECTOR = tmp_vector
for dataset_type in dataset_filepaths.keys():
label_vector_indices[dataset_type] = []
for label_indices_sequence in label_indices[dataset_type]:
vector_sequence = []
for indice in label_indices_sequence:
vector = [0] * len(self.unique_labels)
vector[indice] = 1
vector_sequence.append(vector)
label_vector_indices[dataset_type].append(vector_sequence)
if self.verbose:
print('label_vector_indices[\'train\'][0:2]: {0}'.format(
label_vector_indices['train'][0:2]))
if self.verbose:
print('len(label_vector_indices[\'train\']): {0}'.format(
len(label_vector_indices['train'])))
self.token_to_index = token_to_index
self.index_to_token = index_to_token
self.token_indices = token_indices
self.label_indices = label_indices
#self.character_indices_padded = character_indices_padded
self.index_to_character = index_to_character
self.character_to_index = character_to_index
self.character_indices = character_indices
self.token_lengths = token_lengths
self.sequence_lengths = sequence_lengths
self.longest_token_length_in_sequence = longest_token_length_in_sequence
self.characters = characters
self.tokens = tokens
self.labels = labels
self.label_vector_indices = label_vector_indices
self.index_to_label = index_to_label
self.label_to_index = label_to_index
if self.verbose:
print("len(self.token_to_index): {0}".format(
len(self.token_to_index)))
if self.verbose:
print("len(self.index_to_token): {0}".format(
len(self.index_to_token)))
self.number_of_classes = len(self.unique_labels)
self.vocabulary_size = len(self.index_to_token) if len(
self.index_to_token) > 100000 else 100000
self.alphabet_size = len(self.character_to_index)
if self.verbose:
print("self.number_of_classes: {0}".format(self.number_of_classes))
if self.verbose:
print("self.alphabet_size: {0}".format(self.alphabet_size))
if self.verbose:
print("self.vocabulary_size: {0}".format(self.vocabulary_size))
# unique_labels_of_interest is used to compute F1-scores.
self.unique_labels_of_interest = list(self.unique_labels)
self.unique_labels_of_interest.remove('O')
self.unique_label_indices_of_interest = []
for lab in self.unique_labels_of_interest:
self.unique_label_indices_of_interest.append(label_to_index[lab])
self.infrequent_token_indices = infrequent_token_indices
if self.verbose:
print('self.unique_labels_of_interest: {0}'.format(
self.unique_labels_of_interest))
if self.verbose:
print('self.unique_label_indices_of_interest: {0}'.format(
self.unique_label_indices_of_interest))
print(self.label_to_index)
elapsed_time = time.time() - start_time
print('done ({0:.2f} seconds)'.format(elapsed_time))
def load_dataset(self,
dataset_filepaths,
parameters,
token_to_vector=None):
'''
dataset_filepaths : dictionary with keys 'train', 'valid', 'test', 'deploy'
'''
start_time = time.time()
print('Load dataset... ', end='', flush=True)
if parameters['token_pretrained_embedding_filepath'] != '':
if token_to_vector == None:
token_to_vector = utils_nlp.load_pretrained_token_embeddings(
parameters)
else:
token_to_vector = {}
if self.verbose:
print("len(token_to_vector): {0}".format(len(token_to_vector)))
# Load pretraining dataset to ensure that index to label is compatible to the pretrained model,
# and that token embeddings that are learned in the pretrained model are loaded properly.
all_tokens_in_pretraining_dataset = []
all_characters_in_pretraining_dataset = []
if parameters['use_pretrained_model']:
pretraining_dataset = pickle.load(
open(
os.path.join(parameters['pretrained_model_folder'],
'dataset.pickle'), 'rb'))
all_tokens_in_pretraining_dataset = pretraining_dataset.index_to_token.values(
)
all_characters_in_pretraining_dataset = pretraining_dataset.index_to_character.values(
)
remap_to_unk_count_threshold = 1
self.UNK_TOKEN_INDEX = 0
self.PADDING_CHARACTER_INDEX = 0
self.tokens_mapped_to_unk = []
self.UNK = 'UNK'
self.unique_labels = []
labels = {}
tokens = {}
label_count = {}
token_count = {}
character_count = {}
for dataset_type in ['train', 'valid', 'test', 'deploy']:
labels[dataset_type], tokens[dataset_type], token_count[dataset_type], label_count[dataset_type], character_count[dataset_type] \
= self._parse_dataset(dataset_filepaths.get(dataset_type, None))
if self.verbose: print("dataset_type: {0}".format(dataset_type))
if self.verbose:
print("len(token_count[dataset_type]): {0}".format(
len(token_count[dataset_type])))
token_count['all'] = {}
for token in list(token_count['train'].keys()) + list(
token_count['valid'].keys()) + list(
token_count['test'].keys()) + list(
token_count['deploy'].keys()):
token_count['all'][token] = token_count['train'][
token] + token_count['valid'][token] + token_count['test'][
token] + token_count['deploy'][token]
if parameters['load_all_pretrained_token_embeddings']:
for token in token_to_vector:
if token not in token_count['all']:
token_count['all'][token] = -1
token_count['train'][token] = -1
for token in all_tokens_in_pretraining_dataset:
if token not in token_count['all']:
token_count['all'][token] = -1
token_count['train'][token] = -1
character_count['all'] = {}
for character in list(character_count['train'].keys()) + list(
character_count['valid'].keys()) + list(
character_count['test'].keys()) + list(
character_count['deploy'].keys()):
character_count['all'][character] = character_count['train'][
character] + character_count['valid'][
character] + character_count['test'][
character] + character_count['deploy'][character]
for character in all_characters_in_pretraining_dataset:
if character not in character_count['all']:
character_count['all'][character] = -1
character_count['train'][character] = -1
for dataset_type in dataset_filepaths.keys():
if self.verbose: print("dataset_type: {0}".format(dataset_type))
if self.verbose:
print("len(token_count[dataset_type]): {0}".format(
len(token_count[dataset_type])))
label_count['all'] = {}
for character in list(label_count['train'].keys()) + list(
label_count['valid'].keys()) + list(
label_count['test'].keys()) + list(
label_count['deploy'].keys()):
label_count['all'][character] = label_count['train'][
character] + label_count['valid'][character] + label_count[
'test'][character] + label_count['deploy'][character]
token_count['all'] = utils.order_dictionary(token_count['all'],
'value_key',
reverse=True)
label_count['all'] = utils.order_dictionary(label_count['all'],
'key',
reverse=False)
character_count['all'] = utils.order_dictionary(character_count['all'],
'value',
reverse=True)
if self.verbose:
print('character_count[\'all\']: {0}'.format(
character_count['all']))
token_to_index = {}
token_to_index[self.UNK] = self.UNK_TOKEN_INDEX
iteration_number = 0
number_of_unknown_tokens = 0
if self.verbose:
print("parameters['remap_unknown_tokens_to_unk']: {0}".format(
parameters['remap_unknown_tokens_to_unk']))
if self.verbose:
print("len(token_count['train'].keys()): {0}".format(
len(token_count['train'].keys())))
for token, count in token_count['all'].items():
if iteration_number == self.UNK_TOKEN_INDEX: iteration_number += 1
if parameters['remap_unknown_tokens_to_unk'] == 1 and \
(token_count['train'][token] == 0 or \
parameters['load_only_pretrained_token_embeddings']) and \
not utils_nlp.is_token_in_pretrained_embeddings(token, token_to_vector, parameters) and \
token not in all_tokens_in_pretraining_dataset:
if self.verbose: print("token: {0}".format(token))
if self.verbose:
print("token.lower(): {0}".format(token.lower()))
if self.verbose:
print("re.sub('\d', '0', token.lower()): {0}".format(
re.sub('\d', '0', token.lower())))
token_to_index[token] = self.UNK_TOKEN_INDEX
number_of_unknown_tokens += 1
self.tokens_mapped_to_unk.append(token)
else:
token_to_index[token] = iteration_number
iteration_number += 1
if self.verbose:
print("number_of_unknown_tokens: {0}".format(
number_of_unknown_tokens))
infrequent_token_indices = []
for token, count in token_count['train'].items():
if 0 < count <= remap_to_unk_count_threshold:
infrequent_token_indices.append(token_to_index[token])
if self.verbose:
print("len(token_count['train']): {0}".format(
len(token_count['train'])))
if self.verbose:
print("len(infrequent_token_indices): {0}".format(
len(infrequent_token_indices)))
# Ensure that both B- and I- versions exist for each label
labels_without_bio = set()
for label in label_count['all'].keys():
new_label = utils_nlp.remove_bio_from_label_name(label)
labels_without_bio.add(new_label)
for label in labels_without_bio:
if label == 'O':
continue
if parameters['tagging_format'] == 'bioes':
prefixes = ['B-', 'I-', 'E-', 'S-']
else:
prefixes = ['B-', 'I-']
for prefix in prefixes:
l = prefix + label
if l not in label_count['all']:
label_count['all'][l] = 0
label_count['all'] = utils.order_dictionary(label_count['all'],
'key',
reverse=False)
if parameters['use_pretrained_model']:
self.unique_labels = sorted(
list(pretraining_dataset.label_to_index.keys()))
# Make sure labels are compatible with the pretraining dataset.
for label in label_count['all']:
if label not in pretraining_dataset.label_to_index:
raise AssertionError(
"The label {0} does not exist in the pretraining dataset. "
.format(label) +
"Please ensure that only the following labels exist in the dataset: {0}"
.format(', '.join(self.unique_labels)))
label_to_index = pretraining_dataset.label_to_index.copy()
else:
label_to_index = {}
iteration_number = 0
for label, count in label_count['all'].items():
label_to_index[label] = iteration_number
iteration_number += 1
self.unique_labels.append(label)
if self.verbose:
print('self.unique_labels: {0}'.format(self.unique_labels))
character_to_index = {}
iteration_number = 0
for character, count in character_count['all'].items():
if iteration_number == self.PADDING_CHARACTER_INDEX:
iteration_number += 1
character_to_index[character] = iteration_number
iteration_number += 1
if self.verbose:
print('token_count[\'train\'][0:10]: {0}'.format(
list(token_count['train'].items())[0:10]))
token_to_index = utils.order_dictionary(token_to_index,
'value',
reverse=False)
if self.verbose: print('token_to_index: {0}'.format(token_to_index))
index_to_token = utils.reverse_dictionary(token_to_index)
if parameters['remap_unknown_tokens_to_unk'] == 1:
index_to_token[self.UNK_TOKEN_INDEX] = self.UNK
if self.verbose: print('index_to_token: {0}'.format(index_to_token))
if self.verbose:
print('label_count[\'train\']: {0}'.format(label_count['train']))
label_to_index = utils.order_dictionary(label_to_index,
'value',
reverse=False)
if self.verbose: print('label_to_index: {0}'.format(label_to_index))
index_to_label = utils.reverse_dictionary(label_to_index)
if self.verbose: print('index_to_label: {0}'.format(index_to_label))
character_to_index = utils.order_dictionary(character_to_index,
'value',
reverse=False)
index_to_character = utils.reverse_dictionary(character_to_index)
if self.verbose:
print('character_to_index: {0}'.format(character_to_index))
if self.verbose:
print('index_to_character: {0}'.format(index_to_character))
if self.verbose:
print('labels[\'train\'][0:10]: {0}'.format(labels['train'][0:10]))
if self.verbose:
print('tokens[\'train\'][0:10]: {0}'.format(tokens['train'][0:10]))
if self.verbose:
# Print sequences of length 1 in train set
for token_sequence, label_sequence in zip(tokens['train'],
labels['train']):
if len(label_sequence) == 1 and label_sequence[0] != 'O':
print("{0}\t{1}".format(token_sequence[0],
label_sequence[0]))
self.token_to_index = token_to_index
self.index_to_token = index_to_token
self.index_to_character = index_to_character
self.character_to_index = character_to_index
self.index_to_label = index_to_label
self.label_to_index = label_to_index
if self.verbose:
print("len(self.token_to_index): {0}".format(
len(self.token_to_index)))
if self.verbose:
print("len(self.index_to_token): {0}".format(
len(self.index_to_token)))
self.tokens = tokens
self.labels = labels
token_indices, label_indices, character_indices_padded, character_indices, token_lengths, characters, label_vector_indices = self._convert_to_indices(
dataset_filepaths.keys())
self.token_indices = token_indices
self.label_indices = label_indices
self.character_indices_padded = character_indices_padded
self.character_indices = character_indices
self.token_lengths = token_lengths
self.characters = characters
self.label_vector_indices = label_vector_indices
self.number_of_classes = max(self.index_to_label.keys()) + 1
self.vocabulary_size = max(self.index_to_token.keys()) + 1
self.alphabet_size = max(self.index_to_character.keys()) + 1
if self.verbose:
print("self.number_of_classes: {0}".format(self.number_of_classes))
if self.verbose:
print("self.alphabet_size: {0}".format(self.alphabet_size))
if self.verbose:
print("self.vocabulary_size: {0}".format(self.vocabulary_size))
# unique_labels_of_interest is used to compute F1-scores.
self.unique_labels_of_interest = list(self.unique_labels)
self.unique_labels_of_interest.remove('O')
self.unique_label_indices_of_interest = []
for lab in self.unique_labels_of_interest:
self.unique_label_indices_of_interest.append(label_to_index[lab])
self.infrequent_token_indices = infrequent_token_indices
if self.verbose:
print('self.unique_labels_of_interest: {0}'.format(
self.unique_labels_of_interest))
if self.verbose:
print('self.unique_label_indices_of_interest: {0}'.format(
self.unique_label_indices_of_interest))
elapsed_time = time.time() - start_time
print('done ({0:.2f} seconds)'.format(elapsed_time))
return token_to_vector
def load_dataset(self, dataset_filepaths, parameters):
'''
dataset_filepaths : dictionary with keys 'train', 'valid', 'test', 'deploy'
'''
start_time = time.time()
print('Load dataset... ', end='', flush=True)
all_pretrained_tokens = []
if parameters['token_pretrained_embedding_filepath'] != '':
all_pretrained_tokens = utils_nlp.load_tokens_from_pretrained_token_embeddings(
parameters)
if self.verbose:
print("len(all_pretrained_tokens): {0}".format(
len(all_pretrained_tokens)))
all_tokens_in_pretraining_dataset = []
if parameters['use_pretrained_model']:
pretraining_dataset = pickle.load(
open(
os.path.join(parameters['pretrained_model_folder'],
'dataset.pickle'), 'rb'))
all_tokens_in_pretraining_dataset = pretraining_dataset.index_to_token.values(
)
remap_to_unk_count_threshold = 1
self.UNK_TOKEN_INDEX = 0
self.PADDING_CHARACTER_INDEX = 0
self.tokens_mapped_to_unk = []
self.UNK = 'UNK'
self.unique_labels = []
labels = {}
tokens = {}
if parameters['use_corrector']:
labels_corrector = {}
label_corrector_count = {}
self.unique_labels_corrector = []
if parameters['include_pos']:
labels_pos = {}
label_pos_count = {}
self.unique_labels_pos = []
characters = {}
token_lengths = {}
label_count = {}
token_count = {}
character_count = {}
for dataset_type in ['train', 'valid', 'test', 'deploy']:
# print("what am i getting?? {:s}".format(str(dataset_filepaths.get(dataset_type, None))))
if parameters['use_corrector']:
if parameters['include_pos']:
labels_pos[dataset_type], labels_corrector[dataset_type], labels[dataset_type], \
tokens[dataset_type], token_count[dataset_type], label_pos_count[dataset_type], \
label_corrector_count[dataset_type], label_count[dataset_type], character_count[dataset_type] \
= self._parse_dataset(dataset_filepaths.get(dataset_type, None),
use_corrector=True, include_pos=True, tagging_format=parameters['tagging_format'])
else:
labels_corrector[dataset_type], labels[dataset_type], tokens[dataset_type], \
token_count[dataset_type], label_corrector_count[dataset_type], label_count[dataset_type], \
character_count[dataset_type] \
= self._parse_dataset(dataset_filepaths.get(dataset_type, None), use_corrector=True,
tagging_format=parameters['tagging_format'])
else:
labels[dataset_type], tokens[dataset_type], token_count[dataset_type], label_count[dataset_type], \
character_count[dataset_type] \
= self._parse_dataset(dataset_filepaths.get(dataset_type, None), tagging_format=parameters['tagging_format'])
if self.verbose:
print("len(token_count[{1}]): {0}".format(
len(token_count[dataset_type]), dataset_type))
# sys.exit(0)
token_count['all'] = {}
for token in list(token_count['train'].keys()) + list(
token_count['valid'].keys()) + list(
token_count['test'].keys()) + list(
token_count['deploy'].keys()):
token_count['all'][token] = token_count['train'][
token] + token_count['valid'][token] + token_count['test'][
token] + token_count['deploy'][token]
if self.verbose:
print("len(token_count[all]): {0}".format(len(token_count['all'])))
for dataset_type in dataset_filepaths.keys():
if self.verbose:
print("len(token_count[{1}]): {0}".format(
len(token_count[dataset_type]), dataset_type))
character_count['all'] = {}
for character in list(character_count['train'].keys()) + list(
character_count['valid'].keys()) + list(
character_count['test'].keys()) + list(
character_count['deploy'].keys()):
character_count['all'][character] = character_count['train'][
character] + character_count['valid'][
character] + character_count['test'][
character] + character_count['deploy'][character]
label_count['all'] = {}
for character in list(label_count['train'].keys()) + list(
label_count['valid'].keys()) + list(
label_count['test'].keys()) + list(
label_count['deploy'].keys()):
label_count['all'][character] = label_count['train'][character] + label_count['valid'][character] + \
label_count['test'][character] + label_count['deploy'][character]
if parameters['use_corrector']:
label_corrector_count['all'] = {}
for label in list(label_corrector_count['train'].keys()) + list(
label_corrector_count['valid'].keys()) + list(
label_corrector_count['test'].keys()) + list(
label_corrector_count['deploy'].keys()):
label_corrector_count['all'][label] = label_corrector_count['train'][label] + label_corrector_count['valid'][label] + \
label_corrector_count['test'][label] + label_corrector_count['deploy'][label]
label_corrector_count['all'] = utils.order_dictionary(
label_corrector_count['all'], 'key', reverse=False)
if parameters['include_pos']:
label_pos_count['all'] = {}
for label in list(label_pos_count['train'].keys()) + list(
label_pos_count['valid'].keys()) + list(
label_pos_count['test'].keys()) + list(
label_pos_count['deploy'].keys()):
label_pos_count['all'][label] = label_pos_count['train'][label] + label_pos_count['valid'][label] + \
label_pos_count['test'][label] + label_pos_count['deploy'][label]
label_pos_count['all'] = utils.order_dictionary(
label_pos_count['all'], 'key', reverse=False)
token_count['all'] = utils.order_dictionary(token_count['all'],
'value_key',
reverse=True)
label_count['all'] = utils.order_dictionary(label_count['all'],
'key',
reverse=False)
character_count['all'] = utils.order_dictionary(character_count['all'],
'value',
reverse=True)
if self.verbose:
print('character_count[\'all\']: {0}'.format(
character_count['all']))
token_to_index = {}
token_to_index[self.UNK] = self.UNK_TOKEN_INDEX
iteration_number = 0
number_of_unknown_tokens = 0
if self.verbose:
print("parameters['remap_unknown_tokens_to_unk']: {0}".format(
parameters['remap_unknown_tokens_to_unk']))
if self.verbose:
print("len(token_count['train'].keys()): {0}".format(
len(token_count['train'].keys())))
for token, count in token_count['all'].items():
if iteration_number == self.UNK_TOKEN_INDEX: iteration_number += 1
if parameters['remap_unknown_tokens_to_unk'] == 1 and \
(token_count['train'][token] == 0 or \
parameters['load_only_pretrained_token_embeddings']) and \
not utils_nlp.is_token_in_pretrained_embeddings(token, all_pretrained_tokens, parameters) and \
token not in all_tokens_in_pretraining_dataset:
token_to_index[token] = self.UNK_TOKEN_INDEX
number_of_unknown_tokens += 1
self.tokens_mapped_to_unk.append(token)
else:
token_to_index[token] = iteration_number
iteration_number += 1
if self.verbose:
print("number_of_unknown_tokens: {0}".format(
number_of_unknown_tokens))
infrequent_token_indices = []
for token, count in token_count['train'].items():
if 0 < count <= remap_to_unk_count_threshold:
infrequent_token_indices.append(token_to_index[token])
if self.verbose:
print("len(token_count['train']): {0}".format(
len(token_count['train'])))
if self.verbose:
print("len(infrequent_token_indices): {0}".format(
len(infrequent_token_indices)))
# Ensure that both B- and I- versions exist for each label
labels_without_bio = set()
for label in label_count['all'].keys():
new_label = utils_nlp.remove_bio_from_label_name(label)
labels_without_bio.add(new_label)
for label in labels_without_bio:
if label == 'O':
continue
if parameters['tagging_format'] == 'bioes':
prefixes = ['B-', 'I-', 'E-', 'S-']
else:
prefixes = ['B-', 'I-']
for prefix in prefixes:
l = prefix + label
if l not in label_count['all']:
label_count['all'][l] = 0
label_count['all'] = utils.order_dictionary(label_count['all'],
'key',
reverse=False)
if parameters['use_pretrained_model'] and not parameters['add_class']:
self.unique_labels = sorted(
list(pretraining_dataset.label_to_index.keys()))
# Make sure labels are compatible with the pretraining dataset.
for label in label_count['all']:
if label not in pretraining_dataset.label_to_index:
raise AssertionError(
"The label {0} does not exist in the pretraining dataset. "
.format(label) +
"Please ensure that only the following labels exist in the dataset: {0}"
.format(', '.join(self.unique_labels)))
label_to_index = pretraining_dataset.label_to_index.copy()
elif parameters['use_pretrained_model'] and parameters['add_class']:
# make sure that the added labels are mapped to the end of the dectionary
print('Adding new label-index pair to label_to_index dictionary')
old_label_to_index = pretraining_dataset.label_to_index.copy()
for label, count in label_count['all'].items():
if label not in old_label_to_index.keys():
old_label_to_index[label] = len(old_label_to_index.keys())
label_to_index = old_label_to_index.copy()
self.unique_labels = list(label_to_index.keys())
else:
label_to_index = {}
iteration_number = 0
for label, count in label_count['all'].items():
label_to_index[label] = iteration_number
iteration_number += 1
self.unique_labels.append(label)
if parameters['use_corrector']:
label_corrector_to_index = {}
self.unique_labels_corrector = list(
label_corrector_count['all'].keys())
for n, label in enumerate(self.unique_labels_corrector):
label_corrector_to_index[label] = n
if parameters['include_pos']:
label_pos_to_index = {}
self.unique_labels_pos = list(label_pos_count['all'].keys())
for n, pos in enumerate(self.unique_labels_pos):
label_pos_to_index[pos] = n
if self.verbose:
print('self.unique_labels: {0}'.format(self.unique_labels))
character_to_index = {}
iteration_number = 0
for character, count in character_count['all'].items():
if iteration_number == self.PADDING_CHARACTER_INDEX:
iteration_number += 1
character_to_index[character] = iteration_number
iteration_number += 1
if self.verbose:
print('token_count[\'train\'][0:10]: {0}'.format(
list(token_count['train'].items())[0:10]))
token_to_index = utils.order_dictionary(token_to_index,
'value',
reverse=False)
index_to_token = utils.reverse_dictionary(token_to_index)
if parameters['remap_unknown_tokens_to_unk'] == 1:
index_to_token[self.UNK_TOKEN_INDEX] = self.UNK
if self.verbose:
print('label_count[\'train\']: {0}'.format(label_count['train']))
label_to_index = utils.order_dictionary(label_to_index,
'value',
reverse=False)
if self.verbose: print('label_to_index: {0}'.format(label_to_index))
index_to_label = utils.reverse_dictionary(label_to_index)
if self.verbose: print('index_to_label: {0}'.format(index_to_label))
if parameters['use_corrector']:
label_corrector_to_index = utils.order_dictionary(
label_corrector_to_index, 'value', reverse=False)
index_to_label_corrector = utils.reverse_dictionary(
label_corrector_to_index)
if parameters['include_pos']:
label_pos_to_index = utils.order_dictionary(label_pos_to_index,
'value',
reverse=False)
index_to_label_pos = utils.reverse_dictionary(
label_pos_to_index)
character_to_index = utils.order_dictionary(character_to_index,
'value',
reverse=False)
index_to_character = utils.reverse_dictionary(character_to_index)
if self.verbose:
print('character_to_index: {0}'.format(character_to_index))
if self.verbose:
print('index_to_character: {0}'.format(index_to_character))
if self.verbose:
print('labels[\'train\'][0:10]: {0}'.format(labels['train'][0:10]))
if self.verbose:
print('tokens[\'train\'][0:10]: {0}'.format(tokens['train'][0:10]))
if self.verbose:
# Print sequences of length 1 in train set
for token_sequence, label_sequence in zip(tokens['train'],
labels['train']):
if len(label_sequence) == 1 and label_sequence[0] != 'O':
print("{0}\t{1}".format(token_sequence[0],
label_sequence[0]))
token_indices = {}
label_indices = {}
if parameters['use_corrector']:
label_indices_corrector = {}
if parameters['include_pos']:
label_indices_pos = {}
character_indices = {}
character_indices_padded = {}
for dataset_type in dataset_filepaths.keys():
# print("dataset_type: {:s}".format(dataset_type))
token_indices[dataset_type] = []
characters[dataset_type] = []
character_indices[dataset_type] = []
token_lengths[dataset_type] = []
character_indices_padded[dataset_type] = []
for token_sequence in tokens[dataset_type]:
token_indices[dataset_type].append(
[token_to_index[token] for token in token_sequence])
characters[dataset_type].append(
[list(token) for token in token_sequence])
character_indices[dataset_type].append(
[[character_to_index[character] for character in token]
for token in token_sequence])
token_lengths[dataset_type].append(
[len(token) for token in token_sequence])
longest_token_length_in_sequence = max(
token_lengths[dataset_type][-1])
character_indices_padded[dataset_type].append([
utils.pad_list(temp_token_indices,
longest_token_length_in_sequence,
self.PADDING_CHARACTER_INDEX) for
temp_token_indices in character_indices[dataset_type][-1]
])
label_indices[dataset_type] = []
for label_sequence in labels[dataset_type]:
label_indices[dataset_type].append(
[label_to_index[label] for label in label_sequence])
if parameters['use_corrector']:
label_indices_corrector[dataset_type] = []
for label_sequence_corrector in labels_corrector[dataset_type]:
label_indices_corrector[dataset_type].append([
label_corrector_to_index[label]
for label in label_sequence_corrector
])
if parameters['include_pos']:
label_indices_pos[dataset_type] = []
for label_sequence_pos in labels_pos[dataset_type]:
label_indices_pos[dataset_type].append([
label_pos_to_index[label]
for label in label_sequence_pos
])
if self.verbose:
print('token_lengths[\'train\'][0][0:10]: {0}'.format(
token_lengths['train'][0][0:10]))
if self.verbose:
print('characters[\'train\'][0][0:10]: {0}'.format(
characters['train'][0][0:10]))
if self.verbose:
print('token_indices[\'train\'][0:10]: {0}'.format(
token_indices['train'][0:10]))
if self.verbose:
print('label_indices[\'train\'][0:10]: {0}'.format(
label_indices['train'][0:10]))
if self.verbose:
print('character_indices[\'train\'][0][0:10]: {0}'.format(
character_indices['train'][0][0:10]))
if self.verbose:
print('character_indices_padded[\'train\'][0][0:10]: {0}'.format(
character_indices_padded['train'][0][0:10]))
label_binarizer = sklearn.preprocessing.LabelBinarizer()
label_binarizer.fit(range(max(index_to_label.keys()) + 1))
label_vector_indices = {}
for dataset_type in dataset_filepaths.keys():
label_vector_indices[dataset_type] = []
for label_indices_sequence in label_indices[dataset_type]:
label_vector_indices[dataset_type].append(
label_binarizer.transform(label_indices_sequence))
if parameters['use_corrector']:
label_binarizer_corrector = sklearn.preprocessing.LabelBinarizer()
label_binarizer_corrector.fit(
range(max(index_to_label_corrector.keys()) + 1))
label_corrector_vector_indices = {}
for dataset_type in dataset_filepaths.keys():
label_corrector_vector_indices[dataset_type] = []
for label_indices_sequence in label_indices_corrector[
dataset_type]:
label_corrector_vector_indices[dataset_type].append(
label_binarizer_corrector.transform(
label_indices_sequence))
if parameters['include_pos']:
label_binarizer_pos = sklearn.preprocessing.LabelBinarizer()
label_binarizer_pos.fit(
range(max(index_to_label_pos.keys()) + 1))
label_pos_vector_indices = {}
for dataset_type in dataset_filepaths.keys():
label_pos_vector_indices[dataset_type] = []
for label_indices_sequence in label_indices_pos[
dataset_type]:
label_pos_vector_indices[dataset_type].append(
label_binarizer_pos.transform(
label_indices_sequence))
if self.verbose:
print('label_vector_indices[\'train\'][0:2]: {0}'.format(
label_vector_indices['train'][0:2]))
if self.verbose:
print('len(label_vector_indices[\'train\']): {0}'.format(
len(label_vector_indices['train'])))
self.token_to_index = token_to_index
self.index_to_token = index_to_token
self.token_indices = token_indices
self.label_indices = label_indices
self.character_indices_padded = character_indices_padded
self.index_to_character = index_to_character
self.character_to_index = character_to_index
self.character_indices = character_indices
self.token_lengths = token_lengths
self.characters = characters
self.tokens = tokens
self.labels = labels
self.label_vector_indices = label_vector_indices
self.index_to_label = index_to_label
self.label_to_index = label_to_index
if parameters['use_corrector']:
self.index_to_label_corrector = index_to_label_corrector
self.label_corrector_to_index = label_corrector_to_index
self.label_indices_corrector = label_indices_corrector
self.label_corrector_vector_indices = label_corrector_vector_indices
if parameters['include_pos']:
self.index_to_label_pos = index_to_label_pos
self.label_pos_to_index = label_pos_to_index
self.label_indices_pos = label_indices_pos
self.label_pos_vector_indices = label_pos_vector_indices
if self.verbose:
print("len(self.token_to_index): {0}".format(
len(self.token_to_index)))
if self.verbose:
print("len(self.index_to_token): {0}".format(
len(self.index_to_token)))
if parameters['add_class'] and parameters[
'tagging_format'] == 'bioes' and len(
self.index_to_label) > 100:
self.number_of_classes = max(self.index_to_label.keys()) + 1 - 8
elif parameters['add_class'] and parameters[
'tagging_format'] == 'bioes':
print('here')
self.number_of_classes = max(self.index_to_label.keys()) + 1 - 4
elif parameters['add_class'] and parameters['tagging_format'] == 'bio':
print('here2')
self.number_of_classes = max(self.index_to_label.keys()) + 1 - 2
else:
self.number_of_classes = max(
self.index_to_label.keys()) + 1 # 1 is for O label
print('max(self.index_to_label.keys()) : {:d}'.format(
max(self.index_to_label.keys())))
print(self.index_to_label.keys())
print(self.number_of_classes)
self.vocabulary_size = max(self.index_to_token.keys()) + 1
self.alphabet_size = max(self.index_to_character.keys()) + 1
if self.verbose:
print("self.number_of_classes: {0}".format(self.number_of_classes))
if self.verbose:
print("self.alphabet_size: {0}".format(self.alphabet_size))
if self.verbose:
print("self.vocabulary_size: {0}".format(self.vocabulary_size))
self.unique_labels_of_interest = list(self.unique_labels)
self.unique_labels_of_interest.remove('O')
self.unique_label_indices_of_interest = []
for lab in self.unique_labels_of_interest:
self.unique_label_indices_of_interest.append(label_to_index[lab])
self.infrequent_token_indices = infrequent_token_indices
if self.verbose:
print('self.unique_labels_of_interest: {0}'.format(
self.unique_labels_of_interest))
if self.verbose:
print('self.unique_label_indices_of_interest: {0}'.format(
self.unique_label_indices_of_interest))
elapsed_time = time.time() - start_time
print('done ({0:.2f} seconds)'.format(elapsed_time))
def load_dataset(self,
dataset_filepaths,
parameters,
token_to_vector=None):
'''
dataset_filepaths : dictionary with keys 'train', 'valid', 'test', 'deploy'
Load word vectors từ file đã chuẩn bị sẵn
'''
start_time = time.time()
print('Load dataset... ', end='', flush=True)
if parameters['token_pretrained_embedding_filepath'] != '':
if token_to_vector == None:
token_to_vector = utils_nlp.load_pretrained_token_embeddings(
parameters)
else:
token_to_vector = {}
if self.verbose:
print("len(token_to_vector): {0}".format(len(token_to_vector)))
# Load pretraining dataset to ensure that index to label is compatible to the pretrained model,
# and that token embeddings that are learned in the pretrained model are loaded properly.
all_tokens_in_pretraining_dataset = []
all_characters_in_pretraining_dataset = []
if parameters['use_pretrained_model']:
pretraining_dataset = pickle.load(
open(
os.path.join(parameters['pretrained_model_folder'],
'dataset.pickle'), 'rb'))
all_tokens_in_pretraining_dataset = pretraining_dataset.index_to_token.values(
) # Những token lưu ở đợt train trước
all_characters_in_pretraining_dataset = pretraining_dataset.index_to_character.values(
) # Những character lưu ở đợt train trước
remap_to_unk_count_threshold = 1
self.UNK_TOKEN_INDEX = 0 # Index của những unknow token
self.PADDING_CHARACTER_INDEX = 0
self.tokens_mapped_to_unk = [] # những unknown token
self.UNK = 'UNK'
self.unique_labels = [] # Các nhãn tồn tại trong dataset
labels = {} # nhãn {all: ...., train: ..., test: ...}
tokens = {} # token {all: ...., train: ..., test: ...}
label_count = {} # Đếm số nhãn {all: ...., train: ..., test: ...}
token_count = {} # Đếm số token {all: ...., train: ..., test: ...}
character_count = {} # Đếm số ký tự {all: ...., train: ..., test: ...}
for dataset_type in ['train', 'valid', 'test', 'deploy']:
labels[dataset_type], tokens[dataset_type], token_count[dataset_type], label_count[dataset_type], character_count[dataset_type] \
= self._parse_dataset(dataset_filepaths.get(dataset_type, None))
if self.verbose: print("dataset_type: {0}".format(dataset_type))
if self.verbose:
print("len(token_count[dataset_type]): {0}".format(
len(token_count[dataset_type])))
# Tính tổng hợp lại cho tất cả các dataset
token_count['all'] = {}
for token in list(token_count['train'].keys()) + list(
token_count['valid'].keys()) + list(
token_count['test'].keys()) + list(
token_count['deploy'].keys()):
token_count['all'][token] = token_count['train'][
token] + token_count['valid'][token] + token_count['test'][
token] + token_count['deploy'][token]
# Thêm những token ở pretrained trước với giá trị -1
if parameters['load_all_pretrained_token_embeddings']:
for token in token_to_vector:
if token not in token_count['all']:
token_count['all'][token] = -1
token_count['train'][token] = -1
for token in all_tokens_in_pretraining_dataset:
if token not in token_count['all']:
token_count['all'][token] = -1
token_count['train'][token] = -1
# Tính tổng hợp lại cho tất cả các dataset
character_count['all'] = {}
for character in list(character_count['train'].keys()) + list(
character_count['valid'].keys()) + list(
character_count['test'].keys()) + list(
character_count['deploy'].keys()):
character_count['all'][character] = character_count['train'][
character] + character_count['valid'][
character] + character_count['test'][
character] + character_count['deploy'][character]
# Thêm những token ở pretrained trước với giá trị -1
for character in all_characters_in_pretraining_dataset:
if character not in character_count['all']:
character_count['all'][character] = -1
character_count['train'][character] = -1
for dataset_type in dataset_filepaths.keys():
if self.verbose: print("dataset_type: {0}".format(dataset_type))
if self.verbose:
print("len(token_count[dataset_type]): {0}".format(
len(token_count[dataset_type])))
# Tính tổng hợp lại các nhãn ở đợt train trước
label_count['all'] = {}
for character in list(label_count['train'].keys()) + list(
label_count['valid'].keys()) + list(
label_count['test'].keys()) + list(
label_count['deploy'].keys()):
label_count['all'][character] = label_count['train'][
character] + label_count['valid'][character] + label_count[
'test'][character] + label_count['deploy'][character]
token_count['all'] = utils.order_dictionary(
token_count['all'], 'value_key', reverse=True
) # Sort token count theo các token có freq cao đến thấp, token desc
label_count['all'] = utils.order_dictionary(
label_count['all'], 'key',
reverse=False) # Sort label count theo label asc
character_count['all'] = utils.order_dictionary(
character_count['all'], 'value', reverse=True
) # Sort character count theo các character có freq cao đến thấp
if self.verbose:
print('character_count[\'all\']: {0}'.format(
character_count['all']))
token_to_index = {}
token_to_index[self.UNK] = self.UNK_TOKEN_INDEX
iteration_number = 0
number_of_unknown_tokens = 0
if self.verbose:
print("parameters['remap_unknown_tokens_to_unk']: {0}".format(
parameters['remap_unknown_tokens_to_unk']))
if self.verbose:
print("len(token_count['train'].keys()): {0}".format(
len(token_count['train'].keys())))
for token, count in token_count['all'].items():
if iteration_number == self.UNK_TOKEN_INDEX: iteration_number += 1
'''
UNK_TOKEN: token không xuất hiện trong pretraining_dataset và trong word vectors
'''
if parameters['remap_unknown_tokens_to_unk'] == 1 and \
(token_count['train'][token] == 0 or \
parameters['load_only_pretrained_token_embeddings']) and \
not utils_nlp.is_token_in_pretrained_embeddings(token, token_to_vector, parameters) and \
token not in all_tokens_in_pretraining_dataset:
if self.verbose: print("token: {0}".format(token))
if self.verbose:
print("token.lower(): {0}".format(token.lower()))
if self.verbose:
print("re.sub('\d', '0', token.lower()): {0}".format(
re.sub('\d', '0', token.lower())))
token_to_index[token] = self.UNK_TOKEN_INDEX
number_of_unknown_tokens += 1
self.tokens_mapped_to_unk.append(token)
else:
token_to_index[token] = iteration_number
iteration_number += 1
if self.verbose:
print("number_of_unknown_tokens: {0}".format(
number_of_unknown_tokens))
infrequent_token_indices = [
] # Các token xuất hiện thấp trong train dataset
for token, count in token_count['train'].items():
if 0 < count <= remap_to_unk_count_threshold:
infrequent_token_indices.append(token_to_index[token])
if self.verbose:
print("len(token_count['train']): {0}".format(
len(token_count['train'])))
if self.verbose:
print("len(infrequent_token_indices): {0}".format(
len(infrequent_token_indices)))
# Ensure that both B- and I- versions exist for each label
# Bỏ các tiền tố B-, O-, I-...
labels_without_bio = set()
for label in label_count['all'].keys():
new_label = utils_nlp.remove_bio_from_label_name(label)
labels_without_bio.add(new_label)
# Kết hợp các ENTITY vs các tiền tố B-, I-,... và thêm vào label count
for label in labels_without_bio:
if label == 'O':
continue
if parameters['tagging_format'] == 'bioes':
prefixes = ['B-', 'I-', 'E-', 'S-']
else:
prefixes = ['B-', 'I-']
for prefix in prefixes:
l = prefix + label
if l not in label_count['all']:
label_count['all'][l] = 0
# Sắp xếp label_count theo label asc
label_count['all'] = utils.order_dictionary(label_count['all'],
'key',
reverse=False)
if parameters['use_pretrained_model']:
self.unique_labels = sorted(
list(pretraining_dataset.label_to_index.keys()))
# Make sure labels are compatible with the pretraining dataset.
for label in label_count['all']:
if label not in pretraining_dataset.label_to_index:
raise AssertionError(
"The label {0} does not exist in the pretraining dataset. "
.format(label) +
"Please ensure that only the following labels exist in the dataset: {0}"
.format(', '.join(self.unique_labels)))
label_to_index = pretraining_dataset.label_to_index.copy()
else:
label_to_index = {}
iteration_number = 0
for label, count in label_count['all'].items():
label_to_index[label] = iteration_number
iteration_number += 1
self.unique_labels.append(label)
if self.verbose:
print('self.unique_labels: {0}'.format(self.unique_labels))
character_to_index = {}
iteration_number = 0
for character, count in character_count['all'].items():
if iteration_number == self.PADDING_CHARACTER_INDEX:
iteration_number += 1
character_to_index[character] = iteration_number
iteration_number += 1
if self.verbose:
print('token_count[\'train\'][0:10]: {0}'.format(
list(token_count['train'].items())[0:10]))
token_to_index = utils.order_dictionary(token_to_index,
'value',
reverse=False)
if self.verbose: print('token_to_index: {0}'.format(token_to_index))
index_to_token = utils.reverse_dictionary(token_to_index)
if parameters['remap_unknown_tokens_to_unk'] == 1:
index_to_token[self.UNK_TOKEN_INDEX] = self.UNK
if self.verbose: print('index_to_token: {0}'.format(index_to_token))
if self.verbose:
print('label_count[\'train\']: {0}'.format(label_count['train']))
label_to_index = utils.order_dictionary(label_to_index,
'value',
reverse=False)
if self.verbose: print('label_to_index: {0}'.format(label_to_index))
index_to_label = utils.reverse_dictionary(label_to_index)
if self.verbose: print('index_to_label: {0}'.format(index_to_label))
character_to_index = utils.order_dictionary(character_to_index,
'value',
reverse=False)
index_to_character = utils.reverse_dictionary(character_to_index)
if self.verbose:
print('character_to_index: {0}'.format(character_to_index))
if self.verbose:
print('index_to_character: {0}'.format(index_to_character))
if self.verbose:
print('labels[\'train\'][0:10]: {0}'.format(labels['train'][0:10]))
if self.verbose:
print('tokens[\'train\'][0:10]: {0}'.format(tokens['train'][0:10]))
if self.verbose:
# Print sequences of length 1 in train set
for token_sequence, label_sequence in zip(tokens['train'],
labels['train']):
if len(label_sequence) == 1 and label_sequence[0] != 'O':
print("{0}\t{1}".format(token_sequence[0],
label_sequence[0]))
self.token_to_index = token_to_index # {token: index sau khi sắp xếp theo freq từ cao đến thấp, 0 nếu là unk token}
self.index_to_token = index_to_token # Ngược token_to_index
self.index_to_character = index_to_character # Ngược character_to_index
self.character_to_index = character_to_index # { character: index sau khi sắp xếp freq từ cao đến thấp}
self.index_to_label = index_to_label # Ngược label_to_index
self.label_to_index = label_to_index # {label: index sau khi sắp xếp asc}
if self.verbose:
print("len(self.token_to_index): {0}".format(
len(self.token_to_index)))
if self.verbose:
print("len(self.index_to_token): {0}".format(
len(self.index_to_token)))
self.tokens = tokens
self.labels = labels
token_indices, label_indices, character_indices_padded, character_indices, token_lengths, characters, label_vector_indices = self._convert_to_indices(
dataset_filepaths.keys())
self.token_indices = token_indices
self.label_indices = label_indices
self.character_indices_padded = character_indices_padded
self.character_indices = character_indices
self.token_lengths = token_lengths
self.characters = characters
self.label_vector_indices = label_vector_indices
self.number_of_classes = max(self.index_to_label.keys()) + 1
self.vocabulary_size = max(self.index_to_token.keys()) + 1
self.alphabet_size = max(self.index_to_character.keys()) + 1
if self.verbose:
print("self.number_of_classes: {0}".format(self.number_of_classes))
if self.verbose:
print("self.alphabet_size: {0}".format(self.alphabet_size))
if self.verbose:
print("self.vocabulary_size: {0}".format(self.vocabulary_size))
# unique_labels_of_interest is used to compute F1-scores.
self.unique_labels_of_interest = list(self.unique_labels)
self.unique_labels_of_interest.remove('O')
self.unique_label_indices_of_interest = []
for lab in self.unique_labels_of_interest:
self.unique_label_indices_of_interest.append(label_to_index[lab])
self.infrequent_token_indices = infrequent_token_indices
if self.verbose:
print('self.unique_labels_of_interest: {0}'.format(
self.unique_labels_of_interest))
if self.verbose:
print('self.unique_label_indices_of_interest: {0}'.format(
self.unique_label_indices_of_interest))
elapsed_time = time.time() - start_time
print('done ({0:.2f} seconds)'.format(elapsed_time))
return token_to_vector
def load_dataset(self, dataset_filepaths, parameters, annotator):
'''
dataset_filepaths : dictionary with keys 'train', 'valid', 'test'
'''
start_time = time.time()
print('Load dataset... ', end='', flush=True)
if parameters['do_split']:
dataset_filepaths = self._do_split(parameters)
all_pretrained_tokens = []
if parameters['token_pretrained_embedding_filepath'] != '':
all_pretrained_tokens = utils_nlp.load_tokens_from_pretrained_token_embeddings(
parameters)
if self.verbose:
print("len(all_pretrained_tokens): {0}".format(
len(all_pretrained_tokens)))
# Load pretraining dataset to ensure that index to label is compatible to the pretrained model,
# and that token embeddings that are learned in the pretrained model are loaded properly.
all_tokens_in_pretraining_dataset = []
self.UNK_TOKEN_INDEX = 0
self.PADDING_TOKEN_INDEX = 1
self.tokens_mapped_to_unk = []
self.UNK = '_UNK_'
self.PAD = '_PAD_'
self.unique_labels = []
labels = {}
tokens = {}
token_count = {}
label_count = {}
self.max_tokens = -1
# Look for max length
for dataset_type in ['train', 'valid', 'test']:
max_tokens = self._find_max_length(
dataset_filepaths.get(dataset_type, None),
annotator,
force_preprocessing=parameters['do_split'])
if parameters['max_length_sentence'] == -1:
self.max_tokens = max(self.max_tokens, max_tokens)
else:
if self.max_tokens == -1:
self.max_tokens = max_tokens
self.max_tokens = min(parameters['max_length_sentence'],
self.max_tokens)
for dataset_type in ['train', 'valid', 'test']:
labels[dataset_type], tokens[dataset_type], token_count[
dataset_type], label_count[dataset_type] = self._parse_dataset(
dataset_filepaths.get(dataset_type, None),
annotator,
force_preprocessing=parameters['do_split'],
limit=self.max_tokens)
if self.verbose: print("dataset_type: {0}".format(dataset_type))
if self.verbose:
print("len(token_count[dataset_type]): {0}".format(
len(token_count[dataset_type])))
token_count['all'] = {}
for token in list(token_count['train'].keys()) + list(
token_count['valid'].keys()) + list(
token_count['test'].keys()):
token_count['all'][token] = token_count['train'].get(
token, 0) + token_count['valid'].get(
token, 0) + token_count['test'].get(token, 0)
for dataset_type in dataset_filepaths.keys():
if self.verbose: print("dataset_type: {0}".format(dataset_type))
if self.verbose:
print("len(token_count[dataset_type]): {0}".format(
len(token_count[dataset_type])))
label_count['all'] = {}
for character in list(label_count['train'].keys()) + list(
label_count['valid'].keys()) + list(
label_count['test'].keys()):
label_count['all'][character] = label_count['train'].get(
character, 0) + label_count['valid'].get(
character, 0) + label_count['test'].get(character, 0)
token_count['all'] = utils.order_dictionary(token_count['all'],
'value_key',
reverse=True)
label_count['all'] = utils.order_dictionary(label_count['all'],
'key',
reverse=False)
token_to_index = {}
token_to_index[self.UNK] = self.UNK_TOKEN_INDEX
token_to_index[self.PAD] = self.PADDING_TOKEN_INDEX
iteration_number = 0
number_of_unknown_tokens = 0
if self.verbose:
print("parameters['remap_unknown_tokens_to_unk']: {0}".format(
parameters['remap_unknown_tokens_to_unk']))
if self.verbose:
print("len(token_count['train'].keys()): {0}".format(
len(token_count['train'].keys())))
for token, count in token_count['all'].items():
if iteration_number == self.UNK_TOKEN_INDEX:
iteration_number += 1
if iteration_number == self.PADDING_TOKEN_INDEX:
iteration_number += 1
if parameters['remap_unknown_tokens_to_unk'] and (
token_count['train'].get(token, 0) == 0
or parameters['load_only_pretrained_token_embeddings']
) and not utils_nlp.is_token_in_pretrained_embeddings(
token, all_pretrained_tokens, parameters
) and token not in all_tokens_in_pretraining_dataset:
if self.verbose: print("token: {0}".format(token))
if self.verbose:
print("token.lower(): {0}".format(token.lower()))
if self.verbose:
print("re.sub('\d', '0', token.lower()): {0}".format(
re.sub('\d', '0', token.lower())))
token_to_index[token] = self.UNK_TOKEN_INDEX
number_of_unknown_tokens += 1
self.tokens_mapped_to_unk.append(token)
else:
token_to_index[token] = iteration_number
iteration_number += 1
if self.verbose:
print("number_of_unknown_tokens: {0}".format(
number_of_unknown_tokens))
infrequent_token_indices = []
for token, count in token_count['train'].items():
if 0 < count <= parameters['remap_to_unk_count_threshold']:
infrequent_token_indices.append(token_to_index[token])
if self.verbose:
print("len(token_count['train']): {0}".format(
len(token_count['train'])))
if self.verbose:
print("len(infrequent_token_indices): {0}".format(
len(infrequent_token_indices)))
label_to_index = {}
iteration_number = 0
for label, count in label_count['all'].items():
label_to_index[label] = iteration_number
iteration_number += 1
self.unique_labels.append(label)
if self.verbose:
print('self.unique_labels: {0}'.format(self.unique_labels))
if self.verbose:
print('token_count[\'train\'][0:10]: {0}'.format(
list(token_count['train'].items())[0:10]))
token_to_index = utils.order_dictionary(token_to_index,
'value',
reverse=False)
if self.verbose: print('token_to_index: {0}'.format(token_to_index))
index_to_token = utils.reverse_dictionary(token_to_index)
if parameters['remap_unknown_tokens_to_unk'] == 1:
index_to_token[self.UNK_TOKEN_INDEX] = self.UNK
index_to_token[self.PADDING_TOKEN_INDEX] = self.PAD
if self.verbose: print('index_to_token: {0}'.format(index_to_token))
if self.verbose:
print('label_count[\'train\']: {0}'.format(label_count['train']))
label_to_index = utils.order_dictionary(label_to_index,
'value',
reverse=False)
if self.verbose: print('label_to_index: {0}'.format(label_to_index))
index_to_label = utils.reverse_dictionary(label_to_index)
if self.verbose: print('index_to_label: {0}'.format(index_to_label))
if self.verbose:
print('labels[\'train\'][0:10]: {0}'.format(labels['train'][0:10]))
if self.verbose:
print('tokens[\'train\'][0:10]: {0}'.format(tokens['train'][0:10]))
# Map tokens and labels to their indices
token_indices = {}
label_indices = {}
token_lengths = {}
token_indices_padded = {}
for dataset_type in dataset_filepaths.keys():
token_indices[dataset_type] = []
token_lengths[dataset_type] = []
token_indices_padded[dataset_type] = []
# Tokens
for token_sequence in tokens[dataset_type]:
token_indices[dataset_type].append(
[token_to_index[token] for token in token_sequence])
token_lengths[dataset_type].append(len(token_sequence))
# Labels
label_indices[dataset_type] = []
for label in labels[dataset_type]:
label_indices[dataset_type].append(label_to_index[label])
# Pad tokens
for dataset_type in dataset_filepaths.keys():
token_indices_padded[dataset_type] = []
token_indices_padded[dataset_type] = [
utils.pad_list(temp_token_indices, self.max_tokens,
self.PADDING_TOKEN_INDEX)
for temp_token_indices in token_indices[dataset_type]
]
if self.verbose:
print('token_lengths[\'train\'][0:10]: {0}'.format(
token_lengths['train'][0:10]))
if self.verbose:
print('token_indices[\'train\'][0][0:10]: {0}'.format(
token_indices['train'][0][0:10]))
if self.verbose:
print('token_indices_padded[\'train\'][0][0:10]: {0}'.format(
token_indices_padded['train'][0][0:10]))
if self.verbose:
print('label_indices[\'train\'][0:10]: {0}'.format(
label_indices['train'][0:10]))
self.token_to_index = token_to_index
self.index_to_token = index_to_token
self.token_indices = token_indices
self.label_indices = label_indices
self.token_indices_padded = token_indices_padded
self.token_lengths = token_lengths
self.tokens = tokens
self.labels = labels
self.index_to_label = index_to_label
self.label_to_index = label_to_index
if self.verbose:
print("len(self.token_to_index): {0}".format(
len(self.token_to_index)))
if self.verbose:
print("len(self.index_to_token): {0}".format(
len(self.index_to_token)))
self.number_of_classes = max(self.index_to_label.keys()) + 1
self.vocabulary_size = max(self.index_to_token.keys()) + 1
if self.verbose:
print("self.number_of_classes: {0}".format(self.number_of_classes))
if self.verbose:
print("self.vocabulary_size: {0}".format(self.vocabulary_size))
self.infrequent_token_indices = infrequent_token_indices
# Binarize label
label_vector_indices = {}
for dataset_type, labels in label_indices.items():
label_vector_indices[dataset_type] = []
for label in labels:
label_vector_indices[dataset_type].append(
utils.convert_one_hot(label, self.number_of_classes))
self.label_vector_indices = label_vector_indices
elapsed_time = time.time() - start_time
print('done ({0:.2f} seconds)'.format(elapsed_time))
def main(args):
experiments = utils.load_experiments()
#parameters, conf_parameters = load_parameters2()
#if args.file:
# parameters['predict_text'] = args.file
#parameters = process_input(parameters)
#if not parameters["use_pretrained_model"]:
# raise IOError('Set use_pretrained_model parameter to True if you want to predict')
#dataset_filepaths = get_valid_dataset_filepaths(parameters)
#check_parameter_compatiblity(parameters, dataset_filepaths)
pprint(experiments)
time_stamp = utils.get_current_time_in_miliseconds()
result_file = '{0}_{1}'.format(args.experiment_set + "_" + "results_",
time_stamp + ".txt")
print(result_file)
with open(os.path.join("../predictions",result_file), "w", encoding="utf-8") as file:
for elem in experiments['experiments'][args.experiment_set]:
trained_model = elem[0]
test = elem[1]
print("======================")
print("Train on {0}, test {1}".format(trained_model,test))
print("======================")
pretrained_model_folder = os.path.dirname(experiments['models'][trained_model])
dataset = pickle.load(open(os.path.join(pretrained_model_folder, 'dataset.pickle'), 'rb'))
parameters, conf_parameters = load_parameters(os.path.join(pretrained_model_folder, 'parameters.ini'), verbose=False)
parameters['train_model'] = False
parameters['use_pretrained_model'] = True
parameters['dataset_predict'] = experiments['datasets'][test]
parameters['pretrained_model_name'] = "{0}_on_{1}".format(trained_model,test)
parameters['pretrained_model_checkpoint_filepath'] = experiments['models'][trained_model]
dataset_filepaths = get_valid_dataset_filepaths(parameters)
pprint(parameters)
#sys.exit()
#if args.file:
# parameters['predict_text'] = args.file
#parameters = process_input(parameters)
# Load dataset
#dataset = ds.Dataset(verbose=parameters['verbose'], debug=parameters['debug'])
#dataset.load_vocab_word_embeddings(parameters)
#pretrained_model_folder = os.path.dirname(parameters['pretrained_model_checkpoint_filepath'])
#dataset = pickle.load(open(os.path.join(pretrained_model_folder, 'dataset.pickle'), 'rb'))
#dataset.load_dataset(dataset_filepaths, parameters)
dataset_type = "predict"
dataset.labels[dataset_type], dataset.tokens[dataset_type], _, _, _ = dataset._parse_dataset(dataset_filepaths.get(dataset_type, None), parameters['language'])
#dataset.load_vocab_word_embeddings(parameters)
iteration_number = 0
dataset.token_to_index = dict()
dataset.number_of_unknown_tokens = 0
for token_sentence in tqdm(dataset.tokens['predict']):
for token in token_sentence:
if iteration_number == dataset.UNK_TOKEN_INDEX: iteration_number += 1
if iteration_number == dataset.PADDING_TOKEN_INDEX: iteration_number += 1
if token == "CD":
a=1
if not utils_nlp.is_token_in_pretrained_embeddings(token, dataset.vocab_embeddings, parameters):
if parameters['embedding_type'] == 'fasttext':
dataset.token_to_index[token] = iteration_number
iteration_number += 1
else:
dataset.token_to_index[token] = dataset.UNK_TOKEN_INDEX
dataset.number_of_unknown_tokens += 1
dataset.tokens_mapped_to_unk.append(token)
else:
if token not in dataset.token_to_index:
dataset.token_to_index[token] = iteration_number
iteration_number += 1
dataset_type = "predict"
for dataset_type in dataset_filepaths.keys():
dataset.token_indices[dataset_type] = []
dataset.characters[dataset_type] = []
dataset.character_indices[dataset_type] = []
dataset.token_lengths[dataset_type] = []
dataset.sequence_lengths[dataset_type] = []
dataset.longest_token_length_in_sequence[dataset_type] = []
# character_indices_padded[dataset_type] = []
for token_sequence in dataset.tokens[dataset_type]:
dataset.token_indices[dataset_type].append([dataset.token_to_index.get(token, dataset.UNK_TOKEN_INDEX) for token in token_sequence])
dataset.characters[dataset_type].append([list(token) for token in token_sequence])
dataset.character_indices[dataset_type].append(
[[dataset.character_to_index.get(character,dataset.UNK_CHARACTER_INDEX) for character in token] for token in token_sequence])
dataset.token_lengths[dataset_type].append([len(token) for token in token_sequence])
dataset.sequence_lengths[dataset_type].append(len(token_sequence))
dataset.longest_token_length_in_sequence[dataset_type].append(max(dataset.token_lengths[dataset_type][-1]))
# character_indices_padded[dataset_type].append([ utils.pad_list(temp_token_indices, longest_token_length_in_sequence, self.PADDING_CHARACTER_INDEX)
# for temp_token_indices in character_indices[dataset_type][-1]])
dataset.label_indices[dataset_type] = []
for label_sequence in dataset.labels[dataset_type]:
dataset.label_indices[dataset_type].append([dataset.label_to_index[label] for label in label_sequence])
tmp_vector = [0] * len(dataset.unique_labels)
tmp_vector[dataset.label_to_index["O"]] = 1
dataset.PADDING_LABEL_VECTOR = tmp_vector
for dataset_type in dataset_filepaths.keys():
dataset.label_vector_indices[dataset_type] = []
for label_indices_sequence in dataset.label_indices[dataset_type]:
vector_sequence = []
for indice in label_indices_sequence:
vector = [0] * len(dataset.unique_labels)
vector[indice] = 1
vector_sequence.append(vector)
dataset.label_vector_indices[dataset_type].append(vector_sequence)
# Create graph and session
with tf.Graph().as_default():
session_conf = tf.ConfigProto(
intra_op_parallelism_threads=parameters['number_of_cpu_threads'],
inter_op_parallelism_threads=parameters['number_of_cpu_threads'],
device_count={'CPU': 1, 'GPU': parameters['number_of_gpus']},
allow_soft_placement=True,
# automatically choose an existing and supported device to run the operations in case the specified one doesn't exist
log_device_placement=False,
)
session_conf.gpu_options.allow_growth = True
sess = tf.Session(config=session_conf)
model = EntityLSTM(dataset, parameters)
model_saver = tf.train.Saver()
prediction_folder = os.path.join('..', 'predictions')
utils.create_folder_if_not_exists(prediction_folder)
dataset_name = parameters['pretrained_model_name']
model_name = '{0}_{1}'.format(dataset_name,
time_stamp)
prediction_folder = os.path.join(prediction_folder, model_name)
utils.create_folder_if_not_exists(prediction_folder)
epoch_number = 100
#dataset_name = utils.get_basename_without_extension(parameters['dataset_test'])
with open(os.path.join(prediction_folder, 'parameters.ini'), 'w') as parameters_file:
conf_parameters.write(parameters_file)
if parameters['use_pretrained_model']:
# Restore pretrained model parameters
transition_params_trained = train.restore_model_parameters_from_pretrained_model(parameters, dataset, sess, model, model_saver)
model.load_pretrained_token_embeddings(sess, dataset, parameters)
start_time = time.time()
results = {}
results['epoch'] = {}
results['execution_details'] = {}
results['execution_details']['train_start'] = start_time
results['execution_details']['time_stamp'] = start_time
results['execution_details']['early_stop'] = False
results['execution_details']['keyboard_interrupt'] = False
results['execution_details']['num_epochs'] = epoch_number
results['model_options'] = copy.copy(parameters)
demo = parameters['pretrained_model_name'] == "demo"
y_pred, y_true, output_filepaths = train.predict_labels(sess, model, transition_params_trained, parameters, dataset, epoch_number, prediction_folder, dataset_filepaths, demo=demo)
conll_output_file = evaluate.evaluate_model(results, dataset, y_pred, y_true, prediction_folder, epoch_number,
start_time , output_filepaths, parameters)
file.write(parameters['pretrained_model_name'] + "\n")
with open(conll_output_file, "r") as conll_file:
conll = conll_file.read()
file.write(conll)
file.write("\n\n\n")
if parameters['pretrained_model_name'] == "demo":
print("============")
print(" Prediction ")
print("============")
i = 0
for sentence in dataset.tokens['predict']:
for token in sentence:
predict_label = dataset.index_to_label[y_pred['predict'][i]]
if dataset.index_to_label[y_pred['predict'][i]] != "O":
print(token,predict_label)
else:
print(token)
i += 1
print("")
else:
raise IOError('Set use_pretrained_model parameter to True')
def main(args):
parameters, conf_parameters = load_parameters()
if args.file:
parameters['predict_text'] = args.file
parameters = process_input(parameters)
dataset_filepaths = get_valid_dataset_filepaths(parameters)
# Load dataset
dataset = ds.Dataset(verbose=parameters['verbose'], debug=parameters['debug'])
dataset.load_vocab_word_embeddings(parameters)
pretrained_model_folder = os.path.dirname(parameters['pretrained_model_checkpoint_filepath'])
dataset = pickle.load(open(os.path.join(pretrained_model_folder, 'dataset.pickle'), 'rb'))
dataset.load_dataset(dataset_filepaths, parameters)
dataset_type = "predict"
dataset.labels[dataset_type], dataset.tokens[dataset_type], _, _, _ = dataset._parse_dataset(dataset_filepaths.get(dataset_type, None), parameters['language'])
#dataset.load_vocab_word_embeddings(parameters)
iteration_number = 0
dataset.token_to_index = dict()
for token_sentence in dataset.tokens['predict']:
for token in token_sentence:
if iteration_number == dataset.UNK_TOKEN_INDEX: iteration_number += 1
if iteration_number == dataset.PADDING_TOKEN_INDEX: iteration_number += 1
if not utils_nlp.is_token_in_pretrained_embeddings(token, dataset.vocab_embeddings, parameters):
if parameters['embedding_type'] == 'glove':
dataset.token_to_index[token] = dataset.UNK_TOKEN_INDEX
dataset.number_of_unknown_tokens += 1
dataset.tokens_mapped_to_unk.append(token)
elif parameters['embedding_type'] == 'fasttext':
dataset.token_to_index[token] = iteration_number
iteration_number += 1
else:
raise AssertionError("Embedding type not recognized")
else:
if token not in dataset.token_to_index:
dataset.token_to_index[token] = iteration_number
iteration_number += 1
dataset_type = "predict"
for dataset_type in dataset_filepaths.keys():
dataset.token_indices[dataset_type] = []
dataset.characters[dataset_type] = []
dataset.character_indices[dataset_type] = []
dataset.token_lengths[dataset_type] = []
dataset.sequence_lengths[dataset_type] = []
dataset.longest_token_length_in_sequence[dataset_type] = []
# character_indices_padded[dataset_type] = []
for token_sequence in dataset.tokens[dataset_type]:
dataset.token_indices[dataset_type].append([dataset.token_to_index.get(token, dataset.UNK_TOKEN_INDEX) for token in token_sequence])
dataset.characters[dataset_type].append([list(token) for token in token_sequence])
dataset.character_indices[dataset_type].append(
[[dataset.character_to_index.get(character,dataset.UNK_CHARACTER_INDEX) for character in token] for token in token_sequence])
dataset.token_lengths[dataset_type].append([len(token) for token in token_sequence])
dataset.sequence_lengths[dataset_type].append(len(token_sequence))
dataset.longest_token_length_in_sequence[dataset_type].append(max(dataset.token_lengths[dataset_type][-1]))
# character_indices_padded[dataset_type].append([ utils.pad_list(temp_token_indices, longest_token_length_in_sequence, self.PADDING_CHARACTER_INDEX)
# for temp_token_indices in character_indices[dataset_type][-1]])
dataset.label_indices[dataset_type] = []
for label_sequence in dataset.labels[dataset_type]:
dataset.label_indices[dataset_type].append([dataset.label_to_index[label] for label in label_sequence])
tmp_vector = [0] * len(dataset.unique_labels)
tmp_vector[dataset.label_to_index["O"]] = 1
dataset.PADDING_LABEL_VECTOR = tmp_vector
for dataset_type in dataset_filepaths.keys():
dataset.label_vector_indices[dataset_type] = []
for label_indices_sequence in dataset.label_indices[dataset_type]:
vector_sequence = []
for indice in label_indices_sequence:
vector = [0] * len(dataset.unique_labels)
vector[indice] = 1
vector_sequence.append(vector)
dataset.label_vector_indices[dataset_type].append(vector_sequence)
# Create graph and session
with tf.Graph().as_default():
session_conf = tf.ConfigProto(
intra_op_parallelism_threads=parameters['number_of_cpu_threads'],
inter_op_parallelism_threads=parameters['number_of_cpu_threads'],
device_count={'CPU': 1, 'GPU': parameters['number_of_gpus']},
allow_soft_placement=True,
# automatically choose an existing and supported device to run the operations in case the specified one doesn't exist
log_device_placement=False,
)
session_conf.gpu_options.allow_growth = True
sess = tf.Session(config=session_conf)
model = EntityLSTM(dataset, parameters)
model_saver = tf.train.Saver()
prediction_folder = os.path.join('..', 'predictions')
utils.create_folder_if_not_exists(prediction_folder)
dataset_name = parameters['pretrained_model_name']
model_name = '{0}_{1}'.format(parameters["language"] + "_" + dataset_name,
utils.get_current_time_in_miliseconds())
prediction_folder = os.path.join(prediction_folder, model_name)
utils.create_folder_if_not_exists(prediction_folder)
epoch_number = 100
#dataset_name = utils.get_basename_without_extension(parameters['dataset_test'])
with open(os.path.join(prediction_folder, 'parameters.ini'), 'w') as parameters_file:
conf_parameters.write(parameters_file)
if parameters['use_pretrained_model']:
# Restore pretrained model parameters
transition_params_trained = train.restore_model_parameters_from_pretrained_model(parameters, dataset, sess, model, model_saver)
model.load_pretrained_token_embeddings(sess, dataset, parameters)
demo = parameters['pretrained_model_name'] == "demo"
y_pred, y_true, output_filepaths = train.predict_labels(sess, model, transition_params_trained, parameters, dataset, epoch_number, prediction_folder, dataset_filepaths, demo=demo)
if parameters['pretrained_model_name'] == "demo":
print("============")
print(" Prediction ")
print("============")
i = 0
for sentence in dataset.tokens['predict']:
for token in sentence:
predict_label = dataset.index_to_label[y_pred['predict'][i]]
if dataset.index_to_label[y_pred['predict'][i]] != "O":
print(token,predict_label)
else:
print(token)
i += 1
print("")
else:
raise IOError('Set use_pretrained_model parameter to True')
