def _load_train_lines(corpus_name=TRAIN_CORPUS_NAME):
processed_corpus_path = get_processed_corpus_path(corpus_name)
dialogs = load_processed_dialogs_from_json(
FileTextLinesIterator(processed_corpus_path), text_field_name='text', condition_field_name='condition')
train_lines, _ = get_dialog_lines_and_conditions(
get_alternated_dialogs_lines(dialogs), text_field_name='text', condition_field_name='condition')
return train_lines
def load_context_sensitive_val(token_to_index, condition_to_index):
processed_val_corpus_path = get_processed_corpus_path(
CONTEXT_SENSITIVE_VAL_CORPUS_NAME)
context_sensitive_val_dialogs = load_processed_dialogs_from_json(
FileTextLinesIterator(processed_val_corpus_path),
text_field_name='text',
condition_field_name='condition')
alternated_context_sensitive_val_dialogs = \
get_alternated_dialogs_lines(context_sensitive_val_dialogs)
alternated_context_sensitive_val_lines, alternated_context_sensitive_val_conditions = \
get_dialog_lines_and_conditions(alternated_context_sensitive_val_dialogs,
text_field_name='text', condition_field_name='condition')
tokenized_alternated_context_sensitive_val_lines = ProcessedLinesIterator(
alternated_context_sensitive_val_lines,
processing_callbacks=[get_tokens_sequence])
_logger.info(
'Transform context sensitive validation lines to tensor of indexes')
x_context_sensitive_val, y_context_sensitive_val, num_context_sensitive_val_dialogs = \
transform_lines_to_nn_input(tokenized_alternated_context_sensitive_val_lines, token_to_index)
condition_ids_context_sensitive_val = transform_conditions_to_nn_input(
alternated_context_sensitive_val_conditions, condition_to_index,
num_context_sensitive_val_dialogs)
return Dataset(x=x_context_sensitive_val,
y=y_context_sensitive_val,
condition_ids=condition_ids_context_sensitive_val)
def _load_train_lines(corpus_name=TRAIN_CORPUS_NAME):
processed_corpus_path = get_processed_corpus_path(corpus_name)
dialogs = load_processed_dialogs_from_json(
FileTextLinesIterator(processed_corpus_path), text_field_name='text', condition_field_name='condition')
train_lines, _ = get_dialog_lines_and_conditions(
get_alternated_dialogs_lines(dialogs), text_field_name='text', condition_field_name='condition')
return train_lines
def load_conditioned_dataset(corpus_name,
token_to_index,
condition_to_index,
subset_size=None):
processed_corpus_path = get_processed_corpus_path(corpus_name)
dialogs = load_processed_dialogs_from_json(
FileTextLinesIterator(processed_corpus_path),
text_field_name='text',
condition_field_name='condition')
if subset_size:
_logger.info(
'Slicing dataset to the first {} entries'.format(subset_size))
dialogs = islice(dialogs, subset_size)
train_lines, train_conditions = get_dialog_lines_and_conditions(
get_alternated_dialogs_lines(dialogs),
text_field_name='text',
condition_field_name='condition')
tokenized_alternated_train_lines = ProcessedLinesIterator(
train_lines, processing_callbacks=[get_tokens_sequence])
# prepare train set
x_train, y_train, n_dialogs = transform_lines_to_nn_input(
tokenized_alternated_train_lines, token_to_index)
condition_ids_train = transform_conditions_to_nn_input(
train_conditions, condition_to_index, n_dialogs)
return Dataset(x=x_train, y=y_train, condition_ids=condition_ids_train)
def build_index_mappings(corpus_path, max_tokens_num=MAX_TOKENS_NUM, max_conditions_num=MAX_CONDITIONS_NUM):
if not is_non_empty_file(corpus_path):
raise ValueError('Test corpus file doesn\'t exist: {}'.format(corpus_path))
dialogs = load_processed_dialogs_from_json(
FileTextLinesIterator(corpus_path), text_field_name=TEXT_FIELD_NAME, condition_field_name=CONDITION_FIELD_NAME)
tokens_counter = Counter()
conditions_counter = Counter()
for dialog in dialogs:
for utterance in dialog:
# Tokenize dialog utterance text and update tokens count
tokens = get_tokens_sequence(utterance[TEXT_FIELD_NAME])
tokens_counter += Counter(tokens)
# Update conditions count
conditions_counter[utterance[CONDITION_FIELD_NAME]] += 1
# Build the tokens list
vocab = list(SPECIAL_TOKENS) + \
[token for token, _ in tokens_counter.most_common(max_tokens_num - len(SPECIAL_TOKENS))]
# Build the conditions list
conditions = [condition for condition, _ in conditions_counter.most_common(max_conditions_num)]
# Validate the condition list
if DEFAULT_CONDITION not in conditions:
raise Exception('No default condition "%s" found in the dataset condition list.' % DEFAULT_CONDITION)
# Return index_to_token and index_to_condition mappings
return dict(enumerate(vocab)), dict(enumerate(conditions))
def build_index_mappings(corpus_path, max_tokens_num=VOCABULARY_MAX_SIZE, max_conditions_num=MAX_CONDITIONS_NUM,
simple_tokenize=SIMPLE_TOKENIZE):
if not is_non_empty_file(corpus_path):
raise ValueError('Test corpus file doesn\'t exist: {}'.format(corpus_path))
dialogs = load_processed_dialogs_from_json(
FileTextLinesIterator(corpus_path), text_field_name=TEXT_FIELD_NAME, condition_field_name=CONDITION_FIELD_NAME)
tokens_counter = Counter()
conditions_counter = Counter()
for dialog in tqdm(dialogs):
for utterance in dialog:
tokens = utterance[TEXT_FIELD_NAME].split() if simple_tokenize else \
get_tokens_sequence(utterance[TEXT_FIELD_NAME])
tokens_counter.update(tokens)
conditions_counter[utterance[CONDITION_FIELD_NAME]] += 1
# Build the tokens list
vocab = list(SPECIAL_TOKENS) + \
[token for token, _ in tokens_counter.most_common(max_tokens_num - len(SPECIAL_TOKENS))]
# Build the conditions list
conditions = [condition for condition, _ in conditions_counter.most_common(max_conditions_num)]
# Validate the condition list
if DEFAULT_CONDITION not in conditions:
raise Exception('No default condition "{}" found in the dataset condition list.'.format(DEFAULT_CONDITION))
# Return index_to_token and index_to_condition mappings
return dict(enumerate(vocab)), dict(enumerate(conditions))
def load_conditioned_train_set(token_to_index, condition_to_index, train_subset_size=TRAIN_SUBSET_SIZE):
processed_corpus_path = get_processed_corpus_path(TRAIN_CORPUS_NAME)
dialogs = load_processed_dialogs_from_json(
FileTextLinesIterator(processed_corpus_path), text_field_name='text', condition_field_name='condition')
if train_subset_size:
dialogs = islice(dialogs, train_subset_size)
train_lines, train_conditions = get_dialog_lines_and_conditions(
get_alternated_dialogs_lines(dialogs), text_field_name='text', condition_field_name='condition')
tokenized_alternated_train_lines = ProcessedLinesIterator(train_lines, processing_callbacks=[get_tokens_sequence])
# prepare train set
x_train, y_train, n_dialogs = transform_lines_to_nn_input(tokenized_alternated_train_lines, token_to_index)
condition_ids_train = transform_conditions_to_nn_input(train_conditions, condition_to_index, n_dialogs)
return Dataset(x=x_train, y=y_train, condition_ids=condition_ids_train)
def _get_w2v_embedding_matrix_by_corpus_path(processed_train_corpus_path, index_to_token):
if USE_PRETRAINED_W2V_EMBEDDINGS_LAYER:
_logger.info('Getting train iterator for w2v...')
dialogs_for_w2v = load_processed_dialogs_from_json(
FileTextLinesIterator(processed_train_corpus_path),
text_field_name='text',
condition_field_name='condition')
_logger.info('Getting text-filtered train iterator...')
train_lines_for_w2v = imap(lambda x: x['text'], get_flatten_dialogs(dialogs_for_w2v))
_logger.info('Getting tokenized train iterator...')
tokenized_train_lines_for_w2v = ProcessedLinesIterator(
train_lines_for_w2v, processing_callbacks=[get_tokens_sequence])
return get_w2v_embedding_matrix(tokenized_train_lines_for_w2v, index_to_token, add_start_end=True)
else:
return None
def build_index_mappings(corpus_path,
max_tokens_num=MAX_TOKENS_NUM,
max_conditions_num=MAX_CONDITIONS_NUM):
if not is_non_empty_file(corpus_path):
raise ValueError(
'Test corpus file doesn\'t exist: {}'.format(corpus_path))
dialogs = load_processed_dialogs_from_json(
FileTextLinesIterator(corpus_path),
text_field_name=TEXT_FIELD_NAME,
condition_field_name=CONDITION_FIELD_NAME)
tokens_counter = Counter()
conditions_counter = Counter()
for dialog in dialogs:
for utterance in dialog:
# Tokenize dialog utterance text and update tokens count
tokens = get_tokens_sequence(utterance[TEXT_FIELD_NAME])
tokens_counter += Counter(tokens)
# Update conditions count
conditions_counter[utterance[CONDITION_FIELD_NAME]] += 1
# Build the tokens list
vocab = list(SPECIAL_TOKENS) + \
[token for token, _ in tokens_counter.most_common(max_tokens_num - len(SPECIAL_TOKENS))]
# Build the conditions list
conditions = [
condition
for condition, _ in conditions_counter.most_common(max_conditions_num)
]
# Validate the condition list
if DEFAULT_CONDITION not in conditions:
raise Exception(
'No default condition "%s" found in the dataset condition list.' %
DEFAULT_CONDITION)
# Return index_to_token and index_to_condition mappings
return dict(enumerate(vocab)), dict(enumerate(conditions))
import os
import sys
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
from cakechat.utils.text_processing import get_processed_corpus_path, load_processed_dialogs_from_json, \
FileTextLinesIterator, get_dialog_lines_and_conditions, ProcessedLinesIterator, get_flatten_dialogs
from cakechat.utils.w2v.model import _get_w2v_model as get_w2v_model
from cakechat.config import TRAIN_CORPUS_NAME, VOCABULARY_MAX_SIZE, WORD_EMBEDDING_DIMENSION, W2V_WINDOW_SIZE, \
USE_SKIP_GRAM
if __name__ == '__main__':
processed_corpus_path = get_processed_corpus_path(TRAIN_CORPUS_NAME)
dialogs = load_processed_dialogs_from_json(
FileTextLinesIterator(processed_corpus_path),
text_field_name='text',
condition_field_name='condition')
training_dialogs_lines_for_w2v, _ = get_dialog_lines_and_conditions(
get_flatten_dialogs(dialogs),
text_field_name='text',
condition_field_name='condition')
tokenized_training_lines = ProcessedLinesIterator(
training_dialogs_lines_for_w2v, processing_callbacks=[str.split])
get_w2v_model(tokenized_lines=tokenized_training_lines,
corpus_name=TRAIN_CORPUS_NAME,
voc_size=VOCABULARY_MAX_SIZE,
vec_size=WORD_EMBEDDING_DIMENSION,
window_size=W2V_WINDOW_SIZE,