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_model_if_exists(self):
if is_non_empty_file(self._model_progress_resource_path):
self._model = self._load_model(self._model,
self._model_progress_resource_path)
self._metrics = self._metrics_serializer.load_metrics(
self._metrics_resource_path)
return
self._logger.info(
'Could not find saved model at {}\nModel will be trained from scratch.\n'
.format(self._model_progress_resource_path))
def get_tokenized_test_lines(corpus_name, tokens_voc):
corpus_path = os.path.join(TEST_DATA_DIR, '%s.txt' % corpus_name)
if not is_non_empty_file(corpus_path):
raise ValueError('Test corpus file doesn\'t exist: {}'.format(corpus_path))
test_lines = load_file(corpus_path)
result = []
for line in test_lines:
tokenized_line = get_tokens_sequence(line)
tokenized_line = replace_out_of_voc_tokens(tokenized_line, tokens_voc)
result.append(tokenized_line)
return result
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 predict(model_path,
tokens_index_path=None,
conditions_index_path=None,
default_predictions_path=None,
reverse_model_weights=None,
temperatures=None,
prediction_mode=None):
if not tokens_index_path:
tokens_index_path = get_index_to_token_path(BASE_CORPUS_NAME)
if not conditions_index_path:
conditions_index_path = get_index_to_condition_path(BASE_CORPUS_NAME)
if not temperatures:
temperatures = [DEFAULT_TEMPERATURE]
if not prediction_mode:
prediction_mode = PREDICTION_MODE_FOR_TESTS
# Construct list of parameters values for all possible combinations of passed parameters
prediction_params = [dict()]
if reverse_model_weights:
prediction_params = [
dict(params, mmi_reverse_model_score_weight=w)
for params in prediction_params for w in reverse_model_weights
]
if temperatures:
prediction_params = [
dict(params, temperature=t) for params in prediction_params
for t in temperatures
]
if not is_non_empty_file(tokens_index_path):
_logger.warning(
'Couldn\'t find tokens_index file:\n{}. \nExiting...'.format(
tokens_index_path))
return
index_to_token = load_index_to_item(tokens_index_path)
index_to_condition = load_index_to_item(conditions_index_path)
nn_model, _ = get_nn_model(index_to_token,
index_to_condition,
model_init_path=model_path)
if not default_predictions_path:
default_predictions_path = os.path.join(
DATA_DIR, 'results', 'predictions_' + nn_model.model_name)
# Get path for each combination of parameters
predictions_paths = []
# Add suffix to the filename only for parameters that have a specific value passed as an argument
# If no parameters were specified, no suffix is added
if len(prediction_params) > 1:
for cur_params in prediction_params:
cur_path = '{base_path}_{params_str}.tsv'.format(
base_path=default_predictions_path,
params_str='_'.join(
['{}_{}'.format(k, v) for k, v in cur_params.items()]))
predictions_paths.append(cur_path)
else:
predictions_paths = [default_predictions_path + '.tsv']
_logger.info('Model for prediction:\n{}'.format(nn_model.model_load_path))
_logger.info('Tokens index:\n{}'.format(tokens_index_path))
_logger.info('File with questions:\n{}'.format(QUESTIONS_CORPUS_NAME))
_logger.info('Files to dump responses:\n{}'.format(
'\n'.join(predictions_paths)))
_logger.info('Prediction parameters\n{}'.format('\n'.join(
[str(x) for x in prediction_params])))
processed_test_set = get_tokenized_test_lines(QUESTIONS_CORPUS_NAME,
set(index_to_token.values()))
processed_test_set = list(processed_test_set)
for cur_params, cur_path in zip(prediction_params, predictions_paths):
_logger.info(
'Predicting with the following params: {}'.format(cur_params))
_save_test_results(processed_test_set,
cur_path,
nn_model,
prediction_modes=[prediction_mode])
def _look_for_saved_files(files_paths):
for f_path in files_paths:
if not is_non_empty_file(f_path):
raise Exception('\nCould not find the following file or it\'s empty: {0}'.format(f_path))
def predict(model_path=None,
tokens_index_path=None,
conditions_index_path=None,
default_predictions_path=None,
reverse_model_weights=None,
temperatures=None,
prediction_mode=PREDICTION_MODE_FOR_TESTS):
if not model_path:
model_path = get_model_full_path()
if not tokens_index_path:
tokens_index_path = get_index_to_token_path(BASE_CORPUS_NAME)
if not conditions_index_path:
conditions_index_path = get_index_to_condition_path(BASE_CORPUS_NAME)
if not default_predictions_path:
default_predictions_path = os.path.join(DATA_DIR, 'results', 'predictions_' + get_model_full_params_str())
# Construct list of parameters values for all possible combinations of passed parameters
prediction_params = [dict()]
if reverse_model_weights:
prediction_params = [
dict(params, mmi_reverse_model_score_weight=w)
for params in prediction_params
for w in reverse_model_weights
]
if temperatures:
prediction_params = [dict(params, temperature=t) for params in prediction_params for t in temperatures]
# Get path for each combination of parameters
predictions_paths = []
# Add suffix to the filename only for parameters that have a specific value passed as an argument
# If no parameters were specified, no suffix is added
if len(prediction_params) > 1:
for cur_params in prediction_params:
cur_path = '{base_path}_{params_str}.tsv'.format(
base_path=default_predictions_path,
params_str='_'.join(['{}_{}'.format(k, v) for k, v in cur_params.items()]))
predictions_paths.append(cur_path)
else:
predictions_paths = [default_predictions_path + '.tsv']
if not is_non_empty_file(model_path):
_logger.warn('Couldn\'t find model:\n"{}". \nExiting...'.format(model_path))
return
if not is_non_empty_file(tokens_index_path):
_logger.warn('Couldn\'t find tokens_index file:\n"{}". \nExiting...'.format(tokens_index_path))
return
_logger.info('Model for prediction:\n{}'.format(model_path))
_logger.info('Tokens index:\n{}'.format(tokens_index_path))
_logger.info('File with questions:\n{}'.format(QUESTIONS_CORPUS_NAME))
_logger.info('Files to dump responses:\n{}'.format('\n'.join(predictions_paths)))
_logger.info('Prediction parameters\n{}'.format('\n'.join([str(x) for x in prediction_params])))
index_to_token = load_index_to_item(tokens_index_path)
index_to_condition = load_index_to_item(conditions_index_path)
processed_test_set = get_tokenized_test_lines(QUESTIONS_CORPUS_NAME, set(index_to_token.values()))
processed_test_set = list(processed_test_set)
nn_model, _ = get_nn_model(index_to_token, index_to_condition, nn_model_path=model_path)
for cur_params, cur_path in zip(prediction_params, predictions_paths):
_logger.info('Predicting with the following params: {}'.format(cur_params))
_save_test_results(processed_test_set, cur_path, nn_model, prediction_mode, **cur_params)
