def predict(self, text):
"""
Predict
Args:
text (str): Description.
"""
self.prediction_count += 1
if self.prediction_count == 1:
self.parameters['dataset_text_folder'] = os.path.join('.', 'data', 'temp')
self.stats_graph_folder, _ = self._create_stats_graph_folder(self.parameters)
# Update the deploy folder, file, and modeldata
dataset_type = 'deploy'
# Delete all deployment data
for filepath in glob.glob(os.path.join(self.parameters['dataset_text_folder'],
'{0}*'.format(dataset_type))):
if os.path.isdir(filepath):
shutil.rmtree(filepath)
else:
os.remove(filepath)
# # Create brat folder and file
dataset_brat_deploy_folder = os.path.join(self.parameters['dataset_text_folder'],
dataset_type)
utils.create_folder_if_not_exists(dataset_brat_deploy_folder)
dataset_brat_deploy_filepath = os.path.join(dataset_brat_deploy_folder,
'temp_{0}.txt'.format(str(self.prediction_count).zfill(5)))
#self._get_dataset_brat_deploy_filepath(dataset_brat_deploy_folder)
with codecs.open(dataset_brat_deploy_filepath, 'w', 'UTF-8') as f:
f.write(text)
# Update deploy filepaths
dataset_filepaths, dataset_brat_folders = self._get_valid_dataset_filepaths(self.parameters,
dataset_types=[dataset_type])
self.dataset_filepaths.update(dataset_filepaths)
self.dataset_brat_folders.update(dataset_brat_folders)
# Update the dataset for the new deploy set
self.modeldata.update_dataset(self.dataset_filepaths, [dataset_type])
# Predict labels and output brat
output_filepaths = {}
prediction_output = train.prediction_step(self.sess, self.modeldata,
dataset_type, self.model, self.transition_params_trained,
self.prediction_count, self.parameters)
return prediction_output
def predict(self, text):
"""
Predict
Args:
text (str): Description.
"""
self.prediction_count += 1
if self.prediction_count == 1:
self.parameters['dataset_text_folder'] = os.path.join(
'.', 'data', 'temp')
self.stats_graph_folder, _ = self._create_stats_graph_folder(
self.parameters)
# Update the deploy folder, file, and modeldata
dataset_type = 'deploy'
# Delete all deployment data
for filepath in glob.glob(
os.path.join(self.parameters['dataset_text_folder'],
'{0}*'.format(dataset_type))):
if os.path.isdir(filepath):
shutil.rmtree(filepath)
else:
os.remove(filepath)
# Create brat folder and file
dataset_brat_deploy_folder = os.path.join(
self.parameters['dataset_text_folder'], dataset_type)
utils.create_folder_if_not_exists(dataset_brat_deploy_folder)
dataset_brat_deploy_filepath = os.path.join(
dataset_brat_deploy_folder,
'temp_{0}.txt'.format(str(self.prediction_count).zfill(5)))
#self._get_dataset_brat_deploy_filepath(dataset_brat_deploy_folder)
with codecs.open(dataset_brat_deploy_filepath, 'w', 'UTF-8') as f:
f.write(text)
# Update deploy filepaths
dataset_filepaths, dataset_brat_folders = self._get_valid_dataset_filepaths(
self.parameters, dataset_types=[dataset_type])
self.dataset_filepaths.update(dataset_filepaths)
self.dataset_brat_folders.update(dataset_brat_folders)
# Update the dataset for the new deploy set
self.modeldata.update_dataset(self.dataset_filepaths, [dataset_type])
# Predict labels and output brat
output_filepaths = {}
prediction_output = train.prediction_step(
self.sess, self.modeldata, dataset_type, self.model,
self.transition_params_trained, self.stats_graph_folder,
self.prediction_count, self.parameters, self.dataset_filepaths)
_, _, output_filepaths[dataset_type] = prediction_output
conll_to_brat.output_brat(output_filepaths,
self.dataset_brat_folders,
self.stats_graph_folder,
overwrite=True)
# Print and output result
text_filepath = os.path.join(
self.stats_graph_folder, 'brat', 'deploy',
os.path.basename(dataset_brat_deploy_filepath))
annotation_filepath = os.path.join(
self.stats_graph_folder, 'brat', 'deploy', '{0}.ann'.format(
utils.get_basename_without_extension(
dataset_brat_deploy_filepath)))
text2, entities = brat_to_conll.get_entities_from_brat(
text_filepath, annotation_filepath, verbose=True)
assert (text == text2)
return entities