def convert_training_data(data_file, out_file, output_format, language):
td = training_data.load_data(data_file, language)
if output_format == 'md':
output = td.as_markdown()
else:
output = td.as_json(indent=2)
write_to_file(out_file, output)
def persist(self, dir_name):
# type: (Text) -> Dict[Text, Any]
"""Persists this training data to disk and returns necessary
information to load it again."""
data_file = os.path.join(dir_name, "training_data.json")
write_to_file(data_file, self.as_json(indent=2))
return {"training_data": "training_data.json"}
def dump(self, filename, training_data):
"""Writes a TrainingData object in markdown format to a file."""
s = self.dumps(training_data)
utils.write_to_file(filename, s)
def save_nlu_errors(errors, filename):
"""Write out nlu classification errors to a file."""
utils.write_to_file(filename,
json.dumps(errors, indent=4, ensure_ascii=False))
logger.info("Model prediction errors saved to {}.".format(filename))