def persist(self, dir_name: 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 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 test_remove_model_invalid(empty_model_dir):
test_file = "something.else"
test_content = "Some other stuff"
test_file_path = os.path.join(empty_model_dir, test_file)
write_to_file(test_file_path, test_content)
with pytest.raises(ValueError) as e:
remove_model(empty_model_dir)
os.remove(test_file_path)
def test_remove_model_invalid(empty_model_dir):
test_file = "something.else"
test_content = "Some other stuff"
test_file_path = os.path.join(empty_model_dir, test_file)
write_to_file(test_file_path, test_content)
with pytest.raises(ValueError) as e:
remove_model(empty_model_dir)
os.remove(test_file_path)
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 persist(
self,
dir_name: Text,
filename: Text = DEFAULT_TRAINING_DATA_OUTPUT_PATH
) -> Dict[Text, Any]:
"""Persists this training data to disk and returns necessary
information to load it again."""
if not os.path.exists(dir_name):
os.makedirs(dir_name)
data_file = os.path.join(dir_name, filename)
write_to_file(data_file, self.as_json(indent=2))
return {"training_data": DEFAULT_TRAINING_DATA_OUTPUT_PATH}
def persist(self, dir_name: Text,
filename: Text = DEFAULT_TRAINING_DATA_OUTPUT_PATH
) -> Dict[Text, Any]:
"""Persists this training data to disk and returns necessary
information to load it again."""
if not os.path.exists(dir_name):
os.makedirs(dir_name)
data_file = os.path.join(dir_name, filename)
write_to_file(data_file, self.as_json(indent=2))
return {
"training_data": DEFAULT_TRAINING_DATA_OUTPUT_PATH
}
def save_json(data, filename):
"""Write out nlu classification to a file."""
utils.write_to_file(filename,
json.dumps(data, indent=4, ensure_ascii=False))
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))
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_json(data, filename):
"""Write out nlu classification to a file."""
utils.write_to_file(filename,
json.dumps(data, indent=4, ensure_ascii=False))
def convert_training_data(data_file, out_file, output_format, language):
td = training_data.load_data(data_file, language)
output = td.as_markdown() if output_format == 'md' else td.as_json(indent=2)
write_to_file(out_file, output)
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))
