def train_model_for_bot(bot: str):
"""
loads bot data from mongo into individual files for training
:param bot: bot id
:return: model path
"""
processor = MongoProcessor()
nlu = processor.load_nlu(bot)
if not nlu.training_examples:
raise AppException("Training data does not exists!")
domain = processor.load_domain(bot)
stories = processor.load_stories(bot)
config = processor.load_config(bot)
rules = processor.get_rules_for_training(bot)
directory = Utility.write_training_data(nlu, domain, config, stories,
rules)
output = os.path.join(DEFAULT_MODELS_PATH, bot)
model = train(
domain=os.path.join(directory, DEFAULT_DOMAIN_PATH),
config=os.path.join(directory, DEFAULT_CONFIG_PATH),
training_files=os.path.join(directory, DEFAULT_DATA_PATH),
output=output,
)
Utility.delete_directory(directory)
del processor
del nlu
del domain
del stories
del config
return model
def test_write_training_data_with_rules(self):
from kairon.data_processor.processor import MongoProcessor
processor = MongoProcessor()
training_data = processor.load_nlu("test_load_from_path_yml_training_files")
story_graph = processor.load_stories("test_load_from_path_yml_training_files")
domain = processor.load_domain("test_load_from_path_yml_training_files")
config = processor.load_config("test_load_from_path_yml_training_files")
http_action = processor.load_http_action("test_load_from_path_yml_training_files")
rules = processor.get_rules_for_training("test_load_from_path_yml_training_files")
training_data_path = Utility.write_training_data(training_data, domain, config, story_graph, rules, http_action)
assert os.path.exists(training_data_path)
async def test_write_training_data(self):
from kairon.data_processor.processor import MongoProcessor
processor = MongoProcessor()
await (
processor.save_from_path(
"./tests/testing_data/yml_training_files", bot="test_load_from_path_yml_training_files", user="******"
)
)
training_data = processor.load_nlu("test_load_from_path_yml_training_files")
story_graph = processor.load_stories("test_load_from_path_yml_training_files")
domain = processor.load_domain("test_load_from_path_yml_training_files")
config = processor.load_config("test_load_from_path_yml_training_files")
http_action = processor.load_http_action("test_load_from_path_yml_training_files")
training_data_path = Utility.write_training_data(training_data, domain, config, story_graph, None, http_action)
assert os.path.exists(training_data_path)
def train_model_for_bot(bot: str):
"""
loads bot data from mongo into individual files for training
:param bot: bot id
:return: model path
"""
processor = MongoProcessor()
nlu = processor.load_nlu(bot)
if not nlu.training_examples:
raise AppException("Training data does not exists!")
domain = processor.load_domain(bot)
stories = processor.load_stories(bot)
config = processor.load_config(bot)
directory = Utility.save_files(
nlu.nlu_as_markdown().encode(),
domain.as_yaml().encode(),
stories.as_story_string().encode(),
yaml.dump(config).encode(),
)
output = os.path.join(DEFAULT_MODELS_PATH, bot)
model = train(
domain=os.path.join(directory, DEFAULT_DOMAIN_PATH),
config=os.path.join(directory, DEFAULT_CONFIG_PATH),
training_files=os.path.join(directory, DEFAULT_DATA_PATH),
output=output,
)
Utility.delete_directory(directory)
del processor
del nlu
del domain
del stories
del config
return model