def train_dialogue_model(domain_file,
stories_file,
output_path,
use_online_learning=False,
nlu_model_path=None,
max_history=None,
kwargs=None):
if not kwargs:
kwargs = {}
agent = Agent(
domain_file,
policies=[MemoizationPolicy(max_history=max_history),
KerasPolicy()])
data_load_args, kwargs = utils.extract_args(
kwargs, {
"use_story_concatenation", "unique_last_num_states",
"augmentation_factor", "remove_duplicates", "debug_plots"
})
training_data = agent.load_data(stories_file, **data_load_args)
if use_online_learning:
if nlu_model_path:
agent.interpreter = RasaNLUInterpreter(nlu_model_path)
else:
agent.interpreter = RegexInterpreter()
agent.train_online(training_data,
input_channel=ConsoleInputChannel(),
model_path=output_path,
**kwargs)
else:
agent.train(training_data, **kwargs)
agent.persist(output_path)
def train_dialogue_model(domain_file, stories_file, output_path,
use_online_learning=False, nlu_model_path=None,
kwargs=None):
if not kwargs:
kwargs = {}
agent = Agent(domain_file, policies=[MemoizationPolicy(), KerasPolicy()])
if use_online_learning:
if nlu_model_path:
agent.interpreter = RasaNLUInterpreter(nlu_model_path)
else:
agent.interpreter = RegexInterpreter()
agent.train_online(
stories_file,
input_channel=ConsoleInputChannel(),
epochs=10,
model_path=output_path)
else:
agent.train(
stories_file,
validation_split=0.1,
**kwargs
)
agent.persist(output_path)
def train_dialogue_model(domain_file, stories_file, output_path,
use_online_learning=False,
nlu_model_path=None,
max_history=None,
kwargs=None):
if not kwargs:
kwargs = {}
agent = Agent(domain_file, policies=[
MemoizationPolicy(max_history=max_history),
KerasPolicy()])
training_data = agent.load_data(stories_file)
if use_online_learning:
if nlu_model_path:
agent.interpreter = RasaNLUInterpreter(nlu_model_path)
else:
agent.interpreter = RegexInterpreter()
agent.train_online(
training_data,
input_channel=ConsoleInputChannel(),
model_path=output_path,
**kwargs)
else:
agent.train(training_data, **kwargs)
agent.persist(output_path)
def train_dialog(online=False, nlu=False, policies=[KerasPolicy()]):
agent = Agent("domain.yml", policies=policies)
stories = "data\stories.md"
output_path = "models\dialog"
if online:
if nlu:
agent.interpreter = RasaNLUInterpreter(
"models/infosys_cui/current")
else:
agent.interpreter = RegexInterpreter()
agent.train_online(stories,
input_channel=ConsoleInputChannel(),
epochs=10,
model_path=output_path)
else:
kwargs = {"epochs": 300}
agent.train(stories, validation_split=0.1, **kwargs)
agent.persist(output_path)
def train_model_online():
agent = Agent(RASA_CORE_DOMAIN_PATH,
policies=[MemoizationPolicy(),
StatusPolicy()],
interpreter=RegexInterpreter())
agent.train_online(RASA_CORE_TRAINING_DATA_PATH,
input_channel=FileInputChannel(
RASA_CORE_TRAINING_DATA_PATH,
message_line_pattern='^\s*\*\s(.*)$',
max_messages=10),
epochs=RASA_CORE_EPOCHS)
agent.interpreter = RasaNLUInterpreter(RASA_NLU_MODEL_PATH)
return agent
def run_babi_online(max_messages=10):
training_data = 'examples/babi/data/babi_task5_dev_rasa_even_smaller.md'
logger.info("Starting to train policy")
agent = Agent("examples/restaurant_domain.yml",
policies=[MemoizationPolicy(),
RestaurantPolicy()],
interpreter=RegexInterpreter())
input_c = FileInputChannel(training_data,
message_line_pattern='^\s*\*\s(.*)$',
max_messages=max_messages)
agent.train_online(training_data, input_channel=input_c, epochs=10)
agent.interpreter = RasaNLUInterpreter(nlu_model_path)
return agent
def run_babi_online(max_messages=10):
training_data = 'stories.md'
logger.info("Starting to train policy")
agent = Agent("domain.yml",
policies=[MemoizationPolicy(),
MusicPlayerPolicy()],
interpreter=RegexInterpreter())
input_c = FileInputChannel(training_data,
message_line_pattern='^\s*\*\s(.*)$',
max_messages=max_messages)
agent.train_online(training_data, input_channel=input_c, epochs=10)
agent.interpreter = RasaNLUInterpreter(nlu_model_path)
return agent
def run_babi_online():
training_data = 'data/weather.md'
logger.info("Starting to train policy")
agent = Agent("../weather_domain.yml",
policies=[MemoizationPolicy(),
WeatherPolicy()],
interpreter=RegexInterpreter())
input_c = FileInputChannel(training_data,
message_line_pattern='^\s*\*\s(.*)$',
max_messages=10)
agent.train_online(training_data, input_channel=input_c, epochs=10)
agent.interpreter = RasaNLUHttpInterpreter(
model_name='model_20171013-084449',
token=None,
server='http://localhost:7000')
return agent