def run_ivrbot_online(input_channel=ConsoleInputChannel(),
interpreter=RasaNLUInterpreter("projects/ivr_nlu/demo"),
domain_file="mobile_domain.yml",
training_data_file="data/mobile_story.md"):
agent = Agent(domain_file,
policies=[MemoizationPolicy(), KerasPolicy()],
interpreter=interpreter)
training_data = agent.load_data(training_data_file)
agent.train_online(training_data,
input_channel=input_channel,
batch_size=16,
epochs=200,
max_training_samples=300)
return agent
def run_weather_online(interpreter,
domain_file="domain.yml",
training_data_file='data/stories.md'):
action_endpoint = EndpointConfig(url="http://localhost:5055/webhook")
agent = Agent(domain_file,
policies=[
MemoizationPolicy(max_history=2),
KerasPolicy(max_history=3, epochs=3, batch_size=50)
],
interpreter=interpreter,
action_endpoint=action_endpoint)
data = agent.load_data(training_data_file)
agent.train(data)
interactive.run_interactive_learning(agent, training_data_file)
return agent
def train_dialog(domain_file, training_data_file, model_dir, interpreter):
_agent = Agent(domain_file,
policies=[
MemoizationPolicy(max_history=6),
KerasPolicy(MaxHistoryTrackerFeaturizer(
BinarySingleStateFeaturizer(), max_history=6),
augmentation_factor=50,
epochs=300,
batch_size=50,
validation_split=0.2)
],
interpreter=interpreter)
_training_data = _agent.load_data(training_data_file)
_agent.train(_training_data)
_agent.persist(model_dir)
return _agent
def train_dialogue(domain_file = 'restaurant_domain.yml', model_path = './models/dialogue', training_data_file = './data/core_stories.md'): featurizer = MaxHistoryTrackerFeaturizer(BinarySingleStateFeaturizer(), max_history=5) agent = Agent(domain_file, policies = [MemoizationPolicy(max_history = 5), KerasPolicy(featurizer)]) agent.train( training_data_file, epochs = 300, batch_size = 50, validation_split = 0.2, augmentation_factor = 50) agent.persist(model_path) return agent
def run_weather_online(input_channel,
interpreter,
domain_file="weather_domain.yml",
training_data_file='data/stories.md'):
agent = Agent(domain_file,
policies=[MemoizationPolicy(max_history=2),
KerasPolicy()],
interpreter=interpreter)
agent.train_online(training_data_file,
input_channel=input_channel,
batch_size=50,
epochs=200,
max_training_samples=300)
return agent
def train_dialogue(domain_file = 'domain.yml', model_path = './models/dialogue', training_data_file = './data/stories.md'): agent = Agent(domain_file, policies = [MemoizationPolicy(), KerasPolicy()]) agent.train( training_data_file, max_history = 3, epochs = 300, batch_size = 50, validation_split = 0.2, augmentation_factor = 50) agent.persist(model_path) return agent
def run_fake_user(input_channel, max_training_samples=10, serve_forever=True):
logger.info("Starting to train policy")
agent = Agent(RASA_CORE_DOMAIN_PATH,
policies=[MemoizationPolicy(),
KerasPolicy()],
interpreter=RegexInterpreter())
agent.train_online(RASA_CORE_TRAINING_DATA_PATH,
input_channel=input_channel,
epochs=RASA_CORE_EPOCHS,
max_training_samples=max_training_samples)
while serve_forever:
agent.handle_message(UserMessage(back, ConsoleOutputChannel()))
return agent
def train_dialogue(domain_file="mobile_domain.yml",
model_path="projects/dialogue",
training_data_file="data/mobile_story.md"):
agent = Agent(domain_file,
policies=[MemoizationPolicy(),
KerasPolicy(), fallback])
training_data = agent.load_data(training_data_file)
agent.train(training_data,
epochs=200,
batch_size=16,
augmentation_factor=50,
validation_split=0.2)
agent.persist(model_path)
return agent
def run_online_trainer(
input_channel,
interpreter,
domain_def_file='chat_domain.yml',
training_data_file='./data/stories.md',
):
agent = Agent(domain_def_file,
policies=[KerasPolicy(), MemoizationPolicy()],
interpreter=interpreter)
agent.train_online(training_data_file,
input_channel=input_channel,
max_history=2,
batch_size=500,
epochs=200,
max_training_samples=300)
return agent
def train_agent(interpreter,
domain_file="domain.yml",
training_file='data/stories.md'):
action_endpoint = EndpointConfig('http://localhost:5055/webhook')
policies = [MemoizationPolicy(max_history=3),
KerasPolicy(max_history=3, epochs=10, batch_size=10)]
agent = Agent(domain_file, policies=policies,
interpreter=interpreter,
action_endpoint=action_endpoint)
stories = agent.load_data(training_file)
agent.train(stories)
interactive.run_interactive_learning(agent, training_file)
return agent
def test_agent_and_persist():
policies = config.load("policies.yml")
policies[0] = KerasPolicy(epochs=2) # Keep training times low
agent = Agent("domain.yml", policies=policies)
training_data = agent.load_data("data/stories.md")
agent.train(training_data, validation_split=0.0)
agent.persist("./tests/models/dialogue")
loaded = Agent.load("./tests/models/dialogue")
assert agent.handle_text("/greet") is not None
assert loaded.domain.action_names == agent.domain.action_names
assert loaded.domain.intents == agent.domain.intents
assert loaded.domain.entities == agent.domain.entities
assert loaded.domain.templates == agent.domain.templates
def train_dialogue(domain_file="restaurant_domain.yml", model_path="models/dialogue", training_data_file="data/babi_stories.md"): agent = Agent(domain_file,[MemoizationPolicy(max_history=3), RestaurantPolicy()]) training_data = agent.load_data(training_data_file) agent.train( training_data, epochs=400, batch_size=100, validation_split=0.2 ) agent.persist(model_path) return agent
def train_dialogue(domain_file="restaurant_domain.yml",
model_path="models/dialogue",
training_data_file="data/babi_stories.md"):
agent = Agent(domain_file,
policies=[MemoizationPolicy(),
RestaurantPolicy()])
agent.train(training_data_file,
max_history=7,
epochs=100,
batch_size=50,
augmentation_factor=20,
validation_split=0.1)
agent.persist(model_path)
return agent
def train_dialogue(domain_file='domain.yml',
model_path='./models/dialogue',
training_data_file='./data/stories.md'):
agent = Agent(domain_file,
policies=[
MemoizationPolicy(),
KerasPolicy(max_history=3, epochs=100, batch_size=50),
FormPolicy()
])
data = agent.load_data(training_data_file)
agent.train(data)
agent.persist(model_path)
return agent
def train_dialog(dialog_training_data_file,
domain_file,
path_to_model='models/dialogue'):
logging.basicConfig(level='INFO')
fallback = FallbackPolicy(fallback_action_name="utter_unclear",
core_threshold=0.3,
nlu_threshold=0.3)
agent = Agent(domain_file,
policies=[
MemoizationPolicy(max_history=1),
KerasPolicy(epochs=200, batch_size=20), fallback
])
training_data = agent.load_data(dialog_training_data_file)
agent.train(training_data, augmentation_factor=50, validation_split=0.2)
agent.persist(path_to_model)
def train_dialogue_model(domain_file, stories_file, output_path,
nlu_model_path=None,
endpoints=None,
max_history=None,
dump_flattened_stories=False,
kwargs=None):
if not kwargs:
kwargs = {}
action_endpoint = utils.read_endpoint_config(endpoints, "action_endpoint")
fallback_args, kwargs = utils.extract_args(kwargs,
{"nlu_threshold",
"core_threshold",
"fallback_action_name"})
policies = [
FallbackPolicy(
fallback_args.get("nlu_threshold",
DEFAULT_NLU_FALLBACK_THRESHOLD),
fallback_args.get("core_threshold",
DEFAULT_CORE_FALLBACK_THRESHOLD),
fallback_args.get("fallback_action_name",
DEFAULT_FALLBACK_ACTION)),
MemoizationPolicy(
max_history=max_history),
KerasPolicy(
MaxHistoryTrackerFeaturizer(BinarySingleStateFeaturizer(),
max_history=max_history))]
agent = Agent(domain_file,
action_endpoint=action_endpoint,
interpreter=nlu_model_path,
policies=policies)
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)
agent.train(training_data, **kwargs)
agent.persist(output_path, dump_flattened_stories)
return agent
def run_online_training(self,
ensemble,
domain,
interpreter=None,
input_channel=None):
from rasa_core.agent import Agent
if interpreter is None:
interpreter = RegexInterpreter()
bot = Agent(domain,
ensemble,
featurizer=self.featurizer,
interpreter=interpreter)
bot.toggle_memoization(False)
bot.handle_channel(
input_channel if input_channel else ConsoleInputChannel())
def train_online(project='Lambton'):
domain_file = './Core/models/' + project + '/dialogue/domain.yml'
model_path = './NLU/models/default/' + project,
training_data_file = './Core/models/' + project + '/stories/stories.md'
agent = Agent(domain_file, policies=[MemoizationPolicy(), KerasPolicy()])
agent.train_online(training_data_file,
input_channel=ConsoleInputChannel(),
max_history=2,
batch_size=10,
epochs=250,
max_training_samples=300,
validation_split=0.2)
agent.persist(model_path)
return agent
def run_online(input_channel,
interpreter,
domain_file="domain.yml",
training_data_file='stories.md'):
fallback = FallbackPolicy(fallback_action_name="utter_unclear",
core_threshold=0.2,
nlu_threshold=0.4)
agent = Agent('domain.yml',
policies=[MemoizationPolicy(),
KerasPolicy(), fallback],
interpreter=interpreter)
training_data = agent.load_data(training_data_file)
agent.train_online(training_data, input_channel=input_channel, epochs=200)
return agent
def train_rst_dm():
training_data_file = "../restaurantData/babi_task5_trn_rasa_with_slots.md"
domain_path = '../restaurant_domain.yml'
model_path = '../rst_models/rst_policy/current'
agent = Agent(domain=domain_path,
policies=[MemoizationPolicy(),
RestaurantPolicy()])
agent.train(training_data_file,
max_history=3,
epochs=100,
batch_size=50,
augmentation_factor=50,
validation_split=0.2)
agent.persist(model_path)
def train_dm():
training_data_file = 'stories.md'
model_path = 'models/policy/current'
agent = Agent("domain.yml",
policies=[MemoizationPolicy(), MusicPlayerPolicy()])
agent.train(
training_data_file,
max_history=3,
epochs=100,
batch_size=50,
augmentation_factor=50,
validation_split=0.2
)
agent.persist(model_path)
def train_dialogue(domain_id="default"):
domain_file = "{}/{}/domain.yml".format(data_folder, domain_id)
model_path = "{}/{}/dialogue".format(model_folder, domain_id)
training_data_file = "{}/{}/stories.md".format(data_folder, domain_id)
agent = Agent(
domain_file,
policies=[MemoizationPolicy(max_history=3),
IntelleiPolicy()])
training_data = agent.load_data(training_data_file)
agent.train(training_data,
epochs=400,
batch_size=100,
validation_split=0.2)
agent.persist(model_path)
return agent
def train_tickets_order_dm():
training_data_file = "../tickets/data/stories.md"
domain_path = '../tickets/domain.yml'
model_path = '../models/policy/current'
agent = Agent(domain=domain_path,
policies=[MemoizationPolicy(),
TicketsPolicy()])
agent.train(training_data_file,
max_history=3,
epochs=100,
batch_size=50,
augmentation_factor=50,
validation_split=0.2)
agent.persist(model_path)
def train_dialogue(domain_file='weather_domain.yml',
model_path='./models/dialogue',
training_data_file='./data/stories.md'):
agent = Agent(domain_file,
policies=[MemoizationPolicy(), WeatherPolicy()])
agent.train(#TODO set the model parameters
training_data_file,
max_history = ,
epochs = ,
batch_size = ,
augmentation_factor = ,
validation_split =
)
agent.persist(model_path)
return agent
def run_restaurantbot_online(input_channel=ConsoleInputChannel(),
interpreter=RasaNLUInterpreter("models/default/model_20190702-213207"),
domain_file="hainan_domain.yml",
training_data_file="data/story.md"):
agent = Agent(domain_file,
policies=[MemoizationPolicy(), KerasPolicy()],
interpreter=interpreter)
training_data = agent.load_data(training_data_file)
agent.train_online(training_data,
input_channel=input_channel,
max_history=2,
batch_size=16,
epochs=200,
max_training_samples=300)
return agent
def train_dialogue(domain_file='domain.yml',
model_path='./models/dialogue',
training_data_file='stories.md'):
fallback = FallbackPolicy(fallback_action_name='utter_unclear',
core_threshold=0.2,
nlu_threshold=0.6)
agent = Agent(domain_file,
policies=[MemoizationPolicy(),
KerasPolicy(), fallback])
agent.train(training_data_file,
epochs=300,
batch_size=50,
validation_split=0.2)
agent.persist(model_path)
return agent
def train_dialogue(domain_file='Sell4BidsBot_domain.yml',
model_path='./models/dialogue',
training_data_file='./data/stories.md'):
fallback = FallbackPolicy(fallback_action_name="action_default_fallback",
core_threshold=0.3,
nlu_threshold=0.2)
agent = Agent(domain_file,
policies=[
MemoizationPolicy(),
KerasPolicy(max_history=3, epochs=200, batch_size=50),
fallback
])
data = agent.load_data(training_data_file)
agent.train(data)
agent.persist(model_path)
return agent
def run_restaurant_online(input_channel,
interpreter,
domain_file="restaurant_domain.yml",
training_data_file='./data/core_stories.md'):
agent = Agent(domain_file,
policies=[MemoizationPolicy(),
KerasPolicy()],
interpreter=interpreter)
agent.train_online(training_data_file,
input_channel=input_channel,
max_history=5,
batch_size=50,
epochs=200,
max_training_samples=300)
return agent
def train_dialogue(domain_file = './config/domain.yml',
model_path = './models/dialogue',
training_data_file = './data/stories.md'):
fallback = FallbackPolicy(fallback_action_name="utter_fallback",
core_threshold=0.3,
nlu_threshold=0.3)
agent = Agent(domain_file, policies = [MemoizationPolicy(), KerasPolicy(),fallback])
data = agent.load_data(training_data_file)
agent.train(
data,
epochs = 300,
batch_size = 50,
validation_split = 0.2)
agent.persist(model_path)
return agent
def food_order_online(input_channel,
interpreter,
domain_file="domain.yml",
training_data_file='data/stories.md'):
agent = Agent(domain_file,
policies=[MemoizationPolicy(),
KerasPolicy()],
interpreter=interpreter)
agent.train_online(training_data_file,
input_channel=input_channel,
max_history=3,
batch_size=100,
epochs=400,
max_training_samples=300)
return agent
