def train_dialogue_model(domain_file,
stories_file,
output_path,
interpreter=None,
endpoints=AvailableEndpoints(),
max_history=None,
dump_flattened_stories=False,
policy_config=None,
kwargs=None):
if not kwargs:
kwargs = {}
fallback_args, kwargs = utils.extract_args(
kwargs, {"nlu_threshold", "core_threshold", "fallback_action_name"})
policies = config.load(policy_config, fallback_args, max_history)
agent = Agent(domain_file,
generator=endpoints.nlg,
action_endpoint=endpoints.action,
interpreter=interpreter,
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 train_dialogue_model(domain_file,
stories_file,
output_path,
interpreter=None,
endpoints=AvailableEndpoints(),
dump_stories=False,
policy_config=None,
exclusion_percentage=None,
kwargs=None):
if not kwargs:
kwargs = {}
policies = config.load(policy_config)
agent = Agent(domain_file,
generator=endpoints.nlg,
action_endpoint=endpoints.action,
interpreter=interpreter,
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,
exclusion_percentage=exclusion_percentage,
**data_load_args)
agent.train(training_data, **kwargs)
agent.persist(output_path, dump_stories)
return agent
def train(domain_file: Text,
stories_file: Text,
output_path: Text,
interpreter: Optional[NaturalLanguageInterpreter] = None,
endpoints: AvailableEndpoints = AvailableEndpoints(),
dump_stories: bool = False,
policy_config: Text = None,
exclusion_percentage: int = None,
kwargs: Optional[Dict] = None):
from rasa_core.agent import Agent
if not kwargs:
kwargs = {}
policies = config.load(policy_config)
agent = Agent(domain_file,
generator=endpoints.nlg,
action_endpoint=endpoints.action,
interpreter=interpreter,
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,
exclusion_percentage=exclusion_percentage,
**data_load_args)
agent.train(training_data, **kwargs)
agent.persist(output_path, dump_stories)
return agent
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,
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 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
