def train(self, training_trackers: List[DialogueStateTracker],
**kwargs: Any) -> None:
"""Train the policies / policy ensemble using dialogue data from file.
Args:
training_trackers: trackers to train on
**kwargs: additional arguments passed to the underlying ML
trainer (e.g. keras parameters)
"""
if not self.is_ready():
raise AgentNotReady("Can't train without a policy ensemble.")
# deprecation tests
if kwargs.get('featurizer') or kwargs.get('max_history'):
raise Exception("Passing `featurizer` and `max_history` "
"to `agent.train(...)` is not supported anymore. "
"Pass appropriate featurizer "
"directly to the policy instead. More info "
"https://rasa.com/docs/core/migrations.html#x-to"
"-0-9-0")
if isinstance(training_trackers, str):
# the user most likely passed in a file name to load training
# data from
raise Exception("Passing a file name to `agent.train(...)` is "
"not supported anymore. Rather load the data with "
"`data = agent.load_data(file_name)` and pass it "
"to `agent.train(data)`.")
logger.debug("Agent trainer got kwargs: {}".format(kwargs))
check_domain_sanity(self.domain)
self.policy_ensemble.train(training_trackers, self.domain, **kwargs)
self._set_fingerprint()
def train(
self,
training_trackers, # type: List[DialogueStateTracker]
**kwargs # type: **Any
):
# type: (...) -> None
"""Train the policies / policy ensemble using dialogue data from file.
:param training_trackers: trackers to train on
:param kwargs: additional arguments passed to the underlying ML
trainer (e.g. keras parameters)
"""
# deprecation tests
if kwargs.get('featurizer') or kwargs.get('max_history'):
raise Exception("Passing `featurizer` and `max_history` "
"to `agent.train(...)` is not supported anymore. "
"Pass appropriate featurizer "
"directly to the policy instead. More info "
"https://core.rasa.com/migrations.html#x-to-0-9-0")
# TODO: DEPRECATED - remove in version 0.10
if isinstance(training_trackers, string_types):
# the user most likely passed in a file name to load training
# data from
logger.warning("Passing a file name to `agent.train(...)` is "
"deprecated. Rather load the data with "
"`data = agent.load_data(file_name)` and pass it "
"to `agent.train(data)`.")
training_trackers = self.load_data(training_trackers)
logger.debug("Agent trainer got kwargs: {}".format(kwargs))
check_domain_sanity(self.domain)
self.policy_ensemble.train(training_trackers, self.domain, **kwargs)
def train(self,
training_trackers, # type: List[DialogueStateTracker]
**kwargs # type: **Any
):
# type: (...) -> None
"""Train the policies / policy ensemble using dialogue data from file.
:param training_trackers: trackers to train on
:param kwargs: additional arguments passed to the underlying ML
trainer (e.g. keras parameters)
"""
# deprecation tests
if kwargs.get('featurizer') or kwargs.get('max_history'):
raise Exception("Passing `featurizer` and `max_history` "
"to `agent.train(...)` is not supported anymore. "
"Pass appropriate featurizer "
"directly to the policy instead. More info "
"https://core.rasa.com/migrations.html#x-to-0-9-0")
# TODO: DEPRECATED - remove in version 0.10
if isinstance(training_trackers, string_types):
# the user most likely passed in a file name to load training
# data from
logger.warning("Passing a file name to `agent.train(...)` is "
"deprecated. Rather load the data with "
"`data = agent.load_data(file_name)` and pass it "
"to `agent.train(data)`.")
training_trackers = self.load_data(training_trackers)
logger.debug("Agent trainer got kwargs: {}".format(kwargs))
check_domain_sanity(self.domain)
self.policy_ensemble.train(training_trackers, self.domain,
**kwargs)
def train(self,
resource_name=None,
interpreter=None,
input_channel=None,
max_history=3,
augmentation_factor=20,
max_training_samples=None,
max_number_of_trackers=2000,
**kwargs):
logger.debug("Policy trainer got kwargs: {}".format(kwargs))
check_domain_sanity(self.domain)
training_data = self._prepare_training_data(resource_name, max_history,
augmentation_factor,
max_training_samples,
max_number_of_trackers)
self.ensemble.train(training_data, self.domain, self.featurizer,
**kwargs)
training_data.reset_metadata(
) # online learning doesn't support it yet
ensemble = OnlinePolicyEnsemble(self.ensemble, self.featurizer,
max_history, training_data)
self.run_online_training(ensemble, self.domain, interpreter,
input_channel)
def train_online(
self,
training_trackers, # type: List[DialogueStateTracker]
input_channel=None, # type: Optional[InputChannel]
max_visual_history=3, # type: int
**kwargs # type: **Any
):
# type: (...) -> None
from rasa_core.policies.online_trainer import OnlinePolicyEnsemble
"""Train a policy ensemble in online learning mode."""
if not self.interpreter:
raise ValueError("When using online learning, you need to specify "
"an interpreter for the agent to use.")
# TODO: DEPRECATED - remove in version 0.10
if isinstance(training_trackers, string_types):
# the user most likely passed in a file name to load training
# data from
logger.warning("Passing a file name to `agent.train_online(...)` "
"is deprecated. Rather load the data with "
"`data = agent.load_data(file_name)` and pass it "
"to `agent.train_online(data)`.")
training_trackers = self.load_data(training_trackers)
logger.debug("Agent online trainer got kwargs: {}".format(kwargs))
check_domain_sanity(self.domain)
self.policy_ensemble.train(training_trackers, self.domain, **kwargs)
ensemble = OnlinePolicyEnsemble(self.policy_ensemble,
training_trackers, max_visual_history)
ensemble.run_online_training(self.domain, self.interpreter,
input_channel)
def train(self, filename=None, max_history=3,
augmentation_factor=20, max_training_samples=None,
max_number_of_trackers=2000, **kwargs):
"""Trains a policy on a domain using training data from a file.
:param augmentation_factor: how many stories should be created by
randomly concatenating stories
:param filename: story file containing the training conversations
:param max_history: number of past actions to consider for the
prediction of the next action
:param max_training_samples: specifies how many training samples to
train on - `None` to use all examples
:param max_number_of_trackers: limits the tracker generation during
story file parsing - `None` for unlimited
:param kwargs: additional arguments passed to the underlying ML trainer
(e.g. keras parameters)
:return: trained policy
"""
logger.debug("Policy trainer got kwargs: {}".format(kwargs))
check_domain_sanity(self.domain)
X, y = self._prepare_training_data(filename, max_history,
augmentation_factor,
max_training_samples,
max_number_of_trackers)
self.ensemble.train(X, y, self.domain, self.featurizer, **kwargs)
def train(self,
filename=None, interpreter=None, input_channel=None,
max_history=3, augmentation_factor=20, max_training_samples=None,
max_number_of_trackers=2000, **kwargs):
logger.debug("Policy trainer got kwargs: {}".format(kwargs))
check_domain_sanity(self.domain)
X, y = self._prepare_training_data(filename, max_history,
augmentation_factor,
max_training_samples,
max_number_of_trackers)
self.ensemble.train(X, y, self.domain, self.featurizer, **kwargs)
ensemble = OnlinePolicyEnsemble(self.ensemble, self.featurizer,
max_history, (X, y))
self.run_online_training(ensemble, self.domain, interpreter,
input_channel)
def train_online(self,
training_trackers, # type: List[DialogueStateTracker]
input_channel=None, # type: Optional[InputChannel]
max_visual_history=3, # type: int
**kwargs # type: **Any
):
# type: (...) -> None
from rasa_core.policies.online_trainer import OnlinePolicyEnsemble
"""Train a policy ensemble in online learning mode."""
if not self.interpreter:
raise ValueError(
"When using online learning, you need to specify "
"an interpreter for the agent to use.")
# TODO: DEPRECATED - remove in version 0.10
if isinstance(training_trackers, string_types):
# the user most likely passed in a file name to load training
# data from
logger.warning("Passing a file name to `agent.train_online(...)` "
"is deprecated. Rather load the data with "
"`data = agent.load_data(file_name)` and pass it "
"to `agent.train_online(data)`.")
training_trackers = self.load_data(training_trackers)
logger.debug("Agent online trainer got kwargs: {}".format(kwargs))
check_domain_sanity(self.domain)
self.policy_ensemble.train(training_trackers, self.domain, **kwargs)
ensemble = OnlinePolicyEnsemble(self.policy_ensemble,
training_trackers,
max_visual_history)
ensemble.run_online_training(self.domain, self.interpreter,
input_channel)
