async def train(
domain_file: Union[Domain, Text],
training_resource: Union[Text, "TrainingDataImporter"],
output_path: Text,
interpreter: Optional["NaturalLanguageInterpreter"] = None,
endpoints: "AvailableEndpoints" = None,
policy_config: Optional[Union[Text, Dict]] = None,
exclusion_percentage: Optional[int] = None,
additional_arguments: Optional[Dict] = None,
model_to_finetune: Optional["Agent"] = None,
) -> "Agent":
from rasa.core import config, utils
from rasa.core.utils import AvailableEndpoints
from rasa.core.agent import Agent
if not endpoints:
endpoints = AvailableEndpoints()
if not additional_arguments:
additional_arguments = {}
policies = config.load(policy_config)
agent = Agent(
domain_file,
generator=endpoints.nlg,
action_endpoint=endpoints.action,
interpreter=interpreter,
policies=policies,
)
data_load_args, additional_arguments = utils.extract_args(
additional_arguments,
{
"use_story_concatenation",
"unique_last_num_states",
"augmentation_factor",
"remove_duplicates",
"debug_plots",
},
)
training_data = await agent.load_data(
training_resource,
exclusion_percentage=exclusion_percentage,
**data_load_args)
if model_to_finetune:
agent.policy_ensemble = model_to_finetune.policy_ensemble
agent.train(training_data, **additional_arguments)
agent.persist(output_path)
return agent
async def train(
domain_file: Union[Domain, Text],
stories_file: Text,
output_path: Text,
interpreter: Optional["NaturalLanguageInterpreter"] = None,
endpoints: "AvailableEndpoints" = None,
dump_stories: bool = False,
policy_config: Text = None,
exclusion_percentage: int = None,
kwargs: Optional[Dict] = None,
):
from rasa.core.agent import Agent
from rasa.core import config, utils
from rasa.core.utils import AvailableEndpoints
if not endpoints:
endpoints = AvailableEndpoints()
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 = await 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
async def train(
domain_file: Union[Domain, Text],
training_resource: Union[Text, "TrainingDataImporter"],
output_path: Text,
interpreters: Optional[Dict[Text, "NaturalLanguageInterpreter"]] = None,
endpoints: "AvailableEndpoints" = None,
dump_stories: bool = False,
policy_config: Optional[Union[Text, Dict]] = None,
exclusion_percentage: int = None,
additional_arguments: Optional[Dict] = None,
):
from rasa.core.agent import Agent
from rasa.core import config, utils
from rasa.core.utils import AvailableEndpoints
if not endpoints:
endpoints = AvailableEndpoints()
if not additional_arguments:
additional_arguments = {}
policies = config.load(policy_config)
agent = Agent(
domain_file,
generator=endpoints.nlg,
action_endpoint=endpoints.action,
interpreters=interpreters or {}, # fix to avoid model not ready error
policies=policies,
)
data_load_args, additional_arguments = utils.extract_args(
additional_arguments,
{
"use_story_concatenation",
"unique_last_num_states",
"augmentation_factor",
"remove_duplicates",
"debug_plots",
},
)
training_data = await agent.load_data(
training_resource,
exclusion_percentage=exclusion_percentage,
**data_load_args)
agent.train(training_data, **additional_arguments)
agent.persist(output_path, dump_stories)
return agent
