def test_finetune_after_load(
self,
trained_policy: SklearnPolicy,
trackers: List[TrackerWithCachedStates],
default_domain: Domain,
tmp_path: Path,
):
trained_policy.persist(tmp_path)
loaded_policy = SklearnPolicy.load(tmp_path, should_finetune=True)
assert loaded_policy.finetune_mode
loaded_policy.train(trackers, default_domain, RegexInterpreter())
assert loaded_policy.model
def create_policy(self, featurizer, priority, **kwargs):
p = SklearnPolicy(featurizer, priority, **kwargs)
return p
def create_policy(
self, featurizer: Optional[TrackerFeaturizer], priority: int, **kwargs: Any
) -> SklearnPolicy:
return SklearnPolicy(featurizer, priority, **kwargs)
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
from rasa.core import utils
from rasa.core.agent import Agent
from rasa.core.policies.memoization import MemoizationPolicy
from rasa.core.policies.sklearn_policy import SklearnPolicy
if __name__ == '__main__':
#utils.io.configure_colored_logging(loglevel="DEBUG")
training_data_file = './data/stories.md'
model_path = './models/dialogue'
agent = Agent("spotybot_domain.yml",
policies=[MemoizationPolicy(max_history=2),
SklearnPolicy()])
training_data = agent.load_data(training_data_file)
agent.train(training_data,
augmentation_factor=50,
epochs=500,
batch_size=10,
validation_split=0.2)
agent.persist(model_path)
