def test_diagnostics(default_model_storage: ModelStorage,
default_execution_context: ExecutionContext):
domain = Domain.from_yaml(DOMAIN_YAML)
policy = TEDPolicy(
TEDPolicy.get_default_config(),
default_model_storage,
Resource("TEDPolicy"),
default_execution_context,
)
GREET_RULE = DialogueStateTracker.from_events(
"greet rule",
evts=[
UserUttered(intent={"name": GREET_INTENT_NAME}),
ActionExecuted(UTTER_GREET_ACTION),
ActionExecuted(ACTION_LISTEN_NAME),
UserUttered(intent={"name": GREET_INTENT_NAME}),
ActionExecuted(ACTION_LISTEN_NAME),
],
)
precomputations = None
policy.train([GREET_RULE], domain, precomputations)
prediction = policy.predict_action_probabilities(
GREET_RULE,
domain,
precomputations,
)
assert prediction.diagnostic_data
assert "attention_weights" in prediction.diagnostic_data
assert isinstance(prediction.diagnostic_data.get("attention_weights"),
np.ndarray)
async def test_train_keras_policy():
default_domain = Domain.load(DEFAULT_DOMAIN_PATH_WITH_SLOTS)
trackers = await training.load_data(DEFAULT_STORIES_FILE,
default_domain,
augmentation_factor=0,
debug_plots=False)
config = {"epochs": 100}
policy = TEDPolicy(featurizer=featurizer(), priority=1, **config)
policy.train(trackers, default_domain)
policy.persist("{}/models/ted".format(prj_dir))
def test_diagnostics():
domain = Domain.from_yaml(DOMAIN_YAML)
policy = TEDPolicy()
GREET_RULE = DialogueStateTracker.from_events(
"greet rule",
evts=[
UserUttered(intent={"name": GREET_INTENT_NAME}),
ActionExecuted(UTTER_GREET_ACTION),
ActionExecuted(ACTION_LISTEN_NAME),
UserUttered(intent={"name": GREET_INTENT_NAME}),
ActionExecuted(ACTION_LISTEN_NAME),
],
)
policy.train([GREET_RULE], domain, RegexInterpreter())
prediction = policy.predict_action_probabilities(GREET_RULE, domain,
RegexInterpreter())
assert prediction.diagnostic_data
assert "attention_weights" in prediction.diagnostic_data
assert isinstance(prediction.diagnostic_data.get("attention_weights"),
np.ndarray)
