def create_policy(self, featurizer, priority):
max_history = None
if isinstance(featurizer, MaxHistoryTrackerFeaturizer):
max_history = featurizer.max_history
p = AugmentedMemoizationPolicy(priority=priority,
max_history=max_history)
return p
def test_agent_train(tmpdir, default_domain):
training_data_file = 'examples/moodbot/data/stories.md'
agent = Agent("examples/moodbot/domain.yml",
policies=[AugmentedMemoizationPolicy()])
training_data = agent.load_data(training_data_file)
agent.train(training_data)
agent.persist(tmpdir.strpath)
loaded = Agent.load(tmpdir.strpath)
# test domain
assert [a.name() for a in loaded.domain.actions] == \
[a.name() for a in agent.domain.actions]
assert loaded.domain.intents == agent.domain.intents
assert loaded.domain.entities == agent.domain.entities
assert loaded.domain.templates == agent.domain.templates
assert [s.name for s in loaded.domain.slots] == \
[s.name for s in agent.domain.slots]
# test policies
assert type(loaded.policy_ensemble) is type(
agent.policy_ensemble) # nopep8
assert [type(p) for p in loaded.policy_ensemble.policies] == \
[type(p) for p in agent.policy_ensemble.policies]
def test_agent_wrong_use_of_load(tmpdir, default_domain):
training_data_file = 'examples/moodbot/data/stories.md'
agent = Agent("examples/moodbot/domain.yml",
policies=[AugmentedMemoizationPolicy()])
with pytest.raises(ValueError):
# try to load a model file from a data path, which is nonsense and
# should fail properly
agent.load(training_data_file)
def default_processor(default_domain, default_nlg):
agent = Agent(default_domain,
SimplePolicyEnsemble([AugmentedMemoizationPolicy()]),
interpreter=RegexInterpreter())
training_data = agent.load_data(DEFAULT_STORIES_FILE)
agent.train(training_data)
tracker_store = InMemoryTrackerStore(default_domain)
return MessageProcessor(agent.interpreter, agent.policy_ensemble,
default_domain, tracker_store, default_nlg)
async def prepared_agent(tmpdir_factory) -> Agent:
model_path = tmpdir_factory.mktemp("model").strpath
agent = Agent("data/test_domains/default.yml",
policies=[AugmentedMemoizationPolicy(max_history=3)])
training_data = await agent.load_data(DEFAULT_STORIES_FILE)
agent.train(training_data)
agent.persist(model_path)
return agent
def core_server(tmpdir_factory):
model_path = tmpdir_factory.mktemp("model").strpath
agent = Agent("data/test_domains/default.yml",
policies=[AugmentedMemoizationPolicy(max_history=3)])
training_data = agent.load_data(DEFAULT_STORIES_FILE)
agent.train(training_data)
agent.persist(model_path)
return server.create_app(model_path, interpreter=RegexInterpreter())
def core_server(tmpdir_factory):
model_path = tmpdir_factory.mktemp("model").strpath
agent = Agent("data/test_domains/default.yml",
policies=[AugmentedMemoizationPolicy(max_history=3)])
training_data = agent.load_data(DEFAULT_STORIES_FILE)
agent.train(training_data)
agent.persist(model_path)
loaded_agent = Agent.load(model_path, interpreter=RegexInterpreter())
app = server.create_app(loaded_agent)
channel.register([RestInput()], app, agent.handle_message, "/webhooks/")
return app
def train_dialogue(domain_file='bank_domain.yml',
model_path='./models/dialogue',
training_data_file='./data/stories.md'):
agent = Agent(domain_file,
policies=[
MemoizationPolicy(),
KerasPolicy(max_history=3, epochs=150, batch_size=50),
AugmentedMemoizationPolicy(max_history=3)
])
data = agent.load_data(training_data_file)
agent.train(data)
agent.persist(model_path)
return agent