def test_state():
state = State.new("E=MC2", "Matter and energy are interchangable")
assert state.truth == Truth.TRUE
assert not state.isEnd()
state = State.new("E=MC2", "E=MC2")
assert state.isEnd()
assert state.truth == Truth.TRUE
def test_lexical_model_predict(self, backend, word_map):
model = LexicalModel.build(word_map)
model.compile(
optimizer='adagrad',
loss='categorical_crossentropy',
metrics=['accuracy'])
value = model.predict(State.new("we eat bananas for lunch .", "we eat bananas for lunch ."))
assert value.shape == (1,3)
def test_agent_act():
agent = Agent(MockActorModel(), top_k=1)
state = State.new("E=MC2", "Matter and energy are interchangable")
actions = agent.act(state, [GiveUpAction()])
assert len(actions) == 1
_, action = actions[0]
assert isinstance(action, GiveUpAction)
def test_lexical_model_update(self, backend, word_map):
model = LexicalModel.build(word_map)
model.compile(
optimizer='adagrad',
loss='categorical_crossentropy',
metrics=['accuracy'])
for i in range(10):
model.enqueue((State.new("we eat bananas for lunch .", "we eat bananas for lunch ."), Truth.TRUE))
model.enqueue((State.new("we eat bananas for lunch .", "we don't eat bananas for lunch ."), Truth.FALSE))
model.enqueue((State.new("we eat bananas for lunch .", "we eat bananas ."), Truth.TRUE))
model.enqueue((State.new("we eat bananas for lunch .", "we eat lunch ."), Truth.TRUE))
model.update()
value = model.predict(State.new("we eat bananas for lunch .", "we eat bananas for lunch .", Truth.TRUE))
assert value.shape == (1,3)
assert value.argmax() == Truth.TRUE.value
def test_agent_feedback():
agent = Agent(MockActorModel(), top_k=1)
state = State.new("E=MC2", "Matter and energy are interchangable")
action = GiveUpAction()
reward = 0
agent.incorporate_feedback(state, action, reward)
assert len(agent.actor_model.queue) == 1
segment = agent.actor_model.queue[0]
assert len(segment) == 2
(state_, action_), reward_ = segment
assert state_ == state
assert action_ == action
assert reward_ == reward
def make_state(row, do_annotate=False):
L = {
"entailment": 2,
"neutral": 1,
"contradiction": 0,
}
if do_annotate:
source = row.sentence1
target = row.sentence2
gold_truth = Truth(L[row.gold_label])
return State.new(source, target, gold_truth)
else:
source = make_sentence(row.sentence1, row.sentence1_parse)
target = make_sentence(row.sentence2, row.sentence2_parse)
truth = Truth.TRUE
gold_truth = Truth(L[row.gold_label])
return State(source, target, truth, gold_truth)
def do_rte(args):
tree = ElementTree(file=args.input)
LABELS = {
"YES": Truth.TRUE,
"NO": Truth.FALSE,
"UNKNOWN": Truth.NEUTRAL,
}
for pair in tqdm(tree.findall("pair")):
assert pair.get("entailment") in LABELS
label = LABELS[pair.get("entailment")]
source = pair.findtext("t")
target = pair.findtext("h")
try:
state = State.new(source, target, gold_truth=label)
args.output.write(json.dumps(state.json) + "\n")
except AssertionError as e:
logging.warning(e.args)
def test_agenda_environment():
agent = Agent(MockActorModel(), top_k=1)
agenda = AgendaEnvironment(agent,
MockCriticModel(),
action_generator=lambda _: [GiveUpAction()],
reward_fn=mock_reward_fn)
state = State.new("E=MC2", "Matter and energy are interchangable",
Truth.TRUE)
state_ = agenda.run_episode(state)
assert state_.isEnd()
assert state_.truth == Truth.NEUTRAL
assert len(agent.actor_model.queue) == 1
_, reward = agent.actor_model.queue[0]
assert reward == 0 # Reward here is still 0
assert len(agenda.critic_model.queue) == 1
_, reward_ = agenda.critic_model.queue[0]
assert reward_ < 0