def test_sum_one_info_action_match_predict(self):
learner = SafeLearner(UnsafeFixedLearner([1 / 2, 1 / 2], 1))
predict = learner.predict(None, [1, 2])
self.assertEqual([1 / 2, 1 / 2], predict[0])
self.assertEqual(1, predict[1])
def test_params_fullname_2_params(self):
learner = SafeLearner(
ParamsLearner({
'a': "A",
"b": "B",
"family": "B"
}))
self.assertEqual("B(a=A,b=B)", learner.full_name)
def process(
self, learner: Learner, interactions: Iterable[LoggedInteraction]
) -> Iterable[Dict[Any, Any]]:
learn_time = 0
predict_time = 0
if not isinstance(learner, SafeLearner): learner = SafeLearner(learner)
if not interactions: return
for interaction in interactions:
InteractionContext.learner_info.clear()
if "actions" not in interaction.kwargs:
info = None
interaction_info = {}
if "actions" in interaction.kwargs:
actions = list(interaction.kwargs["actions"])
start_time = time.time()
probs, info = learner.predict(interaction.context, actions)
predict_time = time.time() - start_time
interaction_info = {}
if len(interaction.kwargs.keys()
& {"probability", "actions", "reward"}) == 3:
ratio = probs[actions.index(
interaction.action)] / interaction.kwargs["probability"]
interaction_info = {
'reward': ratio * interaction.kwargs['reward']
}
for k, v in interaction.kwargs.items():
if k not in ["probability", "actions", "reward"]:
interaction_info[k] = v
reveal = interaction.kwargs.get("reveal",
interaction.kwargs.get("reward"))
prob = interaction.kwargs.get("probability")
start_time = time.time()
learner.learn(interaction.context, interaction.action, reveal,
prob, info)
learn_time = time.time() - start_time
learner_info = InteractionContext.learner_info
time_info = {
"predict_time": predict_time,
"learn_time": learn_time
} if self._time else {}
yield {**interaction_info, **learner_info, **time_info}
def process(
self, learner: Learner, interactions: Iterable[SimulatedInteraction]
) -> Iterable[Dict[Any, Any]]:
random = CobaRandom(self._seed)
if not isinstance(learner, SafeLearner): learner = SafeLearner(learner)
if not interactions: return
for interaction in interactions:
InteractionContext.learner_info.clear()
context = interaction.context
actions = interaction.actions
start_time = time.time()
probs, info = learner.predict(context, actions)
predict_time = time.time() - start_time
action = random.choice(actions, probs)
reveal = interaction.kwargs.get(
"reveals",
interaction.kwargs.get("rewards"))[actions.index(action)]
prob = probs[actions.index(action)]
start_time = time.time()
learner.learn(context, action, reveal, prob, info)
learn_time = time.time() - start_time
learner_info = InteractionContext.learner_info
interaction_info = {}
for k, v in interaction.kwargs.items():
if isinstance(v, collections.abc.Sequence) and not isinstance(
v, str):
interaction_info[k] = v[actions.index(action)]
else:
interaction_info[k] = v
time_info = {
"predict_time": predict_time,
"learn_time": learn_time
} if self._time else {}
yield {**interaction_info, **learner_info, **time_info}
def process(
self, learner: Learner,
interactions: Iterable[Interaction]) -> Iterable[Dict[Any, Any]]:
if not isinstance(learner, SafeLearner): learner = SafeLearner(learner)
if not interactions: return
separable_interactions = iter(
self._repeat_first_simulated_interaction(interactions))
logged_interactions = takewhile(
lambda i: isinstance(i, LoggedInteraction), separable_interactions)
simulated_interactions = separable_interactions
for row in OnlineOffPolicyEvalTask(self._time).process(
learner, logged_interactions):
yield row
for row in OnlineOnPolicyEvalTask(self._time, self._seed).process(
learner, simulated_interactions):
yield row
def process(self, item: Learner) -> Dict[Any, Any]:
item = SafeLearner(item)
return {"full_name": item.full_name, **item.params}
def test_sum_one_info_action_mismatch_predict(self):
learner = SafeLearner(UnsafeFixedLearner([1 / 2, 1 / 2], 1))
with self.assertRaises(AssertionError):
learner.predict(None, [1, 2, 3])
def test_no_sum_one_no_info_action_match_predict(self):
learner = SafeLearner(UnsafeFixedLearner([1 / 3, 1 / 2], None))
with self.assertRaises(AssertionError):
learner.predict(None, [1, 2])
def test_params_fullname_0_params(self):
learner = SafeLearner(ParamsLearner({"family": "B"}))
self.assertEqual("B", learner.full_name)
def test_params_family(self):
learner = SafeLearner(ParamsLearner({'a': "A", "family": "B"}))
self.assertDictEqual({"family": "B", "a": "A"}, learner.params)
def test_no_params(self):
learner = SafeLearner(NoParamsLearner())
self.assertEqual("NoParamsLearner", learner.params["family"])