def evaluate_model(self):
logging.info("SimPLe epoch [% 6d]: evaluating model.",
self._simple_epoch)
start_time = time.time()
self._sim_env.initialize(
batch_size=self._simulated_batch_size,
history_stream=itertools.repeat(None),
)
if self._has_own_data:
trajectory_dir = self._trajectory_dump_root_dir
else:
trajectory_dir = self._initial_trajectory_dir
(_, eval_trajectories) = simple.load_trajectories(
trajectory_dir, eval_frac=self._data_eval_frac)
chosen_trajectories = [
random.choice(eval_trajectories)
for _ in range(self._sim_env.batch_size)
]
summaries = simple.evaluate_model(self._sim_env, chosen_trajectories,
plt)
if summaries is not None:
for (name, value) in summaries.items():
self._summary_writer.scalar("simple/{}".format(name),
value,
step=self._simple_epoch)
self._summary_writer.plot("simple/model_eval_plot",
plt,
step=self._simple_epoch)
self.flush_summaries()
logging.vlog(1, "Evaluating model took %0.2f sec.",
time.time() - start_time)
def evaluate_model(self):
logging.info("SimPLe epoch [% 6d]: evaluating model.",
self._simple_epoch)
start_time = time.time()
self._sim_env.initialize(
batch_size=self._simulated_batch_size,
history_stream=itertools.repeat(None),
)
(_, eval_trajectories) = self._load_trajectories(
# If we have any trajectories collected in this run, evaluate on them.
# Otherwise, use the initial dataset.
initial=(not self._has_own_data))
chosen_trajectories = [
random.choice(eval_trajectories)
for _ in range(self._sim_env.batch_size)
]
summaries = simple.evaluate_model(self._sim_env, chosen_trajectories,
plt)
if summaries is not None:
for (name, value) in summaries.items():
self._summary_writer.scalar("simple/{}".format(name),
value,
step=self._simple_epoch)
self._summary_writer.plot("simple/model_eval_plot",
plt,
step=self._simple_epoch)
self.flush_summaries()
logging.vlog(1, "Evaluating model took %0.2f sec.",
time.time() - start_time)
def test_fails_to_evaluate_model_with_matrix_observation_space(self):
with backend.use_backend("numpy"):
env = self._make_env( # pylint: disable=no-value-for-parameter
observation_space=gym.spaces.Box(shape=(2, 2), low=0, high=1),
action_space=gym.spaces.Discrete(n=1),
max_trajectory_length=2,
batch_size=1,
)
trajectories = [
self._make_trajectory(np.array([[0, 1], [2, 3]]),
np.array([0]))
]
metrics = simple.evaluate_model(env, trajectories, plt)
self.assertIsNone(metrics)
def test_evaluates_model_with_vector_observation_space(self):
with backend.use_backend("numpy"):
env = self._make_env( # pylint: disable=no-value-for-parameter
observation_space=gym.spaces.Box(shape=(2, ), low=0, high=1),
action_space=gym.spaces.Discrete(n=1),
max_trajectory_length=2,
batch_size=3,
)
trajectories = [
self._make_trajectory(observations, actions) # pylint: disable=g-complex-comprehension
for (observations, actions) in [
(np.array([[0, 1]]), np.array([0])),
(np.array([[1, 2], [3, 4]]), np.array([0, 0])),
(np.array([[1, 2], [3, 4], [5, 6]]), np.array([0, 0, 0])),
]
]
metrics = simple.evaluate_model(env, trajectories, plt)
self.assertIsNotNone(metrics)
self.assertEqual(len(metrics), 2)
