def test_contains_correct_n_in_vector(self):
# check a vector is contained even if it has n as one of its values.
n = 1 # number of trials
k = 2 # number of categories
multinomial_space = multinomial.Multinomial(k, n)
is_contained_vector = np.asarray([1, 0], dtype=np.uint32)
self.assertTrue(multinomial_space.contains(is_contained_vector))
def test_one_hot_conversion(self):
observation_space = gym.spaces.Dict(
{'x': multinomial.Multinomial(10, 1)})
params = classifier_agents.ScoringAgentParams(
default_action_fn=lambda: 0,
feature_keys=['x'],
convert_one_hot_to_integer=True,
threshold_policy=threshold_policies.ThresholdPolicy.
SINGLE_THRESHOLD)
agent = classifier_agents.ThresholdAgent(
observation_space=observation_space,
reward_fn=rewards.NullReward(),
params=params)
self.assertEqual(agent._get_features({'x': _one_hot(5)}), [5])
def __init__(self, params=None):
if params is None:
params = Params()
self.action_space = multinomial.Multinomial(params.n_locations,
params.n_attention_units)
assert (params.n_locations == len(params.prior_incident_counts)
and params.n_locations == len(params.incident_rates))
# Define the observation space.
# Crimes seen is multidiscrete because it may not sum to n_attention_units.
# MultiDiscrete uses dtype=np.int32.
if params.attention_replacement:
# If there is attention replacement, the number of attention doesn't bound
# the incidents_seen.
incidents_seen_space = spaces.MultiDiscrete(
[np.iinfo(np.int32).max] * params.n_locations)
else:
incidents_seen_space = spaces.MultiDiscrete(
[params.n_attention_units + 1] * params.n_locations)
incidents_reported_space = spaces.MultiDiscrete(
[np.iinfo(np.int32).max] * params.n_locations)
n_features = len(params.feature_means)
location_features_space = spaces.Box(low=-np.inf,
high=np.inf,
shape=(params.n_locations,
n_features),
dtype=np.float32)
# The first observation from this state is not necessarily contained by this
# observation space. It conveys a prior of the initial incident counts.
self.observable_state_vars = {
'incidents_seen': incidents_seen_space,
'incidents_reported': incidents_reported_space,
'location_features': location_features_space
}
super(LocationAllocationEnv, self).__init__(params)
self._state_init()
def test_agent_on_one_hot_vectors(self):
# Space of 1-hot vectors of length 10.
observation_space = gym.spaces.Dict(
{'x': multinomial.Multinomial(10, 1)})
params = classifier_agents.ScoringAgentParams(
default_action_fn=lambda: 0,
feature_keys=['x'],
convert_one_hot_to_integer=True,
burnin=999,
threshold_policy=threshold_policies.ThresholdPolicy.
SINGLE_THRESHOLD)
agent = classifier_agents.ThresholdAgent(
observation_space=observation_space,
reward_fn=rewards.NullReward(),
params=params)
observation_space.seed(100)
# Train a boundary at 3 using 1-hot vectors.
observation = observation_space.sample()
agent._act_impl(observation, reward=None, done=False)
for _ in range(1000):
last_observation = observation
observation = observation_space.sample()
agent._act_impl(observation,
reward=int(np.argmax(last_observation['x']) >= 3),
done=False)
if agent._training_corpus.examples:
assert int(agent._training_corpus.examples[-1].features[0] >= 3
) == agent._training_corpus.examples[-1].label
agent.frozen = True
self.assertTrue(agent.act({'x': _one_hot(3)}, done=False))
self.assertFalse(agent.act({'x': _one_hot(2)}, done=False))
def setUp(self):
self.n = 15 # number of trials
self.k = 6 # number of categories
self.multinomial_space = multinomial.Multinomial(self.k, self.n)
self.multinomial_space.seed(0)
super(MultinomialTest, self).setUp()
def __init__(self, params=None):
super(DelayedImpactEnv, self).__init__(params)
self.observable_state_vars[
'applicant_features'] = multinomial.Multinomial(
self.initial_params.applicant_distribution.dim, 1)
self.observation_space = spaces.Dict(self.observable_state_vars)
