def test_construct_policies_singlefactor(self):
"""
Test policy constructor function for single factor control states
"""
n_states = [3]
n_control = [3]
control_fac_idx = [0]
# one step policies
policy_len = 1
policies = core.construct_policies(n_states, n_control, policy_len,
control_fac_idx)
self.assertEqual(len(policies), n_control[0])
for policy in policies:
self.assertEqual(policy.shape[0], policy_len)
# multistep step policies
policy_len = 3
policies = core.construct_policies(n_states, n_control, policy_len,
control_fac_idx)
for policy in policies:
self.assertEqual(policy.shape[0], policy_len)
# now leave out the optional arguments of `construct_policies` such as `n_control` and `control_fac_idx`
n_states = [3]
# one step policies
policy_len = 1
policies, n_control = core.construct_policies(n_states, None,
policy_len, None)
self.assertEqual(len(policies), n_control[0])
self.assertEqual(n_states[0], n_control[0])
for policy in policies:
self.assertEqual(policy.shape[0], policy_len)
# multistep step policies
policy_len = 3
policies, n_control = core.construct_policies(n_states, None,
policy_len, None)
self.assertEqual(n_states[0], n_control[0])
for policy in policies:
self.assertEqual(policy.shape[0], policy_len)
def test_state_info_gain(self):
"""
Test the states_info_gain function. Demonstrates working
by manipulating uncertainty in the likelihood matrices (A or B)
in a ways that alternatively change the resolvability of uncertainty
(via an imprecise expected state and a precise mapping, or high ambiguity
and imprecise mapping).
"""
n_states = [2]
n_control = [2]
qs = Categorical(values=np.eye(n_states[0])[0])
# add some uncertainty into the consequences of the second policy, which
# leads to increased epistemic value of observations, in case of pursuing
# that policy -- in the case of a precise observation likelihood model
B_matrix = construct_generic_B(n_states, n_control)
B_matrix[:, :, 1] = core.softmax(B_matrix[:, :, 1])
B = Categorical(values=B_matrix)
# single timestep
n_step = 1
policies = core.construct_policies(n_states,
n_control,
policy_len=n_step)
# single observation modality
num_obs = [2]
# create noiseless identity A matrix
A = Categorical(values=np.eye(num_obs[0]))
state_info_gains = np.zeros(len(policies))
for idx, policy in enumerate(policies):
qs_pi = core.get_expected_states(qs, B, policy)
state_info_gains[idx] += core.calc_states_info_gain(A, qs_pi)
self.assertGreater(state_info_gains[1], state_info_gains[0])
# we can 'undo' the epistemic bonus of the second policy by making the A matrix
# totally ambiguous, thus observations cannot resolve uncertainty about hidden states
# - in this case, uncertainty in the posterior beliefs doesn't matter
A = Categorical(values=np.ones((num_obs[0], num_obs[0])))
A.normalize()
state_info_gains = np.zeros(len(policies))
for idx, policy in enumerate(policies):
qs_pi = core.get_expected_states(qs, B, policy)
state_info_gains[idx] += core.calc_states_info_gain(A, qs_pi)
self.assertEqual(state_info_gains[0], state_info_gains[1])
def test_pA_info_gain(self):
"""
Test the pA_info_gain function. Demonstrates operation
by manipulating shape of the Dirichlet priors over likelihood parameters
(pA), which affects information gain for different expected observations
"""
n_states = [2]
n_control = [2]
qs = Categorical(values=np.eye(n_states[0])[0])
B = Categorical(values=construct_generic_B(n_states, n_control))
# single timestep
n_step = 1
policies = core.construct_policies(n_states,
n_control,
policy_len=n_step)
# single observation modality
num_obs = [2]
# create noiseless identity A matrix
A = Categorical(values=np.eye(num_obs[0]))
# create prior over dirichlets such that there is a skew
# in the parameters about the likelihood mapping from the
# second hidden state (index 1) to observations, such that one
# observation is considered to be more likely than the other conditioned on that state.
# Therefore sampling that observation would afford high info gain
# about parameters for that part of the likelhood distribution.
pA_matrix = construct_pA(num_obs, n_states)
pA_matrix[0, 1] = 2.0
pA = Dirichlet(values=pA_matrix)
pA_info_gains = np.zeros(len(policies))
for idx, policy in enumerate(policies):
qs_pi = core.get_expected_states(qs, B, policy)
qo_pi = core.get_expected_obs(qs_pi, A)
pA_info_gains[idx] += core.calc_pA_info_gain(pA, qo_pi, qs_pi)
self.assertGreater(pA_info_gains[1], pA_info_gains[0])
def test_pB_info_gain(self):
"""
Test the pB_info_gain function. Demonstrates operation
by manipulating shape of the Dirichlet priors over likelihood parameters
(pB), which affects information gain for different states
"""
n_states = [2]
n_control = [2]
qs = Categorical(values=np.eye(n_states[0])[0])
B = Categorical(values=construct_generic_B(n_states, n_control))
pB_matrix = construct_pB(n_states, n_control)
# create prior over dirichlets such that there is a skew
# in the parameters about the likelihood mapping from the
# hidden states to hidden states under the second action,
# such that hidden state 0 is considered to be more likely than the other,
# given the action in question
# Therefore taking that action would yield an expected state that afford
# high information gain about that part of the likelihood distribution.
#
pB_matrix[0, :, 1] = 2.0
pB = Dirichlet(values=pB_matrix)
# single timestep
n_step = 1
policies = core.construct_policies(n_states,
n_control,
policy_len=n_step)
pB_info_gains = np.zeros(len(policies))
for idx, policy in enumerate(policies):
qs_pi = core.get_expected_states(qs, B, policy)
pB_info_gains[idx] += core.calc_pB_info_gain(pB, qs_pi, qs, policy)
self.assertGreater(pB_info_gains[1], pB_info_gains[0])
scenes[1][1, 1] = 2
scenes[1][1, 0] = 3
env = VisualForagingEnv(scenes=scenes, n_features=3)
A = env.get_likelihood_dist()
B = env.get_transition_dist()
# if you want parameter information gain and/or learning
# pA = Dirichlet(values = A.values * 1e20)
# pB = Dirichlet(values = B.values * 1e20)
C = np.empty(env.n_modalities, dtype=object)
for g, No in enumerate(env.n_observations):
C[g] = np.zeros(No)
_, possible_policies = core.construct_policies(env.n_states, env.n_factors, [0], 1)
obs = env.reset()
msg = """ === Starting experiment === \n True scene: {} Initial observation {} """
print(msg.format(env.true_scene, obs))
prior = env.get_uniform_posterior()
for t in range(T):
Qs = core.update_posterior_states(A, obs, prior, return_numpy=False)
msg = """[{}] Inference [location {} / scene {}]
Observation [location {} / feature {}] """
print(msg.format(t, Qs[0].sample(), Qs[1].sample(), obs[0], obs[1]))
scenes[1][1, 1] = 2
scenes[1][1, 0] = 3
env = VisualForagingEnv(scenes=scenes, n_features=3)
A = env.get_likelihood_dist()
B = env.get_transition_dist()
# if you want parameter information gain and/or learning
# pA = Dirichlet(values = A.values * 1e20)
# pB = Dirichlet(values = B.values * 1e20)
C = np.empty(env.n_modalities, dtype=object)
for g, No in enumerate(env.n_observations):
C[g] = np.zeros(No)
policies, n_control = core.construct_policies(env.n_states, None, 1, [0])
obs = env.reset()
msg = """ === Starting experiment === \n True scene: {} Initial observation {} """
print(msg.format(env.true_scene, obs))
prior = env.get_uniform_posterior()
for t in range(T):
qs = core.update_posterior_states(A, obs, prior, return_numpy=False)
msg = """[{}] Inference [location {} / scene {}]
Observation [location {} / feature {}] """
print(
msg.format(t, np.argmax(qs[0].values), np.argmax(qs[1].values), obs[0],
def test_expected_utility(self):
"""
Test for the expected utility function, for a simple single factor generative model
where there are imbalances in the preferences for different outcomes. Test for both single
timestep policy horizons and multiple timestep horizons
"""
n_states = [2]
n_control = [2]
qs = Categorical(values=construct_init_qs(n_states))
B = Categorical(values=construct_generic_B(n_states, n_control))
# single timestep
n_step = 1
policies = core.construct_policies(n_states,
n_control,
policy_len=n_step)
# single observation modality
num_obs = [2]
# create noiseless identity A matrix
A = Categorical(values=np.eye(num_obs[0]))
# create imbalance in preferences for observations
C = Categorical(values=np.eye(num_obs[0])[1])
utilities = np.zeros(len(policies))
for idx, policy in enumerate(policies):
qs_pi = core.get_expected_states(qs, B, policy)
qo_pi = core.get_expected_obs(qs_pi, A)
utilities[idx] += core.calc_expected_utility(qo_pi, C)
self.assertGreater(utilities[1], utilities[0])
n_states = [3]
n_control = [3]
qs = Categorical(values=construct_init_qs(n_states))
B = Categorical(values=construct_generic_B(n_states, n_control))
# 3-step policies -- one involves going to state 0 two times in a row, and then state 2 at the end
# -- one involves going to state 1 three times in a row
policies = [
np.array([0, 0, 2]).reshape(-1, 1),
np.array([1, 1, 1]).reshape(-1, 1)
]
# single observation modality
num_obs = [3]
# create noiseless identity A matrix
A = Categorical(values=np.eye(num_obs[0]))
# create imbalance in preferences for observations
# this is designed to illustrate the time-integrated nature of the expected free energy
# -- even though the first observation (index 0) is the most preferred, the policy
# that frequents this observation the most is actually not optimal, because that policy
# ends up visiting a less preferred state at the end.
C = Categorical(values=np.array([1.2, 1, 0.5]))
utilities = np.zeros(len(policies))
for idx, policy in enumerate(policies):
qs_pi = core.get_expected_states(qs, B, policy)
qo_pi = core.get_expected_obs(qs_pi, A)
utilities[idx] += core.calc_expected_utility(qo_pi, C)
self.assertGreater(utilities[1], utilities[0])
def test_multistep_multifac_posteriorPolicies(self):
"""
Test for computing posterior over policies (and associated expected free energies)
in the case of a posterior over hidden states with multiple hidden state factors.
This version tests using a policy horizon of 3 steps ahead
"""
n_states = [3, 4]
n_control = [3, 4]
qs = Categorical(values=construct_init_qs(n_states))
B = Categorical(values=construct_generic_B(n_states, n_control))
pB = Dirichlet(values=construct_pB(n_states, n_control))
# single timestep
n_step = 3
policies = core.construct_policies(n_states,
n_control,
policy_len=n_step)
# single observation modality
num_obs = [4]
A = Categorical(values=construct_generic_A(num_obs, n_states))
pA = Dirichlet(values=construct_pA(num_obs, n_states))
C = Categorical(values=construct_generic_C(num_obs))
q_pi, efe = core.update_posterior_policies(qs,
A,
B,
C,
policies,
use_utility=True,
use_states_info_gain=True,
use_param_info_gain=True,
pA=pA,
pB=pB,
gamma=16.0,
return_numpy=True)
self.assertEqual(len(q_pi), len(policies))
self.assertEqual(len(efe), len(policies))
# multiple observation modalities
num_obs = [3, 2]
A = Categorical(values=construct_generic_A(num_obs, n_states))
pA = Dirichlet(values=construct_pA(num_obs, n_states))
C = Categorical(values=construct_generic_C(num_obs))
q_pi, efe = core.update_posterior_policies(qs,
A,
B,
C,
policies,
use_utility=True,
use_states_info_gain=True,
use_param_info_gain=True,
pA=pA,
pB=pB,
gamma=16.0,
return_numpy=True)
self.assertEqual(len(q_pi), len(policies))
self.assertEqual(len(efe), len(policies))