def test_nested_control_projection_count_controller(self):
# Inner Composition
ia = TransferMechanism(name='ia')
icomp = Composition(name='icomp', pathways=[ia])
# Outer Composition
ocomp = Composition(name='ocomp', pathways=[icomp])
ocm = OptimizationControlMechanism(
name='ocm',
agent_rep=ocomp,
control_signals=[
ControlSignal(projections=[(NOISE, ia)]),
ControlSignal(projections=[(INTERCEPT, ia)]),
ControlSignal(projections=[(SLOPE, ia)]),
],
search_space=[[1], [1], [1]])
ocomp.add_controller(ocm)
assert len(ocm.efferents) == 3
assert all([
proj.receiver.owner == icomp.parameter_CIM
for proj in ocm.efferents
])
assert len(ia.mod_afferents) == 3
assert all([
proj.sender.owner == icomp.parameter_CIM
for proj in ia.mod_afferents
])
def test_parameter_CIM_port_order(self):
# Note: CIM_port order is also tested in TestNodes and test_simplified_necker_cube()
# Inner Composition
ia = TransferMechanism(name='ia')
icomp = Composition(name='icomp', pathways=[ia])
# Outer Composition
ocomp = Composition(name='ocomp', pathways=[icomp])
ocm = OptimizationControlMechanism(
name='ic',
agent_rep=ocomp,
control_signals=[
ControlSignal(projections=[(NOISE, ia)]),
ControlSignal(projections=[(INTERCEPT, ia)]),
ControlSignal(projections=[(SLOPE, ia)]),
])
ocomp.add_controller(ocm)
assert INTERCEPT in icomp.parameter_CIM.output_ports.names[0]
assert NOISE in icomp.parameter_CIM.output_ports.names[1]
assert SLOPE in icomp.parameter_CIM.output_ports.names[2]
def test_predator_prey(benchmark, mode, samples):
if len(samples) > 10 and mode not in {"LLVM", "LLVMRun", "Python-PTX"}:
pytest.skip("This test takes too long")
# OCM default mode is Python
mode, ocm_mode = (mode + "-Python").split('-')[0:2]
benchmark.group = "Predator-Prey " + str(len(samples))
obs_len = 3
obs_coords = 2
player_idx = 0
player_obs_start_idx = player_idx * obs_len
player_value_idx = player_idx * obs_len + obs_coords
player_coord_slice = slice(player_obs_start_idx,player_value_idx)
predator_idx = 1
predator_obs_start_idx = predator_idx * obs_len
predator_value_idx = predator_idx * obs_len + obs_coords
predator_coord_slice = slice(predator_obs_start_idx,predator_value_idx)
prey_idx = 2
prey_obs_start_idx = prey_idx * obs_len
prey_value_idx = prey_idx * obs_len + obs_coords
prey_coord_slice = slice(prey_obs_start_idx,prey_value_idx)
player_len = prey_len = predator_len = obs_coords
# Perceptual Mechanisms
player_obs = ProcessingMechanism(size=prey_len, function=GaussianDistort, name="PLAYER OBS")
prey_obs = ProcessingMechanism(size=prey_len, function=GaussianDistort, name="PREY OBS")
predator_obs = TransferMechanism(size=predator_len, function=GaussianDistort, name="PREDATOR OBS")
# Action Mechanism
# Use ComparatorMechanism to compute direction of action as difference of coordinates between player and prey:
# note: unitization is done in main loop, to allow compilation of LinearCombination function) (TBI)
greedy_action_mech = ComparatorMechanism(name='ACTION',sample=player_obs,target=prey_obs)
# Create Composition
agent_comp = Composition(name='PREDATOR-PREY COMPOSITION')
agent_comp.add_node(player_obs)
agent_comp.add_node(predator_obs)
agent_comp.add_node(prey_obs)
agent_comp.add_node(greedy_action_mech)
agent_comp.exclude_node_roles(predator_obs, NodeRole.OUTPUT)
ocm = OptimizationControlMechanism(features={SHADOW_INPUTS: [player_obs, predator_obs, prey_obs]},
agent_rep=agent_comp,
function=GridSearch(direction=MINIMIZE,
save_values=True),
objective_mechanism=ObjectiveMechanism(function=Distance(metric=NORMED_L0_SIMILARITY),
monitor=[
player_obs,
prey_obs
]),
control_signals=[ControlSignal(modulates=(VARIANCE,player_obs),
allocation_samples=samples),
ControlSignal(modulates=(VARIANCE,predator_obs),
allocation_samples=samples),
ControlSignal(modulates=(VARIANCE,prey_obs),
allocation_samples=samples)
],
)
agent_comp.add_controller(ocm)
agent_comp.enable_controller = True
ocm.comp_execution_mode = ocm_mode
input_dict = {player_obs:[[1.1576537, 0.60782117]],
predator_obs:[[-0.03479106, -0.47666293]],
prey_obs:[[-0.60836214, 0.1760381 ]],
}
run_results = agent_comp.run(inputs=input_dict, num_trials=2, bin_execute=mode)
if len(samples) == 2:
# KDM 12/4/19: modified results due to global seed offset of
# GaussianDistort assignment.
# to produce old numbers, run get_global_seed once before creating
# each Mechanism with GaussianDistort above
assert np.allclose(run_results[0], [[-10.06333025, 2.4845505 ]])
if mode == 'Python':
assert np.allclose(ocm.feature_values, [[ 1.1576537, 0.60782117],
[-0.03479106, -0.47666293],
[-0.60836214, 0.1760381 ]])
if benchmark.enabled:
benchmark(agent_comp.run, inputs=input_dict, bin_execute=mode)
def test_predator_prey(benchmark, mode, samples):
benchmark.group = "Predator-Prey " + str(len(samples))
# These should probably be replaced by reference to ForagerEnv constants:
obs_len = 3
obs_coords = 2
action_len = 2
player_idx = 0
player_obs_start_idx = player_idx * obs_len
player_value_idx = player_idx * obs_len + obs_coords
player_coord_slice = slice(player_obs_start_idx, player_value_idx)
predator_idx = 1
predator_obs_start_idx = predator_idx * obs_len
predator_value_idx = predator_idx * obs_len + obs_coords
predator_coord_slice = slice(predator_obs_start_idx, predator_value_idx)
prey_idx = 2
prey_obs_start_idx = prey_idx * obs_len
prey_value_idx = prey_idx * obs_len + obs_coords
prey_coord_slice = slice(prey_obs_start_idx, prey_value_idx)
player_len = prey_len = predator_len = obs_coords
# Perceptual Mechanisms
player_obs = ProcessingMechanism(size=prey_len,
function=GaussianDistort,
name="PLAYER OBS")
prey_obs = ProcessingMechanism(size=prey_len,
function=GaussianDistort,
name="PREY OBS")
predator_obs = TransferMechanism(size=predator_len,
function=GaussianDistort,
name="PREDATOR OBS")
# Action Mechanism
# Use ComparatorMechanism to compute direction of action as difference of coordinates between player and prey:
# note: unitization is done in main loop, to allow compilation of LinearCombination function) (TBI)
greedy_action_mech = ComparatorMechanism(name='ACTION',
sample=player_obs,
target=prey_obs)
# Create Composition
agent_comp = Composition(name='PREDATOR-PREY COMPOSITION')
agent_comp.add_node(player_obs)
agent_comp.add_node(predator_obs)
agent_comp.add_node(prey_obs)
agent_comp.add_node(greedy_action_mech)
# ControlMechanism
ocm = OptimizationControlMechanism(
features={SHADOW_INPUTS: [player_obs, predator_obs, prey_obs]},
agent_rep=agent_comp,
function=GridSearch(direction=MINIMIZE, save_values=True),
objective_mechanism=ObjectiveMechanism(
function=Distance(metric=NORMED_L0_SIMILARITY),
monitor=[player_obs, predator_obs, prey_obs]),
control_signals=[
ControlSignal(modulates=(VARIANCE, player_obs),
allocation_samples=samples),
ControlSignal(modulates=(VARIANCE, predator_obs),
allocation_samples=samples),
ControlSignal(modulates=(VARIANCE, prey_obs),
allocation_samples=samples),
],
)
agent_comp.add_controller(ocm)
agent_comp.enable_controller = True
ocm.comp_execution_mode = mode
input_dict = {
player_obs: [[1.1576537, 0.60782117]],
predator_obs: [[-0.03479106, -0.47666293]],
prey_obs: [[-0.60836214, 0.1760381]],
}
run_results = agent_comp.run(inputs=input_dict,
num_trials=2,
bin_execute=mode)
if len(samples) == 2:
assert np.allclose(run_results[0], [[-19.06547277, 5.47274121]])
if mode == 'Python':
assert np.allclose(
ocm.feature_values,
[[1.1576537, 0.60782117], [-0.03479106, -0.47666293],
[-0.60836214, 0.1760381]])
benchmark(agent_comp.run, inputs=input_dict, bin_execute=mode)
