def test_model_based_ocm_with_buffer(self):
A = pnl.ProcessingMechanism(name='A')
B = pnl.ProcessingMechanism(name='B')
comp = pnl.Composition(name='comp', controller_mode=pnl.BEFORE)
comp.add_linear_processing_pathway([A, B])
search_range = pnl.SampleSpec(start=0.25, stop=0.75, step=0.25)
control_signal = pnl.ControlSignal(
projections=[(pnl.SLOPE, A)],
function=pnl.Linear,
variable=1.0,
allocation_samples=search_range,
intensity_cost_function=pnl.Linear(slope=0.))
objective_mech = pnl.ObjectiveMechanism(monitor=[B])
ocm = pnl.OptimizationControlMechanism(
agent_rep=comp,
features=[A.input_state],
feature_function=pnl.Buffer(history=2),
objective_mechanism=objective_mech,
function=pnl.GridSearch(),
control_signals=[control_signal])
objective_mech.log.set_log_conditions(pnl.OUTCOME)
comp.add_controller(ocm)
inputs = {A: [[[1.0]], [[2.0]], [[3.0]]]}
for i in range(1, len(ocm.input_states)):
ocm.input_states[i].function.reinitialize()
comp.run(inputs=inputs, retain_old_simulation_data=True)
log = objective_mech.log.nparray_dictionary()
# "outer" composition
assert np.allclose(log["comp"][pnl.OUTCOME], [[0.75], [1.5], [2.25]])
# preprocess to ignore control allocations
log_parsed = {}
for key, value in log.items():
cleaned_key = re.sub(r'comp-sim-(\d).*', r'\1', key)
log_parsed[cleaned_key] = value
# First round of simulations is only one trial.
# (Even though the feature fn is a Buffer, there is no history yet)
for i in range(0, 3):
assert len(log_parsed[str(i)]["Trial"]) == 1
# Second and third rounds of simulations are two trials.
# (The buffer has history = 2)
for i in range(3, 9):
assert len(log_parsed[str(i)]["Trial"]) == 2
signal = pnl.ControlSignal(modulates=[(pnl.GAIN, activation)],
function=pnl.Linear,
variable=1.0,
intensity_cost_function=pnl.Linear(slope=0.),
allocation_samples=search_range)
objective_mech = pnl.ObjectiveMechanism(monitor=[
inputLayer, stimulusInfo, (pnl.PROBABILITY_UPPER_THRESHOLD, decisionMaker),
(pnl.PROBABILITY_LOWER_THRESHOLD, decisionMaker)
],
function=computeAccuracy)
meta_controller = pnl.OptimizationControlMechanism(
agent_rep=stabilityFlexibility,
features=[inputLayer.input_port, stimulusInfo.input_port],
feature_function=pnl.Buffer(history=3),
objective_mechanism=objective_mech,
function=pnl.GridSearch(),
control_signals=[signal])
inputs = {inputLayer: INPUT, stimulusInfo: stimulusInput}
stabilityFlexibility.add_controller(meta_controller)
stabilityFlexibility.enable_model_based_optimizer = True
print("Beginning of Run")
for i in range(1, len(stabilityFlexibility.controller.input_ports)):
stabilityFlexibility.controller.input_ports[i].function.reinitialize()
# stabilityFlexibility.show_graph(show_controller=True)
signal = pnl.ControlSignal(modulates=[(pnl.GAIN, activation)],
function=pnl.Linear,
variable=1.0,
allocation_samples=search_range)
objective_mech = pnl.ObjectiveMechanism(monitor=[
inputLayer, stimulusInfo, (pnl.PROBABILITY_UPPER_THRESHOLD, decisionMaker),
(pnl.PROBABILITY_LOWER_THRESHOLD, decisionMaker)
],
function=computeAccuracy)
meta_controller = pnl.OptimizationControlMechanism(
agent_rep=stabilityFlexibility,
state_features=[inputLayer.input_port, stimulusInfo.input_port],
state_feature_function=pnl.Buffer(history=100),
objective_mechanism=objective_mech,
function=pnl.GridSearch(),
control_signals=[signal])
inputs = {inputLayer: INPUT, stimulusInfo: stimulusInput}
stabilityFlexibility.add_model_based_optimizer(meta_controller)
stabilityFlexibility.enable_model_based_optimizer = True
# stabilityFlexibility.show_graph(show_node_structure=[pnl.FUNCTIONS,
# pnl.PORT_FUNCTION_PARAMS,
# pnl.MECH_FUNCTION_PARAMS],
# show_controller=True)
stabilityFlexibility.show_graph(show_node_structure=pnl.ALL,
show_controller=True,
show_cim=True)
def test_stability_flexibility_susan_and_sebastian(self):
# computeAccuracy(trialInformation)
# Inputs: trialInformation[0, 1, 2, 3]
# trialInformation[0] - Task Dimension : [0, 1] or [1, 0]
# trialInformation[1] - Stimulus Dimension: Congruent {[1, 1] or [-1, -1]} // Incongruent {[-1, 1] or [1, -1]}
# trialInformation[2] - Upper Threshold: Probability of DDM choosing upper bound
# trialInformation[3] - Lower Threshold: Probability of DDM choosing lower bound
def computeAccuracy(trialInformation):
# Unload contents of trialInformation
# Origin Node Inputs
taskInputs = trialInformation[0]
stimulusInputs = trialInformation[1]
# DDM Outputs
upperThreshold = trialInformation[2]
lowerThreshold = trialInformation[3]
# Keep Track of Accuracy
accuracy = []
# Beginning of Accuracy Calculation
colorTrial = (taskInputs[0] == 1)
motionTrial = (taskInputs[1] == 1)
# Based on the task dimension information, decide which response is "correct"
# Obtain accuracy probability from DDM thresholds in "correct" direction
if colorTrial:
if stimulusInputs[0] == 1:
accuracy.append(upperThreshold)
elif stimulusInputs[0] == -1:
accuracy.append(lowerThreshold)
if motionTrial:
if stimulusInputs[1] == 1:
accuracy.append(upperThreshold)
elif stimulusInputs[1] == -1:
accuracy.append(lowerThreshold)
# Accounts for initialization runs that have no variable input
if len(accuracy) == 0:
accuracy = [0]
# print("Accuracy: ", accuracy[0])
# print()
return [accuracy]
# BEGIN: Composition Construction
# Constants as defined in Musslick et al. 2018
tau = 0.9 # Time Constant
DRIFT = 1 # Drift Rate
STARTING_POINT = 0.0 # Starting Point
THRESHOLD = 0.0475 # Threshold
NOISE = 0.04 # Noise
T0 = 0.2 # T0
# Task Layer: [Color, Motion] {0, 1} Mutually Exclusive
# Origin Node
taskLayer = pnl.TransferMechanism(default_variable=[[0.0, 0.0]],
size=2,
function=pnl.Linear(slope=1,
intercept=0),
output_states=[pnl.RESULT],
name='Task Input [I1, I2]')
# Stimulus Layer: [Color Stimulus, Motion Stimulus]
# Origin Node
stimulusInfo = pnl.TransferMechanism(default_variable=[[0.0, 0.0]],
size=2,
function=pnl.Linear(slope=1,
intercept=0),
output_states=[pnl.RESULT],
name="Stimulus Input [S1, S2]")
# Activation Layer: [Color Activation, Motion Activation]
# Recurrent: Self Excitation, Mutual Inhibition
# Controlled: Gain Parameter
activation = pnl.RecurrentTransferMechanism(
default_variable=[[0.0, 0.0]],
function=pnl.Logistic(gain=1.0),
matrix=[[1.0, -1.0], [-1.0, 1.0]],
integrator_mode=True,
integrator_function=pnl.AdaptiveIntegrator(rate=(tau)),
initial_value=np.array([[0.0, 0.0]]),
output_states=[pnl.RESULT],
name='Task Activations [Act 1, Act 2]')
# Hadamard product of Activation and Stimulus Information
nonAutomaticComponent = pnl.TransferMechanism(
default_variable=[[0.0, 0.0]],
size=2,
function=pnl.Linear(slope=1, intercept=0),
input_states=pnl.InputState(combine=pnl.PRODUCT),
output_states=[pnl.RESULT],
name='Non-Automatic Component [S1*Activity1, S2*Activity2]')
# Summation of nonAutomatic and Automatic Components
ddmCombination = pnl.TransferMechanism(
size=1,
function=pnl.Linear(slope=1, intercept=0),
input_states=pnl.InputState(combine=pnl.SUM),
output_states=[pnl.RESULT],
name="Drift = (S1 + S2) + (S1*Activity1 + S2*Activity2)")
decisionMaker = pnl.DDM(function=pnl.DriftDiffusionAnalytical(
drift_rate=DRIFT,
starting_point=STARTING_POINT,
threshold=THRESHOLD,
noise=NOISE,
t0=T0),
output_states=[
pnl.DECISION_VARIABLE, pnl.RESPONSE_TIME,
pnl.PROBABILITY_UPPER_THRESHOLD,
pnl.PROBABILITY_LOWER_THRESHOLD
],
name='DDM')
taskLayer.set_log_conditions([pnl.RESULT])
stimulusInfo.set_log_conditions([pnl.RESULT])
activation.set_log_conditions([pnl.RESULT, "mod_gain"])
nonAutomaticComponent.set_log_conditions([pnl.RESULT])
ddmCombination.set_log_conditions([pnl.RESULT])
decisionMaker.set_log_conditions([
pnl.PROBABILITY_UPPER_THRESHOLD, pnl.PROBABILITY_LOWER_THRESHOLD,
pnl.DECISION_VARIABLE, pnl.RESPONSE_TIME
])
# Composition Creation
stabilityFlexibility = pnl.Composition(controller_mode=pnl.BEFORE)
# Node Creation
stabilityFlexibility.add_node(taskLayer)
stabilityFlexibility.add_node(activation)
stabilityFlexibility.add_node(nonAutomaticComponent)
stabilityFlexibility.add_node(stimulusInfo)
stabilityFlexibility.add_node(ddmCombination)
stabilityFlexibility.add_node(decisionMaker)
# Projection Creation
stabilityFlexibility.add_projection(sender=taskLayer,
receiver=activation)
stabilityFlexibility.add_projection(sender=activation,
receiver=nonAutomaticComponent)
stabilityFlexibility.add_projection(sender=stimulusInfo,
receiver=nonAutomaticComponent)
stabilityFlexibility.add_projection(sender=stimulusInfo,
receiver=ddmCombination)
stabilityFlexibility.add_projection(sender=nonAutomaticComponent,
receiver=ddmCombination)
stabilityFlexibility.add_projection(sender=ddmCombination,
receiver=decisionMaker)
# Beginning of Controller
# Grid Search Range
searchRange = pnl.SampleSpec(start=1.0, stop=1.9, num=10)
# Modulate the GAIN parameter from activation layer
# Initalize cost function as 0
signal = pnl.ControlSignal(
projections=[(pnl.GAIN, activation)],
function=pnl.Linear,
variable=1.0,
intensity_cost_function=pnl.Linear(slope=0.0),
allocation_samples=searchRange)
# Use the computeAccuracy function to obtain selection values
# Pass in 4 arguments whenever computeRewardRate is called
objectiveMechanism = pnl.ObjectiveMechanism(
monitor=[
taskLayer, stimulusInfo,
(pnl.PROBABILITY_UPPER_THRESHOLD, decisionMaker),
(pnl.PROBABILITY_LOWER_THRESHOLD, decisionMaker)
],
function=computeAccuracy,
name="Controller Objective Mechanism")
# Sets trial history for simulations over specified signal search parameters
metaController = pnl.OptimizationControlMechanism(
agent_rep=stabilityFlexibility,
features=[taskLayer.input_state, stimulusInfo.input_state],
feature_function=pnl.Buffer(history=10),
name="Controller",
objective_mechanism=objectiveMechanism,
function=pnl.GridSearch(),
control_signals=[signal])
stabilityFlexibility.add_controller(metaController)
stabilityFlexibility.enable_controller = True
# stabilityFlexibility.model_based_optimizer_mode = pnl.BEFORE
for i in range(1, len(stabilityFlexibility.controller.input_states)):
stabilityFlexibility.controller.input_states[
i].function.reinitialize()
# Origin Node Inputs
taskTrain = [[1, 0], [0, 1], [1, 0], [0, 1]]
stimulusTrain = [[1, -1], [-1, 1], [1, -1], [-1, 1]]
inputs = {taskLayer: taskTrain, stimulusInfo: stimulusTrain}
stabilityFlexibility.run(inputs)