def test_formats_for_control_specification_for_mechanism_and_function_params(
self):
clear_registry()
control_spec_list = [
pnl.CONTROL, pnl.CONTROL_SIGNAL, pnl.CONTROL_PROJECTION,
pnl.ControlSignal,
pnl.ControlSignal(), pnl.ControlProjection, "CP_OBJECT",
pnl.ControlMechanism,
pnl.ControlMechanism(), (0.3, pnl.CONTROL),
(0.3, pnl.CONTROL_SIGNAL), (0.3, pnl.CONTROL_PROJECTION),
(0.3, pnl.ControlSignal), (0.3, pnl.ControlSignal()),
(0.3, pnl.ControlProjection), (0.3, "CP_OBJECT"),
(0.3, pnl.ControlMechanism), (0.3, pnl.ControlMechanism())
]
for i, ctl_tuple in enumerate(
[j for j in zip(control_spec_list, reversed(control_spec_list))]):
C1, C2 = ctl_tuple
# This shenanigans is to avoid assigning the same instantiated ControlProjection more than once
if C1 is 'CP_OBJECT':
C1 = pnl.ControlProjection()
elif isinstance(C1, tuple) and C1[1] is 'CP_OBJECT':
C1 = (C1[0], pnl.ControlProjection())
if C2 is 'CP_OBJECT':
C2 = pnl.ControlProjection()
elif isinstance(C2, tuple) and C2[1] is 'CP_OBJECT':
C2 = (C2[0], pnl.ControlProjection())
R = pnl.RecurrentTransferMechanism(noise=C1,
function=pnl.Logistic(gain=C2))
assert R.parameter_states[pnl.NOISE].mod_afferents[0].name in \
'ControlProjection for RecurrentTransferMechanism-{}[noise]'.format(i)
assert R.parameter_states[pnl.GAIN].mod_afferents[0].name in \
'ControlProjection for RecurrentTransferMechanism-{}[gain]'.format(i)
def test_control_modulation(self):
Tx = pnl.TransferMechanism(name='Tx')
Ty = pnl.TransferMechanism(name='Ty')
Tz = pnl.TransferMechanism(name='Tz')
C = pnl.ControlMechanism(
# function=pnl.Linear,
default_variable=[1],
monitor_for_control=Ty,
objective_mechanism=True,
control_signals=pnl.ControlSignal(modulation=pnl.OVERRIDE,
modulates=(pnl.SLOPE, Tz)))
comp = pnl.Composition(pathways=[[Tx, Tz], [Ty, C]])
# comp.show_graph()
assert Tz.parameter_ports[pnl.SLOPE].mod_afferents[0].sender.owner == C
result = comp.run(inputs={Tx: [1, 1], Ty: [4, 4]})
assert comp.results == [[[4.], [4.]], [[4.], [4.]]]
def test_control_modulation(self):
Tx = pnl.TransferMechanism(name='Tx')
Ty = pnl.TransferMechanism(name='Ty')
Tz = pnl.TransferMechanism(name='Tz')
C = pnl.ControlMechanism(
# function=pnl.Linear,
default_variable=[1],
monitor_for_control=Ty,
control_signals=pnl.ControlSignal(modulation=pnl.OVERRIDE,
projections=(pnl.SLOPE,Tz)))
P1=pnl.Process(pathway=[Tx,Tz])
P2=pnl.Process(pathway=[Ty, C])
S=pnl.System(processes=[P1, P2])
assert Tz.parameter_states[pnl.SLOPE].mod_afferents[0].sender.owner == C
result = S.run(inputs={Tx:[1,1], Ty:[4,4]})
assert result == [[[4.], [4.]], [[4.], [4.]]]
def test_grid_search_random_selection(self):
A = pnl.ProcessingMechanism(name='A')
A.log.set_log_conditions(items="mod_slope")
B = pnl.ProcessingMechanism(name='B', function=pnl.Logistic())
comp = pnl.Composition(name='comp')
comp.add_linear_processing_pathway([A, B])
search_range = pnl.SampleSpec(start=15., stop=35., step=5)
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],
objective_mechanism=objective_mech,
function=pnl.GridSearch(select_randomly_from_optimal_values=True),
control_signals=[control_signal])
comp.add_controller(ocm)
inputs = {A: [[[1.0]]]}
comp.run(inputs=inputs, num_trials=10, execution_id='outer_comp')
log_arr = A.log.nparray_dictionary()
# control signal value (mod slope) is chosen randomly from all of the control signal values
# that correspond to a net outcome of 1
assert np.allclose([[1.], [15.], [15.], [20.], [20.], [15.], [20.],
[25.], [15.], [35.]],
log_arr['outer_comp']['mod_slope'])
def test_model_based_num_estimates(self):
A = pnl.ProcessingMechanism(name='A')
B = pnl.ProcessingMechanism(name='B',
function=pnl.SimpleIntegrator(rate=1))
comp = pnl.Composition(name='comp')
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],
objective_mechanism=objective_mech,
function=pnl.GridSearch(),
num_estimates=5,
control_signals=[control_signal])
comp.add_controller(ocm)
inputs = {A: [[[1.0]]]}
comp.run(inputs=inputs, num_trials=2)
assert np.allclose(
comp.simulation_results,
[[np.array([2.25])], [np.array([3.5])], [np.array([4.75])],
[np.array([3.])], [np.array([4.25])], [np.array([5.5])]])
assert np.allclose(comp.results,
[[np.array([1.])], [np.array([1.75])]])
def test_model_based_ocm_before(self, benchmark, mode):
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],
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]]]}
comp.run(inputs=inputs, bin_execute=mode)
# objective_mech.log.print_entries(pnl.OUTCOME)
assert np.allclose(
comp.results,
[[np.array([0.75])], [np.array([1.5])], [np.array([2.25])]])
benchmark(comp.run, inputs, bin_execute=mode)
TargetAutomatic_pathway = [Target_Stim, Automatic_Component, Decision]
Umemoto_comp.add_linear_processing_pathway(TargetAutomatic_pathway)
FlankerAutomatic_pathway = [Distractor_Stim, Automatic_Component, Decision]
Umemoto_comp.add_linear_processing_pathway(FlankerAutomatic_pathway)
Reward_pathway = [Reward]
Umemoto_comp.add_linear_processing_pathway(Reward_pathway)
Umemoto_comp.add_node(Decision, required_roles=pnl.NodeRole.TERMINAL)
# COMPOSITION
Target_Rep_Control_Signal = pnl.ControlSignal(modulates=[(pnl.SLOPE, Target_Rep)],
function=pnl.Linear,
variable=1.0,
cost_options=[pnl.CostFunctions.INTENSITY, pnl.CostFunctions.ADJUSTMENT],
intensity_cost_function=pnl.Exponential(scale=1, rate=1),
adjustment_cost_function=pnl.Exponential(scale=1, rate=1, offset=-1),
allocation_samples=signalSearchRange)
Distractor_Rep_Control_Signal = pnl.ControlSignal(modulates=[(pnl.SLOPE, Distractor_Rep)],
function=pnl.Linear,
variable=1.0,
cost_options=[pnl.CostFunctions.INTENSITY, pnl.CostFunctions.ADJUSTMENT],
intensity_cost_function=pnl.Exponential(rate=0.8046),
adjustment_cost_function=pnl.Exponential(scale=1, rate=1, offset=-1),
allocation_samples=signalSearchRange)
Umemoto_comp.add_model_based_optimizer(optimizer=pnl.OptimizationControlMechanism(agent_rep=Umemoto_comp,
features={pnl.SHADOW_EXTERNAL_INPUTS: [Target_Stim, Distractor_Stim, Reward]},
feature_function=pnl.AdaptiveIntegrator(rate=1.0),
RewardProcess = pnl.Process(
pathway=[Reward],
name='RewardProcess'
)
# System:
mySystem = pnl.System(processes=[TargetControlProcess,
FlankerControlProcess,
TargetAutomaticProcess,
FlankerAutomaticProcess,
RewardProcess],
controller=pnl.EVCControlMechanism(
control_signals=pnl.ControlSignal(projections=[(pnl.SLOPE, Target_Rep),
(pnl.SLOPE, Distractor_Rep)
],
function=psyneulink.core.components.functions.transferfunctions.Logistic,
cost_options=[pnl.ControlSignalCosts.INTENSITY,
pnl.ControlSignalCosts.ADJUSTMENT],
allocation_samples=signalSearchRange
)),
enable_controller=True,
monitor_for_control=[
# (None, None, np.ones((2,1))), # what the **** is this for? Markus October 25 2018
Reward,
Decision.PROBABILITY_UPPER_THRESHOLD,
('OFFSET RT', 1, -1),
],
name='EVC Markus System')
# log controller
mySystem.loggable_items
receiver=controlledElement)
stabilityFlexibility.add_projection(sender=stimulusInfo,
receiver=controlledElement)
stabilityFlexibility.add_projection(sender=stimulusInfo,
receiver=ddmCombination)
stabilityFlexibility.add_projection(sender=controlledElement,
receiver=ddmCombination)
stabilityFlexibility.add_projection(sender=ddmCombination,
receiver=decisionMaker)
# beginning of Controller
search_range = pnl.SampleSpec(start=1.0, stop=1.3, num=3)
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_features = {pnl.SHADOW_INPUTS: [inputLayer, stimulusInfo]},
# state_features = [(inputLayer, pnl.SHADOW_INPUTS),
# (stimulusInfo, pnl.SHADOW_INPUTS)],
objective_mechanism=objective_mech,
default_variable=[0],
pathway=[Distractor_Stim, Automatic_Component_Flanker, Decision], #
name='Flanker1 Automatic Process')
RewardProcess = pnl.Process(pathway=[Reward], name='RewardProcess')
# System:
mySystem = pnl.System(
processes=[
TargetControlProcess, FlankerControlProcess, TargetAutomaticProcess,
FlankerAutomaticProcess, RewardProcess
],
controller=pnl.EVCControlMechanism(control_signals=pnl.ControlSignal(
modulates=[(pnl.SLOPE, Target_Rep), (pnl.SLOPE, Distractor_Rep)],
function=psyneulink.core.components.functions.nonstateful.
transferfunctions.Logistic,
cost_options=[
pnl.CostFunctions.INTENSITY, pnl.CostFunctions.ADJUSTMENT
],
allocation_samples=signalSearchRange)),
enable_controller=True,
monitor_for_control=[
# (None, None, np.ones((2,1))), # what the **** is this for? Markus October 25 2018
Reward,
Decision.PROBABILITY_UPPER_THRESHOLD,
('OFFSET RT', 1, -1),
],
name='EVC Markus System')
# log controller
mySystem.loggable_items
mu_0=-0.17,
sigma_0=9.0909), # -0.17, 9.0909 precision = 0.11; 1/p = v
prediction_terms=[pnl.PV.FC, pnl.PV.COST]),
function=pnl.GradientOptimization(
convergence_criterion=pnl.VALUE,
convergence_threshold=0.001, #0.001
step_size=1, #1
annealing_function=lambda x, y: x / np.sqrt(y)
# direction=pnl.ASCENT
),
control_signals=pnl.ControlSignal(
projections=[(pnl.SLOPE, color_task), ('color_control', word_task)],
# function=pnl.ReLU,
function=pnl.Logistic,
cost_options=[
pnl.ControlSignalCosts.INTENSITY, pnl.ControlSignalCosts.ADJUSTMENT
],
intensity_cost_function=pnl.Exponential(rate=0.25, bias=-3),
adjustment_cost_function=pnl.Exponential(rate=0.25, bias=-3),
# allocation_samples=[i / 2 for i in list(range(0, 50, 1))]
))
# print(lvoc.loggable_items)
lvoc.set_log_conditions('value')
# print(lvoc.loggable_items)
# lvoc.set_log_conditions('variable')
# lvoc.agent_rep.set_log_conditions('regression_weights')
lvoc.reportOutputPref = True
c.add_node(lvoc)
flanker_automatic_pathway
]
# Composition
evc_gratton = pnl.Composition(name="EVCGratton")
evc_gratton.add_node(Decision, required_roles=pnl.NodeRole.OUTPUT)
for path in pathways:
evc_gratton.add_linear_processing_pathway(path)
evc_gratton.add_node(reward, required_roles=pnl.NodeRole.OUTPUT)
# Control Signals
signalSearchRange = pnl.SampleSpec(start=1.0, stop=1.8, step=0.2)
target_rep_control_signal = pnl.ControlSignal(
projections=[(pnl.SLOPE, Target_Rep)],
variable=1.0,
intensity_cost_function=pnl.Exponential(rate=0.8046),
allocation_samples=signalSearchRange)
flanker_rep_control_signal = pnl.ControlSignal(
projections=[(pnl.SLOPE, Flanker_Rep)],
variable=1.0,
intensity_cost_function=pnl.Exponential(rate=0.8046),
allocation_samples=signalSearchRange)
objective_mech = pnl.ObjectiveMechanism(
function=pnl.LinearCombination(operation=pnl.PRODUCT),
monitor=[
reward, (Decision.output_ports[pnl.PROBABILITY_UPPER_THRESHOLD], 1, -1)
])
# Model Based OCM (formerly controller)
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)
def test_evc_gratton(self):
# Stimulus Mechanisms
target_stim = pnl.TransferMechanism(name='Target Stimulus',
function=pnl.Linear(slope=0.3324))
flanker_stim = pnl.TransferMechanism(
name='Flanker Stimulus', function=pnl.Linear(slope=0.3545221843))
# Processing Mechanisms (Control)
Target_Rep = pnl.TransferMechanism(name='Target Representation')
Flanker_Rep = pnl.TransferMechanism(name='Flanker Representation')
# Processing Mechanism (Automatic)
Automatic_Component = pnl.TransferMechanism(name='Automatic Component')
# Decision Mechanism
Decision = pnl.DDM(name='Decision',
function=pnl.DriftDiffusionAnalytical(
drift_rate=(1.0),
threshold=(0.2645),
noise=(0.5),
starting_point=(0),
t0=0.15),
output_states=[
pnl.DECISION_VARIABLE, pnl.RESPONSE_TIME,
pnl.PROBABILITY_UPPER_THRESHOLD
])
# Outcome Mechanism
reward = pnl.TransferMechanism(name='reward')
# Pathways
target_control_pathway = [target_stim, Target_Rep, Decision]
flanker_control_pathway = [flanker_stim, Flanker_Rep, Decision]
target_automatic_pathway = [target_stim, Automatic_Component, Decision]
flanker_automatic_pathway = [
flanker_stim, Automatic_Component, Decision
]
pathways = [
target_control_pathway, flanker_control_pathway,
target_automatic_pathway, flanker_automatic_pathway
]
# Composition
evc_gratton = pnl.Composition(name="EVCGratton")
evc_gratton.add_node(Decision, required_roles=pnl.NodeRole.OUTPUT)
for path in pathways:
evc_gratton.add_linear_processing_pathway(path)
evc_gratton.add_node(reward, required_roles=pnl.NodeRole.OUTPUT)
# Control Signals
signalSearchRange = pnl.SampleSpec(start=1.0, stop=1.8, step=0.2)
target_rep_control_signal = pnl.ControlSignal(
projections=[(pnl.SLOPE, Target_Rep)],
function=pnl.Linear,
variable=1.0,
intensity_cost_function=pnl.Exponential(rate=0.8046),
allocation_samples=signalSearchRange)
flanker_rep_control_signal = pnl.ControlSignal(
projections=[(pnl.SLOPE, Flanker_Rep)],
function=pnl.Linear,
variable=1.0,
intensity_cost_function=pnl.Exponential(rate=0.8046),
allocation_samples=signalSearchRange)
objective_mech = pnl.ObjectiveMechanism(
function=pnl.LinearCombination(operation=pnl.PRODUCT),
monitor=[
reward,
(Decision.output_states[pnl.PROBABILITY_UPPER_THRESHOLD], 1,
-1)
])
# Model Based OCM (formerly controller)
evc_gratton.add_controller(controller=pnl.OptimizationControlMechanism(
agent_rep=evc_gratton,
features=[
target_stim.input_state, flanker_stim.input_state,
reward.input_state
],
feature_function=pnl.AdaptiveIntegrator(rate=1.0),
objective_mechanism=objective_mech,
function=pnl.GridSearch(),
control_signals=[
target_rep_control_signal, flanker_rep_control_signal
]))
evc_gratton.enable_controller = True
targetFeatures = [1, 1, 1]
flankerFeatures = [1, -1, 1]
rewardValues = [100, 100, 100]
stim_list_dict = {
target_stim: targetFeatures,
flanker_stim: flankerFeatures,
reward: rewardValues
}
evc_gratton.run(inputs=stim_list_dict)
expected_results_array = [[[0.32257752863413636], [0.9481940753514433],
[100.]],
[[0.42963678062444666],
[0.47661180945923376], [100.]],
[[0.300291026852769], [0.97089165101931],
[100.]]]
expected_sim_results_array = [
[[0.32257753], [0.94819408], [100.]],
[[0.31663196], [0.95508757], [100.]],
[[0.31093566], [0.96110142], [100.]],
[[0.30548947], [0.96633839], [100.]],
[[0.30029103], [0.97089165], [100.]],
[[0.3169957], [0.95468427], [100.]],
[[0.31128378], [0.9607499], [100.]],
[[0.30582202], [0.96603252], [100.]],
[[0.30060824], [0.9706259], [100.]],
[[0.29563774], [0.97461444], [100.]],
[[0.31163288], [0.96039533], [100.]],
[[0.30615555], [0.96572397], [100.]],
[[0.30092641], [0.97035779], [100.]],
[[0.2959409], [0.97438178], [100.]],
[[0.29119255], [0.97787196], [100.]],
[[0.30649004], [0.96541272], [100.]],
[[0.30124552], [0.97008732], [100.]],
[[0.29624499], [0.97414704], [100.]],
[[0.29148205], [0.97766847], [100.]],
[[0.28694892], [0.98071974], [100.]],
[[0.30156558], [0.96981445], [100.]],
[[0.29654999], [0.97391021], [100.]],
[[0.29177245], [0.97746315], [100.]],
[[0.28722523], [0.98054192], [100.]],
[[0.28289958], [0.98320731], [100.]],
[[0.42963678], [0.47661181], [100.]],
[[0.42846471], [0.43938586], [100.]],
[[0.42628176], [0.40282965], [100.]],
[[0.42314468], [0.36732207], [100.]],
[[0.41913221], [0.333198], [100.]],
[[0.42978939], [0.51176048], [100.]],
[[0.42959394], [0.47427693], [100.]],
[[0.4283576], [0.43708106], [100.]],
[[0.4261132], [0.40057958], [100.]],
[[0.422919], [0.36514906], [100.]],
[[0.42902209], [0.54679323], [100.]],
[[0.42980788], [0.50942101], [100.]],
[[0.42954704], [0.47194318], [100.]],
[[0.42824656], [0.43477897], [100.]],
[[0.42594094], [0.3983337], [100.]],
[[0.42735293], [0.58136855], [100.]],
[[0.42910149], [0.54447221], [100.]],
[[0.42982229], [0.50708112], [100.]],
[[0.42949608], [0.46961065], [100.]],
[[0.42813159], [0.43247968], [100.]],
[[0.42482049], [0.61516258], [100.]],
[[0.42749136], [0.57908829], [100.]],
[[0.42917687], [0.54214925], [100.]],
[[0.42983261], [0.50474093], [100.]],
[[0.42944107], [0.46727945], [100.]],
[[0.32257753], [0.94819408], [100.]],
[[0.31663196], [0.95508757], [100.]],
[[0.31093566], [0.96110142], [100.]],
[[0.30548947], [0.96633839], [100.]],
[[0.30029103], [0.97089165], [100.]],
[[0.3169957], [0.95468427], [100.]],
[[0.31128378], [0.9607499], [100.]],
[[0.30582202], [0.96603252], [100.]],
[[0.30060824], [0.9706259], [100.]],
[[0.29563774], [0.97461444], [100.]],
[[0.31163288], [0.96039533], [100.]],
[[0.30615555], [0.96572397], [100.]],
[[0.30092641], [0.97035779], [100.]],
[[0.2959409], [0.97438178], [100.]],
[[0.29119255], [0.97787196], [100.]],
[[0.30649004], [0.96541272], [100.]],
[[0.30124552], [0.97008732], [100.]],
[[0.29624499], [0.97414704], [100.]],
[[0.29148205], [0.97766847], [100.]],
[[0.28694892], [0.98071974], [100.]],
[[0.30156558], [0.96981445], [100.]],
[[0.29654999], [0.97391021], [100.]],
[[0.29177245], [0.97746315], [100.]],
[[0.28722523], [0.98054192], [100.]],
[[0.28289958], [0.98320731], [100.]],
]
for trial in range(len(evc_gratton.results)):
assert np.allclose(
expected_results_array[trial],
# Note: Skip decision variable OutputState
evc_gratton.results[trial][1:])
for simulation in range(len(evc_gratton.simulation_results)):
assert np.allclose(
expected_sim_results_array[simulation],
# Note: Skip decision variable OutputState
evc_gratton.simulation_results[simulation][1:])
def test_control_signal_default_allocation_specification(self):
m1 = pnl.ProcessingMechanism()
m2 = pnl.ProcessingMechanism()
m3 = pnl.ProcessingMechanism()
# default_allocation not specified in constructor of pnl.ControlMechanism,
# so should be set to defaultControlAllocation (=[1]) if not specified in pnl.ControlSignal constructor
c1 = pnl.ControlMechanism(
name='C1',
default_variable=[10],
control_signals=[
pnl.ControlSignal(modulates=(
pnl.SLOPE,
m1)), # test for assignment to defaultControlAllocation
pnl.ControlSignal(
default_allocation=2, # test for scalar assignment
modulates=(pnl.SLOPE, m2)),
pnl.ControlSignal(
default_allocation=[3], # test for array assignment
modulates=(pnl.SLOPE, m3))
])
comp = pnl.Composition()
comp.add_nodes([m1, m2, m3])
comp.add_controller(c1)
assert c1.control_signals[0].value == [
10
] # defaultControlAllocation should be assigned
# (as no default_allocation from pnl.ControlMechanism)
assert m1.parameter_ports[pnl.SLOPE].value == [1]
assert c1.control_signals[1].value == [
2
] # default_allocation from pnl.ControlSignal (converted scalar)
assert m2.parameter_ports[pnl.SLOPE].value == [1]
assert c1.control_signals[2].value == [
3
] # default_allocation from pnl.ControlSignal
assert m3.parameter_ports[pnl.SLOPE].value == [1]
result = comp.run(inputs={m1: [2], m2: [3], m3: [4]})
assert np.allclose(result, [[20.], [6.], [12.]])
assert c1.control_signals[0].value == [10]
assert m1.parameter_ports[pnl.SLOPE].value == [10]
assert c1.control_signals[1].value == [10]
assert m2.parameter_ports[pnl.SLOPE].value == [2]
assert c1.control_signals[2].value == [10]
assert m3.parameter_ports[pnl.SLOPE].value == [3]
result = comp.run(inputs={m1: [2], m2: [3], m3: [4]})
assert np.allclose(result, [[20.], [30.], [40.]])
assert c1.control_signals[0].value == [10]
assert m1.parameter_ports[pnl.SLOPE].value == [10]
assert c1.control_signals[1].value == [10]
assert m2.parameter_ports[pnl.SLOPE].value == [10]
assert c1.control_signals[2].value == [10]
assert m3.parameter_ports[pnl.SLOPE].value == [10]
# default_allocation *is* specified in constructor of pnl.ControlMechanism,
# so should be used unless specified in pnl.ControlSignal constructor
c2 = pnl.ControlMechanism(
name='C3',
default_variable=[10],
default_allocation=[4],
control_signals=[
pnl.ControlSignal(modulates=(
pnl.SLOPE,
m1)), # tests for assignment to default_allocation
pnl.ControlSignal(
default_allocation=
5, # tests for override of default_allocation
modulates=(pnl.SLOPE, m2)),
pnl.ControlSignal(
default_allocation=[6], # as above same but with array
modulates=(pnl.SLOPE, m3))
])
comp = pnl.Composition()
comp.add_nodes([m1, m2, m3])
comp.add_controller(c2)
assert c2.control_signals[0].value == [
4
] # default_allocation from pnl.ControlMechanism assigned
assert m1.parameter_ports[pnl.SLOPE].value == [
10
] # has not yet received pnl.ControlSignal value
assert c2.control_signals[1].value == [
5
] # default_allocation from pnl.ControlSignal assigned (converted scalar)
assert m2.parameter_ports[pnl.SLOPE].value == [10]
assert c2.control_signals[2].value == [
6
] # default_allocation from pnl.ControlSignal assigned
assert m3.parameter_ports[pnl.SLOPE].value == [10]
result = comp.run(inputs={m1: [2], m2: [3], m3: [4]})
assert np.allclose(result, [[8.], [15.], [24.]])
assert c2.control_signals[0].value == [10]
assert m1.parameter_ports[pnl.SLOPE].value == [4]
assert c2.control_signals[1].value == [10]
assert m2.parameter_ports[pnl.SLOPE].value == [5]
assert c2.control_signals[2].value == [10]
assert m3.parameter_ports[pnl.SLOPE].value == [6]
result = comp.run(inputs={m1: [2], m2: [3], m3: [4]})
assert np.allclose(result, [[20.], [30.], [40.]])
assert c2.control_signals[0].value == [10]
assert m1.parameter_ports[pnl.SLOPE].value == [10]
assert c2.control_signals[1].value == [10]
assert m2.parameter_ports[pnl.SLOPE].value == [10]
assert c2.control_signals[2].value == [10]
assert m3.parameter_ports[pnl.SLOPE].value == [10]
stabilityFlexibility.add_projection(sender=stimulusInfo,
receiver=controlledElement)
stabilityFlexibility.add_projection(sender=stimulusInfo,
receiver=ddmCombination)
stabilityFlexibility.add_projection(sender=controlledElement,
receiver=ddmCombination)
stabilityFlexibility.add_projection(sender=ddmCombination,
receiver=decisionMaker)
# beginning of Controller
search_range = pnl.SampleSpec(start=0.1, stop=0.3, num=3)
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(),
FlankerAutomatic_pathway = [Distractor_Stim, Distactor_Component]
Umemoto_comp.add_linear_processing_pathway(FlankerAutomatic_pathway)
Reward_pathway = [Reward]
Umemoto_comp.add_linear_processing_pathway(Reward_pathway)
Umemoto_comp.add_node(Decision,
# required_roles=pnl.NodeRole.OUTPUT
)
# COMPOSITION
Target_Rep_Control_Signal = pnl.ControlSignal(
modulates=[(pnl.SLOPE, Target_Rep)],
function=pnl.Linear,
variable=1.0,
cost_options=[pnl.CostFunctions.INTENSITY, pnl.CostFunctions.ADJUSTMENT],
intensity_cost_function=pnl.Exponential(scale=1, rate=1),
# compute_reconfiguration_cost=pnl.Distance(metric=pnl.EUCLIDEAN),
# adjustment_cost_function=pnl.Exponential(scale=1, rate=1, offset=-1),#offset = -1
allocation_samples=signalSearchRange)
Distractor_Rep_Control_Signal = pnl.ControlSignal(
modulates=[(pnl.SLOPE, Distractor_Rep)],
function=pnl.Linear,
variable=1.0,
cost_options=[pnl.CostFunctions.INTENSITY, pnl.CostFunctions.ADJUSTMENT],
intensity_cost_function=pnl.Exponential(scale=1, rate=1),
# adjustment_cost_function=pnl.Exponential(scale=1, rate=1, offset=-1),
#offset = -1
allocation_samples=signalSearchRange)
# save_samples=True,
# save_values=True,
# direction=pnl.ASCENT
),
# opt control alloc used to compute ctrl sigs
control_signals=[
pnl.ControlSignal(
default_allocation=default_control_signal,
modulates=[(pnl.SLOPE, color_task), ('color_control', word_task)],
# function=pnl.ReLU,
# function=pnl.Logistic,
cost_options=[
pnl.CostFunctions.INTENSITY, pnl.CostFunctions.ADJUSTMENT
],
intensity_cost_function=pnl.Exponential(rate=0.25,
bias=-1), # 0.25, -3
# adjustment_cost_function=pnl.Exponential(rate=.25, bias=-1), # 0.25, -3
# adjustment_cost_function=lambda x: np.exp(.25 * np.abs(x) - 1),
adjustment_cost_function=adj_cost_fct,
# intensity_cost_function=pnl.Linear(slope=0, intercept=0), # 0.25, -3
# adjustment_cost_function=pnl.Linear(slope=0, intercept=0), # 0.25, -3
allocation_samples=control_signal_range
# allocation_samples = np.arange(0.1, 1.01, 0.3)
# allocation_samples=[i / 2 for i in list(range(0, 50, 1))]
)
])
lvoc.set_log_conditions('value')
# lvoc.set_log_conditions('features')
# print("LVOC loggable: ", lvoc.loggable_items)
# lvoc.set_log_conditions('variable')
class TestProjectionSpecificationFormats:
def test_projection_specification_formats(self):
"""Test various matrix and Projection specifications
Also tests assignment of Projections to pathay of Composition using add_linear_processing_pathway:
- Projection explicitly specified in sequence (M1_M2_proj)
- Projection pre-constructed and assigned to Mechanisms, but not specified in pathway(M2_M3_proj)
- Projection specified in pathway that is duplicate one preconstructed and assigned to Mechanisms (M3_M4_proj)
(currently it should be ignored; in the future, if/when Projections between the same sender and receiver
in different Compositions are allowed, then it should be used)
"""
M1 = pnl.ProcessingMechanism(size=2)
M2 = pnl.ProcessingMechanism(size=5)
M3 = pnl.ProcessingMechanism(size=4)
M4 = pnl.ProcessingMechanism(size=3)
M1_M2_matrix = (np.arange(2 * 5).reshape((2, 5)) + 1) / (2 * 5)
M2_M3_matrix = (np.arange(5 * 4).reshape((5, 4)) + 1) / (5 * 4)
M3_M4_matrix_A = (np.arange(4 * 3).reshape((4, 3)) + 1) / (4 * 5)
M3_M4_matrix_B = (np.arange(4 * 3).reshape((4, 3)) + 1) / (4 * 3)
M1_M2_proj = pnl.MappingProjection(matrix=M1_M2_matrix)
M2_M3_proj = pnl.MappingProjection(sender=M2,
receiver=M3,
matrix={
pnl.VALUE: M2_M3_matrix,
pnl.FUNCTION:
pnl.AccumulatorIntegrator,
pnl.FUNCTION_PARAMS: {
pnl.DEFAULT_VARIABLE:
M2_M3_matrix,
pnl.INITIALIZER:
M2_M3_matrix
}
})
M3_M4_proj_A = pnl.MappingProjection(sender=M3,
receiver=M4,
matrix=M3_M4_matrix_A)
c = pnl.Composition()
c.add_linear_processing_pathway(
pathway=[M1, M1_M2_proj, M2, M3, M3_M4_matrix_B, M4])
assert np.allclose(M2_M3_proj.matrix.base, M2_M3_matrix)
assert M2.efferents[0] is M2_M3_proj
assert np.allclose(M3.efferents[0].matrix.base, M3_M4_matrix_A)
# This is if different Projections are allowed between the same sender and receiver in different Compositions:
# assert np.allclose(M3.efferents[1].matrix, M3_M4_matrix_B)
c.run(inputs={M1: [2, -30]})
# assert np.allclose(c.results, [[-130.19166667, -152.53333333, -174.875]])
assert np.allclose(c.results, [[-78.115, -91.52, -104.925]])
def test_multiple_modulatory_projection_specs(self):
M = pnl.DDM(name='MY DDM')
C = pnl.ControlMechanism(control_signals=[{
pnl.PROJECTIONS: [
M.parameter_ports[psyneulink.core.components.functions.
distributionfunctions.DRIFT_RATE],
M.parameter_ports[psyneulink.core.globals.keywords.THRESHOLD]
]
}])
G = pnl.GatingMechanism(gating_signals=[{
pnl.PROJECTIONS: [
M.output_ports[pnl.DECISION_VARIABLE], M.output_ports[
pnl.RESPONSE_TIME]
]
}])
assert len(C.control_signals) == 1
assert len(C.control_signals[0].efferents) == 2
assert M.parameter_ports[
psyneulink.core.components.functions.distributionfunctions.
DRIFT_RATE].mod_afferents[0] == C.control_signals[0].efferents[0]
assert M.parameter_ports[
psyneulink.core.globals.keywords.
THRESHOLD].mod_afferents[0] == C.control_signals[0].efferents[1]
assert len(G.gating_signals) == 1
assert len(G.gating_signals[0].efferents) == 2
assert M.output_ports[pnl.DECISION_VARIABLE].mod_afferents[
0] == G.gating_signals[0].efferents[0]
assert M.output_ports[pnl.RESPONSE_TIME].mod_afferents[
0] == G.gating_signals[0].efferents[1]
def test_multiple_modulatory_projections_with_port_Name(self):
M = pnl.DDM(name='MY DDM')
C = pnl.ControlMechanism(control_signals=[{
'DECISION_CONTROL': [
M.parameter_ports[psyneulink.core.components.functions.
distributionfunctions.DRIFT_RATE],
M.parameter_ports[psyneulink.core.globals.keywords.THRESHOLD]
]
}])
G = pnl.GatingMechanism(gating_signals=[{
'DDM_OUTPUT_GATE': [
M.output_ports[pnl.DECISION_VARIABLE], M.output_ports[
pnl.RESPONSE_TIME]
]
}])
assert len(C.control_signals) == 1
assert C.control_signals[0].name == 'DECISION_CONTROL'
assert len(C.control_signals[0].efferents) == 2
assert M.parameter_ports[
psyneulink.core.components.functions.distributionfunctions.
DRIFT_RATE].mod_afferents[0] == C.control_signals[0].efferents[0]
assert M.parameter_ports[
psyneulink.core.globals.keywords.
THRESHOLD].mod_afferents[0] == C.control_signals[0].efferents[1]
assert len(G.gating_signals) == 1
assert G.gating_signals[0].name == 'DDM_OUTPUT_GATE'
assert len(G.gating_signals[0].efferents) == 2
assert M.output_ports[pnl.DECISION_VARIABLE].mod_afferents[
0] == G.gating_signals[0].efferents[0]
assert M.output_ports[pnl.RESPONSE_TIME].mod_afferents[
0] == G.gating_signals[0].efferents[1]
def test_multiple_modulatory_projections_with_mech_and_port_Name_specs(
self):
M = pnl.DDM(name='MY DDM')
C = pnl.ControlMechanism(control_signals=[{
pnl.MECHANISM:
M,
pnl.PARAMETER_PORTS: [
psyneulink.core.components.functions.distributionfunctions.
DRIFT_RATE, psyneulink.core.globals.keywords.THRESHOLD
]
}])
G = pnl.GatingMechanism(gating_signals=[{
pnl.MECHANISM:
M,
pnl.OUTPUT_PORTS: [pnl.DECISION_VARIABLE, pnl.RESPONSE_TIME]
}])
assert len(C.control_signals) == 1
assert len(C.control_signals[0].efferents) == 2
assert M.parameter_ports[
psyneulink.core.components.functions.distributionfunctions.
DRIFT_RATE].mod_afferents[0] == C.control_signals[0].efferents[0]
assert M.parameter_ports[
psyneulink.core.globals.keywords.
THRESHOLD].mod_afferents[0] == C.control_signals[0].efferents[1]
assert len(G.gating_signals) == 1
assert len(G.gating_signals[0].efferents) == 2
assert M.output_ports[pnl.DECISION_VARIABLE].mod_afferents[
0] == G.gating_signals[0].efferents[0]
assert M.output_ports[pnl.RESPONSE_TIME].mod_afferents[
0] == G.gating_signals[0].efferents[1]
def test_mapping_projection_with_mech_and_port_Name_specs(self):
R1 = pnl.TransferMechanism(output_ports=['OUTPUT_1', 'OUTPUT_2'])
R2 = pnl.TransferMechanism(default_variable=[[0], [0]],
input_ports=['INPUT_1', 'INPUT_2'])
T = pnl.TransferMechanism(input_ports=[{
pnl.MECHANISM:
R1,
pnl.OUTPUT_PORTS: ['OUTPUT_1', 'OUTPUT_2']
}],
output_ports=[{
pnl.MECHANISM:
R2,
pnl.INPUT_PORTS: ['INPUT_1', 'INPUT_2']
}])
assert len(R1.output_ports) == 2
assert len(R2.input_ports) == 2
assert len(T.input_ports) == 1
for input_port in T.input_ports:
for projection in input_port.path_afferents:
assert projection.sender.owner is R1
assert len(T.output_ports) == 1
for output_port in T.output_ports:
for projection in output_port.efferents:
assert projection.receiver.owner is R2
def test_mapping_projection_using_2_item_tuple_with_list_of_port_Names(
self):
T1 = pnl.TransferMechanism(name='T1', input_ports=[[0, 0], [0, 0, 0]])
T2 = pnl.TransferMechanism(name='T2',
output_ports=[
(['InputPort-0', 'InputPort-1'], T1)
])
assert len(T2.output_ports) == 1
assert T2.output_ports[0].efferents[0].receiver.name == 'InputPort-0'
assert T2.output_ports[0].efferents[0].matrix.base.shape == (1, 2)
assert T2.output_ports[0].efferents[1].receiver.name == 'InputPort-1'
assert T2.output_ports[0].efferents[1].matrix.base.shape == (1, 3)
def test_mapping_projection_using_2_item_tuple_and_3_item_tuples_with_index_specs(
self):
T1 = pnl.TransferMechanism(name='T1', input_ports=[[0, 0], [0, 0, 0]])
T2 = pnl.TransferMechanism(name='T2',
input_ports=['a', 'b', 'c'],
output_ports=[
(['InputPort-0', 'InputPort-1'], T1),
('InputPort-0', (pnl.OWNER_VALUE, 2),
T1),
(['InputPort-0', 'InputPort-1'], 1, T1)
])
assert len(T2.output_ports) == 3
assert T2.output_ports[0].efferents[0].receiver.name == 'InputPort-0'
assert T2.output_ports[0].efferents[0].matrix.base.shape == (1, 2)
assert T2.output_ports[0].efferents[1].receiver.name == 'InputPort-1'
assert T2.output_ports[0].efferents[1].matrix.base.shape == (1, 3)
assert T2.output_ports[1].owner_value_index == 2
assert T2.output_ports[2].owner_value_index == 1
def test_2_item_tuple_from_control_signal_to_parameter_port(self):
D = pnl.DDM(name='D')
# Single name
C = pnl.ControlMechanism(
control_signals=[(psyneulink.core.components.functions.
distributionfunctions.DRIFT_RATE, D)])
assert C.control_signals[0].name == 'D[drift_rate] ControlSignal'
assert C.control_signals[0].efferents[0].receiver.name == 'drift_rate'
# List of names
C = pnl.ControlMechanism(control_signals=[([
psyneulink.core.components.functions.distributionfunctions.
DRIFT_RATE, psyneulink.core.globals.keywords.THRESHOLD
], D)])
assert C.control_signals[
0].name == 'D[drift_rate, threshold] ControlSignal'
assert C.control_signals[0].efferents[0].receiver.name == 'drift_rate'
assert C.control_signals[0].efferents[1].receiver.name == 'threshold'
def test_2_item_tuple_from_parameter_port_to_control_signals(self):
C = pnl.ControlMechanism(control_signals=['a', 'b'])
D = pnl.DDM(name='D3',
function=psyneulink.core.components.functions.
distributionfunctions.DriftDiffusionAnalytical(
drift_rate=(3, C),
threshold=(2, C.control_signals['b'])))
assert D.parameter_ports[
psyneulink.core.components.functions.distributionfunctions.
DRIFT_RATE].mod_afferents[0].sender == C.control_signals[0]
assert D.parameter_ports[
psyneulink.core.globals.keywords.
THRESHOLD].mod_afferents[0].sender == C.control_signals[1]
def test_2_item_tuple_from_gating_signal_to_output_ports(self):
D4 = pnl.DDM(name='D4')
# Single name
G = pnl.GatingMechanism(gating_signals=[(pnl.DECISION_VARIABLE, D4)])
assert G.gating_signals[0].name == 'D4[DECISION_VARIABLE] GatingSignal'
assert G.gating_signals[0].efferents[
0].receiver.name == 'DECISION_VARIABLE'
# List of names
G = pnl.GatingMechanism(
gating_signals=[([pnl.DECISION_VARIABLE, pnl.RESPONSE_TIME], D4)])
assert G.gating_signals[
0].name == 'D4[DECISION_VARIABLE, RESPONSE_TIME] GatingSignal'
assert G.gating_signals[0].efferents[
0].receiver.name == 'DECISION_VARIABLE'
assert G.gating_signals[0].efferents[
1].receiver.name == 'RESPONSE_TIME'
def test_2_item_tuple_from_input_and_output_ports_to_gating_signals(self):
G = pnl.GatingMechanism(gating_signals=['a', 'b'])
T = pnl.TransferMechanism(name='T',
input_ports=[(3, G)],
output_ports=[(2, G.gating_signals['b'])])
assert T.input_ports[0].mod_afferents[0].sender == G.gating_signals[0]
assert T.output_ports[0].mod_afferents[0].sender == G.gating_signals[1]
control_spec_list = [
pnl.CONTROL, pnl.CONTROL_SIGNAL, pnl.CONTROL_PROJECTION,
pnl.ControlSignal,
pnl.ControlSignal(), pnl.ControlProjection, "CP_OBJECT",
pnl.ControlMechanism,
pnl.ControlMechanism(), pnl.ControlMechanism, (0.3, pnl.CONTROL),
(0.3, pnl.CONTROL_SIGNAL), (0.3, pnl.CONTROL_PROJECTION),
(0.3, pnl.ControlSignal), (0.3, pnl.ControlSignal()),
(0.3, pnl.ControlProjection), (0.3, "CP_OBJECT"),
(0.3, pnl.ControlMechanism), (0.3, pnl.ControlMechanism()),
(0.3, pnl.ControlMechanism)
]
@pytest.mark.parametrize(
'noise, gain',
[(noise, gain) for noise, gain in
[j for j in zip(control_spec_list, reversed(control_spec_list))]])
def test_formats_for_control_specification_for_mechanism_and_function_params(
self, noise, gain):
# This shenanigans is to avoid assigning the same instantiated ControlProjection more than once
if noise == 'CP_OBJECT':
noise = pnl.ControlProjection()
elif isinstance(noise, tuple) and noise[1] == 'CP_OBJECT':
noise = (noise[0], pnl.ControlProjection())
if gain == 'CP_OBJECT':
gain = pnl.ControlProjection()
elif isinstance(gain, tuple) and gain[1] == 'CP_OBJECT':
gain = (gain[0], pnl.ControlProjection())
R = pnl.RecurrentTransferMechanism(
# NOTE: fixed name prevents failures due to registry naming
# for parallel test runs
name='R-CONTROL',
noise=noise,
function=psyneulink.core.components.functions.transferfunctions.
Logistic(gain=gain))
assert R.parameter_ports[pnl.NOISE].mod_afferents[0].name in \
'ControlProjection for R-CONTROL[noise]'
assert R.parameter_ports[pnl.GAIN].mod_afferents[0].name in \
'ControlProjection for R-CONTROL[gain]'
gating_spec_list = [
pnl.GATING, pnl.CONTROL, pnl.GATING_SIGNAL, pnl.CONTROL_SIGNAL,
pnl.GATING_PROJECTION, pnl.CONTROL_PROJECTION, pnl.GatingSignal,
pnl.ControlSignal,
pnl.GatingSignal(),
pnl.ControlSignal(), pnl.GatingProjection, "GP_OBJECT",
pnl.GatingMechanism, pnl.ControlMechanism,
pnl.GatingMechanism(),
pnl.ControlMechanism(), (0.3, pnl.GATING), (0.3, pnl.CONTROL),
(0.3, pnl.GATING_SIGNAL), (0.3, pnl.CONTROL_SIGNAL),
(0.3, pnl.GATING_PROJECTION), (0.3, pnl.CONTROL_PROJECTION),
(0.3, pnl.GatingSignal), (0.3, pnl.ControlSignal),
(0.3, pnl.GatingSignal()), (0.3, pnl.ControlSignal()),
(0.3, pnl.GatingProjection), (0.3, pnl.ControlProjection),
(0.3, "GP_OBJECT"), (0.3, pnl.GatingMechanism),
(0.3, pnl.ControlMechanism), (0.3, pnl.GatingMechanism()),
(0.3, pnl.ControlMechanism())
]
@pytest.mark.parametrize(
'input_port, output_port',
[(inp, outp) for inp, outp in
[j for j in zip(gating_spec_list, reversed(gating_spec_list))]])
def test_formats_for_gating_specification_of_input_and_output_ports(
self, input_port, output_port):
G_IN, G_OUT = input_port, output_port
# This shenanigans is to avoid assigning the same instantiated ControlProjection more than once
if G_IN == 'GP_OBJECT':
G_IN = pnl.GatingProjection()
elif isinstance(G_IN, tuple) and G_IN[1] == 'GP_OBJECT':
G_IN = (G_IN[0], pnl.GatingProjection())
if G_OUT == 'GP_OBJECT':
G_OUT = pnl.GatingProjection()
elif isinstance(G_OUT, tuple) and G_OUT[1] == 'GP_OBJECT':
G_OUT = (G_OUT[0], pnl.GatingProjection())
if isinstance(G_IN, tuple):
IN_NAME = G_IN[1]
else:
IN_NAME = G_IN
IN_CONTROL = pnl.CONTROL in repr(IN_NAME).split(".")[-1].upper()
if isinstance(G_OUT, tuple):
OUT_NAME = G_OUT[1]
else:
OUT_NAME = G_OUT
OUT_CONTROL = pnl.CONTROL in repr(OUT_NAME).split(".")[-1].upper()
T = pnl.TransferMechanism(name='T-GATING',
input_ports=[G_IN],
output_ports=[G_OUT])
if IN_CONTROL:
assert T.input_ports[0].mod_afferents[0].name in \
'ControlProjection for T-GATING[InputPort-0]'
else:
assert T.input_ports[0].mod_afferents[0].name in \
'GatingProjection for T-GATING[InputPort-0]'
if OUT_CONTROL:
assert T.output_ports[0].mod_afferents[0].name in \
'ControlProjection for T-GATING[OutputPort-0]'
else:
assert T.output_ports[0].mod_afferents[0].name in \
'GatingProjection for T-GATING[OutputPort-0]'
# with pytest.raises(pnl.ProjectionError) as error_text:
# T1 = pnl.ProcessingMechanism(name='T1', input_ports=[pnl.ControlMechanism()])
# assert 'Primary OutputPort of ControlMechanism-0 (ControlSignal-0) ' \
# 'cannot be used as a sender of a Projection to InputPort of T1' in error_text.value.args[0]
#
# with pytest.raises(pnl.ProjectionError) as error_text:
# T2 = pnl.ProcessingMechanism(name='T2', output_ports=[pnl.ControlMechanism()])
# assert 'Primary OutputPort of ControlMechanism-1 (ControlSignal-0) ' \
# 'cannot be used as a sender of a Projection to OutputPort of T2' in error_text.value.args[0]
def test_no_warning_when_matrix_specified(self):
with pytest.warns(None) as w:
c = pnl.Composition()
m0 = pnl.ProcessingMechanism(default_variable=[0, 0, 0, 0])
p0 = pnl.MappingProjection(matrix=[[0, 0, 0, 0], [0, 0, 0, 0],
[0, 0, 0, 0], [0, 0, 0, 0]])
m1 = pnl.TransferMechanism(default_variable=[0, 0, 0, 0])
c.add_linear_processing_pathway([m0, p0, m1])
for warn in w:
if r'elementwise comparison failed; returning scalar instead' in warn.message.args[
0]:
raise
# KDM: this is a good candidate for pytest.parametrize
def test_masked_mapping_projection(self):
t1 = pnl.TransferMechanism(size=2)
t2 = pnl.TransferMechanism(size=2)
proj = pnl.MaskedMappingProjection(sender=t1,
receiver=t2,
matrix=[[1, 2], [3, 4]],
mask=[[1, 0], [0, 1]],
mask_operation=pnl.ADD)
c = pnl.Composition(pathways=[[t1, proj, t2]])
val = c.execute(inputs={t1: [1, 2]})
assert np.allclose(val, [[8, 12]])
t1 = pnl.TransferMechanism(size=2)
t2 = pnl.TransferMechanism(size=2)
proj = pnl.MaskedMappingProjection(sender=t1,
receiver=t2,
matrix=[[1, 2], [3, 4]],
mask=[[1, 0], [0, 1]],
mask_operation=pnl.MULTIPLY)
c = pnl.Composition(pathways=[[t1, proj, t2]])
val = c.execute(inputs={t1: [1, 2]})
assert np.allclose(val, [[1, 8]])
t1 = pnl.TransferMechanism(size=2)
t2 = pnl.TransferMechanism(size=2)
proj = pnl.MaskedMappingProjection(sender=t1,
receiver=t2,
mask=[[1, 2], [3, 4]],
mask_operation=pnl.MULTIPLY)
c = pnl.Composition(pathways=[[t1, proj, t2]])
val = c.execute(inputs={t1: [1, 2]})
assert np.allclose(val, [[1, 8]])
def test_masked_mapping_projection_mask_conficts_with_matrix(self):
with pytest.raises(pnl.MaskedMappingProjectionError) as error_text:
t1 = pnl.TransferMechanism(size=2)
t2 = pnl.TransferMechanism(size=2)
pnl.MaskedMappingProjection(sender=t1,
receiver=t2,
mask=[[1, 2, 3], [4, 5, 6]],
mask_operation=pnl.MULTIPLY)
assert "Shape of the 'mask'" in str(error_text.value)
assert "((2, 3)) must be the same as its 'matrix' ((2, 2))" in str(
error_text.value)
# FIX 7/22/15 [JDC] - REPLACE WITH MORE ELABORATE TESTS OF DUPLICATE PROJECTIONS:
# SAME FROM OutputPort; SAME TO InputPort
# TEST ERROR MESSAGES GENERATED BY VARIOUS _check_for_duplicates METHODS
# def test_duplicate_projection_detection_and_warning(self):
#
# with pytest.warns(UserWarning) as record:
# T1 = pnl.TransferMechanism(name='T1')
# T2 = pnl.TransferMechanism(name='T2')
# T3 = pnl.TransferMechanism(name='T3')
# T4 = pnl.TransferMechanism(name='T4')
#
# MP1 = pnl.MappingProjection(sender=T1,receiver=T2,name='MP1')
# MP2 = pnl.MappingProjection(sender=T1,receiver=T2,name='MP2')
# pnl.proc(T1,MP1,T2,T3)
# pnl.proc(T1,MP2,T2,T4)
#
# # hack to find a specific warning (other warnings may be generated by the Process construction)
# correct_message_found = False
# for warning in record:
# if "that already has an identical Projection" in str(warning.message):
# correct_message_found = True
# break
#
# assert len(T2.afferents)==1
# assert correct_message_found
def test_duplicate_projection_creation_error(self):
from psyneulink.core.components.projections.projection import DuplicateProjectionError
with pytest.raises(DuplicateProjectionError) as record:
T1 = pnl.TransferMechanism(name='T1')
T2 = pnl.TransferMechanism(name='T2')
pnl.MappingProjection(sender=T1, receiver=T2, name='MP1')
pnl.MappingProjection(sender=T1, receiver=T2, name='MP2')
assert 'Attempt to assign Projection to InputPort-0 of T2 that already has an identical Projection.' \
in record.value.args[0]
def get_stroop_model(unit_noise_std=.01, dec_noise_std=.1):
# model params
integration_rate = 1
hidden_func = pnl.Logistic(gain=1.0, x_0=4.0)
# input layer, color and word
reward = pnl.TransferMechanism(name='reward')
punish = pnl.TransferMechanism(name='punish')
inp_clr = pnl.TransferMechanism(
size=N_UNITS, function=pnl.Linear, name='COLOR INPUT'
)
inp_wrd = pnl.TransferMechanism(
size=N_UNITS, function=pnl.Linear, name='WORD INPUT'
)
# task layer, represent the task instruction; color naming / word reading
inp_task = pnl.TransferMechanism(
size=N_UNITS, function=pnl.Linear, name='TASK'
)
# hidden layer for color and word
hid_clr = pnl.TransferMechanism(
size=N_UNITS,
function=hidden_func,
integrator_mode=True,
integration_rate=integration_rate,
# noise=pnl.NormalDist(standard_deviation=unit_noise_std).function,
noise=pnl.NormalDist(standard_deviation=unit_noise_std),
name='COLORS HIDDEN'
)
hid_wrd = pnl.TransferMechanism(
size=N_UNITS,
function=hidden_func,
integrator_mode=True,
integration_rate=integration_rate,
# noise=pnl.NormalDist(standard_deviation=unit_noise_std).function,
noise=pnl.NormalDist(standard_deviation=unit_noise_std),
name='WORDS HIDDEN'
)
# output layer
output = pnl.TransferMechanism(
size=N_UNITS,
function=pnl.Logistic,
integrator_mode=True,
integration_rate=integration_rate,
# noise=pnl.NormalDist(standard_deviation=unit_noise_std).function,
noise=pnl.NormalDist(standard_deviation=unit_noise_std),
name='OUTPUT'
)
# decision layer, some accumulator
signalSearchRange = pnl.SampleSpec(start=0.05, stop=5, step=0.05)
decision = pnl.DDM(name='Decision',
input_format=pnl.ARRAY,
function=pnl.DriftDiffusionAnalytical(drift_rate=1,
threshold =1,
noise=1,
starting_point=0,
t0=0.35),
output_ports=[pnl.RESPONSE_TIME,
pnl.PROBABILITY_UPPER_THRESHOLD,
pnl.PROBABILITY_LOWER_THRESHOLD]
)
driftrate_control_signal = pnl.ControlSignal(projections=[(pnl.SLOPE, inp_clr)],
variable=1.0,
intensity_cost_function=pnl.Exponential(rate=1),#pnl.Exponential(rate=0.8),#pnl.Exponential(rate=1),
allocation_samples=signalSearchRange)
threshold_control_signal = pnl.ControlSignal(projections=[(pnl.THRESHOLD, decision)],
variable=1.0,
intensity_cost_function=pnl.Linear(slope=0),
allocation_samples=signalSearchRange)
reward_rate = pnl.ObjectiveMechanism(function=pnl.LinearCombination(operation=pnl.PRODUCT,
exponents=[[1],[1],[-1]]),
monitor=[reward,
decision.output_ports[pnl.PROBABILITY_UPPER_THRESHOLD],
decision.output_ports[pnl.RESPONSE_TIME]])
punish_rate = pnl.ObjectiveMechanism(function=pnl.LinearCombination(operation=pnl.PRODUCT,
exponents=[[1],[1],[-1]]),
monitor=[punish,
decision.output_ports[pnl.PROBABILITY_LOWER_THRESHOLD],
decision.output_ports[pnl.RESPONSE_TIME]])
objective_mech = pnl.ObjectiveMechanism(function=pnl.LinearCombination(operation=pnl.SUM,
weights=[[1],[-1]]),
monitor=[reward_rate, punish_rate])
# objective_mech = pnl.ObjectiveMechanism(function=object_function,
# monitor=[reward,
# punish,
# decision.output_ports[pnl.PROBABILITY_UPPER_THRESHOLD],
# decision.output_ports[pnl.PROBABILITY_LOWER_THRESHOLD],
# (decision.output_ports[pnl.RESPONSE_TIME])])
# PROJECTIONS, weights copied from cohen et al (1990)
wts_clr_ih = pnl.MappingProjection(
matrix=[[2.2, -2.2], [-2.2, 2.2]], name='COLOR INPUT TO HIDDEN')
wts_wrd_ih = pnl.MappingProjection(
matrix=[[2.6, -2.6], [-2.6, 2.6]], name='WORD INPUT TO HIDDEN')
wts_clr_ho = pnl.MappingProjection(
matrix=[[1.3, -1.3], [-1.3, 1.3]], name='COLOR HIDDEN TO OUTPUT')
wts_wrd_ho = pnl.MappingProjection(
matrix=[[2.5, -2.5], [-2.5, 2.5]], name='WORD HIDDEN TO OUTPUT')
wts_tc = pnl.MappingProjection(
matrix=[[4.0, 4.0], [0, 0]], name='COLOR NAMING')
wts_tw = pnl.MappingProjection(
matrix=[[0, 0], [4.0, 4.0]], name='WORD READING')
# build the model
model = pnl.Composition(name='STROOP model')
model.add_node(decision, required_roles=pnl.NodeRole.OUTPUT)
model.add_node(reward, required_roles=pnl.NodeRole.OUTPUT)
model.add_node(punish, required_roles=pnl.NodeRole.OUTPUT)
model.add_linear_processing_pathway([inp_clr, wts_clr_ih, hid_clr])
model.add_linear_processing_pathway([inp_wrd, wts_wrd_ih, hid_wrd])
model.add_linear_processing_pathway([hid_clr, wts_clr_ho, output])
model.add_linear_processing_pathway([hid_wrd, wts_wrd_ho, output])
model.add_linear_processing_pathway([inp_task, wts_tc, hid_clr])
model.add_linear_processing_pathway([inp_task, wts_tw, hid_wrd])
model.add_linear_processing_pathway([output, pnl.IDENTITY_MATRIX, decision]) # 3/15/20
# model.add_linear_processing_pathway([output, [[1,-1]], (decision, pnl.NodeRole.OUTPUT)]) # 3/15/20
# model.add_linear_processing_pathway([output, [[1],[-1]], decision]) # 3/15/20
model.add_nodes([reward_rate, punish_rate])
controller = pnl.OptimizationControlMechanism(agent_rep=model,
features=[inp_clr.input_port,
inp_wrd.input_port,
inp_task.input_port,
reward.input_port,
punish.input_port],
feature_function=pnl.AdaptiveIntegrator(rate=0.1),
objective_mechanism=objective_mech,
function=pnl.GridSearch(),
control_signals=[driftrate_control_signal,
threshold_control_signal])
model.add_controller(controller=controller)
# collect the node handles
nodes = [inp_clr, inp_wrd, inp_task, hid_clr, hid_wrd, output, decision, reward, punish,controller]
metadata = [integration_rate, dec_noise_std, unit_noise_std]
return model, nodes, metadata
# set up inner comp controller and add to comp
icomp.add_controller(
pnl.OptimizationControlMechanism(
agent_rep=icomp,
features=[ia.input_port, ib.input_port],
name="Controller",
objective_mechanism=pnl.ObjectiveMechanism(
monitor=ic.output_port,
function=pnl.SimpleIntegrator,
name="iController Objective Mechanism"
),
function=pnl.GridSearch(direction=pnl.MAXIMIZE),
control_signals=[pnl.ControlSignal(projections=[(pnl.SLOPE, ia)],
variable=1.0,
intensity_cost_function=pnl.Linear(slope=0.0),
allocation_samples=pnl.SampleSpec(start=1.0,
stop=5.0,
num=5))])
)
# instantiate outer comp
ocomp = pnl.Composition(name='ocomp', controller_mode=pnl.BEFORE)
# setup structure for outer comp
ocomp.add_node(icomp)
# add controller to outer comp
ocomp.add_controller(
pnl.OptimizationControlMechanism(
agent_rep=ocomp,
features=[ia.input_port, ib.input_port],
stabilityFlexibility.add_projection(sender=ddmCombination,
receiver=decisionMaker)
# Beginning of Controller
# Grid Search Range
# searchRange = pnl.SampleSpec(start=0.25, stop=4.0, num=16)
# searchRange = pnl.SampleSpec(start=1.0, stop=1.9, num=10)
searchRange = pnl.SampleSpec(start=0.5, stop=5.0, 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=pnl.AccuracyIntegrator,
name="Controller Objective Mechanism")
objectiveMechanism.set_log_conditions(items=pnl.VALUE)
