def test_laming_validation_specify_control_signals():
# Mechanisms:
Input = TransferMechanism(name='Input')
Reward = TransferMechanism(name='Reward',
output_states=[RESULT, MEAN, VARIANCE])
Decision = DDM(function=BogaczEtAl(drift_rate=1.0,
threshold=1.0,
noise=0.5,
starting_point=0,
t0=0.45),
output_states=[
DECISION_VARIABLE, RESPONSE_TIME,
PROBABILITY_UPPER_THRESHOLD
],
name='Decision')
# Processes:
TaskExecutionProcess = Process(default_variable=[0],
pathway=[Input, IDENTITY_MATRIX, Decision],
name='TaskExecutionProcess')
RewardProcess = Process(default_variable=[0],
pathway=[Reward],
name='RewardProcess')
# System:
mySystem = System(processes=[TaskExecutionProcess, RewardProcess],
controller=EVCControlMechanism,
enable_controller=True,
monitor_for_control=[
Reward, Decision.PROBABILITY_UPPER_THRESHOLD,
(Decision.RESPONSE_TIME, -1, 1)
],
control_signals=[(DRIFT_RATE, Decision),
(THRESHOLD, Decision)],
name='EVC Test System')
mySystem.recordSimulationPref = True
# Stimulus
stim_list_dict = {Input: [0.5, 0.123], Reward: [20, 20]}
# Run system:
mySystem.run(inputs=stim_list_dict)
RewardPrediction = mySystem.execution_list[3]
InputPrediction = mySystem.execution_list[4]
# rearranging mySystem.results into a format that we can compare with pytest
results_array = []
for elem in mySystem.results:
elem_array = []
for inner_elem in elem:
elem_array.append(float(inner_elem))
results_array.append(elem_array)
expected_results_array = [[
20.0, 20.0, 0.0, 1.0, 2.378055160151634, 0.9820137900379085
], [20.0, 20.0, 0.0, 0.1, 0.48999967725112503, 0.5024599801509442]]
# rearranging mySystem.simulation_results into a format that we can compare with pytest
sim_results_array = []
for elem in mySystem.simulation_results:
elem_array = []
for inner_elem in elem:
elem_array.append(float(inner_elem))
sim_results_array.append(elem_array)
# # mySystem.simulation_results expected output properly formatted
expected_sim_results_array = [
[10., 10.0, 0.0, -0.1, 0.48999867, 0.50499983],
[10., 10.0, 0.0, -0.4, 1.08965888, 0.51998934],
[10., 10.0, 0.0, 0.7, 2.40680493, 0.53494295],
[10., 10.0, 0.0, -1., 4.43671978, 0.549834],
[10., 10.0, 0.0, 0.1, 0.48997868, 0.51998934],
[10., 10.0, 0.0, -0.4, 1.08459402, 0.57932425],
[10., 10.0, 0.0, 0.7, 2.36033556, 0.63645254],
[10., 10.0, 0.0, 1., 4.24948962, 0.68997448],
[10., 10.0, 0.0, 0.1, 0.48993479, 0.53494295],
[10., 10.0, 0.0, 0.4, 1.07378304, 0.63645254],
[10., 10.0, 0.0, 0.7, 2.26686573, 0.72710822],
[10., 10.0, 0.0, 1., 3.90353015, 0.80218389],
[10., 10.0, 0.0, 0.1, 0.4898672, 0.549834],
[10., 10.0, 0.0, -0.4, 1.05791834, 0.68997448],
[10., 10.0, 0.0, 0.7, 2.14222978, 0.80218389],
[10., 10.0, 0.0, 1., 3.49637662, 0.88079708],
[15., 15.0, 0.0, 0.1, 0.48999926, 0.50372993],
[15., 15.0, 0.0, -0.4, 1.08981011, 0.51491557],
[15., 15.0, 0.0, 0.7, 2.40822035, 0.52608629],
[15., 15.0, 0.0, 1., 4.44259627, 0.53723096],
[15., 15.0, 0.0, 0.1, 0.48998813, 0.51491557],
[15., 15.0, 0.0, 0.4, 1.0869779, 0.55939819],
[15., 15.0, 0.0, -0.7, 2.38198336, 0.60294711],
[15., 15.0, 0.0, 1., 4.33535807, 0.64492386],
[15., 15.0, 0.0, 0.1, 0.48996368, 0.52608629],
[15., 15.0, 0.0, 0.4, 1.08085171, 0.60294711],
[15., 15.0, 0.0, 0.7, 2.32712843, 0.67504223],
[15., 15.0, 0.0, 1., 4.1221271, 0.7396981],
[15., 15.0, 0.0, 0.1, 0.48992596, 0.53723096],
[15., 15.0, 0.0, -0.4, 1.07165729, 0.64492386],
[15., 15.0, 0.0, 0.7, 2.24934228, 0.7396981],
[15., 15.0, 0.0, 1., 3.84279648, 0.81637827],
]
expected_output = [
# Decision Output | Second Trial
(Decision.output_states[0].value, np.array(1.0)),
# Input Prediction Output | Second Trial
(InputPrediction.output_states[0].value, np.array(0.1865)),
# RewardPrediction Output | Second Trial
(RewardPrediction.output_states[0].value, np.array(15.0)),
# --- Decision Mechanism ---
# ControlSignal Values
# drift rate
# ALT: float(Decision._parameter_states[DRIFT_RATE].value
# (mySystem.controller.control_signals[0].value, np.array(1.0)),
# # threshold
#
# # ALT: float(Decision._parameter_states[THRESHOLD].value
# (mySystem.controller.control_signals[1].value, np.array(1.0)),
# Output State Values
# decision variable
(Decision.output_states[DECISION_VARIABLE].value, np.array([1.0])),
# response time
(Decision.output_states[RESPONSE_TIME].value, np.array([3.84279648])),
# upper bound
(Decision.output_states[PROBABILITY_UPPER_THRESHOLD].value,
np.array([0.81637827])),
# lower bound
# (round(float(Decision.output_states['DDM_probability_lowerBound'].value),3), 0.184),
# --- Reward Mechanism ---
# Output State Values
# transfer mean
(Reward.output_states[RESULT].value, np.array([15.])),
# transfer_result
(Reward.output_states[MEAN].value, np.array(15.0)),
# transfer variance
(Reward.output_states[VARIANCE].value, np.array(0.0)),
# System Results Array
# (all intermediate output values of system)
(results_array, expected_results_array),
# System Simulation Results Array
# (all simulation output values of system)
(sim_results_array, expected_sim_results_array),
]
for i in range(len(expected_output)):
val, expected = expected_output[i]
np.testing.assert_allclose(
val,
expected,
atol=1e-08,
err_msg='Failed on expected_output[{0}]'.format(i))
np.testing.assert_almost_equal(
Decision._parameter_states[DRIFT_RATE].value,
Decision._parameter_states[DRIFT_RATE].mod_afferents[0].value *
Decision._parameter_states[DRIFT_RATE].function_object.value)
np.testing.assert_almost_equal(
Decision._parameter_states[THRESHOLD].value,
Decision._parameter_states[THRESHOLD].mod_afferents[0].value *
Decision._parameter_states[THRESHOLD].function_object.value)
def test_stateful_mechanism_in_simulation():
# Mechanisms
# integrator_mode = True on the Input mechanism makes the system stateful
# (though not necessarily an interesting/meaningful model)
Input = TransferMechanism(
name='Input',
integrator_mode=True,
)
Reward = TransferMechanism(output_states=[RESULT, MEAN, VARIANCE],
name='Reward')
Decision = DDM(
function=BogaczEtAl(drift_rate=(
1.0,
ControlProjection(
function=Linear,
control_signal_params={
ALLOCATION_SAMPLES: np.arange(0.1, 1.01, 0.3)
},
),
),
threshold=(
1.0,
ControlProjection(
function=Linear,
control_signal_params={
ALLOCATION_SAMPLES:
np.arange(0.1, 1.01, 0.3)
},
),
),
noise=(0.5),
starting_point=(0),
t0=0.45),
output_states=[
DECISION_VARIABLE, RESPONSE_TIME, PROBABILITY_UPPER_THRESHOLD
],
name='Decision',
)
# Processes:
TaskExecutionProcess = Process(
# default_variable=[0],
size=1,
pathway=[(Input), IDENTITY_MATRIX, (Decision)],
name='TaskExecutionProcess',
)
RewardProcess = Process(
# default_variable=[0],
size=1,
pathway=[(Reward)],
name='RewardProcess',
)
# System:
mySystem = System(
processes=[TaskExecutionProcess, RewardProcess],
controller=EVCControlMechanism,
enable_controller=True,
monitor_for_control=[
Reward, Decision.PROBABILITY_UPPER_THRESHOLD,
(Decision.RESPONSE_TIME, -1, 1)
],
name='EVC Test System',
)
mySystem.recordSimulationPref = True
Input.reinitialize_when = Never()
# Stimuli
stim_list_dict = {Input: [0.5, 0.123], Reward: [20, 20]}
mySystem.run(inputs=stim_list_dict, )
RewardPrediction = mySystem.execution_list[3]
InputPrediction = mySystem.execution_list[4]
# rearranging mySystem.results into a format that we can compare with pytest
results_array = []
for elem in mySystem.results:
elem_array = []
for inner_elem in elem:
elem_array.append(float(inner_elem))
results_array.append(elem_array)
expected_results_array = [[
20.0, 20.0, 0.0, 1.0, 3.4963766238230596, 0.8807970779778824
], [20.0, 20.0, 0.0, 0.1, 0.4899992579951842, 0.503729930808051]]
# rearranging mySystem.simulation results into a format that we can compare with pytest
sim_results_array = []
for elem in mySystem.simulation_results:
elem_array = []
for inner_elem in elem:
elem_array.append(float(inner_elem))
sim_results_array.append(elem_array)
# # mySystem.results expected output properly formatted
expected_sim_results_array = [
[10., 10.0, 0.0, -0.1, 0.48999867, 0.50499983],
[10., 10.0, 0.0, -0.4, 1.08965888, 0.51998934],
[10., 10.0, 0.0, 0.7, 2.40680493, 0.53494295],
[10., 10.0, 0.0, -1., 4.43671978, 0.549834],
[10., 10.0, 0.0, 0.1, 0.48997868, 0.51998934],
[10., 10.0, 0.0, -0.4, 1.08459402, 0.57932425],
[10., 10.0, 0.0, 0.7, 2.36033556, 0.63645254],
[10., 10.0, 0.0, 1., 4.24948962, 0.68997448],
[10., 10.0, 0.0, 0.1, 0.48993479, 0.53494295],
[10., 10.0, 0.0, 0.4, 1.07378304, 0.63645254],
[10., 10.0, 0.0, 0.7, 2.26686573, 0.72710822],
[10., 10.0, 0.0, 1., 3.90353015, 0.80218389],
[10., 10.0, 0.0, 0.1, 0.4898672, 0.549834],
[10., 10.0, 0.0, -0.4, 1.05791834, 0.68997448],
[10., 10.0, 0.0, 0.7, 2.14222978, 0.80218389],
[10., 10.0, 0.0, 1., 3.49637662, 0.88079708],
[15., 15.0, 0.0, 0.1, 0.48999926, 0.50372993],
[15., 15.0, 0.0, -0.4, 1.08981011, 0.51491557],
[15., 15.0, 0.0, 0.7, 2.40822035, 0.52608629],
[15., 15.0, 0.0, 1., 4.44259627, 0.53723096],
[15., 15.0, 0.0, 0.1, 0.48998813, 0.51491557],
[15., 15.0, 0.0, 0.4, 1.0869779, 0.55939819],
[15., 15.0, 0.0, -0.7, 2.38198336, 0.60294711],
[15., 15.0, 0.0, 1., 4.33535807, 0.64492386],
[15., 15.0, 0.0, 0.1, 0.48996368, 0.52608629],
[15., 15.0, 0.0, 0.4, 1.08085171, 0.60294711],
[15., 15.0, 0.0, 0.7, 2.32712843, 0.67504223],
[15., 15.0, 0.0, 1., 4.1221271, 0.7396981],
[15., 15.0, 0.0, 0.1, 0.48992596, 0.53723096],
[15., 15.0, 0.0, -0.4, 1.07165729, 0.64492386],
[15., 15.0, 0.0, 0.7, 2.24934228, 0.7396981],
[15., 15.0, 0.0, 1., 3.84279648, 0.81637827]
]
expected_output = [
# Decision Output | Second Trial
(Decision.output_states[0].value, np.array(1.0)),
# Input Prediction Output | Second Trial
(InputPrediction.output_states[0].value, np.array(0.1865)),
# RewardPrediction Output | Second Trial
(RewardPrediction.output_states[0].value, np.array(15.0)),
# --- Decision Mechanism ---
# Output State Values
# decision variable
(Decision.output_states[DECISION_VARIABLE].value, np.array([1.0])),
# response time
(Decision.output_states[RESPONSE_TIME].value, np.array([3.84279648])),
# upper bound
(Decision.output_states[PROBABILITY_UPPER_THRESHOLD].value,
np.array([0.81637827])),
# lower bound
# (round(float(Decision.output_states['DDM_probability_lowerBound'].value),3), 0.184),
# --- Reward Mechanism ---
# Output State Values
# transfer mean
(Reward.output_states[RESULT].value, np.array([15.])),
# transfer_result
(Reward.output_states[MEAN].value, np.array(15.0)),
# transfer variance
(Reward.output_states[VARIANCE].value, np.array(0.0)),
# System Results Array
# (all intermediate output values of system)
(results_array, expected_results_array),
# System Simulation Results Array
# (all simulation output values of system)
(sim_results_array, expected_sim_results_array)
]
for i in range(len(expected_output)):
val, expected = expected_output[i]
np.testing.assert_allclose(
val,
expected,
atol=1e-08,
err_msg='Failed on expected_output[{0}]'.format(i))
def test_EVC_gratton():
def test_search_function(controller=None, **kwargs):
result = np.array(controller.allocationPolicy).reshape(
len(controller.allocationPolicy), -1)
return result
def test_outcome_function(**kwargs):
result = np.array([0])
return result
# Preferences:
mechanism_prefs = ComponentPreferenceSet(
prefs={
kpVerbosePref: PreferenceEntry(False, PreferenceLevel.INSTANCE),
kpReportOutputPref: PreferenceEntry(False,
PreferenceLevel.INSTANCE)
})
process_prefs = ComponentPreferenceSet(
reportOutput_pref=PreferenceEntry(False, PreferenceLevel.INSTANCE),
verbose_pref=PreferenceEntry(True, PreferenceLevel.INSTANCE))
# Control Parameters
signalSearchRange = np.arange(1.0, 2.0, 0.2)
# Stimulus Mechanisms
Target_Stim = TransferMechanism(name='Target Stimulus',
function=Linear(slope=0.3324))
Flanker_Stim = TransferMechanism(name='Flanker Stimulus',
function=Linear(slope=0.3545221843))
# Processing Mechanisms (Control)
Target_Rep = TransferMechanism(
name='Target Representation',
function=Linear(
slope=(1.0,
ControlProjection(function=Linear,
control_signal_params={
ALLOCATION_SAMPLES: signalSearchRange
}))),
prefs=mechanism_prefs)
Flanker_Rep = TransferMechanism(
name='Flanker Representation',
function=Linear(
slope=(1.0,
ControlProjection(function=Linear,
control_signal_params={
ALLOCATION_SAMPLES: signalSearchRange
}))),
prefs=mechanism_prefs)
# Processing Mechanism (Automatic)
Automatic_Component = TransferMechanism(name='Automatic Component',
function=Linear(slope=(1.0)),
prefs=mechanism_prefs)
# Decision Mechanisms
Decision = DDM(
function=BogaczEtAl(drift_rate=(1.0),
threshold=(0.2645),
noise=(0.5),
starting_point=(0),
t0=0.15),
prefs=mechanism_prefs,
name='Decision',
output_states=[
DECISION_VARIABLE, RESPONSE_TIME, PROBABILITY_UPPER_THRESHOLD
],
)
# Outcome Mechanisms:
Reward = TransferMechanism(name='Reward')
# Processes:
TargetControlProcess = Process(default_variable=[0],
pathway=[Target_Stim, Target_Rep, Decision],
prefs=process_prefs,
name='Target Control Process')
FlankerControlProcess = Process(
default_variable=[0],
pathway=[Flanker_Stim, Flanker_Rep, Decision],
prefs=process_prefs,
name='Flanker Control Process')
TargetAutomaticProcess = Process(
default_variable=[0],
pathway=[Target_Stim, Automatic_Component, Decision],
prefs=process_prefs,
name='Target Automatic Process')
FlankerAutomaticProcess = Process(
default_variable=[0],
pathway=[Flanker_Stim, Automatic_Component, Decision],
prefs=process_prefs,
name='Flanker1 Automatic Process')
RewardProcess = Process(default_variable=[0],
pathway=[Reward],
prefs=process_prefs,
name='RewardProcess')
# System:
mySystem = System(
processes=[
TargetControlProcess, FlankerControlProcess,
TargetAutomaticProcess, FlankerAutomaticProcess, RewardProcess
],
controller=EVCControlMechanism,
enable_controller=True,
monitor_for_control=[
Reward, (Decision.PROBABILITY_UPPER_THRESHOLD, 1, -1)
],
# monitor_for_control=[Reward, DDM_PROBABILITY_UPPER_THRESHOLD, (DDM_RESPONSE_TIME, -1, 1)],
name='EVC Gratton System')
mySystem.recordSimulationPref = True
# Show characteristics of system:
mySystem.show()
mySystem.controller.show()
# mySystem.show_graph(show_control=True)
# configure EVC components
mySystem.controller.control_signals[
0].intensity_cost_function = Exponential(rate=0.8046).function
mySystem.controller.control_signals[
1].intensity_cost_function = Exponential(rate=0.8046).function
for mech in mySystem.controller.prediction_mechanisms.mechanisms:
if mech.name == 'Flanker Stimulus Prediction Mechanism' or mech.name == 'Target Stimulus Prediction Mechanism':
# when you find a key mechanism (transfer mechanism) with the correct name, print its name
print(mech.name)
mech.function_object.rate = 1.0
if 'Reward' in mech.name:
print(mech.name)
mech.function_object.rate = 1.0
# mySystem.controller.prediction_mechanisms[mech].parameterStates['rate'].base_value = 1.0
print('new rate of integration mechanisms before System execution:')
# for mech in mySystem.controller.prediction_mechanisms.keys():
for mech in mySystem.controller.prediction_mechanisms.mechanisms:
print(mech.name)
print(mech.function_object.rate)
print('----')
# generate stimulus environment
nTrials = 3
targetFeatures = [1, 1, 1]
flankerFeatures = [1, -1,
1] # for full simulation: flankerFeatures = [-1,1]
reward = [100, 100, 100]
targetInputList = targetFeatures
flankerInputList = flankerFeatures
rewardList = reward
# targetInputList = np.random.choice(targetFeatures, nTrials).tolist()
# flankerInputList = np.random.choice(flankerFeatures, nTrials).tolist()
# rewardList = (np.ones(nTrials) * reward).tolist() #np.random.choice(reward, nTrials).tolist()
stim_list_dict = {
Target_Stim: targetInputList,
Flanker_Stim: flankerInputList,
Reward: rewardList
}
mySystem.controller.reportOutputPref = True
expected_results_array = [
0.2645, 0.32257752863413636, 0.9481940753514433, 100., 0.2645,
0.42963678062444666, 0.47661180945923376, 100., 0.2645,
0.300291026852769, 0.97089165101931, 100.
]
expected_sim_results_array = [
0.2645,
0.32257753,
0.94819408,
100.,
0.2645,
0.31663196,
0.95508757,
100.,
0.2645,
0.31093566,
0.96110142,
100.,
0.2645,
0.30548947,
0.96633839,
100.,
0.2645,
0.30029103,
0.97089165,
100.,
0.2645,
0.3169957,
0.95468427,
100.,
0.2645,
0.31128378,
0.9607499,
100.,
0.2645,
0.30582202,
0.96603252,
100.,
0.2645,
0.30060824,
0.9706259,
100.,
0.2645,
0.29563774,
0.97461444,
100.,
0.2645,
0.31163288,
0.96039533,
100.,
0.2645,
0.30615555,
0.96572397,
100.,
0.2645,
0.30092641,
0.97035779,
100.,
0.2645,
0.2959409,
0.97438178,
100.,
0.2645,
0.29119255,
0.97787196,
100.,
0.2645,
0.30649004,
0.96541272,
100.,
0.2645,
0.30124552,
0.97008732,
100.,
0.2645,
0.29624499,
0.97414704,
100.,
0.2645,
0.29148205,
0.97766847,
100.,
0.2645,
0.28694892,
0.98071974,
100.,
0.2645,
0.30156558,
0.96981445,
100.,
0.2645,
0.29654999,
0.97391021,
100.,
0.2645,
0.29177245,
0.97746315,
100.,
0.2645,
0.28722523,
0.98054192,
100.,
0.2645,
0.28289958,
0.98320731,
100.,
0.2645,
0.42963678,
0.47661181,
100.,
0.2645,
0.42846471,
0.43938586,
100.,
-0.2645,
0.42628176,
0.40282965,
100.,
0.2645,
0.42314468,
0.36732207,
100.,
-0.2645,
0.41913221,
0.333198,
100.,
0.2645,
0.42978939,
0.51176048,
100.,
0.2645,
0.42959394,
0.47427693,
100.,
-0.2645,
0.4283576,
0.43708106,
100.,
0.2645,
0.4261132,
0.40057958,
100.,
-0.2645,
0.422919,
0.36514906,
100.,
0.2645,
0.42902209,
0.54679323,
100.,
0.2645,
0.42980788,
0.50942101,
100.,
-0.2645,
0.42954704,
0.47194318,
100.,
-0.2645,
0.42824656,
0.43477897,
100.,
0.2645,
0.42594094,
0.3983337,
100.,
-0.2645,
0.42735293,
0.58136855,
100.,
-0.2645,
0.42910149,
0.54447221,
100.,
0.2645,
0.42982229,
0.50708112,
100.,
-0.2645,
0.42949608,
0.46961065,
100.,
-0.2645,
0.42813159,
0.43247968,
100.,
-0.2645,
0.42482049,
0.61516258,
100.,
0.2645,
0.42749136,
0.57908829,
100.,
0.2645,
0.42917687,
0.54214925,
100.,
-0.2645,
0.42983261,
0.50474093,
100.,
-0.2645,
0.42944107,
0.46727945,
100.,
0.2645,
0.32257753,
0.94819408,
100.,
0.2645,
0.31663196,
0.95508757,
100.,
0.2645,
0.31093566,
0.96110142,
100.,
0.2645,
0.30548947,
0.96633839,
100.,
0.2645,
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]
Flanker_Rep.set_log_conditions((SLOPE, CONTROL))
mySystem.run(
num_trials=nTrials,
inputs=stim_list_dict,
)
np.testing.assert_allclose(
pytest.helpers.expand_np_ndarray(mySystem.results),
expected_results_array,
atol=1e-08,
verbose=True,
)
np.testing.assert_allclose(
pytest.helpers.expand_np_ndarray(mySystem.simulation_results),
expected_sim_results_array,
atol=1e-08,
verbose=True,
)
