def test_change_termination_condition(self):
D = DDM(function=DriftDiffusionIntegrator(threshold=10))
P = Process(pathway=[D])
S = System(processes=[P])
D.set_log_conditions(VALUE)
def change_termination_processing():
if S.termination_processing is None:
S.scheduler_processing.termination_conds = {TimeScale.TRIAL: WhenFinished(D)}
S.termination_processing = {TimeScale.TRIAL: WhenFinished(D)}
elif isinstance(S.termination_processing[TimeScale.TRIAL], AllHaveRun):
S.scheduler_processing.termination_conds = {TimeScale.TRIAL: WhenFinished(D)}
S.termination_processing = {TimeScale.TRIAL: WhenFinished(D)}
else:
S.scheduler_processing.termination_conds = {TimeScale.TRIAL: AllHaveRun()}
S.termination_processing = {TimeScale.TRIAL: AllHaveRun()}
change_termination_processing()
S.run(inputs={D: [[1.0], [2.0]]},
# termination_processing={TimeScale.TRIAL: WhenFinished(D)},
call_after_trial=change_termination_processing,
num_trials=4)
# Trial 0:
# input = 1.0, termination condition = WhenFinished
# 10 passes (value = 1.0, 2.0 ... 9.0, 10.0)
# Trial 1:
# input = 2.0, termination condition = AllHaveRun
# 1 pass (value = 2.0)
expected_results = [[np.array([[10.]]), np.array([[10.]])],
[np.array([[2.]]), np.array([[1.]])],
[np.array([[10.]]), np.array([[10.]])],
[np.array([[2.]]), np.array([[1.]])]]
assert np.allclose(expected_results, S.results)
def test_DDM_noise_2_0():
stim = 10
T = DDM(name='DDM',
function=DriftDiffusionIntegrator(noise=2.0,
rate=1.0,
time_step_size=1.0))
val = float(T.execute(stim)[0])
assert val == 11.34362792551828
def test_selected_input_array(self):
action_selection = DDM(
input_format=ARRAY,
function=DriftDiffusionAnalytical(
),
output_ports=[SELECTED_INPUT_ARRAY],
name='DDM'
)
action_selection.execute([1.0])
def test_DDM_input(stim):
T = DDM(
name='DDM',
function=DriftDiffusionIntegrator(noise=0.0,
rate=1.0,
time_step_size=1.0),
)
val = float(T.execute(stim)[0])
assert val == 10
def test_DDM_noise_0_5():
stim = 10
T = DDM(name='DDM',
function=DriftDiffusionIntegrator(noise=0.5,
rate=1.0,
time_step_size=1.0))
val = float(T.execute(stim)[0])
assert val == 10.67181396275914
def test_DDM_rate_list_len_1():
stim = 10
T = DDM(
name='DDM',
function=DriftDiffusionIntegrator(noise=0.0,
rate=[5],
time_step_size=1.0),
)
val = float(T.execute(stim)[0])
assert val == 50
def test_DDM():
myMechanism = DDM(
function=DriftDiffusionAnalytical(
drift_rate=(1.0),
threshold=(10.0),
starting_point=0.0,
),
name='My_DDM',
)
myMechanism_2 = DDM(function=DriftDiffusionAnalytical(drift_rate=2.0,
threshold=20.0),
name='My_DDM_2')
myMechanism_3 = DDM(
function=DriftDiffusionAnalytical(drift_rate=3.0, threshold=30.0),
name='My_DDM_3',
)
z = Process(
default_variable=[[30], [10]],
pathway=[
myMechanism, (IDENTITY_MATRIX), myMechanism_2,
(FULL_CONNECTIVITY_MATRIX), myMechanism_3
],
)
result = z.execute([[30], [10]])
expected_output = [
(myMechanism.input_states[0].parameters.value.get(z), np.array([40.])),
(myMechanism.output_states[0].parameters.value.get(z), np.array([10.
])),
(myMechanism_2.input_states[0].parameters.value.get(z), np.array([10.
])),
(myMechanism_2.output_states[0].parameters.value.get(z),
np.array([20.])),
(myMechanism_3.input_states[0].parameters.value.get(z), np.array([20.
])),
(myMechanism_3.output_states[0].parameters.value.get(z),
np.array([30.])),
(result, np.array([30.])),
]
for i in range(len(expected_output)):
val, expected = expected_output[i]
# setting absolute tolerance to be in accordance with reference_output precision
# if you do not specify, assert_allcose will use a relative tolerance of 1e-07,
# which WILL FAIL unless you gather higher precision values to use as reference
np.testing.assert_allclose(
val,
expected,
atol=1e-08,
err_msg='Failed on expected_output[{0}]'.format(i))
def test_DDM_input_fn():
with pytest.raises(TypeError) as error_text:
stim = NormalDist()
T = DDM(
name='DDM',
function=DriftDiffusionIntegrator(noise=0.0,
rate=1.0,
time_step_size=1.0),
)
float(T.execute(stim))
assert "not supported for the input types" in str(error_text.value)
def test_DDM_input_rate_negative():
stim = [10]
T = DDM(
name='DDM',
default_variable=[0],
function=DriftDiffusionIntegrator(noise=0.0,
rate=-5.0,
time_step_size=1.0),
)
val = float(T.execute(stim)[0])
assert val == -50
def test_DDM_input_list_len_2():
with pytest.raises(DDMError) as error_text:
stim = [10, 10]
T = DDM(
name='DDM',
default_variable=[0, 0],
function=DriftDiffusionIntegrator(noise=0.0,
rate=1.0,
time_step_size=1.0),
)
float(T.execute(stim)[0])
assert "single numeric item" in str(error_text.value)
def test_DDM_rate_fn():
with pytest.raises(typecheck.framework.InputParameterError) as error_text:
stim = [10]
T = DDM(
name='DDM',
default_variable=[0],
function=DriftDiffusionIntegrator(noise=0.0,
rate=NormalDist().function,
time_step_size=1.0),
)
float(T.execute(stim)[0])
assert "incompatible value" in str(error_text.value)
def test_DDM_noise_invalid(noise):
with pytest.raises(FunctionError) as error_text:
stim = 10
T = DDM(
name='DDM',
function=DriftDiffusionIntegrator(noise=noise,
rate=1.0,
time_step_size=1.0),
)
float(T.execute(stim)[0])
assert "DriftDiffusionIntegrator requires noise parameter to be a float" in str(
error_text.value)
def test_threshold_stops_accumulation_negative(self):
D = DDM(name='DDM', function=DriftDiffusionIntegrator(threshold=5.0))
decision_variables = []
time_points = []
for i in range(5):
output = D.execute(-2.0)
decision_variables.append(output[0][0][0])
time_points.append(output[1][0][0])
# decision variable accumulation stops
assert np.allclose(decision_variables, [-2.0, -4.0, -5.0, -5.0, -5.0])
# time accumulation does not stop
assert np.allclose(time_points, [1.0, 2.0, 3.0, 4.0, 5.0])
def test_DDM_size_int_inputs():
T = DDM(
name='DDM',
size=1,
function=DriftDiffusionIntegrator(noise=0.0,
rate=-5.0,
time_step_size=1.0),
)
val = T.execute([.4])
decision_variable = val[0][0]
time = val[1][0]
assert decision_variable == -2.0
assert time == 1.0
def test_DDMMechanism_LCA_equivalent(mode):
ddm = DDM(default_variable=[0], function=DriftDiffusionIntegrator(rate=1, time_step_size=0.1))
comp2 = Composition()
comp2.add_node(ddm)
result2 = comp2.run(inputs={ddm:[1]}, bin_execute=mode)
assert np.allclose(np.asfarray(result2[0]), [0.1])
assert np.allclose(np.asfarray(result2[1]), [0.1])
def test_threshold_param(self):
D = DDM(name='DDM', function=DriftDiffusionIntegrator(threshold=10.0))
assert D.function.threshold == 10.0
D.function.threshold = 5.0
assert D.function.threshold == 5.0
def test_DDM_Integrator_Bogacz(benchmark, mech_mode):
stim = 10
T = DDM(name='DDM', function=DriftDiffusionAnalytical())
ex = pytest.helpers.get_mech_execution(T, mech_mode)
val = ex(stim)[0]
assert np.allclose(val, [1.0])
if benchmark.enabled:
benchmark(ex, stim)
def test_DDM_size_int_check_var():
T = DDM(
name='DDM',
size=1,
function=DriftDiffusionIntegrator(noise=0.0,
rate=-5.0,
time_step_size=1.0),
)
assert len(T.defaults.variable) == 1 and T.defaults.variable[0][0] == 0
def test_DDM_size_too_long():
with pytest.raises(DDMError) as error_text:
T = DDM(
name='DDM',
size=[1, 1],
function=DriftDiffusionIntegrator(noise=0.0,
rate=-5.0,
time_step_size=1.0),
)
assert "is greater than 1, implying there are" in str(error_text.value)
def test_DDM_size_too_large():
with pytest.raises(DDMError) as error_text:
T = DDM(
name='DDM',
size=3.0,
function=DriftDiffusionIntegrator(noise=0.0,
rate=-5.0,
time_step_size=1.0),
)
assert "single numeric item" in str(error_text.value)
def test_DDM_mech_size_negative_one():
with pytest.raises(ComponentError) as error_text:
T = DDM(
name='DDM',
size=-1.0,
function=DriftDiffusionIntegrator(noise=0.0,
rate=-5.0,
time_step_size=1.0),
)
assert "is not a positive number" in str(error_text.value)
def test_is_finished_stops_composition(self):
D = DDM(name='DDM', function=DriftDiffusionIntegrator(threshold=10.0))
C = Composition(pathways=[D], reset_stateful_function_when=Never())
C.run(inputs={D: 2.0},
termination_processing={TimeScale.TRIAL: WhenFinished(D)})
# decision variable's value should match threshold
assert D.parameters.value.get(C)[0] == 10.0
# it should have taken 5 executions (and time_step_size = 1.0)
assert D.parameters.value.get(C)[1] == 5.0
def test_ddm_is_finished(mode, noise, threshold, expected_results):
comp = Composition()
ddm = DDM(execute_until_finished=True,
function=DriftDiffusionIntegrator(threshold=threshold, noise=noise))
comp.add_node(ddm)
results = comp.run([0], bin_execute=mode)
results = [x for x in np.array(results).flatten()] #HACK: The result is an object dtype in Python mode for some reason?
assert np.allclose(results, np.array(expected_results).flatten())
def test_is_finished_stops_system(self):
D = DDM(name='DDM', function=DriftDiffusionIntegrator(threshold=10.0))
P = Process(pathway=[D])
S = System(processes=[P], reinitialize_mechanisms_when=Never())
S.run(inputs={D: 2.0},
termination_processing={TimeScale.TRIAL: WhenFinished(D)})
# decision variable's value should match threshold
assert D.parameters.value.get(S)[0] == 10.0
# it should have taken 5 executions (and time_step_size = 1.0)
assert D.parameters.value.get(S)[1] == 5.0
def test_DDM_noise(mech_mode, benchmark, noise, expected):
T = DDM(name='DDM',
function=DriftDiffusionIntegrator(noise=noise,
rate=1.0,
time_step_size=1.0))
ex = pytest.helpers.get_mech_execution(T, mech_mode)
val = ex([10])
assert np.allclose(val[0][0], expected)
if benchmark.enabled:
benchmark(ex, [10])
def test_drift_difussion_analytical_shenhav_compat_mode():
# Create a DriftDiffusionAnalytical Function, make sure to set shenav_et_al_compat_mode=True to get exact behavior
# of old MATLAB code (Matlab/DDMFunctions/ddmSimFRG.m)
B = DDM(name='DDM',
function=DriftDiffusionAnalytical(shenhav_et_al_compat_mode=True))
# Load a CSV containing random sampled test values
data = np.loadtxt(
os.path.join(__location__, 'matlab_ddm_code_ground_truth.csv'))
check_drift_diffusion_analytical(B, data, degenerate_cases=True)
def test_drift_difussion_analytical():
# Create a DriftDiffusionAnalytical Function, setting shenav_et_al_compat_mode=False (the defualt) should only
# really change degenerate input cases, this test tests non-degenerate cases.
B = DDM(name='DDM', function=DriftDiffusionAnalytical())
# Load a CSV containing random sampled test values
data = np.loadtxt(
os.path.join(__location__,
'matlab_ddm_code_ground_truth_non_degenerate.csv'))
check_drift_diffusion_analytical(B, data, degenerate_cases=True)
def test_DDM_Integrator_Bogacz(benchmark, mode):
stim = 10
T = DDM(name='DDM', function=DriftDiffusionAnalytical())
if mode == "Python":
ex = T.execute
elif mode == "LLVM":
ex = pnlvm.execution.MechExecution(T).execute
elif mode == "PTX":
ex = pnlvm.execution.MechExecution(T).cuda_execute
val = ex(stim)[0]
assert np.allclose(val, [1.0])
benchmark(ex, stim)
def test_DDM_rate(benchmark, rate, expected, mech_mode):
stim = [10]
T = DDM(
name='DDM',
function=DriftDiffusionIntegrator(noise=0.0,
rate=rate,
time_step_size=1.0),
)
ex = pytest.helpers.get_mech_execution(T, mech_mode)
val = float(ex(stim)[0][0])
assert val == expected
if benchmark.enabled:
benchmark(ex, stim)
def test_DDM_noise(mode, benchmark, noise, expected):
T = DDM(name='DDM',
function=DriftDiffusionIntegrator(noise=0.5,
rate=1.0,
time_step_size=1.0))
if mode == "Python":
ex = T.execute
elif mode == "LLVM":
ex = pnlvm.execution.MechExecution(T).execute
elif mode == "PTX":
ex = pnlvm.execution.MechExecution(T).cuda_execute
val = ex([10])
assert np.allclose(val[0][0][0], 8.194383551861414)
benchmark(ex, [10])
