def test_threshold_param(self):
D = DDM(name='DDM', function=DriftDiffusionIntegrator(threshold=10.0))
assert D.function.threshold.base == 10.0
D.function.threshold.base = 5.0
assert D.function.threshold.base == 5.0
def test_DDMMechanism_LCA_equivalent(comp_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]}, execution_mode=comp_mode)
assert np.allclose(np.asfarray(result2[0]), [0.1])
assert np.allclose(np.asfarray(result2[1]), [0.1])
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_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_threshold_sets_is_finished(self):
D = DDM(name='DDM', function=DriftDiffusionIntegrator(threshold=5.0))
D.execute(2.0) # 2.0 < 5.0
assert not D.is_finished()
D.execute(2.0) # 4.0 < 5.0
assert not D.is_finished()
D.execute(2.0) # 5.0 = threshold
assert D.is_finished()
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_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_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_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_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_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_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_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_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_ddm_is_finished(comp_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], execution_mode=comp_mode)
results = [
x for x in np.array(results).flatten()
] #HACK: The result is an object dtype in Python comp_mode for some reason?
assert np.allclose(results, np.array(expected_results).flatten())
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_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_time():
D = DDM(name='DDM',
function=DriftDiffusionIntegrator(noise=0.0,
rate=-5.0,
time_step_size=0.2,
starting_point=0.5))
time_0 = D.function.previous_time # t_0 = 0.5
np.testing.assert_allclose(time_0, 0.5, atol=1e-08)
time_1 = D.execute(10)[1][0] # t_1 = 0.5 + 0.2 = 0.7
np.testing.assert_allclose(time_1, 0.7, atol=1e-08)
for i in range(10): # t_11 = 0.7 + 10*0.2 = 2.7
D.execute(10)
time_12 = D.execute(10)[1][0] # t_12 = 2.7 + 0.2 = 2.9
np.testing.assert_allclose(time_12, 2.9, atol=1e-08)
def test_threshold_stops_accumulation(self, mech_mode, variable, expected,
benchmark):
D = DDM(name='DDM', function=DriftDiffusionIntegrator(threshold=5.0))
ex = pytest.helpers.get_mech_execution(D, mech_mode)
decision_variables = []
time_points = []
for i in range(5):
output = ex([variable])
decision_variables.append(output[0][0])
time_points.append(output[1][0])
# decision variable accumulation stops
assert np.allclose(decision_variables, expected)
# time accumulation does not stop
assert np.allclose(time_points, [1.0, 2.0, 3.0, 4.0, 5.0])
if benchmark.enabled:
benchmark(ex, [variable])
def test_valid(self):
D = DDM(
name='DDM',
function=DriftDiffusionIntegrator(seed=0),
)
# returns previous_value + rate * variable * time_step_size + noise
# 0.0 + 1.0 * 1.0 * 1.0 + 0.0
D.execute(1.0)
assert np.allclose(np.asfarray(D.value), [[1.0], [1.0]])
assert np.allclose(D.output_ports[0].value[0], 1.0)
assert np.allclose(D.output_ports[1].value[0], 1.0)
# reset function
D.function.reset(2.0, 0.1)
assert np.allclose(D.function.value[0], 2.0)
assert np.allclose(D.function.previous_value, 2.0)
assert np.allclose(D.function.previous_time, 0.1)
assert np.allclose(np.asfarray(D.value), [[1.0], [1.0]])
assert np.allclose(D.output_ports[0].value[0], 1.0)
assert np.allclose(D.output_ports[1].value[0], 1.0)
# reset function without value spec
D.function.reset()
assert np.allclose(D.function.value[0], 0.0)
assert np.allclose(D.function.previous_value, 0.0)
assert np.allclose(D.function.previous_time, 0.0)
assert np.allclose(np.asfarray(D.value), [[1.0], [1.0]])
assert np.allclose(D.output_ports[0].value[0], 1.0)
assert np.allclose(D.output_ports[1].value[0], 1.0)
# reset mechanism
D.reset(2.0, 0.1)
assert np.allclose(D.function.value[0], 2.0)
assert np.allclose(D.function.previous_value, 2.0)
assert np.allclose(D.function.previous_time, 0.1)
assert np.allclose(np.asfarray(D.value), [[2.0], [0.1]])
assert np.allclose(D.output_ports[0].value, 2.0)
assert np.allclose(D.output_ports[1].value, 0.1)
D.execute(1.0)
# 2.0 + 1.0 = 3.0 ; 0.1 + 1.0 = 1.1
assert np.allclose(np.asfarray(D.value), [[3.0], [1.1]])
assert np.allclose(D.output_ports[0].value[0], 3.0)
assert np.allclose(D.output_ports[1].value[0], 1.1)
# reset mechanism without value spec
D.reset()
assert np.allclose(D.function.value[0], 0.0)
assert np.allclose(D.function.previous_value, 0.0)
assert np.allclose(D.function.previous_time, 0.0)
assert np.allclose(D.output_ports[0].value[0], 0.0)
assert np.allclose(D.output_ports[1].value[0], 0.0)
# reset only decision variable
D.function.initializer = 1.0
D.function.starting_point = 0.0
D.reset()
assert np.allclose(D.function.value[0], 1.0)
assert np.allclose(D.function.previous_value, 1.0)
assert np.allclose(D.function.previous_time, 0.0)
assert np.allclose(D.output_ports[0].value[0], 1.0)
assert np.allclose(D.output_ports[1].value[0], 0.0)