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_reset_state_integrator_mechanism(self): A = IntegratorMechanism(name='A', function=DriftDiffusionIntegrator()) # Execute A twice # [0] saves decision variable only (not time) original_output = [A.execute(1.0)[0], A.execute(1.0)[0]] # SAVING STATE - - - - - - - - - - - - - - - - - - - - - - - - - reinitialize_values = [] for attr in A.function.stateful_attributes: reinitialize_values.append(getattr(A.function, attr)) # Execute A twice AFTER saving the state so that it continues accumulating. # We expect the next two outputs to repeat once we reset the state b/c we will return it to the current state output_after_saving_state = [A.execute(1.0)[0], A.execute(1.0)[0]] # RESETTING STATE - - - - - - - - - - - - - - - - - - - - - - - - A.reinitialize(*reinitialize_values) # We expect these results to match the results from immediately after saving the state output_after_reinitialization = [A.execute(1.0)[0], A.execute(1.0)[0]] assert np.allclose(output_after_saving_state, output_after_reinitialization) assert np.allclose( original_output, [np.array([[1.0]]), np.array([[2.0]])]) assert np.allclose( output_after_reinitialization, [np.array([[3.0]]), np.array([[4.0]])])
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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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_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])
def test_DDM_noise(mode, benchmark, noise, expected): T = DDM(name='DDM', function=DriftDiffusionIntegrator(noise=noise, 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], expected) if benchmark.enabled: benchmark(ex, [10])
def test_DDM_rate(benchmark, rate, expected, mode): stim = [10] T = DDM( name='DDM', function=DriftDiffusionIntegrator(noise=0.0, rate=rate, 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 = float(ex(stim)[0][0][0]) assert val == expected 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], 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[0], 2.9, atol=1e-08)
def test_threshold_stops_accumulation(self, mode, variable, expected, benchmark): D = DDM(name='DDM', function=DriftDiffusionIntegrator(threshold=5.0)) if mode == "Python": ex = D.execute elif mode == "LLVM": ex = pnlvm.execution.MechExecution(D).execute elif mode == "PTX": ex = pnlvm.execution.MechExecution(D).cuda_execute decision_variables = [] time_points = [] for i in range(5): output = ex([variable]) decision_variables.append(output[0][0][0]) time_points.append(output[1][0][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]) 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][0], 1.0) assert np.allclose(D.output_ports[1].value[0][0], 1.0) # reinitialize function D.function.reinitialize(2.0, 0.1, 0) 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][0], 1.0) assert np.allclose(D.output_ports[1].value[0][0], 1.0) # reinitialize function without value spec D.function.reinitialize() 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][0], 1.0) assert np.allclose(D.output_ports[1].value[0][0], 1.0) # reinitialize mechanism D.reinitialize(2.0, 0.1, 0) 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][0], 3.0) assert np.allclose(D.output_ports[1].value[0][0], 1.1) # reinitialize mechanism without value spec D.reinitialize() 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) # reinitialize only decision variable D.function.initializer = 1.0 D.function.starting_point = 0.0 D.reinitialize() 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)
def test_save_state_before_simulations(self): A = TransferMechanism(name='A', integrator_mode=True, integration_rate=0.2) B = IntegratorMechanism(name='B', function=DriftDiffusionIntegrator(rate=0.1)) C = TransferMechanism(name='C') P = Process(pathway=[A, B, C]) S = System(processes=[P], reinitialize_mechanisms_when=Never()) S.run(inputs={A: [[1.0], [1.0]]}) run_1_values = [ A.parameters.value.get(S), B.parameters.value.get(S)[0], C.parameters.value.get(S) ] # "Save state" code from EVCaux # Get any values that need to be reinitialized for each run reinitialization_values = {} for mechanism in S.stateful_mechanisms: # "save" the current state of each stateful mechanism by storing the values of each of its stateful # attributes in the reinitialization_values dictionary; this gets passed into run and used to call # the reinitialize method on each stateful mechanism. reinitialization_value = [] if isinstance(mechanism.function, IntegratorFunction): for attr in mechanism.function.stateful_attributes: reinitialization_value.append( getattr(mechanism.function.parameters, attr).get(S)) elif hasattr(mechanism, "integrator_function"): if isinstance(mechanism.integrator_function, IntegratorFunction): for attr in mechanism.integrator_function.stateful_attributes: reinitialization_value.append( getattr(mechanism.integrator_function.parameters, attr).get(S)) reinitialization_values[mechanism] = reinitialization_value # Allow values to continue accumulating so that we can set them back to the saved state S.run(inputs={A: [[1.0], [1.0]]}) run_2_values = [ A.parameters.value.get(S), B.parameters.value.get(S)[0], C.parameters.value.get(S) ] S.run(inputs={A: [[1.0], [1.0]]}, reinitialize_values=reinitialization_values) run_3_values = [ A.parameters.value.get(S), B.parameters.value.get(S)[0], C.parameters.value.get(S) ] assert np.allclose(run_2_values, run_3_values) assert np.allclose( run_1_values, [np.array([[0.36]]), np.array([[0.056]]), np.array([[0.056]])]) assert np.allclose(run_2_values, [ np.array([[0.5904]]), np.array([[0.16384]]), np.array([[0.16384]]) ])