def test_python_serial_workflow_function_vectorized():
model = PythonModel(model_script='python_model.py',
model_object_name='sum_rvs')
model_python_serial_function = RunModel(model=model)
model_python_serial_function.run(samples=x_mcs.samples)
assert np.allclose(
np.array(model_python_serial_function.qoi_list).flatten(),
np.sum(x_mcs.samples, axis=1))
def test_python_serial_workflow_class():
model = PythonModel(model_script='python_model.py',
model_object_name='SumRVs')
model_python_serial_class = RunModel(model=model)
model_python_serial_class.run(samples=x_mcs.samples)
assert np.allclose(
np.array(model_python_serial_class.qoi_list).flatten(),
np.sum(x_mcs.samples, axis=1))
def test_python_parallel_workflow_function():
model = PythonModel(model_script='python_model.py',
model_object_name='sum_rvs')
model_python_parallel_function = RunModel(model=model, ntasks=3)
model_python_parallel_function.run(samples=x_mcs.samples)
assert np.allclose(
np.array(model_python_parallel_function.qoi_list).flatten(),
np.sum(x_mcs.samples, axis=1))
shutil.rmtree(model_python_parallel_function.model_dir)
def test_append_samples_true():
model = PythonModel(model_script='python_model.py',
model_object_name='SumRVs')
model_python_serial_class = RunModel(model=model, samples=x_mcs.samples)
assert np.allclose(
np.array(model_python_serial_class.qoi_list).flatten(),
np.sum(x_mcs.samples, axis=1))
model_python_serial_class.run(x_mcs_new.samples, append_samples=True)
assert np.allclose(
np.array(model_python_serial_class.qoi_list).flatten(),
np.sum(np.vstack((x_mcs.samples, x_mcs_new.samples)), axis=1))
def test_rss_runmodel_object():
"""
Check 'runmodel_object' should be a UQpy.RunModel class object.
"""
marginals = [Uniform(loc=0., scale=2.), Uniform(loc=0., scale=1.)]
strata = RectangularStrata(strata_number=[2, 2])
x = TrueStratifiedSampling(distributions=marginals,
strata_object=strata,
nsamples_per_stratum=1,
random_state=1)
from UQpy.surrogates.kriging.regression_models import LinearRegression
from UQpy.surrogates.kriging.correlation_models import ExponentialCorrelation
K = Kriging(
regression_model=LinearRegression(),
correlation_model=ExponentialCorrelation(),
optimizations_number=20,
correlation_model_parameters=[1, 1],
optimizer=MinimizeOptimizer('l-bfgs-b'),
)
model = PythonModel(model_script='python_model_function.py',
model_object_name="y_func")
rmodel = RunModel(model=model)
K.fit(samples=x.samples, values=rmodel.qoi_list)
with pytest.raises(BeartypeCallHintPepParamException):
refinement = GradientEnhancedRefinement(strata=x.strata_object,
runmodel_object='abc',
surrogate=K)
RefinedStratifiedSampling(stratified_sampling=x,
samples_number=6,
samples_per_iteration=2,
refinement_algorithm=refinement)
def test_var_names():
with pytest.raises(BeartypeCallHintPepParamException):
model = PythonModel(model_script='python_model.py',
model_object_name='SumRVs',
var_names=[20],
delete_files=True)
runmodel_object = RunModel(model=model)
def test_third_party_parallel():
names = ['var1', 'var11', 'var111']
model = ThirdPartyModel(model_script='python_model_sum_scalar.py',
fmt="{:>10.4f}",
delete_files=True,
input_template='sum_scalar.py',
var_names=names,
model_object_name="matlab",
output_script='process_third_party_output.py',
output_object_name='read_output')
m = RunModel(model=model, ntasks=3)
m.run(x_mcs.samples)
assert np.allclose(np.array(m.qoi_list).flatten(),
np.sum(x_mcs.samples, axis=1),
atol=1e-4)
shutil.rmtree(m.model.model_dir)
def test_derivatives_6_second():
model = PythonModel(model_script='pfn2.py', model_object_name='model_j', delete_files=True)
h_func = RunModel(model=model)
dist1 = Normal(loc=500, scale=100)
dist2 = Normal(loc=1000, scale=100)
point_u = np.array([1.73673009, 0.16383283])
rx = np.array([[1.0, 0.0], [0.0, 1.0]])
ntf_obj = Nataf(distributions=[dist1, dist2], corr_x=rx)
hessian = TaylorSeries._derivatives(point_u=point_u, runmodel_object=h_func,
nataf_object=ntf_obj, order='second')
np.testing.assert_allclose(hessian, [[-0.00720754, 0.00477726], [0.00477726, -0.00316643]], rtol=1e-04)
def test_third_party_var_names():
names = ['var1', 'var11', 'var111', 'var1111']
with pytest.raises(TypeError):
model = ThirdPartyModel(model_script='python_model_sum_scalar.py',
fmt="{:>10.4f}",
delete_files=True,
input_template='sum_scalar.py',
model_object_name="matlab",
output_script='process_third_party_output.py',
output_object_name='read_output')
model_third_party_default_names = RunModel(model=model,
ntasks=3,
samples=x_mcs.samples)
def test_vor_gerss():
"""
Test the 6 samples generated by GE-RSS using voronoi stratification
"""
marginals = [Uniform(loc=0., scale=2.), Uniform(loc=0., scale=1.)]
strata_vor = VoronoiStrata(seeds_number=4, dimension=2, random_state=10)
x_vor = TrueStratifiedSampling(
distributions=marginals,
strata_object=strata_vor,
nsamples_per_stratum=1,
)
from UQpy.surrogates.kriging.regression_models.LinearRegression import LinearRegression
from UQpy.surrogates.kriging.correlation_models.ExponentialCorrelation import ExponentialCorrelation
model = PythonModel(model_script='python_model_function.py',
model_object_name="y_func")
rmodel = RunModel(model=model)
K_ = Kriging(regression_model=LinearRegression(),
correlation_model=ExponentialCorrelation(),
optimizations_number=20,
optimizer=MinimizeOptimizer('l-bfgs-b'),
random_state=0,
correlation_model_parameters=[1, 1])
K_.fit(samples=x_vor.samples, values=rmodel.qoi_list)
z_vor = RefinedStratifiedSampling(
stratified_sampling=x_vor,
nsamples=6,
random_state=x_vor.random_state,
refinement_algorithm=GradientEnhancedRefinement(
strata=x_vor.strata_object,
runmodel_object=rmodel,
surrogate=K_,
nearest_points_number=4))
assert np.allclose(
z_vor.samples,
np.array([[1.78345908, 0.01640854], [1.46201137, 0.70862104],
[0.4021338, 0.05290083], [0.1062376, 0.88958226],
[0.61246269, 0.47160095], [1.16609055, 0.30832536]]))
assert np.allclose(
z_vor.samplesU01,
np.array([[0.89172954, 0.01640854], [0.73100569, 0.70862104],
[0.2010669, 0.05290083], [0.0531188, 0.88958226],
[0.30623134, 0.47160095], [0.58304527, 0.30832536]]))
def test_rss_kriging_object():
"""
Check 'kriging_object', it should have 'fit' and 'predict' methods.
"""
marginals = [Uniform(loc=0., scale=2.), Uniform(loc=0., scale=1.)]
strata = RectangularStrata(strata_number=[2, 2])
x = TrueStratifiedSampling(distributions=marginals,
strata_object=strata,
nsamples_per_stratum=1,
random_state=1)
model = PythonModel(model_script='python_model_function.py',
model_object_name="y_func")
rmodel = RunModel(model=model)
with pytest.raises(NotImplementedError):
refinement = GradientEnhancedRefinement(strata=x.strata_object,
runmodel_object=rmodel,
surrogate="abc")
RefinedStratifiedSampling(stratified_sampling=x,
nsamples=6,
samples_per_iteration=2,
refinement_algorithm=refinement)
def test_rect_gerss():
"""
Test the 6 samples generated by GE-RSS using rectangular stratification
"""
marginals = [Uniform(loc=0., scale=2.), Uniform(loc=0., scale=1.)]
strata = RectangularStrata(strata_number=[2, 2], random_state=1)
x = TrueStratifiedSampling(distributions=marginals,
strata_object=strata,
nsamples_per_stratum=1)
model = PythonModel(model_script='python_model_function.py',
model_object_name="y_func")
rmodel = RunModel(model=model)
from UQpy.surrogates.kriging.regression_models import LinearRegression
from UQpy.surrogates.kriging.correlation_models import ExponentialCorrelation
K = Kriging(
regression_model=LinearRegression(),
correlation_model=ExponentialCorrelation(),
optimizations_number=20,
random_state=0,
correlation_model_parameters=[1, 1],
optimizer=MinimizeOptimizer('l-bfgs-b'),
)
K.fit(samples=x.samples, values=rmodel.qoi_list)
refinement = GradientEnhancedRefinement(strata=x.strata_object,
runmodel_object=rmodel,
surrogate=K,
nearest_points_number=4)
z = RefinedStratifiedSampling(stratified_sampling=x,
random_state=2,
refinement_algorithm=refinement)
z.run(nsamples=6)
assert np.allclose(
z.samples,
np.array([[0.417022, 0.36016225], [1.00011437, 0.15116629],
[0.14675589, 0.5461693], [1.18626021, 0.67278036],
[1.51296312, 0.77483124], [0.74237455, 0.66026822]]))
def setup():
model = PythonModel(model_script='pfn1.py', model_object_name='model_i', delete_files=True)
h_func = RunModel(model=model)
yield h_func
def test_python_serial_workflow_function_no_objects():
with pytest.raises(TypeError):
model = PythonModel(model_script='python_model_blank.py')
model = RunModel(model=model)
model.run(x_mcs.samples)
def test_python_serial_workflow_function_wrong_object_name():
with pytest.raises(AttributeError):
model = PythonModel(model_script='python_model.py',
model_object_name="random_model_name")
model = RunModel(model=model)
model.run(x_mcs.samples)
# There are two probabilistic input variables, the fire load density and the yield strength.
# %%
var_names = ['qtd', 'fy']
# %% md
#
# Create the model object
# %%
abaqus_sfe_model = RunModel(model_script='abaqus_fire_analysis.py',
input_template='abaqus_input.py',
output_script='extract_abaqus_output.py',
var_names=var_names,
ntasks=24,
model_dir='SFE_MCS',
verbose=True,
cores_per_task=1)
print('Example: Created the model object.')
# %% md
#
# Towards defining the sampling scheme
# The fire load density is assumed to be uniformly distributed between 50 :math:`MJ/m^2` and 450 :math:`MJ/m^2`.
# The yield strength is assumed to be normally distributed, with the parameters
# being: mean = 250 :math:`MPa` and coefficient of variation of :math:`7%`.
#
# Creating samples using MCS.
# %%
def test_model_script():
with pytest.raises(ValueError):
model = PythonModel(model_script='random_file_name',
model_object_name='SumRVs',
delete_files=True)
runmodel_object = RunModel(model=model)
def test_samples():
with pytest.raises(BeartypeCallHintPepParamException):
model = PythonModel(model_script='python_model.py',
model_object_name='SumRVs',
delete_files=True)
runmodel_object = RunModel(model=model, samples="samples_string")
def test_models():
a = os.getcwd()
if "inference" not in a:
os.chdir("../inference")
data_ex1 = np.loadtxt('data_ex1a.txt')
model = PythonModel(model_script='pfn_linear.py',
model_object_name='model_linear',
var_names=['theta_0'])
runmodel4 = RunModel(model=model)
model1 = PythonModel(model_script='pfn_quadratic.py',
model_object_name='model_quadratic',
var_names=['theta_0', 'theta_1'])
runmodel5 = RunModel(model=model1)
model2 = PythonModel(model_script='pfn_cubic.py',
model_object_name='model_cubic',
var_names=['theta_0', 'theta_1', 'theta_2'])
runmodel6 = RunModel(model=model2)
prior1 = Normal()
prior2 = JointIndependent(marginals=[Normal(), Normal()])
prior3 = JointIndependent(marginals=[Normal(), Normal(), Normal()])
model_n_params = [1, 2, 3]
model1 = ComputationalModel(n_parameters=1,
runmodel_object=runmodel4,
prior=prior1,
error_covariance=np.ones(50),
name='model_linear')
model2 = ComputationalModel(n_parameters=2,
runmodel_object=runmodel5,
prior=prior2,
error_covariance=np.ones(50),
name='model_quadratic')
model3 = ComputationalModel(n_parameters=3,
runmodel_object=runmodel6,
prior=prior3,
error_covariance=np.ones(50),
name='model_cubic')
proposals = [
Normal(0, 10),
JointIndependent([Normal(0, 1), Normal(0, 1)]),
JointIndependent([Normal(0, 1),
Normal(0, 2),
Normal(0.025)])
]
# sampling =
mh1 = MetropolisHastings(args_target=(data_ex1, ),
log_pdf_target=model1.evaluate_log_posterior,
jump=1,
burn_length=500,
proposal=proposals[0],
random_state=0,
seed=[0.])
mh2 = MetropolisHastings(args_target=(data_ex1, ),
log_pdf_target=model2.evaluate_log_posterior,
jump=1,
burn_length=500,
proposal=proposals[1],
random_state=0,
seed=[0., 0.])
mh3 = MetropolisHastings(args_target=(data_ex1, ),
log_pdf_target=model3.evaluate_log_posterior,
jump=1,
burn_length=500,
proposal=proposals[2],
random_state=0,
seed=[0., 0., 0.])
e1 = BayesParameterEstimation(inference_model=model1,
data=data_ex1,
sampling_class=mh1)
e2 = BayesParameterEstimation(inference_model=model2,
data=data_ex1,
sampling_class=mh2)
e3 = BayesParameterEstimation(inference_model=model3,
data=data_ex1,
sampling_class=mh3)
selection = BayesModelSelection(
parameter_estimators=[e1, e2, e3],
prior_probabilities=[1. / 3., 1. / 3., 1. / 3.],
nsamples=[2000, 2000, 2000])
selection.sort_models()
assert selection.probabilities[0] == 1.0
assert selection.probabilities[1] == 0.0
assert selection.probabilities[2] == 0.0
assert selection.candidate_models[0].name == 'model_quadratic'
assert selection.candidate_models[1].name == 'model_cubic'
assert selection.candidate_models[2].name == 'model_linear'