def test_rect_rss():
"""
Test the 6 samples generated by 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,
)
y = RefinedStratifiedSampling(
stratified_sampling=x,
nsamples=6,
samples_per_iteration=2,
random_state=2,
refinement_algorithm=RandomRefinement(strata=strata))
assert np.allclose(
y.samples,
np.array([[0.417022, 0.36016225], [1.00011437, 0.15116629],
[0.14675589, 0.5461693], [1.18626021, 0.67278036],
[0.77483124, 0.7176612], [1.7101839, 0.66516741]]))
assert np.allclose(
np.array(y.samplesU01),
np.array([[0.208511, 0.36016225], [0.50005719, 0.15116629],
[0.07337795, 0.5461693], [0.59313011, 0.67278036],
[0.38741562, 0.7176612], [0.85509195, 0.66516741]]))
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_vor_rss():
"""
Test the 6 samples generated by 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,
)
y_vor = RefinedStratifiedSampling(
stratified_sampling=x_vor,
nsamples=6,
samples_per_iteration=2,
refinement_algorithm=RandomRefinement(strata=x_vor.strata_object))
assert np.allclose(
y_vor.samples,
np.array([[1.78345908, 0.01640854], [1.46201137, 0.70862104],
[0.4021338, 0.05290083], [0.1062376, 0.88958226],
[0.61246269, 0.47160095], [0.85778034, 0.72123075]]))
assert np.allclose(
y_vor.samplesU01,
np.array([[0.89172954, 0.01640854], [0.73100569, 0.70862104],
[0.2010669, 0.05290083], [0.0531188, 0.88958226],
[0.30623134, 0.47160095], [0.42889017, 0.72123075]]))
def test_nsamples():
"""
Check 'nsamples' attributes, it should be an integer.
"""
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)
with pytest.raises(BeartypeCallHintPepParamException):
RefinedStratifiedSampling(stratified_sampling=x,
nsamples='a',
samples_per_iteration=2,
refinement_algorithm=RandomRefinement(
x.strata_object))
def test_rss_simple_voronoi():
marginals = [Uniform(loc=0., scale=1.), Uniform(loc=0., scale=1.)]
strata = VoronoiStrata(seeds_number=16, dimension=2, random_state=1)
x = TrueStratifiedSampling(distributions=marginals,
strata_object=strata,
nsamples_per_stratum=1,
random_state=1)
algorithm = RandomRefinement(strata)
y = RefinedStratifiedSampling(stratified_sampling=x,
nsamples=18,
samples_per_iteration=2,
refinement_algorithm=algorithm,
random_state=2)
assert np.round(y.samples[16, 0], 6) == 0.363793
assert np.round(y.samples[16, 1], 6) == 0.467625
assert np.round(y.samples[17, 0], 6) == 0.424586
assert np.round(y.samples[17, 1], 6) == 0.217301
def test_rss_simple_rectangular():
marginals = [Uniform(loc=0., scale=1.), Uniform(loc=0., scale=1.)]
strata = RectangularStrata(strata_number=[4, 4])
x = TrueStratifiedSampling(distributions=marginals,
strata_object=strata,
nsamples_per_stratum=1,
random_state=1)
algorithm = RandomRefinement(strata)
y = RefinedStratifiedSampling(stratified_sampling=x,
nsamples=18,
samples_per_iteration=2,
refinement_algorithm=algorithm,
random_state=2)
assert y.samples[16, 0] == 0.06614276178462988
assert y.samples[16, 1] == 0.7836449863362334
assert y.samples[17, 0] == 0.1891972651582183
assert y.samples[17, 1] == 0.2961099664117288
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]]))