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_voronoi_sts():
marginals = [Exponential(loc=1., scale=1.), Exponential(loc=1., scale=1.)]
strata = VoronoiStrata(seeds_number=8, dimension=2, random_state=3)
x = TrueStratifiedSampling(distributions=marginals,
strata_object=strata,
nsamples_per_stratum=3)
assert np.round(x.samples[7, 0], 8) == 3.69284409
assert np.round(x.samples[20, 1], 8) == 1.15559632
assert np.round(x.samples[1, 0], 8) == 1.8393015
assert np.round(x.samples[15, 1], 8) == 2.11772762
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_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]]))
#%%
from UQpy.sampling.stratified_sampling.strata import VoronoiStrata
from UQpy.sampling import TrueStratifiedSampling, RefinedStratifiedSampling, RandomRefinement
from UQpy.distributions import Uniform
import matplotlib.pyplot as plt
#%% md
#
# Create a distribution object.
#%%
marginals = [Uniform(loc=0., scale=1.), Uniform(loc=0., scale=1.)]
strata = VoronoiStrata(seeds_number=16, dimension=2)
#%% md
#
# Run stratified sampling.
#%%
x = TrueStratifiedSampling(distributions=marginals,
strata_object=strata,
nsamples_per_stratum=1,
random_state=1)
#%% md
#
# Plot the resulting stratified samples and the boundaries of the strata in the :math:`U(0,1)` space.
nsamples_per_stratum[4] = 0
x_sts = TrueStratifiedSampling(distributions=marginals,
strata_object=strata,
nsamples_per_stratum=nsamples_per_stratum,
random_state=1)
strata1 = RectangularStrata(strata_number=[3, 3],
sampling_criterion=SamplingCriterion.CENTERED,
random_state=1)
x_sts1 = TrueStratifiedSampling(
distributions=marginals,
strata_object=strata1,
nsamples_per_stratum=1,
)
# Voronoi
strata_vor = VoronoiStrata(seeds_number=8, dimension=2, random_state=3)
sts_vor = TrueStratifiedSampling(distributions=marginals,
strata_object=strata_vor)
sts_vor.run(nsamples_per_stratum=1)
strata_vor1 = VoronoiStrata(seeds_number=8, dimension=2, random_state=3)
sts_vor2 = TrueStratifiedSampling(
distributions=marginals,
strata_object=strata_vor1,
)
sts_vor2.run(nsamples=8, nsamples_per_stratum=1)
sts_vor2.run()
# Delaunay
seeds = np.array([[0, 0], [0.4, 0.8], [1, 0], [1, 1]])
strata_del = DelaunayStrata(seeds=seeds, random_state=2)
#
# Create a distribution object.
#%%
marginals = [Exponential(loc=1., scale=1.), Exponential(loc=1., scale=1.)]
#%% md
#
# Equal number of samples in each stratum
# ----------------------------------------
# Create strata object using :class:`.VoronoiStrata` class.
#%%
strata_obj = VoronoiStrata(seeds_number=8, dimension=2)
sts_vor_obj = TrueStratifiedSampling(distributions=marginals,
strata_object=strata_obj,
random_state=3)
#%% md
#
# Figure shows the stratification of domain using randomly generated seed points
#%%
strata_obj
voronoi_plot_2d(strata_obj.voronoi)
plt.title('Stratified Sample - U(0,1)')
plt.xlim([0, 1])