def test_random_state(): """ Raises an error if type of random_state is not correct. """ with pytest.raises(BeartypeCallHintPepParamException): Kriging(regression_model=LinearRegression(), correlation_model=GaussianCorrelation(), correlation_model_parameters=[1], random_state='A')
def test_missing_correlation_model_parameters(): """ Raises an error if corr_model_params is not defined. """ with pytest.raises(TypeError): Kriging(regression_model=LinearRegression(), correlation_model=GaussianCorrelation(), bounds=[[0.01, 5]], optimizations_number=100, random_state=1)
def test_optimizer(): """ Raises an error if corr_model_params is not defined. """ with pytest.raises(ValueError): Kriging(regression_model=LinearRegression(), correlation_model=GaussianCorrelation(), correlation_model_parameters=[1], optimizer='A')
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_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_regression_models(): from UQpy.surrogates.kriging.regression_models import ConstantRegression, LinearRegression, QuadraticRegression krig.regression_model = ConstantRegression() tmp = krig.regression_model.r([[0], [1]]) tmp_test1 = (tmp[0] == np.array([[1.], [1.]])).all() and (tmp[1] == np.array([[[0.]], [[0.]]])).all() krig.regression_model = LinearRegression() tmp = krig.regression_model.r([[0], [1]]) tmp_test2 = (tmp[0] == (np.array([[1., 0.], [1., 1.]]))).all() and \ (tmp[1] == np.array([[[0., 1.]], [[0., 1.]]])).all() krig.regression_model = QuadraticRegression() tmp = krig.regression_model.r([[-1, 1], [2, -0.5]]) tmp_test3 = (tmp[0] == np.array([[1., -1., 1., 1., -1., 1.], [1., 2., -0.5, 4., -1., 0.25]])).all() and \ (tmp[1] == np.array([[[0., 1., 0., -2., 1., 0.], [0., 0., 1., 0., -1., 2.]], [[0., 1., 0., 4., -0.5, 0.], [0., 0., 1., 0., 2., -1.]]])).all() assert tmp_test1 and tmp_test2 and tmp_test3
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 test_wrong_correlation_model(): """ Raises an error if corr_model is not callable or a string of an in-built model. """ with pytest.raises(BeartypeCallHintPepParamException): Kriging(regression_model=LinearRegression(), correlation_model='A', correlation_model_parameters=[1])
import pytest from UQpy.utilities.MinimizeOptimizer import MinimizeOptimizer from beartype.roar import BeartypeCallHintPepParamException from UQpy.surrogates.kriging.Kriging import Kriging import numpy as np from UQpy.surrogates.kriging.regression_models import LinearRegression, ConstantRegression from UQpy.surrogates.kriging.correlation_models import GaussianCorrelation samples = np.linspace(0, 5, 20).reshape(-1, 1) values = np.cos(samples) optimizer = MinimizeOptimizer(method="L-BFGS-B") krig = Kriging(regression_model=LinearRegression(), correlation_model=GaussianCorrelation(), optimizer=optimizer, correlation_model_parameters=[0.14], optimize=False, random_state=1) krig.fit(samples=samples, values=values, correlation_model_parameters=[0.3]) optimizer = MinimizeOptimizer(method="L-BFGS-B") krig2 = Kriging(regression_model=ConstantRegression(), correlation_model=GaussianCorrelation(), optimizer=optimizer, correlation_model_parameters=[0.3], bounds=[[0.01, 5]], optimize=False, optimizations_number=100, normalize=False, random_state=2) krig2.fit(samples=samples, values=values) # Using the in-built linear regression model as a function linear_regression_model = Kriging(regression_model=LinearRegression(), correlation_model=GaussianCorrelation(), optimizer=optimizer, correlation_model_parameters=[1]).regression_model optimizer = MinimizeOptimizer(method="L-BFGS-B") gaussian_corrleation_model = Kriging(regression_model=LinearRegression(), correlation_model=GaussianCorrelation(), optimizer=optimizer, correlation_model_parameters=[1]).correlation_model