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diff --git a/Outputs_SeqPosteriorComparison/posterior/Z.npy b/Outputs_SeqPosteriorComparison/posterior/Z.npy
new file mode 100644
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diff --git a/src/bayesvalidrox/.coverage b/src/bayesvalidrox/.coverage
new file mode 100644
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diff --git a/src/bayesvalidrox/surrogate_models/input_space.py b/src/bayesvalidrox/surrogate_models/input_space.py
index 99b0d5d39532faa12d92073eb17942af8d7fe24f..7eedbbe0de17daee7ebc03b44417961c138faeee 100644
--- a/src/bayesvalidrox/surrogate_models/input_space.py
+++ b/src/bayesvalidrox/surrogate_models/input_space.py
@@ -30,7 +30,7 @@ class InputSpace:
self.meta_model_type = meta_model_type
# Other
- self.apce = None
+ self.pce = None
self.bound_tuples = None
self.input_data_given = None
self.JDist = None
@@ -61,12 +61,11 @@ class InputSpace:
inputs = self.InputObj
self.ndim = len(inputs.Marginals)
- # Check if PCE or aPCE metamodel is selected.
- # TODO: test also for 'pce'??
- if self.meta_model_type.lower() == 'apce':
- self.apce = True
+ # Check if PCE metamodel is selected.
+ if self.meta_model_type.lower() == 'pce':
+ self.pce = True
else:
- self.apce = False
+ self.pce = False
# check if marginals given
if not self.ndim >= 1:
@@ -293,7 +292,7 @@ class InputSpace:
f"{parIdx + 1} is not available.")
raise ValueError(message)
- if self.input_data_given or self.apce:
+ if self.input_data_given or not self.pce:
polytype = 'arbitrary'
# Store dists and poly_types
@@ -376,7 +375,7 @@ class InputSpace:
disttypes.append(inputs.Marginals[par_i].dist_type)
# Pass non-transformed X, if arbitrary PCE is selected.
- if None in disttypes or self.input_data_given or self.apce:
+ if None in disttypes or self.input_data_given or not self.pce:
return X
cdfx = np.zeros(X.shape)
diff --git a/tests/test_InputSpace.py b/tests/test_InputSpace.py
index 40f26956376d07e59161cff6418aab1cab2c898f..60893db8f4ab469f40dcfdf02e6c7c844767f73c 100644
--- a/tests/test_InputSpace.py
+++ b/tests/test_InputSpace.py
@@ -125,7 +125,7 @@ def test_check_valid_input_noapc() -> None:
inp.add_marginals()
inp.Marginals[1].dist_type = 'normal'
inp.Marginals[1].parameters = [0, 1]
- InputSpace(inp, meta_Model_type='gpe')
+ InputSpace(inp, meta_model_type='gpe')
#%% Test ExpDesign.build_polytypes
diff --git a/tests/test_MetaModel.py b/tests/test_MetaModel.py
index 5cea0dcae3601e56fa213bb3cb5ba4ffdc44d094..dd1eb29f26b7d7f52f9a2cf0896c61e946e86787 100644
--- a/tests/test_MetaModel.py
+++ b/tests/test_MetaModel.py
@@ -33,8 +33,8 @@ sys.path.append("src/")
from bayesvalidrox.surrogate_models.inputs import Input
from bayesvalidrox.surrogate_models.input_space import InputSpace
-from bayesvalidrox.surrogate_models.surrogate_models import MetaModel, corr_loocv_error, create_psi
-from bayesvalidrox.surrogate_models.surrogate_models import gaussian_process_emulator
+from bayesvalidrox.surrogate_models.surrogate_models import MetaModel
+from bayesvalidrox.surrogate_models.supplementary import create_psi
@@ -51,137 +51,31 @@ def test_metamod() -> None:
MetaModel(inp)
-#%% Test MetaModel.build_metamodel
+#%% Test MetaModel.check_is_gaussian
-def test_build_metamodel_nosamples() -> None:
- """
- Build MetaModel without collocation samples
- """
+def test_check_is_gaussian() -> None:
inp = Input()
inp.add_marginals()
inp.Marginals[0].dist_type = 'normal'
inp.Marginals[0].parameters = [0, 1]
mm = MetaModel(inp)
- with pytest.raises(AttributeError) as excinfo:
- mm.build_metamodel()
- assert str(excinfo.value) == 'Please provide samples to the metamodel before building it.'
-
-
-def test_build_metamodel() -> None:
- """
- Build MetaModel
- """
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = MetaModel(inp)
- mm.CollocationPoints = np.array([[0.2], [0.8]])
- mm.build_metamodel()
-
+ mm.check_is_gaussian()
-def test_build_metamodel_ninitsamples() -> None:
- """
- Build MetaModel with n_init_samples
- """
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = MetaModel(inp)
- mm.CollocationPoints = np.array([[0.2], [0.8]])
- mm.build_metamodel(n_init_samples=2)
+#%% Test MetaModel.build_metamodel
-def test_build_metamodel_gpe() -> None:
+def test_build_metamodel() -> None:
"""
- Build MetaModel gpe
+ Build MetaModel
"""
inp = Input()
inp.add_marginals()
inp.Marginals[0].dist_type = 'normal'
inp.Marginals[0].parameters = [0, 1]
mm = MetaModel(inp)
- mm.meta_model_type = 'gpe'
- mm.CollocationPoints = np.array([[0.2], [0.8]])
mm.build_metamodel()
-
-
-def test_build_metamodel_coldimerr() -> None:
- """
- Build MetaModel with wrong shape collocation samples
- """
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = MetaModel(inp)
- mm.CollocationPoints = [[0.2, 0.8]]
- with pytest.raises(AttributeError) as excinfo:
- mm.build_metamodel()
- assert str(
- excinfo.value) == 'The second dimension of X should be the same size as the number of marginals in the InputObj'
-
-
-#%% Test MetaMod.generate_polynomials
-
-def test_generate_polynomials_noexp() -> None:
- """
- Generate polynomials without ExpDeg
- """
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = MetaModel(inp)
- with pytest.raises(AttributeError) as excinfo:
- mm.generate_polynomials()
- assert str(excinfo.value) == 'Generate or add InputSpace before generating polynomials'
-
-
-def test_generate_polynomials_nodeg() -> None:
- """
- Generate polynomials without max_deg
- """
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = MetaModel(inp)
-
- # Setup
- mm.InputSpace = InputSpace(inp)
- mm.InputSpace.n_init_samples = 2
- mm.InputSpace.init_param_space(np.max(mm.pce_deg))
- mm.ndim = mm.InputSpace.ndim
- mm.n_params = len(mm.input_obj.Marginals)
-
- # Generate
- with pytest.raises(AttributeError) as excinfo:
- mm.generate_polynomials()
- assert str(excinfo.value) == 'MetaModel cannot generate polynomials in the given scenario!'
-
-
-def test_generate_polynomials_deg() -> None:
- """
- Generate polynomials with max_deg
- """
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = MetaModel(inp)
-
- # Setup
- mm.InputSpace = InputSpace(inp)
- mm.InputSpace.n_init_samples = 2
- mm.InputSpace.init_param_space(np.max(mm.pce_deg))
- mm.ndim = mm.InputSpace.ndim
- mm.n_params = len(mm.input_obj.Marginals)
-
- # Generate
- mm.generate_polynomials(4)
-
+
+
#%% Test MetaMod.add_InputSpace
@@ -261,656 +155,6 @@ def test_fit_gpe() -> None:
mm.fit([[0.2], [0.4], [0.8]], {'Z': [[0.4], [0.2], [0.5]]})
-#%% Test MetaModel.create_psi
-
-def test_create_psi() -> None:
- """
- Create psi-matrix
- """
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = MetaModel(inp)
- samples = np.array([[0.2], [0.8]])
- mm.CollocationPoints = samples
- mm.build_metamodel(n_init_samples=2)
- BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
- univ_bas = mm.univ_basis_vals(samples)
- create_psi(BasisIndices, univ_bas)
-
-
-#%% Test MetaModel.regression
-
-def test_regression() -> None:
- """
- Regression without a method
- """
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = MetaModel(inp)
- samples = np.array([[0.2]])
- outputs = np.array([0.5])
-
- mm.CollocationPoints = samples
- mm.build_metamodel(n_init_samples=2)
- BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
- univ_bas = mm.univ_basis_vals(samples)
- psi = create_psi(BasisIndices, univ_bas)
-
- mm.regression(samples, outputs, psi)
-
-
-def test_regression_ols() -> None:
- """
- Regression: ols
- """
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = MetaModel(inp)
- samples = np.array([[0.2]])
- outputs = np.array([0.5])
-
- mm.CollocationPoints = samples
- mm.build_metamodel(n_init_samples=2)
- BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
- univ_bas = mm.univ_basis_vals(samples)
- psi = create_psi(BasisIndices, univ_bas)
-
- mm.regression(samples, outputs, psi, reg_method='ols')
-
-
-def test_regression_olssparse() -> None:
- """
- Regression: ols and sparse
- """
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = MetaModel(inp)
- samples = np.array([[0.2]])
- outputs = np.array([0.5])
-
- mm.CollocationPoints = samples
- mm.build_metamodel(n_init_samples=2)
- BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
- univ_bas = mm.univ_basis_vals(samples)
- psi = create_psi(BasisIndices, univ_bas)
-
- mm.regression(samples, outputs, psi, reg_method='ols', sparsity=True)
-
-
-def test_regression_ard() -> None:
- """
- Regression: ard
- """
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = MetaModel(inp)
- samples = np.array([[0.2], [0.8]])
- outputs = np.array([0.4, 0.5])
-
- mm.CollocationPoints = samples
- mm.build_metamodel(n_init_samples=2)
- BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
- univ_bas = mm.univ_basis_vals(samples)
- psi = create_psi(BasisIndices, univ_bas)
-
- mm.regression(samples, outputs, psi, reg_method='ard')
-
-
-def test_regression_ardssparse() -> None:
- """
- Regression: ard and sparse
- """
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = MetaModel(inp)
- samples = np.array([[0.2], [0.8]])
- outputs = np.array([0.4, 0.5])
-
- mm.CollocationPoints = samples
- mm.build_metamodel(n_init_samples=2)
- BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
- univ_bas = mm.univ_basis_vals(samples)
- psi = create_psi(BasisIndices, univ_bas)
-
- mm.regression(samples, outputs, psi, reg_method='ard', sparsity=True)
-
-
-def test_regression_fastard() -> None:
- """
- Regression: fastard
- """
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = MetaModel(inp)
- samples = np.array([[0.2]])
- outputs = np.array([0.5])
-
- mm.CollocationPoints = samples
- mm.build_metamodel(n_init_samples=2)
- BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
- univ_bas = mm.univ_basis_vals(samples)
- psi = create_psi(BasisIndices, univ_bas)
-
- mm.regression(samples, outputs, psi, reg_method='fastard')
-
-
-def test_regression_fastardssparse() -> None:
- """
- Regression: fastard and sparse
- """
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = MetaModel(inp)
- samples = np.array([[0.2]])
- outputs = np.array([0.5])
-
- mm.CollocationPoints = samples
- mm.build_metamodel(n_init_samples=2)
- BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
- univ_bas = mm.univ_basis_vals(samples)
- psi = create_psi(BasisIndices, univ_bas)
-
- mm.regression(samples, outputs, psi, reg_method='fastard', sparsity=True)
-
-
-def test_regression_brr() -> None:
- """
- Regression: brr
- """
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = MetaModel(inp)
- samples = np.array([[0.2]])
- outputs = np.array([0.5])
-
- mm.CollocationPoints = samples
- mm.build_metamodel(n_init_samples=2)
- BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
- univ_bas = mm.univ_basis_vals(samples)
- psi = create_psi(BasisIndices, univ_bas)
-
- mm.regression(samples, outputs, psi, reg_method='brr')
-
-
-def test_regression_brrssparse() -> None:
- """
- Regression: brr and sparse
- """
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = MetaModel(inp)
- samples = np.array([[0.2]])
- outputs = np.array([0.5])
-
- mm.CollocationPoints = samples
- mm.build_metamodel(n_init_samples=2)
- BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
- univ_bas = mm.univ_basis_vals(samples)
- psi = create_psi(BasisIndices, univ_bas)
-
- mm.regression(samples, outputs, psi, reg_method='brr', sparsity=True)
-
-
-if 0: # Could not figure out these errors, issue most likely in chosen samples/outputs
- def test_regression_bcs() -> None:
- """
- Regression: bcs
- """
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = MetaModel(inp)
- samples = np.array([[0.0], [0.1], [0.2], [0.3], [0.4], [0.5], [0.6], [0.7], [0.8], [0.9]])
- outputs = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])
- mm.pce_deg = 3
- mm.CollocationPoints = samples
- mm.build_metamodel(n_init_samples=2)
- BasisIndices = mm.allBasisIndices[str(mm.pce_deg)][str(1.0)]
- univ_bas = mm.univ_basis_vals(samples)
- psi = create_psi(BasisIndices, univ_bas)
-
- mm.regression(samples, outputs, psi, reg_method='bcs')
-
-
- def test_regression_bcsssparse() -> None:
- """
- Regression: bcs and sparse
- """
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = MetaModel(inp)
- samples = np.array([[0.0], [0.1], [0.2], [0.3], [0.4], [0.5], [0.6], [0.7], [0.8], [0.9], [1.0]])
- outputs = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.1])
-
- mm.CollocationPoints = samples
- mm.build_metamodel(n_init_samples=2)
- BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
- univ_bas = mm.univ_basis_vals(samples)
- psi = create_psi(BasisIndices, univ_bas)
-
- mm.regression(samples, outputs, psi, reg_method='bcs', sparsity=True)
-
-
-def test_regression_lars() -> None:
- """
- Regression: lars
- """
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = MetaModel(inp)
- samples = np.array([[0.0], [0.1], [0.2], [0.3], [0.4], [0.5], [0.6], [0.7], [0.8], [0.9], [1.0]])
- outputs = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.1])
-
- mm.CollocationPoints = samples
- mm.build_metamodel(n_init_samples=2)
- BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
- univ_bas = mm.univ_basis_vals(samples)
- psi = create_psi(BasisIndices, univ_bas)
-
- mm.regression(samples, outputs, psi, reg_method='lars')
-
-
-def test_regression_larsssparse() -> None:
- """
- Regression: lars and sparse
- """
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = MetaModel(inp)
- samples = np.array([[0.0], [0.1], [0.2], [0.3], [0.4], [0.5], [0.6], [0.7], [0.8], [0.9], [1.0]])
- outputs = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.1])
-
- mm.CollocationPoints = samples
- mm.build_metamodel(n_init_samples=2)
- BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
- univ_bas = mm.univ_basis_vals(samples)
- psi = create_psi(BasisIndices, univ_bas)
-
- mm.regression(samples, outputs, psi, reg_method='lars', sparsity=True)
-
-
-def test_regression_sgdr() -> None:
- """
- Regression: sgdr
- """
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = MetaModel(inp)
- samples = np.array([[0.2]])
- outputs = np.array([0.5])
-
- mm.CollocationPoints = samples
- mm.build_metamodel(n_init_samples=2)
- BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
- univ_bas = mm.univ_basis_vals(samples)
- psi = create_psi(BasisIndices, univ_bas)
-
- mm.regression(samples, outputs, psi, reg_method='sgdr')
-
-
-def test_regression_sgdrssparse() -> None:
- """
- Regression: sgdr and sparse
- """
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = MetaModel(inp)
- samples = np.array([[0.2]])
- outputs = np.array([0.5])
-
- mm.CollocationPoints = samples
- mm.build_metamodel(n_init_samples=2)
- BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
- univ_bas = mm.univ_basis_vals(samples)
- psi = create_psi(BasisIndices, univ_bas)
-
- mm.regression(samples, outputs, psi, reg_method='sgdr', sparsity=True)
-
-
-if 0: # Could not figure out these errors, issue most likely in chosen samples/outputs
- def test_regression_omp() -> None:
- """
- Regression: omp
- """
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = MetaModel(inp)
- samples = np.array([[0.0], [0.1], [0.2], [0.3], [0.4], [0.5], [0.6], [0.7], [0.8], [0.9], [1.0]])
- outputs = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1])
-
- mm.CollocationPoints = samples
- mm.build_metamodel(n_init_samples=2)
- BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
- univ_bas = mm.univ_basis_vals(samples)
- psi = create_psi(BasisIndices, univ_bas)
-
- mm.regression(samples, outputs, psi, reg_method='omp')
-
-
- def test_regression_ompssparse() -> None:
- """
- Regression: omp and sparse
- """
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = MetaModel(inp)
- samples = np.array([[0.2]])
- outputs = np.array([0.5])
-
- mm.CollocationPoints = samples
- mm.build_metamodel(n_init_samples=2)
- BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
- univ_bas = mm.univ_basis_vals(samples)
- psi = create_psi(BasisIndices, univ_bas)
-
- mm.regression(samples, outputs, psi, reg_method='omp', sparsity=True)
-
-
-def test_regression_vbl() -> None:
- """
- Regression: vbl
- """
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = MetaModel(inp)
- samples = np.array([[0.2]])
- outputs = np.array([0.5])
-
- mm.CollocationPoints = samples
- mm.build_metamodel(n_init_samples=2)
- BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
- univ_bas = mm.univ_basis_vals(samples)
- psi = create_psi(BasisIndices, univ_bas)
-
- mm.regression(samples, outputs, psi, reg_method='vbl')
-
-
-def test_regression_vblssparse() -> None:
- """
- Regression: vbl and sparse
- """
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = MetaModel(inp)
- samples = np.array([[0.2]])
- outputs = np.array([0.5])
-
- mm.CollocationPoints = samples
- mm.build_metamodel(n_init_samples=2)
- BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
- univ_bas = mm.univ_basis_vals(samples)
- psi = create_psi(BasisIndices, univ_bas)
-
- mm.regression(samples, outputs, psi, reg_method='vbl', sparsity=True)
-
-
-def test_regression_ebl() -> None:
- """
- Regression: ebl
- """
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = MetaModel(inp)
- samples = np.array([[0.2]])
- outputs = np.array([0.5])
-
- mm.CollocationPoints = samples
- mm.build_metamodel(n_init_samples=2)
- BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
- univ_bas = mm.univ_basis_vals(samples)
- psi = create_psi(BasisIndices, univ_bas)
-
- mm.regression(samples, outputs, psi, reg_method='ebl')
-
-
-def test_regression_eblssparse() -> None:
- """
- Regression: ebl and sparse
- """
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = MetaModel(inp)
- samples = np.array([[0.2]])
- outputs = np.array([0.5])
-
- mm.CollocationPoints = samples
- mm.build_metamodel(n_init_samples=2)
- BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
- univ_bas = mm.univ_basis_vals(samples)
- psi = create_psi(BasisIndices, univ_bas)
-
- mm.regression(samples, outputs, psi, reg_method='ebl', sparsity=True)
-
-
-#%% Test Model.update_pce_coeffs
-
-# TODO: very linked to the actual training...
-
-#%% Test MetaModel.univ_basis_vals
-
-def test_univ_basis_vals() -> None:
- """
- Creates univariate polynomials
- """
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = MetaModel(inp)
- samples = np.array([[0.2], [0.8]])
- mm.CollocationPoints = samples
- mm.build_metamodel(n_init_samples=2)
- mm.univ_basis_vals(samples)
-
-
-#%% Test MetaModel.adaptive_regression
-
-def test_adaptive_regression_fewsamples() -> None:
- """
- Adaptive regression, no specific method, too few samples given
- """
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = MetaModel(inp)
- samples = np.array([[0.2]])
- outputs = np.array([0.8])
-
- mm.CollocationPoints = samples
- mm.build_metamodel(n_init_samples=2)
-
- # Evaluate the univariate polynomials on InputSpace
- if mm.meta_model_type.lower() != 'gpe':
- mm.univ_p_val = mm.univ_basis_vals(mm.CollocationPoints)
-
- with pytest.raises(AttributeError) as excinfo:
- mm.adaptive_regression(outputs, 0)
- assert str(excinfo.value) == ('There are too few samples for the corrected loo-cv error. Fit surrogate on at least as '
- 'many samples as parameters to use this')
-
-
-def test_adaptive_regression() -> None:
- """
- Adaptive regression, no specific method
- """
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = MetaModel(inp)
- samples = np.array([[0.0], [0.1]])
- outputs = np.array([0.0, 0.1])
-
- mm.CollocationPoints = samples
- mm.build_metamodel(n_init_samples=2)
-
- # Evaluate the univariate polynomials on InputSpace
- if mm.meta_model_type.lower() != 'gpe':
- mm.univ_p_val = mm.univ_basis_vals(mm.CollocationPoints)
- mm.adaptive_regression(outputs, 0)
-
-
-def test_adaptive_regression_verbose() -> None:
- """
- Adaptive regression, no specific method, verbose output
- """
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = MetaModel(inp)
- samples = np.array([[0.0], [0.1]])
- outputs = np.array([0.0, 0.1])
-
- mm.CollocationPoints = samples
- mm.build_metamodel(n_init_samples=2)
-
- # Evaluate the univariate polynomials on InputSpace
- if mm.meta_model_type.lower() != 'gpe':
- mm.univ_p_val = mm.univ_basis_vals(mm.CollocationPoints)
- mm.adaptive_regression(outputs, 0, True)
-
-
-def test_adaptive_regression_ols() -> None:
- """
- Adaptive regression, ols
- """
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = MetaModel(inp)
- samples = np.array([[0.0], [0.1], [0.2], [0.3], [0.4], [0.5], [0.6], [0.7], [0.8],
- [0.9], [1.0]])
- outputs = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.1])
-
- mm.CollocationPoints = samples
- mm.build_metamodel(n_init_samples=2)
-
- # Evaluate the univariate polynomials on InputSpace
- if mm.meta_model_type.lower() != 'gpe':
- mm.univ_p_val = mm.univ_basis_vals(mm.CollocationPoints)
- mm.pce_reg_method = 'ols'
- mm.adaptive_regression(outputs, 0)
-
-
-#%% Test MetaModel.corr_loocv_error
-
-def test_corr_loocv_error_nosparse() -> None:
- """
- Corrected loocv error
- """
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = MetaModel(inp)
- samples = np.array([[0.0], [0.1], [0.2], [0.3], [0.4], [0.5], [0.6], [0.7],
- [0.8], [0.9], [1.0]])
- outputs = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.1])
-
- mm.CollocationPoints = samples
- mm.build_metamodel(n_init_samples=2)
- BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
- univ_bas = mm.univ_basis_vals(samples)
- psi = create_psi(BasisIndices, univ_bas)
-
- outs = mm.regression(samples, outputs, psi, reg_method='ebl')
- corr_loocv_error(outs['clf_poly'], outs['sparePsi'], outs['coeffs'],
- outputs)
-
-
-def test_corr_loocv_error_singley() -> None:
- """
- Corrected loocv error
- """
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = MetaModel(inp)
- samples = np.array([[0.2]])
- outputs = np.array([0.1])
-
- mm.CollocationPoints = samples
- mm.build_metamodel(n_init_samples=2)
- BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
- univ_bas = mm.univ_basis_vals(samples)
- psi = create_psi(BasisIndices, univ_bas)
-
- outs = mm.regression(samples, outputs, psi, reg_method='ols')
- corr_loocv_error(outs['clf_poly'], outs['sparePsi'], outs['coeffs'],
- outputs)
-
-
-def test_corr_loocv_error_sparse() -> None:
- """
- Corrected loocv error from sparse results
- """
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = MetaModel(inp)
- samples = np.array([[0.0], [0.1], [0.2], [0.3], [0.4], [0.5], [0.6], [0.7],
- [0.8], [0.9], [1.0]])
- outputs = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.1])
-
- mm.CollocationPoints = samples
- mm.build_metamodel(n_init_samples=2)
- BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
- univ_bas = mm.univ_basis_vals(samples)
- psi = create_psi(BasisIndices, univ_bas)
-
- outs = mm.regression(samples, outputs, psi, reg_method='ebl',
- sparsity=True)
- corr_loocv_error(outs['clf_poly'], outs['sparePsi'], outs['coeffs'],
- outputs)
-
#%% Test MetaModel.pca_transformation
@@ -924,7 +168,7 @@ def test_pca_transformation() -> None:
inp.Marginals[0].parameters = [0, 1]
mm = MetaModel(inp)
outputs = np.array([[0.4, 0.4], [0.5, 0.6]])
- mm.pca_transformation(outputs)
+ mm.pca_transformation(outputs, 1)
def test_pca_transformation_varcomp() -> None:
@@ -938,7 +182,7 @@ def test_pca_transformation_varcomp() -> None:
mm = MetaModel(inp)
outputs = np.array([[0.4, 0.4], [0.5, 0.6]])
mm.var_pca_threshold = 1
- mm.pca_transformation(outputs)
+ mm.pca_transformation(outputs, 1)
def test_pca_transformation_ncomp() -> None:
@@ -950,55 +194,8 @@ def test_pca_transformation_ncomp() -> None:
inp.Marginals[0].dist_type = 'normal'
inp.Marginals[0].parameters = [0, 1]
mm = MetaModel(inp)
- outputs = np.array([[0.4, 0.4], [0.5, 0.6]])
- mm.n_pca_components = 1
- mm.pca_transformation(outputs)
-
-
-#%% Test MetaModel.gaussian_process_emulator
-
-def test_gaussian_process_emulator() -> None:
- """
- Create GPE
- """
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- gaussian_process_emulator([[0.2], [0.8]], [0.4, 0.5])
-
-
-def test_gaussian_process_emulator_nug() -> None:
- """
- Create GPEwith nugget
- """
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- gaussian_process_emulator([[0.2], [0.8]], [0.4, 0.5], nug_term=1.0)
-
-
-def test_gaussian_process_emulator_autosel() -> None:
- """
- Fit MetaModel with autoselect
- """
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- gaussian_process_emulator([[0.2], [0.8]], [0.4, 0.5], autoSelect=True)
-
-
-def test_gaussian_process_emulator_varidx() -> None:
- """
- Create GPE with var_idx
- """
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- gaussian_process_emulator([[0.2], [0.8]], [0.4, 0.5], varIdx=1)
+ outputs = np.array([[0.4, 0.4], [0.5, 0.6]])
+ mm.pca_transformation(outputs, 1)
#%% Test MetaModel.eval_metamodel
@@ -1012,70 +209,10 @@ def test_eval_metamodel() -> None:
inp.Marginals[0].dist_type = 'normal'
inp.Marginals[0].parameters = [0, 1]
mm = MetaModel(inp)
- mm.out_names = ['Z']
- mm.fit([[0.2], [0.4], [0.8]], {'Z': [[0.4], [0.2], [0.5]]})
- mm.eval_metamodel([[0.4]])
-
-
-def test_eval_metamodel_normalboots() -> None:
- """
- Eval trained MetaModel with normal bootstrap
- """
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = MetaModel(inp)
- mm.bootstrap_method = 'normal'
- mm.out_names = ['Z']
- mm.fit([[0.2], [0.4], [0.8]], {'Z': [[0.4], [0.2], [0.5]]})
- mm.eval_metamodel([[0.4]])
-
-
-def test_eval_metamodel_highnormalboots() -> None:
- """
- Eval trained MetaModel with higher bootstrap-itrs
- """
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = MetaModel(inp)
- mm.n_bootstrap_itrs = 2
- mm.out_names = ['Z']
- mm.fit([[0.2], [0.4], [0.8]], {'Z': [[0.4], [0.2], [0.5]]})
- mm.eval_metamodel([[0.4]])
-
-
-def test_eval_metamodel_gpe() -> None:
- """
- Eval trained MetaModel - gpe
- """
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = MetaModel(inp)
- mm.meta_model_type = 'gpe'
- mm.out_names = ['Z']
- mm.fit([[0.2], [0.8]], {'Z': np.array([[0.4], [0.5]])})
- mm.eval_metamodel([[0.4]])
+ out = mm.eval_metamodel([[0.4]])
+ assert len(out) == 2
-def test_eval_metamodel_pca() -> None:
- """
- Eval trained MetaModel with pca
- """
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = MetaModel(inp)
- mm.dim_red_method = 'pca'
- mm.out_names = ['Z']
- mm.fit([[0.2], [0.8]], {'Z': [[0.4, 0.4], [0.5, 0.6]]})
- mm.eval_metamodel([[0.4]])
-
#%% Test MetaModel.create_model_error
# TODO: move model out of this function
@@ -1115,7 +252,7 @@ def test_copy_meta_model_opts() -> None:
#%% Test Engine._compute_pce_moments
-def test__compute_pce_moments() -> None:
+def test__compute_moments() -> None:
"""
Compute moments of a pce-surrogate
"""
@@ -1125,37 +262,9 @@ def test__compute_pce_moments() -> None:
inp.Marginals[0].parameters = [0, 1]
mm = MetaModel(inp)
mm.fit([[0.2], [0.4], [0.8]], {'Z': [[0.4], [0.2], [0.5]]})
- mm._compute_pce_moments()
+ mm._compute_moments()
-def test__compute_pce_moments_pca() -> None:
- """
- Compute moments of a pce-surrogate with pca
- """
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = MetaModel(inp)
- mm.dim_red_method = 'pca'
- mm.fit([[0.2], [0.8]], {'Z': [[0.4, 0.4], [0.5, 0.6]]})
- mm._compute_pce_moments()
-
-
-def test__compute_pce_moments_gpe() -> None:
- """
- Compute moments of a gpe-surrogate
- """
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = MetaModel(inp)
- mm.meta_model_type = 'gpe'
- with pytest.raises(AttributeError) as excinfo:
- mm._compute_pce_moments()
- assert str(excinfo.value) == 'Moments can only be computed for pce-type surrogates'
-
#%% Test MetaModel.update_metamodel
# TODO: taken from engine
diff --git a/tests/test_PolynomialChaosEmulator.py b/tests/test_PolynomialChaosEmulator.py
new file mode 100644
index 0000000000000000000000000000000000000000..c15f9e13940ef9ec7604acfe6194ad2165257a12
--- /dev/null
+++ b/tests/test_PolynomialChaosEmulator.py
@@ -0,0 +1,952 @@
+# -*- coding: utf-8 -*-
+"""
+Test the MetaModel class in bayesvalidrox.
+Tests are available for the following functions
+Class MetaModel:
+ build_metamodel - x
+ update_metamodel
+ update_pce_coeffs
+ create_basis_indices --removed, just redirects
+ add_InputSpace -x
+ univ_basis_vals
+ fit
+ adaptive_regression
+ pca_transformation
+ eval_metamodel
+ create_model_error
+ eval_model_error
+ auto_vivification
+ copy_meta_model_opts
+ __select_degree
+ generate_polynomials
+ _compute_pce_moments
+
+"""
+import numpy as np
+import pytest
+import sys
+
+sys.path.append("src/")
+
+from bayesvalidrox.surrogate_models.inputs import Input
+from bayesvalidrox.surrogate_models.input_space import InputSpace
+from bayesvalidrox.surrogate_models.surrogate_models import MetaModel
+from bayesvalidrox.surrogate_models.polynomial_chaos import PCE
+from bayesvalidrox.surrogate_models.supplementary import gaussian_process_emulator, corr_loocv_error, create_psi
+
+
+
+#%% Test MetaMod constructor on its own
+
+def test_metamod() -> None:
+ """
+ Construct PCE without inputs
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ PCE(inp)
+
+
+#%% Test PCE.build_metamodel
+
+def test_build_metamodel_nosamples() -> None:
+ """
+ Build PCE without collocation samples
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = PCE(inp)
+ with pytest.raises(AttributeError) as excinfo:
+ mm.build_metamodel()
+ assert str(excinfo.value) == 'Please provide samples to the metamodel before building it.'
+
+
+def test_build_metamodel() -> None:
+ """
+ Build PCE
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = PCE(inp)
+ mm.CollocationPoints = np.array([[0.2], [0.8]])
+ mm.build_metamodel()
+
+
+def test_build_metamodel_ninitsamples() -> None:
+ """
+ Build PCE with n_init_samples
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = PCE(inp)
+ mm.CollocationPoints = np.array([[0.2], [0.8]])
+ mm.build_metamodel(n_init_samples=2)
+
+
+
+def test_build_metamodel_coldimerr() -> None:
+ """
+ Build PCE with wrong shape collocation samples
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = PCE(inp)
+ mm.CollocationPoints = [[0.2, 0.8]]
+ with pytest.raises(AttributeError) as excinfo:
+ mm.build_metamodel()
+ assert str(
+ excinfo.value) == 'The second dimension of X should be the same size as the number of marginals in the InputObj'
+
+
+#%% Test MetaMod.generate_polynomials
+
+def test_generate_polynomials_noexp() -> None:
+ """
+ Generate polynomials without ExpDeg
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = PCE(inp)
+ with pytest.raises(AttributeError) as excinfo:
+ mm.generate_polynomials()
+ assert str(excinfo.value) == 'Generate or add InputSpace before generating polynomials'
+
+
+def test_generate_polynomials_nodeg() -> None:
+ """
+ Generate polynomials without max_deg
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = PCE(inp)
+
+ # Setup
+ mm.InputSpace = InputSpace(inp)
+ mm.InputSpace.n_init_samples = 2
+ mm.InputSpace.init_param_space(np.max(mm.pce_deg))
+ mm.ndim = mm.InputSpace.ndim
+ mm.n_params = len(mm.input_obj.Marginals)
+
+ # Generate
+ with pytest.raises(AttributeError) as excinfo:
+ mm.generate_polynomials()
+ assert str(excinfo.value) == 'PCE cannot generate polynomials in the given scenario!'
+
+
+def test_generate_polynomials_deg() -> None:
+ """
+ Generate polynomials with max_deg
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = PCE(inp)
+
+ # Setup
+ mm.InputSpace = InputSpace(inp)
+ mm.InputSpace.n_init_samples = 2
+ mm.InputSpace.init_param_space(np.max(mm.pce_deg))
+ mm.ndim = mm.InputSpace.ndim
+ mm.n_params = len(mm.input_obj.Marginals)
+
+ # Generate
+ mm.generate_polynomials(4)
+
+
+#%% Test MetaMod.add_InputSpace
+
+def test_add_inputspace() -> None:
+ """
+ Add InputSpace in PCE
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = PCE(inp)
+ mm.add_InputSpace()
+
+
+#%% Test PCE.fit
+# Faster without these
+def test_fit() -> None:
+ """
+ Fit PCE
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = PCE(inp)
+ mm.fit([[0.2], [0.4], [0.8]], {'Z': [[0.4], [0.2], [0.5]]})
+
+
+def test_fit_parallel() -> None:
+ """
+ Fit PCE in parallel
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = PCE(inp)
+ mm.fit([[0.2], [0.4], [0.8]], {'Z': [[0.4], [0.2], [0.5]]}, parallel=True)
+
+
+def test_fit_verbose() -> None:
+ """
+ Fit PCE verbose
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = PCE(inp)
+ mm.fit([[0.2], [0.4], [0.8]], {'Z': [[0.4], [0.2], [0.5]]}, verbose=True)
+
+
+def test_fit_pca() -> None:
+ """
+ Fit PCE verbose and with pca
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = PCE(inp)
+ mm.dim_red_method = 'pca'
+ mm.fit([[0.2], [0.4], [0.8]], {'Z': [[0.4], [0.2], [0.5]]}, verbose=True)
+
+
+
+#%% Test PCE.regression
+
+def test_regression() -> None:
+ """
+ Regression without a method
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = PCE(inp)
+ samples = np.array([[0.2]])
+ outputs = np.array([0.5])
+
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+ BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
+ univ_bas = mm.univ_basis_vals(samples)
+ psi = create_psi(BasisIndices, univ_bas)
+
+ mm.regression(samples, outputs, psi)
+
+
+def test_regression_ols() -> None:
+ """
+ Regression: ols
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = PCE(inp)
+ samples = np.array([[0.2]])
+ outputs = np.array([0.5])
+
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+ BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
+ univ_bas = mm.univ_basis_vals(samples)
+ psi = create_psi(BasisIndices, univ_bas)
+
+ mm.regression(samples, outputs, psi, reg_method='ols')
+
+
+def test_regression_olssparse() -> None:
+ """
+ Regression: ols and sparse
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = PCE(inp)
+ samples = np.array([[0.2]])
+ outputs = np.array([0.5])
+
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+ BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
+ univ_bas = mm.univ_basis_vals(samples)
+ psi = create_psi(BasisIndices, univ_bas)
+
+ mm.regression(samples, outputs, psi, reg_method='ols', sparsity=True)
+
+
+def test_regression_ard() -> None:
+ """
+ Regression: ard
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = PCE(inp)
+ samples = np.array([[0.2], [0.8]])
+ outputs = np.array([0.4, 0.5])
+
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+ BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
+ univ_bas = mm.univ_basis_vals(samples)
+ psi = create_psi(BasisIndices, univ_bas)
+
+ mm.regression(samples, outputs, psi, reg_method='ard')
+
+
+def test_regression_ardssparse() -> None:
+ """
+ Regression: ard and sparse
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = PCE(inp)
+ samples = np.array([[0.2], [0.8]])
+ outputs = np.array([0.4, 0.5])
+
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+ BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
+ univ_bas = mm.univ_basis_vals(samples)
+ psi = create_psi(BasisIndices, univ_bas)
+
+ mm.regression(samples, outputs, psi, reg_method='ard', sparsity=True)
+
+
+def test_regression_fastard() -> None:
+ """
+ Regression: fastard
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = PCE(inp)
+ samples = np.array([[0.2]])
+ outputs = np.array([0.5])
+
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+ BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
+ univ_bas = mm.univ_basis_vals(samples)
+ psi = create_psi(BasisIndices, univ_bas)
+
+ mm.regression(samples, outputs, psi, reg_method='fastard')
+
+
+def test_regression_fastardssparse() -> None:
+ """
+ Regression: fastard and sparse
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = PCE(inp)
+ samples = np.array([[0.2]])
+ outputs = np.array([0.5])
+
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+ BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
+ univ_bas = mm.univ_basis_vals(samples)
+ psi = create_psi(BasisIndices, univ_bas)
+
+ mm.regression(samples, outputs, psi, reg_method='fastard', sparsity=True)
+
+
+def test_regression_brr() -> None:
+ """
+ Regression: brr
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = PCE(inp)
+ samples = np.array([[0.2]])
+ outputs = np.array([0.5])
+
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+ BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
+ univ_bas = mm.univ_basis_vals(samples)
+ psi = create_psi(BasisIndices, univ_bas)
+
+ mm.regression(samples, outputs, psi, reg_method='brr')
+
+
+def test_regression_brrssparse() -> None:
+ """
+ Regression: brr and sparse
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = PCE(inp)
+ samples = np.array([[0.2]])
+ outputs = np.array([0.5])
+
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+ BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
+ univ_bas = mm.univ_basis_vals(samples)
+ psi = create_psi(BasisIndices, univ_bas)
+
+ mm.regression(samples, outputs, psi, reg_method='brr', sparsity=True)
+
+
+if 0: # Could not figure out these errors, issue most likely in chosen samples/outputs
+ def test_regression_bcs() -> None:
+ """
+ Regression: bcs
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = PCE(inp)
+ samples = np.array([[0.0], [0.1], [0.2], [0.3], [0.4], [0.5], [0.6], [0.7], [0.8], [0.9]])
+ outputs = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9])
+ mm.pce_deg = 3
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+ BasisIndices = mm.allBasisIndices[str(mm.pce_deg)][str(1.0)]
+ univ_bas = mm.univ_basis_vals(samples)
+ psi = create_psi(BasisIndices, univ_bas)
+
+ mm.regression(samples, outputs, psi, reg_method='bcs')
+
+
+ def test_regression_bcsssparse() -> None:
+ """
+ Regression: bcs and sparse
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = PCE(inp)
+ samples = np.array([[0.0], [0.1], [0.2], [0.3], [0.4], [0.5], [0.6], [0.7], [0.8], [0.9], [1.0]])
+ outputs = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.1])
+
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+ BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
+ univ_bas = mm.univ_basis_vals(samples)
+ psi = create_psi(BasisIndices, univ_bas)
+
+ mm.regression(samples, outputs, psi, reg_method='bcs', sparsity=True)
+
+
+def test_regression_lars() -> None:
+ """
+ Regression: lars
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = PCE(inp)
+ samples = np.array([[0.0], [0.1], [0.2], [0.3], [0.4], [0.5], [0.6], [0.7], [0.8], [0.9], [1.0]])
+ outputs = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.1])
+
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+ BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
+ univ_bas = mm.univ_basis_vals(samples)
+ psi = create_psi(BasisIndices, univ_bas)
+
+ mm.regression(samples, outputs, psi, reg_method='lars')
+
+
+def test_regression_larsssparse() -> None:
+ """
+ Regression: lars and sparse
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = PCE(inp)
+ samples = np.array([[0.0], [0.1], [0.2], [0.3], [0.4], [0.5], [0.6], [0.7], [0.8], [0.9], [1.0]])
+ outputs = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.1])
+
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+ BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
+ univ_bas = mm.univ_basis_vals(samples)
+ psi = create_psi(BasisIndices, univ_bas)
+
+ mm.regression(samples, outputs, psi, reg_method='lars', sparsity=True)
+
+
+def test_regression_sgdr() -> None:
+ """
+ Regression: sgdr
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = PCE(inp)
+ samples = np.array([[0.2]])
+ outputs = np.array([0.5])
+
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+ BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
+ univ_bas = mm.univ_basis_vals(samples)
+ psi = create_psi(BasisIndices, univ_bas)
+
+ mm.regression(samples, outputs, psi, reg_method='sgdr')
+
+
+def test_regression_sgdrssparse() -> None:
+ """
+ Regression: sgdr and sparse
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = PCE(inp)
+ samples = np.array([[0.2]])
+ outputs = np.array([0.5])
+
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+ BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
+ univ_bas = mm.univ_basis_vals(samples)
+ psi = create_psi(BasisIndices, univ_bas)
+
+ mm.regression(samples, outputs, psi, reg_method='sgdr', sparsity=True)
+
+
+if 0: # Could not figure out these errors, issue most likely in chosen samples/outputs
+ def test_regression_omp() -> None:
+ """
+ Regression: omp
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = PCE(inp)
+ samples = np.array([[0.0], [0.1], [0.2], [0.3], [0.4], [0.5], [0.6], [0.7], [0.8], [0.9], [1.0]])
+ outputs = np.array([0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.1])
+
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+ BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
+ univ_bas = mm.univ_basis_vals(samples)
+ psi = create_psi(BasisIndices, univ_bas)
+
+ mm.regression(samples, outputs, psi, reg_method='omp')
+
+
+ def test_regression_ompssparse() -> None:
+ """
+ Regression: omp and sparse
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = PCE(inp)
+ samples = np.array([[0.2]])
+ outputs = np.array([0.5])
+
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+ BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
+ univ_bas = mm.univ_basis_vals(samples)
+ psi = create_psi(BasisIndices, univ_bas)
+
+ mm.regression(samples, outputs, psi, reg_method='omp', sparsity=True)
+
+
+def test_regression_vbl() -> None:
+ """
+ Regression: vbl
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = PCE(inp)
+ samples = np.array([[0.2]])
+ outputs = np.array([0.5])
+
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+ BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
+ univ_bas = mm.univ_basis_vals(samples)
+ psi = create_psi(BasisIndices, univ_bas)
+
+ mm.regression(samples, outputs, psi, reg_method='vbl')
+
+
+def test_regression_vblssparse() -> None:
+ """
+ Regression: vbl and sparse
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = PCE(inp)
+ samples = np.array([[0.2]])
+ outputs = np.array([0.5])
+
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+ BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
+ univ_bas = mm.univ_basis_vals(samples)
+ psi = create_psi(BasisIndices, univ_bas)
+
+ mm.regression(samples, outputs, psi, reg_method='vbl', sparsity=True)
+
+
+def test_regression_ebl() -> None:
+ """
+ Regression: ebl
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = PCE(inp)
+ samples = np.array([[0.2]])
+ outputs = np.array([0.5])
+
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+ BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
+ univ_bas = mm.univ_basis_vals(samples)
+ psi = create_psi(BasisIndices, univ_bas)
+
+ mm.regression(samples, outputs, psi, reg_method='ebl')
+
+
+def test_regression_eblssparse() -> None:
+ """
+ Regression: ebl and sparse
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = PCE(inp)
+ samples = np.array([[0.2]])
+ outputs = np.array([0.5])
+
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+ BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
+ univ_bas = mm.univ_basis_vals(samples)
+ psi = create_psi(BasisIndices, univ_bas)
+
+ mm.regression(samples, outputs, psi, reg_method='ebl', sparsity=True)
+
+
+#%% Test Model.update_pce_coeffs
+
+# TODO: very linked to the actual training...
+
+#%% Test PCE.univ_basis_vals
+
+def test_univ_basis_vals() -> None:
+ """
+ Creates univariate polynomials
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = PCE(inp)
+ samples = np.array([[0.2], [0.8]])
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+ mm.univ_basis_vals(samples)
+
+
+#%% Test PCE.adaptive_regression
+
+def test_adaptive_regression_fewsamples() -> None:
+ """
+ Adaptive regression, no specific method, too few samples given
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = PCE(inp)
+ samples = np.array([[0.2]])
+ outputs = np.array([0.8])
+
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+
+ # Evaluate the univariate polynomials on InputSpace
+ if mm.meta_model_type.lower() != 'gpe':
+ mm.univ_p_val = mm.univ_basis_vals(mm.CollocationPoints)
+
+ with pytest.raises(AttributeError) as excinfo:
+ mm.adaptive_regression(outputs, 0)
+ assert str(excinfo.value) == ('There are too few samples for the corrected loo-cv error. Fit surrogate on at least as '
+ 'many samples as parameters to use this')
+
+
+def test_adaptive_regression() -> None:
+ """
+ Adaptive regression, no specific method
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = PCE(inp)
+ samples = np.array([[0.0], [0.1]])
+ outputs = np.array([0.0, 0.1])
+
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+
+ # Evaluate the univariate polynomials on InputSpace
+ if mm.meta_model_type.lower() != 'gpe':
+ mm.univ_p_val = mm.univ_basis_vals(mm.CollocationPoints)
+ mm.adaptive_regression(outputs, 0)
+
+
+def test_adaptive_regression_verbose() -> None:
+ """
+ Adaptive regression, no specific method, verbose output
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = PCE(inp)
+ samples = np.array([[0.0], [0.1]])
+ outputs = np.array([0.0, 0.1])
+
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+
+ # Evaluate the univariate polynomials on InputSpace
+ if mm.meta_model_type.lower() != 'gpe':
+ mm.univ_p_val = mm.univ_basis_vals(mm.CollocationPoints)
+ mm.adaptive_regression(outputs, 0, True)
+
+
+def test_adaptive_regression_ols() -> None:
+ """
+ Adaptive regression, ols
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = PCE(inp)
+ samples = np.array([[0.0], [0.1], [0.2], [0.3], [0.4], [0.5], [0.6], [0.7], [0.8],
+ [0.9], [1.0]])
+ outputs = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.1])
+
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+
+ # Evaluate the univariate polynomials on InputSpace
+ if mm.meta_model_type.lower() != 'gpe':
+ mm.univ_p_val = mm.univ_basis_vals(mm.CollocationPoints)
+ mm.pce_reg_method = 'ols'
+ mm.adaptive_regression(outputs, 0)
+
+
+#%% Test PCE.pca_transformation
+
+def test_pca_transformation() -> None:
+ """
+ Apply PCA
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = PCE(inp)
+ outputs = np.array([[0.4, 0.4], [0.5, 0.6]])
+ mm.pca_transformation(outputs, 1)
+
+
+def test_pca_transformation_varcomp() -> None:
+ """
+ Apply PCA with set var_pca_threshold
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = PCE(inp)
+ outputs = np.array([[0.4, 0.4], [0.5, 0.6]])
+ mm.var_pca_threshold = 1
+ mm.pca_transformation(outputs, 1)
+
+
+
+#%% Test PCE.eval_metamodel
+
+def test_eval_metamodel() -> None:
+ """
+ Eval trained PCE
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = PCE(inp)
+ mm.out_names = ['Z']
+ mm.fit([[0.2], [0.4], [0.8]], {'Z': [[0.4], [0.2], [0.5]]})
+ mm.eval_metamodel([[0.4]])
+
+
+def test_eval_metamodel_normalboots() -> None:
+ """
+ Eval trained PCE with normal bootstrap
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = PCE(inp)
+ mm.bootstrap_method = 'normal'
+ mm.out_names = ['Z']
+ mm.fit([[0.2], [0.4], [0.8]], {'Z': [[0.4], [0.2], [0.5]]})
+ mm.eval_metamodel([[0.4]])
+
+
+def test_eval_metamodel_highnormalboots() -> None:
+ """
+ Eval trained PCE with higher bootstrap-itrs
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = PCE(inp)
+ mm.n_bootstrap_itrs = 2
+ mm.out_names = ['Z']
+ mm.fit([[0.2], [0.4], [0.8]], {'Z': [[0.4], [0.2], [0.5]]})
+ mm.eval_metamodel([[0.4]])
+
+
+def test_eval_metamodel_pca() -> None:
+ """
+ Eval trained PCE with pca
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = PCE(inp)
+ mm.dim_red_method = 'pca'
+ mm.out_names = ['Z']
+ mm.fit([[0.2], [0.8]], {'Z': [[0.4, 0.4], [0.5, 0.6]]})
+ mm.eval_metamodel([[0.4]])
+
+
+#%% Test PCE.create_model_error
+# TODO: move model out of this function
+
+#%% Test PCE.eval_model_error
+# TODO: test create_model_error first
+
+#%% Test PCE.auto_vivification
+def test_auto_vivification() -> None:
+ """
+ Creation of auto-vivification objects
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = PCE(inp)
+ mm.auto_vivification()
+
+
+#%% Test PCE.copy_meta_model_opts
+
+def test_copy_meta_model_opts() -> None:
+ """
+ Copy the PCE with just some stats
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = PCE(inp)
+ mm.add_InputSpace()
+ mm.copy_meta_model_opts()
+
+
+#%% Test PCE.__select_degree
+
+#%% Test Engine._compute_moments
+
+def test__compute_moments() -> None:
+ """
+ Compute moments of a pce-surrogate
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = PCE(inp)
+ mm.fit([[0.2], [0.4], [0.8]], {'Z': [[0.4], [0.2], [0.5]]})
+ mm._compute_moments()
+
+
+def test__compute_moments_pca() -> None:
+ """
+ Compute moments of a pce-surrogate with pca
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = PCE(inp)
+ mm.dim_red_method = 'pca'
+ mm.fit([[0.2], [0.8]], {'Z': [[0.4, 0.4], [0.5, 0.6]]})
+ mm._compute_moments()
+
+
+#%% Test PCE.update_metamodel
+# TODO: taken from engine
diff --git a/tests/test_supplementary.py b/tests/test_supplementary.py
new file mode 100644
index 0000000000000000000000000000000000000000..7537b0ca38d2644748ef626650bc02cf7036324f
--- /dev/null
+++ b/tests/test_supplementary.py
@@ -0,0 +1,162 @@
+# -*- coding: utf-8 -*-
+"""
+Test the supplementary functions in bayesvalidrox.
+Tests are available for the following functions
+ hellinger_distance
+ logpdf
+ subdomain
+ create_psi x
+ corr_loocv_error x
+ gaussian_process_emulator x
+
+"""
+import numpy as np
+import pytest
+import sys
+
+sys.path.append("src/")
+
+from bayesvalidrox.surrogate_models.inputs import Input
+from bayesvalidrox.surrogate_models.input_space import InputSpace
+from bayesvalidrox.surrogate_models.surrogate_models import MetaModel
+from bayesvalidrox.surrogate_models.polynomial_chaos import PCE
+from bayesvalidrox.surrogate_models.supplementary import gaussian_process_emulator, corr_loocv_error, create_psi
+
+
+#%% Test create_psi
+
+def test_create_psi() -> None:
+ """
+ Create psi-matrix
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ samples = np.array([[0.2], [0.8]])
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+ BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
+ univ_bas = mm.univ_basis_vals(samples)
+ create_psi(BasisIndices, univ_bas)
+
+#%% Test corr_loocv_error
+
+def test_corr_loocv_error_nosparse() -> None:
+ """
+ Corrected loocv error
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ samples = np.array([[0.0], [0.1], [0.2], [0.3], [0.4], [0.5], [0.6], [0.7],
+ [0.8], [0.9], [1.0]])
+ outputs = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.1])
+
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+ BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
+ univ_bas = mm.univ_basis_vals(samples)
+ psi = create_psi(BasisIndices, univ_bas)
+
+ outs = mm.regression(samples, outputs, psi, reg_method='ebl')
+ corr_loocv_error(outs['clf_poly'], outs['sparePsi'], outs['coeffs'],
+ outputs)
+
+
+def test_corr_loocv_error_singley() -> None:
+ """
+ Corrected loocv error
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ samples = np.array([[0.2]])
+ outputs = np.array([0.1])
+
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+ BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
+ univ_bas = mm.univ_basis_vals(samples)
+ psi = create_psi(BasisIndices, univ_bas)
+
+ outs = mm.regression(samples, outputs, psi, reg_method='ols')
+ corr_loocv_error(outs['clf_poly'], outs['sparePsi'], outs['coeffs'],
+ outputs)
+
+
+def test_corr_loocv_error_sparse() -> None:
+ """
+ Corrected loocv error from sparse results
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ mm = MetaModel(inp)
+ samples = np.array([[0.0], [0.1], [0.2], [0.3], [0.4], [0.5], [0.6], [0.7],
+ [0.8], [0.9], [1.0]])
+ outputs = np.array([0.0, 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1.1])
+
+ mm.CollocationPoints = samples
+ mm.build_metamodel(n_init_samples=2)
+ BasisIndices = mm.allBasisIndices[str(1)][str(1.0)]
+ univ_bas = mm.univ_basis_vals(samples)
+ psi = create_psi(BasisIndices, univ_bas)
+
+ outs = mm.regression(samples, outputs, psi, reg_method='ebl',
+ sparsity=True)
+ corr_loocv_error(outs['clf_poly'], outs['sparePsi'], outs['coeffs'],
+ outputs)
+
+
+#%% Test gaussian_process_emulator
+
+def test_gaussian_process_emulator() -> None:
+ """
+ Create GPE
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ gaussian_process_emulator([[0.2], [0.8]], [0.4, 0.5])
+
+
+def test_gaussian_process_emulator_nug() -> None:
+ """
+ Create GPEwith nugget
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ gaussian_process_emulator([[0.2], [0.8]], [0.4, 0.5], nug_term=1.0)
+
+
+def test_gaussian_process_emulator_autosel() -> None:
+ """
+ Fit MetaModel with autoselect
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ gaussian_process_emulator([[0.2], [0.8]], [0.4, 0.5], autoSelect=True)
+
+
+def test_gaussian_process_emulator_varidx() -> None:
+ """
+ Create GPE with var_idx
+ """
+ inp = Input()
+ inp.add_marginals()
+ inp.Marginals[0].dist_type = 'normal'
+ inp.Marginals[0].parameters = [0, 1]
+ gaussian_process_emulator([[0.2], [0.8]], [0.4, 0.5], varIdx=1)
+