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diff --git a/src/bayesvalidrox/bayes_inference/bayes_inference.py b/src/bayesvalidrox/bayes_inference/bayes_inference.py
index 7284348266fa88dd93a7380633437cc713e19291..c0c4cd08eb1a07f40a2a397f98cf7d46b4802368 100644
--- a/src/bayesvalidrox/bayes_inference/bayes_inference.py
+++ b/src/bayesvalidrox/bayes_inference/bayes_inference.py
@@ -961,7 +961,7 @@ class BayesInference:
output_names = self.engine.Model.Output.names
# Evaluate MetaModel on the experimental design and ValidSet
- OutputRS, stdOutputRS = MetaModel.eval_metamodel(samples=samples)
+ OutputRS, stdOutputRS = MetaModel.eval_metamodel(samples)
logLik_data = np.zeros(n_samples)
logLik_model = np.zeros(n_samples)
diff --git a/src/bayesvalidrox/surrogate_models/polynomial_chaos.py b/src/bayesvalidrox/surrogate_models/polynomial_chaos.py
index 7d2cf2616ea530ceb3b95f5ca4d1d40e2f11cc11..25b1fdb5224ec42814b764ec56ad917e6a773687 100644
--- a/src/bayesvalidrox/surrogate_models/polynomial_chaos.py
+++ b/src/bayesvalidrox/surrogate_models/polynomial_chaos.py
@@ -55,8 +55,8 @@ class PCE(MetaModel):
----------
input_obj : obj
Input object with the information on the model input parameters.
- _meta_model_type : str
- Surrogate model types. Three surrogate model types are supported:
+ meta_model_type : str
+ PCE-surrogate model types. Two surrogate model types are supported:
polynomial chaos expansion (`PCE`), arbitrary PCE (`aPCE`).
Default is PCE.
_pce_reg_method : str
@@ -729,8 +729,6 @@ class PCE(MetaModel):
mean_pred = {}
std_pred = {}
for output, values in model_dict.items():
- print(output, values)
-
mean = np.empty((len(samples), len(values)))
std = np.empty((len(samples), len(values)))
idx = 0
diff --git a/src/bayesvalidrox/surrogate_models/surrogate_models.py b/src/bayesvalidrox/surrogate_models/surrogate_models.py
index 131e59aa949da1bfb3965a640122f4d184328cb0..5a494135f7046133d2344995bffedb44cdb9197d 100644
--- a/src/bayesvalidrox/surrogate_models/surrogate_models.py
+++ b/src/bayesvalidrox/surrogate_models/surrogate_models.py
@@ -92,7 +92,6 @@ def _preprocessing_fit(fit_function):
# Build the input space
if self.InputSpace is None:
- print(self.meta_model_type)
self.InputSpace = InputSpace(self.input_obj, self.meta_model_type)
n_init_samples = X.shape[0]
self.InputSpace.n_init_samples = n_init_samples
@@ -107,14 +106,14 @@ def _preprocessing_fit(fit_function):
# --- Loop through data points and fit the surrogate ---
if self.verbose:
- print(f"\n>>>> Training the {self._meta_model_type} metamodel "
+ print(f"\n>>>> Training the {self.meta_model_type} metamodel "
"started. <<<<<<\n")
# Call the function for fitting
fit_function(self, X, y, **kwargs)
if self.verbose:
- print(f"\n>>>> Training the {self._meta_model_type} metamodel"
+ print(f"\n>>>> Training the {self.meta_model_type} metamodel"
" sucessfully completed. <<<<<<\n")
return decorator
@@ -415,6 +414,7 @@ class MetaModel:
self.n_pca_components = None
# Build general parameters
+ self._pce_deg = 1 # TODO: rename this more generally, so that it can be used for other types as well
# General warnings
if not self.is_gaussian:
diff --git a/tests/test_BayesInference.py b/tests/test_BayesInference.py
index 4b37f2cbb2776b3c881599c98a2363bc42b3f0d0..9048776c874b488446f040fae71ce03f1d973b30 100644
--- a/tests/test_BayesInference.py
+++ b/tests/test_BayesInference.py
@@ -112,7 +112,7 @@ def test_create_inference() -> None:
mm = MetaModel(inp)
mm.fit(expdes.X, expdes.Y)
- expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
+ expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm._pce_deg))
mod = PL()
mod.observations = {'Z': np.array([0.45])}
diff --git a/tests/test_MCMC.py b/tests/test_MCMC.py
index e38c93b0a8cd79ad43d974e49ef6f6d90ec95559..31b24598683befbbf38c31b1cbe1e7fd22409537 100644
--- a/tests/test_MCMC.py
+++ b/tests/test_MCMC.py
@@ -54,7 +54,7 @@ def test_MCMC() -> None:
mm = MetaModel(inp)
mm.fit(expdes.X, expdes.Y)
- expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
+ expdes.generate_ED(expdes.n_init_samples, max_pce_deg=2)
mod = PL()
mod.observations = {'Z': np.array([0.45]), 'x_values': np.array([0])}
@@ -102,7 +102,7 @@ if 0: # TODO: issue not resolved here, issue appears due to specific test setup,
mm = MetaModel(inp)
mm.fit(expdes.X, expdes.Y)
- expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
+ expdes.generate_ED(expdes.n_init_samples, max_pce_deg=2)
mod = PL()
mod.observations = {'Z': np.array([0.45]), 'x_values': np.array([0])}
@@ -197,7 +197,7 @@ if __name__ == '__main__':
mm = MetaModel(inp)
mm.fit(expdes.X, expdes.Y)
- expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
+ expdes.generate_ED(expdes.n_init_samples, max_pce_deg=2)
mod = PL()
mod.observations = {'Z': np.array([0.45]), 'x_values': np.array([0])}
diff --git a/tests/test_MetaModel.py b/tests/test_MetaModel.py
index dd1eb29f26b7d7f52f9a2cf0896c61e946e86787..f2a4981ea4ee9dca1b38ad5d44ee38f043928378 100644
--- a/tests/test_MetaModel.py
+++ b/tests/test_MetaModel.py
@@ -29,7 +29,7 @@ import numpy as np
import pytest
import sys
-sys.path.append("src/")
+sys.path.append("../src/")
from bayesvalidrox.surrogate_models.inputs import Input
from bayesvalidrox.surrogate_models.input_space import InputSpace
@@ -250,7 +250,7 @@ def test_copy_meta_model_opts() -> None:
#%% Test MetaModel.__select_degree
-#%% Test Engine._compute_pce_moments
+#%% Test MetaModel.compute_moments
def test__compute_moments() -> None:
"""
@@ -262,7 +262,7 @@ def test__compute_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_moments()
+ mm.compute_moments()
diff --git a/tests/test_PolynomialChaosEmulator.py b/tests/test_PolynomialChaosEmulator.py
index fea710dd78a510b7ca19a42c08fd4d3a6314f67d..61c65889722e3a1b8206d12f9ef4b5f6f165d0ea 100644
--- a/tests/test_PolynomialChaosEmulator.py
+++ b/tests/test_PolynomialChaosEmulator.py
@@ -26,7 +26,7 @@ import numpy as np
import pytest
import sys
-sys.path.append("src/")
+sys.path.append("../src/")
from bayesvalidrox.surrogate_models.inputs import Input
from bayesvalidrox.surrogate_models.input_space import InputSpace
@@ -35,6 +35,23 @@ from bayesvalidrox.surrogate_models.polynomial_chaos import PCE
from bayesvalidrox.surrogate_models.supplementary import gaussian_process_emulator, corr_loocv_error, create_psi
+@pytest.fixture
+def PCE_1DwithInputSpace():
+ 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 = PCE(inp)
+ mm.CollocationPoints = samples
+ mm.InputSpace = InputSpace(mm.input_obj, mm.meta_model_type)
+ n_init_samples = samples.shape[0]
+ mm.InputSpace.n_init_samples = n_init_samples
+ # TODO: _pce_deg not necessarily available, generalize this!
+ mm.InputSpace.init_param_space(np.max(mm._pce_deg))
+ return mm
+
#%% Test MetaMod constructor on its own
@@ -51,66 +68,32 @@ def test_metamod() -> None:
#%% 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_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:
+def test_build_metamodel(PCE_1DwithInputSpace) -> None:
"""
Build PCE
"""
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = PCE(inp)
+ mm = PCE_1DwithInputSpace
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)
+#%% Test PCE._generate_polynomials
-
-
-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:
+def test__generate_polynomials_noexp() -> None:
"""
Generate polynomials without ExpDeg
"""
@@ -120,11 +103,11 @@ def test_generate_polynomials_noexp() -> None:
inp.Marginals[0].parameters = [0, 1]
mm = PCE(inp)
with pytest.raises(AttributeError) as excinfo:
- mm.generate_polynomials()
+ mm._generate_polynomials()
assert str(excinfo.value) == 'Generate or add InputSpace before generating polynomials'
-def test_generate_polynomials_nodeg() -> None:
+def test__generate_polynomials_nodeg() -> None:
"""
Generate polynomials without max_deg
"""
@@ -137,17 +120,17 @@ def test_generate_polynomials_nodeg() -> None:
# Setup
mm.InputSpace = InputSpace(inp)
mm.InputSpace.n_init_samples = 2
- mm.InputSpace.init_param_space(np.max(mm.pce_deg))
+ 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!'
+ mm._generate_polynomials()
+ assert str(excinfo.value) == 'MetaModel cannot generate polynomials in the given scenario!'
-def test_generate_polynomials_deg() -> None:
+def test__generate_polynomials_deg() -> None:
"""
Generate polynomials with max_deg
"""
@@ -160,12 +143,12 @@ def test_generate_polynomials_deg() -> None:
# Setup
mm.InputSpace = InputSpace(inp)
mm.InputSpace.n_init_samples = 2
- mm.InputSpace.init_param_space(np.max(mm.pce_deg))
+ 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)
+ mm._generate_polynomials(4)
#%% Test MetaMod.add_InputSpace
@@ -236,447 +219,381 @@ def test_fit_pca() -> None:
#%% Test PCE.regression
-def test_regression() -> None:
+def test_regression(PCE_1DwithInputSpace) -> 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)
+ mm = PCE_1DwithInputSpace
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)]
+ mm.build_metamodel()
+ BasisIndices = mm._all_basis_indices[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:
+def test_regression_ols(PCE_1DwithInputSpace) -> None:
"""
Regression: ols
"""
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = PCE(inp)
+ mm = PCE_1DwithInputSpace
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)]
+ mm.build_metamodel()
+ BasisIndices = mm._all_basis_indices[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')
+ mm._pce_reg_method = 'ols'
+ mm.regression(samples, outputs, psi)
-def test_regression_olssparse() -> None:
+def test_regression_olssparse(PCE_1DwithInputSpace) -> 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)
+ mm = PCE_1DwithInputSpace
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)]
+ mm.build_metamodel()
+ BasisIndices = mm._all_basis_indices[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)
+ mm._pce_reg_method = 'ols'
+ mm.regression(samples, outputs, psi, sparsity=True)
-def test_regression_ard() -> None:
+def test_regression_ard(PCE_1DwithInputSpace) -> None:
"""
Regression: ard
"""
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = PCE(inp)
+ mm = PCE_1DwithInputSpace
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)]
+ mm.build_metamodel()
+ BasisIndices = mm._all_basis_indices[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')
+ mm._pce_reg_method = 'ard'
+ mm.regression(samples, outputs, psi)
-def test_regression_ardssparse() -> None:
+def test_regression_ardssparse(PCE_1DwithInputSpace) -> 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)
+ mm = PCE_1DwithInputSpace
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)]
+ mm.build_metamodel()
+ BasisIndices = mm._all_basis_indices[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)
+ mm._pce_reg_method = 'ard'
+ mm.regression(samples, outputs, psi, sparsity=True)
-def test_regression_fastard() -> None:
+def test_regression_fastard(PCE_1DwithInputSpace) -> None:
"""
Regression: fastard
"""
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = PCE(inp)
+ mm = PCE_1DwithInputSpace
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)]
+ mm.build_metamodel()
+ BasisIndices = mm._all_basis_indices[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')
+ mm._pce_reg_method = 'fastard'
+ mm.regression(samples, outputs, psi)
-def test_regression_fastardssparse() -> None:
+def test_regression_fastardssparse(PCE_1DwithInputSpace) -> 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)
+ mm = PCE_1DwithInputSpace
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)]
+ mm.build_metamodel()
+ BasisIndices = mm._all_basis_indices[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)
+ mm._pce_reg_method = 'fastard'
+ mm.regression(samples, outputs, psi, sparsity=True)
-def test_regression_brr() -> None:
+def test_regression_brr(PCE_1DwithInputSpace) -> None:
"""
Regression: brr
"""
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = PCE(inp)
+ mm = PCE_1DwithInputSpace
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)]
+ mm.build_metamodel()
+ BasisIndices = mm._all_basis_indices[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')
+ mm._pce_reg_method = 'brr'
+ mm.regression(samples, outputs, psi)
-def test_regression_brrssparse() -> None:
+def test_regression_brrssparse(PCE_1DwithInputSpace) -> 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)
+ mm = PCE_1DwithInputSpace
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)]
+ mm.build_metamodel()
+ BasisIndices = mm._all_basis_indices[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)
+ mm._pce_reg_method = 'brr'
+ mm.regression(samples, outputs, psi, sparsity=True)
if 0: # Could not figure out these errors, issue most likely in chosen samples/outputs
- def test_regression_bcs() -> None:
+ def test_regression_bcs(PCE_1DwithInputSpace) -> None:
"""
Regression: bcs
"""
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = PCE(inp)
+ mm = PCE_1DwithInputSpace
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._pce_deg = 3
mm.CollocationPoints = samples
- mm.build_metamodel(n_init_samples=2)
- BasisIndices = mm.allBasisIndices[str(mm.pce_deg)][str(1.0)]
+ mm.build_metamodel()
+ BasisIndices = mm._all_basis_indices[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')
+ mm._pce_reg_method = 'bcs'
+ mm.regression(samples, outputs, psi)
- def test_regression_bcsssparse() -> None:
+ def test_regression_bcsssparse(PCE_1DwithInputSpace) -> 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)
+ mm = PCE_1DwithInputSpace
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)]
+ mm.build_metamodel()
+ BasisIndices = mm._all_basis_indices[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)
+ mm._pce_reg_method = 'bcs'
+ mm.regression(samples, outputs, psi, sparsity=True)
-def test_regression_lars() -> None:
+def test_regression_lars(PCE_1DwithInputSpace) -> None:
"""
Regression: lars
"""
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = PCE(inp)
+ mm = PCE_1DwithInputSpace
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)]
+ mm.build_metamodel()
+ BasisIndices = mm._all_basis_indices[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')
+ mm._pce_reg_method = 'lars'
+ mm.regression(samples, outputs, psi)
-def test_regression_larsssparse() -> None:
+def test_regression_larsssparse(PCE_1DwithInputSpace) -> 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)
+ mm = PCE_1DwithInputSpace
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)]
+ mm.build_metamodel()
+ BasisIndices = mm._all_basis_indices[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)
+ mm._pce_reg_method = 'lars'
+ mm.regression(samples, outputs, psi, sparsity=True)
-def test_regression_sgdr() -> None:
+def test_regression_sgdr(PCE_1DwithInputSpace) -> None:
"""
Regression: sgdr
"""
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = PCE(inp)
+ mm = PCE_1DwithInputSpace
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)]
+ mm.build_metamodel()
+ BasisIndices = mm._all_basis_indices[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')
+ mm._pce_reg_method = 'sgdr'
+ mm.regression(samples, outputs, psi)
-def test_regression_sgdrssparse() -> None:
+def test_regression_sgdrssparse(PCE_1DwithInputSpace) -> 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)
+ mm = PCE_1DwithInputSpace
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)]
+ mm.build_metamodel()
+ BasisIndices = mm._all_basis_indices[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)
+ mm._pce_reg_method = 'sgdr'
+ mm.regression(samples, outputs, psi, sparsity=True)
if 0: # Could not figure out these errors, issue most likely in chosen samples/outputs
- def test_regression_omp() -> None:
+ def test_regression_omp(PCE_1DwithInputSpace) -> None:
"""
Regression: omp
"""
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = PCE(inp)
+ mm = PCE_1DwithInputSpace
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)]
+ mm.build_metamodel()
+ BasisIndices = mm._all_basis_indices[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:
+ def test_regression_ompssparse(PCE_1DwithInputSpace) -> 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)
+ mm = PCE_1DwithInputSpace
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)]
+ mm.build_metamodel()
+ BasisIndices = mm._all_basis_indices[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:
+def test_regression_vbl(PCE_1DwithInputSpace) -> None:
"""
Regression: vbl
"""
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = PCE(inp)
+ mm = PCE_1DwithInputSpace
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)]
+ mm.build_metamodel()
+ BasisIndices = mm._all_basis_indices[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')
+ mm._pce_reg_method = 'vbl'
+ mm.regression(samples, outputs, psi)
-def test_regression_vblssparse() -> None:
+def test_regression_vblssparse(PCE_1DwithInputSpace) -> 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)
+ mm = PCE_1DwithInputSpace
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)]
+ mm.build_metamodel()
+ BasisIndices = mm._all_basis_indices[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)
+ mm._pce_reg_method = 'vbl'
+ mm.regression(samples, outputs, psi, sparsity=True)
-def test_regression_ebl() -> None:
+def test_regression_ebl(PCE_1DwithInputSpace) -> None:
"""
Regression: ebl
"""
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = PCE(inp)
+ mm = PCE_1DwithInputSpace
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)]
+ mm.build_metamodel()
+ BasisIndices = mm._all_basis_indices[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')
+ mm._pce_reg_method = 'ebl'
+ mm.regression(samples, outputs, psi)
-def test_regression_eblssparse() -> None:
+def test_regression_eblssparse(PCE_1DwithInputSpace) -> 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)
+ mm = PCE_1DwithInputSpace
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)]
+ mm.build_metamodel()
+ BasisIndices = mm._all_basis_indices[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)
+ mm._pce_reg_method = 'ebl'
+ mm.regression(samples, outputs, psi, sparsity=True)
#%% Test Model.update_pce_coeffs
@@ -685,107 +602,87 @@ def test_regression_eblssparse() -> None:
#%% Test PCE.univ_basis_vals
-def test_univ_basis_vals() -> None:
+def test_univ_basis_vals(PCE_1DwithInputSpace) -> None:
"""
Creates univariate polynomials
"""
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = PCE(inp)
+ mm = PCE_1DwithInputSpace
samples = np.array([[0.2], [0.8]])
mm.CollocationPoints = samples
- mm.build_metamodel(n_init_samples=2)
+ mm.build_metamodel()
mm.univ_basis_vals(samples)
#%% Test PCE.adaptive_regression
-def test_adaptive_regression_fewsamples() -> None:
+def test_adaptive_regression_fewsamples(PCE_1DwithInputSpace) -> 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)
+ mm = PCE_1DwithInputSpace
samples = np.array([[0.2]])
outputs = np.array([0.8])
mm.CollocationPoints = samples
- mm.build_metamodel(n_init_samples=2)
+ mm.build_metamodel()
# Evaluate the univariate polynomials on InputSpace
mm.univ_p_val = mm.univ_basis_vals(mm.CollocationPoints)
with pytest.raises(AttributeError) as excinfo:
- mm.adaptive_regression(outputs, 0)
+ mm.adaptive_regression(samples, 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:
+def test_adaptive_regression(PCE_1DwithInputSpace) -> 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)
+ mm = PCE_1DwithInputSpace
samples = np.array([[0.0], [0.1]])
outputs = np.array([0.0, 0.1])
mm.CollocationPoints = samples
- mm.build_metamodel(n_init_samples=2)
+ mm.build_metamodel()
# Evaluate the univariate polynomials on InputSpace
mm.univ_p_val = mm.univ_basis_vals(mm.CollocationPoints)
- mm.adaptive_regression(outputs, 0)
+ mm.adaptive_regression(samples, outputs, 0)
-def test_adaptive_regression_verbose() -> None:
+def test_adaptive_regression_verbose(PCE_1DwithInputSpace) -> 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)
+ mm = PCE_1DwithInputSpace
samples = np.array([[0.0], [0.1]])
outputs = np.array([0.0, 0.1])
mm.CollocationPoints = samples
- mm.build_metamodel(n_init_samples=2)
+ mm.build_metamodel()
# Evaluate the univariate polynomials on InputSpace
mm.univ_p_val = mm.univ_basis_vals(mm.CollocationPoints)
- mm.adaptive_regression(outputs, 0, True)
+ mm.adaptive_regression(samples, outputs, 0, True)
-def test_adaptive_regression_ols() -> None:
+def test_adaptive_regression_ols(PCE_1DwithInputSpace) -> None:
"""
Adaptive regression, ols
"""
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = PCE(inp)
+ mm = PCE_1DwithInputSpace
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)
+ mm.build_metamodel()
# Evaluate the univariate polynomials on InputSpace
mm.univ_p_val = mm.univ_basis_vals(mm.CollocationPoints)
- mm.pce_reg_method = 'ols'
- mm.adaptive_regression(outputs, 0)
+ mm._pce_reg_method = 'ols'
+ mm.adaptive_regression(samples, outputs, 0)
#%% Test PCE.pca_transformation
@@ -915,9 +812,9 @@ def test_copy_meta_model_opts() -> None:
#%% Test PCE.__select_degree
-#%% Test Engine._compute_moments
+#%% Test PCE.compute_moments
-def test__compute_moments() -> None:
+def test_compute_moments() -> None:
"""
Compute moments of a pce-surrogate
"""
@@ -927,10 +824,10 @@ def test__compute_moments() -> None:
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()
+ mm.compute_moments()
-def test__compute_moments_pca() -> None:
+def test_compute_moments_pca() -> None:
"""
Compute moments of a pce-surrogate with pca
"""
@@ -941,7 +838,7 @@ def test__compute_moments_pca() -> None:
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()
+ mm.compute_moments()
#%% Test PCE.update_metamodel
diff --git a/tests/test_SequentialDesign.py b/tests/test_SequentialDesign.py
index 52e54be03a22ec15f49bbbe4b9c4a6dd950ace00..f922a77d7b78feee8ca01de8976ed4bd036607a7 100644
--- a/tests/test_SequentialDesign.py
+++ b/tests/test_SequentialDesign.py
@@ -170,7 +170,7 @@ def test_choose_next_sample() -> None:
mm = MetaModel(inp)
mm.fit(expdes.X, expdes.Y)
- expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
+ expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm._pce_deg))
mod = PL()
engine = Engine(mm, mod, expdes)
@@ -200,7 +200,7 @@ def test_choose_next_sample_da_spaceparallel() -> None:
mm = MetaModel(inp)
mm.fit(expdes.X, expdes.Y)
- expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
+ expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm._pce_deg))
mod = PL()
engine = Engine(mm, mod, expdes)
@@ -231,7 +231,7 @@ def test_choose_next_sample_da_spacenoparallel() -> None:
mm = MetaModel(inp)
mm.fit(expdes.X, expdes.Y)
- expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
+ expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm._pce_deg))
mod = PL()
engine = Engine(mm, mod, expdes)
@@ -262,7 +262,7 @@ def test_choose_next_sample_loo_space() -> None:
mm = MetaModel(inp)
mm.fit(expdes.X, expdes.Y)
- expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
+ expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm._pce_deg))
mod = PL()
engine = Engine(mm, mod, expdes)
@@ -292,7 +292,7 @@ def test_choose_next_sample_vor_space() -> None:
mm = MetaModel(inp)
mm.fit(expdes.X, expdes.Y)
- expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
+ expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm._pce_deg))
mod = PL()
engine = Engine(mm, mod, expdes)
@@ -327,7 +327,7 @@ def test_choose_next_sample_latin_space() -> None:
mm = MetaModel(inp)
mm.fit(expdes.X, expdes.Y)
- expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
+ expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm._pce_deg))
mod = PL()
engine = Engine(mm, mod, expdes)
@@ -357,7 +357,7 @@ def test_choose_next_sample_latin_alphD() -> None:
mm = MetaModel(inp)
mm.fit(expdes.X, expdes.Y)
- expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
+ expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm._pce_deg))
mod = PL()
engine = Engine(mm, mod, expdes)
@@ -387,7 +387,7 @@ def test_choose_next_sample_latin_alphK() -> None:
mm = MetaModel(inp)
mm.fit(expdes.X, expdes.Y)
- expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
+ expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm._pce_deg))
mod = PL()
engine = Engine(mm, mod, expdes)
@@ -417,7 +417,7 @@ def test_choose_next_sample_latin_alphA() -> None:
mm = MetaModel(inp)
mm.fit(expdes.X, expdes.Y)
- expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
+ expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm._pce_deg))
mod = PL()
engine = Engine(mm, mod, expdes)
@@ -448,7 +448,7 @@ def test_choose_next_sample_latin_VarALM() -> None:
mm = MetaModel(inp)
mm.fit(expdes.X, expdes.Y)
- expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
+ expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm._pce_deg))
mod = PL()
engine = Engine(mm, mod, expdes)
@@ -479,7 +479,7 @@ def test_choose_next_sample_latin_VarEIGF() -> None:
mm = MetaModel(inp)
mm.fit(expdes.X, expdes.Y)
- expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
+ expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm._pce_deg))
mod = PL()
engine = Engine(mm, mod, expdes)
@@ -512,7 +512,7 @@ if 0:
mm = MetaModel(inp)
mm.fit(expdes.X, expdes.Y)
- expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
+ expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm._pce_deg))
mod = PL()
engine = Engine(mm, mod, expdes)
@@ -543,7 +543,7 @@ def test_choose_next_sample_latin_BODMI() -> None:
expdes.util_func = 'MI'
mm = MetaModel(inp)
mm.fit(expdes.X, expdes.Y)
- expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
+ expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm._pce_deg))
mod = PL()
engine = Engine(mm, mod, expdes)
@@ -576,7 +576,7 @@ def test_choose_next_sample_vor_BODMI() -> None:
expdes.util_func = 'MI'
mm = MetaModel(inp)
mm.fit(expdes.X, expdes.Y)
- expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
+ expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm._pce_deg))
mod = PL()
engine = Engine(mm, mod, expdes)
@@ -614,7 +614,7 @@ def test_choose_next_sample_latin_BODALC() -> None:
expdes.util_func = 'ALC'
mm = MetaModel(inp)
mm.fit(expdes.X, expdes.Y)
- expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
+ expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm._pce_deg))
mod = PL()
engine = Engine(mm, mod, expdes)
@@ -647,7 +647,7 @@ def test_choose_next_sample_latin_BODDKL() -> None:
expdes.util_func = 'DKL'
mm = MetaModel(inp)
mm.fit(expdes.X, expdes.Y)
- expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
+ expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm._pce_deg))
mod = PL()
engine = Engine(mm, mod, expdes)
@@ -680,7 +680,7 @@ def test_choose_next_sample_latin_BODDPP() -> None:
expdes.util_func = 'DPP'
mm = MetaModel(inp)
mm.fit(expdes.X, expdes.Y)
- expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
+ expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm._pce_deg))
mod = PL()
engine = Engine(mm, mod, expdes)
@@ -713,7 +713,7 @@ def test_choose_next_sample_latin_BODAPP() -> None:
expdes.util_func = 'APP'
mm = MetaModel(inp)
mm.fit(expdes.X, expdes.Y)
- expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
+ expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm._pce_deg))
mod = PL()
engine = Engine(mm, mod, expdes)
@@ -746,7 +746,7 @@ def test_choose_next_sample_latin_BODMI_() -> None:
expdes.util_func = 'MI'
mm = MetaModel(inp)
mm.fit(expdes.X, expdes.Y)
- expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
+ expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm._pce_deg))
mod = PL()
engine = Engine(mm, mod, expdes)
@@ -779,7 +779,7 @@ if 0:
expdes.util_func = 'BME'
mm = MetaModel(inp)
mm.fit(expdes.X, expdes.Y)
- expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
+ expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm._pce_deg))
mod = PL()
engine = Engine(mm, mod, expdes)
@@ -813,7 +813,7 @@ if 0:
expdes.util_func = 'DKL'
mm = MetaModel(inp)
mm.fit(expdes.X, expdes.Y)
- expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
+ expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm._pce_deg))
mod = PL()
engine = Engine(mm, mod, expdes)
@@ -847,7 +847,7 @@ if 0:
expdes.util_func = 'infEntropy'
mm = MetaModel(inp)
mm.fit(expdes.X, expdes.Y)
- expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm.pce_deg))
+ expdes.generate_ED(expdes.n_init_samples, max_pce_deg=np.max(mm._pce_deg))
mod = PL()
engine = Engine(mm, mod, expdes)
diff --git a/tests/test_supplementary.py b/tests/test_supplementary.py
index 7537b0ca38d2644748ef626650bc02cf7036324f..1eea5b70ba398cc27219c6d9184d253a30301094 100644
--- a/tests/test_supplementary.py
+++ b/tests/test_supplementary.py
@@ -14,7 +14,7 @@ import numpy as np
import pytest
import sys
-sys.path.append("src/")
+sys.path.append("../src/")
from bayesvalidrox.surrogate_models.inputs import Input
from bayesvalidrox.surrogate_models.input_space import InputSpace
@@ -22,95 +22,101 @@ 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
+@pytest.fixture
+def PCE_withInputSpace():
+ 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 = PCE(inp)
+ mm.CollocationPoints = samples
+ mm.InputSpace = InputSpace(mm.input_obj, mm.meta_model_type)
+ n_init_samples = samples.shape[0]
+ mm.InputSpace.n_init_samples = n_init_samples
+ mm.InputSpace.init_param_space(np.max(mm._pce_deg))
+ return mm
+
+
#%% Test create_psi
-def test_create_psi() -> None:
+def test_create_psi(PCE_withInputSpace) -> None:
"""
Create psi-matrix
"""
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = MetaModel(inp)
+ mm = PCE_withInputSpace#(inp, samples)
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)]
+ #mm.InputSpace = InputSpace(mm.input_obj, mm.meta_model_type)
+ #n_init_samples = samples.shape[0]
+ #mm.InputSpace.n_init_samples = n_init_samples
+ # TODO: _pce_deg not necessarily available, generalize this!
+ #mm.InputSpace.init_param_space(np.max(mm._pce_deg))
+ mm.build_metamodel()
+
+ BasisIndices = mm._all_basis_indices[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:
+def test_corr_loocv_error_nosparse(PCE_withInputSpace) -> None:
"""
Corrected loocv error
"""
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = MetaModel(inp)
+ mm = PCE_withInputSpace
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)]
+ mm.build_metamodel()
+ BasisIndices = mm._all_basis_indices[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')
+ outs = mm.regression(samples, outputs, psi)
corr_loocv_error(outs['clf_poly'], outs['sparePsi'], outs['coeffs'],
outputs)
-def test_corr_loocv_error_singley() -> None:
+def test_corr_loocv_error_singley(PCE_withInputSpace) -> None:
"""
Corrected loocv error
"""
- inp = Input()
- inp.add_marginals()
- inp.Marginals[0].dist_type = 'normal'
- inp.Marginals[0].parameters = [0, 1]
- mm = MetaModel(inp)
+ mm = PCE_withInputSpace
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)]
+ mm.build_metamodel()
+ BasisIndices = mm._all_basis_indices[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')
+ outs = mm.regression(samples, outputs, psi)
corr_loocv_error(outs['clf_poly'], outs['sparePsi'], outs['coeffs'],
outputs)
-def test_corr_loocv_error_sparse() -> None:
+def test_corr_loocv_error_sparse(PCE_withInputSpace) -> 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)
+ mm = PCE_withInputSpace
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)]
+ mm.build_metamodel()
+ BasisIndices = mm._all_basis_indices[str(1)][str(1.0)]
univ_bas = mm.univ_basis_vals(samples)
psi = create_psi(BasisIndices, univ_bas)
+ mm._pce_reg_method='ebl'
- outs = mm.regression(samples, outputs, psi, reg_method='ebl',
- sparsity=True)
+ outs = mm.regression(samples, outputs, psi, sparsity=True)
corr_loocv_error(outs['clf_poly'], outs['sparePsi'], outs['coeffs'],
outputs)
@@ -147,7 +153,7 @@ def test_gaussian_process_emulator_autosel() -> None:
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)
+ gaussian_process_emulator([[0.2], [0.8]], [0.4, 0.5], auto_select=True)
def test_gaussian_process_emulator_varidx() -> None:
@@ -158,5 +164,5 @@ def test_gaussian_process_emulator_varidx() -> None:
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)
+ gaussian_process_emulator([[0.2], [0.8]], [0.4, 0.5], var_idx=1)