diff --git a/src/bayesvalidrox/post_processing/post_processing.py b/src/bayesvalidrox/post_processing/post_processing.py
index bed9851055ccd41df8d67fe17e5d99d96ab9b3f8..b29d8bf40b06dd9a76c92912ad92092407e7a7c5 100644
--- a/src/bayesvalidrox/post_processing/post_processing.py
+++ b/src/bayesvalidrox/post_processing/post_processing.py
@@ -230,7 +230,7 @@ class PostProcessing:
Parameter sets to be checked. The default is None.
outputs : dict, optional
Output dictionary with model outputs for all given output types in
- `Model.Output.names`. The default is None.
+ `engine.out_names`. The default is None.
Raises
------
@@ -555,7 +555,7 @@ class PostProcessing:
# Extract the necessary variables
max_order = np.max(metamod._pce_deg)
- outputs = self.engine.Model.Output.names
+ outputs = self.engine.out_names
if metamod.sobol is None:
metamod.calculate_sobol()
sobol_all, total_sobol_all = metamod.sobol, metamod.total_sobol
@@ -700,6 +700,7 @@ class PostProcessing:
)
plt.clf()
+ return sobol_all, total_sobol_all
# -------------------------------------------------------------------------
def check_reg_quality(self, n_samples: int = 1000, samples=None, outputs: dict = None) -> None:
@@ -715,7 +716,7 @@ class PostProcessing:
Parameter sets to use for the check. The default is None.
outputs : dict, optional
Output dictionary with model outputs for all given output types in
- `Model.Output.names`. The default is None.
+ `engine.out_names`. The default is None.
Return
------
diff --git a/src/bayesvalidrox/surrogate_models/polynomial_chaos.py b/src/bayesvalidrox/surrogate_models/polynomial_chaos.py
index afedbe76440300104ae0f4572d8f47e35ad1071e..c1164b690eb292a536abde52823e9ec90ae56fdc 100644
--- a/src/bayesvalidrox/surrogate_models/polynomial_chaos.py
+++ b/src/bayesvalidrox/surrogate_models/polynomial_chaos.py
@@ -929,7 +929,7 @@ class PCE(MetaModel):
return pce_means, pce_stds
- def calculate_sobol(self, Y_train = None):
+ def calculate_sobol(self, y_train = None):
"""
Provides Sobol' indices as a sensitivity measure to infer the importance
of the input parameters. See Eq. 27 in [1] for more details. For the
@@ -947,7 +947,7 @@ class PCE(MetaModel):
Parameters
----------
- Y_train: dict, optional
+ y_train: dict, optional
Trainings outputs. They are needed when used in combination with PCA.
The default is None
@@ -959,6 +959,9 @@ class PCE(MetaModel):
Total Sobol' indices
"""
+ if self.dim_red_method == 'pca' and y_train is None:
+ raise AttributeError("Calculation of Sobol' indices with PCA expects training outputs, but none are given.")
+
max_order = np.max(self._pce_deg)
n_params = len(self.input_obj.Marginals)
cov_zpq = np.zeros((n_params))
@@ -1061,7 +1064,7 @@ class PCE(MetaModel):
# Compute the sobol indices according to Ref. 2
if self.dim_red_method.lower() == "pca":
- n_c_points = Y_train[output].shape[1]
+ n_c_points = y_train[output].shape[1]
pca = self.pca[f"b_{b_i+1}"][output]
comp_pca = pca.components_
n_comp = comp_pca.shape[0]
@@ -1079,7 +1082,7 @@ class PCE(MetaModel):
for t_idx in range(n_c_points):
var_yt = np.var(
- Y_train[output][:, t_idx]
+ y_train[output][:, t_idx]
)
term1, term2 = 0.0, 0.0
if var_yt != 0.0:
@@ -1106,7 +1109,7 @@ class PCE(MetaModel):
s_zi = total_sobol_array[par_idx]
for t_idx in range(n_c_points):
- var_yt = np.var(Y_train[output][:, t_idx])
+ var_yt = np.var(y_train[output][:, t_idx])
term1, term2 = 0.0, 0.0
if var_yt != 0.0:
for i in range(n_comp):
diff --git a/tests/test_PolynomialChaosEmulator.py b/tests/test_PolynomialChaosEmulator.py
index 64858588d1382caa8df9002fec2ed4c2d7ef9b9a..59477eda68810a68e6aca8c9bb7ce855ce2f674d 100644
--- a/tests/test_PolynomialChaosEmulator.py
+++ b/tests/test_PolynomialChaosEmulator.py
@@ -873,3 +873,20 @@ def test_calculate_sobol_pca():
mm.fit([[0.2], [0.8]], {'Z': [[0.4, 0.4], [0.5, 0.6]]})
sobol, totalsobol = mm.calculate_sobol({'Z': np.array([[0.4, 0.4], [0.5, 0.6]])})
# TODO are there theory-related checks that could be applied here?
+
+def test_calculate_sobol_pcanoy():
+ """
+ Calculate Sobol' indices of a pce-surrogate with PCA but no outputs
+ """
+ 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]]})
+
+ with pytest.raises(AttributeError) as excinfo:
+ mm.calculate_sobol()
+ assert str(excinfo.value) == ("Calculation of Sobol' indices with PCA expects training outputs, but none are given.")
+
diff --git a/tests/test_PostProcessing.py b/tests/test_PostProcessing.py
index a57e8c085cc3f58b20771768743de87ffbec7a24..eb3d194663a3910eba545ee13051a3227180d65b 100644
--- a/tests/test_PostProcessing.py
+++ b/tests/test_PostProcessing.py
@@ -143,10 +143,17 @@ def test_sobol_indices_pce(pce_engine) -> None:
"""
engine = pce_engine
post = PostProcessing(engine)
- sobol = post.sobol_indices()
- assert list(sobol.keys()) == ['Z']
- assert sobol['Z'].shape == (1,1)
- assert sobol['Z'][0,0] == 1
+ sobol, totalsobol = post.sobol_indices()
+
+ assert list(totalsobol.keys()) == ['Z']
+ assert totalsobol['Z'].shape == (1,1)
+ assert totalsobol['Z'][0,0] == 1
+
+ print(sobol)
+ assert list(sobol.keys()) == [1]
+ assert list(sobol[1].keys()) == ['Z']
+ assert sobol[1]['Z'].shape == (1,1,1)
+ assert sobol[1]['Z'][0,0] == 1
#%% check_reg_quality