diff --git a/BayesValidRox/PostProcessing/PostProcessing.py b/BayesValidRox/PostProcessing/PostProcessing.py
index 391d9a9c1b71f23280c6b62aea1b33301995621a..42bf97780c965de5b80e2da73b06ce056b028fd6 100644
--- a/BayesValidRox/PostProcessing/PostProcessing.py
+++ b/BayesValidRox/PostProcessing/PostProcessing.py
@@ -965,8 +965,10 @@ class PostProcessing:
self.sobol_cell[Output] = sobol_array
self.total_sobol[Output] = total_sobol
-
-
+ else:
+ self.sobol_cell[Output] = sobol_cell_array
+ self.total_sobol[Output] = total_sobol_array
+
# ---------------- Plot -----------------------
fig = plt.figure()
parNames = [PCEModel.Inputs.Marginals[i].Name for i in range(NofPa)]
diff --git a/BayesValidRox/PyLink/PyLinkForwardModel.py b/BayesValidRox/PyLink/PyLinkForwardModel.py
index ed6f559d8e56abf06ca7cd3279be42b61b280867..e6201eb27274b7a263d583117421654053059d7e 100644
--- a/BayesValidRox/PyLink/PyLinkForwardModel.py
+++ b/BayesValidRox/PyLink/PyLinkForwardModel.py
@@ -328,7 +328,7 @@ class PyLinkForwardModel:
for path in FilePaths: shutil.rmtree(path)
print("\n")
- print(dir_name+'.zip file is created successfully!')
+ print(dir_name+'.zip file is created successfully!\n')
return
diff --git a/BayesValidRox/surrogate_models/surrogate_models.py b/BayesValidRox/surrogate_models/surrogate_models.py
index ab889fea44a0886364b06eb0573df9776b7d4a05..b357e53e3d6519bd97de651749bf22e53d0aa8ac 100644
--- a/BayesValidRox/surrogate_models/surrogate_models.py
+++ b/BayesValidRox/surrogate_models/surrogate_models.py
@@ -498,10 +498,12 @@ class aPCE:
lambda_1=Lambda, lambda_2=Lambda)
elif RegMethod == 'ARD':
- # clf_poly = ARDRegression(fit_intercept = False,compute_score=True,
- # n_iter=1000, tol= 0.001)
+ clf_poly = ARDRegression(fit_intercept=False,compute_score=compute_score,
+ n_iter=1000, tol= 0.001)
# alpha_1=1e-6, alpha_2=1e-6,
# lambda_1=Lambda, lambda_2=Lambda)
+
+ elif RegMethod == 'FastARD':
clf_poly = RegressionFastARD(start=startBasisIndices,fit_intercept=False,compute_score=compute_score,
tol= 1e-3)
@@ -902,20 +904,21 @@ class aPCE:
"""
- # ---- Experimental Design ------
# Get the collocation points to run the forward model
self.Exe_ExpDesign(Model)
CollocationPoints = self.OptimalCollocationPointsBase
OutputDict = self.ModelOutputDict.copy()
+ # Initialize the nested dictionaries
+ self.CoeffsDict= self.AutoVivification()
+ self.BasisDict = self.AutoVivification()
+ self.ScoresDict = self.AutoVivification()
+ self.clf_poly = self.AutoVivification()
+ self.gp_poly = self.AutoVivification()
+ self.pca = self.AutoVivification()
+ self.LCerror = self.AutoVivification()
+
if self.ExpDesignFlag != 'sequential':
- self.CoeffsDict= self.AutoVivification()
- self.BasisDict = self.AutoVivification()
- self.ScoresDict = self.AutoVivification()
- self.clf_poly = self.AutoVivification()
- self.gp_poly = self.AutoVivification()
- self.pca = self.AutoVivification()
- self.LCerror = self.AutoVivification()
self.allBasisIndices = self.AutoVivification()
# Read observations or MCReference
@@ -939,7 +942,7 @@ class aPCE:
M_uptoMax = lambda maxDeg: np.array([math.factorial(ndim+d)//(math.factorial(ndim)*math.factorial(d)) for d in range(1,maxDeg+1)])
deg = range(1,maxDeg+1)[np.argmin(abs(M_uptoMax(maxDeg)-nSamples*ndim*d))]
self.q = np.array(self.q) if not np.isscalar(self.q) else np.array([self.q])
-
+
if deg not in self.DegreeArray:
self.allBasisIndices = self.AutoVivification()
self.DegreeArray = np.arange(self.MinPceDegree,deg+1)
@@ -947,7 +950,7 @@ class aPCE:
for qidx, q in enumerate(self.q):
# Generate the polynomial basis indices
self.allBasisIndices[str(deg)][str(q)] = self.PolyBasisIndices(degree=deg, q=q)
-
+
# Evaluate the univariate polynomials on ExpDesign
if self.RegMethod != 'GPE':
@@ -1431,7 +1434,7 @@ class aPCE:
return Subdomains
#--------------------------------------------------------------------------------------------------------
- def Utility_runner(self, method, Model, allCandidates,index,X_MC=None, sigma2Dict=None, var=None):
+ def Utility_runner(self, method, Model, allCandidates,index, sigma2Dict=None, var=None, X_MC=None):
if method == 'VarOptDesign':
U_J_d = self.util_VarBasedDesign(allCandidates, index, var)
@@ -1698,7 +1701,7 @@ class aPCE:
# Split the candidates in groups for multiprocessing
split_allCandidates = np.array_split(allCandidates, NrofCandGroups, axis=0)
- args = [(ExploitMethod, Model, split_allCandidates[i], i,X_MC, sigma2Dict , var) for i in range(NrofCandGroups)]
+ args = [(ExploitMethod, Model, split_allCandidates[i], i, sigma2Dict , var, X_MC) for i in range(NrofCandGroups)]
# With Pool.starmap_async()
results = pool.starmap_async(self.Utility_runner, args).get()
diff --git a/BayesValidRox/tests/Co2BenchmarkTest/Test_CO2Benchmark.py b/BayesValidRox/tests/Co2BenchmarkTest/Test_CO2Benchmark.py
old mode 100644
new mode 100755
index 0b7dd91a2d9c5730191f768e74dc5aad9f7b6e25..1eb4684e8e8dfd57b07e393a9342076c308975f5
--- a/BayesValidRox/tests/Co2BenchmarkTest/Test_CO2Benchmark.py
+++ b/BayesValidRox/tests/Co2BenchmarkTest/Test_CO2Benchmark.py
@@ -115,6 +115,7 @@ if __name__ == "__main__":
# 5)SGDRegressor: Stochastic gradient descent learning
MetaModelOpts.metaModel = 'PCEKriging'
MetaModelOpts.RegMethod = 'ARD'
+ MetaModelOpts.DimRedMethod = 'PCA'
# Print summary of the regression results
# MetaModelOpts.DisplayFlag = True
@@ -126,8 +127,8 @@ if __name__ == "__main__":
MetaModelOpts.ExpDesign.MCSize = 10000
# One-shot (normal) or Sequential Adaptive (sequential) Design
- MetaModelOpts.ExpDesign.Method = 'sequential'
- initNSamples = 10
+ MetaModelOpts.ExpDesign.Method = 'normal'
+ initNSamples = 5
MetaModelOpts.ExpDesign.NrSamples = initNSamples
# Sampling methods
@@ -335,4 +336,4 @@ if __name__ == "__main__":
# Plot original posteriors
posteriorPlot(origPosterior, theta_true, parNames, 'origPosterior')
-
\ No newline at end of file
+