diff --git a/BayesValidRox/BayesInference/BayesInference.py b/BayesValidRox/BayesInference/BayesInference.py
index dcf817bd3bf15023583eda77174ed965b9534025..a19e42627c5124a78b7f4a00d3592cbff58a7ed5 100644
--- a/BayesValidRox/BayesInference/BayesInference.py
+++ b/BayesValidRox/BayesInference/BayesInference.py
@@ -44,6 +44,7 @@ class BayesInference:
self.NrofSamples = None
self.Samples = None
self.Samplesu = None
+ self.MCMCnSteps = None
self.ModelOutputs = []
self.meanPCEPriorPred = []
self.stdPCEPriorPred = []
@@ -678,7 +679,7 @@ class BayesInference:
else:
# TODO: MCMC
initsamples = None if self.Samples is None else self.Samples
- nsteps = 1000 if self.NrofSamples is None else self.NrofSamples
+ nsteps = 1000 if self.MCMCnSteps is None else self.MCMCnSteps
multiprocessing = True if self.MultiProcessMCMC is None else self.MultiProcessMCMC
MCMC_ = MCMC(self, initsamples=initsamples, nwalkers=2*self.PCEModel.NofPa,
nsteps = nsteps, multiprocessing=multiprocessing)
@@ -746,7 +747,7 @@ class BayesInference:
bbox_inches='tight')
# -------- Plot logBME dist --------
- if self.BootstrapItrNr != 1:
+ if self.Bootstrap:
# Computing the TOM performance
x_values = np.linspace(np.min(self.BME), np.max(self.BME), 1000)
dof = ObservationData.shape[0]
diff --git a/BayesValidRox/PostProcessing/PostProcessing.py b/BayesValidRox/PostProcessing/PostProcessing.py
index 42bf97780c965de5b80e2da73b06ce056b028fd6..613fea138b228c2e193bc669e1875d7b3a92c507 100644
--- a/BayesValidRox/PostProcessing/PostProcessing.py
+++ b/BayesValidRox/PostProcessing/PostProcessing.py
@@ -44,7 +44,7 @@ class PostProcessing:
self.sobol_cell = {}
self.total_sobol = {}
self.Likelihoods = []
-
+ self.Name = 'calib'
#--------------------------------------------------------------------------------------------------------
def PCEMoments(self, aPCE):
@@ -182,14 +182,33 @@ class PostProcessing:
PCEOutputs_std[:, idx] = y_std
idx += 1
- if PCEModel.DimRedMethod.lower() == 'pca':
- PCA = PCEModel.pca[Outkey]
- self.PCEOutputs[Outkey] = PCA.mean_ + np.dot(PCEOutputs_mean,PCA.components_)
- self.PCEOutputs_std[Outkey] = np.sqrt(np.dot(PCEOutputs_std**2, PCA.components_**2))
+ if PCEModel.index is None:
+ if PCEModel.DimRedMethod.lower() == 'pca':
+ PCA = PCEModel.pca[Outkey]
+ self.PCEOutputs[Outkey] = PCA.mean_ + np.dot(PCEOutputs_mean,PCA.components_)
+ self.PCEOutputs_std[Outkey] = np.sqrt(np.dot(PCEOutputs_std**2, PCA.components_**2))
+ else:
+ self.PCEOutputs[Outkey] = PCEOutputs_mean
+ self.PCEOutputs_std[Outkey] = PCEOutputs_std
else:
- self.PCEOutputs[Outkey] = PCEOutputs_mean
- self.PCEOutputs_std[Outkey] = PCEOutputs_std
-
+ index = PCEModel.index
+ if self.Name.lower() == 'calib':
+ if PCEModel.DimRedMethod.lower() == 'pca':
+ PCA = PCEModel.pca[Outkey]
+ self.PCEOutputs[Outkey] = PCA.mean_[:index] + np.dot(PCEOutputs_mean,PCA.components_)[:,:index]
+ self.PCEOutputs_std[Outkey] = np.sqrt(np.dot(PCEOutputs_std**2, PCA.components_**2))[:,:index]
+ else:
+ self.PCEOutputs[Outkey] = PCEOutputs_mean[:,:index]
+ self.PCEOutputs_std[Outkey] = PCEOutputs_std[:,:index]
+ else:
+ if PCEModel.DimRedMethod.lower() == 'pca':
+ PCA = PCEModel.pca[Outkey]
+ self.PCEOutputs[Outkey] = PCA.mean_[index:] + np.dot(PCEOutputs_mean,PCA.components_)[:,index:]
+ self.PCEOutputs_std[Outkey] = np.sqrt(np.dot(PCEOutputs_std**2, PCA.components_**2))[:,index:]
+ else:
+ self.PCEOutputs[Outkey] = PCEOutputs_mean[:,index:]
+ self.PCEOutputs_std[Outkey] = PCEOutputs_std[:,index:]
+
return self.PCEOutputs, self.PCEOutputs_std
#--------------------------------------------------------------------------------------------------------
diff --git a/BayesValidRox/tests/AnalyticalFunction/AnalytFuncValid_Test.py b/BayesValidRox/tests/AnalyticalFunction/AnalytFuncValid_Test.py
index c47ea65c3b92e92b3228671683f72cc49a7b68c0..22b758590a561ef6800386636dcf63de7e96222b 100644
--- a/BayesValidRox/tests/AnalyticalFunction/AnalytFuncValid_Test.py
+++ b/BayesValidRox/tests/AnalyticalFunction/AnalytFuncValid_Test.py
@@ -37,7 +37,7 @@ if __name__ == "__main__":
#=====================================================
#============= COMPUTATIONAL MODEL ================
#=====================================================
- Model = FuncForwardModel('AnalyticalFunction')
+ Model = FuncForwardModel()
Model.Type = 'Function'
Model.pyFile = 'AnalyticalFunction'
@@ -47,8 +47,8 @@ if __name__ == "__main__":
# For Bayesian inversion synthetic data with X=[0,0]
- Model.Observations['Time [s]'] = np.arange(0, 10, 1.) / 5
- Model.Observations['Z'] = np.array([[2]*5])
+ Model.Observations['Time [s]'] = np.arange(0, 5, 1.) / 5
+ Model.Observations['Z'] = np.repeat([2],5)
# For Checking with the MonteCarlo refrence
# Model.MCReference['Time [s]'] = np.arange(0, 10, 1.) / 9
@@ -60,9 +60,9 @@ if __name__ == "__main__":
# 171.17886925, 171.17660944, 171.17481586])
# For Bayesian inversion synthetic data with X=[1,1]
- Model.ObservationsValid['Time [s]'] = np.arange(0, 10, 1.) / 5
- Model.ObservationsValid['Z']= np.array([[2.21773563, 2.11712764,
- 2.02460905, 1.93849485, 1.85761462]]).reshape(-1,1)
+ Model.ObservationsValid['Time [s]'] = np.arange(0, 5, 1.) / 5
+ Model.ObservationsValid['Z']= np.array([2.21773563, 2.11712764,
+ 2.02460905, 1.93849485, 1.85761462])
#=====================================================
#========= PROBABILISTIC INPUT MODEL ==============
@@ -71,16 +71,12 @@ if __name__ == "__main__":
# standard deviation
Inputs = Input()
- Inputs.addMarginals()
- Inputs.Marginals[0].Name = 'x1'
- Inputs.Marginals[0].DistType = 'unif'
- Inputs.Marginals[0].Parameters = [-5, 5]
-
- Inputs.addMarginals()
- Inputs.Marginals[1].Name = 'x2'
- Inputs.Marginals[1].DistType = 'unif'
- Inputs.Marginals[1].Parameters = [-5, 5]
-
+ ndim = 2
+ for i in range(ndim):
+ Inputs.addMarginals()
+ Inputs.Marginals[i].Name = '$X_{%s}$'%(i+1)
+ Inputs.Marginals[i].DistType = 'unif'
+ Inputs.Marginals[i].Parameters = [-5, 5]
#=====================================================
#====== POLYNOMIAL CHAOS EXPANSION METAMODELS ======
@@ -91,7 +87,7 @@ if __name__ == "__main__":
# The degree with the lowest Leave-One-Out cross-validation (LOO)
# error (or the highest score=1-LOO)estimator is chosen as the final
# metamodel.
- #MetaModelOpts.MinPceDegree = 1 #3
+ MetaModelOpts.MinPceDegree = 1 #3
MetaModelOpts.MaxPceDegree = 8 #15
# q-quasi-norm 0<q<1 (default=1)
@@ -99,27 +95,37 @@ if __name__ == "__main__":
# Select the sparse least-square minimization method for
# the PCE coefficients calculation:
- # 1)AaPCE: Adaptive aPCE 2)BRR: Bayesian Ridge Regression
- # 3)LARS: Least angle regression 4)ARD: Bayesian ARD Regression (Non-degree Adaptive)
-
- MetaModelOpts.RegMethod = 'BRR'
+ # 1)OLS: Ordinary Least Square 2)BRR: Bayesian Ridge Regression
+ # 3)LARS: Least angle regression 4)ARD: Bayesian ARD Regression
+ # 5)FastARD: Fast Bayesian ARD Regression
+ # 6)SGDR: Stochastic gradient descent learning
+ # MetaModelOpts.metaModel = 'PCEKriging'
+ MetaModelOpts.RegMethod = 'FastARD'
+ MetaModelOpts.DimRedMethod = 'PCA'
# Print summary of the regression results
#MetaModelOpts.DisplayFlag = True
+
# ------ Experimental Design --------
# Generate an experimental design of size NrExpDesign based on a latin
# hypercube sampling of the input model or user-defined values of X and/or Y:
MetaModelOpts.addExpDesign()
- NrofInitSamples = 25
+ # One-shot (normal) or Sequential Adaptive (sequential) Design
+ MetaModelOpts.ExpDesign.Method = 'normal'
+ NrofInitSamples = 200 #75
MetaModelOpts.ExpDesign.NrSamples = NrofInitSamples
- MetaModelOpts.ExpDesign.SamplingMethod = 'MC' # 1)MC 2)LHS 3)PCM 4)LSCM 5)user
- MetaModelOpts.ExpDesign.Method = 'normal' # 1) normal 2) sequential
+
+ # Sampling methods
+ # 1) random 2) latin_hypercube 3) sobol 4) halton 5) hammersley 6) chebyshev(FT)
+ # 7) korobov 8) grid(FT) 9) nested_grid(FT) 10)user
+ MetaModelOpts.ExpDesign.SamplingMethod = 'random'
# Sequential experimental design (needed only for sequential ExpDesign)
- MetaModelOpts.ExpDesign.MaxNSamples = 50
- MetaModelOpts.ExpDesign.ModifiedLOOThreshold = 1e-15
+ MetaModelOpts.ExpDesign.NrofNewSample = 1
+ MetaModelOpts.ExpDesign.MaxNSamples = 50 #150
+ MetaModelOpts.ExpDesign.ModifiedLOOThreshold = 1e-16
# Defining the measurement error, if it's known a priori
obsData = pd.DataFrame(Model.Observations, columns=Model.Output.Names)
@@ -171,20 +177,22 @@ if __name__ == "__main__":
# MetaModelOpts.ExpDesign.UtilityFunction = 'D-Opt' #['D-Opt', 'A-Opt', 'K-Opt']
# >>>>>>>>>>>>>>>>>>>>>> Build Surrogate <<<<<<<<<<<<<<<<<<<<<<<<<<<<<<<
- MetaModelOpts.slicingforValidation = True
+ # MetaModelOpts.slicingforValidation = True
MetaModelOpts.index = 5
# Adaptive sparse arbitrary polynomial chaos expansion
- PCEModelCalib, PCEModelValid = MetaModelOpts.create_PCE(Model)
-
+ # PCEModelCalib, PCEModelValid = MetaModelOpts.create_PCE(Model)
+ PCEModel = MetaModelOpts.create_PCE(Model)
#=====================================================
#========= POST PROCESSING OF METAMODELS ===========
#=====================================================
- PostPCE = PostProcessing(PCEModelCalib)
+ #PostPCE = PostProcessing(PCEModelCalib)
+ PostPCE = PostProcessing(PCEModel)
+ PostPCE.Name = 'Calib'
PostPCE.NrofSamples = 3
PostPCE.plotFlag = True
- t = np.arange(0, 5, 1.) / 9
+ t = np.arange(0, 10, 1.) / 5
PostPCE.ValidMetamodel(x_values=t, x_axis="Time [s]")
# # Comparison with Monte-Carlo reference
@@ -203,7 +211,8 @@ if __name__ == "__main__":
SaveFig=True)
# Plot the sobol indices
- sobol_cell, total_sobol = PostPCE.SobolIndicesPCE('Z', SaveFig=True)
+ sobol_cell, total_sobol = PostPCE.SobolIndicesPCE(x_values=t, x_axis="Time [s]",
+ SaveFig=True)
# Compute and print RMSE error
PostPCE.relErrorPCEModel(nSamples=3000)
@@ -212,15 +221,20 @@ if __name__ == "__main__":
#=====================================================
#======= Bayesian calibration with Emulator =========
#=====================================================
- BayesOpts = BayesInference(PCEModelCalib)
+ # BayesOpts = BayesInference(PCEModelCalib)
+ BayesOpts = BayesInference(PCEModel)
BayesOpts.Name = 'Calib'
BayesOpts.emulator = True
+ # MCMC solver
+ BayesOpts.SamplingMethod = 'MCMC'
+ BayesOpts.MCMCnSteps = 1000
+
# Evaluation of surrogate model predictions
BayesOpts.NrofSamples = 100000
# Bootstrap for BME calulations
- BayesOpts.Bootstrap = True
+ BayesOpts.Bootstrap = False
BayesOpts.BootstrapItrNr = 10
BayesOpts.BootstrapNoise = 0.05
@@ -233,7 +247,7 @@ if __name__ == "__main__":
# (Option B)
DiscrepancyOpts = Discrepancy('')
DiscrepancyOpts.Type = 'Gaussian'
- DiscrepancyOpts.Parameters = obsData**2
+ DiscrepancyOpts.Parameters = obsData[:MetaModelOpts.index]**2
BayesOpts.Discrepancy = DiscrepancyOpts
@@ -259,7 +273,7 @@ if __name__ == "__main__":
## Plot the posterior (Model)
with sns.axes_style("white"):
- g = sns.jointplot(x=Bayes_Calib.Posterior_df['x1'], y=Bayes_Calib.Posterior_df['x2'],
+ g = sns.jointplot(x=Bayes_Calib.Posterior_df['$X_{1}$'], y=Bayes_Calib.Posterior_df['$X_{2}$'],
kind="kde", cmap='jet');
g.ax_joint.collections[0].set_alpha(0)
g.set_axis_labels("$X_1$", "$X_2$")
@@ -271,15 +285,18 @@ if __name__ == "__main__":
#=====================================================
#======= Bayesian validation with Emulator =========
#=====================================================
- BayesOpts_Valid = BayesInference(PCEModelValid)
+ # BayesOpts_Valid = BayesInference(PCEModelValid)
+ BayesOpts_Valid = BayesInference(PCEModel)
+
BayesOpts_Valid.Name = 'Valid'
+ BayesOpts_Valid.SamplingMethod = 'MCMC'
+ BayesOpts_Valid.MCMCnSteps = 2000
BayesOpts_Valid.Samples = Bayes_Calib.Posterior_df
-
BayesOpts_Valid.emulator = True
# Bootstrap for BME calulations
- BayesOpts_Valid.Bootstrap = True
- BayesOpts_Valid.BootstrapItrNr = 20
+ BayesOpts_Valid.Bootstrap = False
+ BayesOpts_Valid.BootstrapItrNr = 20
BayesOpts_Valid.BootstrapNoise = 0.05
BayesOpts_Valid.PlotPostDist = True
@@ -300,7 +317,7 @@ if __name__ == "__main__":
## Plot the posterior (PCEModel)
with sns.axes_style("white"):
- g = sns.jointplot(x=Bayes_Valid.Posterior_df['x1'], y=Bayes_Valid.Posterior_df['x2'],
+ g = sns.jointplot(x=Bayes_Valid.Posterior_df['$X_{1}$'], y=Bayes_Valid.Posterior_df['$X_{2}$'],
kind="kde", cmap='jet');
g.ax_joint.collections[0].set_alpha(0)
g.set_axis_labels("$X_1$", "$X_2$")
@@ -312,9 +329,9 @@ if __name__ == "__main__":
#============== Save class objects =================
#=====================================================
with open('AnalyticFunc_Results.pkl', 'wb') as output:
- pickle.dump(PCEModelCalib, output, pickle.HIGHEST_PROTOCOL)
+ pickle.dump(PCEModel, output, pickle.HIGHEST_PROTOCOL)
- pickle.dump(PCEModelValid, output, pickle.HIGHEST_PROTOCOL)
+ # pickle.dump(PCEModelValid, output, pickle.HIGHEST_PROTOCOL)
pickle.dump(PostPCE, output, pickle.HIGHEST_PROTOCOL)
diff --git a/BayesValidRox/tests/AnalyticalFunction/Test_AnalyticalFunction.py b/BayesValidRox/tests/AnalyticalFunction/Test_AnalyticalFunction.py
index ac097c78360f81176af0f693b902c43ad376a944..b0ee08ee2dc91b3a8438fce7902ab830a26d7599 100755
--- a/BayesValidRox/tests/AnalyticalFunction/Test_AnalyticalFunction.py
+++ b/BayesValidRox/tests/AnalyticalFunction/Test_AnalyticalFunction.py
@@ -39,7 +39,7 @@ from BayesInference.BayesInference import BayesInference, Discrepancy
if __name__ == "__main__":
# Number of parameters
- ndim = 10 # 2, 10
+ ndim = 2 # 2, 10
#=====================================================
#============= COMPUTATIONAL MODEL ================
@@ -118,13 +118,13 @@ if __name__ == "__main__":
# One-shot (normal) or Sequential Adaptive (sequential) Design
MetaModelOpts.ExpDesign.Method = 'sequential'
- NrofInitSamples = 10 #75
+ NrofInitSamples = 20 #75
MetaModelOpts.ExpDesign.NrSamples = NrofInitSamples
# Sampling methods
# 1) random 2) latin_hypercube 3) sobol 4) halton 5) hammersley 6) chebyshev(FT)
# 7) korobov 8) grid(FT) 9) nested_grid(FT) 10)user
- MetaModelOpts.ExpDesign.SamplingMethod = 'latin_hypercube'
+ MetaModelOpts.ExpDesign.SamplingMethod = 'random'
# Sequential experimental design (needed only for sequential ExpDesign)
MetaModelOpts.ExpDesign.NrofNewSample = 1
@@ -164,16 +164,16 @@ if __name__ == "__main__":
# -------- Exploitation ------
# 1)'BayesOptDesign' 2)'BayesActDesign' 3)'VarOptDesign' 4)'alphabetic'
# 5)'Space-filling'
- MetaModelOpts.ExpDesign.ExploitMethod = 'BayesActDesign'
+ MetaModelOpts.ExpDesign.ExploitMethod = 'VarOptDesign'
# BayesOptDesign -> when data is available
# 1)DKL (Kullback-Leibler Divergence) 2)DPP (D-Posterior-percision)
# 3)APP (A-Posterior-percision) # ['DKL', 'BME', 'infEntropy']
- MetaModelOpts.ExpDesign.UtilityFunction = 'DKL'
+ # MetaModelOpts.ExpDesign.UtilityFunction = 'DKL'
# VarBasedOptDesign -> when data is not available
# Only with Vornoi >>> 1)Entropy 2)EIGF, 3)LOOCV
- # MetaModelOpts.ExpDesign.UtilityFunction = 'Entropy'#['EIGF', 'Entropy', 'LOOCV']
+ MetaModelOpts.ExpDesign.UtilityFunction = 'Entropy'#['EIGF', 'Entropy', 'LOOCV']
# alphabetic
# 1)D-Opt (D-Optimality) 2)A-Opt (A-Optimality)