From 1dcac61165a5e66021f7b286e26bdb24a77386ec Mon Sep 17 00:00:00 2001
From: faridm69 <faridmohammadi69@gmail.com>
Date: Thu, 1 Oct 2020 15:51:42 +0200
Subject: [PATCH] [tests] made minor changes.
---
.../surrogate_models/surrogate_models.py | 2 +-
.../Test_AnalyticalFunction.py | 52 +++++++++----------
.../Co2BenchmarkTest/Test_CO2Benchmark.py | 6 +--
3 files changed, 29 insertions(+), 31 deletions(-)
diff --git a/BayesValidRox/surrogate_models/surrogate_models.py b/BayesValidRox/surrogate_models/surrogate_models.py
index 791b6dff8..89dc2fca3 100644
--- a/BayesValidRox/surrogate_models/surrogate_models.py
+++ b/BayesValidRox/surrogate_models/surrogate_models.py
@@ -988,7 +988,7 @@ class Metamodel:
for deg in self.DegreeArray:
if deg not in np.fromiter(self.allBasisIndices.keys(), dtype=float):
- self.allBasisIndices = self.AutoVivification()
+ # self.allBasisIndices = self.AutoVivification()
# Generate the polynomial basis indices
for qidx, q in enumerate(self.q):
self.allBasisIndices[str(deg)][str(q)] = self.PolyBasisIndices(degree=deg, q=q)
diff --git a/BayesValidRox/tests/AnalyticalFunction/Test_AnalyticalFunction.py b/BayesValidRox/tests/AnalyticalFunction/Test_AnalyticalFunction.py
index e90372b1f..43dce8c45 100755
--- a/BayesValidRox/tests/AnalyticalFunction/Test_AnalyticalFunction.py
+++ b/BayesValidRox/tests/AnalyticalFunction/Test_AnalyticalFunction.py
@@ -89,7 +89,7 @@ if __name__ == "__main__":
# Select if you want to preserve the spatial/temporal depencencies
MetaModelOpts.DimRedMethod = 'PCA'
- MetaModelOpts.varPCAThreshold = 100 #99.999
+ # MetaModelOpts.varPCAThreshold = 100 #99.999
# Select your metamodel method
# 1) PCE (Polynomial Chaos Expansion) 2) GPE (Gaussian Process Emulator)
@@ -104,16 +104,16 @@ if __name__ == "__main__":
# 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
- MetaModelOpts.RegMethod = 'ARD'
+ MetaModelOpts.RegMethod = 'FastARD'
# Specify the max degree to be compared by the adaptive algorithm:
# 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 = 3
- MetaModelOpts.MaxPceDegree = 8
+ MetaModelOpts.MinPceDegree = 1
+ MetaModelOpts.MaxPceDegree = 12
# q-quasi-norm 0<q<1 (default=1)
- MetaModelOpts.q = 0.6 if ndim<5 else 0.8 #np.linspace(0.3,0.8, 3)
+ MetaModelOpts.q = 1.0 if ndim<5 else 0.85 #np.linspace(0.3,0.8, 3)
# Print summary of the regression results
# MetaModelOpts.DisplayFlag = True
@@ -127,7 +127,7 @@ if __name__ == "__main__":
# One-shot (normal) or Sequential Adaptive (sequential) Design
MetaModelOpts.ExpDesign.Method = 'sequential'
- NrofInitSamples = 50 #50
+ NrofInitSamples = 2*ndim #50
MetaModelOpts.ExpDesign.NrSamples = NrofInitSamples
# Sampling methods
@@ -141,7 +141,7 @@ if __name__ == "__main__":
# Sequential experimental design (needed only for sequential ExpDesign)
MetaModelOpts.ExpDesign.NrofNewSample = 1
- MetaModelOpts.ExpDesign.MaxNSamples = 100 #150
+ MetaModelOpts.ExpDesign.MaxNSamples = 150 #150
MetaModelOpts.ExpDesign.ModifiedLOOThreshold = 1e-16
# Defining the measurement error, if it's known a priori
@@ -160,10 +160,10 @@ if __name__ == "__main__":
# ------------------------------------------------
# ------- Sequential Design configuration --------
# ------------------------------------------------
- MetaModelOpts.adaptVerbose = True
+ # MetaModelOpts.adaptVerbose = True
# 1) 'None' 2) 'equal' 3)'epsilon-decreasing' 4) 'adaptive'
- MetaModelOpts.ExpDesign.TradeOffScheme = 'epsilon-decreasing'
- # MetaModelOpts.ExpDesign.nReprications = 2
+ MetaModelOpts.ExpDesign.TradeOffScheme = 'None'
+ # MetaModelOpts.ExpDesign.nReprications = 20
# -------- Exploration ------
#1)'Voronoi' 2)'random' 3)'latin_hypercube' 4)'LOOCV' 5)'dual annealing'
MetaModelOpts.ExpDesign.ExploreMethod = 'random'
@@ -172,22 +172,22 @@ if __name__ == "__main__":
MetaModelOpts.ExpDesign.MaxFunItr = 200
# Use when 'Voronoi' or 'random' or 'latin_hypercube' chosen
- MetaModelOpts.ExpDesign.NCandidate = 5000 #500
+ MetaModelOpts.ExpDesign.NCandidate = 400 #5000
MetaModelOpts.ExpDesign.NrofCandGroups = 8
# -------- Exploitation ------
# 1)'BayesOptDesign' 2)'BayesActDesign' 3)'VarOptDesign' 4)'alphabetic'
# 5)'Space-filling'
- MetaModelOpts.ExpDesign.ExploitMethod = 'VarOptDesign'
+ MetaModelOpts.ExpDesign.ExploitMethod = 'BayesActDesign'
# 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 = 'EIGF' #['EIGF', 'Entropy', 'LOOCV']
# alphabetic
# 1)D-Opt (D-Optimality) 2)A-Opt (A-Optimality)
@@ -233,9 +233,10 @@ if __name__ == "__main__":
#=====================================================
BayesOpts = BayesInference(PCEModel)
BayesOpts.emulator = True
+ BayesOpts.PlotPostPred = True
# BME Bootstrap
- # BayesOpts.Bootstrap = False
+ # BayesOpts.Bootstrap = True
# BayesOpts.BootstrapItrNr = 10
# BayesOpts.BootstrapNoise = 0.05
@@ -243,10 +244,6 @@ if __name__ == "__main__":
BayesOpts.SamplingMethod = 'MCMC'
BayesOpts.MCMCnwalkers = 100
# BayesOpts.MCMCnSteps = 500
- BayesOpts.MultiProcessMCMC = False
-
-
- BayesOpts.PlotPostPred = True
# ----- Define the discrepancy model -------
obsData = pd.DataFrame(Model.Observations, columns=Model.Output.Names)
@@ -259,18 +256,20 @@ if __name__ == "__main__":
BayesOpts.Discrepancy = DiscrepancyOpts
- Bayes_PCE = BayesOpts.create_Inference()
- sys.exit('STOP!!')
+ Bayes_PCE = BayesOpts.create_Inference()
+ # sys.exit('STOP!!')
#=====================================================
#====== Bayesian inference with Forward Model ========
#=====================================================
- if ndim < 7:
+ if ndim < 7:
+ PCEModel.ModelObj.Name = 'OrigAnalyticFunc'
+
BayesOptsModel = BayesInference(PCEModel)
BayesOptsModel.emulator = False
- BayesOptsModel.NrofSamples = 100000
+ BayesOptsModel.NrofSamples = 1000000
# Evaluation of forward model predictions for the samples used for PCEModel
- BayesOptsModel.Samples = Bayes_PCE.Samples
+ # BayesOptsModel.Samples = Bayes_PCE.Samples
# BayesOptsModel.SamplingMethod = 'MCMC'
# Bootstrap for BME calulations
@@ -279,7 +278,7 @@ if __name__ == "__main__":
BayesOptsModel.BootstrapNoise = 0.05
BayesOptsModel.PlotPostDist = True
- BayesOptsModel.PlotPostPred = True
+ BayesOptsModel.PlotPostPred = False
# ----- Define the discrepancy model -------
@@ -292,8 +291,7 @@ if __name__ == "__main__":
BayesOptsModel.Discrepancy = DiscrepancyOpts
Bayes_Model = BayesOptsModel.create_Inference()
-
-
+
#=====================================================
#============== Save class objects =================
#=====================================================
diff --git a/BayesValidRox/tests/Co2BenchmarkTest/Test_CO2Benchmark.py b/BayesValidRox/tests/Co2BenchmarkTest/Test_CO2Benchmark.py
index 0f831e63b..e42f82f13 100755
--- a/BayesValidRox/tests/Co2BenchmarkTest/Test_CO2Benchmark.py
+++ b/BayesValidRox/tests/Co2BenchmarkTest/Test_CO2Benchmark.py
@@ -122,9 +122,9 @@ if __name__ == "__main__":
# error (or the highest score=1-LOO)estimator is chosen as the final
# metamodel.
MetaModelOpts.MinPceDegree = 1
- MetaModelOpts.MaxPceDegree = 4
+ MetaModelOpts.MaxPceDegree = 10
# q-quasi-norm 0<q<1 (default=1)
- MetaModelOpts.q = 1.0
+ MetaModelOpts.q = 0.8
# Print summary of the regression results
# MetaModelOpts.DisplayFlag = True
@@ -138,7 +138,7 @@ if __name__ == "__main__":
# One-shot (normal) or Sequential Adaptive (sequential) Design
MetaModelOpts.ExpDesign.Method = 'sequential'
- initNSamples = 2 #20
+ initNSamples = 5 #20
MetaModelOpts.ExpDesign.NrSamples = initNSamples
# Sampling methods
--
GitLab