From 672c2088a12ed68b2cfd33fec2bc2ded39aef9ad Mon Sep 17 00:00:00 2001
From: Farid Mohammadi <farid.mohammadi@iws.uni-stuttgart.de>
Date: Mon, 9 Mar 2020 13:53:57 +0100
Subject: [PATCH] [surrogate][SeqDesign] fixed the issue with LOOCV with
Voronoi.
---
.../surrogate_models/surrogate_models.py | 6 +++---
.../AnalyticalFunction_Test.py | 8 ++++----
.../tests/Co2BenchmarkTest/Test_CO2Benchmark.py | 16 ++++++++--------
3 files changed, 15 insertions(+), 15 deletions(-)
diff --git a/BayesValidRox/surrogate_models/surrogate_models.py b/BayesValidRox/surrogate_models/surrogate_models.py
index e896dca3d..49bdb374b 100644
--- a/BayesValidRox/surrogate_models/surrogate_models.py
+++ b/BayesValidRox/surrogate_models/surrogate_models.py
@@ -1076,11 +1076,11 @@ class aPCE:
# find an optimal point subset to add to the initial design
# by maximization of the utility score and taking care of NaN values
temp = ExploitScore.copy()
- bestIdx = np.argsort(temp)[::-1][:NrofNewSample]
- #bestIdx = sorted_idxtotalScore[:NrofNewSample]
+ sorted_idxtotalScore = np.argsort(temp, axis=0)[::-1]
+ bestIdx = sorted_idxtotalScore[:NrofNewSample]
Xnew = np.zeros((NrofNewSample,ndim))
- for i, idx in enumerate(bestIdx):
+ for i, idx in enumerate(bestIdx.flatten()):
X_can = exploration.closestPoints[idx]
#plotter(self.ExpDesign.X, X_can, ExploreMethod, scoreExploration=None)
diff --git a/BayesValidRox/tests/AnalyticalFunction/AnalyticalFunction_Test.py b/BayesValidRox/tests/AnalyticalFunction/AnalyticalFunction_Test.py
index 8143a81b9..0628f19ad 100644
--- a/BayesValidRox/tests/AnalyticalFunction/AnalyticalFunction_Test.py
+++ b/BayesValidRox/tests/AnalyticalFunction/AnalyticalFunction_Test.py
@@ -138,7 +138,7 @@ if __name__ == "__main__":
#MetaModelOpts.ExpDesign.nReprications = 2
# -------- Exploration ------
#1)'Voronoi' 2)'MC' 3)'LHS' 4)'dual annealing'
- MetaModelOpts.ExpDesign.ExploreMethod = 'MC'
+ MetaModelOpts.ExpDesign.ExploreMethod = 'Voronoi'
# Use when 'dual annealing' chosen
MetaModelOpts.ExpDesign.MaxFunItr = 200
@@ -149,16 +149,16 @@ if __name__ == "__main__":
# -------- Exploitation ------
# 1)'BayesOptDesign' 2)'VarOptDesign' 3)'alphabetic' 4)'Space-filling'
- MetaModelOpts.ExpDesign.ExploitMethod = 'BayesOptDesign'
+ MetaModelOpts.ExpDesign.ExploitMethod = 'VarOptDesign'
# BayesOptDesign -> when data is available
# 1)DKL (Kullback-Leibler Divergence) 2)DPP (D-Posterior-percision)
# 3)APP (A-Posterior-percision)
- MetaModelOpts.ExpDesign.UtilityFunction = 'DKL' #['DKL', 'DPP']
+ #MetaModelOpts.ExpDesign.UtilityFunction = 'DKL' #['DKL', 'DPP']
# VarBasedOptDesign -> when data is not available
# Only with Vornoi >>> 1)Entropy 2)EIGF, 3)ALM, 4)LOOCV
- #MetaModelOpts.ExpDesign.UtilityFunction = 'Entropy'#['EIGF', 'Entropy', 'LOOCV']
+ MetaModelOpts.ExpDesign.UtilityFunction = 'LOOCV'#['EIGF', 'Entropy', 'LOOCV']
# alphabetic
# 1)D-Opt (D-Optimality) 2)A-Opt (A-Optimality)
diff --git a/BayesValidRox/tests/Co2BenchmarkTest/Test_CO2Benchmark.py b/BayesValidRox/tests/Co2BenchmarkTest/Test_CO2Benchmark.py
index e6959dd57..4039df9c9 100644
--- a/BayesValidRox/tests/Co2BenchmarkTest/Test_CO2Benchmark.py
+++ b/BayesValidRox/tests/Co2BenchmarkTest/Test_CO2Benchmark.py
@@ -107,16 +107,16 @@ if __name__ == "__main__":
MetaModelOpts.addExpDesign()
MetaModelOpts.ExpDesign.MCSize = 10000
- initNSamples = 200
+ initNSamples = 100
MetaModelOpts.ExpDesign.NrSamples = initNSamples
- MetaModelOpts.ExpDesign.SamplingMethod = 'user' # 1)LHS 2)PCM 3)LSCM 4)user
+ MetaModelOpts.ExpDesign.SamplingMethod = 'MC' # 1)LHS 2)PCM 3)LSCM 4)user
MetaModelOpts.ExpDesign.Method = 'sequential' # 1) normal 2) sequential
- MetaModelOpts.ExpDesign.X = np.load('EDX_CO2Benchmark.npy')
+ #MetaModelOpts.ExpDesign.X = np.load('EDX_CO2Benchmark.npy')
#MetaModelOpts.ExpDesign.Y = ExpDesign_Y
# Sequential experimental design (needed only for sequential ExpDesign)
MetaModelOpts.ExpDesign.NrofNewSample = 10
- MetaModelOpts.ExpDesign.MaxNSamples = 500
+ MetaModelOpts.ExpDesign.MaxNSamples = 1000
MetaModelOpts.ExpDesign.ModifiedLOOThreshold = 1e-4
# Defining the measurement error, if it's known a priori
@@ -143,7 +143,7 @@ if __name__ == "__main__":
#MetaModelOpts.ExpDesign.nReprications = 2
# -------- Exploration ------
#1)'Voronoi' 2)'MC' 3)'LHS' 4)'dual annealing'
- MetaModelOpts.ExpDesign.ExploreMethod = 'MC'
+ MetaModelOpts.ExpDesign.ExploreMethod = 'Voronoi'
# Use when 'dual annealing' chosen
MetaModelOpts.ExpDesign.MaxFunItr = 200
@@ -154,16 +154,16 @@ if __name__ == "__main__":
# -------- Exploitation ------
# 1)'BayesOptDesign' 2)'VarOptDesign' 3)'alphabetic' 4)'Space-filling'
- MetaModelOpts.ExpDesign.ExploitMethod = 'BayesOptDesign'
+ MetaModelOpts.ExpDesign.ExploitMethod = 'VarOptDesign'
# BayesOptDesign -> when data is available
# 1)DKL (Kullback-Leibler Divergence) 2)DPP (D-Posterior-percision)
# 3)APP (A-Posterior-percision)
- MetaModelOpts.ExpDesign.UtilityFunction = 'DKL' #['DKL', 'DPP']
+ #MetaModelOpts.ExpDesign.UtilityFunction = 'DKL' #['DKL', 'DPP']
# VarBasedOptDesign -> when data is not available
# Only with Vornoi >>> 1)Entropy 2)EIGF, 3)ALM, 4)LOOCV
- #MetaModelOpts.ExpDesign.UtilityFunction = 'LOOCV'
+ MetaModelOpts.ExpDesign.UtilityFunction = 'LOOCV'
# alphabetic
# 1)D-Opt (D-Optimality) 2)A-Opt (A-Optimality)
--
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