From 9311723adad546943b3f657a8c26c6e4d5b0eef7 Mon Sep 17 00:00:00 2001
From: faridm69 <faridmohammadi69@gmail.com>
Date: Wed, 15 Jul 2020 13:38:43 +0200
Subject: [PATCH] [surrogate][FastARD] minor changes
---
.../surrogate_models/surrogate_models.py | 22 +++++++++++--------
.../Test_AnalyticalFunction.py | 10 ++++-----
2 files changed, 18 insertions(+), 14 deletions(-)
diff --git a/BayesValidRox/surrogate_models/surrogate_models.py b/BayesValidRox/surrogate_models/surrogate_models.py
index 3518726ce..12e86cb53 100644
--- a/BayesValidRox/surrogate_models/surrogate_models.py
+++ b/BayesValidRox/surrogate_models/surrogate_models.py
@@ -651,18 +651,21 @@ class aPCE:
# Leave the loop, if FastARD did not converge.
if self.RegMethod == 'FastARD' and not clf_poly.converged:
+ print("Degree {0} not converged!".format(deg))
break
-
+
# Store the score in the scores list
bestqIdx = np.nanargmax(qNormScores)
scores[degIdx] = qNormScores[bestqIdx] #np.max(qNormScores)
+
AllCoeffs[str(degIdx+1)] = qAllCoeffs[str(bestqIdx+1)]
AllIndices_Sparse[str(degIdx+1)] = qAllIndices_Sparse[str(bestqIdx+1)]
Allclf_poly[str(degIdx+1)] = qAllclf_poly[str(bestqIdx+1)]
AllnTerms[str(degIdx+1)] = qAllnTerms[str(bestqIdx+1)]
AllLCerror[str(degIdx+1)] = qAllLCerror[str(bestqIdx+1)]
+
# check the direction of the error (on average):
# if it increases consistently stop the iterations
if len(scores[scores!=-np.inf]) > n_checks_degree:
@@ -860,7 +863,7 @@ class aPCE:
var = np.cumsum(np.round(covar_matrix.explained_variance_ratio_, decimals=5)*100)
try:
- selected_n_components = np.where(var>=99.99)[0][0] + 1
+ selected_n_components = np.where(var>=99.999)[0][0] + 1
except:
selected_n_components = min(n_samples, n_features)
@@ -946,13 +949,14 @@ 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.array([deg])#np.arange(self.MinPceDegree,deg+1)
- # Generate the polynomial basis indices
- for qidx, q in enumerate(self.q):
- self.allBasisIndices[str(deg)][str(q)] = self.PolyBasisIndices(degree=deg, q=q)
+ self.DegreeArray = np.array([deg]) #np.arange(self.MinPceDegree,deg+1) #or np.array([deg])
+
+ for deg in self.DegreeArray:
+ # self.allBasisIndices = self.AutoVivification()
+ if deg not in self.allBasisIndices.keys():
+ # Generate the polynomial basis indices
+ for qidx, q in enumerate(self.q):
+ self.allBasisIndices[str(deg)][str(q)] = self.PolyBasisIndices(degree=deg, q=q)
# Evaluate the univariate polynomials on ExpDesign
diff --git a/BayesValidRox/tests/AnalyticalFunction/Test_AnalyticalFunction.py b/BayesValidRox/tests/AnalyticalFunction/Test_AnalyticalFunction.py
index 8f7d7965a..ac097c783 100755
--- a/BayesValidRox/tests/AnalyticalFunction/Test_AnalyticalFunction.py
+++ b/BayesValidRox/tests/AnalyticalFunction/Test_AnalyticalFunction.py
@@ -93,7 +93,7 @@ if __name__ == "__main__":
# error (or the highest score=1-LOO)estimator is chosen as the final
# metamodel.
MetaModelOpts.MinPceDegree = 1 #12
- MetaModelOpts.MaxPceDegree = 6 #12
+ MetaModelOpts.MaxPceDegree = 10 #12
# q-quasi-norm 0<q<1 (default=1)
MetaModelOpts.q = 1.0 if ndim<5 else 0.65
@@ -105,7 +105,7 @@ if __name__ == "__main__":
# 5)FastARD: Fast Bayesian ARD Regression
# 6)SGDR: Stochastic gradient descent learning
# MetaModelOpts.metaModel = 'PCEKriging'
- MetaModelOpts.RegMethod = 'ARD'
+ MetaModelOpts.RegMethod = 'FastARD'
MetaModelOpts.DimRedMethod = 'PCA'
# Print summary of the regression results
@@ -117,8 +117,8 @@ if __name__ == "__main__":
MetaModelOpts.addExpDesign()
# One-shot (normal) or Sequential Adaptive (sequential) Design
- MetaModelOpts.ExpDesign.Method = 'normal'
- NrofInitSamples = 100 #75
+ MetaModelOpts.ExpDesign.Method = 'sequential'
+ NrofInitSamples = 10 #75
MetaModelOpts.ExpDesign.NrSamples = NrofInitSamples
# Sampling methods
@@ -128,7 +128,7 @@ if __name__ == "__main__":
# Sequential experimental design (needed only for sequential ExpDesign)
MetaModelOpts.ExpDesign.NrofNewSample = 1
- MetaModelOpts.ExpDesign.MaxNSamples = 100 #150
+ MetaModelOpts.ExpDesign.MaxNSamples = 50 #150
MetaModelOpts.ExpDesign.ModifiedLOOThreshold = 1e-16
# Defining the measurement error, if it's known a priori
--
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