From e8a2601725dd3d0242eb2b7f2859ce4eecea923f Mon Sep 17 00:00:00 2001
From: kohlhaasrebecca <rebecca.kohlhaas@outlook.com>
Date: Wed, 10 Jul 2024 15:05:57 +0200
Subject: [PATCH] Small fixes
---
src/bayesvalidrox/surrogate_models/engine.py | 3 ++-
src/bayesvalidrox/surrogate_models/sequential_design.py | 8 +++++---
2 files changed, 7 insertions(+), 4 deletions(-)
diff --git a/src/bayesvalidrox/surrogate_models/engine.py b/src/bayesvalidrox/surrogate_models/engine.py
index a6531b978..d0b281f76 100644
--- a/src/bayesvalidrox/surrogate_models/engine.py
+++ b/src/bayesvalidrox/surrogate_models/engine.py
@@ -362,7 +362,8 @@ class Engine:
pce = True
else:
pce = False
- mc_ref = True if bool(self.Model.mc_reference) else False
+ #mc_ref = True if bool(self.Model.mc_reference) else False
+ mc_ref = True if (self.Model.mc_reference is not None) else False
if mc_ref:
self.Model.read_observation('mc_ref')
diff --git a/src/bayesvalidrox/surrogate_models/sequential_design.py b/src/bayesvalidrox/surrogate_models/sequential_design.py
index e020f689f..29b213030 100644
--- a/src/bayesvalidrox/surrogate_models/sequential_design.py
+++ b/src/bayesvalidrox/surrogate_models/sequential_design.py
@@ -23,6 +23,7 @@ from tqdm import tqdm
from bayesvalidrox.bayes_inference.bayes_inference import BayesInference
from bayesvalidrox.bayes_inference.discrepancy import Discrepancy
from .exploration import Exploration
+from .surrogate_models import create_psi
def logpdf(x, mean, cov):
"""
@@ -427,7 +428,7 @@ class SequentialDesign:
X_can = explore.closestPoints[idx]
# Calculate the maxmin score for the region of interest
- newSamples, maxminScore = explore.get_mc_samples(X_can)
+ newSamples, maxminScore = explore.get_mc_samples(X_can) # TODO: Understand this line!
# select the requested number of samples
Xnew[i] = newSamples[np.argmax(maxminScore)]
@@ -550,6 +551,7 @@ class SequentialDesign:
# ToDo: Check function to see what it does for scores/how it chooses points, so it gives as an output the
# scores. See how it works with exploration_scores.
# Todo: Check if it is a minimization or maximization. (We think it is minimization)
+ # TODO: Move the final accumulation of scores outside of this function to match the other methods
# ------- EXPLOITATION: ALPHABETIC -------
Xnew = self.util_AlphOptDesign(allCandidates, var)
@@ -1249,12 +1251,12 @@ class SequentialDesign:
# ------ Old Psi ------------
univ_p_val = self.MetaModel.univ_basis_vals(oldExpDesignX)
- Psi = self.MetaModel.create_psi(BasisIndices, univ_p_val)
+ Psi = create_psi(BasisIndices, univ_p_val)
# ------ New candidates (Psi_c) ------------
# Assemble Psi_c
univ_p_val_c = self.MetaModel.univ_basis_vals(candidates)
- Psi_c = self.MetaModel.create_psi(BasisIndices, univ_p_val_c)
+ Psi_c = create_psi(BasisIndices, univ_p_val_c)
for idx in range(NCandidate):
--
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