From d5b568e8e559e80df5d9d1cd56ba166f463e7e89 Mon Sep 17 00:00:00 2001
From: kohlhaasrebecca <rebecca.kohlhaas@outlook.com>
Date: Mon, 6 May 2024 14:42:56 +0200
Subject: [PATCH] Cleanup
---
docs/diagrams/Structure_BayesInf.drawio | 320 +++++++++++++++++-
.../surrogate_models/sequential_design.py | 44 +--
2 files changed, 316 insertions(+), 48 deletions(-)
diff --git a/docs/diagrams/Structure_BayesInf.drawio b/docs/diagrams/Structure_BayesInf.drawio
index d71ef7225..008e3133a 100644
--- a/docs/diagrams/Structure_BayesInf.drawio
+++ b/docs/diagrams/Structure_BayesInf.drawio
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diff --git a/src/bayesvalidrox/surrogate_models/sequential_design.py b/src/bayesvalidrox/surrogate_models/sequential_design.py
index 72779f022..7a95934f9 100644
--- a/src/bayesvalidrox/surrogate_models/sequential_design.py
+++ b/src/bayesvalidrox/surrogate_models/sequential_design.py
@@ -123,16 +123,10 @@ class SequentialDesign:
# Read observations or MCReference
# TODO: recheck the logic in this if statement
- # ToDo: this could perhaps be moved into the Sequential Design
+ # TODO: TotalSigma2 and self.observations not always defined !?
if (len(self.Model.observations) != 0 or self.Model.meas_file is not None) and hasattr(self.MetaModel,
'Discrepancy'):
self.observations = self.Model.read_observation()
- obs_data = self.observations
- else:
- obs_data = []
- # TODO: TotalSigma2 not defined if not in this else???
- # TODO: no self.observations if in here
- TotalSigma2 = {}
@@ -149,7 +143,7 @@ class SequentialDesign:
n_candidates : int, optional
Number of candidate samples. The default is 5.
var : string, optional
- Utility function. The default is None. # TODO: default is set to DKL, not none
+ Utility function. The default is 'DKL'.
Raises
------
@@ -161,11 +155,11 @@ class SequentialDesign:
Xnew : array (n_samples, n_params)
Selected new training point(s).
"""
+ # Do all necessary initializations
self.start_seqdesign()
# Initialization
# TODO: check if all of these variables are necessary, or if some should be class attributes
- Bounds = self.ExpDesign.bound_tuples
n_new_samples = self.ExpDesign.n_new_samples
explore_method = self.ExpDesign.explore_method
exploit_method = self.ExpDesign.exploit_method
@@ -174,7 +168,6 @@ class SequentialDesign:
old_EDX = self.ExpDesign.X
old_EDY = self.ExpDesign.Y.copy()
- ndim = self.ExpDesign.X.shape[1]
OutputNames = self.out_names
# -----------------------------------------
@@ -182,7 +175,6 @@ class SequentialDesign:
# -----------------------------------------
# Utility function exploit_method provided by user
if exploit_method.lower() == 'user':
- # TODO: is the exploit_method meant here?
if not hasattr(self.ExpDesign, 'ExploitFunction') or self.ExpDesign.ExploitFunction is None:
raise AttributeError(
'Function `ExploitFunction` not given to the ExpDesign, thus cannor run user-defined sequential'
@@ -203,7 +195,7 @@ class SequentialDesign:
start_time = time.time()
# Divide the domain to subdomains
- subdomains = subdomain(Bounds, n_new_samples)
+ subdomains = subdomain(self.ExpDesign.bound_tuples, n_new_samples)
# Multiprocessing
if self.parallel:
@@ -364,7 +356,6 @@ class SequentialDesign:
# Xnew[i] = newSamples[np.argmax(maxminScore)]
# else:
# Changed this from allCandiates to full set of candidates
- # TODO: still not changed for e.g. 'Voronoi'
# Xnew = finalCandidates[sorted_idxtotalScore[:n_new_samples]]
elif exploit_method.lower() == 'varoptdesign':
@@ -372,11 +363,6 @@ class SequentialDesign:
UtilMethod = var.lower()
# ------- Calculate Exoploration weight -------
- # Compute exploration weight based on trade off scheme
-# explore_w, exploit_w = self.tradeoff_weights(tradeoff_scheme,
-# old_EDX,
-# old_EDY)
-
# Generate candidate samples from Exploration class
nMeasurement = old_EDY[OutputNames[0]].shape[1]
@@ -471,34 +457,24 @@ class SequentialDesign:
# select the requested number of samples
# Xnew[i] = newSamples[np.argmax(maxminScore)]
- # TODO: For these 2 last methods, we should find better ways
elif exploit_method.lower() == 'alphabetic':
# 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)
- # ------- EXPLOITATION: ALPHABETIC -------
-
scoreExploitation = -self.util_AlphOptDesign(candidates, var)
elif exploit_method == 'Space-filling':
- # TODO: Set exploitation score to 0, so we can do tradeoff oustide of if/else
- # ------- EXPLOITATION: SPACE-FILLING -------
- scoreExploitation = scoreExploration
-
- # ------- Select the best candidate -------
- # find an optimal point subset to add to the initial design by
- # maximization of the utility score and taking care of NaN values
-# temp = totalScore.copy()
-# temp[np.isnan(totalScore)] = -np.inf
-# sorted_idxtotalScore = np.argsort(temp)[::-1]
-
- # select the requested number of samples
-# Xnew = candidates[sorted_idxtotalScore[:n_new_samples]]
+ # Set exploitation score to 0, so we can do tradeoff oustide of if/else
+ scoreExploitation = np.zeros(scoreExploration.shape[0])
else:
raise NameError('The requested design method is not available.')
# ------------- Select the best candidates ------------------
+ # find an optimal point subset to add to the initial design by
+ # maximization of the utility score and taking care of NaN values
+ scoreExploration[np.isnan(scoreExploration)] = -np.inf
+ scoreExploitation[np.isnan(scoreExploitation)] = -np.inf
totalScore = explore_w * scoreExploration
totalScore += exploit_w * scoreExploitation
--
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