diff --git a/Outputs_Bayes_None_Calib/emcee_sampler.h5 b/Outputs_Bayes_None_Calib/emcee_sampler.h5
index fb19ab42bc20ce5c07901fed541bc29760bfc3d9..81f2373f9817399ab2c7fb908ad03de3f94a7d88 100644
Binary files a/Outputs_Bayes_None_Calib/emcee_sampler.h5 and b/Outputs_Bayes_None_Calib/emcee_sampler.h5 differ
diff --git a/src/bayesvalidrox/surrogate_models/sequential_design.py b/src/bayesvalidrox/surrogate_models/sequential_design.py
index 2885331fd04f227963ca78edcd1afc8b1ef4d88d..f4669a7f29f0cefefdd4ae18f2ea5f0eab0b2317 100644
--- a/src/bayesvalidrox/surrogate_models/sequential_design.py
+++ b/src/bayesvalidrox/surrogate_models/sequential_design.py
@@ -371,20 +371,13 @@ class SequentialDesign:
# Create a sample pool for rejection sampling
# ToDo: remove from here, add only to BayesOptDesign option
- # TODO: reduced for testing
MCsize = 15000
X_MC = self.ExpDesign.generate_samples(MCsize, 'random')
- # ToDo: Get samples from the "do_exploration"
- #candidates = self.ExpDesign.generate_samples(
- # n_candidates, 'latin_hypercube')
-
# Split the candidates in groups for multiprocessing
split_cand = np.array_split(
allCandidates, n_cand_groups, axis=0
)
- # print(candidates)
- # print(split_cand)
if self.parallel:
results = Parallel(n_jobs=-1, backend='multiprocessing')(
delayed(self.run_util_func)(
@@ -411,52 +404,12 @@ class SequentialDesign:
U_J_d = np.mean(U_J_d.reshape(-1, n_candidates), axis=1)
# Normalize U_J_d
- # ToDO: Check if this is working for the case where the util_func should be minimized (e.g. IE)
# norm_U_J_D = U_J_d / np.nansum(np.abs(U_J_d)) # Possible solution
norm_scoreExploitation = U_J_d / np.sum(U_J_d)
else:
norm_scoreExploitation = np.zeros((len(scoreExploration)))
- # ------- Calculate Total score -------
- # ToDo: This should be outside of the exploration/exploitation if/else part
- # ------- Trade off between EXPLORATION & EXPLOITATION -------
- # Accumulate the samples
- # ToDo: Stop assuming 2 sets of samples (should only be 1)
- #finalCandidates = np.concatenate((allCandidates, candidates), axis=0) # ToDo: Remove
- finalCandidates = allCandidates#np.unique(finalCandidates, axis=0) # ToDo: Remove
-
- # Calculations take into account both exploration and exploitation
- # samples without duplicates
- #totalScore = np.zeros(finalCandidates.shape[0])
- # self.totalScore = totalScore
-
- # ToDo: Simplify (remove loop) for only one set of samples
- # final_weights = explore_score*explore_weights + exploit_score*exploit_weight
- #for cand_idx in range(finalCandidates.shape[0]):
- # # find candidate indices
- # idx1 = np.where(allCandidates == finalCandidates[cand_idx])[0]
- # idx2 = np.where(candidates == finalCandidates[cand_idx])[0]#
-
- # exploration
- # if idx1.shape[0] > 0:
- # idx1 = idx1[0]
- # totalScore[cand_idx] += explore_w * scoreExploration[idx1]
-
- # # exploitation
- # if idx2.shape[0] > 0:
- # idx2 = idx2[0]
- # totalScore[cand_idx] += exploit_w * norm_U_J_d[idx2]
-
- # Total score
- print('Shapes of components of the weights')
- print(f'Exploration: {explore_w} * {scoreExploration.shape}')
- print(f'Exploitation: {exploit_w} * {norm_scoreExploitation.shape}')
- # TODO: for the combination none, voronoi, BOD+MI the exploration scores contain 10*more values than the exploitation scores
- # This seems to be an issue resulting from the voronoi sampling, not BODMI
-
- totalScore = exploit_w * norm_scoreExploitation + explore_w * scoreExploration
-
# temp: Plot
# dim = self.ExpDesign.X.shape[1]
# if dim == 2:
@@ -478,8 +431,6 @@ class SequentialDesign:
# Generate candidate samples from Exploration class
nMeasurement = old_EDY[OutputNames[0]].shape[1]
- # print(UtilMethod)
-
# Find sensitive region
if UtilMethod.lower() == 'loocv':
# TODO: why is this not inside the VarOptDesign function?
@@ -496,8 +447,6 @@ class SequentialDesign:
elif UtilMethod in ['EIGF', 'ALM']:
# ToDo: Check the methods it actually can receive (ALC is missing from conditional list and code)
# ----- All other in ['EIGF', 'ALM'] -----
- # Initilize the ExploitScore array
- # ExploitScore = np.zeros((len(old_EDX), len(OutputNames)))
# Split the candidates in groups for multiprocessing
if explore_method.lower() != 'voronoi':
@@ -538,69 +487,40 @@ class SequentialDesign:
else:
raise NameError('The requested utility function is not '
'available.')
-
- # print("ExploitScore:\n", ExploitScore)
-
- # find an optimal point subset to add to the initial design by
- # maximization of the utility score and taking care of NaN values
- # Total score
- # Normalize U_J_d
- # ToDo: MOve this out of the exploitation if/else part (same as with Bayesian approaches)
- norm_scoreExploitation = ExploitScore / np.sum(ExploitScore)
- totalScore = exploit_w * norm_scoreExploitation + explore_w * scoreExploration
-
+
+ # Total score - Normalize U_J_d
+ norm_scoreExploitation = ExploitScore / np.sum(ExploitScore)
opt_type = 'maximization'
- #temp = totalScore.copy()
- #sorted_idxtotalScore = np.argsort(temp, axis=0)[::-1]
- #bestIdx = sorted_idxtotalScore[:n_new_samples]
-
- #Xnew = np.zeros((n_new_samples, ndim))
- #if explore_method.lower() != 'voronoi':
- # Xnew = allCandidates[bestIdx]
- #else:
- # for i, idx in enumerate(bestIdx.flatten()):
- # X_can = explore.closest_points[idx]
- # plotter(self.ExpDesign.X, X_can, explore_method,
- # scoreExploration=None)
-
- # Calculate the maxmin score for the region of interest
- # newSamples, maxminScore = explore.get_mc_samples(X_can)
-
- # 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)
- # TODO: Move the final accumulation of scores outside of this function to match the other methods
+ # TODO: Rethink the final accumulation of scores for this function
+ # Should this be reworked to maximization as well?
# ------- EXPLOITATION: ALPHABETIC -------
- scoreExploitation = self.util_AlphOptDesign(allCandidates, var)
- totalScore = exploit_w * scoreExploitation + explore_w * scoreExploration
+ norm_scoreExploitation = self.util_AlphOptDesign(allCandidates, var)
opt_type = 'minimization'
elif exploit_method.lower() == 'space-filling':
- # ToDo: Set exploitation score to 0, so we can do tradeoff oustide of if/else
# ------- EXPLOITATION: SPACE-FILLING -------
- scoreExploitation = scoreExploration
- totalScore = exploit_w * scoreExploitation + explore_w * scoreExploration
+ norm_scoreExploitation = scoreExploration
+ exploit_w = 0
opt_type = 'maximization'
-
- # ------- 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 = allCandidates[sorted_idxtotalScore[:n_new_samples]]
-
+
else:
raise NameError('The requested design method is not available.')
+ # Accumulate all candidates and scores
+ # TODO: recheck if this line holds true for all combinations of methods
+ # TODO: for the combination none, voronoi, BOD+MI the exploration scores contain 10*more values than the exploitation scores
+ # This seems to be an issue resulting from the voronoi sampling, not BODMI
+ #print('Shapes of components of the weights')
+ #print(f'Exploration: {explore_w} * {scoreExploration.shape}')
+ #print(f'Exploitation: {exploit_w} * {norm_scoreExploitation.shape}')
+ finalCandidates = allCandidates
+ totalScore = exploit_w * norm_scoreExploitation + explore_w * scoreExploration
+
+ # Choose the new training samples
# If the total weights should be maximized
if opt_type == 'maximization':
@@ -630,12 +550,12 @@ class SequentialDesign:
# If the total weights should be maximized
elif opt_type == 'minimization':
- # find an optimal point subset to add to the initial design
- # by minimization of the Phi
- sorted_idxtotalScore = np.argsort(totalScore)
+ # find an optimal point subset to add to the initial design
+ # by minimization of the Phi
+ sorted_idxtotalScore = np.argsort(totalScore)
- # select the requested number of samples
- Xnew = finalCandidates[sorted_idxtotalScore[:n_new_samples]]
+ # select the requested number of samples
+ Xnew = finalCandidates[sorted_idxtotalScore[:n_new_samples]]
print("\n")