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diff --git a/CHANGELOG.md b/CHANGELOG.md
index d1669ae56def290bc636a0c6b39c71e9b1be0d40..daded68320a07869efaa6c5b2d643b97eaa110e7 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -17,6 +17,7 @@ Examples
* Moved functions for sequential training from `Engine` to `SeqDesign`
* Moved `hellinger_distance`, `logpdf`, `subdomain` into `surrogate_models/seq_design`
* Early stop in `BayesInf` for `BayesModelComp`
+* Allow singular matrices in exploitation with `BayesActDesign`
Bug fixes
* Import of `ExpDesign` allowed
diff --git a/src/bayesvalidrox/bayes_inference/bayes_model_comparison.py b/src/bayesvalidrox/bayes_inference/bayes_model_comparison.py
index dc66e1cf5322928c6f7cc16392b96f302bd6d04a..6374cdc01db0535bd255c88e8b6b8065eaba9112 100644
--- a/src/bayesvalidrox/bayes_inference/bayes_model_comparison.py
+++ b/src/bayesvalidrox/bayes_inference/bayes_model_comparison.py
@@ -484,7 +484,6 @@ class BayesModelComparison:
for name in names:
fig, ax = plt.subplots()
for i, model in enumerate(model_names[1:]):
- #print(model, i)
plt.plot(list(range(1, self.n_meas+1)),
model_weights_dict[name][i],
color=Color[i], marker='o',
diff --git a/src/bayesvalidrox/post_processing/post_processing.py b/src/bayesvalidrox/post_processing/post_processing.py
index 7953716513437060e767962e4e64318ae6cb8415..020695c7668a66f8073c76ea8a5fc43cf7b5f189 100644
--- a/src/bayesvalidrox/post_processing/post_processing.py
+++ b/src/bayesvalidrox/post_processing/post_processing.py
@@ -321,7 +321,6 @@ class PostProcessing:
if len(name_util) == 0:
continue
- print(seq_dict)
# Box plot when Replications have been detected.
if any(int(name.split("rep_", 1)[1]) > 1 for name in name_util):
@@ -494,8 +493,8 @@ class PostProcessing:
seq_values = np.nan_to_num(seq_values)
# Plot the error evolution for each output
- print(x_idx.shape)
- print(seq_values.mean(axis=1).shape)
+ #print(x_idx.shape)
+ #print(seq_values.mean(axis=1).shape)
plt.semilogy(x_idx, seq_values.mean(axis=1),
marker=markers[idx], ls='--', lw=2,
color=colors[idx],
diff --git a/src/bayesvalidrox/surrogate_models/engine.py b/src/bayesvalidrox/surrogate_models/engine.py
index ecc9e9485e53b2174ae5c9cfc8c7ebf1676aa5d0..330ac293884516f2eaa107c11d4e661bccfc6ba3 100644
--- a/src/bayesvalidrox/surrogate_models/engine.py
+++ b/src/bayesvalidrox/surrogate_models/engine.py
@@ -97,7 +97,6 @@ class Engine:
# Read ExpDesign (training and targets) from the provided hdf5
if ExpDesign.hdf5_file is not None:
- # TODO: need to run 'generate_ED' as well after this or not?
ExpDesign.read_from_file(self.out_names)
else:
# Check if an old hdf5 file exists: if yes, rename it
@@ -599,7 +598,6 @@ class Engine:
self.seqRMSEMean[strKey] = seqRMSEMean
self.seqRMSEStd[strKey] = seqRMSEStd
- # return self.MetaModel
# -------------------------------------------------------------------------
def _normpdf(self, y_hat_pce, std_pce, obs_data, total_sigma2s,
diff --git a/src/bayesvalidrox/surrogate_models/sequential_design.py b/src/bayesvalidrox/surrogate_models/sequential_design.py
index d4004b8b8c60ce4ffa957f4855d462566630f8f5..878ac740214de987f5fea9e1389e01305d2d044b 100644
--- a/src/bayesvalidrox/surrogate_models/sequential_design.py
+++ b/src/bayesvalidrox/surrogate_models/sequential_design.py
@@ -533,8 +533,6 @@ class SequentialDesign:
temp = totalScore.copy()
temp[np.isnan(totalScore)] = -np.inf # Since we are maximizing
sorted_idxtotalScore = np.argsort(temp)[::-1]
- print(sorted_idxtotalScore)
- print(n_new_samples)
bestIdx = sorted_idxtotalScore[:n_new_samples]
if type(bestIdx) is int:
bestIdx = [bestIdx]
@@ -543,7 +541,6 @@ class SequentialDesign:
if explore_method.lower() == 'voronoi':
Xnew = np.zeros((n_new_samples, ndim))
for i, idx in enumerate(bestIdx):
- print(explore.closestPoints)
X_can = explore.closestPoints[idx]
# Calculate the maxmin score for the region of interest
@@ -708,8 +705,6 @@ class SequentialDesign:
y_hat = {key: items[idx] for key, items in y_can.items()}
std = {key: items[idx] for key, items in std_can.items()}
- # print(y_hat)
- # print(std)
U_J_d[idx] = self.util_BayesianActiveDesign(
y_hat, std, sigma2Dict, var)
@@ -830,8 +825,7 @@ class SequentialDesign:
logPriorLikelihoods = np.zeros(mc_size)
for key in list(y_hat):
cov = np.diag(std[key] ** 2)
- print(key, y_hat[key], std[key])
- # TODO: added the allow_singular = True here
+ # Allow for singular matrices
rv = stats.multivariate_normal(mean=y_hat[key], cov=cov, allow_singular=True)
Y_MC[key] = rv.rvs(size=mc_size)
logPriorLikelihoods += rv.logpdf(Y_MC[key])
@@ -1284,7 +1278,6 @@ class SequentialDesign:
Phi[idx] = np.linalg.cond(M)
else:
- # print(var.lower())
raise Exception('The optimality criterion you requested has '
'not been implemented yet!')
@@ -1325,7 +1318,6 @@ class SequentialDesign:
for idx, out in enumerate(self.out_names):
# (Meta)Model Output
- # print(y_hat_pce[out])
nsamples, nout = y_hat_pce[out].shape
# Prepare data and remove NaN
diff --git a/src/bayesvalidrox/surrogate_models/surrogate_models.py b/src/bayesvalidrox/surrogate_models/surrogate_models.py
index b71040b405769617ab33fbb68d97d652c2e1d2d2..e56cfe40f1592b6d2389b9faf2cf160af0da0fad 100644
--- a/src/bayesvalidrox/surrogate_models/surrogate_models.py
+++ b/src/bayesvalidrox/surrogate_models/surrogate_models.py
@@ -1081,6 +1081,7 @@ class MetaModel:
text = f"$\\alpha={clf_poly.alpha_:.1f}$\n$"
f"\\L={clf_poly.scores_[-1]:.1f}$"
+ # TODO: save this figure and close it, do not show
plt.text(0.75, 0.5, text, fontsize=18, transform=ax.transAxes)
plt.show()
print('=' * 80)