From 7e4aecd095af2e988d99c2614a7a95314a5574fe Mon Sep 17 00:00:00 2001
From: Farid Mohammadi <farid.mohammadi@iws.uni-stuttgart.de>
Date: Fri, 30 Sep 2022 15:50:50 +0200
Subject: [PATCH] fix bugs related to n_bootstrap.
---
.../bayes_inference/bayes_model_comparison.py | 16 +++++++---------
1 file changed, 7 insertions(+), 9 deletions(-)
diff --git a/src/bayesvalidrox/bayes_inference/bayes_model_comparison.py b/src/bayesvalidrox/bayes_inference/bayes_model_comparison.py
index a12e493dd..693afc82d 100644
--- a/src/bayesvalidrox/bayes_inference/bayes_model_comparison.py
+++ b/src/bayesvalidrox/bayes_inference/bayes_model_comparison.py
@@ -38,7 +38,7 @@ class BayesModelComparison:
)
output = {
- 'Bayes factor': bayes_dict_bf,
+ 'Bayes objects BF': bayes_dict_bf,
'Model weights BF': model_weights_dict_bf
}
@@ -48,7 +48,7 @@ class BayesModelComparison:
model_dict, opts_dict, justifiability=True
)
- output['Justifiability analysis'] = bayes_dict_ja
+ output['Bayes objects JA'] = bayes_dict_ja
output['Model weights JA'] = model_weights_dict_ja
return output
@@ -70,12 +70,11 @@ class BayesModelComparison:
# ----- Generate data -----
# Find n_bootstrap
- if not justifiability:
+ if self.perturbed_data is None:
n_bootstarp = self.n_bootstarp
else:
- # find the smallest n_samples
- n_bootstarp = min([len(MetaModel.ExpDesign.X) for MetaModel
- in modelDict.values()])
+ n_bootstarp = self.perturbed_data.shape[0]
+
# Create dataset
justData = self.generate_dataset(
modelDict, justifiability, n_bootstarp=n_bootstarp)
@@ -106,7 +105,7 @@ class BayesModelComparison:
# Compute model weights
BME_Dict = dict()
for modelName, bayesObj in bayesDict.items():
- BME_Dict[modelName] = np.exp(bayesObj.log_BME)
+ BME_Dict[modelName] = np.exp(bayesObj.log_BME, dtype=np.float128)
# BME correction in BayesInference class
model_weights = self.cal_modelWeight(
@@ -169,8 +168,7 @@ class BayesModelComparison:
# Use surrogate runs for data-generating process
for key, metaModel in modelDict.items():
model_data = np.array(
- [runs[key][out][i] for out in out_names]
- ).reshape(y_data.shape)
+ [runs[key][out][i] for out in out_names]).reshape(y_data.shape)
justData = np.vstack((
justData,
np.tril(np.repeat(model_data, model_data.shape[1], axis=0))
--
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