fix bugs related to n_bootstrap.

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))