diff --git a/dicee/evaluator.py b/dicee/evaluator.py
index b005f20a4..f43621992 100644
--- a/dicee/evaluator.py
+++ b/dicee/evaluator.py
@@ -312,6 +312,16 @@ def evaluate_lp_k_vs_all(self, model, triple_idx, info=None, form_of_labelling=N
                     predictions[j, id_e_target] = target_value
                 # (5) Sort predictions.
                 sort_values, sort_idxs = torch.sort(predictions, dim=1, descending=True)
+
+                #probabilities = torch.sigmoid(sort_values)
+                #non_zero_probs = probabilities[probabilities != 0.0]
+                #one_minus_non_zero_probs = 1 - non_zero_probs
+
+                non_zero_probs = sort_values[sort_values != 0.0]
+                probabilities = torch.sigmoid(non_zero_probs)
+                one_minus_non_zero_probs = 1 - probabilities
+
+                ECE = one_minus_non_zero_probs.sum().item()
                 # (6) Compute the filtered ranks.
                 for j in range(data_batch.shape[0]):
                     # index between 0 and \inf
@@ -327,7 +337,7 @@ def evaluate_lp_k_vs_all(self, model, triple_idx, info=None, form_of_labelling=N
         hit_10 = sum(hits[10]) / num_triples
         mean_reciprocal_rank = np.mean(1. / np.array(ranks))
 
-        results = {'H@1': hit_1, 'H@3': hit_3, 'H@10': hit_10, 'MRR': mean_reciprocal_rank}
+        results = {'H@1': hit_1, 'H@3': hit_3, 'H@10': hit_10, 'MRR': mean_reciprocal_rank, 'ECE': ECE }
         if info and self.during_training is False:
             print(info)
             print(results)
diff --git a/dicee/scripts/bayesian_optimization.py b/dicee/scripts/bayesian_optimization.py
index 8b8c9f9f7..64a84e1bc 100644
--- a/dicee/scripts/bayesian_optimization.py
+++ b/dicee/scripts/bayesian_optimization.py
@@ -18,10 +18,10 @@ def objective(trial, model, dataset, loss):
     embedding_dim = 32 #trial.suggest_categorical("embedding_dim", [32, 64])
     optimizer = "Adam" #trial.suggest_categorical("optimizer", ["Adam", "Adopt"])
     batch_size = 1024 #trial.suggest_categorical("batch_size", [512, 1024])
-    learning_rate = trial.suggest_float("learning_rate", 0.01, 0.1)
+    learning_rate = 0.1 #trial.suggest_float("learning_rate", 0.01, 0.1)
 
-    label_relaxation_alpha = trial.suggest_float("label_relaxation_alpha", 0.01, 0.1,) if loss == "LRLoss" else 0.0
-    label_smoothing_rate = trial.suggest_float("label_smoothing_rate", 0.01, 0.1)  if loss == "LS" else 0.0
+    label_relaxation_alpha = trial.suggest_float("label_relaxation_alpha", 0.01, 0.2,) if loss == "LRLoss" else 0.0
+    label_smoothing_rate = trial.suggest_float("label_smoothing_rate", 0.01, 0.2)  if loss == "LS" else 0.0
 
     parser.add_argument('--loss_fn', type=str, default=loss)
     parser.add_argument("--label_smoothing_rate", type=float, default=label_smoothing_rate)
@@ -71,14 +71,14 @@ def objective(trial, model, dataset, loss):
 
 # set according to your environment TODO: make it as a parameter
 main_math = "../../../KGs/Datasets_Perturbed/"
-report_folder_name = "./bo_outputs/"
+report_folder_name = "./bo_outputs/1024/"
 report_file_name = "bayesian_optimization_report.txt"
 
 datasets = ["UMLS", "KINSHIP", "NELL-995-h100", "WN18RR", "FB15k-237"]
-models = ["Keci", "Pykeen_MuRE", "QMult", "Pykeen_DistMult", "Pykeen_ComplEx", "Pykeen_RotatE", "Pykeen_BoxE"]
-losses = ["LRLoss", "LS"]
+models = ["Keci", "Pykeen_MuRE", "QMult", "Pykeen_DistMult", "Pykeen_ComplEx"] #, "Pykeen_RotatE", "Pykeen_BoxE"
+losses = ["LRLoss", "LS", "BCELoss"]
 
-number_of_runs = 30
+number_of_runs = 50
 
 for dataset in datasets:
     for model in models:
@@ -94,6 +94,7 @@ def objective(trial, model, dataset, loss):
 
             os.makedirs(os.path.dirname(report_folder_name), exist_ok=True)
 
+
             fig1 = plot_parallel_coordinate(study)
             fig1.write_image(report_folder_name + f"parallel_coordinate-{dataset}-{model}-{loss}"+ ".png")
 
@@ -102,7 +103,8 @@ def objective(trial, model, dataset, loss):
 
             fig4 = plot_optimization_history(study)
             fig4.write_image(report_folder_name + f"plot_optimization_history-{dataset}-{model}-{loss}" + ".png")
-            
+            """
+
             if loss == "LRLoss":
                 fig2 = plot_contour(study, params=["label_relaxation_alpha", "learning_rate"])
                 fig2.write_image(report_folder_name + f"contour-{dataset}-{model}-{loss}" + ".png")
@@ -110,10 +112,12 @@ def objective(trial, model, dataset, loss):
             if loss == "LS":
                 fig2 = plot_contour(study, params=["label_smoothing_rate", "learning_rate"])
                 fig2.write_image(report_folder_name + f"contour-{dataset}-{model}-{loss}" + ".png")
-
+            """
 
             with open(report_folder_name + report_file_name, "a") as file:
-                file.write(f"Value: {best_trial.value}, Params: {best_trial.params}, Dataset: {dataset}, Model: {model}, Loss: {loss} \n")
-
-
-0
+                file.write(f"Value: {best_trial.value}, "
+                           f"Params: {best_trial.params}, "
+                           f"Dataset: {dataset}, "
+                           f"Model: {model}, "
+                           f"Loss: {loss} "
+                           f"\n")