merge bugfix from main to release_docs

obsidian/dash/optimize.py

@@ -117,7 +117,7 @@ def fit_statistics(opt_save, config, filename):
             [
              dbc.ListGroupItem(['Model Type: ', f'{optimizer.surrogate_type}']),
              dbc.ListGroupItem(['Data Name: ', filename]),
-             dbc.ListGroupItem(['R', html.Sup('2'), ' Score: ', f'{optimizer.surrogate[0].score: .4g}']), # for SOO only
+             dbc.ListGroupItem(['R', html.Sup('2'), ' Score: ', f'{optimizer.surrogate[0].r2_score: .4g}']), # for SOO only
              dbc.ListGroupItem(['Marginal Log Likelihood: ', f'{optimizer.surrogate[0].loss: .4g}']), # for SOO only
             ], flush=True
         )

obsidian/optimizer/bayesian.py

@@ -244,7 +244,7 @@ def fit(self,
 
             if self.verbose > 0:
                 print(f'{self.surrogate_type[i]} model has been fit \
-                      to data with a train-score of: {self.surrogate[i].score:.3g} for response: {self.y_names[i]}')
+                      to data with a train-score of: {self.surrogate[i].r2_score:.3g} for response: {self.y_names[i]}')
         return
 
     def save_state(self) -> dict:

obsidian/surrogates/botorch.py

@@ -46,7 +46,7 @@ class SurrogateBoTorch(SurrogateModel):
         mll (ExactMarginalLogLikelihood): The marginal log likelihood of the model.
         torch_model (torch.nn.Module): The torch model for the surrogate.
         loss (float): The loss of the model.
-        score (float): The R2 score of the model.
+        r2_score (float): The R2 score of the model.
     """
     def __init__(self,
                  model_type: str = 'GP',