basic workflow: change default for save best only

Author: vpratz

bayesflow/workflows/basic_workflow.py

@@ -32,7 +32,7 @@ def __init__(
         checkpoint_filepath: str = None,
         checkpoint_name: str = "model",
         save_weights_only: bool = False,
-        save_best_only: bool = True,
+        save_best_only: bool = False,
         inference_variables: Sequence[str] | str = "theta",
         inference_conditions: Sequence[str] | str = "x",
         summary_variables: Sequence[str] | str = None,
@@ -67,8 +67,10 @@ def __init__(
         save_weights_only : bool, optional
             If True, only the model weights will be saved during checkpointing (default is False).
         save_best_only: bool, optional
-            If only the latest best model according to the quantity monitored (loss or validation) at the end of
-            each epoch will be saved. Consider setting to False when using FlowMatching (default is True)
+            If only the best model according to the quantity monitored (loss or validation) at the end of
+            each epoch will be saved instead of the last model (default is False). Use with caution,
+            as some losses (e.g. flow matching) do not reliably reflect model performance, and outliers in the
+            validation data can cause unwanted effects.
         inference_variables : Sequence[str] or str, optional
             Variables for inference as a sequence of strings or a single string (default is "theta").
             Important for automating diagnostics!
@@ -114,6 +116,25 @@ def __init__(
         self.checkpoint_name = checkpoint_name
         self.save_weights_only = save_weights_only
         self.save_best_only = save_best_only
+        if self.checkpoint_filepath is not None:
+            if self.save_weights_only:
+                file_ext = self.checkpoint_name + ".weights.h5"
+            else:
+                file_ext = self.checkpoint_name + ".keras"
+            checkpoint_full_filepath = os.path.join(self.checkpoint_filepath, file_ext)
+            if os.path.exists(checkpoint_filepath):
+                msg = (
+                    f"Checkpoint file exists: '{checkpoint_full_filepath}'.\n"
+                    "Existing checkpoints can _not_ be restored/loaded using this workflow. "
+                    "Upon refitting, the checkpoints will be overwritten."
+                )
+                if not self.save_weights_only:
+                    msg += (
+                        " To load the stored approximator from the checkpoint, "
+                        "use approximator = keras.saving.load_model(...)"
+                    )
+
+                logging.warning(msg)
         self.history = None
 
     @staticmethod