not clean, but support automatic calibration of uncalibrated predeces…

…sor models

petab_select/ui.py

@@ -1,3 +1,4 @@
+import copy
 from pathlib import Path
 from typing import Dict, List, Optional, Union
 
@@ -76,10 +77,29 @@ def candidates(
     candidate_space.exclude_hashes(calibrated_models)
 
     # Set the predecessor model to the previous predecessor model.
+    predecessor_model = candidate_space.previous_predecessor_model
+
+    # If the predecessor model has not yet been calibrated, then calibrate it.
+    if (
+        predecessor_model.get_criterion(
+            criterion,
+            raise_on_failure=False,
+        )
+        is None
+    ):
+        candidate_space.models = [copy.deepcopy(predecessor_model)]
+        # Dummy zero likelihood, which the predecessor model will
+        # improve on after it's actually calibrated.
+        predecessor_model.set_criterion(Criterion.LH, 0.0)
+        return candidate_space
+
+    # Exclude the calibrated predecessor model.
+    if not candidate_space.excluded(predecessor_model):
+        candidate_space.exclude(predecessor_model)
+
     # Set the new predecessor_model from the initial model or
     # by calling ui.best to find the best model to jump to if
     # this is not the first step of the search.
-    predecessor_model = candidate_space.previous_predecessor_model
     if newly_calibrated_models:
         predecessor_model = problem.get_best(
             newly_calibrated_models.values(),