Select: support user-provided calibration results (#1338)

* let users supply calibration results * handle calibrated model via petab select * custom petab select branch * functionality moved to petab-select * update method for next petab_select version * update for petab select * update for next petab-select version --------- Co-authored-by: Doresic <[email protected]>
ICB-DCM · Nov 11, 2024 · 9a69f3c · 9a69f3c
1 parent d5eedc2
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diff --git a/pypesto/select/method.py b/pypesto/select/method.py
@@ -8,6 +8,10 @@
 import numpy as np
 import petab_select
 from petab_select import (
+    CANDIDATE_SPACE,
+    MODELS,
+    PREDECESSOR_MODEL,
+    UNCALIBRATED_MODELS,
     VIRTUAL_INITIAL_MODEL,
     CandidateSpace,
     Criterion,
@@ -213,6 +217,11 @@ class MethodCaller:
         Specify the predecessor (initial) model for the model selection
         algorithm. If ``None``, then the algorithm will generate an initial
         predecessor model if required.
+    user_calibrated_models:
+        Supply calibration results for models yourself, as a list of models.
+        If a model with the same hash is encountered in the current model
+        selection run, and the user-supplied calibrated model has the
+        `criterion` value set, the model will not be calibrated again.
     select_first_improvement:
         If ``True``, model selection will terminate as soon as a better model
         is found. If `False`, all candidate models will be tested.
@@ -245,6 +254,7 @@ def __init__(
         # TODO deprecated
         model_to_pypesto_problem_method: Callable[[Any], Problem] = None,
         model_problem_options: dict = None,
+        user_calibrated_models: list[Model] = None,
     ):
         """Arguments are used in every `__call__`, unless overridden."""
         self.petab_select_problem = petab_select_problem
@@ -256,6 +266,12 @@ def __init__(
         self.select_first_improvement = select_first_improvement
         self.startpoint_latest_mle = startpoint_latest_mle
 
+        self.user_calibrated_models = {}
+        if user_calibrated_models is not None:
+            self.user_calibrated_models = {
+                model.get_hash(): model for model in user_calibrated_models
+            }
+
         self.logger = MethodLogger()
 
         # TODO deprecated
@@ -335,10 +351,7 @@ def __init__(
         # May have changed from `None` to `petab_select.VIRTUAL_INITIAL_MODEL`
         self.predecessor_model = self.candidate_space.get_predecessor_model()
 
-    def __call__(
-        self,
-        newly_calibrated_models: Optional[dict[str, Model]] = None,
-    ) -> tuple[list[Model], dict[str, Model]]:
+    def __call__(self) -> tuple[list[Model], dict[str, Model]]:
         """Run a single iteration of the model selection method.
 
         A single iteration here refers to calibration of all candidate models.
@@ -347,14 +360,6 @@ def __call__(
         of all models that have both: the same 3 estimated parameters; and 1
         additional estimated parameter.
 
-        The input `newly_calibrated_models` is from the previous iteration. The
-        output `newly_calibrated_models` is from the current iteration.
-
-        Parameters
-        ----------
-        newly_calibrated_models:
-            The newly calibrated models from the previous iteration.
-
         Returns
         -------
         A 2-tuple, with the following values:
@@ -366,39 +371,56 @@ def __call__(
         # All calibrated models in this iteration (see second return value).
         self.logger.new_selection()
 
-        candidate_space = petab_select.ui.candidates(
+        iteration = petab_select.ui.start_iteration(
             problem=self.petab_select_problem,
             candidate_space=self.candidate_space,
             limit=self.limit,
-            calibrated_models=self.calibrated_models,
-            newly_calibrated_models=newly_calibrated_models,
-            excluded_model_hashes=self.calibrated_models.keys(),
             criterion=self.criterion,
+            user_calibrated_models=self.user_calibrated_models,
         )
-        predecessor_model = self.candidate_space.predecessor_model
 
-        if not candidate_space.models:
+        if not iteration[UNCALIBRATED_MODELS]:
             raise StopIteration("No valid models found.")
 
         # TODO parallelize calibration (maybe not sensible if
         #      `self.select_first_improvement`)
-        newly_calibrated_models = {}
-        for candidate_model in candidate_space.models:
-            # autoruns calibration
-            self.new_model_problem(model=candidate_model)
-            newly_calibrated_models[
-                candidate_model.get_hash()
-            ] = candidate_model
+        calibrated_models = {}
+        for model in iteration[UNCALIBRATED_MODELS]:
+            if (
+                model.get_criterion(
+                    criterion=self.criterion,
+                    compute=True,
+                    raise_on_failure=False,
+                )
+                is not None
+            ):
+                self.logger.log(
+                    message=(
+                        "Unexpected calibration result already available for "
+                        f"model: `{model.get_hash()}`. Skipping "
+                        "calibration."
+                    ),
+                    level="warning",
+                )
+            else:
+                self.new_model_problem(model=model)
+
+            calibrated_models[model.get_hash()] = model
             method_signal = self.handle_calibrated_model(
-                model=candidate_model,
-                predecessor_model=predecessor_model,
+                model=model,
+                predecessor_model=iteration[PREDECESSOR_MODEL],
             )
             if method_signal.proceed == MethodSignalProceed.STOP:
                 break
 
-        self.calibrated_models.update(newly_calibrated_models)
+        iteration_results = petab_select.ui.end_iteration(
+            candidate_space=iteration[CANDIDATE_SPACE],
+            calibrated_models=calibrated_models,
+        )
+
+        self.calibrated_models.update(iteration_results[MODELS])
 
-        return predecessor_model, newly_calibrated_models
+        return iteration[PREDECESSOR_MODEL], iteration_results[MODELS]
 
     def handle_calibrated_model(
         self,

diff --git a/pypesto/select/problem.py b/pypesto/select/problem.py
@@ -156,16 +156,13 @@ def select(
         self.handle_select_kwargs(kwargs)
         # TODO handle bidirectional
         method_caller = self.create_method_caller(**kwargs)
-        previous_best_model, newly_calibrated_models = method_caller(
-            # TODO add predecessor model to state
-            newly_calibrated_models=self.newly_calibrated_models,
-        )
+        previous_best_model, newly_calibrated_models = method_caller()
 
         self.update_with_newly_calibrated_models(
             newly_calibrated_models=newly_calibrated_models,
         )
 
-        best_model = petab_select.ui.best(
+        best_model = petab_select.ui.get_best(
             problem=self.petab_select_problem,
             models=self.newly_calibrated_models.values(),
             criterion=method_caller.criterion,
@@ -198,9 +195,7 @@ def select_to_completion(
 
         while True:
             try:
-                previous_best_model, newly_calibrated_models = method_caller(
-                    newly_calibrated_models=self.newly_calibrated_models,
-                )
+                previous_best_model, newly_calibrated_models = method_caller()
                 self.update_with_newly_calibrated_models(
                     newly_calibrated_models=newly_calibrated_models,
                 )
@@ -247,33 +242,18 @@ def multistart_select(
         """
         self.handle_select_kwargs(kwargs)
         model_lists = []
-        newly_calibrated_models_list = [
-            self.newly_calibrated_models for _ in predecessor_models
-        ]
 
-        method_caller = self.create_method_caller(**kwargs)
-        for start_index, predecessor_model in enumerate(predecessor_models):
-            method_caller.candidate_space.previous_predecessor_model = (
-                predecessor_model
-            )
-            (
-                best_model,
-                newly_calibrated_models_list[start_index],
-            ) = method_caller(
-                newly_calibrated_models=newly_calibrated_models_list[
-                    start_index
-                ],
-            )
-            self.calibrated_models.update(
-                newly_calibrated_models_list[start_index]
+        for predecessor_model in predecessor_models:
+            method_caller = self.create_method_caller(
+                **(kwargs | {"predecessor_model": predecessor_model})
             )
+            (best_model, models) = method_caller()
+            self.calibrated_models |= models
 
-            model_lists.append(
-                newly_calibrated_models_list[start_index].values()
-            )
+            model_lists.append(list(models.values()))
             method_caller.candidate_space.reset()
 
-        best_model = petab_select.ui.best(
+        best_model = petab_select.ui.get_best(
             problem=method_caller.petab_select_problem,
             models=[model for models in model_lists for model in models],
             criterion=method_caller.criterion,

diff --git a/setup.cfg b/setup.cfg
@@ -163,10 +163,12 @@ example =
     ipywidgets >= 8.1.5
     benchmark_models_petab @ git+https://github.com/Benchmarking-Initiative/Benchmark-Models-PEtab.git@master#subdirectory=src/python
 select =
-    # Remove when vis is moved to PEtab Select version
+    # Remove when vis is moved to PEtab Select
     networkx >= 2.5.1
     # End remove
-    petab-select >= 0.1.12
+    #petab-select >= 0.1.12
+    # FIXME before merge
+    petab-select @ git+https://github.com/PEtab-dev/petab_select.git@develop
 test =
     pytest >= 5.4.3
     pytest-cov >= 2.10.0

diff --git a/test/select/test_select.py b/test/select/test_select.py
@@ -279,6 +279,8 @@ def test_problem_multistart_select(pypesto_select_problem, initial_models):
         "M1_3": -4.705,
         # 'M1_7': -4.056,  # skipped -- reproducibility requires many starts
     }
+    # As M1_7 criterion comparison is skipped, at least ensure it is present
+    assert {m.model_subspace_id for m in best_models} == {"M1_3", "M1_7"}
     test_best_models_criterion_values = {
         model.model_subspace_id: model.get_criterion(Criterion.AIC)
         for model in best_models