Select: use the new "standardized" petab_select classes

doc/example/model_selection/model_space.tsv

@@ -1,4 +1,4 @@
-model_subspace_id	petab_yaml	k1	k2	k3
+model_subspace_id	model_subspace_petab_yaml	k1	k2	k3
 M1_0	example_modelSelection.yaml	0	0	0
 M1_1	example_modelSelection.yaml	0.2	0.1	estimate
 M1_2	example_modelSelection.yaml	0.2	estimate	0

pypesto/select/__init__.py

@@ -8,6 +8,7 @@
 
 from . import postprocessors
 from .misc import SacessMinimizeMethod, model_to_pypesto_problem
+from .model_problem import ModelProblem
 from .problem import Problem
 
 try:

pypesto/select/method.py

@@ -3,7 +3,7 @@
 import logging
 from dataclasses import dataclass
 from enum import Enum
-from typing import Any, Callable, Optional
+from typing import Any, Callable
 
 import numpy as np
 import petab_select
@@ -17,6 +17,7 @@
     Criterion,
     Method,
     Model,
+    Models,
 )
 
 from ..problem import Problem
@@ -206,8 +207,7 @@ class MethodCaller:
         example, in `ForwardSelector`, test models are compared to the
         previously selected model.
     calibrated_models:
-        The calibrated models of the model selection, as a `dict` where keys
-        are model hashes and values are models.
+        All calibrated models of the model selection.
     limit:
         Limit the number of calibrated models. NB: the number of accepted
         models may (likely) be fewer.
@@ -233,7 +233,7 @@ class MethodCaller:
     def __init__(
         self,
         petab_select_problem: petab_select.Problem,
-        calibrated_models: dict[str, Model],
+        calibrated_models: Models,
         # Arguments/attributes that can simply take the default value here.
         criterion_threshold: float = 0.0,
         limit: int = np.inf,
@@ -266,11 +266,9 @@ def __init__(
         self.select_first_improvement = select_first_improvement
         self.startpoint_latest_mle = startpoint_latest_mle
 
-        self.user_calibrated_models = {}
+        self.user_calibrated_models = Models()
         if user_calibrated_models is not None:
-            self.user_calibrated_models = {
-                model.get_hash(): model for model in user_calibrated_models
-            }
+            self.user_calibrated_models = user_calibrated_models
 
         self.logger = MethodLogger()
 
@@ -351,7 +349,7 @@ 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) -> tuple[list[Model], dict[str, Model]]:
+    def __call__(self) -> tuple[Model, Models]:
         """Run a single iteration of the model selection method.
 
         A single iteration here refers to calibration of all candidate models.
@@ -365,8 +363,7 @@ def __call__(self) -> tuple[list[Model], dict[str, Model]]:
         A 2-tuple, with the following values:
 
            1. the predecessor model for the newly calibrated models; and
-           2. the newly calibrated models, as a `dict` where keys are model
-              hashes and values are models.
+           2. the newly calibrated models.
         """
         # All calibrated models in this iteration (see second return value).
         self.logger.new_selection()
@@ -384,7 +381,7 @@ def __call__(self) -> tuple[list[Model], dict[str, Model]]:
 
         # TODO parallelize calibration (maybe not sensible if
         #      `self.select_first_improvement`)
-        calibrated_models = {}
+        calibrated_models = Models()
         for model in iteration[UNCALIBRATED_MODELS]:
             if (
                 model.get_criterion(
@@ -405,7 +402,7 @@ def __call__(self) -> tuple[list[Model], dict[str, Model]]:
             else:
                 self.new_model_problem(model=model)
 
-            calibrated_models[model.get_hash()] = model
+            calibrated_models.append(model)
             method_signal = self.handle_calibrated_model(
                 model=model,
                 predecessor_model=iteration[PREDECESSOR_MODEL],
@@ -414,18 +411,19 @@ def __call__(self) -> tuple[list[Model], dict[str, Model]]:
                 break
 
         iteration_results = petab_select.ui.end_iteration(
+            problem=self.petab_select_problem,
             candidate_space=iteration[CANDIDATE_SPACE],
             calibrated_models=calibrated_models,
         )
 
-        self.calibrated_models.update(iteration_results[MODELS])
+        self.calibrated_models += iteration_results[MODELS]
 
-        return iteration[PREDECESSOR_MODEL], iteration_results[MODELS]
+        return iteration_results[MODELS]
 
     def handle_calibrated_model(
         self,
         model: Model,
-        predecessor_model: Optional[Model],
+        predecessor_model: Model,
     ) -> MethodSignal:
         """Handle the model selection method, given a new calibrated model.
 
@@ -454,8 +452,7 @@ def handle_calibrated_model(
 
         # Reject the model if it doesn't improve on the predecessor model.
         if (
-            predecessor_model is not None
-            and predecessor_model != VIRTUAL_INITIAL_MODEL
+            predecessor_model.hash != VIRTUAL_INITIAL_MODEL.hash
             and not self.model1_gt_model0(
                 model1=model, model0=predecessor_model
             )
@@ -549,7 +546,9 @@ def new_model_problem(
             predecessor_model = self.calibrated_models[
                 model.predecessor_model_hash
             ]
-            if str(model.petab_yaml) != str(predecessor_model.petab_yaml):
+            if str(model.model_subspace_petab_yaml) != str(
+                predecessor_model.model_subspace_petab_yaml
+            ):
                 raise NotImplementedError(
                     "The PEtab YAML files differ between the model and its "
                     "predecessor model. This may imply different (fixed union "

pypesto/select/model_problem.py

@@ -15,6 +15,9 @@
 TYPE_POSTPROCESSOR = Callable[["ModelProblem"], None]  # noqa: F821
 
 
+__all__ = ["ModelProblem"]
+
+
 class ModelProblem:
     """Handles all required calibration tasks on a model.
 
@@ -149,7 +152,7 @@ def minimize(self) -> Result:
         if isinstance(self.minimize_method, SacessMinimizeMethod):
             return self.minimize_method(
                 self.pypesto_problem,
-                model_hash=self.model.get_hash(),
+                model_hash=self.model.hash,
                 **self.minimize_options,
             )
         return self.minimize_method(

pypesto/select/postprocessors.py

@@ -1,10 +1,11 @@
 """Process a model selection :class:`ModelProblem` after calibration."""
 
+import warnings
 from pathlib import Path
 
 import matplotlib.pyplot as plt
 import numpy as np
-from petab_select.constants import ESTIMATE, TYPE_PATH, Criterion
+from petab_select.constants import TYPE_PATH, Criterion
 
 from .. import store, visualize
 from .model_problem import TYPE_POSTPROCESSOR, ModelProblem
@@ -48,7 +49,7 @@ def waterfall_plot_postprocessor(
     See :meth:`save_postprocessor` for usage hints and argument documentation.
     """
     visualize.waterfall(problem.minimize_result)
-    plot_output_path = Path(output_path) / (problem.model.model_hash + ".png")
+    plot_output_path = Path(output_path) / (str(problem.model.hash) + ".png")
     plt.savefig(str(plot_output_path))
 
 
@@ -85,7 +86,7 @@ def save_postprocessor(
     """
     stem = problem.model.model_id
     if use_model_hash:
-        stem = problem.model.get_hash()
+        stem = str(problem.model.hash)
     store.write_result(
         problem.minimize_result,
         Path(output_path) / (stem + ".hdf5"),
@@ -109,10 +110,15 @@ def model_id_binary_postprocessor(problem: ModelProblem):
     problem:
         A model selection :class:`ModelProblem` that has been optimized.
     """
-    model_id = "M_"
-    for parameter_value in problem.model.parameters.values():
-        model_id += "1" if parameter_value == ESTIMATE else "0"
-    problem.model.model_id = model_id
+    warnings.warn(
+        (
+            "This is obsolete. Model IDs are by default the model hash, which "
+            "is now similar to the binary string."
+        ),
+        DeprecationWarning,
+        stacklevel=2,
+    )
+    problem.model.model_id = str(problem.model.hash)
 
 
 def report_postprocessor(