Add v2.Problem.has_{map,ml}_objective

To check for the type of objective function encoded in the PEtab problem.

Author: dweindl

petab/v2/core.py

@@ -1820,9 +1820,44 @@ def x_fixed_indices(self) -> list[int]:
         """Parameter table non-estimated parameter indices."""
         return [i for i, p in enumerate(self.parameters) if not p.estimate]
 
+    @property
+    def has_map_objective(self) -> bool:
+        """Whether this problem encodes a maximum a posteriori (MAP) objective.
+
+        A PEtab problem is considered to have a MAP objective if there is a
+        prior distribution specified for at least one estimated parameter.
+
+        :returns: ``True`` if MAP objective, ``False`` otherwise.
+        """
+        return any(
+            p.prior_distribution is not None
+            for p in self.parameters
+            if p.estimate
+        )
+
+    @property
+    def has_ml_objective(self) -> bool:
+        """Whether this problem encodes a maximum likelihood (ML) objective.
+
+        A PEtab problem is considered to have an ML objective if there are no
+        prior distributions specified for any estimated parameters.
+
+        :returns: ``True`` if ML objective, ``False`` otherwise.
+        """
+        return all(
+            p.prior_distribution is None for p in self.parameters if p.estimate
+        )
+
     def get_priors(self) -> dict[str, Distribution]:
         """Get prior distributions.
 
+        Note that this will default to uniform distributions over the
+        parameter bounds for parameters without an explicit prior.
+
+        For checking whether this :class:`Problem` encodes a MAP or ML
+        objective, use :attr:`Problem.has_map_objective` or
+        :attr:`Problem.has_ml_objective`.
+
         :returns: The prior distributions for the estimated parameters.
         """
         return {p.id: p.prior_dist for p in self.parameters if p.estimate}

tests/v2/test_core.py

@@ -866,3 +866,22 @@ def test_mapping_validation():
 
     # identity mapping is valid
     Mapping(petab_id="valid_id", model_id="valid_id", name="some name")
+
+
+def test_objective_type():
+    """Test that MAP and ML problems are recognized correctly."""
+    problem = Problem()
+    problem += Parameter(id="par1", lb=0, ub=100, estimate=True)
+    assert problem.has_ml_objective is True
+    assert problem.has_map_objective is False
+
+    problem += Parameter(
+        id="par2",
+        lb=0,
+        ub=100,
+        estimate=True,
+        prior_distribution="normal",
+        prior_parameters=[50, 10],
+    )
+    assert problem.has_map_objective is True
+    assert problem.has_ml_objective is False