optional

Author: dweindl

petab/v1/priors.py

@@ -40,9 +40,6 @@
 
 __all__ = ["priors_to_measurements"]
 
-# TODO: does anybody really rely on the old behavior?
-USE_PROPER_TRUNCATION = True
-
 
 class Prior:
     """A PEtab parameter prior.
@@ -61,6 +58,15 @@ class Prior:
         on the `parameter_scale` scale).
     :param bounds: The untransformed bounds of the sample (lower, upper).
     :param transformation: The transformation of the distribution.
+    :param bounds_truncate: Whether the generated prior will be truncated
+        at the bounds.
+        If ``True``, the probability density will be rescaled
+        accordingly and the sample is generated from the truncated
+        distribution.
+        If ``False``, the probability density will not account for the
+        bounds, but any parameter samples outside the bounds will be set to
+        the value of the closest bound. In this case, the PDF might not match
+        the sample.
     """
 
     def __init__(
@@ -69,6 +75,7 @@ def __init__(
         parameters: tuple,
         bounds: tuple = None,
         transformation: str = C.LIN,
+        bounds_truncate: bool = True,
     ):
         if transformation not in C.PARAMETER_SCALES:
             raise ValueError(
@@ -90,8 +97,9 @@ def __init__(
         self._parameters = parameters
         self._bounds = bounds
         self._transformation = transformation
+        self._bounds_truncate = bounds_truncate
 
-        truncation = bounds if USE_PROPER_TRUNCATION else None
+        truncation = bounds if bounds_truncate else None
         if truncation is not None:
             # for uniform, we don't want to implement truncation and just
             #  adapt the distribution parameters
@@ -184,7 +192,7 @@ def _clip_to_bounds(self, x):
 
         :param x: The values to clip. Assumed to be on the parameter scale.
         """
-        if self.bounds is None or USE_PROPER_TRUNCATION:
+        if self.bounds is None or self._bounds_truncate:
             return x
 
         return np.maximum(
@@ -235,12 +243,16 @@ def neglogprior(self, x):
 
     @staticmethod
     def from_par_dict(
-        d, type_=Literal["initialization", "objective"]
+        d,
+        type_=Literal["initialization", "objective"],
+        bounds_truncate: bool = True,
     ) -> Prior:
         """Create a distribution from a row of the parameter table.
 
         :param d: A dictionary representing a row of the parameter table.
         :param type_: The type of the distribution.
+        :param bounds_truncate: Whether the generated prior will be truncated
+            at the bounds.
         :return: A distribution object.
         """
         dist_type = d.get(f"{type_}PriorType", C.PARAMETER_SCALE_UNIFORM)
@@ -268,6 +280,7 @@ def from_par_dict(
             parameters=params,
             bounds=(d[C.LOWER_BOUND], d[C.UPPER_BOUND]),
             transformation=pscale,
+            bounds_truncate=bounds_truncate,
         )
 
 

petab/v1/sampling.py

@@ -28,7 +28,11 @@ def sample_from_prior(
     # unpack info
     p_type, p_params, scaling, bounds = prior
     prior = Prior(
-        p_type, tuple(p_params), bounds=tuple(bounds), transformation=scaling
+        p_type,
+        tuple(p_params),
+        bounds=tuple(bounds),
+        transformation=scaling,
+        bounds_truncate=True,
     )
     return prior.sample(shape=(n_starts,))
 
@@ -74,7 +78,9 @@ def sample_parameter_startpoints(
     # get types and parameters of priors from dataframe
     return np.array(
         [
-            Prior.from_par_dict(row, type_="initialization").sample(n_starts)
+            Prior.from_par_dict(
+                row, type_="initialization", bounds_truncate=True
+            ).sample(n_starts)
             for row in par_to_estimate.to_dict("records")
         ]
     ).T

tests/v1/test_priors.py

@@ -156,7 +156,9 @@ def apply_parameter_values(row):
     ]
     priors = [
         Prior.from_par_dict(
-            petab_problem_priors.parameter_df.loc[par_id], type_="objective"
+            petab_problem_priors.parameter_df.loc[par_id],
+            type_="objective",
+            bounds_truncate=False,
         )
         for par_id in parameter_ids
     ]