[refactor] rebase from master

Author: domosedy

src/pysatl_core/families/exponential_family.py

@@ -9,9 +9,6 @@
 from scipy.integrate import nquad
 from scipy.linalg import det
 
-from pysatl_core.distributions import (
-    SamplingStrategy,
-)
 from pysatl_core.distributions.support import (
     ContinuousSupport,
     SupportByPredicate,
@@ -73,7 +70,6 @@ def __init__(
         name: str = "ExponentialFamily",
         distr_type: DistributionType | Callable[[Parametrization], DistributionType],
         distr_parametrizations: list[ParametrizationName],
-        sampling_strategy: SamplingStrategy,
         support_by_parametrization: SupportArg = None,
     ):
         self._sufficient = sufficient_statistics
@@ -99,7 +95,6 @@ def __init__(
             distr_type=distr_type,
             distr_parametrizations=distr_parametrizations,
             distr_characteristics=distr_characteristics,
-            sampling_strategy=sampling_strategy,
             support_by_parametrization=support_by_parametrization,
         )
         parametrization(family=self, name="theta")(ExponentialFamilyParametrization)
@@ -137,7 +132,6 @@ def conjugate_sufficient(
 
             if theta not in self._parameter_space:
                 return np.full(len(theta) + 1, float("-inf"))
-            # print("Current sufficient:", theta, self._log_partition(theta))
             return np.append(theta, self._log_partition(theta))
 
         def conjugate_log_partition(
@@ -178,7 +172,6 @@ def conjugate_sufficient_accepts(
             sufficient_statistics_values=self._parameter_space,  # TODO: write convex hull for this
             parameter_space=SupportByPredicate(conjugate_sufficient_accepts),  # type: ignore[arg-type]
             name=self.name,
-            sampling_strategy=self.sampling_strategy,
             distr_type=self._distr_type,
             distr_parametrizations=self.parametrization_names,
             support_by_parametrization=self.support_resolver,
@@ -213,7 +206,6 @@ def new_normalization(x: Any) -> Any:
             name=f"Transformed{self._name}",
             distr_type=self._distr_type,
             distr_parametrizations=self.parametrization_names,
-            sampling_strategy=self.sampling_strategy,
             support_by_parametrization=self.support_resolver,
         )
 

src/pysatl_core/types.py

@@ -240,6 +240,8 @@ def shape(self) -> ContinuousSupportShape1D:
 
 type Method[In, Out] = AnalyticalComputation[In, Out] | FittedComputationMethod[In, Out]
 """Type alias for a distribution computation method (analytical or fitted)."""
+
+
 @dataclass(frozen=True, slots=True)
 class IntervalND:
     intervals: list[Interval1D]

tests/unit/families/test_exponential_family.py

@@ -5,7 +5,6 @@
 import scipy
 from numpy.testing import assert_allclose
 
-from pysatl_core.distributions.strategies import DefaultSamplingUnivariateStrategy
 from pysatl_core.distributions.support import ContinuousNDSupport, SupportByIntervals
 from pysatl_core.families import (
     ContinuousExponentialClassFamily,
@@ -36,7 +35,6 @@ def transform_function(x: list[float] | float) -> list[float] | float:
         support=support_pos,
         distr_type=UnivariateContinuous,
         distr_parametrizations=["theta"],
-        sampling_strategy=DefaultSamplingUnivariateStrategy(),
     )
 
     conjugate_fam = fam.conjugate_prior_family.transform(transform_function)