Remove deprecated features

pymc/distributions/continuous.py

@@ -199,17 +199,6 @@ def transform_params(*args):
     return transforms.Interval(bounds_fn=transform_params)
 
 
-def assert_negative_support(var, label, distname, value=-1e-6):
-    warnings.warn(
-        "The assert_negative_support function will be deprecated in future versions!"
-        " See https://github.com/pymc-devs/pymc/issues/5162",
-        DeprecationWarning,
-    )
-    msg = f"The variable specified for {label} has negative support for {distname}, "
-    msg += "likely making it unsuitable for this parameter."
-    return Assert(msg)(var, pt.all(pt.ge(var, 0.0)))
-
-
 def get_tau_sigma(
     tau: TensorLike | None = None, sigma: TensorLike | None = None
 ) -> tuple[TensorVariable, TensorVariable]:

pymc/distributions/multivariate.py

@@ -2709,7 +2709,6 @@ class ZeroSumNormal(Distribution):
     n_zerosum_axes: int, defaults to 1
         Number of axes along which the zero-sum constraint is enforced, starting from the rightmost position.
         Defaults to 1, i.e the rightmost axis.
-    zerosum_axes: int, deprecated please use n_zerosum_axes as its successor
     dims: sequence of strings, optional
         Dimension names of the distribution. Works the same as for other PyMC distributions.
         Necessary if ``shape`` is not passed.
@@ -2749,15 +2748,7 @@ class ZeroSumNormal(Distribution):
     rv_type = ZeroSumNormalRV
     rv_op = ZeroSumNormalRV.rv_op
 
-    def __new__(
-        cls, *args, zerosum_axes=None, n_zerosum_axes=None, support_shape=None, dims=None, **kwargs
-    ):
-        if zerosum_axes is not None:
-            n_zerosum_axes = zerosum_axes
-            warnings.warn(
-                "The 'zerosum_axes' parameter is deprecated. Use 'n_zerosum_axes' instead.",
-                DeprecationWarning,
-            )
+    def __new__(cls, *args, n_zerosum_axes=None, support_shape=None, dims=None, **kwargs):
         if dims is not None or kwargs.get("observed") is not None:
             n_zerosum_axes = cls.check_zerosum_axes(n_zerosum_axes)
 

pymc/smc/sampling.py

@@ -15,7 +15,6 @@
 import logging
 import multiprocessing
 import time
-import warnings
 
 from collections import defaultdict
 from concurrent.futures import ProcessPoolExecutor, wait
@@ -150,41 +149,6 @@ def sample_smc(
         `link <http://ascelibrary.org/doi/abs/10.1061/%28ASCE%290733-9399
         %282007%29133:7%28816%29>`__
     """
-    if isinstance(kernel, str) and kernel.lower() in ("abc", "metropolis"):
-        warnings.warn(
-            f'The kernel string argument "{kernel}" in sample_smc has been deprecated. '
-            f"It is no longer needed to distinguish between `abc` and `metropolis`",
-            FutureWarning,
-            stacklevel=2,
-        )
-        kernel = IMH
-
-    if kernel_kwargs.pop("save_sim_data", None) is not None:
-        warnings.warn(
-            "save_sim_data has been deprecated. Use pm.sample_posterior_predictive "
-            "to obtain the same type of samples.",
-            FutureWarning,
-            stacklevel=2,
-        )
-
-    if kernel_kwargs.pop("save_log_pseudolikelihood", None) is not None:
-        warnings.warn(
-            "save_log_pseudolikelihood has been deprecated. This information is "
-            "now saved as log_likelihood in models with Simulator distributions.",
-            FutureWarning,
-            stacklevel=2,
-        )
-
-    parallel = kernel_kwargs.pop("parallel", None)
-    if parallel is not None:
-        warnings.warn(
-            "The argument parallel is deprecated, use the argument cores instead.",
-            FutureWarning,
-            stacklevel=2,
-        )
-        if parallel is False:
-            cores = 1
-
     if cores is None:
         cores = _cpu_count()
 

tests/sampling/test_jax.py

@@ -47,13 +47,6 @@
 )
 
 
-def test_old_import_route():
-    import pymc.sampling.jax as new_sj
-    import pymc.sampling_jax as old_sj
-
-    assert set(new_sj.__all__) <= set(dir(old_sj))
-
-
 def test_jax_PosDefMatrix():
     x = pt.tensor(name="x", shape=(2, 2), dtype="float32")
     matrix_pos_def = PosDefMatrix()

tests/smc/test_smc.py

@@ -236,40 +236,6 @@ def test_convergence_checks(self, caplog):
                 pm.sample_smc(draws=99, progressbar=not _IS_WINDOWS)
         assert "The number of samples is too small" in caplog.text
 
-    def test_deprecated_parallel_arg(self):
-        with self.fast_model:
-            with pytest.warns(
-                FutureWarning,
-                match="The argument parallel is deprecated",
-            ):
-                pm.sample_smc(draws=10, chains=1, parallel=False)
-
-    def test_deprecated_abc_args(self):
-        with self.fast_model:
-            with pytest.warns(
-                FutureWarning,
-                match='The kernel string argument "ABC" in sample_smc has been deprecated',
-            ):
-                pm.sample_smc(draws=10, chains=1, kernel="ABC")
-
-            with pytest.warns(
-                FutureWarning,
-                match='The kernel string argument "Metropolis" in sample_smc has been deprecated',
-            ):
-                pm.sample_smc(draws=10, chains=1, kernel="Metropolis")
-
-            with pytest.warns(
-                FutureWarning,
-                match="save_sim_data has been deprecated",
-            ):
-                pm.sample_smc(draws=10, chains=1, save_sim_data=True)
-
-            with pytest.warns(
-                FutureWarning,
-                match="save_log_pseudolikelihood has been deprecated",
-            ):
-                pm.sample_smc(draws=10, chains=1, save_log_pseudolikelihood=True)
-
     def test_ordered(self):
         """
         Test that initial population respects custom initval, especially when applied