Remove deprecated functionality

Author: ricardoV94

pymc/data.py

@@ -14,7 +14,6 @@
 
 import io
 import urllib.request
-import warnings
 
 from collections.abc import Sequence
 from copy import copy
@@ -40,10 +39,8 @@
 from pymc.vartypes import isgenerator
 
 __all__ = [
-    "ConstantData",
     "Data",
     "Minibatch",
-    "MutableData",
     "get_data",
 ]
 BASE_URL = "https://raw.githubusercontent.com/pymc-devs/pymc-examples/main/examples/data/{filename}"
@@ -218,74 +215,13 @@ def determine_coords(
     return coords, new_dims
 
 
-def ConstantData(
-    name: str,
-    value,
-    *,
-    dims: Sequence[str] | None = None,
-    coords: dict[str, Sequence | np.ndarray] | None = None,
-    infer_dims_and_coords=False,
-    **kwargs,
-) -> TensorConstant:
-    """Alias for ``pm.Data``.
-
-    Registers the ``value`` as a :class:`~pytensor.tensor.TensorConstant` with the model.
-    For more information, please reference :class:`pymc.Data`.
-    """
-    warnings.warn(
-        "ConstantData is deprecated. All Data variables are now mutable. Use Data instead.",
-        FutureWarning,
-    )
-
-    var = Data(
-        name,
-        value,
-        dims=dims,
-        coords=coords,
-        infer_dims_and_coords=infer_dims_and_coords,
-        **kwargs,
-    )
-    return cast(TensorConstant, var)
-
-
-def MutableData(
-    name: str,
-    value,
-    *,
-    dims: Sequence[str] | None = None,
-    coords: dict[str, Sequence | np.ndarray] | None = None,
-    infer_dims_and_coords=False,
-    **kwargs,
-) -> SharedVariable:
-    """Alias for ``pm.Data``.
-
-    Registers the ``value`` as a :class:`~pytensor.compile.sharedvalue.SharedVariable`
-    with the model. For more information, please reference :class:`pymc.Data`.
-    """
-    warnings.warn(
-        "MutableData is deprecated. All Data variables are now mutable. Use Data instead.",
-        FutureWarning,
-    )
-
-    var = Data(
-        name,
-        value,
-        dims=dims,
-        coords=coords,
-        infer_dims_and_coords=infer_dims_and_coords,
-        **kwargs,
-    )
-    return cast(SharedVariable, var)
-
-
 def Data(
     name: str,
     value,
     *,
     dims: Sequence[str] | None = None,
     coords: dict[str, Sequence | np.ndarray] | None = None,
     infer_dims_and_coords=False,
-    mutable: bool | None = None,
     **kwargs,
 ) -> SharedVariable | TensorConstant:
     """Create a data container that registers a data variable with the model.
@@ -380,11 +316,6 @@ def Data(
             "Pass them directly to `observed` if you want to trigger auto-imputation"
         )
 
-    if mutable is not None:
-        warnings.warn(
-            "Data is now always mutable. Specifying the `mutable` kwarg will raise an error in a future release",
-            FutureWarning,
-        )
     x = pytensor.shared(arr, name, **kwargs)
 
     if isinstance(dims, str):

pymc/distributions/custom.py

@@ -13,7 +13,6 @@
 #   limitations under the License.
 import functools
 import re
-import warnings
 
 from collections.abc import Callable, Sequence
 
@@ -715,13 +714,6 @@ def __new__(
             )
         dist_params = cls.parse_dist_params(dist_params)
         cls.check_valid_dist_random(dist, random, dist_params)
-        moment = kwargs.pop("moment", None)
-        if moment is not None:
-            warnings.warn(
-                "`moment` argument is deprecated. Use `support_point` instead.",
-                FutureWarning,
-            )
-            support_point = moment
         if dist is not None:
             kwargs.setdefault("class_name", f"CustomDist_{name}")
             return _CustomSymbolicDist(

pymc/distributions/dist_math.py

@@ -18,8 +18,6 @@
 @author: johnsalvatier
 """
 
-import warnings
-
 from collections.abc import Iterable
 from functools import partial
 
@@ -419,12 +417,3 @@ def log_i0(x):
             + 11025.0 / (98304.0 * x**4.0)
         ),
     )
-
-
-def incomplete_beta(a, b, value):
-    warnings.warn(
-        "incomplete_beta has been deprecated. Use pytensor.tensor.betainc instead.",
-        FutureWarning,
-        stacklevel=2,
-    )
-    return pt.betainc(a, b, value)

pymc/distributions/distribution.py

@@ -60,7 +60,7 @@
     convert_observed_data,
     floatX,
 )
-from pymc.util import UNSET, _add_future_warning_tag
+from pymc.util import UNSET
 from pymc.vartypes import continuous_types, string_types
 
 __all__ = [
@@ -571,10 +571,7 @@ def dist(
             ndim_supp = cls.rv_op(*dist_params, **kwargs).owner.op.ndim_supp
 
         create_size = find_size(shape=shape, size=size, ndim_supp=ndim_supp)
-        rv_out = cls.rv_op(*dist_params, size=create_size, **kwargs)
-
-        _add_future_warning_tag(rv_out)
-        return rv_out
+        return cls.rv_op(*dist_params, size=create_size, **kwargs)
 
 
 @node_rewriter([SymbolicRandomVariable])

pymc/distributions/shape_utils.py

@@ -43,7 +43,6 @@
 
 from pymc.exceptions import ShapeError
 from pymc.pytensorf import PotentialShapeType
-from pymc.util import _add_future_warning_tag
 
 
 def to_tuple(shape):
@@ -264,7 +263,6 @@ def change_dist_size(
 
     op = dist.owner.op
     new_dist = _change_dist_size(op, dist, new_size=new_size, expand=expand)
-    _add_future_warning_tag(new_dist)
 
     new_dist.name = dist.name
     for k, v in dist.tag.__dict__.items():

pymc/distributions/timeseries.py

@@ -286,13 +286,6 @@ class GaussianRandomWalk(PredefinedRandomWalk):
 
     @classmethod
     def get_dists(cls, mu=0.0, sigma=1.0, *, init_dist=None, **kwargs):
-        if "init" in kwargs:
-            warnings.warn(
-                "init parameter is now called init_dist. Using init will raise an error in a future release.",
-                FutureWarning,
-            )
-            init_dist = kwargs.pop("init")
-
         if init_dist is None:
             warnings.warn(
                 "Initial distribution not specified, defaulting to `Normal.dist(0, 100)`."
@@ -340,14 +333,6 @@ class MvGaussianRandomWalk(PredefinedRandomWalk):
 
     @classmethod
     def get_dists(cls, mu, *, cov=None, tau=None, chol=None, lower=True, init_dist=None, **kwargs):
-        if "init" in kwargs:
-            warnings.warn(
-                "init parameter is now called init_dist. Using init will raise an error "
-                "in a future release.",
-                FutureWarning,
-            )
-            init_dist = kwargs.pop("init")
-
         if init_dist is None:
             warnings.warn(
                 "Initial distribution not specified, defaulting to `MvNormal.dist(0, I*100)`."
@@ -396,14 +381,6 @@ class MvStudentTRandomWalk(PredefinedRandomWalk):
     def get_dists(
         cls, *, nu, mu, scale=None, tau=None, chol=None, lower=True, init_dist=None, **kwargs
     ):
-        if "init" in kwargs:
-            warnings.warn(
-                "init parameter is now called init_dist. Using init will raise an error "
-                "in a future release.",
-                FutureWarning,
-            )
-            init_dist = kwargs.pop("init")
-
         if init_dist is None:
             warnings.warn(
                 "Initial distribution not specified, defaulting to `MvNormal.dist(0, I*100)`."
@@ -588,13 +565,6 @@ def dist(
         sigma = pt.as_tensor_variable(sigma)
         rhos = pt.atleast_1d(pt.as_tensor_variable(rho))
 
-        if "init" in kwargs:
-            warnings.warn(
-                "init parameter is now called init_dist. Using init will raise an error in a future release.",
-                FutureWarning,
-            )
-            init_dist = kwargs.pop("init")
-
         ar_order = cls._get_ar_order(rhos=rhos, constant=constant, ar_order=ar_order)
         steps = get_support_shape_1d(
             support_shape=steps, shape=kwargs.get("shape", None), support_shape_offset=ar_order

pymc/distributions/transforms.py

@@ -11,7 +11,6 @@
 #   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 #   See the License for the specific language governing permissions and
 #   limitations under the License.
-import warnings
 
 from functools import singledispatch
 
@@ -48,22 +47,6 @@
 ]
 
 
-def __getattr__(name):
-    if name in ("univariate_ordered", "multivariate_ordered"):
-        warnings.warn(f"{name} has been deprecated, use ordered instead.", FutureWarning)
-        return ordered
-
-    if name in ("univariate_sum_to_1", "multivariate_sum_to_1"):
-        warnings.warn(f"{name} has been deprecated, use sum_to_1 instead.", FutureWarning)
-        return sum_to_1
-
-    if name == "RVTransform":
-        warnings.warn("RVTransform has been renamed to Transform", FutureWarning)
-        return Transform
-
-    raise AttributeError(f"module {__name__} has no attribute {name}")
-
-
 @singledispatch
 def _default_transform(op: Op, rv: TensorVariable):
     """Return default transform for a given Distribution `Op`."""
@@ -100,9 +83,7 @@ class Ordered(Transform):
 
     name = "ordered"
 
-    def __init__(self, ndim_supp=None, positive=False, ascending=True):
-        if ndim_supp is not None:
-            warnings.warn("ndim_supp argument is deprecated and has no effect", FutureWarning)
+    def __init__(self, positive=False, ascending=True):
         self.positive = positive
         self.ascending = ascending
 
@@ -142,10 +123,6 @@ class SumTo1(Transform):
 
     name = "sumto1"
 
-    def __init__(self, ndim_supp=None):
-        if ndim_supp is not None:
-            warnings.warn("ndim_supp argument is deprecated and has no effect", FutureWarning)
-
     def backward(self, value, *inputs):
         remaining = 1 - pt.sum(value[..., :], axis=-1, keepdims=True)
         return pt.concatenate([value[..., :], remaining], axis=-1)

pymc/initial_point.py

@@ -240,12 +240,6 @@ def make_initial_point_expression(
             strategy = default_strategy
 
         if isinstance(strategy, str):
-            if strategy == "moment":
-                strategy = "support_point"
-                warnings.warn(
-                    "The 'moment' strategy is deprecated. Use 'support_point' instead.",
-                    FutureWarning,
-                )
             if strategy == "support_point":
                 try:
                     value = support_point(variable)

pymc/logprob/abstract.py

@@ -35,7 +35,6 @@
 #   SOFTWARE.
 
 import abc
-import warnings
 
 from collections.abc import Sequence
 from functools import singledispatch
@@ -48,17 +47,6 @@
 from pytensor.tensor.random.op import RandomVariable
 
 
-def __getattr__(name):
-    if name == "MeasurableVariable":
-        warnings.warn(
-            f"{name} has been deprecated in favor of MeasurableOp. Importing will fail in a future release.",
-            FutureWarning,
-        )
-        return MeasurableOp
-
-    raise AttributeError(f"module {__name__} has no attribute {name}")
-
-
 @singledispatch
 def _logprob(
     op: Op,