Create helper wrapper around Aeppl IntervalTransform

docs/source/api/distributions/transforms.rst

@@ -15,28 +15,30 @@ Transform instances are the entities that should be used in the
 
     simplex
     logodds
-    interval
     log_exp_m1
     ordered
     log
     sum_to_1
     circular
 
-Transform Composition Classes
-~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
-
-.. autosummary::
-   :toctree: generated
-
-    Chain
-    CholeskyCovPacked
 
 Specific Transform Classes
 ~~~~~~~~~~~~~~~~~~~~~~~~~~
 
 .. autosummary::
    :toctree: generated
 
+    CholeskyCovPacked
+    Interval
     LogExpM1
     Ordered
     SumTo1
+
+
+Transform Composition Classes
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+.. autosummary::
+   :toctree: generated
+
+    Chain

pymc/distributions/continuous.py

@@ -195,7 +195,7 @@ def transform_params(*args):
 
             return lower, upper
 
-        return transforms.interval(transform_params)
+        return transforms.Interval(bounds_fn=transform_params)
 
 
 def assert_negative_support(var, label, distname, value=-1e-6):
@@ -3796,7 +3796,7 @@ def transform_params(*params):
                 _, _, _, x_points, _, _ = params
                 return floatX(x_points[0]), floatX(x_points[-1])
 
-            kwargs["transform"] = transforms.interval(transform_params)
+            kwargs["transform"] = transforms.Interval(bounds_fn=transform_params)
         return super().__new__(cls, *args, **kwargs)
 
     @classmethod

pymc/distributions/multivariate.py

@@ -62,7 +62,7 @@
     rv_size_is_none,
     to_tuple,
 )
-from pymc.distributions.transforms import interval
+from pymc.distributions.transforms import Interval
 from pymc.math import kron_diag, kron_dot
 from pymc.util import UNSET, check_dist_not_registered
 
@@ -1571,7 +1571,7 @@ class LKJCorr(BoundedContinuous):
     def __new__(cls, *args, **kwargs):
         transform = kwargs.get("transform", UNSET)
         if transform is UNSET:
-            kwargs["transform"] = interval(lambda *args: (floatX(-1.0), floatX(1.0)))
+            kwargs["transform"] = Interval(floatX(-1.0), floatX(1.0))
         return super().__new__(cls, *args, **kwargs)
 
     @classmethod

pymc/distributions/transforms.py

@@ -13,6 +13,7 @@
 #   limitations under the License.
 
 import aesara.tensor as at
+import numpy as np
 
 from aeppl.transforms import (
     CircularTransform,
@@ -27,7 +28,7 @@
     "RVTransform",
     "simplex",
     "logodds",
-    "interval",
+    "Interval",
     "log_exp_m1",
     "ordered",
     "log",
@@ -174,10 +175,87 @@ def log_jac_det(self, value, *inputs):
 Instantiation of :class:`aeppl.transforms.LogOddsTransform`
 for use in the ``transform`` argument of a random variable."""
 
-interval = IntervalTransform
-interval.__doc__ = """
-Instantiation of :class:`aeppl.transforms.IntervalTransform`
-for use in the ``transform`` argument of a random variable."""
+
+class Interval(IntervalTransform):
+    """Wrapper around  :class:`aeppl.transforms.IntervalTransform` for use in the
+    ``transform`` argument of a random variable.
+
+    Parameters
+    ----------
+    lower: int, float, or None
+        Lower bound of the interval transform. Must be a constant value. If ``None``, the
+        interval is not bounded below.
+    upper: int, float or None
+        Upper bound of the interval transfrom. Must be a finite value. If ``None``, the
+        interval is not bounded above.
+    bounds_fn: callable
+        Alternative to lower and upper. Must return a tuple of lower and upper bounds
+        as a symbolic function of the respective distribution inputs. If lower or
+        upper is ``None``, the interval is unbounded on that edge.
+
+    .. warning:: Expressions returned by `bounds_fn` should depend only on the distribution
+    inputs or other constants.
+
+        Expressions that depend on other symbolic variables, including nonlocal distribution
+        variables defined in the model context will likely break sampling. This can happen
+        silently, and is difficult to debug
+
+
+    Examples
+    --------
+    .. code-block:: python
+
+        # Create an interval transform between -1 and +1
+        with pm.Model():
+            x = pm.Normal("x", transform=pm.transforms.Interval(lower=-1, upper=1))
+
+    .. code-block:: python
+
+        # Create an interval transform between -1 and +1 using a callable
+        def get_bounds(rng, size, dtype, loc, scale):
+            return 0, None
+
+        with pm.Model():
+            x = pm.Normal("x", transform=pm.transforms.Interval(bouns_fn=get_bounds))
+
+    .. code-block:: python
+
+        # Create a lower bounded interval transform based on a distribution parameter
+        def get_bounds(rng, size, dtype, loc, scale):
+            return loc, None
+
+        interval = pm.transforms.Interval(bounds_fn=get_bounds)
+
+        with pm.Model():
+            loc = pm.Normal("loc")
+            x = pm.Normal("x", mu=loc, sigma=2, transform=interval)
+    """
+
+    def __init__(self, lower=None, upper=None, *, bounds_fn=None):
+        if bounds_fn is None:
+            try:
+                bounds = tuple(
+                    None if bound is None else at.constant(bound, ndim=0).data
+                    for bound in (lower, upper)
+                )
+            except (ValueError, TypeError):
+                raise ValueError(
+                    "Interval bounds must be constant values. If you need expressions that "
+                    "depend on symbolic variables use `args_fn`"
+                )
+
+            lower, upper = (
+                None if (bound is None or np.isinf(bound)) else bound for bound in bounds
+            )
+
+            if lower is None and upper is None:
+                raise ValueError("Lower and upper interval bounds cannot both be None")
+
+            def bounds_fn(*rv_inputs):
+                return lower, upper
+
+        super().__init__(args_fn=bounds_fn)
+
 
 log_exp_m1 = LogExpM1()
 log_exp_m1.__doc__ = """

pymc/tests/test_distributions.py

@@ -2710,7 +2710,7 @@ def test_arguments_checks(self):
         with pm.Model() as m:
             x = pm.Poisson.dist(0.5)
             with pytest.raises(ValueError, match=msg):
-                pm.Bound("bound", x, transform=pm.transforms.interval)
+                pm.Bound("bound", x, transform=pm.transforms.log)
 
         msg = "Given dims do not exist in model coordinates."
         with pm.Model() as m:

pymc/tests/test_transforms.py

@@ -177,10 +177,7 @@ def test_logodds():
 
 
 def test_lowerbound():
-    def transform_params(*inputs):
-        return 0.0, None
-
-    trans = tr.interval(transform_params)
+    trans = tr.Interval(0.0, None)
     check_transform(trans, Rplusbig)
 
     check_jacobian_det(trans, Rplusbig, elemwise=True)
@@ -191,10 +188,7 @@ def transform_params(*inputs):
 
 
 def test_upperbound():
-    def transform_params(*inputs):
-        return None, 0.0
-
-    trans = tr.interval(transform_params)
+    trans = tr.Interval(None, 0.0)
     check_transform(trans, Rminusbig)
 
     check_jacobian_det(trans, Rminusbig, elemwise=True)
@@ -208,10 +202,7 @@ def test_interval():
     for a, b in [(-4, 5.5), (0.1, 0.7), (-10, 4.3)]:
         domain = Unit * np.float64(b - a) + np.float64(a)
 
-        def transform_params(z=a, y=b):
-            return z, y
-
-        trans = tr.interval(transform_params)
+        trans = tr.Interval(a, b)
         check_transform(trans, domain)
 
         check_jacobian_det(trans, domain, elemwise=True)
@@ -375,7 +366,7 @@ def transform_params(*inputs):
             upper = at.as_tensor_variable(upper) if upper is not None else None
             return lower, upper
 
-        interval = tr.interval(transform_params)
+        interval = tr.Interval(bounds_fn=transform_params)
         model = self.build_model(
             pm.Uniform, {"lower": lower, "upper": upper}, size=size, transform=interval
         )
@@ -396,7 +387,7 @@ def transform_params(*inputs):
             upper = at.as_tensor_variable(upper) if upper is not None else None
             return lower, upper
 
-        interval = tr.interval(transform_params)
+        interval = tr.Interval(bounds_fn=transform_params)
         model = self.build_model(
             pm.Triangular, {"lower": lower, "c": c, "upper": upper}, size=size, transform=interval
         )
@@ -491,7 +482,7 @@ def transform_params(*inputs):
             upper = at.as_tensor_variable(upper) if upper is not None else None
             return lower, upper
 
-        interval = tr.interval(transform_params)
+        interval = tr.Interval(bounds_fn=transform_params)
 
         initval = np.sort(np.abs(np.random.rand(*size)))
         model = self.build_model(
@@ -556,3 +547,13 @@ def test_triangular_transform():
     transform = x.tag.value_var.tag.transform
     assert np.isclose(transform.backward(-np.inf, *x.owner.inputs).eval(), 0)
     assert np.isclose(transform.backward(np.inf, *x.owner.inputs).eval(), 2)
+
+
+def test_interval_transform_raises():
+    with pytest.raises(ValueError, match="Lower and upper interval bounds cannot both be None"):
+        tr.Interval(None, None)
+
+    with pytest.raises(ValueError, match="Interval bounds must be constant values"):
+        tr.Interval(at.constant(5) + 1, None)
+
+    assert tr.Interval(at.constant(5), None)