keep transforms.ordered for backward compatibility

Author: TimOliverMaier

pymc/distributions/transforms.py

@@ -352,14 +352,28 @@ def extend_axis_rev(array, axis):
 Instantiation of :class:`pymc.distributions.transforms.Ordered`
 for use in the ``transform`` argument of a multivariate random variable."""
 
-ordered = univariate_ordered
+ordered = Ordered(ndim_supp=1)
+ordered.__doc__ = """
+Instantiation of :class:`pymc.distributions.transforms.Ordered`
+for use in the ``transform`` argument. """
+
 
 log = LogTransform()
 log.__doc__ = """
 Instantiation of :class:`aeppl.transforms.LogTransform`
 for use in the ``transform`` argument of a random variable."""
 
-sum_to_1 = SumTo1()
+univariate_sum_to_1 = SumTo1(ndim_supp=0)
+univariate_sum_to_1.__doc__ = """
+Instantiation of :class:`pymc.distributions.transforms.SumTo1`
+for use in the ``transform`` argument of a univariate random variable."""
+
+multivariate_sum_to_1 = SumTo1(ndim_supp=1)
+multivariate_sum_to_1.__doc__ = """
+Instantiation of :class:`pymc.distributions.transforms.SumTo1`
+for use in the ``transform`` argument of a multivariate random variable."""
+
+sum_to_1 = SumTo1(ndim_supp=1)
 sum_to_1.__doc__ = """
 Instantiation of :class:`pymc.distributions.transforms.SumTo1`
 for use in the ``transform`` argument of a random variable."""

pymc/tests/distributions/test_transform.py

@@ -327,7 +327,7 @@ def check_vectortransform_elementwise_logp(self, model):
         jacob_det = transform.log_jac_det(test_array_transf, *x.owner.inputs)
         # Original distribution is univariate
         if x.owner.op.ndim_supp == 0:
-            assert model.logp(x, sum=False)[0].ndim == x.ndim == jacob_det.ndim
+            assert model.logp(x, sum=False)[0].ndim == x.ndim == (jacob_det.ndim + 1)
         # Original distribution is multivariate
         else:
             assert model.logp(x, sum=False)[0].ndim == (x.ndim - 1) == jacob_det.ndim
@@ -573,7 +573,7 @@ def test_mvnormal_ordered(self, mu, cov, size, shape):
             {"mu": mu, "cov": cov},
             size=size,
             initval=initval,
-            transform=tr.multivariate_ordered,
+            transform=tr.ordered,
         )
         self.check_vectortransform_elementwise_logp(model)
 
@@ -607,11 +607,11 @@ def test_discrete_trafo():
 def test_transforms_ordered():
     with pm.Model() as model:
         pm.Normal(
-            "x",
+            "x_univariate",
             mu=[-3, -1, 1, 2],
             sigma=1,
             size=(10, 4),
-            transform=pm.distributions.transforms.ordered,
+            transform=tr.univariate_ordered,
         )
 
     log_prob = model.point_logps()
@@ -625,7 +625,7 @@ def test_transforms_sumto1():
             mu=[-3, -1, 1, 2],
             sigma=1,
             size=(10, 4),
-            transform=pm.distributions.transforms.sum_to_1,
+            transform=tr.univariate_sum_to_1,
         )
 
     log_prob = model.point_logps()