Do not make transforms module accessible at root level

pymc-devs · Mar 18, 2022 · 3a184e7 · 3a184e7
1 parent f12edd3
commit 3a184e7
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Showing 4 changed files with 4 additions and 5 deletions.
diff --git a/docs/source/api/distributions/transforms.rst b/docs/source/api/distributions/transforms.rst
@@ -2,7 +2,7 @@
 Transformations
 ***************
 
-.. currentmodule:: pymc.transforms
+.. currentmodule:: pymc.distributions.transforms
 
 Transform Instances
 ~~~~~~~~~~~~~~~~~~~

diff --git a/pymc/__init__.py b/pymc/__init__.py
@@ -55,7 +55,6 @@ def __set_compiler_flags():
 from pymc.blocking import *
 from pymc.data import *
 from pymc.distributions import *
-from pymc.distributions import transforms
 from pymc.exceptions import *
 from pymc.func_utils import find_constrained_prior
 from pymc.math import (

diff --git a/pymc/tests/test_distributions.py b/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.log)
+                pm.Bound("bound", x, transform=pm.distributions.transforms.log)
 
         msg = "Given dims do not exist in model coordinates."
         with pm.Model() as m:

diff --git a/pymc/tests/test_sampling.py b/pymc/tests/test_sampling.py
@@ -327,11 +327,11 @@ def test_deterministic_of_unobserved(self):
 
         np.testing.assert_allclose(idata.posterior["y"], idata.posterior["x"] + 100)
 
-    def test_transform_with_rv_depenency(self):
+    def test_transform_with_rv_dependency(self):
         # Test that untransformed variables that depend on upstream variables are properly handled
         with pm.Model() as m:
             x = pm.HalfNormal("x", observed=1)
-            transform = pm.transforms.IntervalTransform(lambda *inputs: (inputs[-2], inputs[-1]))
+            transform = pm.distributions.transforms.IntervalTransform(lambda *inputs: (inputs[-2], inputs[-1]))
             y = pm.Uniform("y", lower=0, upper=x, transform=transform)
             trace = pm.sample(tune=10, draws=50, return_inferencedata=False, random_seed=336)