Pin Aesara 2.3.6

* Remove deprecated kwarg in `broadcast_to`
* Update type equality check in `DifferentialEquation` `Op`

Author: twiecki

conda-envs/environment-dev-py37.yml

@@ -5,7 +5,7 @@ channels:
 - defaults
 dependencies:
 - aeppl=0.0.18
-- aesara=2.3.2
+- aesara=2.3.6
 - arviz>=0.11.4
 - cachetools>=4.2.1
 - cloudpickle

conda-envs/environment-dev-py38.yml

@@ -5,7 +5,7 @@ channels:
 - defaults
 dependencies:
 - aeppl=0.0.18
-- aesara=2.3.2
+- aesara=2.3.6
 - arviz>=0.11.4
 - cachetools>=4.2.1
 - cloudpickle

conda-envs/environment-dev-py39.yml

@@ -5,7 +5,7 @@ channels:
 - defaults
 dependencies:
 - aeppl=0.0.18
-- aesara=2.3.2
+- aesara=2.3.6
 - arviz>=0.11.4
 - cachetools>=4.2.1
 - cloudpickle

conda-envs/environment-test-py37.yml

@@ -5,7 +5,7 @@ channels:
 - defaults
 dependencies:
 - aeppl=0.0.18
-- aesara=2.3.2
+- aesara=2.3.6
 - arviz>=0.11.4
 - cachetools>=4.2.1
 - cloudpickle

conda-envs/environment-test-py38.yml

@@ -5,7 +5,7 @@ channels:
 - defaults
 dependencies:
 - aeppl=0.0.18
-- aesara=2.3.2
+- aesara=2.3.6
 - arviz>=0.11.4
 - cachetools>=4.2.1
 - cloudpickle

conda-envs/environment-test-py39.yml

@@ -5,7 +5,7 @@ channels:
 - defaults
 dependencies:
 - aeppl=0.0.18
-- aesara=2.3.2
+- aesara=2.3.6
 - arviz>=0.11.4
 - cachetools
 - cloudpickle

conda-envs/windows-environment-dev-py38.yml

@@ -5,7 +5,7 @@ channels:
 dependencies:
  # base dependencies (see install guide for Windows)
 - aeppl=0.0.18
-- aesara=2.3.2
+- aesara=2.3.6
 - arviz>=0.11.4
 - cachetools>=4.2.1
 - cloudpickle

conda-envs/windows-environment-test-py38.yml

@@ -5,7 +5,7 @@ channels:
 dependencies:
  # base dependencies (see install guide for Windows)
 - aeppl=0.0.18
-- aesara=2.3.2
+- aesara=2.3.6
 - arviz>=0.11.2
 - cachetools
 - cloudpickle

pymc/distributions/multivariate.py

@@ -1749,7 +1749,7 @@ def dist(
         dist_shape = (rowchol_cov.shape[0], colchol_cov.shape[0])
 
         # Broadcasting mu
-        mu = at.extra_ops.broadcast_to(a=mu, shape=dist_shape)
+        mu = at.extra_ops.broadcast_to(mu, shape=dist_shape)
 
         mu = at.as_tensor_variable(floatX(mu))
         # mean = median = mode = mu

pymc/ode/ode.py

@@ -158,7 +158,7 @@ def __call__(self, y0, theta, return_sens=False, **kwargs):
         theta = at.cast(at.unbroadcast(at.as_tensor_variable(theta), 0), floatX)
         inputs = [y0, theta]
         for i, (input_val, itype) in enumerate(zip(inputs, self._itypes)):
-            if not input_val.type == itype:
+            if not input_val.type.in_same_class(itype):
                 raise ValueError(
                     f"Input {i} of type {input_val.type} does not have the expected type of {itype}"
                 )