Bump PyTensor dependency

conda-envs/environment-alternative-backends.yml

@@ -22,7 +22,7 @@ dependencies:
 - numpyro>=0.8.0
 - pandas>=0.24.0
 - pip
-- pytensor>=2.31.7,<2.32
+- pytensor>=2.32.0,<2.33
 - python-graphviz
 - networkx
 - rich>=13.7.1

conda-envs/environment-dev.yml

@@ -12,7 +12,7 @@ dependencies:
 - numpy>=1.25.0
 - pandas>=0.24.0
 - pip
-- pytensor>=2.31.7,<2.32
+- pytensor>=2.32.0,<2.33
 - python-graphviz
 - networkx
 - scipy>=1.4.1

conda-envs/environment-docs.yml

@@ -11,7 +11,7 @@ dependencies:
 - numpy>=1.25.0
 - pandas>=0.24.0
 - pip
-- pytensor>=2.31.7,<2.32
+- pytensor>=2.32.0,<2.33
 - python-graphviz
 - rich>=13.7.1
 - scipy>=1.4.1

conda-envs/environment-test.yml

@@ -14,7 +14,7 @@ dependencies:
 - pandas>=0.24.0
 - pip
 - polyagamma
-- pytensor>=2.31.7,<2.32
+- pytensor>=2.32.0,<2.33
 - python-graphviz
 - networkx
 - rich>=13.7.1

conda-envs/windows-environment-dev.yml

@@ -12,7 +12,7 @@ dependencies:
 - numpy>=1.25.0
 - pandas>=0.24.0
 - pip
-- pytensor>=2.31.7,<2.32
+- pytensor>=2.32.0,<2.33
 - python-graphviz
 - networkx
 - rich>=13.7.1

conda-envs/windows-environment-test.yml

@@ -15,7 +15,7 @@ dependencies:
 - pandas>=0.24.0
 - pip
 - polyagamma
-- pytensor>=2.31.7,<2.32
+- pytensor>=2.32.0,<2.33
 - python-graphviz
 - networkx
 - rich>=13.7.1

pymc/distributions/continuous.py

@@ -1618,7 +1618,7 @@ def support_point(rv, size, b, kappa, mu):
 
     def logp(value, b, kappa, mu):
         value = value - mu
-        res = pt.log(b / (kappa + (kappa**-1))) + (
+        res = pt.log(b / (kappa + pt.reciprocal(kappa))) + (
             -value * b * pt.sign(value) * (kappa ** pt.sign(value))
         )
 

pymc/logprob/linalg.py

@@ -14,7 +14,7 @@
 import pytensor.tensor as pt
 
 from pytensor.graph.rewriting.basic import node_rewriter
-from pytensor.tensor.math import _matrix_matrix_matmul
+from pytensor.tensor.math import _matmul
 
 from pymc.logprob.abstract import MeasurableBlockwise, MeasurableOp, _logprob, _logprob_helper
 from pymc.logprob.rewriting import measurable_ir_rewrites_db
@@ -55,7 +55,7 @@ def logprob_measurable_matmul(op, values, l, r):  # noqa: E741
     return x_logp - log_abs_jac_det
 
 
-@node_rewriter(tracks=[_matrix_matrix_matmul])
+@node_rewriter(tracks=[_matmul])
 def find_measurable_matmul(fgraph, node):
     """Find measurable matrix-matrix multiplication operations."""
     if isinstance(node.op, MeasurableOp):

pymc/ode/utils.py

@@ -83,7 +83,7 @@ def augment_system(ode_func, n_states, n_theta):
         Augemted system of differential equations.
     """
     # Present state of the system
-    t_y = pt.vector("y", dtype="float64")
+    t_y = pt.vector("y", dtype="float64", shape=(n_states,))
     t_y.tag.test_value = np.ones((n_states,), dtype="float64")
     # Parameter(s).  Should be vector to allow for generaliztion to multiparameter
     # systems of ODEs.  Is m dimensional because it includes all initial conditions as well as ode parameters

requirements-dev.txt

@@ -16,7 +16,7 @@ pandas>=0.24.0
 polyagamma
 pre-commit>=2.8.0
 pymc-sphinx-theme>=0.16.0
-pytensor>=2.31.7,<2.32
+pytensor>=2.32.0,<2.33
 pytest-cov>=2.5
 pytest>=3.0
 rich>=13.7.1

requirements.txt

@@ -3,7 +3,7 @@ cachetools>=4.2.1
 cloudpickle
 numpy>=1.25.0
 pandas>=0.24.0
-pytensor>=2.31.7,<2.32
+pytensor>=2.32.0,<2.33
 rich>=13.7.1
 scipy>=1.4.1
 threadpoolctl>=3.1.0,<4.0.0

tests/distributions/test_dist_math.py

@@ -59,7 +59,7 @@ def test_check_parameters(conditions, succeeds):
     if succeeds:
         assert ret.eval()
     else:
-        with pytest.raises(ParameterValueError, match="^parameter check msg$"):
+        with pytest.raises(ParameterValueError, match="^parameter check msg*"):
             ret.eval()
 
 

tests/distributions/test_distribution.py

@@ -440,7 +440,7 @@ def test_multivariate_shared_mask_separable(self):
         obs_logp, unobs_logp = logp_fn()
         expected_logp = pm.logp(rv, unobs_data).eval()
         np.testing.assert_almost_equal(obs_logp, [])
-        np.testing.assert_array_equal(unobs_logp, expected_logp)
+        np.testing.assert_almost_equal(unobs_logp, expected_logp)
 
         # Test that we can update a shared mask
         mask.set_value(np.array([False]))

tests/logprob/test_transforms.py

@@ -615,10 +615,10 @@ def test_measurable_power_exponent_with_constant_base():
 
     np.testing.assert_allclose(x_logp_fn_pow(0.1), x_logp_fn_exp2(0.1))
 
+    x_rv_neg = pt.pow(-2, pt.random.normal())
+    x_vv_neg = x_rv_neg.clone()
     with pytest.raises(ParameterValueError, match="base >= 0"):
-        x_rv_neg = pt.pow(-2, pt.random.normal())
-        x_vv_neg = x_rv_neg.clone()
-        logp(x_rv_neg, x_vv_neg)
+        logp(x_rv_neg, x_vv_neg).eval({x_vv_neg: 1.5})
 
 
 def test_measurable_power_exponent_with_variable_base():

tests/ode/test_ode.py

@@ -412,7 +412,9 @@ def system(y, t, p):
                     warnings.filterwarnings(
                         "ignore", "invalid value encountered in log", RuntimeWarning
                     )
-                    idata = pm.sample(50, tune=0, chains=1)
+                    idata = pm.sample(
+                        50, tune=0, chains=1, progressbar=False, compute_convergence_checks=False
+                    )
 
         assert idata.posterior["R"].shape == (1, 50)
         assert idata.posterior["sigma"].shape == (1, 50, 2)

tests/test_printing.py

@@ -137,7 +137,7 @@ def setup_class(self):
             ("plain", True): [
                 r"alpha ~ Normal(0, 10)",
                 r"sigma ~ HalfNormal(0, 1)",
-                r"mu ~ Deterministic(f(beta, alpha))",
+                r"mu ~ Deterministic(f(alpha, beta))",
                 r"beta ~ Normal(0, 10)",
                 r"Z ~ MultivariateNormal(f(), f())",
                 r"nb_with_p_n ~ NegativeBinomial(10, nbp)",
@@ -149,7 +149,7 @@ def setup_class(self):
                     r"Censored(Bernoulli(0.5), -1, 1))"
                 ),
                 r"Y_obs ~ Normal(mu, sigma)",
-                r"pot ~ Potential(f(beta, alpha))",
+                r"pot ~ Potential(f(alpha, beta))",
                 r"pred ~ Deterministic(f(<normal>))",
             ],
             ("plain", False): [
@@ -169,7 +169,7 @@ def setup_class(self):
             ("latex", True): [
                 r"$\text{alpha} \sim \operatorname{Normal}(0,~10)$",
                 r"$\text{sigma} \sim \operatorname{HalfNormal}(0,~1)$",
-                r"$\text{mu} \sim \operatorname{Deterministic}(f(\text{beta},~\text{alpha}))$",
+                r"$\text{mu} \sim \operatorname{Deterministic}(f(\text{alpha},~\text{beta}))$",
                 r"$\text{beta} \sim \operatorname{Normal}(0,~10)$",
                 r"$\text{Z} \sim \operatorname{MultivariateNormal}(f(),~f())$",
                 r"$\text{nb\_with\_p\_n} \sim \operatorname{NegativeBinomial}(10,~\text{nbp})$",
@@ -181,7 +181,7 @@ def setup_class(self):
                     r"~\operatorname{Censored}(\operatorname{Bernoulli}(0.5),~-1,~1))$"
                 ),
                 r"$\text{Y\_obs} \sim \operatorname{Normal}(\text{mu},~\text{sigma})$",
-                r"$\text{pot} \sim \operatorname{Potential}(f(\text{beta},~\text{alpha}))$",
+                r"$\text{pot} \sim \operatorname{Potential}(f(\text{alpha},~\text{beta}))$",
                 r"$\text{pred} \sim \operatorname{Deterministic}(f(\text{<normal>}))",
             ],
             ("latex", False): [