Bump Pytensor dependency

ricardoV94 · twiecki · commit 2a324bc04d65 · 2023-04-12T10:54:36.000+02:00
diff --git a/conda-envs/environment-dev.yml b/conda-envs/environment-dev.yml
@@ -14,7 +14,7 @@ dependencies:
 - numpy>=1.15.0
 - pandas>=0.24.0
 - pip
-- pytensor=2.10.1
+- pytensor>=2.11.0,<2.12
 - python-graphviz
 - networkx
 - scipy>=1.4.1
diff --git a/conda-envs/environment-docs.yml b/conda-envs/environment-docs.yml
@@ -12,7 +12,7 @@ dependencies:
 - numpy>=1.15.0
 - pandas>=0.24.0
 - pip
-- pytensor=2.9.1
+- pytensor>=2.11.0,<2.12
 - python-graphviz
 - scipy>=1.4.1
 - typing-extensions>=3.7.4
diff --git a/conda-envs/environment-test.yml b/conda-envs/environment-test.yml
@@ -17,7 +17,7 @@ dependencies:
 - numpy>=1.15.0
 - pandas>=0.24.0
 - pip
-- pytensor=2.10.1
+- pytensor>=2.11.0,<2.12
 - python-graphviz
 - networkx
 - scipy>=1.4.1
diff --git a/conda-envs/windows-environment-dev.yml b/conda-envs/windows-environment-dev.yml
@@ -14,7 +14,7 @@ dependencies:
 - numpy>=1.15.0
 - pandas>=0.24.0
 - pip
-- pytensor=2.10.1
+- pytensor>=2.11.0,<2.12
 - python-graphviz
 - networkx
 - scipy>=1.4.1
diff --git a/conda-envs/windows-environment-test.yml b/conda-envs/windows-environment-test.yml
@@ -17,7 +17,7 @@ dependencies:
 - numpy>=1.15.0
 - pandas>=0.24.0
 - pip
-- pytensor=2.10.1
+- pytensor>=2.11.0,<2.12
 - python-graphviz
 - networkx
 - scipy>=1.4.1
diff --git a/pymc/distributions/continuous.py b/pymc/distributions/continuous.py
@@ -235,7 +235,7 @@ def get_tau_sigma(tau=None, sigma=None):
         else:
             if isinstance(sigma, Variable):
                 # Keep tau negative, if sigma was negative, so that it will fail when used
-                tau = (sigma**-2.0) * pt.sgn(sigma)
+                tau = (sigma**-2.0) * pt.sign(sigma)
             else:
                 sigma_ = np.asarray(sigma)
                 if np.any(sigma_ <= 0):
@@ -248,7 +248,7 @@ def get_tau_sigma(tau=None, sigma=None):
         else:
             if isinstance(tau, Variable):
                 # Keep sigma negative, if tau was negative, so that it will fail when used
-                sigma = pt.abs(tau) ** (-0.5) * pt.sgn(tau)
+                sigma = pt.abs(tau) ** (-0.5) * pt.sign(tau)
             else:
                 tau_ = np.asarray(tau)
                 if np.any(tau_ <= 0):
diff --git a/pymc/distributions/dist_math.py b/pymc/distributions/dist_math.py
@@ -370,7 +370,7 @@ def impl(self, x):
     def grad(self, inp, grads):
         (x,) = inp
         (gz,) = grads
-        return (gz * (i1e_scalar(x) - pytensor.scalar.sgn(x) * i0e_scalar(x)),)
+        return (gz * (i1e_scalar(x) - pytensor.scalar.sign(x) * i0e_scalar(x)),)
 
 
 i0e_scalar = I0e(upgrade_to_float_no_complex, name="i0e")
diff --git a/pymc/distributions/multivariate.py b/pymc/distributions/multivariate.py
@@ -2059,8 +2059,8 @@ def make_node(self, rng, size, dtype, mu, W, alpha, tau):
         sparse = isinstance(W, pytensor.sparse.SparseVariable)
         msg = "W must be a symmetric adjacency matrix."
         if sparse:
-            abs_diff = pytensor.sparse.basic.mul(pytensor.sparse.basic.sgn(W - W.T), W - W.T)
-            W = Assert(msg)(W, pt.isclose(pytensor.sparse.basic.sp_sum(abs_diff), 0))
+            abs_diff = pytensor.sparse.basic.mul(pytensor.sparse.sign(W - W.T), W - W.T)
+            W = Assert(msg)(W, pt.isclose(pytensor.sparse.sp_sum(abs_diff), 0))
         else:
             W = Assert(msg)(W, pt.allclose(W, W.T))
 
diff --git a/pymc/logprob/transforms.py b/pymc/logprob/transforms.py
@@ -608,7 +608,7 @@ def find_measurable_transforms(fgraph: FunctionGraph, node: Node) -> Optional[Li
             return None
         try:
             (power,) = other_inputs
-            power = pt.get_scalar_constant_value(power).item()
+            power = pt.get_underlying_scalar_constant_value(power).item()
         # Power needs to be a constant
         except NotScalarConstantError:
             return None
diff --git a/pymc/model.py b/pymc/model.py
@@ -1443,7 +1443,7 @@ def make_obs_var(
             # values, and another for the non-missing values.
 
             antimask_idx = (~mask).nonzero()
-            nonmissing_data = pt.as_tensor_variable(data[antimask_idx])
+            nonmissing_data = pt.as_tensor_variable(data[antimask_idx].data)
             unmasked_rv_var = rv_var[antimask_idx]
             unmasked_rv_var = unmasked_rv_var.owner.clone().default_output()
 
diff --git a/requirements-dev.txt b/requirements-dev.txt
@@ -18,7 +18,7 @@ numpydoc
 pandas>=0.24.0
 polyagamma
 pre-commit>=2.8.0
-pytensor==2.10.1
+pytensor>=2.11.0,<2.12
 pytest-cov>=2.5
 pytest>=3.0
 scipy>=1.4.1
diff --git a/requirements.txt b/requirements.txt
@@ -4,6 +4,6 @@ cloudpickle
 fastprogress>=0.2.0
 numpy>=1.15.0
 pandas>=0.24.0
-pytensor==2.10.1
+pytensor>=2.11.0,<2.12
 scipy>=1.4.1
 typing-extensions>=3.7.4
diff --git a/tests/test_printing.py b/tests/test_printing.py
@@ -132,63 +132,63 @@ def setup_class(self):
         self.formats = [("plain", True), ("plain", False), ("latex", True), ("latex", False)]
         self.expected = {
             ("plain", True): [
-                r"alpha ~ N(0, 10)",
-                r"sigma ~ N**+(0, 1)",
+                r"alpha ~ Normal(0, 10)",
+                r"sigma ~ HalfNormal(0, 1)",
                 r"mu ~ Deterministic(f(beta, alpha))",
-                r"beta ~ N(0, 10)",
-                r"Z ~ N(f(), f())",
-                r"nb_with_p_n ~ NB(10, nbp)",
-                r"zip ~ MarginalMixture(f(), DiracDelta(0), Pois(5))",
-                r"w ~ Dir(<constant>)",
+                r"beta ~ Normal(0, 10)",
+                r"Z ~ MultivariateNormal(f(), f())",
+                r"nb_with_p_n ~ NegativeBinomial(10, nbp)",
+                r"zip ~ MarginalMixture(f(), DiracDelta(0), Poisson(5))",
+                r"w ~ Dirichlet(<constant>)",
                 (
                     r"nested_mix ~ MarginalMixture(w, "
-                    r"MarginalMixture(f(), DiracDelta(0), Pois(5)), "
-                    r"Censored(Bern(0.5), -1, 1))"
+                    r"MarginalMixture(f(), DiracDelta(0), Poisson(5)), "
+                    r"Censored(Bernoulli(0.5), -1, 1))"
                 ),
-                r"Y_obs ~ N(mu, sigma)",
+                r"Y_obs ~ Normal(mu, sigma)",
                 r"pot ~ Potential(f(beta, alpha))",
             ],
             ("plain", False): [
-                r"alpha ~ N",
-                r"sigma ~ N**+",
+                r"alpha ~ Normal",
+                r"sigma ~ HalfNormal",
                 r"mu ~ Deterministic",
-                r"beta ~ N",
-                r"Z ~ N",
-                r"nb_with_p_n ~ NB",
+                r"beta ~ Normal",
+                r"Z ~ MultivariateNormal",
+                r"nb_with_p_n ~ NegativeBinomial",
                 r"zip ~ MarginalMixture",
-                r"w ~ Dir",
+                r"w ~ Dirichlet",
                 r"nested_mix ~ MarginalMixture",
-                r"Y_obs ~ N",
+                r"Y_obs ~ Normal",
                 r"pot ~ Potential",
             ],
             ("latex", True): [
-                r"$\text{alpha} \sim \operatorname{N}(0,~10)$",
-                r"$\text{sigma} \sim \operatorname{N^{+}}(0,~1)$",
+                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"$\text{beta} \sim \operatorname{N}(0,~10)$",
-                r"$\text{Z} \sim \operatorname{N}(f(),~f())$",
-                r"$\text{nb_with_p_n} \sim \operatorname{NB}(10,~\text{nbp})$",
-                r"$\text{zip} \sim \operatorname{MarginalMixture}(f(),~\operatorname{DiracDelta}(0),~\operatorname{Pois}(5))$",
-                r"$\text{w} \sim \operatorname{Dir}(\text{<constant>})$",
+                r"$\text{beta} \sim \operatorname{Normal}(0,~10)$",
+                r"$\text{Z} \sim \operatorname{MultivariateNormal}(f(),~f())$",
+                r"$\text{nb_with_p_n} \sim \operatorname{NegativeBinomial}(10,~\text{nbp})$",
+                r"$\text{zip} \sim \operatorname{MarginalMixture}(f(),~\operatorname{DiracDelta}(0),~\operatorname{Poisson}(5))$",
+                r"$\text{w} \sim \operatorname{Dirichlet}(\text{<constant>})$",
                 (
                     r"$\text{nested_mix} \sim \operatorname{MarginalMixture}(\text{w},"
-                    r"~\operatorname{MarginalMixture}(f(),~\operatorname{DiracDelta}(0),~\operatorname{Pois}(5)),"
-                    r"~\operatorname{Censored}(\operatorname{Bern}(0.5),~-1,~1))$"
+                    r"~\operatorname{MarginalMixture}(f(),~\operatorname{DiracDelta}(0),~\operatorname{Poisson}(5)),"
+                    r"~\operatorname{Censored}(\operatorname{Bernoulli}(0.5),~-1,~1))$"
                 ),
-                r"$\text{Y_obs} \sim \operatorname{N}(\text{mu},~\text{sigma})$",
+                r"$\text{Y_obs} \sim \operatorname{Normal}(\text{mu},~\text{sigma})$",
                 r"$\text{pot} \sim \operatorname{Potential}(f(\text{beta},~\text{alpha}))$",
             ],
             ("latex", False): [
-                r"$\text{alpha} \sim \operatorname{N}$",
-                r"$\text{sigma} \sim \operatorname{N^{+}}$",
+                r"$\text{alpha} \sim \operatorname{Normal}$",
+                r"$\text{sigma} \sim \operatorname{HalfNormal}$",
                 r"$\text{mu} \sim \operatorname{Deterministic}$",
-                r"$\text{beta} \sim \operatorname{N}$",
-                r"$\text{Z} \sim \operatorname{N}$",
-                r"$\text{nb_with_p_n} \sim \operatorname{NB}$",
+                r"$\text{beta} \sim \operatorname{Normal}$",
+                r"$\text{Z} \sim \operatorname{MultivariateNormal}$",
+                r"$\text{nb_with_p_n} \sim \operatorname{NegativeBinomial}$",
                 r"$\text{zip} \sim \operatorname{MarginalMixture}$",
-                r"$\text{w} \sim \operatorname{Dir}$",
+                r"$\text{w} \sim \operatorname{Dirichlet}$",
                 r"$\text{nested_mix} \sim \operatorname{MarginalMixture}$",
-                r"$\text{Y_obs} \sim \operatorname{N}$",
+                r"$\text{Y_obs} \sim \operatorname{Normal}$",
                 r"$\text{pot} \sim \operatorname{Potential}$",
             ],
         }
@@ -210,28 +210,28 @@ def setup_class(self):
         self.formats = [("plain", True), ("plain", False), ("latex", True), ("latex", False)]
         self.expected = {
             ("plain", True): [
-                r"a ~ N(2, 1)",
-                r"b ~ N(<shared>, 1)",
-                r"c ~ N(2, 1)",
-                r"d ~ N(<constant>, 1)",
+                r"a ~ Normal(2, 1)",
+                r"b ~ Normal(<shared>, 1)",
+                r"c ~ Normal(2, 1)",
+                r"d ~ Normal(<constant>, 1)",
             ],
             ("plain", False): [
-                r"a ~ N",
-                r"b ~ N",
-                r"c ~ N",
-                r"d ~ N",
+                r"a ~ Normal",
+                r"b ~ Normal",
+                r"c ~ Normal",
+                r"d ~ Normal",
             ],
             ("latex", True): [
-                r"$\text{a} \sim \operatorname{N}(2,~1)$",
-                r"$\text{b} \sim \operatorname{N}(\text{<shared>},~1)$",
-                r"$\text{c} \sim \operatorname{N}(2,~1)$",
-                r"$\text{d} \sim \operatorname{N}(\text{<constant>},~1)$",
+                r"$\text{a} \sim \operatorname{Normal}(2,~1)$",
+                r"$\text{b} \sim \operatorname{Normal}(\text{<shared>},~1)$",
+                r"$\text{c} \sim \operatorname{Normal}(2,~1)$",
+                r"$\text{d} \sim \operatorname{Normal}(\text{<constant>},~1)$",
             ],
             ("latex", False): [
-                r"$\text{a} \sim \operatorname{N}$",
-                r"$\text{b} \sim \operatorname{N}$",
-                r"$\text{c} \sim \operatorname{N}$",
-                r"$\text{d} \sim \operatorname{N}$",
+                r"$\text{a} \sim \operatorname{Normal}$",
+                r"$\text{b} \sim \operatorname{Normal}$",
+                r"$\text{c} \sim \operatorname{Normal}$",
+                r"$\text{d} \sim \operatorname{Normal}$",
             ],
         }
 
@@ -249,9 +249,9 @@ def test_model_latex_repr_three_levels_model():
     expected = [
         "$$",
         "\\begin{array}{rcl}",
-        "\\text{mu} &\\sim & \\operatorname{N}(0,~5)\\\\\\text{sigma} &\\sim & "
-        "\\operatorname{C^{+}}(0,~2.5)\\\\\\text{censored_normal} &\\sim & "
-        "\\operatorname{Censored}(\\operatorname{N}(\\text{mu},~\\text{sigma}),~-2,~2)",
+        "\\text{mu} &\\sim & \\operatorname{Normal}(0,~5)\\\\\\text{sigma} &\\sim & "
+        "\\operatorname{HalfCauchy}(0,~2.5)\\\\\\text{censored_normal} &\\sim & "
+        "\\operatorname{Censored}(\\operatorname{Normal}(\\text{mu},~\\text{sigma}),~-2,~2)",
         "\\end{array}",
         "$$",
     ]
@@ -268,8 +268,8 @@ def test_model_latex_repr_mixture_model():
         "$$",
         "\\begin{array}{rcl}",
         "\\text{w} &\\sim & "
-        "\\operatorname{Dir}(\\text{<constant>})\\\\\\text{mix} &\\sim & "
-        "\\operatorname{MarginalMixture}(\\text{w},~\\operatorname{N}(0,~5),~\\operatorname{StudentT}(7,~0,~1))",
+        "\\operatorname{Dirichlet}(\\text{<constant>})\\\\\\text{mix} &\\sim & "
+        "\\operatorname{MarginalMixture}(\\text{w},~\\operatorname{Normal}(0,~5),~\\operatorname{StudentT}(7,~0,~1))",
         "\\end{array}",
         "$$",
     ]
