pre-commit ruff 0.8.0 update (#7595)

Author: juanitorduz

.pre-commit-config.yaml

@@ -48,7 +48,7 @@ repos:
         - --exclude=binder/
         - --exclude=versioneer.py
 - repo: https://github.com/astral-sh/ruff-pre-commit
-  rev: v0.7.4
+  rev: v0.8.0
   hooks:
     - id: ruff
       args: [--fix, --show-fixes]

pymc/backends/__init__.py

@@ -89,7 +89,7 @@
     RunType = type(None)  # type: ignore[assignment, misc]
 
 
-__all__ = ["to_inference_data", "predictions_to_inference_data"]
+__all__ = ["predictions_to_inference_data", "to_inference_data"]
 
 
 def _init_trace(

pymc/data.py

@@ -42,12 +42,12 @@
 from pymc.vartypes import isgenerator
 
 __all__ = [
-    "get_data",
+    "ConstantData",
+    "Data",
     "GeneratorAdapter",
     "Minibatch",
-    "Data",
-    "ConstantData",
     "MutableData",
+    "get_data",
 ]
 BASE_URL = "https://raw.githubusercontent.com/pymc-devs/pymc-examples/main/examples/data/{filename}"
 

pymc/distributions/__init__.py

@@ -116,93 +116,93 @@
 from pymc.distributions.truncated import Truncated
 
 __all__ = [
-    "Uniform",
-    "Flat",
-    "HalfFlat",
-    "Normal",
-    "TruncatedNormal",
+    "AR",
+    "CAR",
+    "GARCH11",
+    "ICAR",
+    "AsymmetricLaplace",
+    "Bernoulli",
     "Beta",
-    "Kumaraswamy",
-    "Exponential",
-    "Laplace",
-    "StudentT",
+    "BetaBinomial",
+    "Binomial",
+    "Categorical",
     "Cauchy",
-    "HalfCauchy",
-    "Gamma",
-    "Weibull",
-    "LogNormal",
-    "Lognormal",
-    "HalfStudentT",
+    "Censored",
     "ChiSquared",
-    "HalfNormal",
-    "Wald",
-    "Pareto",
-    "InverseGamma",
-    "ExGaussian",
-    "VonMises",
-    "Binomial",
-    "BetaBinomial",
-    "Bernoulli",
-    "Poisson",
-    "NegativeBinomial",
+    "Continuous",
+    "CustomDist",
+    "DensityDist",
     "DiracDelta",
-    "ZeroInflatedPoisson",
-    "ZeroInflatedNegativeBinomial",
-    "ZeroInflatedBinomial",
+    "Dirichlet",
+    "DirichletMultinomial",
+    "Discrete",
     "DiscreteUniform",
+    "DiscreteWeibull",
+    "Distribution",
+    "EulerMaruyama",
+    "ExGaussian",
+    "Exponential",
+    "Flat",
+    "Gamma",
+    "GaussianRandomWalk",
     "Geometric",
+    "Gumbel",
+    "HalfCauchy",
+    "HalfFlat",
+    "HalfNormal",
+    "HalfStudentT",
+    "HurdleGamma",
+    "HurdleLogNormal",
+    "HurdleNegativeBinomial",
+    "HurdlePoisson",
     "HyperGeometric",
-    "Categorical",
-    "OrderedLogistic",
-    "OrderedProbit",
-    "DensityDist",
-    "CustomDist",
-    "Distribution",
-    "SymbolicRandomVariable",
-    "Continuous",
-    "Discrete",
-    "MvNormal",
-    "ZeroSumNormal",
-    "MatrixNormal",
+    "Interpolated",
+    "InverseGamma",
     "KroneckerNormal",
-    "MvStudentT",
-    "Dirichlet",
-    "StickBreakingWeights",
-    "Multinomial",
-    "DirichletMultinomial",
-    "OrderedMultinomial",
-    "Wishart",
-    "WishartBartlett",
+    "Kumaraswamy",
     "LKJCholeskyCov",
     "LKJCorr",
-    "AsymmetricLaplace",
-    "RandomWalk",
-    "GaussianRandomWalk",
+    "Laplace",
+    "LogNormal",
+    "Logistic",
+    "LogitNormal",
+    "Lognormal",
+    "MatrixNormal",
+    "Mixture",
+    "Moyal",
+    "Multinomial",
     "MvGaussianRandomWalk",
+    "MvNormal",
+    "MvStudentT",
     "MvStudentTRandomWalk",
-    "AR",
-    "EulerMaruyama",
-    "GARCH11",
-    "SkewNormal",
-    "Mixture",
+    "NegativeBinomial",
+    "Normal",
     "NormalMixture",
-    "Triangular",
-    "DiscreteWeibull",
-    "Gumbel",
-    "Logistic",
-    "LogitNormal",
-    "Interpolated",
+    "OrderedLogistic",
+    "OrderedMultinomial",
+    "OrderedProbit",
+    "Pareto",
+    "Poisson",
+    "PolyaGamma",
+    "RandomWalk",
     "Rice",
-    "Moyal",
     "Simulator",
-    "Truncated",
-    "Censored",
-    "CAR",
-    "ICAR",
-    "PolyaGamma",
-    "HurdleGamma",
-    "HurdleLogNormal",
-    "HurdleNegativeBinomial",
-    "HurdlePoisson",
+    "SkewNormal",
     "SkewStudentT",
+    "StickBreakingWeights",
+    "StudentT",
+    "SymbolicRandomVariable",
+    "Triangular",
+    "Truncated",
+    "TruncatedNormal",
+    "Uniform",
+    "VonMises",
+    "Wald",
+    "Weibull",
+    "Wishart",
+    "WishartBartlett",
+    "ZeroInflatedBinomial",
+    "ZeroInflatedNegativeBinomial",
+    "ZeroInflatedPoisson",
+    "ZeroSumNormal",
 ]

pymc/distributions/continuous.py

@@ -95,40 +95,40 @@ def polyagamma_cdf(*args, **kwargs):
 from pymc.math import invlogit, logdiffexp, logit
 
 __all__ = [
-    "Uniform",
-    "Flat",
-    "HalfFlat",
-    "Normal",
-    "TruncatedNormal",
+    "AsymmetricLaplace",
     "Beta",
-    "Kumaraswamy",
-    "Exponential",
-    "Laplace",
-    "StudentT",
     "Cauchy",
-    "HalfCauchy",
-    "Gamma",
-    "Weibull",
-    "HalfStudentT",
-    "LogNormal",
     "ChiSquared",
-    "HalfNormal",
-    "Wald",
-    "Pareto",
-    "InverseGamma",
     "ExGaussian",
-    "VonMises",
-    "SkewNormal",
-    "Triangular",
+    "Exponential",
+    "Flat",
+    "Gamma",
     "Gumbel",
+    "HalfCauchy",
+    "HalfFlat",
+    "HalfNormal",
+    "HalfStudentT",
+    "Interpolated",
+    "InverseGamma",
+    "Kumaraswamy",
+    "Laplace",
+    "LogNormal",
     "Logistic",
     "LogitNormal",
-    "Interpolated",
-    "Rice",
     "Moyal",
-    "AsymmetricLaplace",
+    "Normal",
+    "Pareto",
     "PolyaGamma",
+    "Rice",
+    "SkewNormal",
     "SkewStudentT",
+    "StudentT",
+    "Triangular",
+    "TruncatedNormal",
+    "Uniform",
+    "VonMises",
+    "Wald",
+    "Weibull",
 ]
 
 

pymc/distributions/discrete.py

@@ -52,18 +52,18 @@
 from pymc.math import sigmoid
 
 __all__ = [
-    "Binomial",
-    "BetaBinomial",
     "Bernoulli",
-    "DiscreteWeibull",
-    "Poisson",
-    "NegativeBinomial",
+    "BetaBinomial",
+    "Binomial",
+    "Categorical",
     "DiscreteUniform",
+    "DiscreteWeibull",
     "Geometric",
     "HyperGeometric",
-    "Categorical",
+    "NegativeBinomial",
     "OrderedLogistic",
     "OrderedProbit",
+    "Poisson",
 ]
 
 from pymc.pytensorf import normalize_rng_param

pymc/distributions/distribution.py

@@ -64,10 +64,10 @@
 from pymc.vartypes import continuous_types, string_types
 
 __all__ = [
-    "DiracDelta",
-    "Distribution",
     "Continuous",
+    "DiracDelta",
     "Discrete",
+    "Distribution",
     "SymbolicRandomVariable",
 ]
 

pymc/distributions/multivariate.py

@@ -83,22 +83,22 @@
 from pymc.util import check_dist_not_registered
 
 __all__ = [
-    "MvNormal",
-    "ZeroSumNormal",
-    "MvStudentT",
+    "CAR",
+    "ICAR",
     "Dirichlet",
-    "Multinomial",
     "DirichletMultinomial",
-    "OrderedMultinomial",
-    "Wishart",
-    "WishartBartlett",
-    "LKJCorr",
+    "KroneckerNormal",
     "LKJCholeskyCov",
+    "LKJCorr",
     "MatrixNormal",
-    "KroneckerNormal",
-    "CAR",
-    "ICAR",
+    "Multinomial",
+    "MvNormal",
+    "MvStudentT",
+    "OrderedMultinomial",
     "StickBreakingWeights",
+    "Wishart",
+    "WishartBartlett",
+    "ZeroSumNormal",
 ]
 
 solve_lower = partial(solve_triangular, lower=True)

pymc/distributions/shape_utils.py

@@ -36,9 +36,9 @@
 from pymc.pytensorf import convert_observed_data
 
 __all__ = [
-    "to_tuple",
-    "rv_size_is_none",
     "change_dist_size",
+    "rv_size_is_none",
+    "to_tuple",
 ]
 
 from pymc.exceptions import ShapeError

pymc/distributions/timeseries.py

@@ -49,13 +49,13 @@
 from pymc.util import check_dist_not_registered
 
 __all__ = [
-    "RandomWalk",
-    "GaussianRandomWalk",
-    "MvGaussianRandomWalk",
-    "MvStudentTRandomWalk",
     "AR",
     "GARCH11",
     "EulerMaruyama",
+    "GaussianRandomWalk",
+    "MvGaussianRandomWalk",
+    "MvStudentTRandomWalk",
+    "RandomWalk",
 ]
 
 

pymc/distributions/transforms.py

@@ -36,18 +36,18 @@
 )
 
 __all__ = [
-    "Transform",
-    "simplex",
-    "logodds",
+    "Chain",
+    "CholeskyCovPacked",
     "Interval",
+    "Transform",
+    "ZeroSumTransform",
+    "circular",
+    "log",
     "log_exp_m1",
+    "logodds",
     "ordered",
-    "log",
+    "simplex",
     "sum_to_1",
-    "circular",
-    "CholeskyCovPacked",
-    "Chain",
-    "ZeroSumTransform",
 ]