⬆️ UPGRADE: Autoupdate pre-commit config

Author: pymc-bot

.pre-commit-config.yaml

@@ -34,12 +34,12 @@ repos:
     - id: pyupgrade
       args: [--py37-plus]
 - repo: https://github.com/psf/black
-  rev: 22.12.0
+  rev: 23.1.0
   hooks:
     - id: black
     - id: black-jupyter
 - repo: https://github.com/PyCQA/pylint
-  rev: v2.16.0b1
+  rev: v2.16.1
   hooks:
     - id: pylint
       args: [--rcfile=.pylintrc]

docs/source/learn/core_notebooks/dimensionality.ipynb

@@ -1661,7 +1661,6 @@
     "        \"year\": [2020, 2021, 2022],\n",
     "    }\n",
     ") as pmodel:\n",
-    "\n",
     "    pm.Normal(\"profit\", dims=\"year\")\n",
     "\n",
     "pm.model_to_graphviz(pmodel)"

docs/source/learn/core_notebooks/model_comparison.ipynb

@@ -135,7 +135,6 @@
    ],
    "source": [
     "with pm.Model() as pooled:\n",
-    "\n",
     "    # Latent pooled effect size\n",
     "    mu = pm.Normal(\"mu\", 0, sigma=1e6)\n",
     "\n",
@@ -242,7 +241,6 @@
    ],
    "source": [
     "with pm.Model() as hierarchical:\n",
-    "\n",
     "    eta = pm.Normal(\"eta\", 0, 1, shape=J)\n",
     "    # Hierarchical mean and SD\n",
     "    mu = pm.Normal(\"mu\", 0, sigma=10)\n",

docs/source/learn/core_notebooks/pymc_overview.ipynb

@@ -205,7 +205,6 @@
     "basic_model = pm.Model()\n",
     "\n",
     "with basic_model:\n",
-    "\n",
     "    # Priors for unknown model parameters\n",
     "    alpha = pm.Normal(\"alpha\", mu=0, sigma=10)\n",
     "    beta = pm.Normal(\"beta\", mu=0, sigma=10, shape=2)\n",
@@ -3417,7 +3416,6 @@
     "import pytensor.tensor as at\n",
     "\n",
     "with pm.Model(coords={\"predictors\": X.columns.values}) as test_score_model:\n",
-    "\n",
     "    # Prior on error SD\n",
     "    sigma = pm.HalfNormal(\"sigma\", 25)\n",
     "\n",
@@ -3752,7 +3750,6 @@
    ],
    "source": [
     "with test_score_model:\n",
-    "\n",
     "    idata = pm.sample(1000, tune=2000, random_seed=42)"
    ]
   },
@@ -3832,7 +3829,6 @@
    ],
    "source": [
     "with test_score_model:\n",
-    "\n",
     "    idata = pm.sample(1000, tune=2000, random_seed=42, target_accept=0.99)"
    ]
   },
@@ -4048,7 +4044,6 @@
    ],
    "source": [
     "with pm.Model() as disaster_model:\n",
-    "\n",
     "    switchpoint = pm.DiscreteUniform(\"switchpoint\", lower=years.min(), upper=years.max())\n",
     "\n",
     "    # Priors for pre- and post-switch rates number of disasters\n",
@@ -4356,7 +4351,6 @@
    "outputs": [],
    "source": [
     "class Beta(pm.Continuous):\n",
-    "\n",
     "    rv_op = beta\n",
     "\n",
     "    @classmethod\n",

pymc/backends/arviz.py

@@ -71,6 +71,7 @@ def find_observations(model: "Model") -> Dict[str, Var]:
 
 def find_constants(model: "Model") -> Dict[str, Var]:
     """If there are constants available, return them as a dictionary."""
+
     # The constant data vars must be either pm.Data or TensorConstant or SharedVariable
     def is_data(name, var, model) -> bool:
         observations = find_observations(model)
@@ -174,7 +175,6 @@ def __init__(
         save_warmup: Optional[bool] = None,
         include_transformed: bool = False,
     ):
-
         self.save_warmup = rcParams["data.save_warmup"] if save_warmup is None else save_warmup
         self.include_transformed = include_transformed
         self.trace = trace

pymc/backends/ndarray.py

@@ -219,6 +219,7 @@ def point_list_to_multitrace(
     with _model:
         chain = NDArray(model=_model, vars=[_model[vn] for vn in varnames])
         chain.setup(draws=len(point_list), chain=0)
+
         # since we are simply loading a trace by hand, we need only a vacuous function for
         # chain.record() to use. This crushes the default.
         def point_fun(point):

pymc/blocking.py

@@ -34,6 +34,7 @@
 StatsDict: TypeAlias = Dict[str, Any]
 StatsType: TypeAlias = List[StatsDict]
 
+
 # `point_map_info` is a tuple of tuples containing `(name, shape, dtype)` for
 # each of the raveled variables.
 class RaveledVars(NamedTuple):

pymc/distributions/bound.py

@@ -182,7 +182,6 @@ def __new__(
         dims=None,
         **kwargs,
     ):
-
         warnings.warn(
             "Bound has been deprecated in favor of Truncated, and will be removed in a "
             "future release. If Truncated is not an option, Bound can be implemented by"
@@ -234,7 +233,6 @@ def dist(
         shape=None,
         **kwargs,
     ):
-
         cls._argument_checks(dist, **kwargs)
         lower, upper, initval = cls._set_values(lower, upper, size, shape, initval=None)
         dist = ignore_logprob(dist)

pymc/distributions/censored.py

@@ -101,7 +101,6 @@ def dist(cls, dist, lower, upper, **kwargs):
 
     @classmethod
     def rv_op(cls, dist, lower=None, upper=None, size=None):
-
         lower = at.constant(-np.inf) if lower is None else at.as_tensor_variable(lower)
         upper = at.constant(np.inf) if upper is None else at.as_tensor_variable(upper)
 

pymc/distributions/continuous.py

@@ -171,7 +171,6 @@ def bounded_cont_transform(op, rv, bound_args_indices=None):
         raise ValueError(f"Must specify bound_args_indices for {op} bounded distribution")
 
     def transform_params(*args):
-
         lower, upper = None, None
         if bound_args_indices[0] is not None:
             lower = args[bound_args_indices[0]]
@@ -3474,7 +3473,6 @@ class Interpolated(BoundedContinuous):
 
     @classmethod
     def dist(cls, x_points, pdf_points, *args, **kwargs):
-
         interp = InterpolatedUnivariateSpline(x_points, pdf_points, k=1, ext="zeros")
 
         Z = interp.integral(x_points[0], x_points[-1])