⬆️ 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])

pymc/distributions/distribution.py

@@ -97,7 +97,6 @@ class DistributionMeta(ABCMeta):
     """
 
     def __new__(cls, name, bases, clsdict):
-
         # Forcefully deprecate old v3 `Distribution`s
         if "random" in clsdict:
 
@@ -453,7 +452,6 @@ class Discrete(Distribution):
     """Base class for discrete distributions"""
 
     def __new__(cls, name, *args, **kwargs):
-
         if kwargs.get("transform", None):
             raise ValueError("Transformations for discrete distributions")
 
@@ -500,7 +498,6 @@ def dist(
         dtype: str = "floatX",
         **kwargs,
     ):
-
         dist_params = [as_tensor_variable(param) for param in dist_params]
 
         # Assume scalar ndims_params
@@ -608,7 +605,6 @@ def update(self, node: Node):
 
 
 class _CustomSymbolicDist(Distribution):
-
     rv_type = CustomSymbolicDistRV
 
     @classmethod

pymc/distributions/mixture.py

@@ -358,7 +358,6 @@ def marginal_mixture_logprob(op, values, rng, weights, *components, **kwargs):
 
 @_logcdf.register(MarginalMixtureRV)
 def marginal_mixture_logcdf(op, value, rng, weights, *components, **kwargs):
-
     # single component
     if len(components) == 1:
         # Need to broadcast value across mixture axis

pymc/distributions/multivariate.py

@@ -310,7 +310,6 @@ def make_node(self, rng, size, dtype, nu, mu, cov):
         return super().make_node(rng, size, dtype, nu, mu, cov)
 
     def __call__(self, nu, mu=None, cov=None, size=None, **kwargs):
-
         dtype = pytensor.config.floatX if self.dtype == "floatX" else self.dtype
 
         if mu is None:
@@ -326,7 +325,6 @@ def _supp_shape_from_params(self, dist_params, rep_param_idx=1, param_shapes=Non
 
     @classmethod
     def rng_fn(cls, rng, nu, mu, cov, size):
-
         mv_samples = multivariate_normal.rng_fn(rng=rng, mean=np.zeros_like(mu), cov=cov, size=size)
 
         # Take chi2 draws and add an axis of length 1 to the right for correct broadcasting below
@@ -618,7 +616,6 @@ def _supp_shape_from_params(self, dist_params, rep_param_idx=1, param_shapes=Non
 
     @classmethod
     def rng_fn(cls, rng, n, a, size):
-
         if n.ndim > 0 or a.ndim > 1:
             n, a = broadcast_params([n, a], cls.ndims_params)
             size = tuple(size or ())
@@ -865,7 +862,6 @@ def make_node(self, x):
 
     # Python implementation:
     def perform(self, node, inputs, outputs):
-
         (x,) = inputs
         (z,) = outputs
         try:
@@ -1494,7 +1490,6 @@ def _supp_shape_from_params(self, dist_params, **kwargs):
 
     @classmethod
     def rng_fn(cls, rng, n, eta, size):
-
         # We flatten the size to make operations easier, and then rebuild it
         if size is None:
             flat_size = 1
@@ -1651,7 +1646,6 @@ def _infer_shape(self, size, dist_params, param_shapes=None):
 
     @classmethod
     def rng_fn(cls, rng, mu, rowchol, colchol, size=None):
-
         size = to_tuple(size)
         dist_shape = to_tuple([rowchol.shape[0], colchol.shape[0]])
         output_shape = size + dist_shape
@@ -1773,7 +1767,6 @@ def dist(
         *args,
         **kwargs,
     ):
-
         cholesky = Cholesky(lower=True, on_error="raise")
 
         # Among-row matrices
@@ -1971,7 +1964,6 @@ class KroneckerNormal(Continuous):
 
     @classmethod
     def dist(cls, mu, covs=None, chols=None, evds=None, sigma=None, *args, **kwargs):
-
         if len([i for i in [covs, chols, evds] if i is not None]) != 1:
             raise ValueError(
                 "Incompatible parameterization. Specify exactly one of covs, chols, or evds."
@@ -2236,7 +2228,6 @@ class StickBreakingWeightsRV(RandomVariable):
     _print_name = ("StickBreakingWeights", "\\operatorname{StickBreakingWeights}")
 
     def make_node(self, rng, size, dtype, alpha, K):
-
         alpha = at.as_tensor_variable(alpha)
         K = at.as_tensor_variable(intX(K))
 
@@ -2519,7 +2510,6 @@ def check_zerosum_axes(cls, zerosum_axes: Optional[int]) -> int:
 
     @classmethod
     def rv_op(cls, sigma, zerosum_axes, support_shape, size=None):
-
         shape = to_tuple(size) + tuple(support_shape)
         normal_dist = ignore_logprob(pm.Normal.dist(sigma=sigma, shape=shape))
 
@@ -2546,7 +2536,6 @@ def rv_op(cls, sigma, zerosum_axes, support_shape, size=None):
 
 @_change_dist_size.register(ZeroSumNormalRV)
 def change_zerosum_size(op, normal_dist, new_size, expand=False):
-
     normal_dist, sigma, support_shape = normal_dist.owner.inputs
 
     if expand:

pymc/distributions/simulator.py

@@ -167,7 +167,6 @@ def dist(  # type: ignore
         dtype="floatX",
         **kwargs,
     ):
-
         if not isinstance(distance, Op):
             if distance == "gaussian":
                 distance = gaussian

pymc/distributions/timeseries.py

@@ -677,7 +677,6 @@ def step(*args):
 
 @_change_dist_size.register(AutoRegressiveRV)
 def change_ar_size(op, dist, new_size, expand=False):
-
     if expand:
         old_size = dist.shape[:-1]
         new_size = tuple(new_size) + tuple(old_size)
@@ -850,7 +849,6 @@ def step(prev_y, prev_sigma, omega, alpha_1, beta_1, rng):
 
 @_change_dist_size.register(GARCH11RV)
 def change_garch11_size(op, dist, new_size, expand=False):
-
     if expand:
         old_size = dist.shape[:-1]
         new_size = tuple(new_size) + tuple(old_size)
@@ -1035,7 +1033,6 @@ def step(*prev_args):
 
 @_change_dist_size.register(EulerMaruyamaRV)
 def change_eulermaruyama_size(op, dist, new_size, expand=False):
-
     if expand:
         old_size = dist.shape[:-1]
         new_size = tuple(new_size) + tuple(old_size)

pymc/distributions/transforms.py

@@ -164,7 +164,6 @@ def log_jac_det(self, value, *inputs):
 
 
 class Chain(RVTransform):
-
     __slots__ = ("param_extract_fn", "transform_list", "name")
 
     def __init__(self, transform_list):

pymc/distributions/truncated.py

@@ -161,7 +161,6 @@ def dist(cls, dist, lower=None, upper=None, max_n_steps: int = 10_000, **kwargs)
 
     @classmethod
     def rv_op(cls, dist, lower, upper, max_n_steps, size=None):
-
         # Try to use specialized Op
         try:
             return _truncated(dist.owner.op, lower, upper, size, *dist.owner.inputs)

pymc/initial_point.py

@@ -161,7 +161,6 @@ def make_initial_point_fn(
         varnames.append(name)
 
     def make_seeded_function(func):
-
         rngs = find_rng_nodes(func.maker.fgraph.outputs)
 
         @functools.wraps(func)

pymc/logprob/censoring.py

@@ -178,7 +178,6 @@ class MeasurableRound(MeasurableElemwise):
 
 @node_rewriter(tracks=[ceil, floor, round_half_to_even])
 def find_measurable_roundings(fgraph: FunctionGraph, node: Node) -> Optional[List[MeasurableRound]]:
-
     rv_map_feature = getattr(fgraph, "preserve_rv_mappings", None)
     if rv_map_feature is None:
         return None  # pragma: no cover

pymc/logprob/joint_logprob.py

@@ -196,7 +196,6 @@ def factorized_joint_logprob(
             q_logprob_vars = [q_logprob_vars]
 
         for q_value_var, q_logprob_var in zip(q_value_vars, q_logprob_vars):
-
             q_value_var = original_values[q_value_var]
 
             if q_value_var.name:

pymc/logprob/mixture.py

@@ -138,7 +138,6 @@ def expand_indices(
     shape_copy = list(shape)
     n_preceding_basics = 0
     for d, idx in enumerate(full_indices):
-
         if not is_basic_idx(idx):
             s = shape_copy.pop(0)
 
@@ -326,7 +325,6 @@ def mixture_replace(fgraph, node):
     # that belong to each one (by way of their indices).
     new_mixture_rvs = []
     for i, component_rv in enumerate(mixture_rvs):
-
         # We create custom types for the mixture components and assign them
         # null `get_measurable_outputs` dispatches so that they aren't
         # erroneously encountered in places like `factorized_joint_logprob`.