[pre-commit.ci] pre-commit autoupdate

.pre-commit-config.yaml

@@ -27,7 +27,7 @@ repos:
         - --exclude=binder/
         - --exclude=versioneer.py
 - repo: https://github.com/astral-sh/ruff-pre-commit
-  rev: v0.4.1
+  rev: v0.4.5
   hooks:
     - id: ruff
       args: ["--fix", "--output-format=full"]

pymc/backends/base.py

@@ -186,7 +186,7 @@
         for stats in sampler_vars:
             for key, dtype in stats.items():
                 if dtypes.setdefault(key, dtype) != dtype:
-                    raise ValueError("Sampler statistic %s appears with " "different types." % key)
+                    raise ValueError(f"Sampler statistic {key} appears with different types.")
 
         self.sampler_vars = sampler_vars
 
@@ -247,7 +247,7 @@
 
         sampler_idxs = [i for i, s in enumerate(self.sampler_vars) if stat_name in s]
         if not sampler_idxs:
-            raise KeyError("Unknown sampler stat %s" % stat_name)
+            raise KeyError(f"Unknown sampler stat {stat_name}")
 
         vals = np.stack(
             [self._get_sampler_stats(stat_name, i, burn, thin) for i in sampler_idxs], axis=-1
@@ -388,7 +388,7 @@
             return self.get_values(var, burn=burn, thin=thin)
         if var in self.stat_names:
             return self.get_sampler_stats(var, burn=burn, thin=thin)
-        raise KeyError("Unknown variable %s" % var)
+        raise KeyError(f"Unknown variable {var}")
 
     _attrs = {"_straces", "varnames", "chains", "stat_names", "_report"}
 
@@ -512,7 +512,7 @@
             List or ndarray depending on parameters.
         """
         if stat_name not in self.stat_names:
-            raise KeyError("Unknown sampler statistic %s" % stat_name)
+            raise KeyError(f"Unknown sampler statistic {stat_name}")
 
         if chains is None:
             chains = self.chains

pymc/distributions/multivariate.py

@@ -1047,11 +1047,11 @@
         tril_testval = None
 
     c = pt.sqrt(
-        ChiSquared("%s_c" % name, nu - np.arange(2, 2 + n_diag), shape=n_diag, initval=diag_testval)
+        ChiSquared(f"{name}_c", nu - np.arange(2, 2 + n_diag), shape=n_diag, initval=diag_testval)
     )
-    pm._log.info("Added new variable %s_c to model diagonal of Wishart." % name)
-    z = Normal("%s_z" % name, 0.0, 1.0, shape=n_tril, initval=tril_testval)
-    pm._log.info("Added new variable %s_z to model off-diagonals of Wishart." % name)
+    pm._log.info(f"Added new variable {name}_c to model diagonal of Wishart.")
+    z = Normal(f"{name}_z", 0.0, 1.0, shape=n_tril, initval=tril_testval)
+    pm._log.info(f"Added new variable {name}_z to model off-diagonals of Wishart.")
     # Construct A matrix
     A = pt.zeros(S.shape, dtype=np.float32)
     A = pt.set_subtensor(A[diag_idx], c)

pymc/distributions/shape_utils.py

@@ -141,11 +141,8 @@
     if size is None:
         broadcasted_shape = np.broadcast_shapes(*shapes)
         if broadcasted_shape is None:
-            raise ValueError(
-                "Cannot broadcast provided shapes {} given size: {}".format(
-                    ", ".join([f"{s}" for s in shapes]), size
-                )
-            )
+            tmp = ", ".join([f"{s}" for s in shapes])
+            raise ValueError(f"Cannot broadcast provided shapes {tmp} given size: {size}")
         return broadcasted_shape
     shapes = [_check_shape_type(s) for s in shapes]
     _size = to_tuple(size)
@@ -154,11 +151,8 @@
     try:
         broadcast_shape = np.broadcast_shapes(*sp_shapes)
     except ValueError:
-        raise ValueError(
-            "Cannot broadcast provided shapes {} given size: {}".format(
-                ", ".join([f"{s}" for s in shapes]), size
-            )
-        )
+        tmp = ", ".join([f"{s}" for s in shapes])
+        raise ValueError(f"Cannot broadcast provided shapes {tmp} given size: {size}")
     broadcastable_shapes = []
     for shape, sp_shape in zip(shapes, sp_shapes):
         if _size == shape[: len(_size)]:

pymc/gp/util.py

@@ -143,9 +143,8 @@ def getter(self):
                 value = getattr(self, name, None)
                 if value is None:
                     raise AttributeError(
-                        "'{}' not set.  Provide as argument "
-                        "to condition, or call 'prior' "
-                        "first".format(name.lstrip("_"))
+                        f"'{name.lstrip('_')}' not set.  Provide as argument "
+                        "to condition, or call 'prior' first"
                     )
                 else:
                     return value

pymc/model/core.py

@@ -209,7 +209,7 @@ def __init_subclass__(cls, **kwargs):
     def __call__(cls, *args, **kwargs):
         # We type hint Model here so type checkers understand that Model is a context manager.
         # This metaclass is only used for Model, so this is safe to do. See #6809 for more info.
-        instance: "Model" = cls.__new__(cls, *args, **kwargs)
+        instance: Model = cls.__new__(cls, *args, **kwargs)
         with instance:  # appends context
             instance.__init__(*args, **kwargs)
         return instance

pymc/printing.py

@@ -65,12 +65,11 @@ def str_for_dist(
             else r"\\operatorname{Unknown}"
         )
         if include_params:
+            params = ",~".join([d.strip("$") for d in dist_args])
             if print_name:
-                return r"${} \sim {}({})$".format(
-                    print_name, op_name, ",~".join([d.strip("$") for d in dist_args])
-                )
+                return rf"${print_name} \sim {op_name}({params})$"
             else:
-                return r"${}({})$".format(op_name, ",~".join([d.strip("$") for d in dist_args]))
+                return rf"${op_name}({params})$"
 
         else:
             if print_name:
@@ -83,10 +82,11 @@ def str_for_dist(
             dist.owner.op._print_name[0] if hasattr(dist.owner.op, "_print_name") else "Unknown"
         )
         if include_params:
+            params = ", ".join(dist_args)
             if print_name:
-                return r"{} ~ {}({})".format(print_name, dist_name, ", ".join(dist_args))
+                return rf"{print_name} ~ {dist_name}({params})"
             else:
-                return r"{}({})".format(dist_name, ", ".join(dist_args))
+                return rf"{dist_name}({params})"
         else:
             if print_name:
                 return rf"{print_name} ~ {dist_name}"

pymc/sampling/forward.py

@@ -419,7 +419,7 @@
 
     data = {k: np.stack(v) for k, v in zip(names, values)}
     if data is None:
-        raise AssertionError("No variables sampled: attempting to sample %s" % names)
+        raise AssertionError(f"No variables sampled: attempting to sample {names}")
 
     prior: dict[str, np.ndarray] = {}
     for var_name in vars_:
@@ -765,8 +765,7 @@
         samples = len(_trace)
     else:
         raise TypeError(
-            "Do not know how to compute number of samples for trace argument of type %s"
-            % type(_trace)
+            f"Do not know how to compute number of samples for trace argument of type {type(_trace)}"
         )
 
     assert samples is not None

pymc/sampling/mcmc.py

@@ -697,10 +697,7 @@ def joined_blas_limiter():
         msg = "Tuning was enabled throughout the whole trace."
         _log.warning(msg)
     elif draws < 100:
-        msg = (
-            "Only %s samples per chain. Reliable r-hat and ESS diagnostics require longer chains for accurate estimate."
-            % draws
-        )
+        msg = f"Only {draws} samples per chain. Reliable r-hat and ESS diagnostics require longer chains for accurate estimate."
         _log.warning(msg)
 
     auto_nuts_init = True

pymc/sampling/parallel.py

@@ -58,7 +58,7 @@
         tb = traceback.format_exception(type(exc), exc, tb)
         tb = "".join(tb)
         self.exc = exc
-        self.tb = '\n"""\n%s"""' % tb
+        self.tb = f'\n"""\n{tb}"""'
 
     def __reduce__(self):
         return rebuild_exc, (self.exc, self.tb)
@@ -216,7 +216,7 @@
         mp_ctx,
     ):
         self.chain = chain
-        process_name = "worker_chain_%s" % chain
+        process_name = f"worker_chain_{chain}"
         self._msg_pipe, remote_conn = multiprocessing.Pipe()
 
         self._shared_point = {}
@@ -228,7 +228,7 @@
                 size *= int(dim)
             size *= dtype.itemsize
             if size != ctypes.c_size_t(size).value:
-                raise ValueError("Variable %s is too large" % name)
+                raise ValueError(f"Variable {name} is too large")
 
             array = mp_ctx.RawArray("c", size)
             self._shared_point[name] = (array, shape, dtype)
@@ -388,7 +388,7 @@
         mp_ctx=None,
     ):
         if any(len(arg) != chains for arg in [seeds, start_points]):
-            raise ValueError("Number of seeds and start_points must be %s." % chains)
+            raise ValueError(f"Number of seeds and start_points must be {chains}.")
 
         if mp_ctx is None or isinstance(mp_ctx, str):
             # Closes issue https://github.com/pymc-devs/pymc/issues/3849

pymc/step_methods/hmc/integration.py

@@ -51,7 +51,7 @@
     def compute_state(self, q: RaveledVars, p: RaveledVars):
         """Compute Hamiltonian functions using a position and momentum."""
         if q.data.dtype != self._dtype or p.data.dtype != self._dtype:
-            raise ValueError("Invalid dtype. Must be %s" % self._dtype)
+            raise ValueError(f"Invalid dtype. Must be {self._dtype}")
 
         logp, dlogp = self._logp_dlogp_func(q)
 

pymc/step_methods/metropolis.py

@@ -178,7 +178,7 @@
         elif S.ndim == 2:
             self.proposal_dist = MultivariateNormalProposal(S)
         else:
-            raise ValueError("Invalid rank for variance: %s" % S.ndim)
+            raise ValueError(f"Invalid rank for variance: {S.ndim}")
 
         self.scaling = np.atleast_1d(scaling).astype("d")
         self.tune = tune

pymc/variational/inference.py

@@ -72,8 +72,8 @@ def _maybe_score(self, score):
             score = returns_loss
         elif score and not returns_loss:
             warnings.warn(
-                "method `fit` got `score == True` but %s "
-                "does not return loss. Ignoring `score` argument" % self.objective.op
+                f"method `fit` got `score == True` but {self.objective.op} "
+                "does not return loss. Ignoring `score` argument"
             )
             score = False
         else:

pymc/variational/opvi.py

@@ -375,7 +375,7 @@
         if fn_kwargs is None:
             fn_kwargs = {}
         if score and not self.op.returns_loss:
-            raise NotImplementedError("%s does not have loss" % self.op)
+            raise NotImplementedError(f"{self.op} does not have loss")
         updates = self.updates(
             obj_n_mc=obj_n_mc,
             tf_n_mc=tf_n_mc,
@@ -416,7 +416,7 @@
         if fn_kwargs is None:
             fn_kwargs = {}
         if not self.op.returns_loss:
-            raise NotImplementedError("%s does not have loss" % self.op)
+            raise NotImplementedError(f"{self.op} does not have loss")
         if more_replacements is None:
             more_replacements = {}
         loss = self(sc_n_mc, more_replacements=more_replacements)
@@ -496,13 +496,13 @@
     def __call__(self, f=None):
         if self.has_test_function:
             if f is None:
-                raise ParametrizationError("Operator %s requires TestFunction" % self)
+                raise ParametrizationError(f"Operator {self} requires TestFunction")
             else:
                 if not isinstance(f, TestFunction):
                     f = TestFunction.from_function(f)
         else:
             if f is not None:
-                warnings.warn("TestFunction for %s is redundant and removed" % self, stacklevel=3)
+                warnings.warn(f"TestFunction for {self} is redundant and removed", stacklevel=3)
             else:
                 pass
             f = TestFunction()
@@ -555,7 +555,7 @@
     @classmethod
     def from_function(cls, f):
         if not callable(f):
-            raise ParametrizationError("Need callable, got %r" % f)
+            raise ParametrizationError(f"Need callable, got {f!r}")
         obj = TestFunction()
         obj.__call__ = f
         return obj
@@ -1512,7 +1512,7 @@
                 found.name = name + "_vi_random_slice"
                 break
         else:
-            raise KeyError("%r not found" % name)
+            raise KeyError(f"{name!r} not found")
         return found
 
     @node_property

pyproject.toml

@@ -7,11 +7,10 @@ addopts = ["--color=yes"]
 [tool.ruff]
 line-length = 100
 target-version = "py310"
-exclude = ["versioneer.py"]
+extend-exclude = ["versioneer.py", "_version.py"]
 
 [tool.ruff.lint]
 select = ["D", "E", "F", "I", "UP", "W", "RUF"]
-ignore-init-module-imports = true
 ignore = [
   "E501",
   "F841", # Local variable name is assigned to but never used

tests/variational/test_opvi.py

@@ -261,7 +261,7 @@ def test_logq_mini_2_sample_2_var(parametric_grouped_approxes, three_var_model):
 
 def test_logq_globals(three_var_approx):
     if not three_var_approx.has_logq:
-        pytest.skip("%s does not implement logq" % three_var_approx)
+        pytest.skip(f"{three_var_approx} does not implement logq")
     approx = three_var_approx
     logq, symbolic_logq = approx.set_size_and_deterministic(
         [approx.logq, approx.symbolic_logq], 1, 0