pre-commit update ruff 0.9.1 (#7648)

Co-authored-by: Thomas Wiecki <[email protected]>

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: v0.8.4
+  rev: v0.9.1
   hooks:
     - id: ruff
       args: [--fix, --show-fixes]

docs/source/learn/core_notebooks/pymc_pytensor.ipynb

@@ -1849,7 +1849,7 @@
     "print(\n",
     "    f\"\"\"\n",
     "mu_value -> {scipy.stats.norm.logpdf(x=0, loc=0, scale=2)}\n",
-    "sigma_log_value -> {- 10 + scipy.stats.halfnorm.logpdf(x=np.exp(-10), loc=0, scale=3)}\n",
+    "sigma_log_value -> {-10 + scipy.stats.halfnorm.logpdf(x=np.exp(-10), loc=0, scale=3)}\n",
     "x_value -> {scipy.stats.norm.logpdf(x=0, loc=0, scale=np.exp(-10))}\n",
     "\"\"\"\n",
     ")"

pymc/data.py

@@ -257,7 +257,7 @@ def determine_coords(
     if isinstance(value, np.ndarray) and dims is not None:
         if len(dims) != value.ndim:
             raise pm.exceptions.ShapeError(
-                "Invalid data shape. The rank of the dataset must match the " "length of `dims`.",
+                "Invalid data shape. The rank of the dataset must match the length of `dims`.",
                 actual=value.shape,
                 expected=value.ndim,
             )

pymc/distributions/continuous.py

@@ -992,8 +992,7 @@ def get_mu_lam_phi(mu, lam, phi):
                     return mu, lam, lam / mu
 
         raise ValueError(
-            "Wald distribution must specify either mu only, "
-            "mu and lam, mu and phi, or lam and phi."
+            "Wald distribution must specify either mu only, mu and lam, mu and phi, or lam and phi."
         )
 
     def logp(value, mu, lam, alpha):
@@ -1603,8 +1602,7 @@ def dist(cls, kappa=None, mu=None, b=None, q=None, *args, **kwargs):
     def get_kappa(cls, kappa=None, q=None):
         if kappa is not None and q is not None:
             raise ValueError(
-                "Incompatible parameterization. Either use "
-                "kappa or q to specify the distribution."
+                "Incompatible parameterization. Either use kappa or q to specify the distribution."
             )
         elif q is not None:
             if isinstance(q, Variable):
@@ -3483,7 +3481,7 @@ def get_nu_b(cls, nu, b, sigma):
         elif nu is not None and b is None:
             b = nu / sigma
             return nu, b, sigma
-        raise ValueError("Rice distribution must specify either nu" " or b.")
+        raise ValueError("Rice distribution must specify either nu or b.")
 
     def support_point(rv, size, nu, sigma):
         nu_sigma_ratio = -(nu**2) / (2 * sigma**2)

pymc/distributions/multivariate.py

@@ -247,7 +247,9 @@ class MvNormal(Continuous):
         data = np.random.multivariate_normal(mu, true_cov, 10)
 
         sd_dist = pm.Exponential.dist(1.0, shape=3)
-        chol, corr, stds = pm.LKJCholeskyCov("chol_cov", n=3, eta=2, sd_dist=sd_dist, compute_corr=True)
+        chol, corr, stds = pm.LKJCholeskyCov(
+            "chol_cov", n=3, eta=2, sd_dist=sd_dist, compute_corr=True
+        )
         vals = pm.MvNormal("vals", mu=mu, chol=chol, observed=data)
 
     For unobserved values it can be better to use a non-centered
@@ -2793,9 +2795,9 @@ def dist(cls, sigma=1.0, n_zerosum_axes=None, support_shape=None, **kwargs):
 
         support_shape = pt.as_tensor(support_shape, dtype="int64", ndim=1)
 
-        assert n_zerosum_axes == pt.get_vector_length(
-            support_shape
-        ), "support_shape has to be as long as n_zerosum_axes"
+        assert n_zerosum_axes == pt.get_vector_length(support_shape), (
+            "support_shape has to be as long as n_zerosum_axes"
+        )
 
         return super().dist([sigma, support_shape], **kwargs)
 

pymc/gp/cov.py

@@ -328,8 +328,7 @@ def power_spectral_density(self, omega: TensorLike) -> TensorVariable:
         check = Counter([isinstance(factor, Covariance) for factor in self._factor_list])
         if check.get(True, 0) >= 2:
             raise NotImplementedError(
-                "The power spectral density of products of covariance "
-                "functions is not implemented."
+                "The power spectral density of products of covariance functions is not implemented."
             )
         return reduce(mul, self._merge_factors_psd(omega))
 

pymc/gp/util.py

@@ -211,8 +211,7 @@ def plot_gp_dist(
         samples_kwargs = {}
     if np.any(np.isnan(samples)):
         warnings.warn(
-            "There are `nan` entries in the [samples] arguments. "
-            "The plot will not contain a band!",
+            "There are `nan` entries in the [samples] arguments. The plot will not contain a band!",
             UserWarning,
         )
 

pymc/sampling/jax.py

@@ -108,8 +108,7 @@ def _replace_shared_variables(graph: list[TensorVariable]) -> list[TensorVariabl
 
     if any(var.default_update is not None for var in shared_variables):
         raise ValueError(
-            "Graph contains shared variables with default_update which cannot "
-            "be safely replaced."
+            "Graph contains shared variables with default_update which cannot be safely replaced."
         )
 
     replacements = {var: pt.constant(var.get_value(borrow=True)) for var in shared_variables}
@@ -360,7 +359,7 @@ def _sample_blackjax_nuts(
         map_fn = jax.vmap
     else:
         raise ValueError(
-            "Only supporting the following methods to draw chains:" ' "parallel" or "vectorized"'
+            "Only supporting the following methods to draw chains: 'parallel' or 'vectorized'"
         )
 
     if chains == 1:

pymc/sampling/mcmc.py

@@ -1000,7 +1000,7 @@ def _sample_return(
         total_draws = draws_per_chain.sum()
 
         _log.info(
-            f'Sampling {n_chains} chain{"s" if n_chains > 1 else ""} for {desired_tune:_d} desired tune and {desired_draw:_d} desired draw iterations '
+            f"Sampling {n_chains} chain{'s' if n_chains > 1 else ''} for {desired_tune:_d} desired tune and {desired_draw:_d} desired draw iterations "
             f"(Actually sampled {total_n_tune:_d} tune and {total_draws:_d} draws total) "
             f"took {t_sampling:.0f} seconds."
         )
@@ -1062,8 +1062,8 @@ def _sample_return(
 
     n_chains = len(mtrace.chains)
     _log.info(
-        f'Sampling {n_chains} chain{"s" if n_chains > 1 else ""} for {n_tune:_d} tune and {n_draws:_d} draw iterations '
-        f"({n_tune*n_chains:_d} + {n_draws*n_chains:_d} draws total) "
+        f"Sampling {n_chains} chain{'s' if n_chains > 1 else ''} for {n_tune:_d} tune and {n_draws:_d} draw iterations "
+        f"({n_tune * n_chains:_d} + {n_draws * n_chains:_d} draws total) "
         f"took {t_sampling:.0f} seconds."
     )
 

pymc/sampling/population.py

@@ -386,9 +386,9 @@ def _prepare_iter_population(
 
     # 2. Set up the steppers
     steppers: list[Step] = []
-    assert (
-        len(rngs) == nchains
-    ), f"There must be one random Generator per chain. Got {len(rngs)} instead of {nchains}"
+    assert len(rngs) == nchains, (
+        f"There must be one random Generator per chain. Got {len(rngs)} instead of {nchains}"
+    )
     for c, rng in enumerate(rngs):
         # need independent samplers for each chain
         # it is important to copy the actual steppers (but not the delta_logp)

pymc/step_methods/compound.py

@@ -282,9 +282,9 @@ def sampling_state(self) -> DataClassState:
 
     @sampling_state.setter
     def sampling_state(self, state: DataClassState):
-        assert isinstance(
-            state, self._state_class
-        ), f"Invalid sampling state class {type(state)}. Expected {self._state_class}"
+        assert isinstance(state, self._state_class), (
+            f"Invalid sampling state class {type(state)}. Expected {self._state_class}"
+        )
         for method, state_method in zip(self.methods, state.methods):
             method.sampling_state = state_method
 

pymc/step_methods/state.py

@@ -90,9 +90,9 @@ def sampling_state(self) -> DataClassState:
     @sampling_state.setter
     def sampling_state(self, state: DataClassState):
         state_class = self._state_class
-        assert isinstance(
-            state, state_class
-        ), f"Encountered invalid state class '{state.__class__}'. State must be '{state_class}'"
+        assert isinstance(state, state_class), (
+            f"Encountered invalid state class '{state.__class__}'. State must be '{state_class}'"
+        )
         for field in fields(state_class):
             is_tensor_name = field.metadata.get("tensor_name", False)
             state_val = deepcopy(getattr(state, field.name))

pymc/testing.py

@@ -964,9 +964,9 @@ def check_rv_size(self):
                 assert actual == expected_symbolic == expected
 
     def validate_tests_list(self):
-        assert len(self.checks_to_run) == len(
-            set(self.checks_to_run)
-        ), "There are duplicates in the list of checks_to_run"
+        assert len(self.checks_to_run) == len(set(self.checks_to_run)), (
+            "There are duplicates in the list of checks_to_run"
+        )
 
 
 def seeded_scipy_distribution_builder(dist_name: str) -> Callable:

pymc/variational/opvi.py

@@ -710,9 +710,9 @@ class Group(WithMemoization):
 
     @classmethod
     def register(cls, sbcls):
-        assert (
-            frozenset(sbcls.__param_spec__) not in cls.__param_registry
-        ), "Duplicate __param_spec__"
+        assert frozenset(sbcls.__param_spec__) not in cls.__param_registry, (
+            "Duplicate __param_spec__"
+        )
         cls.__param_registry[frozenset(sbcls.__param_spec__)] = sbcls
         assert sbcls.short_name not in cls.__name_registry, "Duplicate short_name"
         cls.__name_registry[sbcls.short_name] = sbcls
@@ -1234,7 +1234,7 @@ def __init__(self, groups, model=None):
         for g in groups:
             if g.group is None:
                 if rest is not None:
-                    raise GroupError("More than one group is specified for " "the rest variables")
+                    raise GroupError("More than one group is specified for the rest variables")
                 else:
                     rest = g
             else:

pymc/variational/updates.py

@@ -1006,7 +1006,7 @@ def norm_constraint(tensor_var, max_norm, norm_axes=None, epsilon=1e-7):
     elif ndim in [3, 4, 5]:  # Conv{1,2,3}DLayer
         sum_over = tuple(range(1, ndim))
     else:
-        raise ValueError(f"Unsupported tensor dimensionality {ndim}." "Must specify `norm_axes`")
+        raise ValueError(f"Unsupported tensor dimensionality {ndim}. Must specify `norm_axes`")
 
     dtype = np.dtype(pytensor.config.floatX).type
     norms = pt.sqrt(pt.sum(pt.sqr(tensor_var), axis=sum_over, keepdims=True))

tests/distributions/test_multivariate.py

@@ -1531,14 +1531,14 @@ class TestZeroSumNormal:
     def assert_zerosum_axes(self, random_samples, axes_to_check, check_zerosum_axes=True):
         if check_zerosum_axes:
             for ax in axes_to_check:
-                assert np.isclose(
-                    random_samples.mean(axis=ax), 0
-                ).all(), f"{ax} is a zerosum_axis but is not summing to 0 across all samples."
+                assert np.isclose(random_samples.mean(axis=ax), 0).all(), (
+                    f"{ax} is a zerosum_axis but is not summing to 0 across all samples."
+                )
         else:
             for ax in axes_to_check:
-                assert not np.isclose(
-                    random_samples.mean(axis=ax), 0
-                ).all(), f"{ax} is not a zerosum_axis, but is nonetheless summing to 0 across all samples."
+                assert not np.isclose(random_samples.mean(axis=ax), 0).all(), (
+                    f"{ax} is not a zerosum_axis, but is nonetheless summing to 0 across all samples."
+                )
 
     @pytest.mark.parametrize(
         "dims, n_zerosum_axes",

tests/gp/test_hsgp_approx.py

@@ -135,9 +135,9 @@ def test_mean_invariance(self):
         with model:
             pm.set_data({"X": x_new})
 
-        assert np.allclose(
-            gp._X_center, original_center
-        ), "gp._X_center should not change after updating data for out-of-sample predictions."
+        assert np.allclose(gp._X_center, original_center), (
+            "gp._X_center should not change after updating data for out-of-sample predictions."
+        )
 
     def test_parametrization(self):
         err_msg = (
@@ -188,9 +188,9 @@ def test_parametrization_drop_first(self, model, cov_func, X1, drop_first):
 
             n_coeffs = model.f1_hsgp_coeffs.type.shape[0]
             if drop_first:
-                assert (
-                    n_coeffs == n_basis - 1
-                ), f"one basis vector should have been dropped, {n_coeffs}"
+                assert n_coeffs == n_basis - 1, (
+                    f"one basis vector should have been dropped, {n_coeffs}"
+                )
             else:
                 assert n_coeffs == n_basis, "one was dropped when it shouldn't have been"
 

tests/test_data.py

@@ -318,8 +318,8 @@ def test_explicit_coords(self, seeded_test):
         N_cols = 7
         data = np.random.uniform(size=(N_rows, N_cols))
         coords = {
-            "rows": [f"R{r+1}" for r in range(N_rows)],
-            "columns": [f"C{c+1}" for c in range(N_cols)],
+            "rows": [f"R{r + 1}" for r in range(N_rows)],
+            "columns": [f"C{c + 1}" for c in range(N_cols)],
         }
         # pass coordinates explicitly, use numpy array in Data container
         with pm.Model(coords=coords) as pmodel:
@@ -391,7 +391,7 @@ def test_implicit_coords_dataframe(self, seeded_test):
         N_cols = 7
         df_data = pd.DataFrame()
         for c in range(N_cols):
-            df_data[f"Column {c+1}"] = np.random.normal(size=(N_rows,))
+            df_data[f"Column {c + 1}"] = np.random.normal(size=(N_rows,))
         df_data.index.name = "rows"
         df_data.columns.name = "columns"