Fix some code lints found with ruff (#6545)

* Make isort happy with imports order

* Avoid shadowing the logprob fn with a local name

* Deal with unused error names

* Remove unreachable return

* Remove unread variable

* Fix type checking for variational/approximations

* A few typing fixes

Author: Armavica

pymc/distributions/dist_math.py

@@ -61,10 +61,10 @@ def check_parameters(logp: Variable, *conditions: Iterable[Variable], msg: str =
     check_bounds = False in pm.Model()
     """
     # at.all does not accept True/False, but accepts np.array(True)/np.array(False)
-    conditions = [
+    conditions_ = [
         cond if (cond is not True and cond is not False) else np.array(cond) for cond in conditions
     ]
-    all_true_scalar = at.all([at.all(cond) for cond in conditions])
+    all_true_scalar = at.all([at.all(cond) for cond in conditions_])
     return CheckParameterValue(msg)(logp, all_true_scalar)
 
 

pymc/distributions/distribution.py

@@ -988,21 +988,19 @@ def __new__(
                 ndim_supp=ndim_supp,
                 **kwargs,
             )
-        else:
-            return _CustomDist(
-                name,
-                *dist_params,
-                class_name=name,
-                random=random,
-                logp=logp,
-                logcdf=logcdf,
-                moment=moment,
-                ndim_supp=ndim_supp,
-                ndims_params=ndims_params,
-                dtype=dtype,
-                **kwargs,
-            )
-        return super().__new__(cls, name, *args, **kwargs)
+        return _CustomDist(
+            name,
+            *dist_params,
+            class_name=name,
+            random=random,
+            logp=logp,
+            logcdf=logcdf,
+            moment=moment,
+            ndim_supp=ndim_supp,
+            ndims_params=ndims_params,
+            dtype=dtype,
+            **kwargs,
+        )
 
     @classmethod
     def dist(

pymc/gp/util.py

@@ -28,10 +28,10 @@
 
 # Avoid circular dependency when importing modelcontext
 from pymc.distributions.distribution import Distribution
+from pymc.model import modelcontext
 from pymc.pytensorf import compile_pymc, walk_model
 
 _ = Distribution  # keep both pylint and black happy
-from pymc.model import modelcontext
 
 JITTER_DEFAULT = 1e-6
 

pymc/logprob/transforms.py

@@ -961,16 +961,16 @@ def transformed_logprob(op, values, *inputs, use_jacobian=True, **kwargs):
         if use_jacobian:
             assert len(values) == len(logprobs) == len(op.transforms)
             logprobs_jac = []
-            for value, transform, logprob in zip(values, op.transforms, logprobs):
+            for value, transform, logp in zip(values, op.transforms, logprobs):
                 if transform is None:
-                    logprobs_jac.append(logprob)
+                    logprobs_jac.append(logp)
                     continue
                 assert isinstance(value.owner.op, TransformedVariable)
                 original_forward_value = value.owner.inputs[1]
                 jacobian = transform.log_jac_det(original_forward_value, *inputs).copy()
                 if value.name:
                     jacobian.name = f"{value.name}_jacobian"
-                logprobs_jac.append(logprob + jacobian)
+                logprobs_jac.append(logp + jacobian)
             logprobs = logprobs_jac
 
         return logprobs

pymc/model.py

@@ -195,7 +195,7 @@ def get_context(cls, error_if_none=True) -> Optional[T]:
         raise a ``TypeError`` instead of returning ``None``."""
         try:
             candidate: Optional[T] = cls.get_contexts()[-1]
-        except IndexError as e:
+        except IndexError:
             # Calling code expects to get a TypeError if the entity
             # is unfound, and there's too much to fix.
             if error_if_none:

pymc/model_graph.py

@@ -73,7 +73,7 @@ def _expand(x):
             return []
 
         parents = {
-            get_var_name(x)
+            VarName(get_var_name(x))
             for x in walk(nodes=_filter_non_parameter_inputs(var), expand=_expand)
             # Only consider nodes that are in the named model variables.
             if x.name and x.name in self._all_var_names
@@ -109,7 +109,7 @@ def vars_to_plot(self, var_names: Optional[Iterable[VarName]] = None) -> List[Va
                 selected_ancestors.add(self.model.rvs_to_values[var])
 
         # ordering of self._all_var_names is important
-        return [var.name for var in selected_ancestors]
+        return [VarName(var.name) for var in selected_ancestors]
 
     def make_compute_graph(
         self, var_names: Optional[Iterable[VarName]] = None
@@ -230,7 +230,7 @@ def get_plates(self, var_names: Optional[Iterable[VarName]] = None) -> Dict[str,
                 plate_label = " x ".join(dim_labels)
             else:
                 # The RV has no `dims` information.
-                dim_labels = map(str, shape)
+                dim_labels = [str(x) for x in shape]
                 plate_label = " x ".join(map(str, shape))
             plates[plate_label].add(var_name)
 

pymc/sampling/jax.py

@@ -15,20 +15,10 @@
 import re
 import sys
 
+from datetime import datetime
 from functools import partial
 from typing import Any, Callable, Dict, List, Optional, Sequence, Union
 
-from pytensor.tensor.random.type import RandomType
-
-from pymc.initial_point import StartDict
-from pymc.sampling.mcmc import _init_jitter
-
-xla_flags = os.getenv("XLA_FLAGS", "")
-xla_flags = re.sub(r"--xla_force_host_platform_device_count=.+\s", "", xla_flags).split()
-os.environ["XLA_FLAGS"] = " ".join([f"--xla_force_host_platform_device_count={100}"] + xla_flags)
-
-from datetime import datetime
-
 import arviz as az
 import jax
 import numpy as np
@@ -43,18 +33,25 @@
 from pytensor.link.jax.dispatch import jax_funcify
 from pytensor.raise_op import Assert
 from pytensor.tensor import TensorVariable
+from pytensor.tensor.random.type import RandomType
 from pytensor.tensor.shape import SpecifyShape
 
 from pymc import Model, modelcontext
 from pymc.backends.arviz import find_constants, find_observations
+from pymc.initial_point import StartDict
 from pymc.logprob.utils import CheckParameterValue
+from pymc.sampling.mcmc import _init_jitter
 from pymc.util import (
     RandomSeed,
     RandomState,
     _get_seeds_per_chain,
     get_default_varnames,
 )
 
+xla_flags_env = os.getenv("XLA_FLAGS", "")
+xla_flags = re.sub(r"--xla_force_host_platform_device_count=.+\s", "", xla_flags_env).split()
+os.environ["XLA_FLAGS"] = " ".join([f"--xla_force_host_platform_device_count={100}"] + xla_flags)
+
 __all__ = (
     "get_jaxified_graph",
     "get_jaxified_logp",
@@ -111,7 +108,7 @@ def get_jaxified_graph(
 ) -> List[TensorVariable]:
     """Compile an PyTensor graph into an optimized JAX function"""
 
-    graph = _replace_shared_variables(outputs)
+    graph = _replace_shared_variables(outputs) if outputs is not None else None
 
     fgraph = FunctionGraph(inputs=inputs, outputs=graph, clone=True)
     # We need to add a Supervisor to the fgraph to be able to run the
@@ -254,12 +251,10 @@ def _get_batched_jittered_initial_points(
         jitter=jitter,
         jitter_max_retries=jitter_max_retries,
     )
-    initial_points = [list(initial_point.values()) for initial_point in initial_points]
+    initial_points_values = [list(initial_point.values()) for initial_point in initial_points]
     if chains == 1:
-        initial_points = initial_points[0]
-    else:
-        initial_points = [np.stack(init_state) for init_state in zip(*initial_points)]
-    return initial_points
+        return initial_points_values[0]
+    return [np.stack(init_state) for init_state in zip(*initial_points_values)]
 
 
 def _update_coords_and_dims(

pymc/step_methods/hmc/integration.py

@@ -82,7 +82,7 @@ def step(self, epsilon, state):
             return self._step(epsilon, state)
         except linalg.LinAlgError as err:
             msg = "LinAlgError during leapfrog step."
-            raise IntegrationError(msg)
+            raise IntegrationError(msg) from err
         except ValueError as err:
             # Raised by many scipy.linalg functions
             scipy_msg = "array must not contain infs or nans"

pymc/step_methods/hmc/nuts.py

@@ -210,7 +210,6 @@ def _hamiltonian_step(self, start, p0, step_size):
     def competence(var, has_grad):
         """Check how appropriate this class is for sampling a random variable."""
 
-        dist = getattr(var.owner, "op", None)
         if var.dtype in continuous_types and has_grad:
             return Competence.PREFERRED
         return Competence.INCOMPATIBLE

pymc/variational/approximations.py

@@ -11,7 +11,7 @@
 #   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 #   See the License for the specific language governing permissions and
 #   limitations under the License.
-
+from typing import Optional
 
 import numpy as np
 import pytensor
@@ -331,7 +331,7 @@ def sample_approx(approx, draws=100, include_transformed=True):
 class SingleGroupApproximation(Approximation):
     """Base class for Single Group Approximation"""
 
-    _group_class = None
+    _group_class: Optional[type] = None
 
     def __init__(self, *args, **kwargs):
         groups = [self._group_class(None, *args, **kwargs)]