Precise blanket type ignore and noqa statements

Author: Armavica

pymc/backends/__init__.py

@@ -85,8 +85,8 @@
     RunType: TypeAlias = Run
     HAS_MCB = True
 except ImportError:
-    TraceOrBackend = BaseTrace  # type: ignore
-    RunType = type(None)  # type: ignore
+    TraceOrBackend = BaseTrace  # type: ignore[misc]
+    RunType = type(None)  # type: ignore[assignment, misc]
 
 
 __all__ = ["to_inference_data", "predictions_to_inference_data"]

pymc/distributions/shape_utils.py

@@ -254,13 +254,13 @@ def change_dist_size(
 
     """
     # Check the dimensionality of the `new_size` kwarg
-    new_size_ndim = np.ndim(new_size)  # type: ignore
+    new_size_ndim = np.ndim(new_size)  # type: ignore[arg-type]
     if new_size_ndim > 1:
         raise ShapeError("The `new_size` must be ≤1-dimensional.", actual=new_size_ndim)
     elif new_size_ndim == 0:
-        new_size = (new_size,)  # type: ignore
+        new_size = (new_size,)  # type: ignore[assignment]
     else:
-        new_size = tuple(new_size)  # type: ignore
+        new_size = tuple(new_size)  # type: ignore[arg-type]
 
     op = dist.owner.op
     new_dist = _change_dist_size(op, dist, new_size=new_size, expand=expand)
@@ -325,7 +325,7 @@ def change_specify_shape_size(op, ss, new_size, expand) -> TensorVariable:
             new_shapes[-ndim_supp:] = shapes[-ndim_supp:]
 
     # specify_shape has a wrong signature https://github.com/aesara-devs/aesara/issues/1164
-    return pt.specify_shape(new_var, new_shapes)  # type: ignore
+    return pt.specify_shape(new_var, new_shapes)  # type: ignore[arg-type]
 
 
 def get_support_shape(
@@ -389,8 +389,7 @@ def get_support_shape(
             raise ValueError(f"Number of dims is too small for ndim_supp of {ndim_supp}")
         model = modelcontext(None)
         inferred_support_shape = [
-            model.dim_lengths[dims[i]] - support_shape_offset[i]  # type: ignore
-            for i in np.arange(-ndim_supp, 0)
+            model.dim_lengths[dims[i]] - support_shape_offset[i] for i in np.arange(-ndim_supp, 0)
         ]
 
     if inferred_support_shape is None and observed is not None:

pymc/distributions/simulator.py

@@ -144,7 +144,7 @@ def __new__(cls, name, *args, **kwargs):
         return super().__new__(cls, name, *args, **kwargs)
 
     @classmethod
-    def dist(  # type: ignore
+    def dist(  # type: ignore[override]
         cls,
         fn,
         *unnamed_params,
@@ -256,7 +256,7 @@ def rv_op(
         return sim_op(*params, **kwargs)
 
 
-@_support_point.register(SimulatorRV)  # type: ignore
+@_support_point.register(SimulatorRV)
 def simulator_support_point(op, rv, *inputs):
     sim_inputs = op.dist_params(rv.owner)
     # Take the mean of 10 draws

pymc/distributions/transforms.py

@@ -21,7 +21,7 @@
 
 # ignore mypy error because it somehow considers that
 # "numpy.core.numeric has no attribute normalize_axis_tuple"
-from numpy.core.numeric import normalize_axis_tuple  # type: ignore
+from numpy.core.numeric import normalize_axis_tuple  # type: ignore[attr-defined]
 from pytensor.graph import Op
 from pytensor.tensor import TensorVariable
 

pymc/gp/cov.py

@@ -143,7 +143,7 @@ def __array_wrap__(self, result):
 
     @staticmethod
     def _alloc(X, *shape: int) -> TensorVariable:
-        return pt.alloc(X, *shape)  # type: ignore
+        return pt.alloc(X, *shape)  # type: ignore[return-value]
 
 
 class Covariance(BaseCovariance):

pymc/gp/hsgp_approx.py

@@ -432,7 +432,7 @@ def prior(
         gp_dims: str | None = None,
         *args,
         **kwargs,
-    ):  # type: ignore
+    ):
         R"""
         Return the (approximate) GP prior distribution evaluated over the input locations `X`.
 
@@ -496,7 +496,7 @@ def _build_conditional(self, Xnew):
         elif self._parametrization == "centered":
             return self.mean_func(Xnew) + phi[:, i:] @ beta
 
-    def conditional(self, name: str, Xnew: TensorLike, dims: str | None = None):  # type: ignore
+    def conditional(self, name: str, Xnew: TensorLike, dims: str | None = None):  # type: ignore[override]
         R"""
         Return the (approximate) conditional distribution evaluated over new input locations `Xnew`.
 
@@ -695,7 +695,7 @@ def prior_linearized(self, X: TensorLike):
         psd = self.scale * self.cov_func.power_spectral_density_approx(J)
         return (phi_cos, phi_sin), psd
 
-    def prior(self, name: str, X: TensorLike, dims: str | None = None):  # type: ignore
+    def prior(self, name: str, X: TensorLike, dims: str | None = None):  # type: ignore[override]
         R"""
         Return the (approximate) GP prior distribution evaluated over the input locations `X`.
 
@@ -718,8 +718,8 @@ def prior(self, name: str, X: TensorLike, dims: str | None = None):  # type: ign
         # and so does not contribute to the approximation.
         f = (
             self.mean_func(X)
-            + phi_cos @ (psd * self._beta[:m])  # type: ignore
-            + phi_sin[..., 1:] @ (psd[1:] * self._beta[m:])  # type: ignore
+            + phi_cos @ (psd * self._beta[:m])  # type: ignore[index]
+            + phi_sin[..., 1:] @ (psd[1:] * self._beta[m:])  # type: ignore[index]
         )
 
         self.f = pm.Deterministic(name, f, dims=dims)
@@ -747,7 +747,7 @@ def _build_conditional(self, Xnew):
         phi = phi_cos @ (psd * beta[:m]) + phi_sin[..., 1:] @ (psd[1:] * beta[m:])
         return self.mean_func(Xnew) + phi
 
-    def conditional(self, name: str, Xnew: TensorLike, dims: str | None = None):  # type: ignore
+    def conditional(self, name: str, Xnew: TensorLike, dims: str | None = None):  # type: ignore[override]
         R"""
         Return the (approximate) conditional distribution evaluated over new input locations `Xnew`.
 

pymc/logprob/basic.py

@@ -589,7 +589,7 @@ def transformed_conditional_logp(
     }
     if values_to_transforms:
         # There seems to be an incorrect type hint in TransformValuesRewrite
-        transform_rewrite = TransformValuesRewrite(values_to_transforms)  # type: ignore
+        transform_rewrite = TransformValuesRewrite(values_to_transforms)  # type: ignore[arg-type]
 
     kwargs.setdefault("warn_rvs", False)
     temp_logp_terms = conditional_logp(

pymc/logprob/tensor.py

@@ -34,7 +34,6 @@
 #   OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
 #   SOFTWARE.
 
-
 from pathlib import Path
 
 from pytensor import tensor as pt
@@ -165,7 +164,7 @@ def find_measurable_stacks(fgraph, node) -> list[TensorVariable] | None:
     # the IR construction
     replacements = [(base_var, promised_valued_rv(base_var)) for base_var in base_vars]
     temp_fgraph = FunctionGraph(outputs=base_vars, clone=False)
-    toposort_replace(temp_fgraph, replacements)  # type: ignore
+    toposort_replace(temp_fgraph, replacements)  # type: ignore[arg-type]
     new_base_vars = temp_fgraph.outputs
 
     if is_join:
@@ -182,7 +181,7 @@ class MeasurableDimShuffle(MeasurableOp, DimShuffle):
 
     # Need to get the absolute path of `c_func_file`, otherwise it tries to
     # find it locally and fails when a new `Op` is initialized
-    c_func_file = str(DimShuffle.get_path(Path(DimShuffle.c_func_file)))
+    c_func_file = str(DimShuffle.get_path(Path(DimShuffle.c_func_file)))  # type: ignore[arg-type]
 
 
 @_logprob.register(MeasurableDimShuffle)

pymc/model/transform/optimization.py

@@ -77,14 +77,14 @@ def freeze_dims_and_data(
         if isinstance(datum, SharedVariable)
     }
 
-    old_outs, old_coords, old_dim_lenghts = fg.outputs, fg._coords, fg._dim_lengths  # type: ignore
+    old_outs, old_coords, old_dim_lenghts = fg.outputs, fg._coords, fg._dim_lengths  # type: ignore[attr-defined]
     # Rebuild strict will force the recreation of RV nodes with updated static types
-    new_outs = clone_replace(old_outs, replace=frozen_replacements, rebuild_strict=False)  # type: ignore
+    new_outs = clone_replace(old_outs, replace=frozen_replacements, rebuild_strict=False)  # type: ignore[arg-type]
     for old_out, new_out in zip(old_outs, new_outs):
         new_out.name = old_out.name
     fg = FunctionGraph(outputs=new_outs, clone=False)
-    fg._coords = old_coords  # type: ignore
-    fg._dim_lengths = {  # type: ignore
+    fg._coords = old_coords  # type: ignore[attr-defined]
+    fg._dim_lengths = {  # type: ignore[attr-defined]
         dim: frozen_replacements.get(dim_length, dim_length)
         for dim, dim_length in old_dim_lenghts.items()
     }
@@ -99,7 +99,7 @@ def freeze_dims_and_data(
         if transform is None:
             new_value = rv.type()
         else:
-            new_value = transform.forward(rv, *rv.owner.inputs).type()  # type: ignore
+            new_value = transform.forward(rv, *rv.owner.inputs).type()  # type: ignore[arg-type]
         new_value.name = old_value.name
         replacements[old_value] = new_value
     fg.replace_all(tuple(replacements.items()), import_missing=True)

pymc/model_graph.py

@@ -234,7 +234,7 @@ def _make_node(
         kwargs["cluster"] = cluster
 
     var_name: str = cast(str, node.var.name)
-    add_node(var_name.replace(":", "&"), **kwargs)  # type: ignore
+    add_node(var_name.replace(":", "&"), **kwargs)  # type: ignore[call-arg]
 
 
 class ModelGraph: