Update MyPy 14

.pre-commit-config.yaml

@@ -12,14 +12,14 @@ ci:
 
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.8.3
+    rev: v0.8.4
     hooks:
       - id: ruff
         args: ["--fix", "--output-format=full"]
       - id: ruff-format
         args: ["--line-length=100"]
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.13.0
+    rev: v1.14.0
     hooks:
       - id: mypy
         args: [--ignore-missing-imports]

pymc_bart/bart.py

@@ -132,7 +132,7 @@ def __new__(
         alpha: float = 0.95,
         beta: float = 2.0,
         response: str = "constant",
-        split_prior: Optional[npt.NDArray[np.float64]] = None,
+        split_prior: Optional[npt.NDArray] = None,
         split_rules: Optional[list[SplitRule]] = None,
         separate_trees: Optional[bool] = False,
         **kwargs,
@@ -203,9 +203,7 @@ def get_moment(cls, rv, size, *rv_inputs):
         return mean
 
 
-def preprocess_xy(
-    X: TensorLike, Y: TensorLike
-) -> tuple[npt.NDArray[np.float64], npt.NDArray[np.float64]]:
+def preprocess_xy(X: TensorLike, Y: TensorLike) -> tuple[npt.NDArray, npt.NDArray]:
     if isinstance(Y, (Series, DataFrame)):
         Y = Y.to_numpy()
     if isinstance(X, (Series, DataFrame)):

pymc_bart/pgbart.py

@@ -16,6 +16,8 @@
 
 import numpy as np
 import numpy.typing as npt
+import pymc as pm
+import pytensor.tensor as pt
 from numba import njit
 from pymc.initial_point import PointType
 from pymc.model import Model, modelcontext
@@ -120,15 +122,15 @@ class PGBART(ArrayStepShared):
         "tune": (bool, []),
     }
 
-    def __init__(  # noqa: PLR0915
+    def __init__(  # noqa: PLR0912, PLR0915
         self,
-        vars=None,  # pylint: disable=redefined-builtin
+        vars: list[pm.Distribution] | None = None,
         num_particles: int = 10,
         batch: tuple[float, float] = (0.1, 0.1),
         model: Optional[Model] = None,
         initial_point: PointType | None = None,
-        compile_kwargs: dict | None = None,  # pylint: disable=unused-argument
-    ):
+        compile_kwargs: dict | None = None,
+    ) -> None:
         model = modelcontext(model)
         if initial_point is None:
             initial_point = model.initial_point()
@@ -137,6 +139,10 @@ def __init__(  # noqa: PLR0915
         else:
             vars = [model.rvs_to_values.get(var, var) for var in vars]
             vars = inputvars(vars)
+
+        if vars is None:
+            raise ValueError("Unable to find variables to sample")
+
         value_bart = vars[0]
         self.bart = model.values_to_rvs[value_bart].owner.op
 
@@ -325,7 +331,7 @@ def normalize(self, particles: list[ParticleTree]) -> float:
         return wei / wei.sum()
 
     def resample(
-        self, particles: list[ParticleTree], normalized_weights: npt.NDArray[np.float64]
+        self, particles: list[ParticleTree], normalized_weights: npt.NDArray
     ) -> list[ParticleTree]:
         """
         Use systematic resample for all but the first particle
@@ -347,7 +353,7 @@ def resample(
         return particles
 
     def get_particle_tree(
-        self, particles: list[ParticleTree], normalized_weights: npt.NDArray[np.float64]
+        self, particles: list[ParticleTree], normalized_weights: npt.NDArray
     ) -> tuple[ParticleTree, Tree]:
         """
         Sample a new particle and associated tree
@@ -359,7 +365,7 @@ def get_particle_tree(
 
         return new_particle, new_particle.tree
 
-    def systematic(self, normalized_weights: npt.NDArray[np.float64]) -> npt.NDArray[np.int_]:
+    def systematic(self, normalized_weights: npt.NDArray) -> npt.NDArray[np.int_]:
         """
         Systematic resampling.
 
@@ -395,7 +401,7 @@ def update_weight(self, particle: ParticleTree, odim: int) -> None:
         particle.log_weight = new_likelihood
 
     @staticmethod
-    def competence(var, has_grad):
+    def competence(var: pm.Distribution, has_grad: bool) -> Competence:
         """PGBART is only suitable for BART distributions."""
         dist = getattr(var.owner, "op", None)
         if isinstance(dist, BARTRV):
@@ -406,12 +412,12 @@ def competence(var, has_grad):
 class RunningSd:
     """Welford's online algorithm for computing the variance/standard deviation"""
 
-    def __init__(self, shape: tuple) -> None:
+    def __init__(self, shape: tuple[int, ...]) -> None:
         self.count = 0  # number of data points
         self.mean = np.zeros(shape)  # running mean
         self.m_2 = np.zeros(shape)  # running second moment
 
-    def update(self, new_value: npt.NDArray[np.float64]) -> Union[float, npt.NDArray[np.float64]]:
+    def update(self, new_value: npt.NDArray) -> Union[float, npt.NDArray]:
         self.count = self.count + 1
         self.mean, self.m_2, std = _update(self.count, self.mean, self.m_2, new_value)
         return fast_mean(std)
@@ -420,10 +426,10 @@ def update(self, new_value: npt.NDArray[np.float64]) -> Union[float, npt.NDArray
 @njit
 def _update(
     count: int,
-    mean: npt.NDArray[np.float64],
-    m_2: npt.NDArray[np.float64],
-    new_value: npt.NDArray[np.float64],
-) -> tuple[npt.NDArray[np.float64], npt.NDArray[np.float64], Union[float, npt.NDArray[np.float64]]]:
+    mean: npt.NDArray,
+    m_2: npt.NDArray,
+    new_value: npt.NDArray,
+) -> tuple[npt.NDArray, npt.NDArray, Union[float, npt.NDArray]]:
     delta = new_value - mean
     mean += delta / count
     delta2 = new_value - mean
@@ -434,7 +440,7 @@ def _update(
 
 
 class SampleSplittingVariable:
-    def __init__(self, alpha_vec: npt.NDArray[np.float64]) -> None:
+    def __init__(self, alpha_vec: npt.NDArray) -> None:
         """
         Sample splitting variables proportional to `alpha_vec`.
 
@@ -547,16 +553,16 @@ def filter_missing_values(available_splitting_values, idx_data_points, missing_d
 
 
 def draw_leaf_value(
-    y_mu_pred: npt.NDArray[np.float64],
-    x_mu: npt.NDArray[np.float64],
+    y_mu_pred: npt.NDArray,
+    x_mu: npt.NDArray,
     m: int,
-    norm: npt.NDArray[np.float64],
+    norm: npt.NDArray,
     shape: int,
     response: str,
-) -> tuple[npt.NDArray[np.float64], Optional[npt.NDArray[np.float64]]]:
+) -> tuple[npt.NDArray, Optional[npt.NDArray]]:
     """Draw Gaussian distributed leaf values."""
     linear_params = None
-    mu_mean = np.empty(shape)
+    mu_mean: npt.NDArray
     if y_mu_pred.size == 0:
         return np.zeros(shape), linear_params
 
@@ -571,7 +577,7 @@ def draw_leaf_value(
 
 
 @njit
-def fast_mean(ari: npt.NDArray[np.float64]) -> Union[float, npt.NDArray[np.float64]]:
+def fast_mean(ari: npt.NDArray) -> Union[float, npt.NDArray]:
     """Use Numba to speed up the computation of the mean."""
     if ari.ndim == 1:
         count = ari.shape[0]
@@ -590,11 +596,11 @@ def fast_mean(ari: npt.NDArray[np.float64]) -> Union[float, npt.NDArray[np.float
 
 @njit
 def fast_linear_fit(
-    x: npt.NDArray[np.float64],
-    y: npt.NDArray[np.float64],
+    x: npt.NDArray,
+    y: npt.NDArray,
     m: int,
-    norm: npt.NDArray[np.float64],
-) -> tuple[npt.NDArray[np.float64], list[npt.NDArray[np.float64]]]:
+    norm: npt.NDArray,
+) -> tuple[npt.NDArray, list[npt.NDArray]]:
     n = len(x)
     y = y / m + np.expand_dims(norm, axis=1)
 
@@ -678,17 +684,17 @@ def update(self):
 
 @njit
 def inverse_cdf(
-    single_uniform: npt.NDArray[np.float64], normalized_weights: npt.NDArray[np.float64]
+    single_uniform: npt.NDArray, normalized_weights: npt.NDArray
 ) -> npt.NDArray[np.int_]:
     """
     Inverse CDF algorithm for a finite distribution.
 
     Parameters
     ----------
-    single_uniform: npt.NDArray[np.float64]
+    single_uniform: npt.NDArray
         Ordered points in [0,1]
 
-    normalized_weights: npt.NDArray[np.float64])
+    normalized_weights: npt.NDArray)
         Normalized weights
 
     Returns
@@ -711,7 +717,7 @@ def inverse_cdf(
 
 
 @njit
-def jitter_duplicated(array: npt.NDArray[np.float64], std: float) -> npt.NDArray[np.float64]:
+def jitter_duplicated(array: npt.NDArray, std: float) -> npt.NDArray:
     """
     Jitter duplicated values.
     """
@@ -727,12 +733,17 @@ def jitter_duplicated(array: npt.NDArray[np.float64], std: float) -> npt.NDArray
 
 
 @njit
-def are_whole_number(array: npt.NDArray[np.float64]) -> np.bool_:
+def are_whole_number(array: npt.NDArray) -> np.bool_:
     """Check if all values in array are whole numbers"""
     return np.all(np.mod(array[~np.isnan(array)], 1) == 0)
 
 
-def logp(point, out_vars, vars, shared):  # pylint: disable=redefined-builtin
+def logp(
+    point,
+    out_vars: list[pm.Distribution],
+    vars: list[pm.Distribution],
+    shared: list[pt.TensorVariable],
+):
     """Compile PyTensor function of the model and the input and output variables.
 
     Parameters