update pre-commit (#171)

Author: juanitorduz

.pre-commit-config.yaml

@@ -12,14 +12,14 @@ ci:
 
 repos:
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.4.7
+    rev: v0.5.0
     hooks:
       - id: ruff
-        args: ["--fix", "--show-source"]
+        args: ["--fix", "--output-format=full"]
       - id: ruff-format
         args: ["--line-length=100"]
   - repo: https://github.com/pre-commit/mirrors-mypy
-    rev: v1.10.0
+    rev: v1.10.1
     hooks:
       - id: mypy
         args: [--ignore-missing-imports]

pymc_bart/bart.py

@@ -125,7 +125,7 @@ def __new__(
         alpha: float = 0.95,
         beta: float = 2.0,
         response: str = "constant",
-        split_prior: Optional[npt.NDArray[np.float_]] = None,
+        split_prior: Optional[npt.NDArray[np.float64]] = None,
         split_rules: Optional[List[SplitRule]] = None,
         separate_trees: Optional[bool] = False,
         **kwargs,
@@ -198,7 +198,7 @@ def get_moment(cls, rv, size, *rv_inputs):
 
 def preprocess_xy(
     X: TensorLike, Y: TensorLike
-) -> Tuple[npt.NDArray[np.float_], npt.NDArray[np.float_]]:
+) -> Tuple[npt.NDArray[np.float64], npt.NDArray[np.float64]]:
     if isinstance(Y, (Series, DataFrame)):
         Y = Y.to_numpy()
     if isinstance(X, (Series, DataFrame)):

pymc_bart/pgbart.py

@@ -313,7 +313,7 @@ def normalize(self, particles: List[ParticleTree]) -> float:
         return wei / wei.sum()
 
     def resample(
-        self, particles: List[ParticleTree], normalized_weights: npt.NDArray[np.float_]
+        self, particles: List[ParticleTree], normalized_weights: npt.NDArray[np.float64]
     ) -> List[ParticleTree]:
         """
         Use systematic resample for all but the first particle
@@ -335,7 +335,7 @@ def resample(
         return particles
 
     def get_particle_tree(
-        self, particles: List[ParticleTree], normalized_weights: npt.NDArray[np.float_]
+        self, particles: List[ParticleTree], normalized_weights: npt.NDArray[np.float64]
     ) -> Tuple[ParticleTree, Tree]:
         """
         Sample a new particle and associated tree
@@ -347,7 +347,7 @@ def get_particle_tree(
 
         return new_particle, new_particle.tree
 
-    def systematic(self, normalized_weights: npt.NDArray[np.float_]) -> npt.NDArray[np.int_]:
+    def systematic(self, normalized_weights: npt.NDArray[np.float64]) -> npt.NDArray[np.int_]:
         """
         Systematic resampling.
 
@@ -399,7 +399,7 @@ def __init__(self, shape: tuple) -> None:
         self.mean = np.zeros(shape)  # running mean
         self.m_2 = np.zeros(shape)  # running second moment
 
-    def update(self, new_value: npt.NDArray[np.float_]) -> Union[float, npt.NDArray[np.float_]]:
+    def update(self, new_value: npt.NDArray[np.float64]) -> Union[float, npt.NDArray[np.float64]]:
         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)
@@ -408,10 +408,10 @@ def update(self, new_value: npt.NDArray[np.float_]) -> Union[float, npt.NDArray[
 @njit
 def _update(
     count: int,
-    mean: npt.NDArray[np.float_],
-    m_2: npt.NDArray[np.float_],
-    new_value: npt.NDArray[np.float_],
-) -> Tuple[npt.NDArray[np.float_], npt.NDArray[np.float_], Union[float, npt.NDArray[np.float_]]]:
+    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]]]:
     delta = new_value - mean
     mean += delta / count
     delta2 = new_value - mean
@@ -422,7 +422,7 @@ def _update(
 
 
 class SampleSplittingVariable:
-    def __init__(self, alpha_vec: npt.NDArray[np.float_]) -> None:
+    def __init__(self, alpha_vec: npt.NDArray[np.float64]) -> None:
         """
         Sample splitting variables proportional to `alpha_vec`.
 
@@ -535,13 +535,13 @@ def filter_missing_values(available_splitting_values, idx_data_points, missing_d
 
 
 def draw_leaf_value(
-    y_mu_pred: npt.NDArray[np.float_],
-    x_mu: npt.NDArray[np.float_],
+    y_mu_pred: npt.NDArray[np.float64],
+    x_mu: npt.NDArray[np.float64],
     m: int,
-    norm: npt.NDArray[np.float_],
+    norm: npt.NDArray[np.float64],
     shape: int,
     response: str,
-) -> Tuple[npt.NDArray[np.float_], Optional[npt.NDArray[np.float_]]]:
+) -> Tuple[npt.NDArray[np.float64], Optional[npt.NDArray[np.float64]]]:
     """Draw Gaussian distributed leaf values."""
     linear_params = None
     mu_mean = np.empty(shape)
@@ -559,7 +559,7 @@ def draw_leaf_value(
 
 
 @njit
-def fast_mean(ari: npt.NDArray[np.float_]) -> Union[float, npt.NDArray[np.float_]]:
+def fast_mean(ari: npt.NDArray[np.float64]) -> Union[float, npt.NDArray[np.float64]]:
     """Use Numba to speed up the computation of the mean."""
     if ari.ndim == 1:
         count = ari.shape[0]
@@ -578,11 +578,11 @@ def fast_mean(ari: npt.NDArray[np.float_]) -> Union[float, npt.NDArray[np.float_
 
 @njit
 def fast_linear_fit(
-    x: npt.NDArray[np.float_],
-    y: npt.NDArray[np.float_],
+    x: npt.NDArray[np.float64],
+    y: npt.NDArray[np.float64],
     m: int,
-    norm: npt.NDArray[np.float_],
-) -> Tuple[npt.NDArray[np.float_], List[npt.NDArray[np.float_]]]:
+    norm: npt.NDArray[np.float64],
+) -> Tuple[npt.NDArray[np.float64], List[npt.NDArray[np.float64]]]:
     n = len(x)
     y = y / m + np.expand_dims(norm, axis=1)
 
@@ -666,17 +666,17 @@ def update(self):
 
 @njit
 def inverse_cdf(
-    single_uniform: npt.NDArray[np.float_], normalized_weights: npt.NDArray[np.float_]
+    single_uniform: npt.NDArray[np.float64], normalized_weights: npt.NDArray[np.float64]
 ) -> npt.NDArray[np.int_]:
     """
     Inverse CDF algorithm for a finite distribution.
 
     Parameters
     ----------
-    single_uniform: npt.NDArray[np.float_]
+    single_uniform: npt.NDArray[np.float64]
         Ordered points in [0,1]
 
-    normalized_weights: npt.NDArray[np.float_])
+    normalized_weights: npt.NDArray[np.float64])
         Normalized weights
 
     Returns
@@ -699,7 +699,7 @@ def inverse_cdf(
 
 
 @njit
-def jitter_duplicated(array: npt.NDArray[np.float_], std: float) -> npt.NDArray[np.float_]:
+def jitter_duplicated(array: npt.NDArray[np.float64], std: float) -> npt.NDArray[np.float64]:
     """
     Jitter duplicated values.
     """
@@ -715,7 +715,7 @@ def jitter_duplicated(array: npt.NDArray[np.float_], std: float) -> npt.NDArray[
 
 
 @njit
-def are_whole_number(array: npt.NDArray[np.float_]) -> np.bool_:
+def are_whole_number(array: npt.NDArray[np.float64]) -> np.bool_:
     """Check if all values in array are whole numbers"""
     return np.all(np.mod(array[~np.isnan(array)], 1) == 0)
 

pymc_bart/tree.py

@@ -27,7 +27,7 @@ class Node:
 
     Attributes
     ----------
-    value : npt.NDArray[np.float_]
+    value : npt.NDArray[np.float64]
     idx_data_points : Optional[npt.NDArray[np.int_]]
     idx_split_variable : int
     linear_params: Optional[List[float]] = None
@@ -37,11 +37,11 @@ class Node:
 
     def __init__(
         self,
-        value: npt.NDArray[np.float_] = np.array([-1.0]),
+        value: npt.NDArray[np.float64] = np.array([-1.0]),
         nvalue: int = 0,
         idx_data_points: Optional[npt.NDArray[np.int_]] = None,
         idx_split_variable: int = -1,
-        linear_params: Optional[List[npt.NDArray[np.float_]]] = None,
+        linear_params: Optional[List[npt.NDArray[np.float64]]] = None,
     ) -> None:
         self.value = value
         self.nvalue = nvalue
@@ -52,11 +52,11 @@ def __init__(
     @classmethod
     def new_leaf_node(
         cls,
-        value: npt.NDArray[np.float_],
+        value: npt.NDArray[np.float64],
         nvalue: int = 0,
         idx_data_points: Optional[npt.NDArray[np.int_]] = None,
         idx_split_variable: int = -1,
-        linear_params: Optional[List[npt.NDArray[np.float_]]] = None,
+        linear_params: Optional[List[npt.NDArray[np.float64]]] = None,
     ) -> "Node":
         return cls(
             value=value,
@@ -100,7 +100,7 @@ class Tree:
         The dictionary's keys are integers that represent the nodes position.
         The dictionary's values are objects of type Node that represent the split and leaf nodes
         of the tree itself.
-    output: Optional[npt.NDArray[np.float_]]
+    output: Optional[npt.NDArray[np.float64]]
         Array of shape number of observations, shape
     split_rules : List[SplitRule]
         List of SplitRule objects, one per column in input data.
@@ -121,7 +121,7 @@ class Tree:
     def __init__(
         self,
         tree_structure: Dict[int, Node],
-        output: npt.NDArray[np.float_],
+        output: npt.NDArray[np.float64],
         split_rules: List[SplitRule],
         idx_leaf_nodes: Optional[List[int]] = None,
     ) -> None:
@@ -133,7 +133,7 @@ def __init__(
     @classmethod
     def new_tree(
         cls,
-        leaf_node_value: npt.NDArray[np.float_],
+        leaf_node_value: npt.NDArray[np.float64],
         idx_data_points: Optional[npt.NDArray[np.int_]],
         num_observations: int,
         shape: int,
@@ -189,7 +189,7 @@ def grow_leaf_node(
         self,
         current_node: Node,
         selected_predictor: int,
-        split_value: npt.NDArray[np.float_],
+        split_value: npt.NDArray[np.float64],
         index_leaf_node: int,
     ) -> None:
         current_node.value = split_value
@@ -221,7 +221,7 @@ def get_split_variables(self) -> Generator[int, None, None]:
             if node.is_split_node():
                 yield node.idx_split_variable
 
-    def _predict(self) -> npt.NDArray[np.float_]:
+    def _predict(self) -> npt.NDArray[np.float64]:
         output = self.output
 
         if self.idx_leaf_nodes is not None:
@@ -232,23 +232,23 @@ def _predict(self) -> npt.NDArray[np.float_]:
 
     def predict(
         self,
-        x: npt.NDArray[np.float_],
+        x: npt.NDArray[np.float64],
         excluded: Optional[List[int]] = None,
         shape: int = 1,
-    ) -> npt.NDArray[np.float_]:
+    ) -> npt.NDArray[np.float64]:
         """
         Predict output of tree for an (un)observed point x.
 
         Parameters
         ----------
-        x : npt.NDArray[np.float_]
+        x : npt.NDArray[np.float64]
             Unobserved point
         excluded: Optional[List[int]]
             Indexes of the variables to exclude when computing predictions
 
         Returns
         -------
-        npt.NDArray[np.float_]
+        npt.NDArray[np.float64]
             Value of the leaf value where the unobserved point lies.
         """
         if excluded is None:
@@ -258,16 +258,16 @@ def predict(
 
     def _traverse_tree(
         self,
-        X: npt.NDArray[np.float_],
+        X: npt.NDArray[np.float64],
         excluded: Optional[List[int]] = None,
         shape: Union[int, Tuple[int, ...]] = 1,
-    ) -> npt.NDArray[np.float_]:
+    ) -> npt.NDArray[np.float64]:
         """
         Traverse the tree starting from the root node given an (un)observed point.
 
         Parameters
         ----------
-        X : npt.NDArray[np.float_]
+        X : npt.NDArray[np.float64]
             (Un)observed point(s)
         node_index : int
             Index of the node to start the traversal from
@@ -278,7 +278,7 @@ def _traverse_tree(
 
         Returns
         -------
-        npt.NDArray[np.float_]
+        npt.NDArray[np.float64]
             Leaf node value or mean of leaf node values
         """
 
@@ -327,14 +327,14 @@ def _traverse_tree(
         return p_d
 
     def _traverse_leaf_values(
-        self, leaf_values: List[npt.NDArray[np.float_]], leaf_n_values: List[int], node_index: int
+        self, leaf_values: List[npt.NDArray[np.float64]], leaf_n_values: List[int], node_index: int
     ) -> None:
         """
         Traverse the tree appending leaf values starting from a particular node.
 
         Parameters
         ----------
-        leaf_values : List[npt.NDArray[np.float_]]
+        leaf_values : List[npt.NDArray[np.float64]]
         node_index : int
         """
         node = self.get_node(node_index)