pre-commit autoupdate 2025-09-11

Author: cclauss

.pre-commit-config.yaml

@@ -16,7 +16,7 @@ repos:
       - id: auto-walrus
 
   - repo: https://github.com/astral-sh/ruff-pre-commit
-    rev: v0.12.12
+    rev: v0.13.0
     hooks:
       - id: ruff-check
       - id: ruff-format

data_structures/arrays/sudoku_solver.py

@@ -149,7 +149,7 @@ def search(values):
     if all(len(values[s]) == 1 for s in squares):
         return values  ## Solved!
     ## Chose the unfilled square s with the fewest possibilities
-    n, s = min((len(values[s]), s) for s in squares if len(values[s]) > 1)
+    _n, s = min((len(values[s]), s) for s in squares if len(values[s]) > 1)
     return some(search(assign(values.copy(), s, d)) for d in values[s])
 
 

data_structures/trie/radix_tree.py

@@ -115,7 +115,7 @@ def find(self, word: str) -> bool:
         if not incoming_node:
             return False
         else:
-            matching_string, remaining_prefix, remaining_word = incoming_node.match(
+            _matching_string, remaining_prefix, remaining_word = incoming_node.match(
                 word
             )
             # If there is remaining prefix, the word can't be on the tree
@@ -144,7 +144,7 @@ def delete(self, word: str) -> bool:
         if not incoming_node:
             return False
         else:
-            matching_string, remaining_prefix, remaining_word = incoming_node.match(
+            _matching_string, remaining_prefix, remaining_word = incoming_node.match(
                 word
             )
             # If there is remaining prefix, the word can't be on the tree

graphs/graph_adjacency_list.py

@@ -448,7 +448,7 @@ def test_remove_edge(self) -> None:
         (
             undirected_graph,
             directed_graph,
-            random_vertices,
+            _random_vertices,
             random_edges,
         ) = self.__generate_graphs(20, 0, 100, 4)
 
@@ -502,7 +502,7 @@ def test_add_vertex_exception_check(self) -> None:
             undirected_graph,
             directed_graph,
             random_vertices,
-            random_edges,
+            _random_edges,
         ) = self.__generate_graphs(20, 0, 100, 4)
 
         for vertex in random_vertices:
@@ -516,7 +516,7 @@ def test_remove_vertex_exception_check(self) -> None:
             undirected_graph,
             directed_graph,
             random_vertices,
-            random_edges,
+            _random_edges,
         ) = self.__generate_graphs(20, 0, 100, 4)
 
         for i in range(101):
@@ -530,7 +530,7 @@ def test_add_edge_exception_check(self) -> None:
         (
             undirected_graph,
             directed_graph,
-            random_vertices,
+            _random_vertices,
             random_edges,
         ) = self.__generate_graphs(20, 0, 100, 4)
 
@@ -569,7 +569,7 @@ def test_contains_edge_exception_check(self) -> None:
             undirected_graph,
             directed_graph,
             random_vertices,
-            random_edges,
+            _random_edges,
         ) = self.__generate_graphs(20, 0, 100, 4)
 
         for vertex in random_vertices:

graphs/graph_adjacency_matrix.py

@@ -469,7 +469,7 @@ def test_remove_edge(self) -> None:
         (
             undirected_graph,
             directed_graph,
-            random_vertices,
+            _random_vertices,
             random_edges,
         ) = self.__generate_graphs(20, 0, 100, 4)
 
@@ -523,7 +523,7 @@ def test_add_vertex_exception_check(self) -> None:
             undirected_graph,
             directed_graph,
             random_vertices,
-            random_edges,
+            _random_edges,
         ) = self.__generate_graphs(20, 0, 100, 4)
 
         for vertex in random_vertices:
@@ -537,7 +537,7 @@ def test_remove_vertex_exception_check(self) -> None:
             undirected_graph,
             directed_graph,
             random_vertices,
-            random_edges,
+            _random_edges,
         ) = self.__generate_graphs(20, 0, 100, 4)
 
         for i in range(101):
@@ -551,7 +551,7 @@ def test_add_edge_exception_check(self) -> None:
         (
             undirected_graph,
             directed_graph,
-            random_vertices,
+            _random_vertices,
             random_edges,
         ) = self.__generate_graphs(20, 0, 100, 4)
 
@@ -590,7 +590,7 @@ def test_contains_edge_exception_check(self) -> None:
             undirected_graph,
             directed_graph,
             random_vertices,
-            random_edges,
+            _random_edges,
         ) = self.__generate_graphs(20, 0, 100, 4)
 
         for vertex in random_vertices:

knapsack/tests/test_greedy_knapsack.py

@@ -28,7 +28,7 @@ def test_negative_max_weight(self):
         # profit = [10, 20, 30, 40, 50, 60]
         # weight = [2, 4, 6, 8, 10, 12]
         # max_weight = -15
-        pytest.raises(ValueError, match="max_weight must greater than zero.")
+        pytest.raises(ValueError, match=r"max_weight must greater than zero.")
 
     def test_negative_profit_value(self):
         """
@@ -38,7 +38,7 @@ def test_negative_profit_value(self):
         # profit = [10, -20, 30, 40, 50, 60]
         # weight = [2, 4, 6, 8, 10, 12]
         # max_weight = 15
-        pytest.raises(ValueError, match="Weight can not be negative.")
+        pytest.raises(ValueError, match=r"Weight can not be negative.")
 
     def test_negative_weight_value(self):
         """
@@ -48,7 +48,7 @@ def test_negative_weight_value(self):
         # profit = [10, 20, 30, 40, 50, 60]
         # weight = [2, -4, 6, -8, 10, 12]
         # max_weight = 15
-        pytest.raises(ValueError, match="Profit can not be negative.")
+        pytest.raises(ValueError, match=r"Profit can not be negative.")
 
     def test_null_max_weight(self):
         """
@@ -58,7 +58,7 @@ def test_null_max_weight(self):
         # profit = [10, 20, 30, 40, 50, 60]
         # weight = [2, 4, 6, 8, 10, 12]
         # max_weight = null
-        pytest.raises(ValueError, match="max_weight must greater than zero.")
+        pytest.raises(ValueError, match=r"max_weight must greater than zero.")
 
     def test_unequal_list_length(self):
         """
@@ -68,7 +68,7 @@ def test_unequal_list_length(self):
         # profit = [10, 20, 30, 40, 50]
         # weight = [2, 4, 6, 8, 10, 12]
         # max_weight = 100
-        pytest.raises(IndexError, match="The length of profit and weight must be same.")
+        pytest.raises(IndexError, match=r"The length of profit and weight must be same.")
 
 
 if __name__ == "__main__":

linear_algebra/gaussian_elimination.py

@@ -33,7 +33,7 @@ def retroactive_resolution(
            [ 0.5]])
     """
 
-    rows, columns = np.shape(coefficients)
+    rows, _columns = np.shape(coefficients)
 
     x: NDArray[float64] = np.zeros((rows, 1), dtype=float)
     for row in reversed(range(rows)):

linear_algebra/jacobi_iteration_method.py

@@ -112,7 +112,7 @@ def jacobi_iteration_method(
         (coefficient_matrix, constant_matrix), axis=1
     )
 
-    rows, cols = table.shape
+    rows, _cols = table.shape
 
     strictly_diagonally_dominant(table)
 
@@ -149,7 +149,7 @@ def jacobi_iteration_method(
 
     # Here we get 'i_col' - these are the column numbers, for each row
     # without diagonal elements, except for the last column.
-    i_row, i_col = np.where(masks)
+    _i_row, i_col = np.where(masks)
     ind = i_col.reshape(-1, rows - 1)
 
     #'i_col' is converted to a two-dimensional list 'ind', which will be

machine_learning/polynomial_regression.py

@@ -93,7 +93,7 @@ def _design_matrix(data: np.ndarray, degree: int) -> np.ndarray:
         ...
         ValueError: Data must have dimensions N x 1
         """
-        rows, *remaining = data.shape
+        _rows, *remaining = data.shape
         if remaining:
             raise ValueError("Data must have dimensions N x 1")
 

machine_learning/principle_component_analysis.py

@@ -65,7 +65,7 @@ def main() -> None:
     """
     Driver function to execute PCA and display results.
     """
-    data_x, data_y = collect_dataset()
+    data_x, _data_y = collect_dataset()
 
     # Number of principal components to retain
     n_components = 2