ty: un-ignore invalid-type-arguments

data_structures/heap/heap.py

@@ -7,15 +7,7 @@
 
 class Comparable(Protocol):
     @abstractmethod
-    def __lt__(self: T, other: T) -> bool:
-        pass
-
-    @abstractmethod
-    def __gt__(self: T, other: T) -> bool:
-        pass
-
-    @abstractmethod
-    def __eq__(self: T, other: object) -> bool:
+    def __gt__(self: T, other: T, /) -> bool:
         pass
 
 
@@ -54,7 +46,7 @@ def __repr__(self) -> str:
 
     def parent_index(self, child_idx: int) -> int | None:
         """
-        returns the parent index based on the given child index
+        Returns the parent index based on the given child index
 
         >>> h = Heap()
         >>> h.build_max_heap([103, 9, 1, 7, 11, 15, 25, 201, 209, 107, 5])
@@ -90,8 +82,8 @@ def parent_index(self, child_idx: int) -> int | None:
 
     def left_child_idx(self, parent_idx: int) -> int | None:
         """
-        return the left child index if the left child exists.
-        if not, return None.
+        Return the left child index if the left child exists.
+        If not, return None.
         """
         left_child_index = 2 * parent_idx + 1
         if left_child_index < self.heap_size:
@@ -100,8 +92,8 @@ def left_child_idx(self, parent_idx: int) -> int | None:
 
     def right_child_idx(self, parent_idx: int) -> int | None:
         """
-        return the right child index if the right child exists.
-        if not, return None.
+        Return the right child index if the right child exists.
+        If not, return None.
         """
         right_child_index = 2 * parent_idx + 2
         if right_child_index < self.heap_size:
@@ -110,10 +102,10 @@ def right_child_idx(self, parent_idx: int) -> int | None:
 
     def max_heapify(self, index: int) -> None:
         """
-        correct a single violation of the heap property in a subtree's root.
+        Correct a single violation of the heap property in a subtree's root.
 
         It is the function that is responsible for restoring the property
-        of Max heap i.e the maximum element is always at top.
+        of a max heap, i.e the maximum element is always at the top.
         """
         if index < self.heap_size:
             violation: int = index
@@ -133,7 +125,7 @@ def max_heapify(self, index: int) -> None:
 
     def build_max_heap(self, collection: Iterable[T]) -> None:
         """
-        build max heap from an unsorted array
+        Build a max heap from an unsorted array
 
         >>> h = Heap()
         >>> h.build_max_heap([20,40,50,20,10])
@@ -164,7 +156,7 @@ def build_max_heap(self, collection: Iterable[T]) -> None:
 
     def extract_max(self) -> T:
         """
-        get and remove max from heap
+        Get and remove max from heap
 
         >>> h = Heap()
         >>> h.build_max_heap([20,40,50,20,10])
@@ -195,7 +187,7 @@ def extract_max(self) -> T:
 
     def insert(self, value: T) -> None:
         """
-        insert a new value into the max heap
+        Insert a new value into the max heap
 
         >>> h = Heap()
         >>> h.insert(10)
@@ -241,7 +233,6 @@ def heap_sort(self) -> None:
 if __name__ == "__main__":
     import doctest
 
-    # run doc test
     doctest.testmod()
 
     # demo

machine_learning/k_nearest_neighbors.py

@@ -16,15 +16,16 @@
 from heapq import nsmallest
 
 import numpy as np
+from numpy.typing import NDArray
 from sklearn import datasets
 from sklearn.model_selection import train_test_split
 
 
 class KNN:
     def __init__(
         self,
-        train_data: np.ndarray[float],
-        train_target: np.ndarray[int],
+        train_data: NDArray[np.float64],
+        train_target: NDArray[np.int64],
         class_labels: list[str],
     ) -> None:
         """
@@ -34,7 +35,7 @@ def __init__(
         self.labels = class_labels
 
     @staticmethod
-    def _euclidean_distance(a: np.ndarray[float], b: np.ndarray[float]) -> float:
+    def _euclidean_distance(a: NDArray[np.float64], b: NDArray[np.float64]) -> float:
         """
         Calculate the Euclidean distance between two points
         >>> KNN._euclidean_distance(np.array([0, 0]), np.array([3, 4]))
@@ -44,7 +45,7 @@ def _euclidean_distance(a: np.ndarray[float], b: np.ndarray[float]) -> float:
         """
         return float(np.linalg.norm(a - b))
 
-    def classify(self, pred_point: np.ndarray[float], k: int = 5) -> str:
+    def classify(self, pred_point: NDArray[np.float64], k: int = 5) -> str:
         """
         Classify a given point using the kNN algorithm
         >>> train_X = np.array(

pyproject.toml

@@ -205,7 +205,6 @@ rules.call-non-callable = "ignore"
 rules.deprecated = "ignore"
 rules.invalid-argument-type = "ignore"
 rules.invalid-return-type = "ignore"
-rules.invalid-type-arguments = "ignore"
 rules.no-matching-overload = "ignore"
 rules.not-iterable = "ignore"
 rules.not-subscriptable = "ignore"