Fix ruff linting errors

- Fix ambiguous minus sign in B-Tree docstring
- Import Hashable from collections.abc instead of typing
- Remove unused numpy import from gaussian_naive_bayes.py
- Prefix unused fig variables with underscore in ML files
- Rename unused loop variable i to _i in rabin_karp_search.py
- Combine nested if statements in rabin_karp_search.py

All ruff checks now pass for contributed files.

Author: M-H-Jishan

data_structures/binary_tree/b_tree.py

@@ -63,7 +63,7 @@ class BTree:
     - Every non-leaf node (except root) has at least ⌈m/2⌉ children
     - The root has at least 2 children if it is not a leaf
     - All leaves appear on the same level
-    - A non-leaf node with k children contains k−1 keys
+    - A non-leaf node with k children contains k-1 keys
 
     Examples:
     >>> btree = BTree(order=3)

graphs/check_bipartite.py

@@ -1,5 +1,5 @@
 from collections import defaultdict, deque
-from typing import Hashable
+from collections.abc import Hashable
 
 
 def is_bipartite_dfs(graph: dict[Hashable, list[Hashable]]) -> bool:

machine_learning/gaussian_naive_bayes.py

@@ -5,7 +5,6 @@
 on the Iris dataset with proper visualization using modern sklearn methods.
 """
 
-import numpy as np
 from sklearn.datasets import load_iris
 from sklearn.metrics import ConfusionMatrixDisplay, accuracy_score
 from sklearn.model_selection import train_test_split

machine_learning/gradient_boosting_regressor.py

@@ -52,7 +52,7 @@ def main() -> None:
     print(f"Mean squared error: {mean_squared_error(y_test, y_pred):.2f}")
     print(f"Test R² score: {r2_score(y_test, y_pred):.2f}")
 
-    fig, ax = plt.subplots()
+    _fig, ax = plt.subplots()
     ax.scatter(y_test, y_pred, edgecolors=(0, 0, 0), alpha=0.4)
     ax.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], "r--", lw=3)
     ax.set_xlabel("Actual")

machine_learning/random_forest_regressor.py

@@ -52,7 +52,7 @@ def main() -> None:
     print(f"Mean squared error: {mean_squared_error(y_test, y_pred):.2f}")
     print(f"Test R² score: {r2_score(y_test, y_pred):.2f}")
 
-    fig, ax = plt.subplots()
+    _fig, ax = plt.subplots()
     ax.scatter(y_test, y_pred, edgecolors=(0, 0, 0), alpha=0.4)
     ax.plot([y_test.min(), y_test.max()], [y_test.min(), y_test.max()], "r--", lw=3)
     ax.set_xlabel("Actual")

strings/rabin_karp_search.py

@@ -54,17 +54,16 @@ def rabin_karp_search(text: str, pattern: str, prime: int = 101) -> list[int]:
     h = 1
     results: list[int] = []
 
-    for i in range(m - 1):
+    for _i in range(m - 1):
         h = (h * d) % prime
 
     for i in range(m):
         pattern_hash = (d * pattern_hash + ord(pattern[i])) % prime
         text_hash = (d * text_hash + ord(text[i])) % prime
 
     for i in range(n - m + 1):
-        if pattern_hash == text_hash:
-            if text[i : i + m] == pattern:
-                results.append(i)
+        if pattern_hash == text_hash and text[i : i + m] == pattern:
+            results.append(i)
 
         if i < n - m:
             text_hash = (d * (text_hash - ord(text[i]) * h) + ord(text[i + m])) % prime