Fix ruff lint issues

Author: Sananda-Dutta

digital_image_processing/filters/local_binary_pattern.py

@@ -19,7 +19,7 @@ def get_neighbors_pixel(
 
     try:
         return int(image[x_coordinate][y_coordinate] >= center)
-    except (IndexError, TypeError):
+    except IndexError, TypeError:
         return 0
 
 

divide_and_conquer/convex_hull.py

@@ -124,7 +124,7 @@ def _construct_points(
             else:
                 try:
                     points.append(Point(p[0], p[1]))
-                except (IndexError, TypeError):
+                except IndexError, TypeError:
                     print(
                         f"Ignoring deformed point {p}. All points"
                         " must have at least 2 coordinates."

dynamic_programming/catalan_numbers.py

@@ -71,7 +71,7 @@ def catalan_numbers(upper_limit: int) -> "list[int]":
                 print(f"The Catalan numbers from 0 through {N} are:")
                 print(catalan_numbers(N))
                 print("Try another upper limit for the sequence: ", end="")
-    except (NameError, ValueError):
+    except NameError, ValueError:
         print("\n********* Invalid input, goodbye! ************\n")
 
     import doctest

machine_learning/decision_tree.py

@@ -4,9 +4,10 @@
 Output: The decision tree maps a real number input to a real number output.
 """
 
-import numpy as np
 from collections import Counter
 
+import numpy as np
+
 
 class DecisionTree:
     def __init__(self, depth=5, min_leaf_size=5, task="regression", criterion="gini"):
@@ -54,7 +55,7 @@ def gini(self, y):
 
         Lower Gini value indicates better purity (best split).
         """
-        classes, counts = np.unique(y, return_counts=True)
+        _, counts = np.unique(y, return_counts=True)
         prob = counts / counts.sum()
         return 1 - np.sum(prob**2)
 
@@ -67,7 +68,7 @@ def entropy(self, y):
 
         Lower entropy means higher purity.
         """
-        classes, counts = np.unique(y, return_counts=True)
+        _, counts = np.unique(y, return_counts=True)
         prob = counts / counts.sum()
         return -np.sum(prob * np.log2(prob + 1e-9))
 
@@ -76,7 +77,8 @@ def information_gain(self, parent, left, right):
         Computes the information gain from splitting a dataset.
         Information gain represents the reduction in impurity
         after a dataset is split into left and right subsets.
-        Formula: IG = Impurity(parent) - [weighted impurity(left) + weighted impurity(right)]
+        Formula: IG = Impurity(parent) - [
+            weighted impurity(left) + weighted impurity(right)]
 
         Higher information gain indicates a better split.
         """
@@ -155,10 +157,7 @@ def train(self, x, y):
         then the data set is not split and the average for the entire array is used as
         the predictor
         """
-        if self.task == "regression":
-            best_score = float("inf")
-        else:
-            best_score = -float("inf")
+        best_score = float("inf") if self.task == "regression" else -float("inf")
 
         for i in range(len(x)):
             if len(x[:i]) < self.min_leaf_size:
@@ -209,11 +208,10 @@ def train(self, x, y):
             self.left.train(left_x, left_y)
             self.right.train(right_x, right_y)
 
+        elif self.task == "regression":
+            self.prediction = np.mean(y)
         else:
-            if self.task == "regression":
-                self.prediction = np.mean(y)
-            else:
-                self.prediction = self.most_common_label(y)
+            self.prediction = self.most_common_label(y)
 
     def predict(self, x):
         """

maths/greatest_common_divisor.py

@@ -73,7 +73,7 @@ def main():
             f"{greatest_common_divisor(num_1, num_2)}"
         )
         print(f"By iterative gcd({num_1}, {num_2}) = {gcd_by_iterative(num_1, num_2)}")
-    except (IndexError, UnboundLocalError, ValueError):
+    except IndexError, UnboundLocalError, ValueError:
         print("Wrong input")
 
 

project_euler/problem_002/sol4.py

@@ -56,7 +56,7 @@ def solution(n: int = 4000000) -> int:
 
     try:
         n = int(n)
-    except (TypeError, ValueError):
+    except TypeError, ValueError:
         raise TypeError("Parameter n must be int or castable to int.")
     if n <= 0:
         raise ValueError("Parameter n must be greater than or equal to one.")

project_euler/problem_003/sol1.py

@@ -80,7 +80,7 @@ def solution(n: int = 600851475143) -> int:
 
     try:
         n = int(n)
-    except (TypeError, ValueError):
+    except TypeError, ValueError:
         raise TypeError("Parameter n must be int or castable to int.")
     if n <= 0:
         raise ValueError("Parameter n must be greater than or equal to one.")

project_euler/problem_003/sol2.py

@@ -44,7 +44,7 @@ def solution(n: int = 600851475143) -> int:
 
     try:
         n = int(n)
-    except (TypeError, ValueError):
+    except TypeError, ValueError:
         raise TypeError("Parameter n must be int or castable to int.")
     if n <= 0:
         raise ValueError("Parameter n must be greater than or equal to one.")

project_euler/problem_003/sol3.py

@@ -44,7 +44,7 @@ def solution(n: int = 600851475143) -> int:
 
     try:
         n = int(n)
-    except (TypeError, ValueError):
+    except TypeError, ValueError:
         raise TypeError("Parameter n must be int or castable to int.")
     if n <= 0:
         raise ValueError("Parameter n must be greater than or equal to one.")

project_euler/problem_005/sol1.py

@@ -47,7 +47,7 @@ def solution(n: int = 20) -> int:
 
     try:
         n = int(n)
-    except (TypeError, ValueError):
+    except TypeError, ValueError:
         raise TypeError("Parameter n must be int or castable to int.")
     if n <= 0:
         raise ValueError("Parameter n must be greater than or equal to one.")