fix: type error, code refactore

machine_learning/apriori_algorithm.py

@@ -11,10 +11,7 @@
 Examples: https://www.kaggle.com/code/earthian/apriori-association-rules-mining
 """
 
-from typing import List, Tuple
-
-
-def load_data() -> List[List[str]]:
+def load_data() -> list[list[str]]:
     """
     Returns a sample transaction dataset.
 
@@ -35,7 +32,7 @@ def load_data() -> List[List[str]]:
     ]
     return data
 
-def generate_candidates(itemset: List[str], length: int) -> List[List[str]]:
+def generate_candidates(itemset: list[str], length: int):
     """
     Generates candidate itemsets of size k from the given itemsets.
 
@@ -47,8 +44,6 @@ def generate_candidates(itemset: List[str], length: int) -> List[List[str]]:
     >>> generate_candidates(itemsets, 3)
     [['milk', 'bread', 'butter']]
     """
-
-def generate_candidates(itemset: List[str], length: int):
     candidates = []
     for i in range(len(itemset)):
         for j in range(i + 1, len(itemset)):
@@ -63,8 +58,8 @@ def generate_candidates(itemset: List[str], length: int):
 
 
 def prune(
-    itemset: List[str], candidates: List[List[str]], length: int
-) -> List[List[str]]:
+    itemset: list[str], candidates: list[list[str]], length: int
+) -> list[list[str]]:
     # Prune candidate itemsets
     """
     The goal of pruning is to filter out candidate itemsets that are not frequent. This is done by checking if all the (k-1) subsets of a candidate itemset are present in the frequent itemsets of the previous iteration (valid subsequences of the frequent itemsets from the previous iteration).
@@ -93,7 +88,7 @@ def prune(
     return pruned
 
 
-def apriori(data: List[List[str]], min_support: int) -> List[Tuple[List[str], int]]:
+def apriori(data: list[list[str]], min_support: int) -> list[tuple[list[str], int]]:
     """
     Returns a list of frequent itemsets and their support counts.
 
@@ -138,11 +133,11 @@ def apriori(data: List[List[str]], min_support: int) -> List[Tuple[List[str], in
     Apriori algorithm for finding frequent itemsets.
 
     Args:
-        data (List[List[str]]): A list of transactions, where each transaction is a list of items.
+        data (list[list[str]]): A list of transactions, where each transaction is a list of items.
         min_support (int): The minimum support threshold for frequent itemsets.
 
     Returns:
-        List[Tuple[List[str], int]]: A list of frequent itemsets along with their support counts.
+        list[Tuple[list[str], int]]: A list of frequent itemsets along with their support counts.
 
     Example:
     >>> data = [["milk", "bread"], ["milk", "butter"], ["milk", "bread", "nuts"]]