[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

machine_learning/apriori_algorithm.py

@@ -11,6 +11,7 @@
 from collections import defaultdict
 from itertools import combinations
 
+
 def load_data() -> list[list[str]]:
     """
     Returns a sample transaction dataset.
@@ -24,7 +25,9 @@ def load_data() -> list[list[str]]:
 class Apriori:
     """Apriori algorithm class with support, confidence, and lift filtering."""
 
-    def __init__(self, transactions, min_support=0.25, min_confidence=0.5, min_lift=1.0):
+    def __init__(
+        self, transactions, min_support=0.25, min_confidence=0.5, min_lift=1.0
+    ):
         self.transactions = [set(t) for t in transactions]
         self.min_support = min_support
         self.min_confidence = min_confidence
@@ -37,7 +40,9 @@ def __init__(self, transactions, min_support=0.25, min_confidence=0.5, min_lift=
 
     def _get_support(self, itemset: frozenset) -> float:
         """Return support of an itemset."""
-        return sum(1 for t in self.transactions if itemset.issubset(t)) / len(self.transactions)
+        return sum(1 for t in self.transactions if itemset.issubset(t)) / len(
+            self.transactions
+        )
 
     def confidence(self, antecedent: frozenset, consequent: frozenset) -> float:
         """Calculate confidence of a rule A -> B."""
@@ -59,7 +64,11 @@ def find_frequent_itemsets(self):
                 item_counts[frozenset([item])] += 1
 
         total = len(self.transactions)
-        current_itemsets = {k: v / total for k, v in item_counts.items() if v / total >= self.min_support}
+        current_itemsets = {
+            k: v / total
+            for k, v in item_counts.items()
+            if v / total >= self.min_support
+        }
         self.itemsets.append(current_itemsets)
 
         k = 2
@@ -69,10 +78,17 @@ def find_frequent_itemsets(self):
             for i in range(len(keys)):
                 for j in range(i + 1, len(keys)):
                     union = keys[i] | keys[j]
-                    if len(union) == k and all(frozenset(sub) in current_itemsets for sub in combinations(union, k - 1)):
-                            candidates.add(union)
-
-            freq_candidates = {c: self._get_support(c) for c in candidates if self._get_support(c) >= self.min_support}
+                    if len(union) == k and all(
+                        frozenset(sub) in current_itemsets
+                        for sub in combinations(union, k - 1)
+                    ):
+                        candidates.add(union)
+
+            freq_candidates = {
+                c: self._get_support(c)
+                for c in candidates
+                if self._get_support(c) >= self.min_support
+            }
             if not freq_candidates:
                 break
 
@@ -115,6 +131,6 @@ def generate_association_rules(self):
     print("\nAssociation Rules:")
     for rule in model.rules:
         antecedent, consequent, conf, lift = rule
-        print(f"{set(antecedent)} -> {set(consequent)}, conf={conf:.2f}, lift={lift:.2f}")
-
-
+        print(
+            f"{set(antecedent)} -> {set(consequent)}, conf={conf:.2f}, lift={lift:.2f}"
+        )