Create test.py

Author: Programmer-RD-AI

test.py

@@ -0,0 +1,66 @@
+from pprint import pprint
+
+
+def apriori(transactions: dict, min_support: int) -> dict:
+    # Generate initial candidate itemsets (C1)
+    item_count = {}
+    for transaction in transactions:
+        for item in transaction:
+            item = frozenset([item])  # Ensure each item is a frozenset for consistency
+            if item in item_count:
+                item_count[item] += 1
+            else:
+                item_count[item] = 1
+
+    # Filter out items that don't meet the minimum support to form L1
+    Lk = {item for item, count in item_count.items() if count >= min_support}
+    k = 1
+    frequent_itemsets = [
+        set()
+    ]  # Start with an empty set to index frequent sets by size
+
+    # Main loop to generate Lk from Ck
+    while Lk:
+        frequent_itemsets.append(Lk)
+        Ck_plus_1 = set()
+
+        # Join step: Generate Ck+1 from Lk by finding all pairs of frequent item sets that can be merged
+        Lk_list = list(Lk)
+        for i in range(len(Lk_list)):
+            for j in range(i + 1, len(Lk_list)):
+                itemset1, itemset2 = Lk_list[i], Lk_list[j]
+                new_candidate = itemset1.union(itemset2)
+                if len(new_candidate) == k + 1:
+                    Ck_plus_1.add(new_candidate)
+
+        # Test each candidate in Ck+1 for minimum support
+        candidate_count = {candidate: 0 for candidate in Ck_plus_1}
+        for transaction in transactions:
+            for candidate in Ck_plus_1:
+                if candidate.issubset(transaction):
+                    candidate_count[candidate] += 1
+
+        # Form Lk+1 from candidates that meet the minimum support
+        Lk = {
+            candidate
+            for candidate, count in candidate_count.items()
+            if count >= min_support
+        }
+        k += 1
+
+    # Return the union of all Lk
+    return {item for sublist in frequent_itemsets for item in sublist}
+
+
+# Example usage:
+transactions = [
+    {"bread", "milk"},
+    {"bread", "diaper", "beer", "eggs"},
+    {"milk", "diaper", "beer", "coke"},
+    {"bread", "milk", "diaper", "beer"},
+    {"bread", "milk", "diaper", "coke"},
+]
+
+min_support = 2
+frequent_itemsets = apriori(transactions, min_support)
+pprint(frequent_itemsets)