class Bucket:
def __init__(self, transaction=None):
self.tree = FPTree()
self.sorting_counter = None
if transaction is not None:
self.add(transaction)
def add(self, transaction):
self.tree.insert(sort_transaction(transaction, self.sorting_counter))
def __len__(self):
return self.tree.num_transactions
def append(self, other_bucket):
for (transaction, count) in other_bucket.tree:
self.tree.insert(
sort_transaction(transaction, self.sorting_counter), count)
self.tree.sort() # TODO: Is this necessary?
self.sorting_counter = self.tree.item_count.copy()
def __str__(self):
return str(self.transactions)
def test_tree_sorting():
expected_tree = construct_initial_tree(test_transactions)
assert(expected_tree.is_sorted())
tree = FPTree()
for transaction in test_transactions:
# Insert reversed, since lexicographical order is already decreasing
# frequency order in this example.
tree.insert(map(Item, reversed(transaction)))
assert(str(expected_tree) != str(tree))
tree.sort()
assert(tree.is_sorted())
assert(str(expected_tree) == str(tree))
datasets = [
"datasets/UCI-zoo.csv",
"datasets/mushroom.csv",
# "datasets/BMS-POS.csv",
# "datasets/kosarak.csv",
]
for csvFilePath in datasets:
print("Loading FPTree for {}".format(csvFilePath))
start = time.time()
tree = FPTree()
with open(csvFilePath, newline='') as csvfile:
for line in list(csv.reader(csvfile)):
# Insert sorted lexicographically
transaction = sorted(map(Item, line))
tree.insert(transaction)
duration = time.time() - start
print("Loaded in {:.2f} seconds".format(duration))
print("Sorting...")
start = time.time()
tree.sort()
duration = time.time() - start
print("Sorting took {:.2f} seconds".format(duration))
assert(tree.is_sorted())
def mine_cp_tree_stream(transactions, min_support, sort_interval, window_size):
# Yields (window_start_index, window_length, patterns)
tree = FPTree()
sliding_window = deque()
frequency = None
num_transactions = 0
for transaction in transactions:
num_transactions += 1
transaction = sort_transaction(map(Item, transaction), frequency)
tree.insert(transaction)
sliding_window.append(transaction)
if len(sliding_window) > window_size:
transaction = sliding_window.popleft()
transaction = sort_transaction(transaction, frequency)
tree.remove(transaction, 1)
assert (len(sliding_window) == window_size)
assert (tree.num_transactions == window_size)
if (num_transactions % sort_interval) == 0:
tree.sort()
frequency = tree.item_count.copy()
if (num_transactions % window_size) == 0:
if (num_transactions % sort_interval) != 0:
# We won't have sorted due to the previous check, so we
# need to sort before mining.
tree.sort()
frequency = tree.item_count.copy()
assert (tree.num_transactions == len(sliding_window))
assert (len(sliding_window) == window_size)
min_count = min_support * tree.num_transactions
patterns = fp_growth(tree, min_count, [])
yield (num_transactions - len(sliding_window), len(sliding_window),
patterns)
else:
# We didn't just mine on the last transaction, we need to mine now,
# else we'll miss data.
if (num_transactions % window_size) != 0:
if (num_transactions % sort_interval) != 0:
tree.sort()
frequency = tree.item_count.copy()
min_count = min_support * tree.num_transactions
patterns = fp_growth(tree, min_count, [])
yield (num_transactions - len(sliding_window), len(sliding_window),
patterns)