def process(infile, algorithm_name, support, confidence, m, random, partial):
stats = Stats()
transactions = TransactionsList(infile)
stats.record_post_large_sets()
stats.record_post_rules()
last_total_time = stats.real_time
last_user_time = stats.user_time
stats = Stats()
if algorithm_name == 'apriori':
algorithm = Apriori(transactions, support)
else:
algorithm = Dic(transactions, support, m, random, partial)
large_sets, counter = algorithm.get_large_sets_and_counter()
stats.record_post_large_sets()
rules = RulesGenerator.generate_rules(large_sets, confidence, counter, transactions)
stats.record_post_rules()
large_len = len(large_sets)
total_time = stats.real_time - last_total_time
user_time = stats.user_time - last_user_time
large_sets_time = stats.set_gen_time - last_total_time
last_total_time = stats.real_time
last_user_time = stats.user_time
memory = stats.memory_use
rules_no = len(rules)
print "{infile}\t{algorithm_name}\t{support}\t{confidence}\t{m}\t{rules_no}\t{large_len}\t{memory}\t{total_time}\t{user_time}\t{large_sets_time}\t{partial}\t{random}".format(**locals())
def testRules(self):
extract = lambda x: (x[0], tuple(x[1]))
result = RulesGenerator.generate_rules(self.large_sets, self.minconf, self.counter, self.transactions)
result = [ extract(x) for x in result ]
expected = [
((2,), (3,)), #conf=0.75
((3,), (2,)), #conf=1.0
((4,), (2,)), #conf=1.0
((4,), (3,)), #conf=1.0
#((4,), (2, 3)), #conf=0.5
#((2, 4), (3,)), #conf=0.5
#((3, 4), (2,)), #conf=0.5
]
self.assertEqual(result, expected)
def testRules(self):
extract = lambda x: (x[0], tuple(x[1]))
result = RulesGenerator.generate_rules(self.large_sets, self.minconf,
self.counter, self.transactions)
result = [extract(x) for x in result]
expected = [
((2, ), (3, )), #conf=0.75
((3, ), (2, )), #conf=1.0
((4, ), (2, )), #conf=1.0
((4, ), (3, )), #conf=1.0
#((4,), (2, 3)), #conf=0.5
#((2, 4), (3,)), #conf=0.5
#((3, 4), (2,)), #conf=0.5
]
self.assertEqual(result, expected)
def main(args):
stats = Stats()
transactions = TransactionsList(args.infile)
if args.algorithm == 'apriori':
algorithm = Apriori(transactions, args.minsup)
else:
algorithm = Dic(transactions, args.minsup, args.m)
large_sets, counter = algorithm.get_large_sets_and_counter()
stats.record_post_large_sets()
rules = RulesGenerator.generate_rules(large_sets, args.minconf, counter, transactions)
stats.record_post_rules()
writer = Writer(args.outfile)
writer.add_args(args)
writer.add_stats(stats)
writer.add_rules(rules)
writer.write()
def testGetAllSubsets(self):
result = RulesGenerator._RulesGenerator__get_all_subsets(self.set)
expected = [(1, ), (2, ), (3, ), (1, 2), (2, 3), (1, 3)]
self.assertEqual(sorted(result), sorted(expected))
def testGetAllSubsets(self):
result = RulesGenerator._RulesGenerator__get_all_subsets(self.set)
expected = [(1,), (2,), (3,), (1, 2), (2, 3), (1, 3)]
self.assertEqual(sorted(result), sorted(expected))
