def main():
parser = argparse.ArgumentParser()
parser.add_argument('--input', '-i', help='input file (csv file)')
parser.add_argument('--minsup', '-m', type=float, help='minimum support')
parser.add_argument('--minconf',
'-c',
type=float,
help='minimun confidence')
parser.add_argument('--numrule',
'-n',
type=int,
help='max number of rules output')
args = parser.parse_args()
db = CsvDatabase(args.input)
Factory.setup_db(db)
apriori = Apriori(db, args.minsup, args.minconf, args.numrule)
frequent_itemsets = apriori.generate_frequent_itemset()
for level in frequent_itemsets:
print(level, ': ', len(frequent_itemsets[level]))
for itemset in frequent_itemsets[level]:
print(itemset)
print('Rules:')
for rule in apriori.generate_all_confidence_rules():
print(rule, '(confidence: %.4f)' % (rule.confidence))
def main():
db = CsvDatabase('test_data/zero_one.csv')
Factory.setup_db(db)
runtimes = [] # store runtime (in second)
numrules = [] # store number of confident rules
for support in np.linspace(low_support, high_support, interval):
start_time = time.monotonic()
apriori = Apriori(db, support, confidence, None)
rules = apriori.generate_all_confidence_rules()
#for rule in rules:
# print(rule, ': ', rule.confidence)
end_time = time.monotonic()
runtimes.append(end_time - start_time)
numrule = len(rules)
numrules.append(numrule)
print('Support %.2f, runtime %.9f, numrules %d' %
(support, end_time - start_time, numrule))
graph_runtime(runtimes)
graph_numrules(numrules)
def setUp(self):
db = CsvDatabase(
os.path.join(os.getcwd(), 'test_data/contact-lenses.csv'))
Factory.setup_db(db)
