def test_accuracy(self):
expected_accuracy = 0.5
cba = CBA(algorithm="m2")
test_dataframe = pd.read_csv(dataset_file, sep=",")
transactions = TransactionDB.from_DataFrame(test_dataframe)
transactions_test = TransactionDB.from_DataFrame(test_dataframe[:2])
cba.fit(transactions)
accuracy = cba.rule_model_accuracy(transactions_test)
self.assertAlmostEqual(accuracy, expected_accuracy, places=3)
def train_model(data, support, confidence, rule_length):
print('Mining rules...')
headers = data.columns
data = TransactionDB.from_DataFrame(data)
cba = CBA(support=support,
confidence=confidence,
maxlen=rule_length,
algorithm="m1")
cba.fit(data)
accuracy = cba.rule_model_accuracy(data)
print('Determining semantic coherence...')
semantics.precompute_attributes(headers)
for rule in cba.clf.rules:
antecedent = list(rule.antecedent.itemset.keys())
coherence = semantics.get_rule_coherence(antecedent)
mined_rules.append((f'{rule_to_em_rule(rule)}', rule.confidence,
coherence, rule.consequent.value))
print_model()
print(f'Model accuracy: {round(accuracy * 100, 2)} %')
print(f'Model support: {cba.support}')
import pandas as pd
from pyarc import CBA, TransactionDB
from sklearn.model_selection import train_test_split
print("")
print("Rule Generation")
data = pd.read_csv('total_data.csv') #disc_data/Known_attack_data
print("Data Size : %s" % str(data.shape))
# 데이터 분할
train, test = train_test_split(data, test_size=0.2, random_state=123)
txns_train = TransactionDB.from_DataFrame(train, target="class")
txns_test = TransactionDB.from_DataFrame(test)
print("Association Rule Generation")
cba = CBA(support=0.1)
cba.fit(txns_train)
print(cba.fit(txns_train))
print("\nRULES : ({})".format(len(cba.clf.rules)))
for i in cba.clf.rules:
print(i)
accuracy = cba.rule_model_accuracy(txns_test)
print("")
print(accuracy)