def test_timestamp_api():
host = '127.0.0.1'
user = '******'
password = '******'
algorithm = ['luminol']
rng = np.random.RandomState(42)
np.random.seed(42)
conn, cursor = connect_server(host, user, password)
ground_truth_whole = insert_demo_data(conn,
cursor,
'db',
't',
ground_truth_flag=True)
data, ground_truth = query_data(conn,
cursor,
'db',
't',
time_serie_name='ts',
ground_truth=ground_truth_whole,
start_time='2019-07-20 00:00:00',
end_time='2019-08-20 00:00:00',
time_serie=True,
ground_truth_flag=True)
for alg in algorithm:
clf = algorithm_selection(alg, random_state=rng, contamination=0.1)
print('Start processing:')
start_time = time.clock()
clf.fit(data)
prediction_result = clf.predict(data)
outlierness = clf.decision_function(data)
output_performance(alg, ground_truth, prediction_result,
time.clock() - start_time, outlierness)
conn.close()
def test_static_api():
host = '127.0.0.1'
user = '******'
password = '******'
algorithm = [
'iforest', 'ocsvm', 'lof', 'robustcovariance', 'cblof', 'knn', 'hbos',
'sod', 'pca', 'dagmm', 'autoencoder', 'lstm_ad', 'lstm_ed',
'staticautoencoder'
]
rng = np.random.RandomState(42)
np.random.seed(42)
conn, cursor = connect_server(host, user, password)
ground_truth_whole = insert_demo_data(conn,
cursor,
'db',
't',
ground_truth_flag=True)
data, ground_truth = query_data(conn,
cursor,
'db',
't',
time_serie_name='ts',
ground_truth=ground_truth_whole,
start_time='2019-07-20 00:00:00',
end_time='2019-08-20 00:00:00',
time_serie=False,
ground_truth_flag=True)
for alg in algorithm:
clf = algorithm_selection(alg, random_state=rng, contamination=0.1)
print('Start processing:')
start_time = time.clock()
clf.fit(data)
prediction_result = clf.predict(data)
outlierness = clf.decision_function(data)
output_performance(alg, ground_truth, prediction_result,
time.clock() - start_time, outlierness)
data, ground_truth = query_data(conn,
cursor,
'db',
't',
time_serie_name='ts',
ground_truth=ground_truth_whole,
start_time=None,
end_time=None,
time_serie=False,
ground_truth_flag=True)
data, ground_truth = query_data(conn,
cursor,
'db',
't',
time_serie_name='ts',
ground_truth=ground_truth_whole,
start_time=None,
end_time=None,
time_serie=False,
ground_truth_flag=False)
conn.close()
def test_io_static():
host = '127.0.0.1'
user = '******'
password = '******'
alg = 'iforest'
rng = np.random.RandomState(42)
np.random.seed(42)
conn, cursor = connect_server(host, user, password)
ground_truth_whole = insert_demo_data(conn,
cursor,
'db',
't',
ground_truth_flag=True)
data, ground_truth = query_data(conn,
cursor,
'db',
't',
time_serie_name='ts',
ground_truth=ground_truth_whole,
start_time='2019-07-20 00:00:00',
end_time='2019-08-20 00:00:00',
time_serie=False,
ground_truth_flag=True)
clf = algorithm_selection(alg, random_state=rng, contamination=0.1)
print('Start processing:')
start_time = time.clock()
clf.fit(data)
prediction_result = clf.predict(data)
outlierness = clf.decision_function(data)
output_performance(alg, ground_truth, prediction_result,
time.clock() - start_time, outlierness)
visualize_distribution_static(data, prediction_result, outlierness)
visualize_distribution(data, prediction_result, outlierness)
visualize_outlierscore(outlierness, prediction_result, contamination=0.1)
data, ground_truth = query_data(conn,
cursor,
'db',
't',
time_serie_name='ts',
ground_truth=ground_truth_whole,
start_time=None,
end_time='2019-08-20 00:00:00',
time_serie=False,
ground_truth_flag=True)
data, ground_truth = query_data(conn,
cursor,
'db',
't',
time_serie_name='ts',
ground_truth=ground_truth_whole,
start_time='2019-07-20 00:00:00',
end_time=None,
time_serie=False,
ground_truth_flag=True)
if args.ground_truth:
alg_selector = Cash(data, ground_truth)
clf = alg_selector.model_selector(max_evals=2)
else:
clf = algorithm_selection(args.algorithm,
random_state=rng,
contamination=args.contamination)
start_time = time.clock()
clf.fit(data)
prediction_result = clf.predict(data)
outlierness = clf.decision_function(data)
anomaly_scores = clf.anomaly_likelihood(data)
if args.ground_truth:
output_performance(args.algorithm, ground_truth, prediction_result,
time.clock() - start_time, anomaly_scores)
if args.visualize_distribution and args.ground_truth:
if not args.time_stamp:
visualize_distribution_static(data, prediction_result, outlierness,
args.saving_path)
visualize_distribution(data, prediction_result, outlierness,
args.saving_path)
visualize_outlierscore(outlierness, prediction_result,
args.contamination, args.saving_path)
else:
visualize_distribution_time_serie(clf.ts, data, args.saving_path)
visualize_outlierscore(outlierness, prediction_result,
args.contamination, args.saving_path)
conn.close()
print('Loading cost: %.6f seconds' % (time.clock() - start_time))
print('Load data successful')
#algorithm
clf = algorithm_selection(args.algorithm,
random_state=rng,
contamination=args.contamination)
print('Start processing:')
start_time = time.clock()
clf.fit(data)
prediction_result = clf.predict(data)
outlierness = clf.decision_function(data)
if args.ground_truth:
output_performance(args.algorithm, ground_truth, prediction_result,
time.clock() - start_time, outlierness)
if args.visualize_distribution and args.ground_truth:
if not args.time_stamp:
visualize_distribution_static(data, prediction_result, outlierness,
args.saving_path)
visualize_distribution(data, prediction_result, outlierness,
args.saving_path)
visualize_outlierscore(outlierness, prediction_result,
args.contamination)
else:
visualize_distribution_time_serie(clf.ts, data, args.saving_path)
visualize_outlierscore(outlierness, prediction_result,
args.contamination, args.saving_path)
conn.close()
