def duration_test():
path = "../Data/Experiments_Duration/"
train_rates = [0, 5, 10, 25]
test_rates = [1, 5, 10, 25, 50, 100, 250, 500]
anoms_rates = []
for train_rate in train_rates:
for test_rate in test_rates:
anoms_rates.append((train_rate, test_rate))
for i in range(len(anoms_rates)):
print(anoms_rates[i])
scores = []
for run in range(RUNS):
print("Run %i" % run)
train_file = path + "%i_train_%i.csv" % (i, anoms_rates[i][0])
test_file = path + "%i_test_%i.csv" % (i, anoms_rates[i][1])
duration_generator.generate(10000, 10000, anoms_rates[i][0],
anoms_rates[i][1], train_file,
test_file)
train_data = LogFile(train_file, ",", 0, 1000000, "date", "trace")
train_data.remove_attributes(["Anomaly"])
test_data = LogFile(test_file,
",",
0,
1000000,
"date",
"trace",
values=train_data.values)
train_data.keep_attributes(
["event", "date", "trace", "process", "resource", "random"])
train_data.create_k_context()
train_data.add_duration_to_k_context()
bins = train_data.discretize("duration_0")
test_data.create_k_context()
test_data.add_duration_to_k_context()
test_data.discretize("duration_0", bins)
model = edbn.train(train_data)
edbn.test(test_data, path + "Output_%i_%i.csv" % anoms_rates[i],
model, "anomaly", "0")
output_file = path + "Output_%i_%i.csv" % anoms_rates[i]
output_roc = path + "roc_%i_%i.png" % anoms_rates[i]
output_prec = path + "prec_recall_%i_%i.png" % anoms_rates[i]
score = plt.get_roc_auc(output_file)
scores.append(plt.get_roc_auc(output_file))
print("Score = %f" % score)
with open(path + "results.txt", "a") as fout:
fout.write("Testing:\ntrain rate: %i\ntest rate: %i\n" %
(anoms_rates[i][0], anoms_rates[i][1]))
fout.write("Result: " + str(scores) + "\n")
fout.write("Mean: %f Median: %f\n" %
(np.mean(scores), np.median(scores)))
fout.write("Variance: %f\n\n" % np.var(scores))
def categorical_test():
path = "../Data/Experiments/"
train_rates = [0, 5, 10, 25]
test_rates = [1, 5, 10, 25, 50, 100, 250, 500]
anoms_rates = []
for train_rate in train_rates:
for test_rate in test_rates:
anoms_rates.append((train_rate, test_rate))
for i in range(len(anoms_rates)):
print(anoms_rates[i])
scores = []
for run in range(RUNS):
print("Run %i" % run)
train_file = path + "%i_train_%i.csv" % (i, anoms_rates[i][0])
test_file = path + "%i_test_%i.csv" % (i, anoms_rates[i][1])
generator.create_shipment_data(10000, 10000, anoms_rates[i][0],
anoms_rates[i][1], train_file,
test_file)
train_data = LogFile(train_file, ",", 0, 1000000, None, "Case")
train_data.remove_attributes(["Anomaly"])
test_data = LogFile(test_file,
",",
0,
1000000,
None,
"Case",
values=train_data.values)
model = edbn.train(train_data)
edbn.test(test_data, path + "Output_%i_%i.csv" % anoms_rates[i],
model, "Anomaly", "0")
output_file = path + "Output_%i_%i.csv" % anoms_rates[i]
output_roc = path + "roc_%i_%i.png" % anoms_rates[i]
output_prec = path + "prec_recall_%i_%i.png" % anoms_rates[i]
score = plt.get_roc_auc(output_file)
scores.append(plt.get_roc_auc(output_file))
print("Score = %f" % score)
with open(path + "results.txt", "a") as fout:
fout.write("Testing:\ntrain rate: %i\ntest rate: %i\n" %
(anoms_rates[i][0], anoms_rates[i][1]))
fout.write("Result: " + str(scores) + "\n")
fout.write("Mean: %f Median: %f\n" %
(np.mean(scores), np.median(scores)))
fout.write("Variance: %f\n\n" % np.var(scores))
test_date = LogFile(test_file,
",",
0,
1000000,
None,
"Case",
string_2_int=train_date.string_2_int,
int_2_string=train_date.int_2_string)
model = edbn.train(train_date)
edbn.test(test_date, path + "Output_%i_%i.csv" % anoms_rates[i],
model, "Anomaly", "0")
output_file = path + "Output_%i_%i.csv" % anoms_rates[i]
output_roc = path + "roc_%i_%i.png" % anoms_rates[i]
output_prec = path + "prec_recall_%i_%i.png" % anoms_rates[i]
score = plt.get_roc_auc(output_file)
scores.append(plt.get_roc_auc(output_file))
print("Score = %f" % score)
with open(path + "results.txt", "a") as fout:
fout.write("Testing:\ntrain rate: %i\ntest rate: %i\n" %
(anoms_rates[i][0], anoms_rates[i][1]))
fout.write("Result: " + str(scores) + "\n")
fout.write("Mean: %f Median: %f\n" %
(np.mean(scores), np.median(scores)))
fout.write("Variance: %f\n\n" % np.var(scores))
#plt.plot_single_roc_curve(output_file, output_roc)
#plt.plot_single_prec_recall_curve(output_file, None, output_prec)
