def task3(train_set, test_set, algo):
ar_utils.test_dataset(train_set['Bearing1'])
ar_utils.plot_series(train_set, "train set")
ar_utils.plot_series(test_set, "test set")
if check_ds == True:
print('adf fuller testing ...')
ar_utils.adfuller_test(train_set['Bearing1'])
ar_utils.plot_pacf(train_set['Bearing1'])
ar_utils.plot_acf(train_set['Bearing1'])
t0 = times.time()
cpu_t0 = psutil.cpu_percent(interval=0.5)
model = ar_utils.generate_model(train_set, 0, 3, 3, 'Bearing1', algo)
t1 = times.time()
cpu_t1 = psutil.cpu_percent(interval=0.5)
train_t = t1 - t0
cpu_train_perc = abs(cpu_t1 - cpu_t0)
cpu_p0 = psutil.cpu_percent(interval=0.5)
pred_set = ar_utils.predict(model, len(test_set))
t2 = times.time()
cpu_p1 = psutil.cpu_percent(interval=0.5)
pred_t = t2 - t1
cpu_pred_perc = abs(cpu_p1 - cpu_p0)
ar_utils.plot_difference_model(test_set, pred_set, algo)
return train_t, pred_t, cpu_train_perc, cpu_pred_perc
def predict_request_body_len(train_set, test_set, method):
arima_utils.adfuller_test(train_set['request_body_len'].dropna())
arima_utils.plot_series(train_set['request_body_len'], 'Original Series')
'''
train_set['Value First Difference'] = train_set['request_body_len'] - train_set['request_body_len'].shift(1)
#dropdna, borra todos los vacios
arima_utils.adfuller_test(train_set['Value First Difference'].dropna())
arima_utils.plot_series(train_set['Value First Difference'], 'Value First Difference')
'''
arima_utils.plot_pacf(train_set['request_body_len'])
arima_utils.plot_acf(train_set['request_body_len'])
# use request_body_len, response_body_len
# usar p = 12 (intento original)
# usar q = 34 (intento original)
p = 2
q = 1
start_time = time.time()
print("STARTING TIMER REQUEST ", method)
model = pf.ARIMA(data=train_set,
ar=p,
ma=q,
integ=0,
target='request_body_len')
x = model.fit(method=method)
end_time = time.time()
total_time = end_time - start_time
print("TIME: ", total_time)
# PRINT DATA
#print(x.summary())
print(x.scores)
model.plot_fit()
plt.show()
# model.plot_predict_is(h=30)
# firstRegister = conn.head(30)
#plt.plot(test_set['ts'], test_set['request_body_len'])
#model.plot_predict_is(h=100, past_values=40)
#print(model.predict(h=100))
start_time = time.time()
print("STARTING TIMER, PREDICT REQUEST ", method)
plt.plot(test_set.index,
test_set['request_body_len'],
label='REAL',
color='pink')
plt.plot(model.predict(h=100), label='PREDICTION', color='cyan')
plt.legend(['REAL', 'PREDICTION'])
# model.plot_predict(h=200, past_values=40)
# plt.plot(firstRegister['ts'], firstRegister['request_body_len'])
end_time = time.time()
total_time = end_time - start_time
print("TIME: ", total_time)
plt.show()
def predict_response_body_len(train_set, test_set):
arima_utils.adfuller_test(train_set['response_body_len'])
arima_utils.plot_series(train_set['response_body_len'], 'Original Series')
arima_utils.plot_pacf(train_set['response_body_len'])
arima_utils.plot_acf(train_set['response_body_len'])
# use request_body_len, response_body_len
# usar p = 8 (intento original)
# usar q = 7 (intento original)
p = 2
q = 8
start_time = time.time()
print("STARTING TIMER, RESPONSE")
model = pf.ARIMA(data=train_set,
ar=p,
ma=q,
integ=0,
target='response_body_len')
x = model.fit(method=method)
#model.fit(method='BBVI', iterations='10000', optimizer='ADAM') ###
#model.fit(method='Laplace')
#model.fit(method='M-H')
end_time = time.time()
total_time = end_time - start_time
print("TIME: ", total_time)
# PRINT DATA
print(x.summary())
print(x.scores)
model.plot_fit()
plt.show()
# model.plot_predict_is(h=30)
# firstRegister = conn.head(30)
start_time = time.time()
print("STARTING TIMER PREDICT RESPONSE")
plt.plot(test_set.index,
test_set['response_body_len'],
label='REAL',
color='pink')
plt.plot(model.predict(h=100), label='PREDICTION', color='cyan')
plt.legend(['REAL', 'PREDICTION'])
#end_time = time.time()
#total_time = end_time - start_time
#print("TIME: " , total_time)
end_time = time.time()
total_time = end_time - start_time
print("TIME: ", total_time)
# model.plot_predict(h=200, past_values=40)
# plt.plot(firstRegister['ts'], firstRegister['response_body_len'])
plt.show()
def predict_request_body_len(train_set, test_set):
arima_utils.adfuller_test(train_set['request_body_len'].dropna())
arima_utils.plot_series(train_set['request_body_len'], 'Original Series')
'''
train_set['Value First Difference'] = train_set['request_body_len'] - train_set['request_body_len'].shift(1)
#dropdna, borra todos los vacios
arima_utils.adfuller_test(train_set['Value First Difference'].dropna())
arima_utils.plot_series(train_set['Value First Difference'], 'Value First Difference')
'''
arima_utils.plot_pacf(train_set['request_body_len'])
arima_utils.plot_acf(train_set['request_body_len'])
# use request_body_len, response_body_len
# usar p = 12 (intento original)
# usar q = 34 (intento original)
p = 2
q = 1
start_time = time.time()
print("STARTING TIMER REQUEST")
model = pf.ARIMA(data=train_set,
ar=p,
ma=q,
integ=0,
target='request_body_len')
x = model.fit(method=method)
##itentando la wea bayesiana
#https://pyflux.readthedocs.io/en/latest/var.html
#model.list_priors()
#model.adjust_prior(2, pf.Normal(0, 1))
#x = model.fit(method='BBVI', iterations='10000', optimizer='ADAM') ###
#x = model.fit(method='Laplace')
#x = model.fit(method="M-H")
end_time = time.time()
total_time = end_time - start_time
print("TIME: ", total_time)
# PRINT DATA
print(x.summary())
print(x.scores)
model.plot_fit()
plt.show()
# model.plot_predict_is(h=30)
# firstRegister = conn.head(30)
#plt.plot(test_set['ts'], test_set['request_body_len'])
#model.plot_predict_is(h=100, past_values=40)
#print(model.predict(h=100))
start_time = time.time()
print("STARTING TIMER, PREDICT REQUEST")
plt.plot(test_set.index,
test_set['request_body_len'],
label='REAL',
color='pink')
plt.plot(model.predict(h=100), label='PREDICTION', color='cyan')
plt.legend(['REAL', 'PREDICTION'])
# model.plot_predict(h=200, past_values=40)
# plt.plot(firstRegister['ts'], firstRegister['request_body_len'])
end_time = time.time()
total_time = end_time - start_time
print("TIME: ", total_time)
plt.show()
def malware_resp(conn):
conn['check_respmimetypes']=conn['resp_mime_types'].apply(malware_resp_mime_types)
arima_utils.adfuller_test(conn['check_respmimetypes'])
arima_utils.plot_series(conn['check_respmimetypes'],"check respmime")
arima_utils.plot_pacf(conn['check_respmimetypes'])
arima_utils.plot_acf(conn['check_respmimetypes'])
import arima_utils
arima_utils.adfuller_test(data['cpu'])
arima_utils.plot_series(data['cpu'],'Original Series')
data['Value First Difference'] = data['cpu'] - data['cpu'].shift(1)
arima_utils.adfuller_test(data['Value First Difference'].dropna())
arima_utils.plot_series(data['Value First Difference'],'Value First Difference')
data['Value 12 Difference'] = data['cpu'] - data['cpu'].shift(12)
arima_utils.adfuller_test(data['Value 12 Difference'].dropna())
arima_utils.plot_series(data['Value 12 Difference'],'Value 12 Difference')
arima_utils.plot_pacf(data['cpu'])
arima_utils.plot_acf(data['cpu'])
# Define the model
model = pf.ARIMA(data=data,
ar=2, ma=20, integ=0, target='cpu')
x = model.fit("PML")
#x = model.fit("M-H")
#https://pyflux.readthedocs.io/en/latest/bayes.html
print(x.summary())
print(x.scores)
model.plot_fit()
plt.show()