def predict(self, point, last_price):
"""Point debe ser un Data Frame de Pandas con las información
necesaria para realizar la predicción."""
# 1. Standardize point with training mean and standard deviation.
test_data = self.__standardize_features_for_test(
point, self.columns_to_standardize, self.column_means,
self.column_stds)
# 2. Add it to the data.
df = pd.concat([self.data, test_data])
# 3. Windowize.
fmt = DataFormatter()
X, Y = fmt.windowize_series(df.as_matrix(),
size=self.input_window_size,
column_indexes=self.columns_to_windowize)
# 4. Extract the last window.
last_window = fmt.get_last_window(
df.as_matrix(),
size=self.input_window_size,
column_indexes=self.columns_to_windowize)
# 5. Compute the error.
train_score = self.model.evaluate(X, Y, verbose=0)
train_score = np.array([
train_score[0],
np.sqrt(train_score[0]), train_score[1], train_score[2] * 100
])
# 6. Make the prediction.
prediction = self.model.predict(last_window)
# 7. Computing prediction intervals
pred_upper = prediction + 1.96 * train_score[1]
pred_lower = prediction - 1.96 * train_score[1]
# 8. Transform back the prediction.
prediction = last_price * np.exp(prediction)
pred_upper = last_price * np.exp(pred_upper)
pred_lower = last_price * np.exp(pred_lower)
return prediction, [pred_lower, pred_upper]
def stormDump(dmonEndpoint, qgte, qlte, qsize, qinterval):
'''
:param dmonEndpoint: -> DMON endpoint
:param qgte: -> greater than timestamp
:param qlte: -> less than timestamp
:param qsize: -> query size
:param qinterval: -> query interval
:return:
'''
dmonConnector = Connector(dmonEndpoint)
stormTopology = dmonConnector.getStormTopology()
bolts = stormTopology['bolts']
spouts = stormTopology['spouts']
print "Detected %s bolts" % str(bolts)
print "Detected %s spouts" % str(spouts)
qConstructor = QueryConstructor()
dformat = DataFormatter(dataDir)
storm, storm_file = qConstructor.stormString()
print "Query string -> %s" % storm
qstorm = qConstructor.stormQuery(storm, qgte, qlte, qsize, qinterval, bolts=bolts, spouts=spouts)
print "Query -> %s" % qstorm
gstorm = dmonConnector.aggQuery(qstorm)
print "Response:"
print gstorm
dformat.dict2csv(gstorm, qstorm, storm_file)
def process_csv(self, csv_line, updown):
data = csv_line.split(',')
if self.cli:
print ' %s sec \t%s\t%s/sec' % \
(data[self.TIME_RANGE],
DataFormatter.format_bytes(data[IPerfTest.TRANSFERED]),
DataFormatter.format_bits(data[IPerfTest.SPEED])
)
if updown == 'up':
self.process_up_csv(data)
else:
self.process_down_csv(data)
def fit_model(self, epochs=200, verbose=0):
"""Entrenar el modelo para producción."""
# Patching
self.column_means = {}
self.column_stds = {}
# Windowize dataset
fmt = DataFormatter()
self.X, self.Y = fmt.windowize_series(
self.data.as_matrix(),
size=self.input_window_size,
column_indexes=self.columns_to_windowize)
self.model.fit(self.X,
self.Y,
epochs=epochs,
batch_size=32,
verbose=verbose)
def cassandraDump(dmonEndpoint, qgte, qlte, qsize, qinterval):
'''
:param dmonEndpoint: -> DMON endpoint
:param qgte: -> greater than timestamp
:param qlte: -> less than timestamp
:param qsize: -> query size
:param qinterval: -> query interval
:return:
'''
dmonConnector = Connector(dmonEndpoint)
qConstructor = QueryConstructor()
dformat = DataFormatter(dataDir)
roles = dmonConnector.roles()
cassandra_hosts = []
for el in roles['Nodes']:
for k, v in el.iteritems():
if 'cassandra' in v:
print 'Found cassandra role at %s' % k
cassandra_hosts.append(k)
for host in cassandra_hosts:
print "-" * 50
cassandra, cassandra_file = qConstructor.cassandraCounterString(host=host)
print "Query string -> %s" % cassandra
qcassandra = qConstructor.cassandraQuery(cassandra, qgte, qlte, qsize, qinterval)
print "Query -> %s" % qcassandra
gcassandra = dmonConnector.aggQuery(qcassandra)
print "Response:"
print gcassandra
dformat.dict2csv(gcassandra, qcassandra, cassandra_file)
cassandragauge, cassandragauge_file = qConstructor.cassandraGaugeString(host=host)
qcassandragauge = qConstructor.cassandraQuery(cassandragauge, qgte, qlte, qsize, qinterval)
print "Query -> %s" % qcassandragauge
gcassandragauge = dmonConnector.aggQuery(qcassandragauge)
print "Response:"
print gcassandragauge
dformat.dict2csv(gcassandragauge, qcassandragauge, cassandragauge_file)
print "-" * 50
def predict(self, point=None):
"""Point debe ser un Data Frame de Pandas con las información
necesaria para realizar la predicción."""
# 1. Standardize point with training mean and standard deviation.
# 2. Add it to the data.
if point is None:
df = self.data
else:
test_data = self.__standardize_features_for_test(
point, self.columns_to_standardize, self.column_means,
self.column_stds)
df = pd.concat([self.data, test_data])
# 3. Windowize.
fmt = DataFormatter()
X, Y = fmt.windowize_series(df.as_matrix(),
size=self.input_window_size,
column_indexes=self.columns_to_windowize)
# 4. Extract the last window.
last_window = fmt.get_last_window(
df.as_matrix(),
size=self.input_window_size,
column_indexes=self.columns_to_windowize)
last_window = last_window[None, :]
# 5. Compute the error.
train_score = self.model.evaluate(X, Y, verbose=0)
train_score = np.array([
train_score[0],
np.sqrt(train_score[0]), train_score[1], train_score[2] * 100
])
# 6. Make the prediction.
prediction = np.squeeze(self.model.predict(last_window))
# 7. Computing prediction intervals
pred_upper = prediction + 1.96 * train_score[1]
pred_lower = prediction - 1.96 * train_score[1]
# Revert standardization
prediction = prediction * self.column_stds[
u'Close'] + self.column_means[u'Close']
pred_upper = pred_upper * self.column_stds[
u'Close'] + self.column_means[u'Close']
pred_lower = pred_lower * self.column_stds[
u'Close'] + self.column_means[u'Close']
return prediction, pred_lower, pred_upper
def get_summary(self):
try:
summary = 'Upload:\n'
summary += ' %s: %s\t%s: %s\n' % \
('Transferred'.ljust(11), DataFormatter.format_bytes(self.get_up_transferred()),
'Speed'.ljust(11), DataFormatter.format_bits(self.get_up_speed()))
summary += ' %s: %s\t%s: %s\n' % \
('Min'.ljust(11), DataFormatter.format_bits(self.get_up_speed_min()),
'Max'.ljust(11), DataFormatter.format_bits(self.get_up_speed_max()))
summary += ' %s: %2.2f%%\n' % ('Jitter'.ljust(11), self.get_up_jitter())
summary += 'Download:\n'
summary += ' %s: %s\t%s: %s\n' % \
('Transferred'.ljust(11), DataFormatter.format_bytes(self.get_down_transferred()),
'Speed'.ljust(11), DataFormatter.format_bits(self.get_down_speed()))
summary += ' %s: %s\t%s: %s\n' % \
('Min'.ljust(11), DataFormatter.format_bits(self.get_down_speed_min()),
'Max'.ljust(11), DataFormatter.format_bits(self.get_down_speed_max()))
summary += ' %s: %2.2f%%\n' % ('Jitter'.ljust(11), self.get_down_jitter())
return summary
except:
pass
def print_summary(self):
print '-' * IPerfTest.PRINT_WIDTH
print '|%s|' % self.description.center(IPerfTest.PRINT_WIDTH - 2)
print '|%s|' % 'Summary'.center(IPerfTest.PRINT_WIDTH - 2)
print '-' * IPerfTest.PRINT_WIDTH
try:
print 'Upload:'
print ' %s: %s\t%s: %s' % \
('Transferred'.ljust(11), DataFormatter.format_bytes(self.get_up_transferred()),
'Speed'.ljust(11), DataFormatter.format_bits(self.get_avg_up_speed()))
print ' %s: %s\t%s: %s' % \
('Min'.ljust(11), DataFormatter.format_bits(self.total_up_min),
'Max'.ljust(11), DataFormatter.format_bits(self.total_up_max))
print ' %s: %2.2f%%' % ('Jitter'.ljust(11), self.get_up_jitter())
print 'Download:'
print ' %s: %s\t%s: %s' % \
('Transferred'.ljust(11), DataFormatter.format_bytes(self.get_down_transferred()),
'Speed'.ljust(11), DataFormatter.format_bits(self.get_avg_down_speed()))
print ' %s: %s\t%s: %s' % \
('Min'.ljust(11), DataFormatter.format_bits(self.total_down_min),
'Max'.ljust(11), DataFormatter.format_bits(self.total_down_max))
print ' %s: %2.2f%%' % ('Jitter'.ljust(11), self.get_down_jitter())
except:
pass
import os
import tempfile
import traceback
import weka.core.jvm as jvm
import edeweka.helper as helper
from weka.clusterers import Clusterer
import weka.core.converters as converters
import weka.core.serialization as serialization
from dataformatter import DataFormatter
import weka.core.packages as packages
dataDir = os.path.join(os.path.dirname(os.path.abspath('')), 'data')
modelDir = os.path.join(os.path.dirname(os.path.abspath('')), 'models')
dformat = DataFormatter(dataDir)
dformat.dict2arff(os.path.join(dataDir, 'System.csv'),
os.path.join(dataDir, 'System.arff'))
#Arff_file = os.path.join(dataDir, 'System.arff')
jvm.start(packages=True)
data = converters.load_any_file(os.path.join(dataDir, 'System.arff'))
clusterer = Clusterer(classname="weka.clusterers.SimpleKMeans",
options=["-N", "10", "-S", "10"])
clusterer.build_clusterer(data)
# print clusterer
# cluster the data
# for inst in data:
def test_model(self, n_splits=9, cv_runs=10, epochs=100, verbose=2):
"""Evaluación del modelo usando validación cruzada
hacia adelante."""
from sklearn.model_selection import TimeSeriesSplit
self.metrics = ['MSE', 'RMSE', 'MAE', 'MAPE']
train_scores = np.zeros((cv_runs, n_splits, len(self.metrics)))
test_scores = np.zeros((cv_runs, n_splits, len(self.metrics)))
fmt = DataFormatter()
tscv = TimeSeriesSplit(n_splits=n_splits)
for j in xrange(cv_runs):
# print('\nCross-validation run %i' % (j+1))
i = 1
for train_index, test_index in tscv.split(
self.data['LogReturn'].values):
# División del conjunto de datos en entrenamiento y prueba
train_df = self.data.loc[train_index]
test_df = self.data.loc[test_index]
# Estandarización del conjunto de datos
if len(self.columns_to_standardize) != 0:
train_data, training_means, training_stds = self.__standardize_features(
train_df, self.columns_to_standardize)
test_data = self.__standardize_features_for_test(
test_df, self.columns_to_standardize, training_means,
training_stds)
else:
train_data = train_df
test_data = test_df
# Extracción de ventanas de datos
trainX, trainY = fmt.windowize_series(
train_data.as_matrix(),
size=self.input_window_size,
column_indexes=self.columns_to_windowize)
testX, testY = fmt.windowize_series(
test_data.as_matrix(),
size=self.input_window_size,
column_indexes=self.columns_to_windowize)
# Ajustando el modelo
# print('Fold %i' % (i))
self.model.fit(trainX,
trainY,
epochs=epochs,
batch_size=32,
validation_data=(testX, testY),
verbose=verbose)
# Evaluando cada partición de la validación cruzada hacia adelante
train_score = self.model.evaluate(trainX,
trainY,
verbose=verbose)
train_score = np.array([
train_score[0],
np.sqrt(train_score[0]), train_score[1],
train_score[2] * 100
])
test_score = self.model.evaluate(testX, testY, verbose=verbose)
test_score = np.array([
test_score[0],
np.sqrt(test_score[0]), test_score[1], test_score[2] * 100
])
# print('Train Score: %.5f MSE, %.5f RMSE, %.5f MAE, %.5f%% MAPE' % (train_score[0], train_score[1], train_score[2], train_score[3]))
# print('Test Score: %.5f MSE, %.5f RMSE, %.5f MAE, %.5f%% MAPE\n' % (test_score[0], test_score[1], test_score[2], test_score[3]))
# [0: MSE, 1: RMSE, 2: MAE, 3: MAPE]
train_scores[j, i - 1, :] = train_score
test_scores[j, i - 1, :] = test_score
i += 1
self.train_results = train_scores.mean(axis=0).mean(axis=0)
self.test_results = test_scores.mean(axis=0).mean(axis=0)
import os, sys
from dataformatter import DataFormatter
from pyQueryConstructor import QueryConstructor
from edeconnector import Connector
if __name__ == '__main__':
dataDir = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'data')
prometheus_endpoint = '194.102.62.155'
prometheus_port = '9090'
print("Collecting data from Monitoring at: {}".format(prometheus_endpoint))
prometheus_query = {"query": '''{__name__=~"node.+"}[80m]'''}
# prometheus_query = qContructor.pr_query_node(time="1h")
edeConnector = Connector(prEndpoint=prometheus_endpoint,
MInstancePort=prometheus_port)
test = edeConnector.pr_targets()
print("Current target information:")
print(test)
test1 = edeConnector.pr_labels('cpu')
print(test1)
test2 = edeConnector.pr_status()
print("Status information")
print(test2)
print("Executing query ....")
test3 = edeConnector.pr_query(query=prometheus_query)
dformat = DataFormatter(dataDir)
print("Query completed ....")
print("Saving ...")
test_format = dformat.prtoDF(test3, checkpoint=True, verbose=True)
print("Saved")
dataDir = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'data')
modelDir = os.path.join(os.path.dirname(os.path.abspath(__file__)), 'models')
#Standard query values
# qte = 1475842980000
# qlte = 1475845200000
qgte = 1477911300000
qlte = 1477914720000
qsize = 0
qinterval = "10s"
dmonEndpoint = '85.120.206.27'
dmonConnector = Connector(dmonEndpoint)
qConstructor = QueryConstructor()
dformat = DataFormatter(dataDir)
nodeList = dmonConnector.getNodeList()
interval = dmonConnector.getInterval()
if int(qinterval[:-1]) < interval['System']:
logger.warning('[%s] : [WARN] System Interval smaller than set interval!',
datetime.fromtimestamp(time.time()).strftime('%Y-%m-%d %H:%M:%S'))
# per slave unique process name list
nodeProcessReduce = {}
nodeProcessMap = {}
# Get host based metrics
for node in nodeList:
modelDir = os.path.join(os.path.dirname(os.path.abspath(__file__)),
'models')
#Standard query values
# qte = 1475842980000
# qlte = 1475845200000
qgte = 1477911300000
qlte = 1477914720000
qsize = 0
qinterval = "10s"
dmonEndpoint = '85.120.206.27'
dmonConnector = Connector(dmonEndpoint)
qConstructor = QueryConstructor()
dformat = DataFormatter(dataDir)
nodeList = dmonConnector.getNodeList()
interval = dmonConnector.getInterval()
if int(qinterval[:-1]) < interval['System']:
logger.warning(
'[%s] : [WARN] System Interval smaller than set interval!',
datetime.fromtimestamp(time.time()).strftime('%Y-%m-%d %H:%M:%S'))
# per slave unique process name list
nodeProcessReduce = {}
nodeProcessMap = {}
# Get host based metrics
for node in nodeList:
