def test_dataset(self):
dataset = DataSet()
dataset.set([('a', 1), ('b', 2), ('c', 3), ('d', 4), ('e', 5),
('f', 6), ('g', 7), ('h', 8), ('i', 9), ('j', 10)])
training_set, validation_set, test_set = dataset.split_train_validation_test_data(
)
print dataset.get()
print len(training_set.get()), training_set.get()
print len(validation_set.get()), validation_set.get()
print len(test_set.get()), test_set.get()
training_list = training_set.get()
variance = Variance()
variance_value = variance.calculate(training_list,
is_tuple=True,
index=1)
standart_deviation = StandartDeviation()
standart_deviation_value = standart_deviation.calculate(training_list,
is_tuple=True,
index=1)
mean = Mean()
mean_value = mean.calculate(training_list, is_tuple=True, index=1)
print "Variance: %f" % variance_value
print "Standart Deviation: %f" % standart_deviation_value
print "Mean: %f" % mean_value
print "*" * 10, datetime.now().strftime(
'%Y-%m-%d %H:%M:%S'), "Finish Test", "*" * 10
def demo3():
data_list = [3, 55, 10000, 2, 100, 104, 23, 1, 22, 20, 303219, 50, 21]
shuffle(data_list)
dataset = DataSet()
dataset.set(data_list)
train, validation, test = dataset.split_train_validation_test_data()
training_list = train.get()
validation_list = validation.get()
test_list = test.get()
print training_list, validation_list, test_list
standart_deviation = StandartDeviation()
standart_deviation_value = standart_deviation.calculate(training_list)
mean = Mean()
mean_value = mean.calculate(training_list)
# print "Training Set: %s, Validation Set: %s, Test Set: %s" % (training_list, validation_list, test_list)
print "Standart Deviation: %f, Mean Value: %f" % (standart_deviation_value, mean_value)
z_value = ZValue()
counter = 0
for val in validation_list:
z_value.calculate(val, mean=mean_value, standart_deviation=standart_deviation_value)
table_value = z_value.find_from_table()
if table_value == -1:
print "This val is anomaly:", val
counter += 1
print "Anomaly Count: %d, Dataset Count: %d" % (counter, dataset.__len__())
counter = 0
for val in test_list:
z_value.calculate(val, mean=mean_value, standart_deviation=standart_deviation_value)
table_value = z_value.find_from_table()
if table_value == -1:
print "This val is anomaly:", val
counter += 1
print "Anomaly Count: %d, Dataset Count: %d" % (counter, dataset.__len__())
def main():
write_klass = CreateData(**{
'log_active': settings.LOG,
'limit': settings.LIMIT
})
write_klass.start()
for interface_name in settings.INTERFACE_NAMES:
print "Interface Name: %s" % interface_name
klass = ReadCsv(**{
'log_active': False,
'interface_name': interface_name
})
klass.start()
data, name = klass.get_data()
dataset = DataSet()
dataset.set(eval(data.get(name)))
if dataset.__len__() <= 1:
return
train, validation, test = dataset.split_train_validation_test_data()
training_list = train.get()
validation_list = validation.get()
test_list = test.get()
if train.__len__() <= 1:
return
standart_deviation = StandartDeviation()
standart_deviation_value = standart_deviation.calculate(training_list,
is_tuple=True,
index=1)
mean = Mean()
mean_value = mean.calculate(training_list, is_tuple=True, index=1)
print "Standart Deviation: %f, Mean Value: %f" % (
standart_deviation_value, mean_value)
z_value = ZValue()
counter = 0
for val in validation_list:
z_value.calculate(val,
mean=mean_value,
standart_deviation=standart_deviation_value,
is_tuple=True,
index=1)
table_value = z_value.find_from_table()
if table_value == -1:
print "This val is anomaly:", val
counter += 1
print "Anomaly Count: %d, Dataset Count: %d" % (counter,
dataset.__len__())
counter = 0
for val in test_list:
z_value.calculate(val,
mean=mean_value,
standart_deviation=standart_deviation_value,
is_tuple=True,
index=1)
table_value = z_value.find_from_table()
if table_value == -1:
print "This val is anomaly:", val
counter += 1
print "Anomaly Count: %d, Dataset Count: %d" % (counter,
dataset.__len__())
def test_algorithm_with_tuple(self):
standart_deviation = StandartDeviation()
data_list = [("a", 1), ("b", 2), ("c", 3), ("d", 4), ("e", 5)]
self.assertEquals(
1.5811,
standart_deviation.calculate(data_list, is_tuple=True, index=1))
data_list = [("a", "a", 1), ("b", "b", 2), ("c", "c", 3),
("d", "d", 4), ("e", "e", 5)]
self.assertEquals(
1.5811,
standart_deviation.calculate(data_list, is_tuple=True, index=2))
data_list = [("a", "a", 1), ("b", "b", 2), ("c", "c", 3),
("d", "d", 4)]
self.assertEquals(
1.291,
standart_deviation.calculate(data_list, is_tuple=True, index=2))
def test_algorithm_with_list(self):
data_list = [1, 2, 3, 4, 5]
standart_deviation = StandartDeviation()
standart_deviation_value = standart_deviation.calculate(data_list)
mean = Mean()
mean_value = mean.calculate(data_list)
print standart_deviation_value, mean_value
z_value = ZValue()
z1 = z_value.calculate(88, mean=100, standart_deviation=10)
z2 = z_value.calculate(112, mean=100, standart_deviation=10)
z3 = z_value.calculate(5, mean=100, standart_deviation=10)
print z1, z2, z3
def demo1():
data_list = []
value_size = 1000000
val = 0
while val < value_size:
data_list.append(val)
val += 1
shuffle(data_list)
dataset = DataSet()
dataset.set(data_list)
train, validation, test = dataset.split_train_validation_test_data()
training_list = train.get()
validation_list = validation.get()
test_list = test.get()
standart_deviation = StandartDeviation()
standart_deviation_value = standart_deviation.calculate(training_list)
mean = Mean()
mean_value = mean.calculate(training_list)
# print "Training Set: %s, Validation Set: %s, Test Set: %s" % (training_list, validation_list, test_list)
print "Standart Deviation: %f, Mean Value: %f" % (standart_deviation_value,
mean_value)
z_value = ZValue()
counter = 0
for val in validation_list:
z_value.calculate(val,
mean=mean_value,
standart_deviation=standart_deviation_value)
table_value = z_value.find_from_table()
if table_value == -1:
print "This val is anomaly:", val
counter += 1
print "Anomaly Count: %d, Dataset Count: %d" % (counter, dataset.__len__())
counter = 0
for val in test_list:
z_value.calculate(val,
mean=mean_value,
standart_deviation=standart_deviation_value)
table_value = z_value.find_from_table()
if table_value == -1:
print "This val is anomaly:", val
counter += 1
print "Anomaly Count: %d, Dataset Count: %d" % (counter, dataset.__len__())
def demo4(limit):
path = os.path.join(BASE_PATH, '../data/demo' + str(limit) + '.csv')
for interface_name in INTERFACE_NAMES:
print "Interface Name: %s" % interface_name
klass = ReadCsv(**{'log_active': False, 'path': path, 'interface_name': interface_name})
klass.start()
data, name = klass.get_data()
dataset = DataSet()
dataset.set(eval(data.get(name)))
if dataset.__len__() <= 1:
return
train, validation, test = dataset.split_train_validation_test_data()
training_list = train.get()
validation_list = validation.get()
test_list = test.get()
if train.__len__() <= 1:
return
standart_deviation = StandartDeviation()
standart_deviation_value = standart_deviation.calculate(training_list, is_tuple=True, index=1)
mean = Mean()
mean_value = mean.calculate(training_list, is_tuple=True, index=1)
print "Standart Deviation: %f, Mean Value: %f" % (standart_deviation_value, mean_value)
z_value = ZValue()
counter = 0
for val in validation_list:
z_value.calculate(val, mean=mean_value, standart_deviation=standart_deviation_value, is_tuple=True, index=1)
table_value = z_value.find_from_table()
if table_value == -1:
print "This val is anomaly:", val
counter += 1
print "Anomaly Count: %d, Dataset Count: %d" % (counter, dataset.__len__())
counter = 0
for val in test_list:
z_value.calculate(val, mean=mean_value, standart_deviation=standart_deviation_value, is_tuple=True, index=1)
table_value = z_value.find_from_table()
if table_value == -1:
print "This val is anomaly:", val
counter += 1
print "Anomaly Count: %d, Dataset Count: %d" % (counter, dataset.__len__())
def test_algorithm_with_list(self):
standart_deviation = StandartDeviation()
data_list = [1, 2, 3, 4, 5]
self.assertEquals(1.5811, standart_deviation.calculate(data_list))
data_list = [1, 2, 3, 4]
self.assertEquals(1.291, standart_deviation.calculate(data_list))