def test_detector_should_be_able_to_calculate_total_probability():
detector = Detector()
models.add_event('edge', 'edge_01', {u'cpu': u'2', u'mem': u'10'}, time.time())
event = helpers.get_fake_event()
cpu_prob = detector.calculate_probability_by_metric("cpu", event)
mem_prob = detector.calculate_probability_by_metric("mem", event)
assert cpu_prob * mem_prob == detector.calculate_total_probability(event)
def test_detector_should_fetch_calculus_base_of_bucket_and_key():
models.add_event('edge', 'edge_01', {u'cpu': u'2', u'mem': u'10'}, time.time())
models.add_event('edge', 'edge_02', {u'cpu': u'3', u'mem': u'20'}, time.time())
detector = Detector()
expected = (5, 2, 13)
assert expected == detector.fetch_data_from_calculus_base("edge", "cpu")
def test_detector_should_be_able_to_calculate_variance():
'''
variance = (sum(values^2) / n) - average^2
'''
occurrences = [1, 2, 3, 4]
number_of_occurrences = len(occurrences)
total = sum(occurrences)
sum_of_square_of_occurrences = sum([pow(number, 2) for number in occurrences])
detector = Detector()
assert 1.25 == detector.calculate_variance(total, sum_of_square_of_occurrences, number_of_occurrences)
def test_detector_should_calculate_probability_for_each_metric(monkeypatch):
event = helpers.get_fake_event()
list_of_keys = ['cpu', 'mem']
def fake_calculate_probability_by_metric(key, event):
list_of_keys.remove(key)
return 1
detector = Detector()
monkeypatch.setattr(detector, 'calculate_probability_by_metric', fake_calculate_probability_by_metric)
detector.detect_anomaly(event)
assert not list_of_keys
def test_detector_should_return_0_for_calculate_the_number_of_standard_deviations_of_metric_of_a_new_metric():
# adding this two events will generate a standard deviation of: cpu: 0.5, mem: 5
detector = Detector()
event = {
'target' : 'edge_01',
'bucket' : 'edge',
'metrics' : {'cpu': 4.0, 'mem': 30},
'timestamp': 8192819082,
}
assert 0 == detector.calculate_the_number_of_standard_deviations(event, "cpu")
assert 0 == detector.calculate_the_number_of_standard_deviations(event, "mem")
def test_detector_should_be_able_to_calculate_probability():
'''
This probability mathematics was extracted from:
http://s3.amazonaws.com/mlclass-resources/docs/slides/Lecture15.pdf
>>> 1/(math.sqrt(math.pi * 2) * 20) * math.exp(-0.5)
0.01209853622595717
'''
current_metric = 80
avg_of_metric = 100
variance = 400
detector = Detector()
assert round(0.01209853622595717 -
detector.calculate_probability(current_metric, avg_of_metric, variance), 5) == 0
def test_detector_should_be_able_to_calculate_the_number_of_standard_deviations_of_metric():
# adding this two events will generate a standard deviation of: cpu: 0.5, mem: 5
models.add_event('edge', 'edge_01', {u'cpu': u'2', u'mem': u'10'}, time.time())
models.add_event('edge', 'edge_02', {u'cpu': u'3', u'mem': u'20'}, time.time())
detector = Detector()
event = {
'target' : 'edge_01',
'bucket' : 'edge',
'metrics' : {'cpu': 4.0, 'mem': 30},
'timestamp': 8192819082,
}
assert 3 == detector.calculate_the_number_of_standard_deviations(event, "cpu")
assert 3 == detector.calculate_the_number_of_standard_deviations(event, "mem")
class AddMetricsHandler(RequestHandler):
API_ERROR_CODES = 400,
def initialize(self):
self.detector = Detector()
def post(self, bucket, target, timestamp=None):
event = self.create_event(bucket, target, self.request.arguments, timestamp)
if self.detector.detect_anomaly(event):
models.mark_event_as_anomalous(event)
def create_event(self, *args, **kwargs):
try:
return models.add_event(*args, **kwargs)
except models.ValidationError as ex:
raise HTTPError(400, str(ex))
def write_error(self, status_code, **kwargs):
if status_code in self.API_ERROR_CODES and 'exc_info' in kwargs:
exc_info = kwargs.pop('exc_info')
self.write({'error_message': exc_info[1].log_message})
self.finish()
else:
super(AddMetricsHandler, self).write_error(status_code, **kwargs)
def test_detector_should_be_able_to_get_bucket():
detector = Detector()
event = helpers.get_fake_event()
assert 'edge' == detector.get_bucket(event)
def test_detector_should_be_able_to_get_current_value():
detector = Detector()
event = helpers.get_fake_event()
assert 2.5 == detector.get_current_value(event, "cpu")
assert 40 == detector.get_current_value(event, "mem")
def test_detector_should_be_able_to_fetch_the_metrics_from_event():
event = helpers.get_fake_event()
detector = Detector()
assert sorted(["cpu", "mem"]) == sorted(detector.get_metrics(event))
def test_detector_should_be_able_to_detect_anomaly():
detector = Detector()
event = helpers.get_fake_event()
assert False == detector.detect_anomaly(event)
def test_detector_should_be_able_to_calculate_probability_with_zero_variance():
detector = Detector()
assert 1 == detector.calculate_probability(1, 1, 0)
def initialize(self):
self.detector = Detector()
