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
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_detect_anomaly():
detector = Detector()
event = helpers.get_fake_event()
assert False == detector.detect_anomaly(event)
