def concat_detection_results(
self, detections: List[DetectionResult]) -> DetectionResult:
result = DetectionResult()
for detection in detections:
result.segments.extend(detection.segments)
result.last_detection_time = detection.last_detection_time
result.cache = detection.cache
return result
def concat_detection_results(self, detections: List[DetectionResult]) -> DetectionResult:
result = DetectionResult()
time_step = detections[0].cache['timeStep']
for detection in detections:
result.segments.extend(detection.segments)
result.last_detection_time = detection.last_detection_time
result.cache = detection.cache
result.segments = utils.merge_intersecting_segments(result.segments, time_step)
return result
def detect(self, dataframe: pd.DataFrame,
cache: Optional[ModelCache]) -> DetectionResult:
logger.debug('Unit {} got {} data points for detection'.format(
self.analytic_unit_id, len(dataframe)))
# TODO: split and sleep (https://github.com/hastic/hastic-server/pull/124#discussion_r214085643)
if cache is None:
msg = f'{self.analytic_unit_id} detection got invalid cache, skip detection'
logger.error(msg)
raise ValueError(msg)
self.model.state = self.model.get_state(cache)
window_size = self.model.state.window_size
if window_size is None:
message = '{} got cache without window_size for detection'.format(
self.analytic_unit_id)
logger.error(message)
raise ValueError(message)
if len(dataframe) < window_size * 2:
message = f'{self.analytic_unit_id} skip detection: dataset length {len(dataframe)} points less than minimal length {window_size * 2} points'
logger.error(message)
raise ValueError(message)
detected = self.model.detect(dataframe, self.analytic_unit_id)
segments = [
Segment(segment[0], segment[1]) for segment in detected['segments']
]
new_cache = detected['cache'].to_json()
last_dataframe_time = dataframe.iloc[-1]['timestamp']
last_detection_time = convert_pd_timestamp_to_ms(last_dataframe_time)
return DetectionResult(new_cache, segments, last_detection_time)
def detect(self, dataframe: pd.DataFrame,
cache: Optional[ModelCache]) -> DetectionResult:
if cache == None:
raise f'Analytic unit {self.analytic_unit_id} got empty cache'
data = dataframe['value']
cache = AnomalyCache.from_json(cache)
segments = cache.segments
enabled_bounds = cache.get_enabled_bounds()
smoothed_data = utils.exponential_smoothing(data, cache.alpha)
lower_bound = smoothed_data - cache.confidence
upper_bound = smoothed_data + cache.confidence
if len(segments) > 0:
data_start_time = utils.convert_pd_timestamp_to_ms(
dataframe['timestamp'][0])
for segment in segments:
seasonality_index = cache.seasonality // cache.time_step
seasonality_offset = self.get_seasonality_offset(
segment.from_timestamp, cache.seasonality, data_start_time,
cache.time_step)
segment_data = pd.Series(segment.data)
lower_bound = self.add_season_to_data(lower_bound,
segment_data,
seasonality_offset,
seasonality_index,
Bound.LOWER)
upper_bound = self.add_season_to_data(upper_bound,
segment_data,
seasonality_offset,
seasonality_index,
Bound.UPPER)
detected_segments = list(
self.detections_generator(dataframe, upper_bound, lower_bound,
enabled_bounds))
last_dataframe_time = dataframe.iloc[-1]['timestamp']
last_detection_time = utils.convert_pd_timestamp_to_ms(
last_dataframe_time)
return DetectionResult(cache.to_json(), detected_segments,
last_detection_time)
def detect(self, dataframe: pd.DataFrame, cache: ModelCache) -> DetectionResult:
if cache is None or cache == {}:
raise ValueError('Threshold detector error: cannot detect before learning')
if len(dataframe) == 0:
return None
value = cache['value']
condition = cache['condition']
segments = []
for index, row in dataframe.iterrows():
current_value = row['value']
current_timestamp = utils.convert_pd_timestamp_to_ms(row['timestamp'])
segment = Segment(current_timestamp, current_timestamp)
# TODO: merge segments
if pd.isnull(current_value):
if condition == 'NO_DATA':
segment.message = 'NO_DATA detected'
segments.append(segment)
continue
comparators = {
'>': operator.gt,
'<': operator.lt,
'=': operator.eq,
'>=': operator.ge,
'<=': operator.le
}
assert condition in comparators.keys(), f'condition {condition} not allowed'
if comparators[condition](current_value, value):
segment.message = f"{current_value} {condition} threshold's value {value}"
segments.append(segment)
last_entry = dataframe.iloc[-1]
last_detection_time = utils.convert_pd_timestamp_to_ms(last_entry['timestamp'])
return DetectionResult(cache, segments, last_detection_time)