def detectSignalAnomalyData( metricInfo, modelHolder, metricType):
series = metricInfo.metricDF
bound = modelHolder.getModelConfigByKey(BOUND)
if bound == None:
bound = IS_UPPER_BOUND
if modelHolder.model_name == AI_MODEL.MOVING_AVERAGE.value or modelHolder.model_name == AI_MODEL.EXPONENTIAL_SMOOTHING.value or modelHolder.model_name == AI_MODEL.DOUBLE_EXPONENTIAL_SMOOTHING.value:
lower_bound = modelHolder.getModelByKey(metricType,LOWER_BOUND)
if lower_bound == None:
pass
#TODO: raise error
upper_bound = modelHolder.getModelByKey(metricType,UPPER_BOUND)
if upper_bound == None:
pass
#TODO: raise error
ts,data,flags =detectLowerUpperAnomalies(series, lower_bound , upper_bound, bound)
triggerAnomalyMetric(metricInfo, ts)
return ts,data
elif modelHolder.model_name == AI_MODEL.PROPHET.value:
lower_bound = modelHolder.getModelByKey(metricType,LOWER_BOUND)
if lower_bound == None:
pass
#TODO: raise error
upper_bound = modelHolder.getModelByKey(metricType,UPPER_BOUND)
if upper_bound == None:
pass
#TODO: raise error
ts,data,flags =detectLowerUpperAnomalies(series, lower_bound , upper_bound, bound)
triggerAnomalyMetric(metricInfo, ts)
return ts,data
elif modelHolder.model_name == AI_MODEL.HOLT_WINDER.value:
upper_bound = modelHolder.getModelByKey(metricType,UPPER_BOUND)
if upper_bound == None:
pass
#TODO raise error and also need to make upperBound as narray
lower_bound = modelHolder.gettModelByKey(metricType,LOWER_BOUND)
if lower_bound == None:
pass
ts,adata,flags = retrieveHW_Anomalies( y, upper_bound, lower_bound, bound)
triggerAnomalyMetric(metricInfo, ts)
return ts,data
else:
#default is modelHolder.model_name == AI_MODEL.MOVING_AVERAGE_ALL.value:
mean = modelHolder.getModelByKey(metricType,MAE)
if mean == None:
pass
#TODO: raise error
stdev = modelHolder.getModelByKey(metricType,DEVIATION)
if stdev == None:
pass
#TODO: raise error
#threshold = modelHolder.getModelConfigByKey(THRESHOLD)
threshold = globalConfig.getThresholdByKey(metricType,THRESHOLD)
if threshold == None:
threshold = DEFAULT_THRESHOLD
#TODO: need to make sure return all df
ts,data,flags = detectAnomalies(series, mean, stdev, threshold , bound)
#print(metricType, "threshold -----", threshold)
triggerAnomalyMetric(metricInfo, ts)
return ts, data
def detectSignalAnomalyData( metricInfo, modelHolder, metricType, strategy=None):
series = metricInfo.metricDF
bound = modelHolder.getModelConfigByKey(BOUND)
if bound == None:
bound = IS_UPPER_BOUND
if modelHolder.model_name == AI_MODEL.MOVING_AVERAGE.value or modelHolder.model_name == AI_MODEL.EXPONENTIAL_SMOOTHING.value or modelHolder.model_name == AI_MODEL.DOUBLE_EXPONENTIAL_SMOOTHING.value:
lower_bound = modelHolder.getModelByKey(metricType,LOWER_BOUND)
if lower_bound == None:
pass
#TODO: raise error
upper_bound = modelHolder.getModelByKey(metricType,UPPER_BOUND)
if upper_bound == None:
pass
#TODO: raise error
ts,data,flags =detectLowerUpperAnomalies(series, lower_bound , upper_bound, bound)
print("Anomaly data: %s" % data)
triggerAnomalyMetric(metricInfo, ts, data)
return ts,data
elif modelHolder.model_name == AI_MODEL.PROPHET.value:
lower_bound = modelHolder.getModelByKey(metricType,LOWER_BOUND)
if lower_bound == None:
pass
#TODO: raise error
upper_bound = modelHolder.getModelByKey(metricType,UPPER_BOUND)
if upper_bound == None:
pass
#TODO: raise error
ts,data,flags =detectLowerUpperAnomalies(series, lower_bound , upper_bound, bound)
print("Anomaly data: %s" % data)
triggerAnomalyMetric(metricInfo, ts, data)
return ts,data
else:
#default is modelHolder.model_name == AI_MODEL.MOVING_AVERAGE_ALL.value:
mean = modelHolder.getModelByKey(metricType,MAE)
if mean == None:
return [],[]
#TODO: raise error
stdev = modelHolder.getModelByKey(metricType,DEVIATION)
if stdev == None:
return [],[]
#TODO: raise error
#threshold = modelHolder.getModelConfigByKey(THRESHOLD)
threshold = globalConfig.getThresholdByKey(metricType,THRESHOLD)
if threshold == None:
threshold = DEFAULT_THRESHOLD
#TODO: need to make sure return all df
if strategy== 'hpa':
print("~~~~~~~~~~~~~~~~~~",metricInfo.metricName, mean,stdev, threshold)
ts,data,anomalies,zscore = detectAnomalies(series, mean, stdev, threshold , bound, minvalue=0, returnAnomaliesOnly= False)
print("Anomaly data: %s\n anomalies: %s\n zscore: %s\n" % (data, anomalies, zscore))
triggerAnomalyMetric(metricInfo, filterTS(ts,anomalies), data)
return ts, data, anomalies, zscore,mean,stdev
ts,data,_ = detectAnomalies(series, mean, stdev, threshold , bound)
print("Anomaly data: %s" % data)
triggerAnomalyMetric(metricInfo, ts, data)
return ts, data