def RunDetection(TS_ID):
#Get Data
dfds = d.GetTimeSeries(TS_ID)
targets = dfds['target'].unique()
#Delete Output Data for TS_ID
d._deleteTimeSeriesOutput(TS_ID)
#Run detection
for target in targets:
try:
titleName = TS_ID + '_' + target
df = dfds[dfds['target'] == target]
print(target)
#Change Detection
detector = ADetector(df)
detector.Run()
dfResults = detector.GetResults()
dfResults['ts_id'] = TS_ID
dfResults['target'] = target
d.SaveResults(dfResults)
visuals = Visualizer(dfResults,
plt_ymin=min(df.y),
plt_ymax=max(df.y))
visuals.PlotChangePoints(Title=titleName, fileName=titleName)
except Exception as ex:
print("Error with {0}".format(TS_ID + " - " + target))
def RunBackpropagation(self,
minPeriods=30,
chartTitle="Trend",
metric='AlgoOutlierDetected'):
AS_OF_DATES = self.df['ds'].unique()
for i, toDate in enumerate(AS_OF_DATES):
print(toDate)
dfSource = self.df[self.df['ds'] <= toDate]
#need at least 10 records to run model
if dfSource.shape[0] > minPeriods:
detector = ADetector(dfSource)
detector.Run()
dfResults = detector.GetResults()
dfResults['AsOfDate'] = toDate
#Prepare for Visualization (including future dates with 0's)
xDates = self.df['ds'].values
y = list(dfResults['y'].values)
changepointChanges = list(dfResults[metric].values)
while len(y) < len(xDates):
y.append(0)
changepointChanges.append(0)
d = {'ds': xDates, 'y': y, metric: changepointChanges}
dfResultsCharting = pd.DataFrame(d)
#Save Image
visuals = Visualizer(dfResultsCharting,
plt_ymin=min(self.df.y),
plt_ymax=max(self.df.y))
visuals.PlotChangePoints(Title=chartTitle,
fileName=str(i),
folderName="seqimg",
metric=metric)
