def forecast_all_signals_hierarchical(self, project_data, last_date, H):
import pyaf.HierarchicalForecastEngine as hautof
lEngine = hautof.cHierarchicalForecastEngine()
lEngine.mOptions.mHierarchicalCombinationMethod = "TD"
# lEngine
lEngine.mOptions.mAddPredictionIntervals = False
# lEngine.mOptions.mParallelMode = False
lEngine.mOptions.set_active_transformations(
['None', 'Difference', 'Anscombe'])
lEngine.mOptions.mMaxAROrder = 16
lHierarchy = self.define_hierarchy(project_data)
# lEngine
df1 = self.mHierarchyVisits.fillna(0.0)
lEngine.train(df1, 'Date', None, 1, lHierarchy, None)
lEngine.getModelInfo()
# lEngine.standrdPlots()
df_forecast = lEngine.forecast(iInputDS=df1, iHorizon=H)
logger.info(str(df_forecast.columns))
df_forecast.to_csv("hierarchical_td_Forecast.csv")
dates = df_forecast['Date'].tail(H).values
forecasts = {}
for col in project_data.mVisitsDF.columns:
if (col != 'Date'):
logger.info("FORECAST_SIGNAL " +
str([self.__class__.__name__, col]))
predictions = None
if (col in self.mHierarchyVisits.columns):
predictions = df_forecast[
str(col) + '_AHP_TD_Forecast'].tail(H).values
else:
nb_other = self.nb_discarded[self.mHierarchyClass[col]]
predictions = df_forecast[str(self.mOtherArticleId) +
'_AHP_TD_Forecast'] / nb_other
predictions = predictions.tail(H).values
# logger.info(dates)
# logger.info(predictions)
fcst_dict = {}
for i in range(H):
ts = pd.to_datetime(str(dates[i]))
date_str = ts.strftime('%Y-%m-%d')
fcst_dict[date_str] = int(predictions[i])
logger.info("SIGNAL_FORECAST " + str([col, fcst_dict]))
forecasts[col] = fcst_dict
return forecasts
def train_and_force_fail(b, error_message):
try:
df = b.mPastData
lEngine = hautof.cHierarchicalForecastEngine()
lEngine.mOptions.mHierarchicalCombinationMethod = "TD"
lEngine.mOptions.set_active_autoregressions([])
lEngine
H = b.mHorizon
lEngine.train(df, b.mTimeVar, b.mSignalVar, H, b.mHierarchy, None)
raise Exception("NOT_OK")
except Exception as e:
# should fail
print(str(e))
assert (str(e) == error_message)
if (str(e) == "NOT_OK"):
raise
pass
'Data':
pd.DataFrame({
'area': areas,
'tract': tracts,
'district': districts,
'austin': area_list
}),
'Type':
'Hierarchical'
}
# Need to complete the time series...
dat = dat.set_index('time').groupby(['area']).resample('15T').mean().fillna(0)
dat.reset_index(inplace=True)
lEngine = hautof.cHierarchicalForecastEngine()
lSignalHierarchy = lEngine.plot_Hierarchy(dat, "time", "n", 1, lHierarchy,
None)
# lSignalHierarchy.mStructure
# drop final day for predictions
train_df = dat[dat['time'] < pd.to_datetime('2019-01-30')].copy().pivot(
index='time', columns='area', values='n').reset_index()
lSignalHierarchy = lEngine.train(train_df, 'time', 'n', 1, lHierarchy, None)
# And now results time
lInfo = lEngine.to_json()
perfs = []
for model in sorted(lInfo['Models'].keys()):