collinrooney12 / htsprophet Goto Github PK

When trying to run the example script I get the following error:

INFO:matplotlib.font_manager:font search path ['/opt/conda/lib/python3.6/site-packages/matplotlib/mpl-data/fonts/ttf', '/opt/conda/lib/python3.6/site-packages/matplotlib/mpl-data/fonts/afm', '/opt/conda/lib/python3.6/site-packages/matplotlib/mpl-data/fonts/pdfcorefonts']
INFO:matplotlib.font_manager:generated new fontManager

TypeError Traceback (most recent call last)
in
44 # NOTE: CVselect takes a while, so if you want results in minutes instead of half-hours pick a different method
45 ##
---> 46 myDict = hts(data2, 52, nodes, holidays = holidays, method = "FP", transform = "BoxCox")
47 ##
48 # This output is a dictionary of dataframes, so you can do any further analysis that you may want. It also allows you to plot the forecasts.

/opt/conda/lib/python3.6/site-packages/htsprophet/hts.py in hts(y, h, nodes, method, freq, transform, include_history, cap, capF, changepoints, n_changepoints, yearly_seasonality, weekly_seasonality, holidays, seasonality_prior_scale, holidays_prior_scale, changepoint_prior_scale, mcmc_samples, interval_width, uncertainty_samples, skipFitting, numThreads)
270 ynew = fitForecast(y, h, sumMat, nodes, method, freq, include_history, cap, capF, changepoints, n_changepoints,
271 yearly_seasonality, weekly_seasonality, holidays, seasonality_prior_scale, holidays_prior_scale,
--> 272 changepoint_prior_scale, mcmc_samples, interval_width, uncertainty_samples, boxcoxT, skipFitting)
273 ##
274 # Inverse boxcox the data

/opt/conda/lib/python3.6/site-packages/htsprophet/fitForecast.py in fitForecast(y, h, sumMat, nodes, method, freq, include_history, cap, capF, changepoints, n_changepoints, yearly_seasonality, weekly_seasonality, holidays, seasonality_prior_scale, holidays_prior_scale, changepoint_prior_scale, mcmc_samples, interval_width, uncertainty_samples, boxcoxT, skipFitting)
72 growth = 'linear'
73 m = Prophet(growth, changepoints1, n_changepoints1, yearly_seasonality, weekly_seasonality, holidays, seasonality_prior_scale,
---> 74 holidays_prior_scale, changepoint_prior_scale, mcmc_samples, interval_width, uncertainty_samples)
75 else:
76 growth = 'logistic'

/opt/conda/lib/python3.6/site-packages/fbprophet/forecaster.py in init(self, growth, changepoints, n_changepoints, changepoint_range, yearly_seasonality, weekly_seasonality, daily_seasonality, holidays, seasonality_mode, seasonality_prior_scale, holidays_prior_scale, changepoint_prior_scale, mcmc_samples, interval_width, uncertainty_samples)
140 self.component_modes = None
141 self.train_holiday_names = None
--> 142 self.validate_inputs()
143
144 def validate_inputs(self):

/opt/conda/lib/python3.6/site-packages/fbprophet/forecaster.py in validate_inputs(self)
147 raise ValueError(
148 "Parameter 'growth' should be 'linear' or 'logistic'.")
--> 149 if ((self.changepoint_range < 0) or (self.changepoint_range > 1)):
150 raise ValueError("Parameter 'changepoint_range' must be in [0, 1]")
151 if self.holidays is not None:

TypeError: '<' not supported between instances of 'str' and 'int'

in your post the nodes has this structure :[[3],[2,2,2],[4,4,4,4,4,4]] which is a strict rules
in general we will have bottom layer not same leaf values

like [[3],[2,1,3],[4,1,1,1,1,4]] ?? can this be possible ???

I already generated independent base forecast for each series in the hierarchy, As these base forecasts are independently generated they will not be aggregate consistent" (i.e.,
they will not add up according to the hierarchical structure).I just want to generates a
set of revised forecasts that would aggregate consistently with the hierarchical structure. Can use you py funciton? I looking forward to hearing from u , thanks

d = {'Total': [100],'GP48_1': [100], 'GP48_2': [100]
, 'GP48_1_GP48_cogenpct': [100], 'GP48_1_GP48_coalpct': [100]
, 'GP48_1_GP48_CCpct': [100], 'GP48_2_GP48_windpct': [100]
, 'GP48_2_GP48_biomasspct': [100],'GP48_2_GP48_thermosolarpct': [100]
, 'GP48_2_GP48_PVpct': [100], 'GP48_2_GP48_nuclearpct': [100]
, 'GP48_2_GP48_hydrototalpct': [100]
}
capDataset=pd.DataFrame(d,columns=['Total','GP48_1', 'GP48_2', 'GP48_1_GP48_cogenpct', 'GP48_1_GP48_coalpct',
'GP48_1_GP48_CCpct', 'GP48_2_GP48_windpct', 'GP48_2_GP48_biomasspct', 'GP48_2_GP48_thermosolarpct',
'GP48_2_GP48_PVpct', 'GP48_2_GP48_nuclearpct', 'GP48_2_GP48_hydrototalpct'])

pred = hts(data3, 1, nodes, method = "BU",cap=100,capF=capDataset)

Hi,

Seems to be a bug when running runHTS.py in python 3. There is an issue with holidays producing ValueError: holidays must be a DataFrame with 'ds' and 'holiday' columns, despite it being clearly defined.

Hi Collin, great work, wanted to point out the fit function breaks with our without holidays input with an error related to holidays dataframe.