def _prophet_seasonality(request, context):
"""
Provide the seasonality component of the Prophet timeseries forecast. Scalar function.
:param request: an iterable sequence of RowData
:param context: not used for now
:return: the forecasted value for each row
:
:Qlik expression example:
:<AAI Connection Name>.Prophet_Seasonality(Month, $(vConcatSeries), $(vHolidays), 'seasonality=yearly, freq=MS, debug=true')
:The fourth argument in the Qlik expression is a string of parameters.
:This should take the form of a comma separated string:
:e.g 'seasonality=yearly, freq=MS, debug=true' or 'seasonality=weekly, freq=D'
:
:Parameters accepted for the Prophet() function are: cap, floor, changepoint_prior_scale, interval_width,
:lower_window, upper_window
:
:Parameters accepted for the make_future_dataframe() function are: freq
:
:For more information on these parameters go here: https://facebook.github.io/prophet/docs/quick_start.html
:
:Additional parameters used are: return, take_log, debug
:
:cap = 1000 : A logistic growth model can be defined using cap and floor. Values should be double or integer
:changepoint_prior_scale = 0.05 : Decrease if the trend changes are being overfit, increase for underfit
:interval_width = 0.08 : Set the width of the uncertainty intervals
:lower_window = -1 : Only used with holidays. Extend the holiday by certain no. of days prior to the date.
:upper_window = 1 : Only used with holidays. Extend the holiday by certain no. of days after the date.
:freq = MS : The frequency of the time series. e.g. MS for Month Start. See the possible options here:
: : http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases
:return = yhat : Any of the options in the forecast result. You can see these options with debug=true
: : yhat, yhat_upper, yhat_lower : Forecast, upper and lower limits
: : y_then_yhat, y_then_yhat_upper, y_then_yhat_lower : Return forecast only for forecast periods
: : trend, trend_upper, trend_lower : Trend component of the timeseries
: : seasonal, seasonal_upper, seasonal_lower: Seasonal component of the timeseries
:take_log = false : Apply logarithm to the values before the forecast. Default is true
:debug = true : Print exexution information to the terminal and logs in ..\logs\Prophet Log <n>.txt
"""
# Get a list from the generator object so that it can be iterated over multiple times
request_list = [request_rows for request_rows in request]
# Create an instance of the ProphetForQlik class
# This will take the request data from Qlik and prepare it for forecasting
predictor = ProphetForQlik.init_seasonality(request_list)
# Calculate the forecast and store in a Pandas series
forecast = predictor.predict()
# Values in the series are converted to type SSE.Dual
response_rows = forecast.apply(lambda result: iter([SSE.Dual(numData=result)]))
# Values in the series are converted to type SSE.Row
# The series is then converted to a list
response_rows = response_rows.apply(lambda duals: SSE.Row(duals=duals)).tolist()
# Iterate over bundled rows
for request_rows in request_list:
# Yield Row data as Bundled rows
yield SSE.BundledRows(rows=response_rows)
def _prophet(request, context):
"""
Provide a timeseries forecast using Facebook's Prophet library. Scalar function.
:param request: an iterable sequence of RowData
:param context: not used for now
:return: the forecasted value for each row
:
:Qlik expression example:
:<AAI Connection Name>.Prophet(MonthStartDate, sum(Value), 'return=yhat, freq=MS, debug=true')
:The third argument in the Qlik expression is a string of parameters.
:This should take the form of a comma separated string:
:e.g 'return=yhat, freq=MS, debug=true' or 'return=yhat_upper, freq=MS'
:
:<AAI Connection Name>.Prophet_Holidays(ForecastDate, sum(Value), Holiday, 'return=yhat, freq=D, debug=true')
:In the holidays variant the third argument is a field containing the holiday name or NULL for each row.
:
:Parameters accepted for the Prophet() function are: cap, floor, changepoint_prior_scale, interval_width,
:lower_window, upper_window
:
:Parameters accepted for the make_future_dataframe() function are: freq
:
:For more information on these parameters go here: https://facebook.github.io/prophet/docs/quick_start.html
:
:Additional parameters used are: return, take_log, debug, load_script
:
:cap = 1000 : A logistic growth model can be defined using cap and floor. Values should be double or integer
:changepoint_prior_scale = 0.05 : Decrease if the trend changes are being overfit, increase for underfit
:interval_width = 0.08 : Set the width of the uncertainty intervals
:lower_window = 1 : Only used with holidays. Extend the holiday by certain no. of days prior to the date.
:upper_window = 1 : Only used with holidays. Extend the holiday by certain no. of days after the date.
:freq = MS : The frequency of the time series. e.g. MS for Month Start. See the possible options here:
: : http://pandas.pydata.org/pandas-docs/stable/timeseries.html#offset-aliases
:return = yhat : Any of the options in the forecast result. You can see these options with debug=true
: : yhat, yhat_upper, yhat_lower : Forecast, upper and lower limits
: : y_then_yhat, y_then_yhat_upper, y_then_yhat_lower : Return forecast only for forecast periods
: : trend, trend_upper, trend_lower : Trend component of the timeseries
: : seasonal, seasonal_upper, seasonal_lower: Seasonal component of the timeseries
:take_log = false : Apply logarithm to the values before the forecast. Default is true
:debug = true : Print execution information to the terminal and logs in ..\logs\Prophet Log <n>.txt
"""
# Get a list from the generator object so that it can be iterated over multiple times
request_list = [request_rows for request_rows in request]
# Calculate timings for the components of the forecasting
# The results will be stored in ..\logs\Prophet Performance Log.txt
# The request_list line above is not timed as the generator can only be iterated once
# ProphetForQlik.timeit(request_list)
# Create an instance of the ProphetForQlik class
# This will take the request data from Qlik and prepare it for forecasting
predictor = ProphetForQlik(request_list, context)
# Calculate the forecast and store in a Pandas series
forecast = predictor.predict()
# Check if the response is a DataFrame.
# This occurs when the load_script=true argument is passed in the Qlik expression.
response_is_df = isinstance(forecast, pd.DataFrame)
# Convert the response to a list of rows
forecast = forecast.values.tolist()
# We convert values to type SSE.Dual, and group columns into a iterable
if response_is_df:
response_rows = [
iter([SSE.Dual(strData=row[0]),
SSE.Dual(numData=row[1])]) for row in forecast
]
else:
response_rows = [iter([SSE.Dual(numData=row)]) for row in forecast]
# Values are then structured as SSE.Rows
response_rows = [SSE.Row(duals=duals) for duals in response_rows]
# Get the number of rows in the request
num_request_bundles = len(request_list)
# Get the number of rows in the response
num_rows = len(response_rows)
# Calculate the number of rows to send per bundle
if num_rows >= num_request_bundles:
rows_per_bundle = len(response_rows) // len(request_list)
else:
rows_per_bundle = num_rows
# Stream response as BundledRows
for i in range(0, len(response_rows), rows_per_bundle):
# Yield Row data as Bundled rows
yield SSE.BundledRows(rows=response_rows[i:i + rows_per_bundle])