def test_events(self):
log.info("testing: Events")
df = pd.read_csv(PEYTON_FILE)[-NROWS:]
playoffs = pd.DataFrame(
{
"event": "playoff",
"ds": pd.to_datetime(
[
"2008-01-13",
"2009-01-03",
"2010-01-16",
"2010-01-24",
"2010-02-07",
"2011-01-08",
"2013-01-12",
"2014-01-12",
"2014-01-19",
"2014-02-02",
"2015-01-11",
"2016-01-17",
"2016-01-24",
"2016-02-07",
]
),
}
)
superbowls = pd.DataFrame(
{
"event": "superbowl",
"ds": pd.to_datetime(["2010-02-07", "2014-02-02", "2016-02-07"]),
}
)
events_df = pd.concat((playoffs, superbowls))
m = NeuralProphet(
n_lags=2,
n_forecasts=30,
daily_seasonality=False,
epochs=EPOCHS,
batch_size=BATCH_SIZE,
)
# set event windows
m = m.add_events(
["superbowl", "playoff"], lower_window=-1, upper_window=1, mode="multiplicative", regularization=0.5
)
# add the country specific holidays
m = m.add_country_holidays("US", mode="additive", regularization=0.5)
history_df = m.create_df_with_events(df, events_df)
metrics_df = m.fit(history_df, freq="D")
future = m.make_future_dataframe(df=history_df, events_df=events_df, periods=30, n_historic_predictions=90)
forecast = m.predict(df=future)
log.debug("Event Parameters:: {}".format(m.model.event_params))
if self.plot:
m.plot_components(forecast)
m.plot(forecast)
m.plot_parameters()
plt.show()
def seek_the_oracle(current_series, args, series, forecast_length,
future_regressor):
"""Prophet for for loop or parallel."""
current_series = current_series.rename(columns={series: 'y'})
current_series['ds'] = current_series.index
try:
quant_range = (1 - args['prediction_interval']) / 2
quantiles = [quant_range, 0.5, (1 - quant_range)]
m = NeuralProphet(
quantiles=quantiles,
growth=self.growth,
n_changepoints=self.n_changepoints,
changepoints_range=self.changepoints_range,
trend_reg=self.trend_reg,
trend_reg_threshold=self.trend_reg_threshold,
ar_sparsity=self.ar_sparsity,
yearly_seasonality=self.yearly_seasonality,
weekly_seasonality=self.weekly_seasonality,
daily_seasonality=self.daily_seasonality,
seasonality_mode=self.seasonality_mode,
seasonality_reg=self.seasonality_reg,
n_lags=self.n_lags,
n_forecasts=forecast_length,
num_hidden_layers=self.num_hidden_layers,
d_hidden=self.d_hidden,
learning_rate=self.learning_rate,
loss_func=self.loss_func,
train_speed=self.train_speed,
normalize=self.normalize,
collect_metrics=False,
)
except Exception:
m = NeuralProphet(
growth=self.growth,
n_changepoints=self.n_changepoints,
changepoints_range=self.changepoints_range,
trend_reg=self.trend_reg,
trend_reg_threshold=self.trend_reg_threshold,
ar_sparsity=self.ar_sparsity,
yearly_seasonality=self.yearly_seasonality,
weekly_seasonality=self.weekly_seasonality,
daily_seasonality=self.daily_seasonality,
seasonality_mode=self.seasonality_mode,
seasonality_reg=self.seasonality_reg,
n_lags=self.n_lags,
n_forecasts=forecast_length,
num_hidden_layers=self.num_hidden_layers,
d_hidden=self.d_hidden,
learning_rate=self.learning_rate,
loss_func=self.loss_func,
train_speed=self.train_speed,
normalize=self.normalize,
collect_metrics=False,
)
if args['holiday']:
m.add_country_holidays(country_name=args['holiday_country'])
if args['regression_type'] == 'User':
current_series = pd.concat(
[current_series, args['regressor_train']], axis=1)
for nme in args['regressor_name']:
m.add_future_regressor(nme)
m.fit(current_series,
freq=args['freq'],
progress_print=False,
minimal=True)
if args['regression_type'] == 'User':
if future_regressor.ndim > 1:
if future_regressor.shape[1] > 1:
ft_regr = (future_regressor.mean(
axis=1).to_frame().merge(
future_regressor.std(axis=1).to_frame(),
left_index=True,
right_index=True,
))
else:
ft_regr = future_regressor.copy()
ft_regr.columns = args['regressor_train'].columns
regr = pd.concat([args['regressor_train'], ft_regr])
regr.columns = args['regressor_train'].columns
# regr.index.name = 'ds'
# regr.reset_index(drop=False, inplace=True)
# future = future.merge(regr, on="ds", how='left')
else:
# a = np.append(args['regressor_train'], future_regressor.values)
regr = future_regressor
future = m.make_future_dataframe(current_series,
periods=forecast_length,
regressors_df=regr)
else:
future = m.make_future_dataframe(current_series,
periods=forecast_length)
fcst = m.predict(future, decompose=False)
fcst = fcst.tail(forecast_length) # remove the backcast
# predicting that someday they will change back to fbprophet format
if "yhat2" in fcst.columns:
fcst['yhat1'] = fcst.fillna(0).sum(axis=1, numeric_only=True)
try:
forecast = fcst['yhat1']
except Exception:
forecast = fcst['yhat']
forecast.name = series
# not yet supported, so fill with the NaN column for now if missing
try:
lower_forecast = fcst['yhat_lower']
upper_forecast = fcst['yhat_upper']
except Exception:
lower_forecast = fcst['y']
upper_forecast = fcst['y']
lower_forecast.name = series
upper_forecast.name = series
return (forecast, lower_forecast, upper_forecast)
