def debug_logger():
log.info("testing: Logger")
log.setLevel("ERROR")
log.parent.setLevel("WARNING")
log.warning("### this WARNING should not show ###")
log.parent.warning("this WARNING should show")
log.error("this ERROR should show")
log.setLevel("DEBUG")
log.parent.setLevel("ERROR")
log.debug("this DEBUG should show")
log.parent.warning("### this WARNING not show ###")
log.error("this ERROR should show")
log.parent.error("this ERROR should show, too")
# test existing test cases
# test_all(log_level="DEBUG")
# test the set_log_level function
log.parent.parent.setLevel("INFO")
m = NeuralProphet(
n_forecasts=3,
n_lags=5,
yearly_seasonality=False,
weekly_seasonality=False,
daily_seasonality=False,
log_level="DEBUG",
epochs=5,
)
log.parent.parent.debug("this DEBUG should show")
m.set_log_level(log_level="WARNING")
log.parent.parent.debug("### this DEBUG should not show ###")
log.parent.parent.info("### this INFO should not show ###")
def nprophet_fit_and_predict_simple(
y: [float],
k: int,
freq: str = None,
model_params: dict = None) -> Tuple[List, List, Any, Any]:
""" Simpler wrapper for testing - univariate only """
assert isinstance(y[0], float)
freq = freq or NPROPHET_META['freq']
used_params = NPROPHET_MODEL
used_params.update({'n_forecasts': k})
if model_params:
used_params.update(model_params)
if len(y) < used_params['n_lags']:
x = [wrap(y)[0]] * k
x_std = [1.0] * k
return x, x_std, None, None
else:
model = NeuralProphet(**used_params)
model.set_log_level(log_level='CRITICAL')
df = pd.DataFrame(columns=['y'], data=y)
df['ds'] = pd.date_range(start='2021-01-01',
periods=len(y),
freq=freq)
metrics = model.fit(df, freq=freq, epochs=40, use_tqdm=False)
future = model.make_future_dataframe(df)
forecast = model.predict(future)
x = [
forecast['yhat' + str(j + 1)].values[-k + j] for j in range(k)
]
x_std = [1.0] * k
return x, x_std, forecast, model
