Closed microprediction closed 2 years ago
This is a very easy one
y = [157.0, 157.0, 155.0, 155.0, 230.0, 230.0, ...], k = 5, freq = 'H'
model_params = {'n_lags': 1}
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 offlinetesting - 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')
E AttributeError: 'NeuralProphet' object has no attribute 'set_log_level'
Probably something obvious