王梦石
11/01/2023, 7:16 AM### NBeatsx HyperParameter Config
nbeatsx_config = {
"input_size": tune.choice([1 * input_sequence]), # input_size = multiplier * horizon
"step_size": tune.choice([4]), # Window shift size
"hist_exog_list": tune.choice(['x_7']), #df.columns[1:-2].tolist(),
}
And put the config to the model iniation then fit the trian input
NBeatsx = AutoNBEATSx(h=output_sequence, config=nbeatsx_config, loss=MAE(), gpus=1, search_alg=HyperOptSearch(), backend='ray', num_samples=40)
nf = NeuralForecast(models=[NBeatsx], freq='H', local_scaler_type='standard')
nf.fit(Y_train_df, val_size=val_size, verbose=True)
Then, the training session failed with the output error
File "/usr/local/lib/python3.10/dist-packages/neuralforecast/common/_base_auto.py", line 336, in _fit_model
model.fit(dataset, val_size=val_size, test_size=test_size)
File "/usr/local/lib/python3.10/dist-packages/neuralforecast/common/_base_windows.py", line 697, in fit
raise Exception(
Exception: {'7', 'x', '_'} historical exogenous variables not found in input dataset
Anyone could please explain this?
Also, one extra question is that, If I am going to have about 700 exogenous variables as the input, is it OKAY/BAD to have huge exogenous dimensions. Thanks!José Morales
11/01/2023, 5:21 PM'hist_exog_list': 'x_7'
and it should be 'hist_exog_list': ['x_7']
so the config for tune should be something like: hist_exog_list: tune.choice([['x_7']])
About the 700 exogenous the problem is memory, so it's really up to the hardware, but i'd say it's probably going to run out of memory.王梦石
11/02/2023, 12:17 AM(_train_tune pid=2432) File "/usr/local/lib/python3.10/dist-packages/neuralforecast/common/_base_windows.py", line 336, in _get_temporal_data_cols
(_train_tune pid=2432) set(temporal_cols.tolist()) & set(self.hist_exog_list + self.futr_exog_list)
(_train_tune pid=2432) TypeError: can only concatenate tuple (not "list") to tuple
The value I used is pure integer value.José Morales
11/02/2023, 5:37 PMlobbie lobbie
11/05/2023, 6:46 AM王梦石
11/05/2023, 7:02 AMJosé Morales
11/06/2023, 3:27 PM