#neural-forecast
Title
p
Patricio
05/27/2022, 2:40 PMHi guys, any clues as to what this error could mean in the context of auto.NHITS?
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self.model.fit(Y_df=Y_df,
File "/Users/Pato/Work/env/lib/python3.8/site-packages/neuralforecast/auto.py", line 35, in fit
self.model, self.trials = hyperopt_tunning(space=<http://self.space|self.space>,
File "/Users/Pato/Work/env/lib/python3.8/site-packages/neuralforecast/experiments/utils.py", line 1123, in hyperopt_tunning
fmin(fmin_objective, space=space, algo=tpe.suggest,
File "/Users/Pato/Work/env/lib/python3.8/site-packages/hyperopt/fmin.py", line 507, in fmin
return trials.fmin(
File "/Users/Pato/Work/env/lib/python3.8/site-packages/hyperopt/base.py", line 682, in fmin
return fmin(
File "/Users/Pato/Work/env/lib/python3.8/site-packages/hyperopt/fmin.py", line 553, in fmin
rval.exhaust()
File "/Users/Pato/Work/env/lib/python3.8/site-packages/hyperopt/fmin.py", line 356, in exhaust
self.run(self.max_evals - n_done, block_until_done=self.asynchronous)
File "/Users/Pato/Work/env/lib/python3.8/site-packages/hyperopt/fmin.py", line 292, in run
self.serial_evaluate()
File "/Users/Pato/Work/env/lib/python3.8/site-packages/hyperopt/fmin.py", line 170, in serial_evaluate
result = self.domain.evaluate(spec, ctrl)
File "/Users/Pato/Work/env/lib/python3.8/site-packages/hyperopt/base.py", line 907, in evaluate
rval = self.fn(pyll_rval)
File "/Users/Pato/Work/env/lib/python3.8/site-packages/neuralforecast/experiments/utils.py", line 991, in evaluate_model
results, _, trainer = model_fit_predict(mc=mc,
File "/Users/Pato/Work/env/lib/python3.8/site-packages/neuralforecast/experiments/utils.py", line 891, in model_fit_predict
model, trainer, val_loader, test_loader, scaler_y = fit(
File "/Users/Pato/Work/env/lib/python3.8/site-packages/neuralforecast/experiments/utils.py", line 755, in predict
outputs = trainer.predict(model, loader)
File "/Users/Pato/Work/env/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1026, in predict
return self._call_and_handle_interrupt(
File "/Users/Pato/Work/env/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 724, in _call_and_handle_interrupt
return trainer_fn(*args, **kwargs)
File "/Users/Pato/Work/env/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1073, in _predict_impl
results = self._run(model, ckpt_path=self.ckpt_path)
File "/Users/Pato/Work/env/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1237, in _run
results = self._run_stage()
File "/Users/Pato/Work/env/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1323, in _run_stage
return self._run_predict()
File "/Users/Pato/Work/env/lib/python3.8/site-packages/pytorch_lightning/trainer/trainer.py", line 1382, in _run_predict
return self.predict_loop.run()
File "/Users/Pato/Work/env/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 204, in run
self.advance(*args, **kwargs)
File "/Users/Pato/Work/env/lib/python3.8/site-packages/pytorch_lightning/loops/dataloader/prediction_loop.py", line 101, in advance
dl_predictions, dl_batch_indices = self.epoch_loop.run(
File "/Users/Pato/Work/env/lib/python3.8/site-packages/pytorch_lightning/loops/base.py", line 204, in run
self.advance(*args, **kwargs)
File "/Users/Pato/Work/env/lib/python3.8/site-packages/pytorch_lightning/loops/epoch/prediction_epoch_loop.py", line 92, in advance
batch_idx, batch = next(dataloader_iter)
File "/Users/Pato/Work/env/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 517, in __next__
data = self._next_data()
File "/Users/Pato/Work/env/lib/python3.8/site-packages/torch/utils/data/dataloader.py", line 557, in _next_data
data = self._dataset_fetcher.fetch(index) # may raise StopIteration
File "/Users/Pato/Work/env/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/Users/Pato/Work/env/lib/python3.8/site-packages/torch/utils/data/_utils/fetch.py", line 44, in <listcomp>
data = [self.dataset[idx] for idx in possibly_batched_index]
File "/Users/Pato/Work/env/lib/python3.8/site-packages/neuralforecast/data/tsdataset.py", line 793, in __getitem__
windows, S, ts_idxs = self._create_windows_tensor(idx=idx)
File "/Users/Pato/Work/env/lib/python3.8/site-packages/neuralforecast/data/tsdataset.py", line 696, in _create_windows_tensor
ts_idxs = t.as_tensor(ts_idxs, dtype=t.long)
Exception: Time Series [0] are not sampleable. Check the data, masks, window_sampling_limit, input_size, output_size, masks.Y_dff
fede (nixtla) (they/them)
05/27/2022, 9:38 PMHi @Patricio! Thanks for using NeuralForecast 🙂 I think it may be a problem with the size of the series. Would it be possible for you to share your data with us?
🙌 1
p
Patricio
05/28/2022, 7:09 PMHey @fede! I think we could share, but it will probably require an NDA 🤔, let me get back to you on Monday once I’ve checked with the team 👍
k
Kin Gtz. Olivares
05/29/2022, 9:33 PMLast time @Patricio, that we observed this unsampleable windows error was because some series are smaller than the n_time_in parameter and are unable to be fed to the N-HiTS
Two possibilities come to my mind:
• add more data to the problem series repeating observations to the past (hacky)
reduce the input size of the model (n_time_in)
Let us know if that works
If that is the case can you help us with a github ISSUE so future users know too?
p
Patricio
05/30/2022, 4:09 PMOhhh yes @Kin Gtz. Olivares, that actually makes sense for this input df and I didn’t think about this. Will check it and post an issue if that’s the case! Thank you for the pointer 🙏
k
Kin Gtz. Olivares
05/30/2022, 4:09 PM👍
p
Patricio
05/31/2022, 10:36 PMI’ve done a bit more digging and experimenting. Seems to me the issue is triggered whenever the data amount (for any group) is such that either the validation or test masks yield an amount of data points smaller than
n_time_inn_time_out