Patricio
05/27/2022, 2:40 PMself.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_df
looks like this, can’t find any obvious issues with it:fede (nixtla) (they/them)
05/27/2022, 9:38 PMPatricio
05/28/2022, 7:09 PMKin Gtz. Olivares
05/29/2022, 9:33 PMPatricio
05/30/2022, 4:09 PMKin Gtz. Olivares
05/30/2022, 4:09 PMPatricio
05/31/2022, 10:36 PMn_time_in
or n_time_out
. Could this be it?