Hi All, I'm trying to follow the tutorial for Hierarchical Forecast using the HINT class. https://nixtla.github.io/neuralforecast/examples/hierarchicalnetworks.html I am getting an error running this step:

nf.fit(df=Y_train_df, val_size=12)

The final error message is:

File ~/Documents/pyTorch/pytorch/lib/python3.9/site-packages/neuralforecast/common/_base_windows.py:460, in BaseWindows.training_step(self, batch, batch_idx)
    458     print("insample_y", torch.isnan(insample_y).sum())
    459     print("outsample_y", torch.isnan(outsample_y).sum())
--> 460     print("output", torch.isnan(output).sum())
    461     raise Exception("Loss is NaN, training stopped.")
    463 self.log("train_loss", loss, prog_bar=True, on_epoch=True)

TypeError: isnan(): argument 'input' (position 1) must be Tensor, not tuple

I am running this tutorial on a MacBook Pro 13.5 (22G74) with python version 3.9. Any idea what is going on?

what model are you using for cls_model

Sure. I am following the tutorial here line by line. https://nixtla.github.io/neuralforecast/examples/hierarchicalnetworks.html

# Horizon and quantiles
level = np.arange(0, 100, 2)
qs = [[50-lv/2, 50+lv/2] if lv!=0 else [50] for lv in level]
quantiles = np.sort(np.concatenate(qs)/100)

# HINT := BaseNetwork + Distribution + Reconciliation
nhits = NHITS(h=horizon,
              input_size=24,
              loss=GMM(n_components=10, quantiles=quantiles),
              hist_exog_list=['month'],
              max_steps=2000,
              early_stop_patience_steps=10,
              val_check_steps=50,
              scaler_type='robust',
              learning_rate=1e-3,
              valid_loss=sCRPS(quantiles=quantiles))

model = HINT(h=horizon, S=S_df.values,
             model=nhits,  reconciliation='BottomUp')

you're using the dataset used int he example?

yes

one sec, running my notebook

Thank you

sorry, i dont know... I'm using my own dataset but it works for me =/

Could it be a version problem? By similar layout, do you mean you are running it on the same specs? MacOS, etc...

no i meant dataframe layout is similar. Maybe? I'm on databricks

I'll try to see if I can run it on Linux... I might have more luck there. Is it possible for you to check if it works with the toy example in the tutorial on databricks

sure

Thanks Mark! Much appreciated!

how long does it take to fail for you? running 7 minutes sof ar

Oh immediately

😬

Ok I'm installing what I need on my linux VM. Will see if it works there.