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# neural-forecast
s
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m
what model are you using for cls_model
can you show me the code for your HINT class?
p
Sure. I am following the tutorial here line by line. https://nixtla.github.io/neuralforecast/examples/hierarchicalnetworks.html
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# 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')
m
you're using the dataset used int he example?
p
yes
m
one sec, running my notebook
p
Thank you
m
sorry, i dont know... I'm using my own dataset but it works for me =/
similar layout
p
Could it be a version problem? By similar layout, do you mean you are running it on the same specs? MacOS, etc...
m
no i meant dataframe layout is similar. Maybe? I'm on databricks
torch does have a specific version for mac iirc, not sure if that's the issue
p
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
for my own sanity 😅
m
sure
p
Thanks Mark! Much appreciated!
m
how long does it take to fail for you? running 7 minutes sof ar
p
Oh immediately
m
😬
yeah, def something with your environment i guess
i was thinking about buying a macbook air, good thing i didnt i guess 😉
🤣 1
p
Ok I'm installing what I need on my linux VM. Will see if it works there.
👍 1