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[DOC] Correct argument for optimizer ranger in Temporal Fusion Transformer tutorial #1724

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merged 7 commits into from
Jan 20, 2025

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@fnhirwa fnhirwa commented Dec 9, 2024

fixes error in the tutorial which was causing examples to fail.
fixes #1722

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@fnhirwa fnhirwa marked this pull request as ready for review December 9, 2024 11:14
@fkiraly fkiraly added the bug Something isn't working label Dec 10, 2024
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the notebook CI failed, looks like a timeout? I will restart

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fnhirwa commented Dec 11, 2024

The issue was the training of the TFT model, which is taking longer than the nb sphinx timeout time, which is 600s. I ran the example locally and disabled notebooks run on CI in conf.py. You can check the notebook output from ReviewNB before merging.

Also increased the timeout in the tests to 1200s due to the training time

@fnhirwa fnhirwa requested a review from fkiraly December 11, 2024 10:11
@fnhirwa fnhirwa marked this pull request as draft December 11, 2024 16:23
@fnhirwa fnhirwa marked this pull request as ready for review December 11, 2024 20:54
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I'm unable to see this diff in Github (it saus unable to render rich diff), and ReviewNB also seems to lack the notebook.

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I'm not quite sure about that, I logged in with my github and was able to see diff on ReviewNB
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Not sure why it's not working for me, but not a big deal. I checked it from branch it directly.

I am not what change of yours is making the predictions worse. In main branch, the predicted orange line and shaded area seemed to contain the blue actuals pretty well, but in the modified one the gaps seem to have increased notably. Do you agree with observation? Any guess what's causing it (seed remained same at 42 in both cases)?

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I'm not sure about the cause as I can see even the learning_rate got changed from 0.041 to 0.009. I think I'll need to check the logs and see where it is going wrong

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I rerun the notebook now the losses seems to have been reduced

@fnhirwa fnhirwa requested a review from yarnabrina December 12, 2024 11:22
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Great!

Question, non-blocking: can we perhaps change the examples that they are still meaningful, but have lower runtime?

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fnhirwa commented Jan 20, 2025

Great!

Question, non-blocking: can we perhaps change the examples that they are still meaningful, but have lower runtime?

Yeah, this sounds great we can do it.

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fnhirwa commented Jan 20, 2025

I think the best approach is to have a broader task for reworking the examples as the ones available now seem to be written a long time ago. With this we can ensure that we have low runtime working examples.

@fnhirwa fnhirwa merged commit ace42be into sktime:main Jan 20, 2025
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[DOC] Incorrect name on tutorial "Demand forecasting with the Temporal Fusion Transformer"
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