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Fix: broken dataset links in transfer learning notebook #2067

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merged 5 commits into from Nov 16, 2023

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madtoinou
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Fixes #1948.

Summary

  • Replaced links to a private github repository with links to public websites
  • Pre-processing takes more time as the datasets are not prepared

Other informations

  • Had to rerun the whole notebook since the datasets are slightly different
  • Changed some observations/conclusions accordingly

@madtoinou madtoinou added this to In review in darts via automation Nov 13, 2023
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codecov-commenter commented Nov 14, 2023

Codecov Report

All modified and coverable lines are covered by tests ✅

Comparison is base (d206055) 93.82% compared to head (3655e32) 93.81%.

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Additional details and impacted files
@@            Coverage Diff             @@
##           master    #2067      +/-   ##
==========================================
- Coverage   93.82%   93.81%   -0.01%     
==========================================
  Files         134      134              
  Lines       13091    13077      -14     
==========================================
- Hits        12282    12268      -14     
  Misses        809      809              

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review-notebook-app bot commented Nov 15, 2023

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dennisbader commented on 2023-11-15T09:30:48Z
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The compute durations written for the different models have been obtained by running the notebook on a i9-10900K CPU, with an RTX 2080s GPU, with Python 3.9.7 and Darts 0.18.0.

Can you update this with the setup you used?


madtoinou commented on 2023-11-15T22:37:27Z
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I updated the config, I could try to run the notebook on a more powerful config if you think that it would be more meaningful for users.

dennisbader commented on 2023-11-16T07:26:20Z
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All good like this, thanks

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dennisbader commented on 2023-11-15T09:30:49Z
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you might want to cut out the cell output here to hide your machine paths :)


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review-notebook-app bot commented Nov 15, 2023

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dennisbader commented on 2023-11-15T09:30:50Z
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sMAPE of about 27.2.


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review-notebook-app bot commented Nov 15, 2023

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dennisbader commented on 2023-11-15T09:30:51Z
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The median is better for with the naive seasonal

->

Slightly better than the naive seasonal model.


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dennisbader commented on 2023-11-15T09:30:51Z
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with good forecasting accuracy and about 10x faster

->

with good forecasting accuracy and lower computational cost.

---

Not related to this PR but it's a bit worrying how ARIMA takes much longer than in the original version


madtoinou commented on 2023-11-15T22:38:52Z
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I don't know if it's caused by the hardware or a change in the implementation since darts 0.18.0. Another difference is that the series are slightly longer on average with this version of the passengers per carrier.

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dennisbader commented on 2023-11-15T09:30:52Z
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Line #20.            # change this one to "gpu" if your notebook does run in a GPU environment:

you can use “auto” so it automatically uses gpu if available


madtoinou commented on 2023-11-15T22:41:02Z
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I forced "cpu" because with the Apple Silicon macbook, the gpu (mps) acceleration does not support float64 and would require all the datasets to be converted to float32. Let me know what you think is the best.

dennisbader commented on 2023-11-16T07:28:31Z
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Ah okay, than it's alright

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dennisbader commented on 2023-11-15T09:30:53Z
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tradeoff between accuracy and speed (about 85x faster than ARIMA for similar accuracy).

->

tradeoff between accuracy and speed.


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dennisbader commented on 2023-11-15T09:30:54Z
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Line #18.            # change this one to "gpu" if your notebook does run in a GPU environment:

same here with "auto"


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dennisbader commented on 2023-11-15T09:30:55Z
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The forecasting step with N-BEATS is ~350x faster than the fit-predict step we needed with ARIMA, and about 4x faster than the fit-predict step of linear regression.

->

The forecasting step with N-BEATS is more than 1000x faster than the fit-predict step we needed with ARIMA, and also faster than with linear regression.


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review-notebook-app bot commented Nov 15, 2023

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dennisbader commented on 2023-11-15T09:30:56Z
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The part about regression being slower can be removed now, as this was optimized


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dennisbader commented on 2023-11-15T09:30:57Z
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ARIMA performs best but is about 170x slower than N-BEATS...

now it's around 1k times slower

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Note that two models out of the 3 most accurate (Exponential Smoothing and Kalman Filter) did not perform so well when used on the air passengers series

--->

Note also that Exponential Smoothing and Kalman Filter now perform much better than when we used them on the air passengers series.


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Looks great, thanks a lot @madtoinou 🚀

Had some suggestions here and there mainly about updating the plot explanations with the new numbers

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I updated the config, I could try to run the notebook on a more powerful config if you think that it would be more meaningful for users.


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I don't know if it's caused by the hardware or a change in the implementation since darts 0.18.0. Another difference is that the series are slightly longer on average with this version of the passengers per carrier.


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I forced "cpu" because with the Apple Silicon macbook, the gpu (mps) acceleration does not support float64 and would require all the datasets to be converted to float32. Let me know what you think is the best.


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All good like this, thanks


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Ah okay, than it's alright


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@dennisbader dennisbader merged commit a7f5d09 into master Nov 16, 2023
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darts automation moved this from In review to Done Nov 16, 2023
@dennisbader dennisbader deleted the fix/broken_datasets branch November 16, 2023 07:38
@dennisbader dennisbader moved this from Done to Released in darts Apr 9, 2024
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[BUG] dataset link is broken
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