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[MRG] Fix batch issue when generating features + add sample_weight in deep models #220
[MRG] Fix batch issue when generating features + add sample_weight in deep models #220
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I'm wondering if you can not just create a dataloader on your own, without using skorch function. Like that you only create a dataloader with X, iterate on the dataloader and that's it. It seems very long to do something simple, but maybe I'm wrong
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sample_weight
is everywhere but in practice we only use it to reweight the loss, right? IMO, it should used only when calling the loss but maybe I misunderstood something.
If you run coverage run -m pytest -v -s && coverage html && open htmlcov/index.html
on your branch and on the main branch, you will see that you have added 24 lines in skada/deep/base.py
that are not covered by tests.
From Skorch FAQ: when X is a dict, its keys are passed as kwargs to |
Codecov ReportAttention: Patch coverage is
Additional details and impacted files@@ Coverage Diff @@
## main #220 +/- ##
==========================================
- Coverage 97.01% 96.31% -0.70%
==========================================
Files 54 54
Lines 5429 5486 +57
==========================================
+ Hits 5267 5284 +17
- Misses 162 202 +40 |
Very good PR, the only thing missing is to modify also |
Before when doing a model.predict_features(X) we were passing all the input in a single batch.
This created CUDA out of memory issues when working with big datasets.
Thus here I tried to mimic the behaviour of skorch models to use batching.
Might not be the best way to fix that issue though, would love your opinion @tgnassou