[WIP] [clean version] DomainAwareNet supports regression and binary classification#351
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vloison wants to merge 11 commits intoscikit-adaptation:mainfrom
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[WIP] [clean version] DomainAwareNet supports regression and binary classification#351vloison wants to merge 11 commits intoscikit-adaptation:mainfrom
vloison wants to merge 11 commits intoscikit-adaptation:mainfrom
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vloison
changed the title
Codecov Report❌ Patch coverage is Additional details and impacted files@@ Coverage Diff @@
## main #351 +/- ##
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- Coverage 95.89% 94.77% -1.12%
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Files 64 64
Lines 7087 7255 +168
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+ Hits 6796 6876 +80
- Misses 291 379 +88 🚀 New features to boost your workflow:
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tgnassou
reviewed
Oct 23, 2025
| nonlin = _identity | ||
| elif nonlin == 'auto': | ||
| nonlin = _infer_predict_nonlinearity(self) | ||
| if not callable(nonlin): |
| """ | ||
| loss = super().get_loss(y_pred, y_true, X, *args, **kwargs) | ||
|
|
||
| if "sample_weight" in X and X["sample_weight"] is not None: |
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Just need to test with sample_weight for the test coverage
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DomainAwareNet implements a classifier. The goal is to implement dual classification and regression, too.
To do so, I replaced DomainAwareNet with a mother class _DomainAwareNet and two classes DomainAwareNetRegressor and DomainNetClassifier that inherit from _DomainAwareNet. Arthur added a class DomainNetBinaryClassifier, which follows the same principle.
Done:
To do: