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chore: scikit compatible version #42
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Training and validation dataloaders | ||
------- | ||
""" | ||
raise NotImplementedError('users must define construct_loaders to use this base class') | ||
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def fit(self, X_train, y_train, X_valid=None, y_valid=None, |
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Shouldn't we call load_best_model
at the end of this function? I'm not sure this is the best solution, but it can be misleading if the output model after the fit is completed is not the one that performs best on the validation set.
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I agree added it!
pytorch_tabnet/tab_model.py
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lambda_sparse=1e-3, | ||
clip_value=1, verbose=1, | ||
lr=2e-2, optimizer_fn=torch.optim.Adam, | ||
lr_params={}, scheduler=None, scheduler_fn=None, |
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Do we still have scheduler_params
?
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disappeared by mistake I'm on it
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IMPORTANT: Please do not create a Pull Request without creating an issue first.
Any change needs to be discussed before proceeding. Failure to do so may result in the rejection of the pull request.
What kind of change does this PR introduce?
Make TabNet Scikit compatible!
Does this PR introduce a breaking change?
What needs to be documented once your changes are merged?
Closing issues
Put
closes #XXXX
in your comment to auto-close the issue that your PR fixes (if such).closes #41