Comments (8)
@Optimox Yes, I can look into it. Will keep you posted here.
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Thank you, it's very much appreciated!
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Hi @Optimox, I am looking into it. Will share an update once it is taken care of. Thank you.
EDIT: The conda-forge version for pytorch-tabnet is the same as that on PyPI.
v4.1.0
👉 Please check once from your end and close this issue if everything looks fine. Thank you.
https://anaconda.org/conda-forge/pytorch-tabnet
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Thank you very much! Looks good to me!
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@sugatoray Any chance you know how to do this ?
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@Optimox Should be doable, since PyTorch 2.0.0 is available in principle, on conda-forge channel. All we need is a PR updating the recipe (with successful build of the library in conda-forge channel).
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Ok, is there any chance you could take care of this? That would be very helpful for a lot of conda users!
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is this resolved ? @sugatoray
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Related Issues (20)
- Wrapper for GridSearchCV with RuntimeError: "Cannot clone object..." for embeddings HOT 3
- Count the number of parameters HOT 2
- Loss goes to -inf HOT 1
- The mask tensor M in script tab_network.py needs to be transformed to realize the objective stated in the paper: "γ is a relaxation parameter – when γ = 1, a feature is enforced to be used only at one decision step".
- Minimal working example for TabNetRegressor/Classifier HOT 4
- Transfer learning, capability to change structure of model HOT 1
- Generate Embeddings for Tabular Data HOT 1
- TabNet overfits (help wanted, not a bug) HOT 9
- TabNetRegressor vs other networks HOT 1
- spike in memory when training ends HOT 8
- Severe overfitting HOT 18
- OOM problem when I search hyperparameters with Tabnet HOT 3
- Support for complex-valued datasets HOT 4
- Different classification variables in the test set and train set HOT 1
- Struggling to get model to fit - Help Wanted HOT 7
- Optimizing TabNet for Disease Classification with Continuous Audio Features HOT 1
- Interpreting Sparsity on Global Importance HOT 5
- ValueError: The truth value of an array with more than one element is ambiguous. Use a.any() or a.all() HOT 1
- Validation loss HOT 1
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