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tabnet 0.6.0

New features

• parsnip models now allow transparently passing case weights through workflows::add_case_weights() parameters (#151)
• parsnip models now support tabnet_model and from_epoch parameters (#143)

Bugfixes

• Adapt tune::finalize_workflow() test to {parsnip} v1.2 breaking change. (#155)
• autoplot() now position the “has_checkpoint” points correctly when a tabnet_fit() is continuing a previous training using tabnet_model =. (#150)
• Explicitely warn that tabnet_model option will not be used in tabnet_pretrain() tasks. (#150)

tabnet 0.5.0

New features

• {tabnet} now allows hierarchical multi-label classification through {data.tree} hierarchical Node dataset. (#126)
• tabnet_pretrain() now allows different GLU blocks in GLU layers in encoder and in decoder through the config() parameters num_idependant_decoder and num_shared_decoder (#129)
• Add reduce_on_plateau as option for lr_scheduler at tabnet_config() (@SvenVw, #120)
• use zeallot internally with %<-% for code readability (#133)
• add FR translation (#131)

tabnet 0.4.0

New features

• Add explicit legend in autoplot.tabnet_fit() (#67)
• Improve unsupervised vignette content. (#67)
• tabnet_pretrain() now allows missing values in predictors. (#68)
• tabnet_explain() now works for tabnet_pretrain models. (#68)
• Allow missing-values values in predictor for unsupervised training. (#68)
• Improve performance of random_obfuscator() torch_nn module. (#68)
• Add support for early stopping (#69)
• tabnet_fit() and predict() now allow missing values in predictors. (#76)
• tabnet_config() now supports a num_workers= parameters to control parallel dataloading (#83)
• Add a vignette on missing data (#83)
• tabnet_config() now has a flag skip_importance to skip calculating feature importance (@egillax, #91)
• Export and document tabnet_nn
• Added min_grid.tabnet method for tune (@cphaarmeyer, #107)
• Added tabnet_explain() method for parsnip models (@cphaarmeyer, #108)
• tabnet_fit() and predict() now allow multi-outcome, all numeric or all factors but not mixed. (#118)

Bugfixes

• tabnet_explain() is now correctly handling missing values in predictors. (#77)
• dataloader can now use num_workers>0 (#83)
• new default values for batch_size and virtual_batch_size improves performance on mid-range devices.
• add default engine="torch" to tabnet parsnip model (#114)
• fix autoplot() warnings turned into errors with {ggplot2} v3.4 (#113)

tabnet 0.3.0

• Added an update method for tabnet models to allow the correct usage of finalize_workflow (#60).

tabnet 0.2.0

New features

• Allow model fine-tuning through passing a pre-trained model to tabnet_fit() (@cregouby, #26)
• Explicit error in case of missing values (@cregouby, #24)
• Better handling of larger datasets when running tabnet_explain().
• Add tabnet_pretrain() for unsupervised pretraining (@cregouby, #29)
• Add autoplot() of model loss among epochs (@cregouby, #36)
• Added a config argument to fit() / pretrain() so one can pass a pre-made config list. (#42)
• In tabnet_config(), new mask_type option with entmax additional to default sparsemax (@cmcmaster1, #48)
• In tabnet_config(), loss now also takes function (@cregouby, #55)

Bugfixes

• Fixed bug in GPU training. (#22)
• Fixed memory leaks when using custom autograd function.
• Batch predictions to avoid OOM error.

Internal improvements

• Added GPU CI. (#22)

tabnet 0.1.0

• Added a NEWS.md file to track changes to the package.

These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.