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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.

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