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)