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
tabnet 0.1.0
- Added a
NEWS.md file to track changes to the
package.