Package: tabnet 0.6.0
Christophe Regouby
tabnet: Fit 'TabNet' Models for Classification and Regression
Implements the 'TabNet' model by Sercan O. Arik et al. (2019) <doi:10.48550/arXiv.1908.07442> with 'Coherent Hierarchical Multi-label Classification Networks' by Giunchiglia et al. <doi:10.48550/arXiv.2010.10151> and provides a consistent interface for fitting and creating predictions. It's also fully compatible with the 'tidymodels' ecosystem.
Authors:
tabnet_0.6.0.tar.gz
tabnet_0.6.0.zip(r-4.5)tabnet_0.6.0.zip(r-4.4)tabnet_0.6.0.zip(r-4.3)
tabnet_0.6.0.tgz(r-4.5-any)tabnet_0.6.0.tgz(r-4.4-any)tabnet_0.6.0.tgz(r-4.3-any)
tabnet_0.6.0.tar.gz(r-4.5-noble)tabnet_0.6.0.tar.gz(r-4.4-noble)
tabnet_0.6.0.tgz(r-4.4-emscripten)tabnet_0.6.0.tgz(r-4.3-emscripten)
tabnet.pdf |tabnet.html✨
tabnet/json (API)
NEWS
| # Install 'tabnet' in R: |
| install.packages('tabnet', repos = c('https://cranhaven.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/mlverse/tabnet/issues
Pkgdown site:https://mlverse.github.io
Last updated 2 days agofrom:10e58523b4 (on package/tabnet). Checks:9 OK. Indexed: no.
| Target | Result | Latest binary |
|---|---|---|
| Doc / Vignettes | OK | Apr 01 2025 |
| R-4.5-win | OK | Apr 01 2025 |
| R-4.5-mac | OK | Apr 01 2025 |
| R-4.5-linux | OK | Apr 01 2025 |
| R-4.4-win | OK | Apr 01 2025 |
| R-4.4-mac | OK | Apr 01 2025 |
| R-4.4-linux | OK | Apr 01 2025 |
| R-4.3-win | OK | Apr 01 2025 |
| R-4.3-mac | OK | Apr 01 2025 |
Exports:%>%attention_widthcheck_compliant_nodedecision_widthfeature_reusagemask_typemomentumnn_prune_head.tabnet_fitnn_prune_head.tabnet_pretrainnode_to_dfnum_independentnum_sharednum_stepstabnettabnet_configtabnet_explaintabnet_fittabnet_nntabnet_pretrain
Dependencies:bitbit64callrclasscliclockcodetoolscolorspacecorocpp11crayondata.tabledata.treedescdiagramdialsDiceDesigndigestdoFuturedplyrfansifarverforeachfurrrfuturefuture.applygenericsggplot2globalsgluegowerGPfitgtablehardhathmsipredisobanditeratorsjsonliteKernSmoothlabelinglatticelavalhslifecyclelistenvlubridatemagrittrMASSMatrixmgcvmodelenvmunsellnlmennetnumDerivparallellyparsnippillarpkgconfigprettyunitsprocessxprodlimprogressprogressrpspurrrR6RColorBrewerRcpprecipesrlangrpartrsamplesafetensorsscalessfdshapeslidersparsevctrsSQUAREMstringistringrsurvivaltibbletidyrtidyselecttimechangetimeDatetorchtunetzdbutf8vctrsviridisLitewarpwithrworkflowsyardstickzeallot
Fitting tabnet with tidymodels
Rendered fromtidymodels-interface.Rmdusingknitr::rmarkdownon Apr 01 2025.Last update: 2025-04-01
Started: 2025-04-01
Hierarchical Classification
Rendered fromHierarchical_classification.Rmdusingknitr::rmarkdownon Apr 01 2025.Last update: 2025-04-01
Started: 2025-04-01
Interpretation tools
Rendered frominterpretation.Rmdusingknitr::rmarkdownon Apr 01 2025.Last update: 2025-04-01
Started: 2025-04-01
Self-supervised training and fine-tuning
Rendered fromselfsupervised_training.Rmdusingknitr::rmarkdownon Apr 01 2025.Last update: 2025-04-01
Started: 2025-04-01
Training a Tabnet model from missing-values dataset
Rendered fromMissing_data_predictors.Rmdusingknitr::rmarkdownon Apr 01 2025.Last update: 2025-04-01
Started: 2025-04-01
Readme and manuals
Help Manual
| Help page | Topics |
|---|---|
| Plot tabnet_explain mask importance heatmap | autoplot.tabnet_explain |
| Plot tabnet_fit model loss along epochs | autoplot.tabnet_fit autoplot.tabnet_pretrain |
| Check that Node object names are compliant | check_compliant_node |
| Parameters for the tabnet model | attention_width decision_width feature_reusage mask_type momentum num_independent num_shared num_steps |
| Prune top layer(s) of a tabnet network | nn_prune_head.tabnet_fit nn_prune_head.tabnet_pretrain |
| Turn a Node object into predictor and outcome. | node_to_df |
| Parsnip compatible tabnet model | tabnet |
| Configuration for TabNet models | tabnet_config |
| Interpretation metrics from a TabNet model | tabnet_explain tabnet_explain.default tabnet_explain.model_fit tabnet_explain.tabnet_fit tabnet_explain.tabnet_pretrain |
| Tabnet model | tabnet_fit tabnet_fit.data.frame tabnet_fit.default tabnet_fit.formula tabnet_fit.Node tabnet_fit.recipe |
| TabNet Model Architecture | tabnet_nn |
| Tabnet model | tabnet_pretrain tabnet_pretrain.data.frame tabnet_pretrain.default tabnet_pretrain.formula tabnet_pretrain.Node tabnet_pretrain.recipe |
