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multi-outcome tabnet-fit and predict (#118)
* add multi-output and multilabel test * switch to `expect_no_error()` for readability * `momentum` consistently default to 0.02 switch target `y` from vector to array * turn `output_dim` into vector when multi_output manage loss cases for multi_output lint and refactor code * pass `is_multi_outcome` to predict encode output_dim for multi-outcome improve multi-outcome classification loss split predict based on `is_multi_outcome` * working predict_impl_class and predict_numeric switch to hardhat v1.3.0 * refactor predict_impl_ for a clearer case_when() call * improve `check_type` to manage multi-outcome fix tests vqlues add mixed-outcome and multi-outcome with valid test * add consistency checks for outcome types * add multi-output description in `tabnet-fit` move multi-output tests is a dedicated file * improve multi-outcome tests fix multi-outcome classification
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‎.Rbuildignore

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^cran-comments\.md$
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‎.github/workflows/R-CMD-check.yaml

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steps:
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r-version: ${{ matrix.config.r }}
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install.packages('remotes')
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saveRDS(remotes::dev_package_deps(dependencies = TRUE), ".github/depends.Rds", version = 2)
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writeLines(sprintf("R-%i.%i", getRversion()$major, getRversion()$minor), ".github/R-version")
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path: ${{ env.R_LIBS_USER }}
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key: ${{ runner.os }}-${{ hashFiles('.github/R-version') }}-1-${{ hashFiles('.github/depends.Rds') }}
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while read -r cmd
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done < <(Rscript -e 'writeLines(remotes::system_requirements("ubuntu", "20.04"))')
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install.packages('remotes')
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saveRDS(remotes::dev_package_deps(dependencies = TRUE), ".github/depends.Rds", version = 2)
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writeLines(sprintf("R-%i.%i", getRversion()$major, getRversion()$minor), ".github/R-version")
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remotes::install_deps(dependencies = TRUE)
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path: check
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‎.github/workflows/test-coverage.yaml

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install.packages("pak", repos = "https://r-lib.github.io/p/pak/dev/")
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extra-packages: |
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any::covr
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pak::pkg_system_requirements("covr", execute = TRUE)
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covr::codecov(
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install_path = file.path(Sys.getenv("RUNNER_TEMP"), "package")
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)
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## --------------------------------------------------------------------
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find ${{ runner.temp }}/package -name 'testthat.Rout*' -exec cat '{}' \; || true
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‎.gitignore

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revdep

‎DESCRIPTION

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Package: tabnet
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Title: Fit 'TabNet' Models for Classification and Regression
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Version: 0.3.0.9000
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Version: 0.4.0
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Authors@R: c(
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person(given = "Daniel", family = "Falbel", role = c("aut"), email = "[email protected]"),
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BugReports: https://github.com/mlverse/tabnet/issues
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Imports:
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torch (>= 0.4.0),
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hardhat,
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hardhat (>= 1.3.0),
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glue,
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‎NEWS.md

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# tabnet (development version)
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# tabnet 0.4.0
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## New features
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* Allow missing-values values in predictor for unsupervised training. (#68)
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* Improve performance of `random_obfuscator()` torch_nn module. (#68)
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* Add support for early stopping (#69)
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* `tabnet_fit()` and `predict()` now allow missing values in predictors. (#76)
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* `tabnet_fit()` and `predict()` now allow **missing values** in predictors. (#76)
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* `tabnet_config()` now supports a `num_workers=` parameters to control parallel dataloading (#83)
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* Add a vignette on missing data (#83)
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* `tabnet_config()` now has a flag `skip_importance` to skip calculating feature importance (@egillax, #91)
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* Export and document `tabnet_nn`
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* Added `min_grid.tabnet` method for `tune` (@cphaarmeyer, #107)
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* Added `tabnet_explain()` method for parsnip models (@cphaarmeyer, #108)
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* `tabnet_fit()` and `predict()` now allow **multi-outcome**, all numeric or all factors but not mixed. (#118)
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## Bugfixes
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* `tabnet_explain()` is now correctly handling missing values in predictors. (#77)
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* `dataloader` can now use `num_workers>0` (#83)
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* new default values for `batch_size` and `virtual_batch_size` do not limit performance on mid-range devices.
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* new default values for `batch_size` and `virtual_batch_size` improves performance on mid-range devices.
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* add default `engine="torch"` to tabnet parsnip model (#114)
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* fix `autoplot()` warnings turned into errors with {ggplot2} v3.4 (#113)
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