From 2964dc42a000d2955a6321f52dd7974cb776cf5e Mon Sep 17 00:00:00 2001 From: "C. Regouby" Date: Sat, 28 Jun 2025 17:36:28 +0200 Subject: [PATCH 1/5] prevent vignette rendering when torch is not installed --- vignettes/aum_loss.qmd | 13 ++++++++----- 1 file changed, 8 insertions(+), 5 deletions(-) diff --git a/vignettes/aum_loss.qmd b/vignettes/aum_loss.qmd index b31ab9a..e5d0c66 100644 --- a/vignettes/aum_loss.qmd +++ b/vignettes/aum_loss.qmd @@ -9,13 +9,16 @@ format: fig-width: 9 fig-height: 6 fig-cap-location: "top" -knitr: - opts_chunk: - collapse: true - comment: "#>" - eval: true --- +```{r, include = FALSE} +knitr::opts_chunk$set( + collapse = TRUE, + comment = "#>", + eval = torch::torch_is_installed() +) +``` + ```{r setup} library(tabnet) suppressPackageStartupMessages(library(tidymodels)) From 9878986196c2492e5041722528b2d3374eb2d06d Mon Sep 17 00:00:00 2001 From: "C. Regouby" Date: Mon, 30 Jun 2025 22:23:35 +0200 Subject: [PATCH 2/5] turn into ExamplesIf --- R/mask-type.R | 4 +++- cran-comments.md | 4 +--- man/entmax15.Rd | 4 ++++ 3 files changed, 8 insertions(+), 4 deletions(-) diff --git a/R/mask-type.R b/R/mask-type.R index af053dc..6385451 100644 --- a/R/mask-type.R +++ b/R/mask-type.R @@ -297,11 +297,13 @@ entmax_15_function <- torch::autograd_function( #' @return The projection result P of the same shape as input, such that #' \eqn{\sum_{dim} P = 1 \forall dim} elementwise. #' -#' @examples +#' @examplesIf torch::torch_is_installed() +#' \dontrun{ #' input <- torch::torch_randn(10,5, requires_grad = TRUE) #' # create a top3 alpha=1.5 entmax on last input dimension #' nn_entmax <- entmax15(dim=-1L, k = 3) #' result <- nn_entmax(input) +#' } #' @export entmax15 <- torch::nn_module( "entmax_15", diff --git a/cran-comments.md b/cran-comments.md index dcdbd93..0636d49 100644 --- a/cran-comments.md +++ b/cran-comments.md @@ -4,6 +4,4 @@ * This is a new release. -Note 1 : Package new submission after fixing the issues that led to archiving on 2025-04-01. - Misspelled words in description are names and have been added to package `inst/WORDLIST` -Note 2 : Example duration is inherent to fitting a model with the underlying torch framework. +Note 1 : Example duration is inherent to fitting a model with the underlying torch framework. diff --git a/man/entmax15.Rd b/man/entmax15.Rd index 565c727..6c04e65 100644 --- a/man/entmax15.Rd +++ b/man/entmax15.Rd @@ -31,8 +31,12 @@ Solves the optimization problem: where \eqn{H_{1.5}(P)} is the Tsallis alpha-entropy with \eqn{\alpha=1.5}. } \examples{ +\dontshow{if (torch::torch_is_installed()) (if (getRversion() >= "3.4") withAutoprint else force)(\{ # examplesIf} +\dontrun{ input <- torch::torch_randn(10,5, requires_grad = TRUE) # create a top3 alpha=1.5 entmax on last input dimension nn_entmax <- entmax15(dim=-1L, k = 3) result <- nn_entmax(input) } +\dontshow{\}) # examplesIf} +} From 1426951f5efcae1a3025d03f475a0476aea053e2 Mon Sep 17 00:00:00 2001 From: "C. Regouby" Date: Sun, 24 Aug 2025 18:08:57 +0200 Subject: [PATCH 3/5] back to knitr for aum_loss vignette --- vignettes/{aum_loss.qmd => aum_loss.Rmd} | 13 ++++++++----- 1 file changed, 8 insertions(+), 5 deletions(-) rename vignettes/{aum_loss.qmd => aum_loss.Rmd} (98%) diff --git a/vignettes/aum_loss.qmd b/vignettes/aum_loss.Rmd similarity index 98% rename from vignettes/aum_loss.qmd rename to vignettes/aum_loss.Rmd index e5d0c66..f919ece 100644 --- a/vignettes/aum_loss.qmd +++ b/vignettes/aum_loss.Rmd @@ -1,17 +1,19 @@ --- title: "Using ROC AUM loss for imbalanced binary classification" +output: rmarkdown::html_vignette vignette: > %\VignetteIndexEntry{Using ROC AUM loss for imbalanced binary classification} %\VignetteEngine{quarto::html} %\VignetteEncoding{UTF-8} -format: - html: +editor_options: + markdown: fig-width: 9 fig-height: 6 fig-cap-location: "top" --- -```{r, include = FALSE} +```{r} +#| include: false knitr::opts_chunk$set( collapse = TRUE, comment = "#>", @@ -19,12 +21,13 @@ knitr::opts_chunk$set( ) ``` -```{r setup} +```{r} +#| label: setup library(tabnet) suppressPackageStartupMessages(library(tidymodels)) library(modeldata) data("lending_club", package = "modeldata") -set.seed(20250409) +set.seed(20250809) ``` ::: callout-note From e4d6c8ea6b7176b7d0eb3973d705caa0ef4bb86b Mon Sep 17 00:00:00 2001 From: "C. Regouby" Date: Mon, 25 Aug 2025 08:34:41 +0200 Subject: [PATCH 4/5] add alt-text to interpretation vignette --- DESCRIPTION | 5 +---- vignettes/interpretation.Rmd | 16 +++++++++++----- 2 files changed, 12 insertions(+), 9 deletions(-) diff --git a/DESCRIPTION b/DESCRIPTION index 55de0f4..d227d0a 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -62,8 +62,7 @@ Suggests: workflows, xgboost, yardstick -VignetteBuilder: - quarto +VignetteBuilder: knitr Config/testthat/edition: 3 Config/testthat/parallel: false Config/testthat/start-first: interface, explain, params @@ -71,5 +70,3 @@ Encoding: UTF-8 Roxygen: list(markdown = TRUE) RoxygenNote: 7.3.2 Language: en-US -SystemRequirements: Quarto command line tool - (). diff --git a/vignettes/interpretation.Rmd b/vignettes/interpretation.Rmd index 215a946..ea172ba 100644 --- a/vignettes/interpretation.Rmd +++ b/vignettes/interpretation.Rmd @@ -105,13 +105,14 @@ Let's fit a TabNet model to the `syn2` dataset and analyze the interpretation metrics. ```{r} -fit_syn2 <- tabnet_fit(y ~ ., syn2, epochs = 10, verbose = TRUE, device = "cpu") +fit_syn2 <- tabnet_fit(y ~ ., syn2, epochs = 45, learn_rate = 0.06, verbose = TRUE, device = "cpu") ``` In the feature importance plot we can see that, as expected, features `V03-V06` are by far the most important ones. ```{r} +#| fig.alt: "A variable importance plot of the fitted model on syn2 dataset showing V03 then V06, V04, v10 and V5 as the 5 most important features, in that order." vip::vip(fit_syn2) ``` @@ -121,10 +122,11 @@ colors represent the importance of the feature in predicting the value for each observation. ```{r} +#| fig.alt: "A tabnet explaination plot of the fitted model on syn2 dataset. The plot shows numerous important observations in V03 then V06 and V04. the other variables are shown with low importance points or sparse observations with importance" library(tidyverse) ex_syn2 <- tabnet_explain(fit_syn2, syn2) -autoplot(ex_syn2) +autoplot(ex_syn2, quantile = 0.99) ``` We can see that the region between V03 and V06 concentrates most of the @@ -136,6 +138,7 @@ Next, we can visualize the attention masks for each step in the architecture. ```{r} +#| fig.alt: "3 tabnet explaination plots, one for each step of the fitted model on syn2 dataset. The Step 1 plot shows numerous important observations with V03 and V03 having high importance, step 2 plot highlight the importance of V03 and V06. Third step plot highlight V03 and V10 as important variables" autoplot(ex_syn2, type="steps") ``` @@ -152,7 +155,7 @@ create the response variable and we expect to see this in the masks. First we fit the model for 10 epochs. ```{r} -fit_syn4 <- tabnet_fit(y ~ ., syn4, epochs = 10, verbose = TRUE, device = "cpu") +fit_syn4 <- tabnet_fit(y ~ ., syn4, epochs = 50, verbose = TRUE, device = "cpu", learn_rate = 0.08) ``` In the feature importance plot we have, as expected, strong importance @@ -160,19 +163,21 @@ for `V10`, and the other features that are used conditionally - either `V01-V02` or `V05-V06`. ```{r} +#| fig.alt: "A variable importance plot of the fitted model on syn4 dataset" vip::vip(fit_syn4) ``` Now let's visualize the attention masks. Notice that we arranged the dataset by `V10` so we can easily visualize the interaction effects. -We also trimmed to the 99th percentile so the colors shows the +We also trimmed to the 98th percentile so the colors shows the importance even if there are strong outliers. ```{r} +#| fig.alt: "A tabnet explaination plot of the fitted model on syn4 dataset. The plot shows numerous important observations in V06 for low values of V10, and importance of V01 and V02 for high values of V10." ex_syn4 <- tabnet_explain(fit_syn4, arrange(syn4, V10)) -autoplot(ex_syn4, quantile=.995) +autoplot(ex_syn4, quantile=.98) ``` From the figure we see that V10 is important for all observations. We @@ -183,6 +188,7 @@ the important ones. We can also visualize the masks at each step in the architecture. ```{r} +#| fig.alt: "3 tabnet explaination plots, one for each step of the fitted model on syn4 dataset." autoplot(ex_syn4, type="steps", quantile=.995) ``` From cb75bc6561536e77305cc89fbf38a495487b9f25 Mon Sep 17 00:00:00 2001 From: "C. Regouby" Date: Mon, 25 Aug 2025 08:52:40 +0200 Subject: [PATCH 5/5] add readme alt-test --- README.Rmd | 4 ++++ README.md | 9 +++++---- man/figures/README-unnamed-chunk-2-1.png | Bin 20558 -> 0 bytes man/figures/README-unnamed-chunk-4-1.png | Bin 50470 -> 0 bytes man/figures/README-unnamed-chunk-5-1.png | Bin 50047 -> 0 bytes vignettes/interpretation.Rmd | 6 +++--- 6 files changed, 12 insertions(+), 7 deletions(-) delete mode 100644 man/figures/README-unnamed-chunk-2-1.png delete mode 100644 man/figures/README-unnamed-chunk-4-1.png delete mode 100644 man/figures/README-unnamed-chunk-5-1.png diff --git a/README.Rmd b/README.Rmd index bd89465..26a8e86 100644 --- a/README.Rmd +++ b/README.Rmd @@ -51,6 +51,7 @@ pak::pak("mlverse/tabnet") Here we show a **binary classification** example of the `attrition` dataset, using a **recipe** for dataset input specification. ```{r model-fit} +#| fig.alt: "A training loss line-plot along training epochs. Both validation loss and training loss are shown. Training loss line includes regular dots at epochs where a checkpoint is recorded." library(tabnet) suppressPackageStartupMessages(library(recipes)) library(yardstick) @@ -92,6 +93,7 @@ cbind(test, predict(fit, test, type = "prob")) %>% TabNet has intrinsic explainability feature through the visualization of attention map, either **aggregated**: ```{r model-explain} +#| fig.alt: "An expainability plot showing for each variable of the test-set on the y axis the importance along each observation on the x axis. The value is a mask agggregate." explain <- tabnet_explain(fit, test) autoplot(explain) ``` @@ -99,6 +101,7 @@ autoplot(explain) or at **each layer** through the `type = "steps"` option: ```{r step-explain} +#| fig.alt: "An small-multiple expainability plot for each step of the Tabnet network. Each plot shows for each variable of the test-set on the y axis the importance along each observation on the x axis." autoplot(explain, type = "steps") ``` @@ -107,6 +110,7 @@ autoplot(explain, type = "steps") For cases when a consistent part of your dataset has no outcome, TabNet offers a self-supervised training step allowing to model to capture predictors intrinsic features and predictors interactions, upfront the supervised task. ```{r step-pretrain} +#| fig.alt: "A training loss line-plot along pre-training epochs. Both validation loss and training loss are shown. Training loss line includes regular dots at epochs where a checkpoint is recorded." pretrain <- tabnet_pretrain(rec, train, epochs = 50, valid_split=0.1, learn_rate = 1e-2) autoplot(pretrain) ``` diff --git a/README.md b/README.md index 754a4f9..1c57068 100644 --- a/README.md +++ b/README.md @@ -19,6 +19,7 @@ status](https://www.r-pkg.org/badges/version/tabnet)](https://CRAN.R-project.org An R implementation of: [TabNet: Attentive Interpretable Tabular Learning](https://arxiv.org/abs/1908.07442) [(Sercan O. Arik, Tomas Pfister)](https://doi.org/10.48550/arXiv.1908.07442). + The code in this repository started by an R port using the [torch](https://github.com/mlverse/torch) package of [dreamquark-ai/tabnet](https://github.com/dreamquark-ai/tabnet) @@ -79,7 +80,7 @@ fit <- tabnet_fit(rec, train, epochs = 30, valid_split=0.1, learn_rate = 5e-3) autoplot(fit) ``` - +A training loss line-plot along training epochs. Both validation loss and training loss are shown. Training loss line includes regular dots at epochs where a checkpoint is recorded. The plots gives you an immediate insight about model over-fitting, and if any, the available model checkpoints available before the @@ -125,7 +126,7 @@ explain <- tabnet_explain(fit, test) autoplot(explain) ``` - +An expainability plot showing for each variable of the test-set on the y axis the importance along each observation on the x axis. The value is a mask agggregate. or at **each layer** through the `type = "steps"` option: @@ -133,7 +134,7 @@ or at **each layer** through the `type = "steps"` option: autoplot(explain, type = "steps") ``` - +An small-multiple expainability plot for each step of the Tabnet network. Each plot shows for each variable of the test-set on the y axis the importance along each observation on the x axis. ## Self-supervised pretraining @@ -147,7 +148,7 @@ pretrain <- tabnet_pretrain(rec, train, epochs = 50, valid_split=0.1, learn_rate autoplot(pretrain) ``` - +A training loss line-plot along pre-training epochs. Both validation loss and training loss are shown. Training loss line includes regular dots at epochs where a checkpoint is recorded. The example here is a toy example as the `train` dataset does actually contain outcomes. 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interpretation metrics. ```{r} -fit_syn2 <- tabnet_fit(y ~ ., syn2, epochs = 45, learn_rate = 0.06, verbose = TRUE, device = "cpu") +fit_syn2 <- tabnet_fit(y ~ ., syn2, epochs = 45, learn_rate = 0.06, device = "cpu") ``` In the feature importance plot we can see that, as expected, features @@ -138,7 +138,7 @@ Next, we can visualize the attention masks for each step in the architecture. ```{r} -#| fig.alt: "3 tabnet explaination plots, one for each step of the fitted model on syn2 dataset. The Step 1 plot shows numerous important observations with V03 and V03 having high importance, step 2 plot highlight the importance of V03 and V06. Third step plot highlight V03 and V10 as important variables" +#| fig.alt: "3 tabnet explaination plots, one for each step of the fitted model on syn2 dataset. The Step 1 plot shows numerous important observations with V02 and V03 having high importance, step 2 plot highlight the importance of V03 and V06. Third step plot highlight V03 and V10 as important variables" autoplot(ex_syn2, type="steps") ``` @@ -155,7 +155,7 @@ create the response variable and we expect to see this in the masks. First we fit the model for 10 epochs. ```{r} -fit_syn4 <- tabnet_fit(y ~ ., syn4, epochs = 50, verbose = TRUE, device = "cpu", learn_rate = 0.08) +fit_syn4 <- tabnet_fit(y ~ ., syn4, epochs = 50, device = "cpu", learn_rate = 0.08) ``` In the feature importance plot we have, as expected, strong importance