diff --git a/articles/Hierarchical_classification.html b/articles/Hierarchical_classification.html index ecd314c9..a0d3e60e 100644 --- a/articles/Hierarchical_classification.html +++ b/articles/Hierarchical_classification.html @@ -400,8 +400,8 @@

Model inference#> .pred_class_level_2 .pred_class_level_3 level_2 level_3 #> <fct> <fct> <fct> <fct> #> 1 Human male Kel Dor male -#> 2 Human male Aleena male -#> 3 Hutt male Togruta female +#> 2 Kel Dor male Aleena male +#> 3 Human male Togruta female #> 4 Human male Togruta female #> 5 Rodian male Kaleesh male

We can see in the Warnings that the dataset is a challenge as many diff --git a/articles/Hierarchical_classification_files/figure-html/unnamed-chunk-10-1.png b/articles/Hierarchical_classification_files/figure-html/unnamed-chunk-10-1.png index 6855cdc7..b0c6bc1f 100644 Binary files a/articles/Hierarchical_classification_files/figure-html/unnamed-chunk-10-1.png and b/articles/Hierarchical_classification_files/figure-html/unnamed-chunk-10-1.png differ diff --git a/articles/Hierarchical_classification_files/figure-html/unnamed-chunk-10-2.png b/articles/Hierarchical_classification_files/figure-html/unnamed-chunk-10-2.png index f8277da1..543414ab 100644 Binary files a/articles/Hierarchical_classification_files/figure-html/unnamed-chunk-10-2.png and b/articles/Hierarchical_classification_files/figure-html/unnamed-chunk-10-2.png differ diff --git a/articles/Hierarchical_classification_files/figure-html/unnamed-chunk-7-1.png b/articles/Hierarchical_classification_files/figure-html/unnamed-chunk-7-1.png index 6e560ea1..ad41bf12 100644 Binary files a/articles/Hierarchical_classification_files/figure-html/unnamed-chunk-7-1.png and b/articles/Hierarchical_classification_files/figure-html/unnamed-chunk-7-1.png differ diff --git a/articles/Hierarchical_classification_files/figure-html/unnamed-chunk-8-1.png b/articles/Hierarchical_classification_files/figure-html/unnamed-chunk-8-1.png index 1ace1092..7471f43c 100644 Binary files a/articles/Hierarchical_classification_files/figure-html/unnamed-chunk-8-1.png and b/articles/Hierarchical_classification_files/figure-html/unnamed-chunk-8-1.png differ diff --git a/articles/Missing_data_predictors.html b/articles/Missing_data_predictors.html index f6facc50..fe40f927 100644 --- a/articles/Missing_data_predictors.html +++ b/articles/Missing_data_predictors.html @@ -95,7 +95,7 @@

Training a Tabnet model from missing-values

Christophe Regouby

-

2023-09-27

+

2023-10-18

Source: vignettes/Missing_data_predictors.Rmd diff --git a/articles/interpretation.html b/articles/interpretation.html index 41afa1a0..79db6bc8 100644 --- a/articles/interpretation.html +++ b/articles/interpretation.html @@ -106,8 +106,8 @@

Interpretation tools

#> ── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ── #> ✔ dplyr 1.1.3 ✔ readr 2.1.4 #> ✔ forcats 1.0.0 ✔ stringr 1.5.0 -#> ✔ ggplot2 3.4.3 ✔ tibble 3.2.1 -#> ✔ lubridate 1.9.2 ✔ tidyr 1.3.0 +#> ✔ ggplot2 3.4.4 ✔ tibble 3.2.1 +#> ✔ lubridate 1.9.3 ✔ tidyr 1.3.0 #> ✔ purrr 1.0.2 #> ── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ── #> ✖ dplyr::filter() masks stats::filter() @@ -190,12 +190,12 @@

Syn2 #> [Epoch 002] Loss: 0.784014 #> [Epoch 003] Loss: 0.676509 #> [Epoch 004] Loss: 0.639690 -#> [Epoch 005] Loss: 0.611586 -#> [Epoch 006] Loss: 0.585089 -#> [Epoch 007] Loss: 0.552268 -#> [Epoch 008] Loss: 0.517715 -#> [Epoch 009] Loss: 0.490631 -#> [Epoch 010] Loss: 0.463379 +#> [Epoch 005] Loss: 0.611584 +#> [Epoch 006] Loss: 0.585045 +#> [Epoch 007] Loss: 0.549735 +#> [Epoch 008] Loss: 0.513075 +#> [Epoch 009] Loss: 0.484156 +#> [Epoch 010] Loss: 0.461366

In the feature importance plot we can see that, as expected, features V03-V06 are by far the most important ones.

@@ -236,14 +236,14 @@ 
Syn 4
 fit_syn4 <- tabnet_fit(y ~ ., syn4, epochs = 10, verbose = TRUE)
 #> [Epoch 001] Loss: 1.289555
 #> [Epoch 002] Loss: 0.929622
-#> [Epoch 003] Loss: 0.745959
-#> [Epoch 004] Loss: 0.685981
-#> [Epoch 005] Loss: 0.661809
-#> [Epoch 006] Loss: 0.646003
-#> [Epoch 007] Loss: 0.632528
-#> [Epoch 008] Loss: 0.615357
-#> [Epoch 009] Loss: 0.599160
-#> [Epoch 010] Loss: 0.583839
+#> [Epoch 003] Loss: 0.745960 +#> [Epoch 004] Loss: 0.685982 +#> [Epoch 005] Loss: 0.661789 +#> [Epoch 006] Loss: 0.645513 +#> [Epoch 007] Loss: 0.633089 +#> [Epoch 008] Loss: 0.616770 +#> [Epoch 009] Loss: 0.602258 +#> [Epoch 010] Loss: 0.589325

In the feature importance plot we have, as expected, strong importance for V10, and the other features that are used conditionally - either V01-V02 or V05-V06.

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Examples

x <- ames[ids,-which(names(ames) %in% c("Sale_Price", "Pool_Area"))] y <- ames[ids, c("Sale_Price", "Pool_Area")] ames_fit <- tabnet_fit(x, y, epochs = 5, verbose=TRUE) -#> [Epoch 001] Loss: 19040073728.000000 -#> [Epoch 002] Loss: 19040014336.000000 -#> [Epoch 003] Loss: 19039924224.000000 -#> [Epoch 004] Loss: 19039889408.000000 -#> [Epoch 005] Loss: 19039801344.000000 +#> [Epoch 001] Loss: 21255729152.000000 +#> [Epoch 002] Loss: 21255641088.000000 +#> [Epoch 003] Loss: 21255581696.000000 +#> [Epoch 004] Loss: 21255522304.000000 +#> [Epoch 005] Loss: 21255479296.000000 ames_explain <- tabnet_explain(ames_fit, x) autoplot(ames_explain, quantile = 0.99) diff --git a/reference/autoplot.tabnet_fit-1.png b/reference/autoplot.tabnet_fit-1.png index f86f1c2d..88914a94 100644 Binary files a/reference/autoplot.tabnet_fit-1.png and b/reference/autoplot.tabnet_fit-1.png differ diff --git a/reference/tabnet_fit.html b/reference/tabnet_fit.html index 2ecf6098..548637da 100644 --- a/reference/tabnet_fit.html +++ b/reference/tabnet_fit.html @@ -244,7 +244,7 @@

Examples

data(acme, package = "data.tree") acme_fit <- tabnet_fit(acme, epochs = 2, verbose = TRUE) -#> [Epoch 001] Loss: 2.754613 +#> [Epoch 001] Loss: 2.754614 #> [Epoch 002] Loss: 1.907744 # Note: Dataset number of rows and model number of epochs should be increased