fix URLs, fix alt-text

Author: cregouby

README.Rmd

@@ -93,15 +93,15 @@ 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."
+#| fig.alt: "An heatmap as explainability 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)
 ```
 
 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."
+#| fig.alt: "An small-multiple heatmap as explainability 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")
 ```
 
@@ -115,25 +115,25 @@ pretrain <- tabnet_pretrain(rec, train, epochs = 50, valid_split=0.1, learn_rate
 autoplot(pretrain)
 ```
 
-The example here is a toy example as the `train` dataset does actually contain outcomes. The vignette [`vignette("selfsupervised_training")`](articles/selfsupervised_training.html) will gives you the complete correct workflow step-by-step.
+The example here is a toy example as the `train` dataset does actually contain outcomes. The vignette [`vignette("selfsupervised_training")`](https://mlverse.github.io/tabnet/articles/selfsupervised_training.html) will gives you the complete correct workflow step-by-step.
 
 ## {tidymodels} integration
 
 The integration within tidymodels workflows offers you unlimited opportunity to compare {tabnet} models with challengers.
 
-Don't miss the [`vignette("tidymodels-interface")`](articles/tidymodels-interface.html) for that.
+Don't miss the [`vignette("tidymodels-interface")`](https://mlverse.github.io/tabnet/articles/tidymodels-interface.html) for that.
 
 ## Missing data in predictors
 
 {tabnet} leverage the masking mechanism to deal with missing data, so you don't have to remove the entries in your dataset with some missing values in the predictors variables.
 
-See [`vignette("Missing_data_predictors")`](articles/Missing_data_predictors.html)
+See [`vignette("Missing_data_predictors")`](https://mlverse.github.io/tabnet/articles/Missing_data_predictors.html)
 
 ## Imbalanced binary classification
 
 {tabnet} includes a Area under the $Min(FPR,FNR)$ (AUM) loss function `nn_aum_loss()` dedicated to your imbalanced binary classification tasks.
 
-Try it out in [`vignette("aum_loss")`](articles/aum_loss.html)
+Try it out in [`vignette("aum_loss")`](https://mlverse.github.io/tabnet/articles/aum_loss.html)
 
 # Comparison with other implementations
 

README.md

@@ -126,15 +126,15 @@ explain <- tabnet_explain(fit, test)
 autoplot(explain)
 ```
 
-<img src="man/figures/README-model-explain-1.png" 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." width="100%" />
+<img src="man/figures/README-model-explain-1.png" alt="An heatmap as explainability 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." width="100%" />
 
 or at **each layer** through the `type = "steps"` option:
 
 ``` r
 autoplot(explain, type = "steps")
 ```
 
-<img src="man/figures/README-step-explain-1.png" 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." width="100%" />
+<img src="man/figures/README-step-explain-1.png" alt="An small-multiple heatmap as explainability 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." width="100%" />
 
 ## Self-supervised pretraining
 
@@ -152,7 +152,7 @@ autoplot(pretrain)
 
 The example here is a toy example as the `train` dataset does actually
 contain outcomes. The vignette
-[`vignette("selfsupervised_training")`](articles/selfsupervised_training.html)
+[`vignette("selfsupervised_training")`](https://mlverse.github.io/tabnet/articles/selfsupervised_training.html)
 will gives you the complete correct workflow step-by-step.
 
 ## {tidymodels} integration
@@ -161,7 +161,7 @@ The integration within tidymodels workflows offers you unlimited
 opportunity to compare {tabnet} models with challengers.
 
 Don’t miss the
-[`vignette("tidymodels-interface")`](articles/tidymodels-interface.html)
+[`vignette("tidymodels-interface")`](https://mlverse.github.io/tabnet/articles/tidymodels-interface.html)
 for that.
 
 ## Missing data in predictors
@@ -171,15 +171,16 @@ you don’t have to remove the entries in your dataset with some missing
 values in the predictors variables.
 
 See
-[`vignette("Missing_data_predictors")`](articles/Missing_data_predictors.html)
+[`vignette("Missing_data_predictors")`](https://mlverse.github.io/tabnet/articles/Missing_data_predictors.html)
 
 ## Imbalanced binary classification
 
 {tabnet} includes a Area under the $Min(FPR,FNR)$ (AUM) loss function
 `nn_aum_loss()` dedicated to your imbalanced binary classification
 tasks.
 
-Try it out in [`vignette("aum_loss")`](articles/aum_loss.html)
+Try it out in
+[`vignette("aum_loss")`](https://mlverse.github.io/tabnet/articles/aum_loss.html)
 
 # Comparison with other implementations