Merged origin/main into main

Author: EmilHvitfeldt

.github/workflows/R-CMD-check.yaml

@@ -59,20 +59,19 @@ jobs:
           extra-packages: any::rcmdcheck
           needs: check
 
-      - name: Install reticulate
-        run: pak::pkg_install('reticulate')
+      - name: Install dev reticulate
+        run: pak::pkg_install('rstudio/reticulate')
         shell: Rscript {0}
 
-      - name: Install Miniconda
-        # conda can fail at downgrading python, so we specify python version in advance
-        env:
-          RETICULATE_MINICONDA_PYTHON_VERSION: "3.8"
-        run: reticulate::install_miniconda() # creates r-reticulate conda env by default
-        shell: Rscript {0}
+      - uses: actions/setup-python@v4
+        with:
+          python-version: 3.11
 
       - name: Install TensorFlow
         run: |
-          tensorflow::install_tensorflow(version='2.13', conda_python_version = NULL)
+          reticulate::virtualenv_create('r-reticulate', python='3.11')
+          reticulate::use_virtualenv('r-reticulate')
+          tensorflow::install_tensorflow(version='2.16')
         shell: Rscript {0}
 
       - uses: r-lib/actions/check-r-package@v2

.github/workflows/pkgdown.yaml

@@ -37,15 +37,14 @@ jobs:
           needs: website
 
       - name: Install Miniconda
-        # conda can fail at downgrading python, so we specify python version in advance
-        env:
-          RETICULATE_MINICONDA_PYTHON_VERSION: "3.8"
-        run: reticulate::install_miniconda() # creates r-reticulate conda env by default
+        run: |
+          reticulate::install_miniconda()
         shell: Rscript {0}
-
+ 
       - name: Install TensorFlow
         run: |
-          tensorflow::install_tensorflow(version='2.13', conda_python_version = NULL)
+          reticulate::conda_create('r-reticulate', packages = c('python==3.11'))
+          tensorflow::install_tensorflow(version='2.16')
         shell: Rscript {0}
 
       - name: Install Torch

.github/workflows/test-coverage.yaml

@@ -32,15 +32,14 @@ jobs:
         shell: Rscript {0}
 
       - name: Install Miniconda
-        # conda can fail at downgrading python, so we specify python version in advance
-        env:
-          RETICULATE_MINICONDA_PYTHON_VERSION: "3.8"
-        run: reticulate::install_miniconda() # creates r-reticulate conda env by default
+        run: |
+          reticulate::install_miniconda()
         shell: Rscript {0}
-
+ 
       - name: Install TensorFlow
         run: |
-          tensorflow::install_tensorflow(version='2.13', conda_python_version = NULL)
+          reticulate::conda_create('r-reticulate', packages = c('python==3.11'))
+          tensorflow::install_tensorflow(version='2.16')
         shell: Rscript {0}
 
       - name: Test coverage

_pkgdown.yml

@@ -2,7 +2,7 @@ url: https://parsnip.tidymodels.org/
 
 navbar:
   components:
-    home: ~
+    home:
     tutorials:
       text: Learn more
       menu:
@@ -20,8 +20,9 @@ template:
     primary: "#CA225E"
 
   includes:
-      in_header: |
-        <script defer data-domain="parsnip.tidymodels.org,all.tidymodels.org" src="https://plausible.io/js/plausible.js"></script>
+    in_header: |
+      <script src="https://cdn.jsdelivr.net/gh/posit-dev/supported-by-posit/js/badge.min.js" data-max-height="43" data-hide-below="1200" data-light-bg="#666f76" data-light-fg="#f9f9f9"></script>
+      <script defer data-domain="parsnip.tidymodels.org,all.tidymodels.org" src="https://plausible.io/js/plausible.js"></script>
 
 development:
   mode: auto
@@ -31,84 +32,84 @@ figures:
   fig.height: 5.75
 
 reference:
-  - title: Models
-    contents:
-      - auto_ml
-      - bag_mars
-      - bag_mlp
-      - bag_tree
-      - bart
-      - boost_tree
-      - cubist_rules
-      - C5_rules
-      - decision_tree
-      - discrim_flexible
-      - discrim_linear
-      - discrim_quad
-      - discrim_regularized
-      - gen_additive_mod
-      - glm_grouped
-      - linear_reg
-      - logistic_reg
-      - mars
-      - mlp
-      - multinom_reg
-      - naive_Bayes
-      - nearest_neighbor
-      - null_model
-      - pls
-      - poisson_reg
-      - proportional_hazards
-      - rand_forest
-      - rule_fit
-      - survival_reg
-      - svm_linear
-      - svm_poly
-      - svm_rbf
-  - title: Infrastructure
-    contents:
-      - autoplot.model_fit
-      - add_rowindex
-      - augment.model_fit
-      - case_weights
-      - case_weights_allowed
-      - descriptors
-      - extract-parsnip
-      - fit.model_spec
-      - fit_xy
-      - control_parsnip
-      - glance.model_fit
-      - matrix_to_quantile_pred
-      - model_fit
-      - model_formula
-      - model_spec
-      - multi_predict
-      - parsnip_addin
-      - predict.model_fit
-      - reexports
-      - repair_call
-      - set_args
-      - set_engine
-      - set_mode
-      - show_engines
-      - sparse_data
-      - tidy.model_fit
-      - translate
-      - starts_with("update")
-      - matches("_train")
+- title: Models
+  contents:
+  - auto_ml
+  - bag_mars
+  - bag_mlp
+  - bag_tree
+  - bart
+  - boost_tree
+  - cubist_rules
+  - C5_rules
+  - decision_tree
+  - discrim_flexible
+  - discrim_linear
+  - discrim_quad
+  - discrim_regularized
+  - gen_additive_mod
+  - glm_grouped
+  - linear_reg
+  - logistic_reg
+  - mars
+  - mlp
+  - multinom_reg
+  - naive_Bayes
+  - nearest_neighbor
+  - null_model
+  - pls
+  - poisson_reg
+  - proportional_hazards
+  - rand_forest
+  - rule_fit
+  - survival_reg
+  - svm_linear
+  - svm_poly
+  - svm_rbf
+- title: Infrastructure
+  contents:
+  - autoplot.model_fit
+  - add_rowindex
+  - augment.model_fit
+  - case_weights
+  - case_weights_allowed
+  - descriptors
+  - extract-parsnip
+  - fit.model_spec
+  - fit_xy
+  - control_parsnip
+  - glance.model_fit
+  - matrix_to_quantile_pred
+  - model_fit
+  - model_formula
+  - model_spec
+  - multi_predict
+  - parsnip_addin
+  - predict.model_fit
+  - reexports
+  - repair_call
+  - set_args
+  - set_engine
+  - set_mode
+  - show_engines
+  - sparse_data
+  - tidy.model_fit
+  - translate
+  - starts_with("update")
+  - matches("_train")
 
-  - title: Developer tools
-    contents:
-      - condense_control
-      - contr_one_hot
-      - set_new_model
-      - maybe_matrix
-      - min_cols
-      - max_mtry_formula
-      - required_pkgs
-      - required_pkgs.model_spec
-      - req_pkgs
-      - .extract_surv_status
-      - .extract_surv_time
-      - .model_param_name_key
-      - .get_prediction_column_names
+- title: Developer tools
+  contents:
+  - condense_control
+  - contr_one_hot
+  - set_new_model
+  - maybe_matrix
+  - min_cols
+  - max_mtry_formula
+  - required_pkgs
+  - required_pkgs.model_spec
+  - req_pkgs
+  - .extract_surv_status
+  - .extract_surv_time
+  - .model_param_name_key
+  - .get_prediction_column_names

vignettes/articles/Examples.Rmd

@@ -553,6 +553,7 @@ The following examples use consistent data sets throughout. For regression, we u
   Now we create the model fit object:
 
   ```{r}
+  #| eval: false
   set.seed(1)
   linreg_reg_fit <- linreg_reg_spec |> fit(ridership ~ ., data = Chicago_train)
   linreg_reg_fit
@@ -561,6 +562,7 @@ The following examples use consistent data sets throughout. For regression, we u
   The holdout data can be predicted:
 
   ```{r}
+  #| eval: false
   predict(linreg_reg_fit, Chicago_test)
   ```
 
@@ -791,6 +793,7 @@ The following examples use consistent data sets throughout. For regression, we u
   Now we create the model fit object:
 
   ```{r}
+  #| eval: false
   set.seed(1)
   logreg_cls_fit <- logreg_cls_spec |> fit(Class ~ ., data = data_train)
   logreg_cls_fit
@@ -799,6 +802,7 @@ The following examples use consistent data sets throughout. For regression, we u
   The holdout data can be predicted for both hard class predictions and probabilities. We'll bind these together into one tibble:
 
   ```{r}
+  #| eval: false
   bind_cols(
     predict(logreg_cls_fit, data_test),
     predict(logreg_cls_fit, data_test, type = "prob")
@@ -1103,6 +1107,7 @@ The following examples use consistent data sets throughout. For regression, we u
   Now we create the model fit object:
 
   ```{r}
+  #| eval: false
   set.seed(1)
   mlp_reg_fit <- mlp_reg_spec |> fit(ridership ~ ., data = Chicago_train)
   mlp_reg_fit
@@ -1111,6 +1116,7 @@ The following examples use consistent data sets throughout. For regression, we u
   The holdout data can be predicted:
 
   ```{r}
+  #| eval: false
   predict(mlp_reg_fit, Chicago_test)
   ```
 
@@ -1136,6 +1142,7 @@ The following examples use consistent data sets throughout. For regression, we u
   Now we create the model fit object:
 
   ```{r}
+  #| eval: false
   set.seed(1)
   mlp_cls_fit <- mlp_cls_spec |> fit(Class ~ ., data = data_train)
   mlp_cls_fit
@@ -1144,6 +1151,7 @@ The following examples use consistent data sets throughout. For regression, we u
   The holdout data can be predicted for both hard class predictions and probabilities. We'll bind these together into one tibble:
 
   ```{r}
+  #| eval: false
   bind_cols(
     predict(mlp_cls_fit, data_test),
     predict(mlp_cls_fit, data_test, type = "prob")
@@ -1365,6 +1373,7 @@ The following examples use consistent data sets throughout. For regression, we u
   Now we create the model fit object:
 
   ```{r}
+  #| eval: false
   set.seed(1)
   mr_cls_fit <- mr_cls_spec |> fit(island ~ ., data = penguins_train)
   mr_cls_fit
@@ -1373,6 +1382,7 @@ The following examples use consistent data sets throughout. For regression, we u
   The holdout data can be predicted for both hard class predictions and probabilities. We'll bind these together into one tibble:
 
   ```{r}
+  #| eval: false
   bind_cols(
     predict(mr_cls_fit, penguins_test),
     predict(mr_cls_fit, penguins_test, type = "prob")