RC 1.4.0

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

@@ -67,11 +67,10 @@ jobs:
         with:
           python-version: 3.11
 
-      - name: Install TensorFlow
+      - name: Install TensorFlow/Keras
         run: |
-          reticulate::virtualenv_create('r-reticulate', python='3.11')
-          reticulate::use_virtualenv('r-reticulate')
-          tensorflow::install_tensorflow(version='2.16')
+          install.packages(c("keras3"))
+          keras3::install_keras()
         shell: Rscript {0}
 
       - uses: r-lib/actions/check-r-package@v2

.github/workflows/test-coverage.yaml

@@ -27,19 +27,10 @@ jobs:
           extra-packages: any::covr, any::xml2
           needs: coverage
 
-      - name: Install dev reticulate
-        run: pak::pkg_install('rstudio/reticulate')
-        shell: Rscript {0}
-
-      - name: Install Miniconda
-        run: |
-          reticulate::install_miniconda()
-        shell: Rscript {0}
-
-      - name: Install TensorFlow
+      - name: Install TensorFlow/Keras
         run: |
-          reticulate::conda_create('r-reticulate', packages = c('python==3.11'))
-          tensorflow::install_tensorflow(version='2.16')
+          install.packages(c("keras3"))
+          keras3::install_keras()
         shell: Rscript {0}
 
       - name: Test coverage

DESCRIPTION

@@ -1,8 +1,9 @@
 Package: parsnip
 Title: A Common API to Modeling and Analysis Functions
-Version: 1.3.3.9001
+Version: 1.4.0
 Authors@R: c(
-    person("Max", "Kuhn", , "[email protected]", role = c("aut", "cre")),
+    person("Max", "Kuhn", , "[email protected]", role = c("cre", "aut"),
+           comment = c(ORCID = "0000-0003-2402-136X")),
     person("Davis", "Vaughan", , "[email protected]", role = "aut"),
     person("Emil", "Hvitfeldt", , "[email protected]", role = "ctb"),
     person("Posit Software, PBC", role = c("cph", "fnd"),

NEWS.md

@@ -1,4 +1,4 @@
-# parsnip (development version)
+# parsnip 1.4.0
 
 * Fixes issue with running predictions for Decision Trees in Spark (#1309)
 

inst/add-in/parsnip_model_db.R

@@ -9,28 +9,66 @@ library(usethis)
 # also requires installation of:
 packages <- c(
   "parsnip",
-  "discrim",
-  "plsmod",
-  "rules",
-  "baguette",
-  "poissonreg",
-  "multilevelmod",
-  "modeltime",
-  "modeltime.gluonts"
+  parsnip:::extensions(),
+  "modeltime"
+  # "modeltime.gluonts" # required python packages to create spec
 )
 
+loaded <- map(packages, library, character.only = TRUE)
+
 # ------------------------------------------------------------------------------
 
-# Detects model specifications via their print methods
-print_methods <- function(x) {
-  require(x, character.only = TRUE)
-  ns <- asNamespace(ns = x)
-  mthds <- ls(envir = ns, pattern = "^print\\.")
-  mthds <- gsub("^print\\.", "", mthds)
-  purrr::map(mthds, get_engines) |>
+get_model <- function(x) {
+  res <- get_from_env(x)
+  if (!is.null(res)) {
+    res <- dplyr::mutate(res, model = x)
+  }
+  res
+}
+
+get_packages <- function(x) {
+  res <- get_from_env(paste0(x, "_pkgs"))
+  if (is.null(res)) {
+    return(res)
+  }
+  res <-
+    res |>
+    tidyr::unnest(pkg) |>
+    dplyr::mutate(
+      model = x
+    )
+
+  res
+}
+
+get_models <- function() {
+  res <- ls(envir = get_model_env(), pattern = "_fit$")
+  models <- gsub("_fit$", "", res)
+  models <-
+    purrr::map(models, get_model) |>
+    purrr::list_rbind()
+
+  # get source package
+  pkgs <- gsub("_fit$", "_pkgs", res)
+  pkgs <-
+    unique(models$model) |>
+    purrr::map(get_packages) |>
     purrr::list_rbind() |>
-    dplyr::mutate(package = x)
+    dplyr::filter(pkg %in% packages)
+  dplyr::left_join(models, pkgs, by = dplyr::join_by(engine, mode, model)) |>
+    dplyr::rename(package = pkg) |>
+    dplyr::mutate(
+      package = dplyr::if_else(is.na(package), "parsnip", package),
+      call_from_parsnip = package %in% parsnip:::extensions(),
+      caller_package = dplyr::if_else(
+        call_from_parsnip,
+        "parsnip",
+        package
+      )
+    )
 }
+
+
 get_engines <- function(x) {
   eng <- try(parsnip::show_engines(x), silent = TRUE)
   if (inherits(eng, "try-error")) {
@@ -77,8 +115,8 @@ get_tunable_param <- function(mode, package, model, engine) {
 # ------------------------------------------------------------------------------
 
 model_db <-
-  purrr::map(packages, print_methods) |>
-  purrr::list_rbind() |>
+  get_models() |>
+  dplyr::filter(mode %in% c("regression", "classification")) |>
   dplyr::filter(engine != "liquidSVM") |>
   dplyr::filter(model != "surv_reg") |>
   dplyr::filter(engine != "spark") |>
@@ -98,9 +136,10 @@ model_db <-
   dplyr::left_join(model_db, num_modes, by = c("package", "model", "engine")) |>
   dplyr::mutate(
     parameters = purrr::pmap(
-      list(mode, package, model, engine),
+      list(mode, caller_package, model, engine),
       get_tunable_param
     )
-  )
+  ) |>
+  dplyr::select(-call_from_parsnip, -caller_package)
 
 usethis::use_data(model_db, overwrite = TRUE)

inst/models.tsv

@@ -14,11 +14,13 @@
 "bart"	"regression"	"dbarts"	NA
 "boost_tree"	"censored regression"	"mboost"	"censored"
 "boost_tree"	"classification"	"C5.0"	NA
+"boost_tree"	"classification"	"catboost"	"bonsai"
 "boost_tree"	"classification"	"h2o"	"agua"
 "boost_tree"	"classification"	"h2o_gbm"	"agua"
 "boost_tree"	"classification"	"lightgbm"	"bonsai"
 "boost_tree"	"classification"	"spark"	NA
 "boost_tree"	"classification"	"xgboost"	NA
+"boost_tree"	"regression"	"catboost"	"bonsai"
 "boost_tree"	"regression"	"h2o"	"agua"
 "boost_tree"	"regression"	"h2o_gbm"	"agua"
 "boost_tree"	"regression"	"lightgbm"	"bonsai"

man/rmd/decision_tree_partykit.md

@@ -1,18 +1,18 @@
 
 
 
-For this engine, there are multiple modes: regression, classification, and censored regression
+For this engine, there are multiple modes: censored regression, regression, and classification
 
 ## Tuning Parameters
 
 
 
 This model has 2 tuning parameters:
 
-- `min_n`: Minimal Node Size (type: integer, default: 20L)
-
 - `tree_depth`: Tree Depth (type: integer, default: see below)
 
+- `min_n`: Minimal Node Size (type: integer, default: 20L)
+
 The `tree_depth` parameter defaults to `0` which means no restrictions are applied to tree depth.
 
 An engine-specific parameter for this model is: 
@@ -128,11 +128,11 @@ parsnip:::get_from_env("decision_tree_predict") |>
 ## # A tibble: 5 x 2
 ##   mode                type    
 ##   <chr>               <chr>   
-## 1 regression          numeric 
-## 2 classification      class   
-## 3 classification      prob    
-## 4 censored regression time    
-## 5 censored regression survival
+## 1 censored regression time    
+## 2 censored regression survival
+## 3 regression          numeric 
+## 4 classification      class   
+## 5 classification      prob
 ```
 
 ## Other details

man/rmd/rand_forest_aorsf.Rmd

@@ -96,7 +96,8 @@ rand_forest() |>
 
 parsnip:::get_from_env("rand_forest_predict") |>
   dplyr::filter(engine == "aorsf") |>
-  dplyr::select(mode, type)
+  dplyr::select(mode, type)|> 
+  print(n = Inf)
 
 ```