RC 1.3.0 (#1253)

* overdue examples for brulee

* namespace to anchor links

* polish news

* update model file

* redoc

* small updates

* doc update

* install torch

Author: topepo

.github/workflows/pkgdown.yaml

@@ -49,6 +49,11 @@ jobs:
           tensorflow::install_tensorflow(version='2.13', conda_python_version = NULL)
         shell: Rscript {0}
 
+      - name: Install Torch
+        run: |
+          torch::install_torch()
+        shell: Rscript {0}
+
       - name: Build site
         run: pkgdown::build_site_github_pages(new_process = FALSE, install = FALSE)
         shell: Rscript {0}

DESCRIPTION

@@ -1,11 +1,11 @@
 Package: parsnip
 Title: A Common API to Modeling and Analysis Functions
-Version: 1.2.1.9004
+Version: 1.3.0
 Authors@R: c(
     person("Max", "Kuhn", , "[email protected]", role = c("aut", "cre")),
     person("Davis", "Vaughan", , "[email protected]", role = "aut"),
     person("Emil", "Hvitfeldt", , "[email protected]", role = "ctb"),
-    person("Posit Software, PBC", role = c("cph", "fnd"), comment = c(ROR = "03wc8by49"))
+    person("Posit Software, PBC", role = c("cph", "fnd"))
   )
 Maintainer: Max Kuhn <[email protected]>
 Description: A common interface is provided to allow users to specify a
@@ -70,10 +70,10 @@ Suggests:
 VignetteBuilder: 
     knitr
 ByteCompile: true
-Config/Needs/website: C50, dbarts, earth, glmnet, keras, kernlab, kknn,
-    LiblineaR, mgcv, nnet, parsnip, quantreg, randomForest, ranger, rpart, 
-    rstanarm, tidymodels/tidymodels, tidyverse/tidytemplate, rstudio/reticulate,
-    xgboost, rmarkdown
+Config/Needs/website: brulee, C50, dbarts, earth, glmnet, keras, kernlab,
+    kknn, LiblineaR, mgcv, nnet, parsnip, quantreg, randomForest, ranger,
+    rpart, rstanarm, tidymodels/tidymodels, tidyverse/tidytemplate,
+    rstudio/reticulate, xgboost, rmarkdown
 Config/rcmdcheck/ignore-inconsequential-notes: true
 Config/testthat/edition: 3
 Encoding: UTF-8

NEWS.md

@@ -1,4 +1,4 @@
-# parsnip (development version)
+# parsnip 1.3.0
 
 ## New Features
 
@@ -7,16 +7,17 @@
   * Predictions are encoded via a custom vector type. See [hardhat::quantile_pred()].
   * Predicted quantile levels are designated when the new mode is specified. See `?set_mode`.
 
-* `fit_xy()` can now take dgCMatrix input for `x` argument (#1121).
-
-* `fit_xy()` can now take sparse tibbles as data values (#1165).
-
-* `predict()` can now take dgCMatrix and sparse tibble input for `new_data` argument, and error informatively when model doesn't support it (#1167).
+* Updates for sparse data formats:  
+  * `fit_xy()` can now take dgCMatrix input for `x` argument (#1121).
+  * `fit_xy()` can now take sparse tibbles as data values (#1165).
+  * `predict()` can now take dgCMatrix and sparse tibble input for `new_data` argument, and error informatively when model doesn't support it (#1167).
 
 * New `extract_fit_time()` method has been added that returns the time it took to train the model (#853).
 
 * `mlp()` with `keras` engine now work for all activation functions currently supported by `keras` (#1127).
 
+* `mlp()` now has a  `brulee_two_layer` engine.
+
 ## Other Changes
 
 * Transitioned package errors and warnings to use cli (#1147 and #1148 by @shum461, #1153 by @RobLBaker and @wright13, #1154 by @JamesHWade, #1160, #1161, #1081).
@@ -49,7 +50,6 @@
 
 * `NULL` is no longer accepted as an engine (#1242).
 
-
 # parsnip 1.2.1
 
 * Added a missing `tidy()` method for survival analysis glmnet models (#1086).

inst/models.tsv

@@ -44,6 +44,7 @@
 "discrim_regularized"	"classification"	"klaR"	"discrim"
 "gen_additive_mod"	"classification"	"mgcv"	NA
 "gen_additive_mod"	"regression"	"mgcv"	NA
+"linear_reg"	"quantile regression"	"quantreg"	NA
 "linear_reg"	"regression"	"brulee"	NA
 "linear_reg"	"regression"	"gee"	"multilevelmod"
 "linear_reg"	"regression"	"glm"	NA
@@ -55,7 +56,6 @@
 "linear_reg"	"regression"	"lm"	NA
 "linear_reg"	"regression"	"lme"	"multilevelmod"
 "linear_reg"	"regression"	"lmer"	"multilevelmod"
-"linear_reg"	"quantile regression"	"quantreg"	NA
 "linear_reg"	"regression"	"spark"	NA
 "linear_reg"	"regression"	"stan"	NA
 "linear_reg"	"regression"	"stan_glmer"	"multilevelmod"

man/details_boost_tree_lightgbm.Rd

@@ -133,6 +133,11 @@ To effectively enable bagging, the user would also need to set the `bagging_freq
 
 bonsai quiets much of the logging output from [lightgbm::lgb.train()] by default. With default settings, logged warnings and errors will still be passed on to the user. To print out all logs during training, set `quiet = TRUE`.
 
+## Sparse Data
+
+
+This model can utilize sparse data during model fitting and prediction. Both sparse matrices such as dgCMatrix from the `Matrix` package and sparse tibbles from the `sparsevctrs` package are supported. See [sparse_data] for more information.
+
 ## Examples 
 
 The "Introduction to bonsai" article contains [examples](https://bonsai.tidymodels.org/articles/bonsai.html) of `boost_tree()` with the `"lightgbm"` engine.

man/details_decision_tree_partykit.Rd

@@ -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: 

man/details_linear_reg_lme.Rd

@@ -39,7 +39,7 @@ This model can use subject-specific coefficient estimates to make predictions (i
 \eta_{i} = (\beta_0 + b_{0i}) + \beta_1x_{i1}
 ```
 
-where $i$ denotes the `i`th independent experimental unit (e.g. subject). When the model has seen subject `i`, it can use that subject's data to adjust the _population_ intercept to be more specific to that subjects results. 
+where `i` denotes the `i`th independent experimental unit (e.g. subject). When the model has seen subject `i`, it can use that subject's data to adjust the _population_ intercept to be more specific to that subjects results. 
 
 What happens when data are being predicted for a subject that was not used in the model fit? In that case, this package uses _only_ the population parameter estimates for prediction: 
 

man/details_rand_forest_aorsf.Rd

@@ -1,20 +1,20 @@
 
 
 
-For this engine, there are multiple modes: classification, regression, and censored regression
+For this engine, there are multiple modes: censored regression, classification, and regression
 
 ## Tuning Parameters
 
 
 
 This model has 3 tuning parameters:
 
-- `mtry`: # Randomly Selected Predictors (type: integer, default: ceiling(sqrt(n_predictors)))
-
 - `trees`: # Trees (type: integer, default: 500L)
 
 - `min_n`: Minimal Node Size (type: integer, default: 5L)
 
+- `mtry`: # Randomly Selected Predictors (type: integer, default: ceiling(sqrt(n_predictors)))
+
 Additionally, this model has one engine-specific tuning parameter:
 
  * `split_min_stat`: Minimum test statistic required to split a node. Defaults are `3.841459` for censored regression (which is roughly a p-value of 0.05) and `0` for classification and regression. For classification, this tuning parameter should be between 0 and 1, and for regression it should be greater than or equal to 0. Higher values of this parameter cause trees grown by `aorsf` to have less depth.

man/details_rand_forest_partykit.Rd

@@ -1,20 +1,20 @@
 
 
 
-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 3 tuning parameters:
 
+- `trees`: # Trees (type: integer, default: 500L)
+
 - `min_n`: Minimal Node Size (type: integer, default: 20L)
 
 - `mtry`: # Randomly Selected Predictors (type: integer, default: 5L)
 
-- `trees`: # Trees (type: integer, default: 500L)
-
 ## Translation from parsnip to the original package (regression)
 
 The **bonsai** extension package is required to fit this model.

man/details_rand_forest_ranger.Rd

@@ -108,6 +108,8 @@ The `fit()` and `fit_xy()` arguments have arguments called `case_weights` that e
 
 This model can utilize sparse data during model fitting and prediction. Both sparse matrices such as dgCMatrix from the `Matrix` package and sparse tibbles from the `sparsevctrs` package are supported. See [sparse_data] for more information.
 
+While this engine supports sparse data as an input, it doesn't use it any differently than dense data. Hence there it no reason to convert back and forth.
+
 ## Saving fitted model objects
 
 

man/details_survival_reg_flexsurv.Rd

@@ -40,7 +40,7 @@ survival_reg(dist = character(1)) %>%
 
 The main interface for this model uses the formula method since the model specification typically involved the use of [survival::Surv()]. 
 
-For this engine, stratification cannot be specified via [`strata()`], please see [flexsurv::flexsurvreg()] for alternative specifications.
+For this engine, stratification cannot be specified via [`survival::strata()`], please see [flexsurv::flexsurvreg()] for alternative specifications.
 
 ```{r child = "template-survival-mean.Rmd"}
 ```

man/details_survival_reg_flexsurvspline.Rd

@@ -42,7 +42,7 @@ survival_reg(dist = character(1)) %>%
 
 The main interface for this model uses the formula method since the model specification typically involved the use of [survival::Surv()]. 
 
-For this engine, stratification cannot be specified via [`strata()`], please see [flexsurv::flexsurvreg()] for alternative specifications.
+For this engine, stratification cannot be specified via [`survival::strata()`], please see [flexsurv::flexsurvreg()] for alternative specifications.
 
 
 

man/parsnip-package.Rd

@@ -26,7 +26,7 @@ survival_reg() %>%
 
 The main interface for this model uses the formula method since the model specification typically involved the use of [survival::Surv()]. 
 
-For this engine, stratification cannot be specified via [`strata()`], please see [flexsurv::flexsurvspline()] for alternative specifications.
+For this engine, stratification cannot be specified via [`survival::strata()`], please see [flexsurv::flexsurvspline()] for alternative specifications.
 
 ```{r child = "template-survival-mean.Rmd"}
 ```

man/rmd/boost_tree_lightgbm.md

@@ -37,7 +37,7 @@ survival_reg() %>%
 
 The main interface for this model uses the formula method since the model specification typically involved the use of [survival::Surv()]. 
 
-For this engine, stratification cannot be specified via [`strata()`], please see [flexsurv::flexsurvspline()] for alternative specifications.
+For this engine, stratification cannot be specified via [`survival::strata()`], please see [flexsurv::flexsurvspline()] for alternative specifications.
 
 
 

man/rmd/decision_tree_partykit.md

@@ -156,7 +156,7 @@ test_that("overhead of parsnip interface is minimal (#1071)", {
   skip_on_cran()
   skip_on_covr()
   skip_if_not_installed("bench")
-  skip_if_not_installed("parsnip", minimum_version = "1.3.0")
+  skip_if_not_installed("parsnip", minimum_version = "1.4.0")
 
   bm <- bench::mark(
     time_engine = lm(mpg ~ ., mtcars),