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Add quantile regression mode (#1209)
* add a quantile regression mode to test with * update type checkers * avoid confusion with global all_models object * add quantile_level argument to set_mode() * initial data for quantreg * some initial tests * fix some issues * enable quantile prediction * tests for quantreg * Quantile predictions output constructor (#1191) * small change to predict checks * add vctrs for quantiles and test, refactor *_rq_preds * revise tests * Apply some of the suggestions from code review Co-authored-by: Simon P. Couch <simonpatrickcouch@gmail.com> * rename tests on suggestion from code review * export missing funs from vctrs for formatting * convert errors to snapshot tests * pass call through input check * update snapshots for caller_env * rename to parsnip_quantiles, add format snapshot tests * Apply suggestions from @topepo Co-authored-by: Max Kuhn <mxkuhn@gmail.com> * rename parsnip_quantiles to quantile_pred * rename parsnip_quantiles to quantile_pred and add vector probability check * fix: two bugs introduced earlier * add formatting tests for single quantile * replace walk with a loop to avoid "Error in map()" * remove row/col names * adjust quantile_pred format * as_tibble method * updated NEWS file * add PR number * small new update * helper methods * update docs * re-enable quantiles prediction for #1203 * update some tests * no longer needed * use tibble::new_tibble * braces * test as_tibble * remove print methods --------- Co-authored-by: Simon P. Couch <simonpatrickcouch@gmail.com> Co-authored-by: Max Kuhn <mxkuhn@gmail.com> Co-authored-by: ‘topepo’ <‘mxkuhn@gmail.com’> * quantile regression updates for new hardhat model (#1207) * bump hardhat version * remove parts now in hardhat * update for new hardhat version * quantile_levels (plural now) * news update * typo * rename helper function * run CI on PRs from branches * forgotten remote * actions for edited PRs * plural * expand branch list * export function for censored to use * updated snapshot * remake snapshot * Revert "remake snapshot" This reverts commit 954e326. * updated snapshot * Update R/arguments.R Co-authored-by: Hannah Frick <hfrick@users.noreply.github.com> * typo * changes from reviewer feedback --------- Co-authored-by: ‘topepo’ <‘mxkuhn@gmail.com’> Co-authored-by: Hannah Frick <hfrick@users.noreply.github.com> * Change to `quantile` argument to `quantile levels` (#1208) * quantile -> quantile_levels for #1203 * defer test until censored updates in new PR * update docs for quantile_levels * update test * disable quantile predictions for surv_reg --------- Co-authored-by: ‘topepo’ <‘mxkuhn@gmail.com’> * post conflict merge updates * update news * version bump and fix typo * revert GHA branches * small bug fix * Apply suggestions from code review Co-authored-by: Hannah Frick <hfrick@users.noreply.github.com> Co-authored-by: Emil Hvitfeldt <emil.hvitfeldt@posit.co> * don't export median * add call arg * added documentation on model * add mode * convert error to warning * remove rankdeficient * added skip * add deprecated `quantile` arg back in * remove numeric prediction --------- Co-authored-by: ‘topepo’ <‘mxkuhn@gmail.com’> Co-authored-by: Daniel McDonald <dajmcdon@gmail.com> Co-authored-by: Simon P. Couch <simonpatrickcouch@gmail.com> Co-authored-by: Hannah Frick <hfrick@users.noreply.github.com> Co-authored-by: Emil Hvitfeldt <emil.hvitfeldt@posit.co>
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‎DESCRIPTION

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@@ -1,6 +1,6 @@
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Package: parsnip
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Title: A Common API to Modeling and Analysis Functions
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Version: 1.2.1.9002
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Version: 1.2.1.9003
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Authors@R: c(
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person("Max", "Kuhn", , "max@posit.co", role = c("aut", "cre")),
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person("Davis", "Vaughan", , "davis@posit.co", role = "aut"),
@@ -25,7 +25,7 @@ Imports:
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ggplot2,
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globals,
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glue,
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hardhat (>= 1.4.0),
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hardhat (>= 1.4.0.9002),
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lifecycle,
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magrittr,
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pillar,
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vctrs (>= 0.6.0),
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withr
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Suggests:
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C50,
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bench,
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C50,
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covr,
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dials (>= 1.1.0),
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earth,
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xgboost (>= 1.5.0.1)
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VignetteBuilder:
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knitr
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Remotes:
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r-lib/sparsevctrs,
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tidymodels/hardhat
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ByteCompile: true
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Config/Needs/website: C50, dbarts, earth, glmnet, keras, kernlab, kknn,
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LiblineaR, mgcv, nnet, parsnip, randomForest, ranger, rpart, rstanarm,
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tidymodels/tidymodels, tidyverse/tidytemplate, rstudio/reticulate,
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LiblineaR, mgcv, nnet, parsnip, quantreg, randomForest, ranger, rpart,
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rstanarm, tidymodels/tidymodels, tidyverse/tidytemplate, rstudio/reticulate,
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xgboost
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Config/rcmdcheck/ignore-inconsequential-notes: true
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Config/testthat/edition: 3
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Encoding: UTF-8
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LazyData: true
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Roxygen: list(markdown = TRUE)
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Remotes:
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r-lib/sparsevctrs
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RoxygenNote: 7.3.2

‎NAMESPACE

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@@ -264,6 +264,7 @@ export(make_classes)
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export(make_engine_list)
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export(make_seealso_list)
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export(mars)
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export(matrix_to_quantile_pred)
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export(max_mtry_formula)
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export(maybe_data_frame)
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export(maybe_matrix)

‎NEWS.md

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# parsnip (development version)
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## New Features
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* A new model mode (`"quantile regression"`) was added. Including:
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* A `linear_reg()` engine for `"quantreg"`.
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* Predictions are encoded via a custom vector type. See [hardhat::quantile_pred()].
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* Predicted quantile levels are designated when the new mode is specified. See `?set_mode`.
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* `fit_xy()` can now take dgCMatrix input for `x` argument (#1121).
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* `fit_xy()` can now take sparse tibbles as data values (#1165).
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* `predict()` can now take dgCMatrix and sparse tibble input for `new_data` argument, and error informatively when model doesn't support it (#1167).
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* Transitioned package errors and warnings to use cli (#1147 and #1148 by
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@shum461, #1153 by @RobLBaker and @wright13, #1154 by @JamesHWade, #1160,
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#1161, #1081).
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* New `extract_fit_time()` method has been added that returns the time it took to train the model (#853).
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## Other Changes
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* Transitioned package errors and warnings to use cli (#1147 and #1148 by @shum461, #1153 by @RobLBaker and @wright13, #1154 by @JamesHWade, #1160, #1161, #1081).
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1322
* `fit_xy()` currently raises an error for `gen_additive_mod()` model specifications as the default engine (`"mgcv"`) specifies smoothing terms in model formulas. However, some engines specify smooths via additional arguments, in which case the restriction on `fit_xy()` is excessive. parsnip will now only raise an error when fitting a `gen_additive_mod()` with `fit_xy()` when using the `"mgcv"` engine (#775).
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* Aligned `null_model()` with other model types; the model type now has an engine argument that defaults to `"parsnip"` and is checked with the same machinery that checks other model types in the package (#1083).
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17-
* New `extract_fit_time()` method has been added that returns the time it took to train the model (#853).
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## Bug Fixes
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* Ensure that `knit_engine_docs()` has the required packages installed (#1156).
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* Fixed bug where some models fit using `fit_xy()` couldn't predict (#1166).
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32+
## Breaking Change
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34+
* For quantile prediction, the `quantile` argument to `predict()` has been deprecate in facor of `quantile_levels`. This does not affect models with mode `"quantile regression"`.
35+
36+
* The quantile regression prediction type was disabled for the deprecated `surv_reg()` model.
37+
38+
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# parsnip 1.2.1
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* Added a missing `tidy()` method for survival analysis glmnet models (#1086).

‎R/aaa-import-standalone-types-check.R

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# Standalone file: do not edit by hand
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# Source: <https://github.com/r-lib/rlang/blob/main/R/standalone-types-check.R>
3-
# ----------------------------------------------------------------------
4-
#
51
# ---
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# repo: r-lib/rlang
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# file: standalone-types-check.R
@@ -13,6 +9,9 @@
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#
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# ## Changelog
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#
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# 2024-08-15:
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# - `check_character()` gains an `allow_na` argument (@martaalcalde, #1724)
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#
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# 2023-03-13:
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# - Improved error messages of number checkers (@teunbrand)
1817
# - Added `allow_infinite` argument to `check_number_whole()` (@mgirlich).
@@ -461,15 +460,28 @@ check_formula <- function(x,
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462461
# Vectors -----------------------------------------------------------------
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463+
# TODO: Figure out what to do with logical `NA` and `allow_na = TRUE`
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464465
check_character <- function(x,
465466
...,
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allow_na = TRUE,
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allow_null = FALSE,
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arg = caller_arg(x),
468470
call = caller_env()) {
471+
469472
if (!missing(x)) {
470473
if (is_character(x)) {
474+
if (!allow_na && any(is.na(x))) {
475+
abort(
476+
sprintf("`%s` can't contain NA values.", arg),
477+
arg = arg,
478+
call = call
479+
)
480+
}
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471482
return(invisible(NULL))
472483
}
484+
473485
if (allow_null && is_null(x)) {
474486
return(invisible(NULL))
475487
}
@@ -479,7 +491,6 @@ check_character <- function(x,
479491
x,
480492
"a character vector",
481493
...,
482-
allow_na = FALSE,
483494
allow_null = allow_null,
484495
arg = arg,
485496
call = call

‎R/aaa_models.R

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11
# Initialize model environments
22

3-
all_modes <- c("classification", "regression", "censored regression")
3+
all_modes <- c("classification", "regression", "censored regression", "quantile regression")
44

55
# ------------------------------------------------------------------------------
66

@@ -195,8 +195,8 @@ stop_missing_engine <- function(cls, call) {
195195
}
196196

197197
check_mode_for_new_engine <- function(cls, eng, mode, call = caller_env()) {
198-
all_modes <- get_from_env(paste0(cls, "_modes"))
199-
if (!(mode %in% all_modes)) {
198+
model_modes <- get_from_env(paste0(cls, "_modes"))
199+
if (!(mode %in% model_modes)) {
200200
cli::cli_abort(
201201
"{.val {mode}} is not a known mode for model {.fn {cls}}.",
202202
call = call

‎R/aaa_quantiles.R

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1+
#' Reformat quantile predictions
2+
#'
3+
#' @param x A matrix of predictions with rows as samples and columns as quantile
4+
#' levels.
5+
#' @param object A parsnip `model_fit` object from a quantile regression model.
6+
#' @keywords internal
7+
#' @export
8+
matrix_to_quantile_pred <- function(x, object) {
9+
if (!is.matrix(x)) {
10+
x <- as.matrix(x)
11+
}
12+
rownames(x) <- NULL
13+
n_pred_quantiles <- ncol(x)
14+
quantile_levels <- object$spec$quantile_levels
15+
16+
tibble::new_tibble(x = list(.pred_quantile = hardhat::quantile_pred(x, quantile_levels)))
17+
}

‎R/arguments.R

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@@ -49,6 +49,8 @@ check_eng_args <- function(args, obj, core_args) {
4949
#' set_args(mtry = 3, importance = TRUE) %>%
5050
#' set_mode("regression")
5151
#'
52+
#' linear_reg() %>%
53+
#' set_mode("quantile regression", quantile_levels = c(0.2, 0.5, 0.8))
5254
#' @export
5355
set_args <- function(object, ...) {
5456
UseMethod("set_args")
@@ -89,12 +91,18 @@ set_args.default <- function(object,...) {
8991

9092
#' @rdname set_args
9193
#' @export
92-
set_mode <- function(object, mode) {
94+
set_mode <- function(object, mode, ...) {
9395
UseMethod("set_mode")
9496
}
9597

98+
#' @rdname set_args
99+
#' @param quantile_levels A vector of values between zero and one (only for the
100+
#' `"quantile regression"` mode); otherwise, it is `NULL`. The model uses these
101+
#' values to appropriately train quantile regression models to make predictions
102+
#' for these values (e.g., `quantile_levels = 0.5` is the median).
96103
#' @export
97-
set_mode.model_spec <- function(object, mode) {
104+
set_mode.model_spec <- function(object, mode, quantile_levels = NULL, ...) {
105+
check_dots_empty()
98106
cls <- class(object)[1]
99107
if (rlang::is_missing(mode)) {
100108
spec_modes <- rlang::env_get(get_model_env(), paste0(cls, "_modes"))
@@ -111,11 +119,21 @@ set_mode.model_spec <- function(object, mode) {
111119

112120
object$mode <- mode
113121
object$user_specified_mode <- TRUE
122+
if (mode == "quantile regression") {
123+
hardhat::check_quantile_levels(quantile_levels)
124+
} else {
125+
if (!is.null(quantile_levels)) {
126+
cli::cli_warn("{.arg quantile_levels} is only used when the mode is
127+
{.val quantile regression}.")
128+
}
129+
}
130+
131+
object$quantile_levels <- quantile_levels
114132
object
115133
}
116134

117135
#' @export
118-
set_mode.default <- function(object, mode) {
136+
set_mode.default <- function(object, mode, ...) {
119137
error_set_object(object, func = "set_mode")
120138

121139
invisible(FALSE)

‎R/fit.R

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176176
eval_env$formula <- formula
177177
eval_env$weights <- wts
178178

179+
if (!is.null(object$quantile_levels)) {
180+
eval_env$quantile_levels <- object$quantile_levels
181+
}
182+
179183
data <- materialize_sparse_tibble(data, object, "data")
180184

181185
fit_interface <-
@@ -187,7 +191,6 @@ fit.model_spec <-
187191
with a spark data object."
188192
)
189193

190-
191194
# populate `method` with the details for this model type
192195
object <- add_methods(object, engine = object$engine)
193196

@@ -295,6 +298,10 @@ fit_xy.model_spec <-
295298
eval_env$y_var <- y_var
296299
eval_env$weights <- weights_to_numeric(case_weights, object)
297300

301+
if (!is.null(object$quantile_levels)) {
302+
eval_env$quantile_levels <- object$quantile_levels
303+
}
304+
298305
# TODO case weights: pass in eval_env not individual elements
299306
fit_interface <- check_xy_interface(eval_env$x, eval_env$y, cl, object)
300307

‎R/install_packages.R

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2626
}
2727

2828
if (extras) {
29-
rmd_pkgs <- c("tidymodels", "broom.mixed", "glmnet", "Cubist", "xrf", "ape",
30-
"rmarkdown")
29+
rmd_pkgs <- c("ape", "broom.mixed", "Cubist", "glmnet", "quantreg",
30+
"rmarkdown", "tidymodels", "xrf")
3131
engine_packages <- unique(c(engine_packages, rmd_pkgs))
3232
}
3333

‎R/linear_reg_data.R

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11
set_new_model("linear_reg")
22

33
set_model_mode("linear_reg", "regression")
4+
set_model_mode("linear_reg", "quantile regression")
45

56
# ------------------------------------------------------------------------------
67

@@ -582,3 +583,48 @@ set_pred(
582583
)
583584
)
584585

586+
# ------------------------------------------------------------------------------
587+
588+
set_model_engine(model = "linear_reg", mode = "quantile regression", eng = "quantreg")
589+
set_dependency(model = "linear_reg", eng = "quantreg", pkg = "quantreg", mode = "quantile regression")
590+
591+
set_fit(
592+
model = "linear_reg",
593+
eng = "quantreg",
594+
mode = "quantile regression",
595+
value = list(
596+
interface = "formula",
597+
protect = c("formula", "data", "weights"),
598+
func = c(pkg = "quantreg", fun = "rq"),
599+
defaults = list(tau = expr(quantile_levels))
600+
)
601+
)
602+
603+
set_encoding(
604+
model = "linear_reg",
605+
eng = "quantreg",
606+
mode = "quantile regression",
607+
options = list(
608+
predictor_indicators = "traditional",
609+
compute_intercept = TRUE,
610+
remove_intercept = TRUE,
611+
allow_sparse_x = FALSE
612+
)
613+
)
614+
615+
set_pred(
616+
model = "linear_reg",
617+
eng = "quantreg",
618+
mode = "quantile regression",
619+
type = "quantile",
620+
value = list(
621+
pre = NULL,
622+
post = matrix_to_quantile_pred,
623+
func = c(fun = "predict"),
624+
args =
625+
list(
626+
object = expr(object$fit),
627+
newdata = expr(new_data)
628+
)
629+
)
630+
)

‎R/linear_reg_quantreg.R

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1+
#' Linear quantile regression via the quantreg package
2+
#'
3+
#' [quantreg::rq()] optimizes quantile loss to fit models with numeric outcomes.
4+
#'
5+
#' @includeRmd man/rmd/linear_reg_quantreg.md details
6+
#'
7+
#' @name details_linear_reg_quantreg
8+
#' @keywords internal
9+
NULL
10+
11+
# See inst/README-DOCS.md for a description of how these files are processed

‎R/predict.R

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201201
regression = "numeric",
202202
classification = "class",
203203
"censored regression" = "time",
204+
"quantile regression" = "quantile",
204205
cli::cli_abort(
205-
"{.arg type} should be 'regression', 'censored regression', or 'classification'.",
206+
"{.arg type} should be one of {.or {.val {all_modes}}}.",
206207
call = call
207208
)
208209
)
209210
}
211+
210212
if (!(type %in% pred_types))
211213
cli::cli_abort(
212214
"{.arg type} should be one of {.or {.arg {pred_types}}}.",
@@ -373,7 +375,7 @@ check_pred_type_dots <- function(object, type, ..., call = rlang::caller_env())
373375

374376
# ----------------------------------------------------------------------------
375377

376-
other_args <- c("interval", "level", "std_error", "quantile",
378+
other_args <- c("interval", "level", "std_error", "quantile_levels",
377379
"time", "eval_time", "increasing")
378380

379381
eval_time_types <- c("survival", "hazard")

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