add toggle_sparsity()

DESCRIPTION

@@ -25,9 +25,10 @@ Imports:
     lifecycle (>= 1.0.3),
     modelenv (>= 0.1.0),
     parsnip (>= 1.2.1.9000),
+    recipes (>= 1.0.10.9000),
     rlang (>= 1.1.0),
     tidyselect (>= 1.2.0),
-    sparsevctrs (>= 0.1.0.9002),
+    sparsevctrs (>= 0.1.0.9003),
     vctrs (>= 0.4.1),
     withr
 Suggests: 
@@ -42,7 +43,6 @@ Suggests:
     methods,
     modeldata (>= 1.0.0),
     probably,
-    recipes (>= 1.0.10.9000),
     rmarkdown,
     testthat (>= 3.0.0)
 VignetteBuilder: 

R/fit.R

@@ -71,6 +71,8 @@ fit.workflow <- function(object, data, ..., calibration = NULL, control = contro
     )
   }
 
+  object <- toggle_sparsity(object, data)
+
   workflow <- object
   workflow <- .fit_pre(workflow, data)
   workflow <- .fit_model(workflow, control)

R/sparsevctrs.R

@@ -1,3 +1,99 @@
 is_sparse_matrix <- function(x) {
   methods::is(x, "sparseMatrix")
 }
+
+# This function takes a workflow and its data. If the model supports sparse data
+# And there is a recipe, then it uses `should_use_sparsity()` to determine
+# whether all the `sparse = "auto"` should be turned to `"yes"` or `"no"` in the
+# recipe.
+#
+# Done using flow chart in https://github.com/tidymodels/workflows/issues/271
+toggle_sparsity <- function(object, data) {
+  if (
+    allow_sparse(object$fit$actions$model$spec) &&
+      has_preprocessor_recipe(object)
+  ) {
+    est_sparsity <- recipes::.recipes_estimate_sparsity(
+      extract_preprocessor(object)
+    )
+
+    toggle_sparse <- should_use_sparsity(
+      est_sparsity,
+      extract_spec_parsnip(object)$engine,
+      nrow(data)
+    )
+
+    object$pre$actions$recipe$recipe <- recipes::.recipes_toggle_sparse_args(
+      object$pre$actions$recipe$recipe,
+      choice = toggle_sparse
+    )
+  }
+
+  object
+}
+
+allow_sparse <- function(x) {
+  if (inherits(x, "model_fit")) {
+    x <- x$spec
+  }
+  res <- parsnip::get_from_env(paste0(class(x)[1], "_encoding"))
+  all(res$allow_sparse_x[res$engine == x$engine])
+}
+
+# This function was created using from the output of a mars model fit on the
+# simulation data generated in `analysis/time_analysis.R`
+# https://github.com/tidymodels/benchmark-sparsity-threshold
+#
+# The model was extracted using {tidypredict} and hand-tuned for speed.
+#
+# The model was fit on `sparsity`, `engine` and `n_rows` and the outcome was 
+# `log_fold` which is defined as 
+# `log(time to fit with dense data / time to fit with sparse data)`.
+# Meaning that values above above 0 would reflects longer fit times for dense,
+# Hence we want to use sparse data.
+#
+# At this time the only engines that support sparse data are glmnet, LiblineaR, 
+# ranger, and xgboost. Which is why they are the only ones listed here.
+# This is fine as this code will only run if `allow_sparse()` returns `TRUE`
+# Which only happens for these engines.
+# 
+# Ranger is hard-coded to always fail since they appear to use the same 
+# algorithm for sparse and dense data, resulting in identical times.
+should_use_sparsity <- function(sparsity, engine, n_rows) {
+  if (is.null(engine) || engine == "ranger") {
+    return("no")
+  }
+
+  log_fold <- -0.599333138645995 +
+    ifelse(sparsity < 0.836601307189543, 0.836601307189543 - sparsity, 0) *
+      -0.541581853008009 +
+    ifelse(n_rows < 16000, 16000 - n_rows, 0) * 3.23980908942813e-05 +
+    ifelse(n_rows > 16000, n_rows - 16000, 0) * -2.81001152147355e-06 +
+    ifelse(sparsity > 0.836601307189543, sparsity - 0.836601307189543, 0) *
+      9.82444255114058 +
+    ifelse(sparsity > 0.836601307189543, sparsity - 0.836601307189543, 0) *
+      ifelse(n_rows > 8000, n_rows - 8000, 0) *
+      7.27456967763306e-05 +
+    ifelse(sparsity > 0.836601307189543, sparsity - 0.836601307189543, 0) *
+      ifelse(n_rows < 8000, 8000 - n_rows, 0) *
+      -0.000798307404212627
+
+  if (engine == "xgboost") {
+    log_fold <- log_fold +
+      ifelse(sparsity < 0.984615384615385, 0.984615384615385 - sparsity, 0) *
+        0.113098025073806 +
+      ifelse(n_rows < 8000, 8000 - n_rows, 0) * -9.77914237255269e-05 +
+      ifelse(n_rows > 8000, n_rows - 8000, 0) * 3.22657666511869e-06 +
+      ifelse(sparsity > 0.984615384615385, sparsity - 0.984615384615385, 0) *
+        41.5180348086939 +
+      0.913457808326756
+  }
+
+  if (engine == "LiblineaR") {
+    log_fold <- log_fold +
+      ifelse(sparsity > 0.836601307189543, sparsity - 0.836601307189543, 0) *
+        -5.39592564852111
+  }
+
+  ifelse(log_fold > 0, "yes", "no")
+}

tests/testthat/test-sparsevctrs.R

@@ -191,3 +191,108 @@ test_that("fit() errors if sparse matrix has no colnames", {
     fit(wf_spec, hotel_data)
   )
 })
+
+test_that("toggle_sparsity changes auto to yes", {
+  skip_if_not_installed("glmnet")
+  skip_if_not_installed("modeldata")
+
+  data("ames", package = "modeldata")
+
+  tree_spec <- parsnip::boost_tree("regression", "xgboost")
+
+  rec_spec <- recipes::recipe(Sale_Price ~ ., data = ames) %>%
+    recipes::step_dummy(recipes::all_nominal_predictors())
+
+  wf_spec <- workflow(rec_spec, tree_spec)
+
+  res <- toggle_sparsity(wf_spec, ames)
+
+  expect_identical(
+    extract_preprocessor(res)$steps[[1]]$sparse,
+    "yes"
+  )
+})
+
+test_that("toggle_sparsity doesn't change no", {
+  skip_if_not_installed("glmnet")
+  skip_if_not_installed("modeldata")
+
+  data("ames", package = "modeldata")
+
+  tree_spec <- parsnip::boost_tree("regression", "xgboost")
+
+  rec_spec <- recipes::recipe(Sale_Price ~ ., data = ames) %>%
+    recipes::step_dummy(recipes::all_nominal_predictors(), sparse = "no")
+
+  wf_spec <- workflow(rec_spec, tree_spec)
+
+  res <- toggle_sparsity(wf_spec, ames)
+
+  expect_identical(
+    extract_preprocessor(res)$steps[[1]]$sparse,
+    "no"
+  )
+})
+
+test_that("toggle_sparsity changes auto to no", {
+  skip_if_not_installed("glmnet")
+  skip_if_not_installed("modeldata")
+
+  data("ames", package = "modeldata")
+
+  tree_spec <- parsnip::boost_tree("regression", "xgboost")
+
+  # if we only dummy 1 variable it doesn't make the data sparse enough
+  rec_spec <- recipes::recipe(Sale_Price ~ ., data = ames) %>%
+    recipes::step_dummy(MS_Zoning)
+
+  wf_spec <- workflow(rec_spec, tree_spec)
+
+  res <- toggle_sparsity(wf_spec, ames)
+
+  expect_identical(
+    extract_preprocessor(res)$steps[[1]]$sparse,
+    "no"
+  )
+})
+
+test_that("toggle_sparsity doesn't change yes", {
+  skip_if_not_installed("glmnet")
+  skip_if_not_installed("modeldata")
+
+  data("ames", package = "modeldata")
+
+  tree_spec <- parsnip::boost_tree("regression", "xgboost")
+
+  # if we only dummy 1 variable it doesn't make the data sparse enough
+  rec_spec <- recipes::recipe(Sale_Price ~ ., data = ames) %>%
+    recipes::step_dummy(MS_Zoning, sparse = "yes")
+
+  wf_spec <- workflow(rec_spec, tree_spec)
+
+  res <- toggle_sparsity(wf_spec, ames)
+
+  expect_identical(
+    extract_preprocessor(res)$steps[[1]]$sparse,
+    "yes"
+  )
+})
+
+test_that("toggle_sparsity doesn't break fit", {
+  skip_if_not_installed("glmnet")
+  skip_if_not_installed("modeldata")
+
+  data("ames", package = "modeldata")
+
+  tree_spec <- parsnip::boost_tree("regression", "xgboost")
+
+  rec_spec <- recipes::recipe(Sale_Price ~ ., data = ames) %>%
+    recipes::step_dummy(recipes::all_nominal_predictors())
+
+  wf_spec <- workflow(rec_spec, tree_spec)
+
+  expect_no_error(
+    fit(wf_spec, ames)
+  )
+})
+