Merge pull request #1211 from tidymodels/misc-sparsevctrs

Misc sparsevctrs

DESCRIPTION

@@ -32,7 +32,7 @@ Imports:
     prettyunits,
     purrr (>= 1.0.0),
     rlang (>= 1.1.0),
-    sparsevctrs (>= 0.1.0.9000),
+    sparsevctrs (>= 0.1.0.9002),
     stats,
     tibble (>= 2.1.1),
     tidyr (>= 1.3.0),

R/convert_data.R

@@ -48,7 +48,7 @@
     )
   }
 
-  if (is_sparse_tibble(data)) {
+  if (sparsevctrs::has_sparse_elements(data)) {
     cli::cli_abort(
       "Sparse data cannot be used with formula interface. Please use 
       {.fn fit_xy} instead."
@@ -417,7 +417,7 @@ maybe_sparse_matrix <- function(x) {
     return(x)
   }
 
-  if (is_sparse_tibble(x)) {
+  if (sparsevctrs::has_sparse_elements(x)) {
     res <- sparsevctrs::coerce_to_sparse_matrix(x)
   } else {
     res <- as.matrix(x)

R/fit.R

@@ -136,7 +136,7 @@ fit.model_spec <-
 
 
     if (is_sparse_matrix(data)) {
-      data <- sparsevctrs::coerce_to_sparse_tibble(data)
+      data <- sparsevctrs::coerce_to_sparse_tibble(data, rlang::caller_env(0))
     }
 
     dots <- quos(...)

R/fit_helpers.R

@@ -131,7 +131,7 @@ form_xy <- function(object, control, env,
   remove_intercept <- encoding_info %>% dplyr::pull(remove_intercept)
   allow_sparse_x <- encoding_info %>% dplyr::pull(allow_sparse_x)
 
-  if (allow_sparse_x && is_sparse_tibble(env$data)) {
+  if (allow_sparse_x && sparsevctrs::has_sparse_elements(env$data)) {
     target <- "dgCMatrix"
   }
 

R/predict.R

@@ -470,7 +470,7 @@ prepare_data <- function(object, new_data) {
   if (allow_sparse(object) && inherits(new_data, "dgCMatrix")) {
     return(new_data)
   }
-  if (allow_sparse(object) && is_sparse_tibble(new_data)) {
+  if (allow_sparse(object) && sparsevctrs::has_sparse_elements(new_data)) {
     new_data <- sparsevctrs::coerce_to_sparse_matrix(new_data)
     return(new_data)
   }

R/sparsevctrs.R

@@ -37,16 +37,12 @@ to_sparse_data_frame <- function(x, object, call = rlang::caller_env()) {
   x
 }
 
-is_sparse_tibble <- function(x) {
-  any(vapply(x, sparsevctrs::is_sparse_vector, logical(1)))
-}
-
 is_sparse_matrix <- function(x) {
   methods::is(x, "sparseMatrix")
 }
 
 materialize_sparse_tibble <- function(x, object, input) {
-  if (is_sparse_tibble(x) && (!allow_sparse(object))) {
+  if (sparsevctrs::has_sparse_elements(x) && (!allow_sparse(object))) {
     if (inherits(object, "model_fit")) {
       object <- object$spec
     }

tests/testthat/_snaps/sparsevctrs.md

@@ -30,6 +30,14 @@
       Warning:
       `data` is a sparse tibble, but `linear_reg()` with engine "lm" doesn't accept that. Converting to non-sparse.
 
+---
+
+    Code
+      fit(spec, avg_price_per_room ~ ., data = hotel_data)
+    Condition
+      Error in `fit()`:
+      ! `x` must have column names.
+
 # sparse tibble can be passed to `fit_xy() - unsupported
 
     Code

tests/testthat/test-sparsevctrs.R

@@ -282,7 +282,7 @@ test_that("maybe_sparse_matrix() is used correctly", {
 
   local_mocked_bindings(
     maybe_sparse_matrix = function(x) {
-      if (is_sparse_tibble(x)) {
+      if (sparsevctrs::has_sparse_elements(x)) {
         stop("sparse vectors detected")
       } else {
         stop("no sparse vectors detected")
@@ -313,3 +313,17 @@ test_that("maybe_sparse_matrix() is used correctly", {
     fit_xy(spec, x = tibble::as_tibble(mtcars)[, -1], y = tibble::as_tibble(mtcars)[, 1])
   )
 })
+
+test_that("fit() errors if sparse matrix has no colnames", {
+  hotel_data <- sparse_hotel_rates()
+  colnames(hotel_data) <- NULL
+
+  spec <- boost_tree() %>%
+    set_mode("regression") %>%
+    set_engine("xgboost")
+
+  expect_snapshot(
+    error = TRUE,
+    fit(spec, avg_price_per_room ~ ., data = hotel_data)
+  )
+})