Make sure all sparse data errors look nice

R/fit.R

@@ -137,6 +137,10 @@ fit.model_spec <-
       cli::cli_abort(msg)
     }
 
+    if (is_sparse_matrix(data)) {
+      data <- sparsevctrs::coerce_to_sparse_tibble(data)
+    }
+
     dots <- quos(...)
 
     if (length(possible_engines(object)) == 0) {

R/sparsevctrs.R

@@ -1,5 +1,5 @@
-to_sparse_data_frame <- function(x, object) {
-  if (methods::is(x, "sparseMatrix")) {
+to_sparse_data_frame <- function(x, object, call = rlang::caller_env()) {
+  if (is_sparse_matrix(x)) {
     if (allow_sparse(object)) {
       x <- sparsevctrs::coerce_to_sparse_data_frame(x)
     } else {
@@ -8,8 +8,10 @@ to_sparse_data_frame <- function(x, object) {
       }
 
       cli::cli_abort(
-      "{.arg x} is a sparse matrix, but {.fn {class(object)[1]}} with
-       engine {.code {object$engine}} doesn't accept that.")
+        "{.arg x} is a sparse matrix, but {.fn {class(object)[1]}} with
+        engine {.val {object$engine}} doesn't accept that.",
+        call = call
+      )
     }
   } else if (is.data.frame(x)) {
     x <- materialize_sparse_tibble(x, object, "x")
@@ -21,6 +23,10 @@ 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 (inherits(object, "model_fit")) {
@@ -29,7 +35,7 @@ materialize_sparse_tibble <- function(x, object, input) {
 
     cli::cli_warn(
       "{.arg {input}} is a sparse tibble, but {.fn {class(object)[1]}} with
-      engine {.code {object$engine}} doesn't accept that. Converting to 
+      engine {.val {object$engine}} doesn't accept that. Converting to 
       non-sparse."
     )
     for (i in seq_along(ncol(x))) {

tests/testthat/_snaps/sparsevctrs.md

@@ -4,39 +4,47 @@
       lm_fit <- fit(spec, avg_price_per_room ~ ., data = hotel_data[1:100, ])
     Condition
       Warning:
-      `data` is a sparse tibble, but `linear_reg()` with engine `lm` doesn't accept that. Converting to non-sparse.
+      `data` is a sparse tibble, but `linear_reg()` with engine "lm" doesn't accept that. Converting to non-sparse.
+
+# sparse matrix can be passed to `fit()
+
+    Code
+      lm_fit <- fit(spec, avg_price_per_room ~ ., data = hotel_data[1:100, ])
+    Condition
+      Warning:
+      `data` is a sparse tibble, but `linear_reg()` with engine "lm" doesn't accept that. Converting to non-sparse.
 
 # sparse tibble can be passed to `fit_xy()
 
     Code
       lm_fit <- fit_xy(spec, x = hotel_data[1:100, -1], y = hotel_data[1:100, 1])
     Condition
       Warning:
-      `x` is a sparse tibble, but `linear_reg()` with engine `lm` doesn't accept that. Converting to non-sparse.
+      `x` is a sparse tibble, but `linear_reg()` with engine "lm" doesn't accept that. Converting to non-sparse.
 
 # sparse matrices can be passed to `fit_xy()
 
     Code
       lm_fit <- fit_xy(spec, x = hotel_data[1:100, -1], y = hotel_data[1:100, 1])
     Condition
-      Error in `to_sparse_data_frame()`:
-      ! `x` is a sparse matrix, but `linear_reg()` with engine `lm` doesn't accept that.
+      Error in `fit_xy()`:
+      ! `x` is a sparse matrix, but `linear_reg()` with engine "lm" doesn't accept that.
 
 # sparse tibble can be passed to `predict()
 
     Code
       preds <- predict(lm_fit, sparse_mtcars)
     Condition
       Warning:
-      `x` is a sparse tibble, but `linear_reg()` with engine `lm` doesn't accept that. Converting to non-sparse.
+      `x` is a sparse tibble, but `linear_reg()` with engine "lm" doesn't accept that. Converting to non-sparse.
 
 # sparse matrices can be passed to `predict()
 
     Code
       predict(lm_fit, sparse_mtcars)
     Condition
-      Error in `to_sparse_data_frame()`:
-      ! `x` is a sparse matrix, but `linear_reg()` with engine `lm` doesn't accept that.
+      Error in `predict()`:
+      ! `x` is a sparse matrix, but `linear_reg()` with engine "lm" doesn't accept that.
 
 # to_sparse_data_frame() is used correctly
 

tests/testthat/test-sparsevctrs.R

@@ -21,6 +21,28 @@ test_that("sparse tibble can be passed to `fit()", {
   )
 })
 
+test_that("sparse matrix can be passed to `fit()", {
+  skip_if_not_installed("xgboost")
+
+  hotel_data <- sparse_hotel_rates()
+
+  spec <- boost_tree() %>%
+    set_mode("regression") %>%
+    set_engine("xgboost")
+
+  expect_no_error(
+    lm_fit <- fit(spec, avg_price_per_room ~ ., data = hotel_data)
+  )
+
+  spec <- linear_reg() %>%
+    set_mode("regression") %>%
+    set_engine("lm")
+
+  expect_snapshot(
+    lm_fit <- fit(spec, avg_price_per_room ~ ., data = hotel_data[1:100, ])
+  )
+})
+
 test_that("sparse tibble can be passed to `fit_xy()", {
   skip_if_not_installed("xgboost")