make predict work with sparse tibbles

Author: EmilHvitfeldt

NEWS.md

@@ -4,7 +4,7 @@
 
 * `fit()` and `fit_xy()` can now take sparse tibbles as data values (#1165).
 
-* `predict()` can now take dgCMatrix input for `new_data` argument, and error informatively when model doesn't support it (#1167).
+* `predict()` can now take dgCMatrix and sparse tibble input for `new_data` argument, and error informatively when model doesn't support it (#1167).
 
 
 * Transitioned package errors and warnings to use cli (#1147 and #1148 by

R/sparsevctrs.R

@@ -23,6 +23,10 @@ is_sparse_tibble <- function(x) {
 
 materialize_sparse_tibble <- function(x, object, input) {
   if ((!allow_sparse(object)) && is_sparse_tibble(x)) {
+    if (inherits(object, "model_fit")) {
+      object <- object$spec
+    }
+    
     cli::cli_warn(
       "{.arg {input}} is a sparse tibble, but {.fn {class(object)[1]}} with
       engine {.code {object$engine}} doesn't accept that. Converting to 

tests/testthat/_snaps/sparsevctrs.md

@@ -22,6 +22,14 @@
       Error in `to_sparse_data_frame()`:
       ! `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.
+
 # sparse matrices can be passed to `predict()
 
     Code

tests/testthat/test-sparsevctrs.R

@@ -67,6 +67,37 @@ test_that("sparse matrices can be passed to `fit_xy()", {
   )
 })
 
+test_that("sparse tibble can be passed to `predict()", {
+  skip_if_not_installed("ranger")
+
+  hotel_data <- sparse_hotel_rates()
+  hotel_data <- sparsevctrs::coerce_to_sparse_tibble(hotel_data)
+
+  spec <- rand_forest(trees = 10) %>%
+    set_mode("regression") %>%
+    set_engine("ranger")
+
+  tree_fit <- fit_xy(spec, x = hotel_data[, -1], y = hotel_data[, 1])
+
+  expect_no_error(
+    predict(tree_fit, hotel_data)
+  )
+
+  spec <- linear_reg() %>%
+    set_mode("regression") %>%
+    set_engine("lm")
+
+  lm_fit <- fit(spec, mpg ~ ., data = mtcars)
+
+  sparse_mtcars <- mtcars %>%
+    sparsevctrs::coerce_to_sparse_matrix() %>%
+    sparsevctrs::coerce_to_sparse_tibble()
+
+  expect_snapshot(
+    preds <- predict(lm_fit, sparse_mtcars)
+  )
+})
+
 test_that("sparse matrices can be passed to `predict()", {
   skip_if_not_installed("ranger")