Merge branch 'sparse-input' of github.com:tidymodels/parsnip into sparse-input

Author: EmilHvitfeldt

NEWS.md

@@ -2,6 +2,10 @@
 
 * `fit_xy()` can now take dgCMatrix input for `x` argument (#1121).
 
+* Transitioned package errors and warnings to use cli (#1147 and #1148 by
+  @shum461, #1153 by @RobLBaker and @wright13, #1154 by @JamesHWade, #1160, 
+  #1161).
+
 * `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).
 
 * 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).

R/bart.R

@@ -142,7 +142,9 @@ update.bart <-
 #' @param std_err Attach column for standard error of prediction or not.
 bartMachine_interval_calc <- function(new_data, obj, ci = TRUE, level = 0.95) {
   if (obj$spec$mode == "classification") {
-    rlang::abort("In bartMachine: Prediction intervals are not possible for classification")
+    cli::cli_abort(
+      "Prediction intervals are not possible for classification"
+    )
   }
   get_std_err <- obj$spec$method$pred$pred_int$extras$std_error
 

R/boost_tree.R

@@ -134,11 +134,9 @@ translate.boost_tree <- function(x, engine = x$engine, ...) {
 
   if (engine == "spark") {
     if (x$mode == "unknown") {
-      rlang::abort(
-        glue::glue(
-          "For spark boosted trees models, the mode cannot be 'unknown' ",
-          "if the specification is to be translated."
-        )
+      cli::cli_abort(
+        "For spark boosted tree models, the mode cannot be {.val unknown}
+         if the specification is to be translated."
       )
     } else {
       arg_vals$type <- x$mode
@@ -172,7 +170,7 @@ check_args.boost_tree <- function(object, call = rlang::caller_env()) {
   check_number_decimal(args$sample_size, min = 0, max = 1, allow_null = TRUE, call = call, arg = "sample_size")
   check_number_whole(args$tree_depth, min = 0, allow_null = TRUE, call = call, arg = "tree_depth")
   check_number_whole(args$min_n, min = 0, allow_null = TRUE, call = call, arg = "min_n")
-  
+
   invisible(object)
 }
 
@@ -229,15 +227,15 @@ xgb_train <- function(
   num_class <- length(levels(y))
 
   if (!is.numeric(validation) || validation < 0 || validation >= 1) {
-    rlang::abort("`validation` should be on [0, 1).")
+    cli::cli_abort("{.arg validation} should be on [0, 1).")
   }
 
   if (!is.null(early_stop)) {
     if (early_stop <= 1) {
-      rlang::abort(paste0("`early_stop` should be on [2, ",  nrounds, ")."))
+      cli::cli_abort("{.arg early_stop} should be on [2, {nrounds}).")
     } else if (early_stop >= nrounds) {
       early_stop <- nrounds - 1
-      rlang::warn(paste0("`early_stop` was reduced to ", early_stop, "."))
+      cli::cli_warn("{.arg early_stop} was reduced to {early_stop}.")
     }
   }
 
@@ -252,7 +250,7 @@ xgb_train <- function(
 
 
   if (!is.numeric(subsample) || subsample < 0 || subsample > 1) {
-    rlang::abort("`subsample` should be on [0, 1].")
+    cli::cli_abort("{.arg subsample} should be on [0, 1].")
   }
 
   # initialize
@@ -268,9 +266,13 @@ xgb_train <- function(
   }
 
   if (min_child_weight > n) {
-    msg <- paste0(min_child_weight, " samples were requested but there were ",
-                  n, " rows in the data. ", n, " will be used.")
-    rlang::warn(msg)
+    cli::cli_warn(
+      c(
+       "!" = "{min_child_weight} samples were requested but there were {n} rows
+              in the data.",
+       "i" = "{n} will be used."
+      )
+    )
     min_child_weight <- min(min_child_weight, n)
   }
 
@@ -369,14 +371,16 @@ recalc_param <- function(x, counts, denom) {
   x
 }
 
-maybe_proportion <- function(x, nm) {
+maybe_proportion <- function(x, nm, call = rlang::caller_env()) {
   if (x < 1) {
-    msg <- paste0(
-      "The option `counts = TRUE` was used but parameter `", nm,
-      "` was given as ", signif(x, 3), ". Please use a value >= 1 or use ",
-      "`counts = FALSE`."
+    cli::cli_abort(
+      c(
+        "The option `counts = TRUE` was used but {.arg {nm}} was given
+         as {signif(x, 3)}.",
+        "i" = "Please use a value >= 1 or use {.code counts = FALSE}."
+      ),
+      call = call
     )
-    rlang::abort(msg)
   }
 }
 
@@ -418,7 +422,9 @@ as_xgb_data <- function(x, y, validation = 0, weights = NULL, event_level = "fir
         y <- as.numeric(y) - 1
       }
     } else {
-      if (event_level == "second") rlang::warn("`event_level` can only be set for binary variables.")
+      if (event_level == "second") {
+        cli::cli_warn("{.arg event_level} can only be set for binary outcomes.")
+      }
       y <- as.numeric(y) - 1
     }
   }
@@ -573,15 +579,19 @@ C5.0_train <-
 
     n <- nrow(x)
     if (n == 0) {
-      rlang::abort("There are zero rows in the predictor set.")
+      cli::cli_abort("There are zero rows in the predictor set.")
     }
 
 
     ctrl <- call2("C5.0Control", .ns = "C50")
     if (minCases > n) {
-      msg <- paste0(minCases, " samples were requested but there were ",
-                    n, " rows in the data. ", n, " will be used.")
-      rlang::warn(msg)
+
+      cli::cli_warn(
+        c(
+          "!" = "{minCases} samples were requested but there were {n} rows in the data.",
+          "i" = "{n} will be used."
+        )
+      )
       minCases <- n
     }
     ctrl$minCases <- minCases

R/decision_tree.R

@@ -97,11 +97,9 @@ translate.decision_tree <- function(x, engine = x$engine, ...) {
 
   if (x$engine == "spark") {
     if (x$mode == "unknown") {
-      rlang::abort(
-        glue::glue(
-          "For spark decision tree models, the mode cannot be 'unknown' ",
-          "if the specification is to be translated."
-        )
+      cli::cli_abort(
+        "For spark decision tree models, the mode cannot be {.val unknown}
+         if the specification is to be translated."
       )
     }
   }

R/nearest_neighbor_data.R

@@ -93,9 +93,9 @@ set_pred(
     # model mode
     pre = function(x, object) {
       if (object$fit$response != "continuous") {
-        rlang::abort(
-          glue::glue("`kknn` model does not appear to use numeric predictions.",
-                     " Was the model fit with a continuous response variable?")
+        cli::cli_abort(
+          c("`kknn` model does not appear to use numeric predictions.",
+            "i" = "Was the model fit with a continuous response variable?")
         )
       }
       x
@@ -136,9 +136,9 @@ set_pred(
   value = list(
     pre = function(x, object) {
       if (!(object$fit$response %in% c("ordinal", "nominal"))) {
-        rlang::abort(
-          glue::glue("`kknn` model does not appear to use class predictions.",
-                     " Was the model fit with a factor response variable?")
+        cli::cli_abort(
+          c("`kknn` model does not appear to use class predictions.",
+            "i" = "Was the model fit with a factor response variable?")
         )
       }
       x
@@ -162,9 +162,9 @@ set_pred(
   value = list(
     pre = function(x, object) {
       if (!(object$fit$response %in% c("ordinal", "nominal"))) {
-        rlang::abort(
-          glue::glue("`kknn` model does not appear to use class predictions.",
-                     " Was the model fit with a factor response variable?")
+        cli::cli_abort(
+          c("`kknn` model does not appear to use class predictions.",
+            "i" = "Was the model fit with a factor response variable?")
         )
       }
       x