Merge commit 'd3744d2e9106b4e04761bba8f9c0605e4ac949d2'

Author: EmilHvitfeldt

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: parsnip
 Title: A Common API to Modeling and Analysis Functions
-Version: 1.2.1.9001
+Version: 1.2.1.9002
 Authors@R: c(
     person("Max", "Kuhn", , "[email protected]", role = c("aut", "cre")),
     person("Davis", "Vaughan", , "[email protected]", role = "aut"),

NEWS.md

@@ -6,14 +6,16 @@
 
 * Transitioned package errors and warnings to use cli (#1147 and #1148 by
   @shum461, #1153 by @RobLBaker and @wright13, #1154 by @JamesHWade, #1160, 
-  #1161).
+  #1161, #1081).
 
 * `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).
 
 * New `extract_fit_time()` method has been added that returns the time it took to train the model (#853).
 
+* Ensure that `knit_engine_docs()` has the required packages installed (#1156).
+
 
 # parsnip 1.2.1
 

R/aaa_multi_predict.R

@@ -19,7 +19,7 @@
 #' @export
 multi_predict <- function(object, ...) {
   if (inherits(object$fit, "try-error")) {
-    rlang::warn("Model fit failed; cannot make predictions.")
+    cli::cli_warn("Model fit failed; cannot make predictions.")
     return(NULL)
   }
   check_for_newdata(...)
@@ -28,17 +28,19 @@ multi_predict <- function(object, ...) {
 
 #' @export
 #' @rdname multi_predict
-multi_predict.default <- function(object, ...)
-  rlang::abort(
-    glue::glue(
-      "No `multi_predict` method exists for objects with classes ",
-      glue::glue_collapse(glue::glue("'{class(object)}'"), sep = ", ")
-      )
-    )
+multi_predict.default <- function(object, ...) {
+  cli::cli_abort(
+    "No {.fun multi_predict} method exists for objects with classes
+     {.cls {class(object)}}."
+  )
+}
 
 #' @export
 predict.model_spec <- function(object, ...) {
-  rlang::abort("You must use `fit()` on your model specification before you can use `predict()`.")
+  cli::cli_abort(
+    "You must {.fun fit} your model specification
+     before you can use {.fun predict}."
+  )
 }
 
 #' Tools for models that predict on sub-models

R/adds.R

@@ -7,7 +7,7 @@
 #' @export
 add_rowindex <- function(x) {
   if (!is.data.frame(x)) {
-    rlang::abort("`x` should be a data frame.")
+    cli::cli_abort("{.arg x} should be a data frame.")
   }
   x <- dplyr::mutate(x, .row = seq_len(nrow(x)))
   x

R/arguments.R

@@ -22,8 +22,10 @@ check_eng_args <- function(args, obj, core_args) {
   if (length(common_args) > 0) {
     args <- args[!(names(args) %in% common_args)]
     common_args <- paste0(common_args, collapse = ", ")
-    rlang::warn(glue::glue("The following arguments cannot be manually modified ",
-                           "and were removed: {common_args}."))
+    cli::cli_warn(
+      "The argument{?s} {.arg {common_args}} cannot be manually
+       modified and {?was/were} removed."
+    )
   }
   args
 }
@@ -56,7 +58,7 @@ set_args <- function(object, ...) {
 set_args.model_spec <- function(object, ...) {
   the_dots <- enquos(...)
   if (length(the_dots) == 0)
-    rlang::abort("Please pass at least one named argument.")
+    cli::cli_abort("Please pass at least one named argument.")
   main_args <- names(object$args)
   new_args <- names(the_dots)
   for (i in new_args) {
@@ -263,7 +265,7 @@ make_xy_call <- function(object, target, env) {
       data.frame = rlang::expr(maybe_data_frame(x)),
       matrix = rlang::expr(maybe_matrix(x)),
       dgCMatrix = rlang::expr(maybe_sparse_matrix(x)),
-      rlang::abort(glue::glue("Invalid data type target: {target}."))
+      cli::cli_abort("Invalid data type target: {target}.")
     )
   if (uses_weights) {
     object$method$fit$args[[ unname(data_args["weights"]) ]] <- rlang::expr(weights)
@@ -317,9 +319,13 @@ min_cols <- function(num_cols, source) {
     p <- ncol(source)
   }
   if (num_cols > p) {
-    msg <- paste0(num_cols, " columns were requested but there were ", p,
-                 " predictors in the data. ", p, " will be used.")
-    rlang::warn(msg)
+    cli::cli_warn(
+      c(
+        "!" = "{num_cols} column{?s} {?was/were} requested but there {cli::qty(p)} {?was/were}
+               {p} predictor{?s} in the data.",
+        "i" = "{p} predictor{?s} will be used."
+      )
+    )
     num_cols <- p
   }
 
@@ -336,9 +342,14 @@ min_rows <- function(num_rows, source, offset = 0) {
   }
 
   if (num_rows > n - offset) {
-    msg <- paste0(num_rows, " samples were requested but there were ", n,
-                  " rows in the data. ", n - offset, " will be used.")
-    rlang::warn(msg)
+    cli::cli_warn(
+      c(
+        "!" = "{num_rows} sample{?s} {?was/were} requested but there were
+               {n} rows in the data.",
+        "i" = "{n - offset} sample{?s} will be used."
+      )
+    )
+
     num_rows <- n - offset
   }
 

R/augment.R

@@ -86,7 +86,12 @@ augment.model_fit <- function(x, new_data, eval_time = NULL, ...) {
       "regression"          = augment_regression(x, new_data),
       "classification"      = augment_classification(x, new_data),
       "censored regression" = augment_censored(x, new_data, eval_time = eval_time),
-      rlang::abort(paste("Unknown mode:", x$spec$mode))
+      cli::cli_abort(
+        c(
+          "Unknown mode {.val {x$spec$mode}}.",
+          "i" = "Model mode should be one of {.or {.val {all_modes}}}."
+        )
+      )
     )
   tibble::new_tibble(res)
 }

R/autoplot.R

@@ -44,7 +44,9 @@ map_glmnet_coefs <- function(x) {
   # work. If an object is loaded from a new session, they will need to load the
   # package.
   if (is.null(coefs)) {
-    rlang::abort("Please load the glmnet package before running `autoplot()`.")
+    cli::cli_abort(
+      "Please load the {.pkg glmnet} package before running {.fun autoplot}."
+    )
   }
   p <- x$dim[1]
   if (is.list(coefs)) {
@@ -161,8 +163,7 @@ check_penalty_value <- function(x) {
   cl <- match.call()
   arg_val <- as.character(cl$x)
   if (!is.vector(x) || length(x) != 1 || !is.numeric(x) || x < 0) {
-    msg <- paste0("Argument '", arg_val, "' should be a single, non-negative value.")
-    rlang::abort(msg)
+    cli::cli_abort("{.arg {arg_val}} should be a single, non-negative value.")
   }
   invisible(x)
 }

R/case_weights.R

@@ -87,10 +87,9 @@ case_weights_allowed <- function(spec) {
     get_from_env(paste0(mod_type, "_fit")) %>%
     dplyr::filter(engine == mod_eng & mode == mod_mode)
   if (nrow(model_info) != 1) {
-    rlang::abort(
-      glue::glue(
-        "Error in getting model information for model {mod_type} with engine {mod_eng} and mode {mod_mode}."
-      )
+    cli::cli_abort(
+      "Error in getting model information for model {mod_type} with
+       engine {mod_eng} and mode {mod_mode}."
     )
   }
   # If weights are used, they are protected data arguments with the canonical

R/condense_control.R

@@ -23,17 +23,12 @@
 condense_control <- function(x, ref) {
   mismatch <- setdiff(names(ref), names(x))
   if (length(mismatch)) {
-    rlang::abort(
+    cli::cli_abort(
       c(
-        glue::glue(
-          "Object of class `{class(x)[1]}` cannot be coerced to ",
-          "object of class `{class(ref)[1]}`."
-        ),
-        "The following arguments are missing:",
-        glue::glue_collapse(
-          glue::single_quote(mismatch),
-          sep = ", ", last = ", and "
-        )
+        "Object of class {.cls class(x)[1]} cannot be coerced to
+         object of class {.cls class(ref)[1]}.",
+        "i" = "{cli::qty(mismatch)} The argument{?s} {.arg {mismatch}}
+               {?is/are} missing."
       )
     )
   }

R/contr_one_hot.R

@@ -16,11 +16,11 @@
 #' @export
 contr_one_hot <- function(n, contrasts = TRUE, sparse = FALSE) {
   if (sparse) {
-    rlang::warn("`sparse = TRUE` not implemented for `contr_one_hot()`.")
+    cli::cli_warn("{.code sparse = TRUE} not implemented for {.fun contr_one_hot}.")
   }
 
   if (!contrasts) {
-    rlang::warn("`contrasts = FALSE` not implemented for `contr_one_hot()`.")
+    cli::cli_warn("{.code contrasts = FALSE} not implemented for {.fun contr_one_hot}.")
   }
 
   if (is.character(n)) {
@@ -30,12 +30,12 @@ contr_one_hot <- function(n, contrasts = TRUE, sparse = FALSE) {
     n <- as.integer(n)
 
     if (length(n) != 1L) {
-      rlang::abort("`n` must have length 1 when an integer is provided.")
+      cli::cli_abort("{.arg n} must have length 1 when an integer is provided.")
     }
 
     names <- as.character(seq_len(n))
   } else {
-    rlang::abort("`n` must be a character vector or an integer of size 1.")
+    cli::cli_abort("{.arg n} must be a character vector or an integer of size 1.")
   }
 
   out <- diag(n)