operation -> adjustment (closes #19)

R/adjust-equivocal-zone.R

@@ -32,8 +32,8 @@ adjust_equivocal_zone <- function(x, value = 0.1, threshold = 1 / 2) {
     check_number_decimal(threshold, min = 10^-10, max = 1 - 10^-10)
   }
 
-  op <-
-    new_operation(
+  adj <-
+    new_adjustment(
       "equivocal_zone",
       inputs = "probability",
       outputs = "class",
@@ -45,7 +45,7 @@ adjust_equivocal_zone <- function(x, value = 0.1, threshold = 1 / 2) {
 
   new_tailor(
     type = x$type,
-    operations = c(x$operations, list(op)),
+    adjustments = c(x$adjustments, list(adj)),
     columns = x$dat,
     ptype = x$ptype,
     call = current_env()
@@ -70,7 +70,7 @@ print.equivocal_zone <- function(x, ...) {
 
 #' @export
 fit.equivocal_zone <- function(object, data, tailor = NULL, ...) {
-  new_operation(
+  new_adjustment(
     class(object),
     inputs = object$inputs,
     outputs = object$outputs,

R/adjust-numeric-calibration.R

@@ -38,8 +38,8 @@ adjust_numeric_calibration <- function(x, method = NULL) {
     )
   }
 
-  op <-
-    new_operation(
+  adj <-
+    new_adjustment(
       "numeric_calibration",
       inputs = "numeric",
       outputs = "numeric",
@@ -51,7 +51,7 @@ adjust_numeric_calibration <- function(x, method = NULL) {
 
   new_tailor(
     type = x$type,
-    operations = c(x$operations, list(op)),
+    adjustments = c(x$adjustments, list(adj)),
     columns = x$dat,
     ptype = x$ptype,
     call = current_env()
@@ -81,7 +81,7 @@ fit.numeric_calibration <- function(object, data, tailor = NULL, ...) {
       )
     )
 
-  new_operation(
+  new_adjustment(
     class(object),
     inputs = object$inputs,
     outputs = object$outputs,

R/adjust-numeric-range.R

@@ -8,8 +8,8 @@ adjust_numeric_range <- function(x, lower_limit = -Inf, upper_limit = Inf) {
   # remaining input checks are done via probably::bound_prediction
   check_tailor(x)
 
-  op <-
-    new_operation(
+  adj <-
+    new_adjustment(
       "numeric_range",
       inputs = "numeric",
       outputs = "numeric",
@@ -21,7 +21,7 @@ adjust_numeric_range <- function(x, lower_limit = -Inf, upper_limit = Inf) {
 
   new_tailor(
     type = x$type,
-    operations = c(x$operations, list(op)),
+    adjustments = c(x$adjustments, list(adj)),
     columns = x$dat,
     ptype = x$ptype,
     call = current_env()
@@ -59,7 +59,7 @@ print.numeric_range <- function(x, ...) {
 
 #' @export
 fit.numeric_range <- function(object, data, tailor = NULL, ...) {
-  new_operation(
+  new_adjustment(
     class(object),
     inputs = object$inputs,
     outputs = object$outputs,

R/adjust-predictions-custom.R

@@ -28,8 +28,8 @@ adjust_predictions_custom <- function(x, ..., .pkgs = character(0)) {
   check_tailor(x)
   cmds <- enquos(...)
 
-  op <-
-    new_operation(
+  adj <-
+    new_adjustment(
       "predictions_custom",
       inputs = "everything",
       outputs = "everything",
@@ -43,7 +43,7 @@ adjust_predictions_custom <- function(x, ..., .pkgs = character(0)) {
 
   new_tailor(
     type = x$type,
-    operations = c(x$operations, list(op)),
+    adjustments = c(x$adjustments, list(adj)),
     columns = x$dat,
     ptype = x$ptype,
     call = current_env()
@@ -59,7 +59,7 @@ print.predictions_custom <- function(x, ...) {
 
 #' @export
 fit.predictions_custom <- function(object, data, tailor = NULL, ...) {
-  new_operation(
+  new_adjustment(
     class(object),
     inputs = object$inputs,
     outputs = object$outputs,

R/adjust-probability-calibration.R

@@ -17,8 +17,8 @@ adjust_probability_calibration <- function(x, method = NULL) {
     )
   }
 
-  op <-
-    new_operation(
+  adj <-
+    new_adjustment(
       "probability_calibration",
       inputs = "probability",
       outputs = "probability_class",
@@ -30,7 +30,7 @@ adjust_probability_calibration <- function(x, method = NULL) {
 
   new_tailor(
     type = x$type,
-    operations = c(x$operations, list(op)),
+    adjustments = c(x$adjustments, list(adj)),
     columns = x$dat,
     ptype = x$ptype,
     call = current_env()
@@ -62,7 +62,7 @@ fit.probability_calibration <- function(object, data, tailor = NULL, ...) {
       )
     )
 
-  new_operation(
+  new_adjustment(
     class(object),
     inputs = object$inputs,
     outputs = object$outputs,

R/adjust-probability-threshold.R

@@ -28,8 +28,8 @@ adjust_probability_threshold <- function(x, threshold = 0.5) {
     check_number_decimal(threshold, min = 10^-10, max = 1 - 10^-10)
   }
 
-  op <-
-    new_operation(
+  adj <-
+    new_adjustment(
       "probability_threshold",
       inputs = "probability",
       outputs = "class",
@@ -41,7 +41,7 @@ adjust_probability_threshold <- function(x, threshold = 0.5) {
 
   new_tailor(
     type = x$type,
-    operations = c(x$operations, list(op)),
+    adjustments = c(x$adjustments, list(adj)),
     columns = x$dat,
     ptype = x$ptype,
     call = current_env()
@@ -66,7 +66,7 @@ print.probability_threshold <- function(x, ...) {
 
 #' @export
 fit.probability_threshold <- function(object, data, tailor = NULL, ...) {
-  new_operation(
+  new_adjustment(
     class(object),
     inputs = object$inputs,
     outputs = object$outputs,

R/tailor.R

@@ -27,33 +27,33 @@ tailor <- function(type = "unknown", outcome = NULL, estimate = NULL,
 
   new_tailor(
     type,
-    operations = list(),
+    adjustments = list(),
     columns = columns,
     ptype = tibble::new_tibble(list()),
     call = current_env()
   )
 }
 
-new_tailor <- function(type, operations, columns, ptype, call) {
+new_tailor <- function(type, adjustments, columns, ptype, call) {
   type <- arg_match0(type, c("unknown", "regression", "binary", "multiclass"))
 
-  if (!is.list(operations)) {
-    cli_abort("The {.arg operations} argument should be a list.", call = call)
+  if (!is.list(adjustments)) {
+    cli_abort("The {.arg adjustments} argument should be a list.", call = call)
   }
 
-  is_oper <- purrr::map_lgl(operations, ~ inherits(.x, "operation"))
-  if (length(is_oper) > 0 && !any(is_oper)) {
-    bad_oper <- names(is_oper)[!is_oper]
-    cli_abort("The following {.arg operations} do not have the class \\
-                   {.val operation}: {bad_oper}.", call = call)
+  is_adjustment <- purrr::map_lgl(adjustments, ~ inherits(.x, "adjustment"))
+  if (length(is_adjustment) > 0 && !any(is_adjustment)) {
+    bad_adjustment <- names(is_adjustment)[!is_adjustment]
+    cli_abort("The following {.arg adjustments} do not have the class \\
+                   {.val adjustment}: {bad_adjustment}.", call = call)
   }
 
-  # validate operation order and check duplicates
-  validate_order(operations, type, call)
+  # validate adjustment order and check duplicates
+  validate_order(adjustments, type, call)
 
   # check columns
   res <- list(
-    type = type, operations = operations,
+    type = type, adjustments = adjustments,
     columns = columns, ptype = ptype
   )
   class(res) <- "tailor"
@@ -64,15 +64,15 @@ new_tailor <- function(type, operations, columns, ptype, call) {
 print.tailor <- function(x, ...) {
   cli::cli_h1("tailor")
 
-  num_op <- length(x$operations)
+  num_adj <- length(x$adjustments)
   cli::cli_text(
     "A {ifelse(x$type == 'unknown', '', x$type)} postprocessor \\
-     with {num_op} operation{?s}{cli::qty(num_op+1)}{?./:}"
+     with {num_adj} adjustment{?s}{cli::qty(num_adj+1)}{?./:}"
   )
 
-  if (num_op > 0) {
+  if (num_adj > 0) {
     cli::cli_text("\n")
-    res <- purrr::map(x$operations, print)
+    res <- purrr::map(x$adjustments, print)
   }
 
   invisible(x)
@@ -112,16 +112,16 @@ fit.tailor <- function(object, .data, outcome, estimate, probabilities = c(),
 
   object <- new_tailor(
     object$type,
-    operations = object$operations,
+    adjustments = object$adjustments,
     columns = columns,
     ptype = ptype,
     call = current_env()
   )
 
-  num_oper <- length(object$operations)
-  for (op in seq_len(num_oper)) {
-    object$operations[[op]] <- fit(object$operations[[op]], .data, object)
-    .data <- predict(object$operations[[op]], .data, object)
+  num_adjustment <- length(object$adjustments)
+  for (adj in seq_len(num_adjustment)) {
+    object$adjustments[[adj]] <- fit(object$adjustments[[adj]], .data, object)
+    .data <- predict(object$adjustments[[adj]], .data, object)
   }
 
   # todo Add a fitted tailor class?
@@ -131,9 +131,9 @@ fit.tailor <- function(object, .data, outcome, estimate, probabilities = c(),
 #' @export
 predict.tailor <- function(object, new_data, ...) {
   # validate levels/classes
-  num_oper <- length(object$operations)
-  for (op in seq_len(num_oper)) {
-    new_data <- predict(object$operations[[op]], new_data, object)
+  num_adjustment <- length(object$adjustments)
+  for (adj in seq_len(num_adjustment)) {
+    new_data <- predict(object$adjustments[[adj]], new_data, object)
   }
   if (!tibble::is_tibble(new_data)) {
     new_data <- tibble::as_tibble(new_data)

R/utils.R

@@ -14,7 +14,7 @@ is_tune <- function(x) {
   isTRUE(identical(quote(tune), x[[1]]))
 }
 
-# for operations with no tunable parameters
+# for adjustments with no tunable parameters
 
 no_param <-
   tibble::tibble(
@@ -33,7 +33,7 @@ no_param <-
 input_vals <- c("numeric", "probability", "class", "everything")
 output_vals <- c("numeric", "probability_class", "class", "everything")
 
-new_operation <- function(cls, inputs, outputs, arguments, results = list(),
+new_adjustment <- function(cls, inputs, outputs, arguments, results = list(),
                           trained, requires_fit, ...) {
   inputs <- arg_match0(inputs, input_vals)
   outputs <- arg_match0(outputs, output_vals)
@@ -49,7 +49,7 @@ new_operation <- function(cls, inputs, outputs, arguments, results = list(),
       trained = trained,
       requires_fit = requires_fit
     )
-  class(res) <- c(cls, "operation")
+  class(res) <- c(cls, "adjustment")
   res
 }
 
@@ -62,25 +62,25 @@ is_tailor <- function(x) {
 #' @keywords internal
 #' @rdname tailor-internals
 tailor_fully_trained <- function(x) {
-  if (length(x$operations) == 0L) {
+  if (length(x$adjustments) == 0L) {
     return(FALSE)
   }
 
-  all(purrr::map_lgl(x$operations, tailor_operation_trained))
+  all(purrr::map_lgl(x$adjustments, tailor_adjustment_trained))
 }
 
-tailor_operation_trained <- function(x) {
+tailor_adjustment_trained <- function(x) {
   isTRUE(x$trained)
 }
 
 #' @export
 #' @keywords internal
 #' @rdname tailor-internals
 tailor_requires_fit <- function(x) {
-  any(purrr::map_lgl(x$operations, tailor_operation_requires_fit))
+  any(purrr::map_lgl(x$adjustments, tailor_adjustment_requires_fit))
 }
 
-tailor_operation_requires_fit <- function(x) {
+tailor_adjustment_requires_fit <- function(x) {
   isTRUE(x$requires_fit)
 }
 
@@ -114,7 +114,7 @@ check_calibration_type <- function(calibration_type, calibration_type_expected,
                                    tailor_type, call) {
   if (!identical(calibration_type, calibration_type_expected)) {
     cli_abort(
-      "A {.field {tailor_type}} tailor is incompatible with the operation \\
+      "A {.field {tailor_type}} tailor is incompatible with the adjustment \\
        {.fun {paste0('adjust_', calibration_type, '_calibration')}}.",
       call = call
     )