simplify concept of outcome type in the package (#14)

* remove `container(mode)`
* rename `adjust_*_calibration(type)` to `adjust_*_calibration(method)`

DESCRIPTION

@@ -1,6 +1,6 @@
 Package: container
 Title: Sandbox for a postprocessor object
-Version: 0.0.0.9000
+Version: 0.0.0.9001
 Authors@R: c(
     person("Simon", "Couch", , "[email protected]", role = "aut"),
     person("Hannah", "Frick", , "[email protected]", role = "aut"),

R/adjust-equivocal-zone.R

@@ -9,7 +9,7 @@
 #' library(modeldata)
 #'
 #' post_obj <-
-#'   container(mode = "classification") %>%
+#'   container() %>%
 #'   adjust_equivocal_zone(value = 1 / 4)
 #'
 #'
@@ -43,7 +43,6 @@ adjust_equivocal_zone <- function(x, value = 0.1, threshold = 1 / 2) {
     )
 
   new_container(
-    mode = x$mode,
     type = x$type,
     operations = c(x$operations, list(op)),
     columns = x$dat,

R/adjust-numeric-calibration.R

@@ -1,7 +1,7 @@
 #' Re-calibrate numeric predictions
 #'
 #' @param x A [container()].
-#' @param type Character. One of `"linear"`, `"isotonic"`, or
+#' @param method Character. One of `"linear"`, `"isotonic"`, or
 #' `"isotonic_boot"`, corresponding to the function from the \pkg{probably}
 #' package [probably::cal_estimate_linear()],
 #' [probably::cal_estimate_isotonic()], or
@@ -19,21 +19,21 @@
 #'
 #' # specify calibration
 #' reg_ctr <-
-#'   container(mode = "regression") %>%
-#'   adjust_numeric_calibration(type = "linear")
+#'   container() %>%
+#'   adjust_numeric_calibration(method = "linear")
 #'
 #' # train container
 #' reg_ctr_trained <- fit(reg_ctr, dat, outcome = y, estimate = y_pred)
 #'
 #' predict(reg_ctr_trained, dat)
 #' @export
-adjust_numeric_calibration <- function(x, type = NULL) {
+adjust_numeric_calibration <- function(x, method = NULL) {
   # to-do: add argument specifying `prop` in initial_split
   check_container(x, calibration_type = "numeric")
-  # wait to `check_type()` until `fit()` time
-  if (!is.null(type)) {
+  # wait to `check_method()` until `fit()` time
+  if (!is.null(method)) {
     arg_match0(
-      type,
+      method,
       c("linear", "isotonic", "isotonic_boot")
     )
   }
@@ -43,13 +43,12 @@ adjust_numeric_calibration <- function(x, type = NULL) {
       "numeric_calibration",
       inputs = "numeric",
       outputs = "numeric",
-      arguments = list(type = type),
+      arguments = list(method = method),
       results = list(),
       trained = FALSE
     )
 
   new_container(
-    mode = x$mode,
     type = x$type,
     operations = c(x$operations, list(op)),
     columns = x$dat,
@@ -67,13 +66,13 @@ print.numeric_calibration <- function(x, ...) {
 
 #' @export
 fit.numeric_calibration <- function(object, data, container = NULL, ...) {
-  type <- check_type(object$type, container$type)
+  method <- check_method(object$method, container$type)
   # todo: adjust_numeric_calibration() should take arguments to pass to
   # cal_estimate_* via dots
   fit <-
     eval_bare(
       call2(
-        paste0("cal_estimate_", type),
+        paste0("cal_estimate_", method),
         .data = data,
         truth = container$columns$outcome,
         estimate = container$columns$estimate,

R/adjust-numeric-range.R

@@ -19,7 +19,6 @@ adjust_numeric_range <- function(x, lower_limit = -Inf, upper_limit = Inf) {
     )
 
   new_container(
-    mode = x$mode,
     type = x$type,
     operations = c(x$operations, list(op)),
     columns = x$dat,

R/adjust-predictions-custom.R

@@ -9,7 +9,7 @@
 #' library(modeldata)
 #'
 #' post_obj <-
-#'   container(mode = "classification") %>%
+#'   container() %>%
 #'   adjust_equivocal_zone() %>%
 #'   adjust_predictions_custom(linear_predictor = binomial()$linkfun(Class2))
 #'
@@ -39,7 +39,6 @@ adjust_predictions_custom <- function(x, ..., .pkgs = character(0)) {
     )
 
   new_container(
-    mode = x$mode,
     type = x$type,
     operations = c(x$operations, list(op)),
     columns = x$dat,

R/adjust-probability-calibration.R

@@ -1,18 +1,18 @@
 #' Re-calibrate classification probability predictions
 #'
 #' @param x A [container()].
-#' @param type Character. One of `"logistic"`, `"multinomial"`,
+#' @param method Character. One of `"logistic"`, `"multinomial"`,
 #' `"beta"`, `"isotonic"`, or `"isotonic_boot"`, corresponding to the
 #' function from the \pkg{probably} package [probably::cal_estimate_logistic()],
 #' [probably::cal_estimate_multinomial()], etc., respectively.
 #' @export
-adjust_probability_calibration <- function(x, type = NULL) {
+adjust_probability_calibration <- function(x, method = NULL) {
   # to-do: add argument specifying `prop` in initial_split
   check_container(x, calibration_type = "probability")
-  # wait to `check_type()` until `fit()` time
-  if (!is.null(type)) {
+  # wait to `check_method()` until `fit()` time
+  if (!is.null(method)) {
     arg_match(
-      type,
+      method,
       c("logistic", "multinomial", "beta", "isotonic", "isotonic_boot")
     )
   }
@@ -22,13 +22,12 @@ adjust_probability_calibration <- function(x, type = NULL) {
       "probability_calibration",
       inputs = "probability",
       outputs = "probability_class",
-      arguments = list(type = type),
+      arguments = list(method = method),
       results = list(),
       trained = FALSE
     )
 
   new_container(
-    mode = x$mode,
     type = x$type,
     operations = c(x$operations, list(op)),
     columns = x$dat,
@@ -46,14 +45,14 @@ print.probability_calibration <- function(x, ...) {
 
 #' @export
 fit.probability_calibration <- function(object, data, container = NULL, ...) {
-  type <- check_type(object$type, container$type)
+  method <- check_method(object$method, container$type)
   # todo: adjust_probability_calibration() should take arguments to pass to
   # cal_estimate_* via dots
   # to-do: add argument specifying `prop` in initial_split
   fit <-
     eval_bare(
       call2(
-        paste0("cal_estimate_", type),
+        paste0("cal_estimate_", method),
         .data = data,
         # todo: make getters for the entries in `columns`
         truth = container$columns$outcome,

R/adjust-probability-threshold.R

@@ -7,7 +7,7 @@
 #' library(modeldata)
 #'
 #' post_obj <-
-#'   container(mode = "classification") %>%
+#'   container() %>%
 #'   adjust_probability_threshold(threshold = .1)
 #'
 #' two_class_example %>% count(predicted)
@@ -39,7 +39,6 @@ adjust_probability_threshold <- function(x, threshold = 0.5) {
     )
 
   new_container(
-    mode = x$mode,
     type = x$type,
     operations = c(x$operations, list(op)),
     columns = x$dat,

R/container.R

@@ -1,7 +1,5 @@
 #' Declare post-processing for model predictions
 #'
-#' @param mode The model's mode, one of `"classification"`, or `"regression"`.
-#' Modes of `"censored regression"` are not currently supported.
 #' @param type The model sub-type. Possible values are `"unknown"`, `"regression"`,
 #' `"binary"`, or `"multiclass"`.
 #' @param outcome The name of the outcome variable.
@@ -14,9 +12,9 @@
 #' @param time The name of the predicted event time. (not yet supported)
 #' @examples
 #'
-#' container(mode = "regression")
+#' container()
 #' @export
-container <- function(mode, type = "unknown", outcome = NULL, estimate = NULL,
+container <- function(type = "unknown", outcome = NULL, estimate = NULL,
                       probabilities = NULL, time = NULL) {
   columns <-
     list(
@@ -28,7 +26,6 @@ container <- function(mode, type = "unknown", outcome = NULL, estimate = NULL,
     )
 
   new_container(
-    mode,
     type,
     operations = list(),
     columns = columns,
@@ -37,13 +34,7 @@ container <- function(mode, type = "unknown", outcome = NULL, estimate = NULL,
   )
 }
 
-new_container <- function(mode, type, operations, columns, ptype, call) {
-  mode <- arg_match0(mode, c("regression", "classification"))
-
-  if (mode == "regression") {
-    type <- "regression"
-  }
-
+new_container <- function(type, operations, columns, ptype, call) {
   type <- arg_match0(type, c("unknown", "regression", "binary", "multiclass"))
 
   if (!is.list(operations)) {
@@ -58,11 +49,11 @@ new_container <- function(mode, type, operations, columns, ptype, call) {
   }
 
   # validate operation order and check duplicates
-  validate_order(operations, mode, call)
+  validate_order(operations, type, call)
 
   # check columns
   res <- list(
-    mode = mode, type = type, operations = operations,
+    type = type, operations = operations,
     columns = columns, ptype = ptype
   )
   class(res) <- "container"
@@ -120,7 +111,6 @@ fit.container <- function(object, .data, outcome, estimate, probabilities = c(),
   object <- set_container_type(object, .data[[columns$outcome]])
 
   object <- new_container(
-    object$mode,
     object$type,
     operations = object$operations,
     columns = columns,

R/utils.R

@@ -61,13 +61,13 @@ check_container <- function(x, calibration_type = NULL, call = caller_env(), arg
   # check that the type of calibration ("numeric" or "probability") is
   # compatible with the container type
   if (!is.null(calibration_type)) {
-    container_type <- x$type
+    type <- x$type
     switch(
-      container_type,
+      type,
       regression =
-        check_calibration_type(calibration_type, "numeric", container_type, call = call),
-      binary = , multinomial =
-        check_calibration_type(calibration_type, "probability", container_type, call = call)
+        check_calibration_type(calibration_type, "numeric", type, call = call),
+      binary = , multiclass =
+        check_calibration_type(calibration_type, "probability", type, call = call)
     )
   }
 
@@ -90,54 +90,53 @@ types_binary <- c("logistic", "beta", "isotonic", "isotonic_boot")
 types_multiclass <- c("multinomial", "beta", "isotonic", "isotonic_boot")
 # a check function to be called when a container is being `fit()`ted.
 # by the time a container is fitted, we have:
-# * `adjust_type`, the `type` argument passed to an `adjust_*` function
+# * `method`, the `method` argument passed to an `adjust_*` function
 #     * this argument has already been checked to agree with the kind of
 #       `adjust_*()` function via `arg_match0()`.
 # * `container_type`, the `type` argument either specified in `container()`
 #   or inferred in `fit.container()`.
-check_type <- function(adjust_type,
-                       container_type,
-                       arg = caller_arg(adjust_type),
+check_method <- function(method,
+                       type,
+                       arg = caller_arg(method),
                        call = caller_env()) {
-  # if no `adjust_type` was supplied, infer a reasonable one based on the
-  # `container_type`
-  if (is.null(adjust_type)) {
+  # if no `method` was supplied, infer a reasonable one based on the `type`
+  if (is.null(method)) {
     switch(
-      container_type,
+      type,
       regression = return("linear"),
       binary = return("logistic"),
       multiclass = return("multinomial")
     )
   }
 
   switch(
-    container_type,
+    type,
     regression = arg_match0(
-      adjust_type,
+      method,
       types_regression,
       arg_nm = arg,
       error_call = call
     ),
     binary = arg_match0(
-      adjust_type,
+      method,
       types_binary,
       arg_nm = arg,
       error_call = call
     ),
     multiclass = arg_match0(
-      adjust_type,
+      method,
       types_multiclass,
       arg_nm = arg,
       error_call = call
     ),
     arg_match0(
-      adjust_type,
+      method,
       unique(c(types_regression, types_binary, types_multiclass)),
       arg_nm = arg,
       error_call = call
     )
   )
 
-  adjust_type
+  method
 }
 

R/validation-rules.R

@@ -1,4 +1,4 @@
-validate_order <- function(ops, mode, call) {
+validate_order <- function(ops, type, call = caller_env()) {
   orderings <-
     tibble::new_tibble(list(
       name = purrr::map_chr(ops, ~ class(.x)[1]),
@@ -13,12 +13,17 @@ validate_order <- function(ops, mode, call) {
     return(invisible(orderings))
   }
 
-  if (mode == "classification") {
-    check_classification_order(orderings, call)
-  } else {
-    check_regression_order(orderings, call)
+  if (type == "unknown") {
+    type <- infer_type(orderings)
   }
 
+  switch(
+    type,
+    regression = check_regression_order(orderings, call),
+    binary = , multiclass = check_classification_order(orderings, call),
+    invisible()
+  )
+
   invisible(orderings)
 }
 
@@ -83,3 +88,19 @@ check_duplicates <- function(x, call) {
   }
   invisible(x)
 }
+
+infer_type <- function(orderings) {
+  if (all(orderings$output_all)) {
+    return("unknown")
+  }
+
+  if (all(orderings$output_numeric | orderings$output_all)) {
+    return("regression")
+  }
+
+  if (all(orderings$output_prob | orderings$output_class | orderings$output_all)) {
+    return("binary")
+  }
+
+  "unknown"
+}

inst/examples/container_regression_example.qmd

@@ -101,7 +101,7 @@ We could manually use `cal_apply()` to adjust predictions, but instead, we'll ad
 #| label: post-1
 
 post_obj <-
-  container(mode = "regression") %>%
+  container() %>%
   adjust_numeric_calibration(bst_cal) 
 post_obj
 ```