first pass at the post-processing container

DESCRIPTION

@@ -6,18 +6,28 @@ Authors@R: c(
     person("Hannah", "Frick", , "[email protected]", role = "aut"),
     person("Emil", "HvitFeldt", , "[email protected]", role = "aut"),
     person("Max", "Kuhn", , "[email protected]", role = c("aut", "cre")),
-    person(given = "Posit Software, PBC", role = c("cph", "fnd"))
+    person("Posit Software, PBC", role = c("cph", "fnd"))
   )
 Description: Sandbox for a postprocessor object.
 License: MIT + file LICENSE
+URL: https://github.com/tidymodels/container
+BugReports: https://github.com/tidymodels/container/issues
+Imports: 
+    cli,
+    dplyr,
+    generics,
+    hardhat,
+    probably (>= 1.0.3.9000),
+    purrr,
+    rlang (>= 1.1.0),
+    tibble,
+    tidyselect
 Suggests: 
+    modeldata,
     testthat (>= 3.0.0)
+Remotes:
+    tidymodels/probably
 Config/testthat/edition: 3
 Encoding: UTF-8
 Roxygen: list(markdown = TRUE)
 RoxygenNote: 7.3.1
-URL: https://github.com/tidymodels/container
-BugReports: https://github.com/tidymodels/container/issues
-Imports: 
-    cli,
-    rlang (>= 1.1.0)

NAMESPACE

@@ -1,6 +1,63 @@
 # Generated by roxygen2: do not edit by hand
 
+S3method(fit,container)
+S3method(fit,equivocal_zone)
+S3method(fit,numeric_calibration)
+S3method(fit,numeric_range)
+S3method(fit,predictions_custom)
+S3method(fit,probability_calibration)
+S3method(fit,probability_threshold)
+S3method(predict,container)
+S3method(predict,equivocal_zone)
+S3method(predict,numeric_calibration)
+S3method(predict,numeric_range)
+S3method(predict,predictions_custom)
+S3method(predict,probability_calibration)
+S3method(predict,probability_threshold)
+S3method(print,container)
+S3method(print,equivocal_zone)
+S3method(print,numeric_calibration)
+S3method(print,numeric_range)
+S3method(print,predictions_custom)
+S3method(print,probability_calibration)
+S3method(print,probability_threshold)
+S3method(required_pkgs,equivocal_zone)
+S3method(required_pkgs,numeric_calibration)
+S3method(required_pkgs,numeric_range)
+S3method(required_pkgs,predictions_custom)
+S3method(required_pkgs,probability_calibration)
+S3method(required_pkgs,probability_threshold)
+S3method(tunable,equivocal_zone)
+S3method(tunable,numeric_calibration)
+S3method(tunable,numeric_range)
+S3method(tunable,predictions_custom)
+S3method(tunable,probability_calibration)
+S3method(tunable,probability_threshold)
+export("%>%")
+export(adjust_equivocal_zone)
+export(adjust_numeric_calibration)
+export(adjust_numeric_range)
+export(adjust_predictions_custom)
+export(adjust_probability_calibration)
+export(adjust_probability_threshold)
+export(container)
+export(extract_parameter_dials)
+export(extract_parameter_set_dials)
+export(fit)
+export(required_pkgs)
+export(tidy)
+export(tunable)
+export(tune_args)
 import(rlang)
 importFrom(cli,cli_abort)
 importFrom(cli,cli_inform)
 importFrom(cli,cli_warn)
+importFrom(dplyr,"%>%")
+importFrom(generics,fit)
+importFrom(generics,required_pkgs)
+importFrom(generics,tidy)
+importFrom(generics,tunable)
+importFrom(generics,tune_args)
+importFrom(hardhat,extract_parameter_dials)
+importFrom(hardhat,extract_parameter_set_dials)
+importFrom(stats,predict)

R/adjust-equivocal-zone.R

@@ -0,0 +1,118 @@
+#' Apply an equivocal zone to a binary classification model.
+#'
+#' @param x A [container()].
+#' @param value A numeric value (between zero and 1/2) or [hardhat::tune()]. The
+#' value is the size of the buffer around the threshold.
+#' @param threshold A numeric value (between zero and one) or [hardhat::tune()].
+#' @examples
+#' library(dplyr)
+#' library(modeldata)
+#'
+#' post_obj <-
+#'   container(mode = "classification") %>%
+#'   adjust_equivocal_zone(value = 1 / 4)
+#'
+#'
+#' post_res <- fit(
+#'   post_obj,
+#'   two_class_example,
+#'   outcome = c(truth),
+#'   estimate = c(predicted),
+#'   probabilities = c(Class1, Class2)
+#' )
+#'
+#' predict(post_res, two_class_example)
+#' @export
+adjust_equivocal_zone <- function(x, value = 0.1, threshold = 1 / 2) {
+  check_container(x)
+  if (!is_tune(value)) {
+    check_number_decimal(value, min = 0, max = 1 / 2)
+  }
+  if (!is_tune(threshold)) {
+    check_number_decimal(threshold, min = 10^-10, max = 1 - 10^-10)
+  }
+
+  op <-
+    new_operation(
+      "equivocal_zone",
+      inputs = "probability",
+      outputs = "class",
+      arguments = list(value = value, threshold = threshold),
+      results = list(),
+      trained = FALSE
+    )
+
+  new_container(
+    mode = x$mode,
+    type = x$type,
+    operations = c(x$operations, list(op)),
+    columns = x$dat,
+    ptype = x$ptype,
+    call = current_env()
+  )
+}
+
+#' @export
+print.equivocal_zone <- function(x, ...) {
+  # check for tune() first
+
+  if (is_tune(x$arguments$value)) {
+    cli::cli_bullets(c("*" = "Add equivocal zone of optimized size."))
+  } else {
+    trn <- ifelse(x$trained, " [trained]", "")
+    cli::cli_bullets(c(
+      "*" = "Add equivocal zone of size
+             {signif(x$arguments$value, digits = 3)}.{trn}"
+    ))
+  }
+  invisible(x)
+}
+
+#' @export
+fit.equivocal_zone <- function(object, data, container = NULL, ...) {
+  new_operation(
+    class(object),
+    inputs = object$inputs,
+    outputs = object$outputs,
+    arguments = object$arguments,
+    results = list(),
+    trained = TRUE
+  )
+}
+
+#' @export
+predict.equivocal_zone <- function(object, new_data, container, ...) {
+  est_nm <- container$columns$estimate
+  prob_nm <- container$columns$probabilities[1]
+  lvls <- levels(new_data[[est_nm]])
+  col_syms <- syms(prob_nm[1])
+  cls_pred <- probably::make_two_class_pred(
+    new_data[[prob_nm]],
+    levels = lvls,
+    buffer = object$arguments$value,
+    threshold = object$arguments$threshold
+  )
+  new_data[[est_nm]] <- cls_pred # todo convert to factor?
+  new_data
+}
+
+#' @export
+required_pkgs.equivocal_zone <- function(x, ...) {
+  c("container", "probably")
+}
+
+#' @export
+tunable.equivocal_zone <- function(x, ...) {
+  tibble::new_tibble(list(
+    name = "buffer",
+    call_info = list(list(pkg = "dials", fun = "buffer")),
+    source = "container",
+    component = "equivocal_zone",
+    component_id = "equivocal_zone"
+  ))
+}
+
+# todo missing methods:
+# todo tune_args
+# todo tidy
+# todo extract_parameter_set_dials

R/adjust-numeric-calibration.R

@@ -0,0 +1,99 @@
+#' Re-calibrate numeric predictions
+#'
+#' @param x A [container()].
+#' @param calibrator A pre-trained calibration method from the \pkg{probably}
+#' package, such as [probably::cal_estimate_linear()].
+#' @examples
+#' library(modeldata)
+#' library(probably)
+#' library(tibble)
+#'
+#' # create example data
+#' set.seed(1)
+#' dat <- tibble(y = rnorm(100), y_pred = y/2 + rnorm(100))
+#'
+#' dat
+#'
+#' # calibrate numeric predictions
+#' reg_cal <- cal_estimate_linear(dat, truth = y, estimate = y_pred)
+#'
+#' # specify calibration
+#' reg_ctr <-
+#'   container(mode = "regression") %>%
+#'   adjust_numeric_calibration(reg_cal)
+#'
+#' # "train" container
+#' reg_ctr_trained <- fit(reg_ctr, dat, outcome = y, estimate = y_pred)
+#'
+#' predict(reg_ctr, dat)
+#' @export
+adjust_numeric_calibration <- function(x, calibrator) {
+  check_container(x)
+  check_required(calibrator)
+  if (!inherits(calibrator, "cal_regression")) {
+    cli_abort(
+      "{.arg calibrator} should be a \\
+       {.help [<cal_regression> object](probably::cal_estimate_linear)}, \\
+       not {.obj_type_friendly {calibrator}}."
+    )
+  }
+
+  op <-
+    new_operation(
+      "numeric_calibration",
+      inputs = "numeric",
+      outputs = "numeric",
+      arguments = list(calibrator = calibrator),
+      results = list(),
+      trained = FALSE
+    )
+
+  new_container(
+    mode = x$mode,
+    type = x$type,
+    operations = c(x$operations, list(op)),
+    columns = x$dat,
+    ptype = x$ptype,
+    call = current_env()
+  )
+}
+
+#' @export
+print.numeric_calibration <- function(x, ...) {
+  trn <- ifelse(x$trained, " [trained]", "")
+  cli::cli_bullets(c("*" = "Re-calibrate numeric predictions.{trn}"))
+  invisible(x)
+}
+
+#' @export
+fit.numeric_calibration <- function(object, data, container = NULL, ...) {
+  new_operation(
+    class(object),
+    inputs = object$inputs,
+    outputs = object$outputs,
+    arguments = object$arguments,
+    results = list(),
+    trained = TRUE
+  )
+}
+
+#' @export
+predict.numeric_calibration <- function(object, new_data, container, ...) {
+  probably::cal_apply(new_data, object$argument$calibrator)
+}
+
+# todo probably needs required_pkgs methods for cal objects
+#' @export
+required_pkgs.numeric_calibration <- function(x, ...) {
+  c("container", "probably")
+}
+
+#' @export
+tunable.numeric_calibration <- function(x, ...) {
+  no_param
+}
+
+# todo missing methods:
+# todo tune_args
+# todo tidy
+# todo extract_parameter_set_dials