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#-------------------------------- Plot -----------------------------------------
#------------------------------ >> Methods -------------------------------------
#' Probability calibration plots via moving windows
#'
#' @description
#' A plot is created to assess whether the observed rate of the event is about
#' the sample as the predicted probability of the event from some model. This
#' is similar to [cal_plot_breaks()], except that the bins are overlapping.
#'
#' A sequence of bins are created from zero to one. For each bin, the data whose
#' predicted probability falls within the range of the bin is used to calculate
#' the observed event rate (along with confidence intervals for the event rate).
#'
#' If the predictions are well calibrated, the fitted curve should align with
#' the diagonal line.
#' @param window_size The size of segments. Used for the windowed probability
#' calculations. It defaults to 10% of segments.
#' @param step_size The gap between segments. Used for the windowed probability
#' calculations. It defaults to half the size of `window_size`
#' @return A ggplot object.
#' @seealso
#' \url{https://www.tidymodels.org/learn/models/calibration/},
#' [cal_plot_logistic()], [cal_plot_breaks()]
#' @examples
#'
#' library(ggplot2)
#' library(dplyr)
#'
#' cal_plot_windowed(
#' segment_logistic,
#' Class,
#' .pred_good
#' )
#'
#' # More breaks
#' cal_plot_windowed(
#' segment_logistic,
#' Class,
#' .pred_good,
#' window_size = 0.05
#' )
#' @inheritParams cal_plot_breaks
#' @seealso [cal_plot_breaks()], [cal_plot_logistic()]
#' @export
cal_plot_windowed <- function(.data,
truth = NULL,
estimate = dplyr::starts_with(".pred"),
window_size = 0.1,
step_size = window_size / 2,
conf_level = 0.90,
include_ribbon = TRUE,
include_rug = TRUE,
include_points = TRUE,
event_level = c("auto", "first", "second"),
...) {
UseMethod("cal_plot_windowed")
}
#' @export
#' @rdname cal_plot_windowed
cal_plot_windowed.data.frame <- function(.data,
truth = NULL,
estimate = dplyr::starts_with(".pred"),
window_size = 0.1,
step_size = window_size / 2,
conf_level = 0.90,
include_ribbon = TRUE,
include_rug = TRUE,
include_points = TRUE,
event_level = c("auto", "first", "second"),
...,
.by = NULL) {
group <- get_group_argument({{ .by }}, .data)
.data <- dplyr::group_by(.data, dplyr::across({{ group }}))
cal_plot_windowed_impl(
.data = .data,
truth = {{ truth }},
estimate = {{ estimate }},
group = {{ group }},
window_size = window_size,
step_size = step_size,
conf_level = conf_level,
include_ribbon = include_ribbon,
include_rug = include_rug,
include_points = include_points,
event_level = event_level,
is_tune_results = FALSE
)
}
#' @export
#' @rdname cal_plot_windowed
cal_plot_windowed.tune_results <- function(.data,
truth = NULL,
estimate = dplyr::starts_with(".pred"),
window_size = 0.1,
step_size = window_size / 2,
conf_level = 0.90,
include_ribbon = TRUE,
include_rug = TRUE,
include_points = TRUE,
event_level = c("auto", "first", "second"),
...) {
tune_args <- tune_results_args(
.data = .data,
truth = {{ truth }},
estimate = {{ estimate }},
event_level = event_level,
...
)
cal_plot_windowed_impl(
.data = tune_args$predictions,
truth = !!tune_args$truth,
estimate = !!tune_args$estimate,
group = !!tune_args$group,
window_size = window_size,
step_size = step_size,
conf_level = conf_level,
include_ribbon = include_ribbon,
include_rug = include_rug,
include_points = include_points,
event_level = event_level,
is_tune_results = TRUE
)
}
#' @export
#' @rdname cal_plot_windowed
cal_plot_windowed.grouped_df <- function(.data,
truth = NULL,
estimate = NULL,
window_size = 0.1,
step_size = window_size / 2,
conf_level = 0.90,
include_ribbon = TRUE,
include_rug = TRUE,
include_points = TRUE,
event_level = c("auto", "first", "second"),
...) {
abort_if_grouped_df()
}
#--------------------------- >> Implementation ---------------------------------
cal_plot_windowed_impl <- function(.data,
truth = NULL,
estimate = dplyr::starts_with(".pred"),
group = NULL,
window_size = 0.1,
step_size = window_size / 2,
conf_level = 0.90,
include_ribbon = TRUE,
include_rug = TRUE,
include_points = TRUE,
event_level = c("auto", "first", "second"),
is_tune_results = FALSE,
...) {
truth <- enquo(truth)
estimate <- enquo(estimate)
group <- enquo(group)
prob_tbl <- .cal_table_windowed(
.data = .data,
truth = !!truth,
estimate = !!estimate,
group = !!group,
window_size = window_size,
step_size = step_size,
conf_level = conf_level,
event_level = event_level
)
cal_plot_impl(
tbl = prob_tbl,
x = predicted_midpoint,
y = event_rate,
.data = .data,
truth = !!truth,
estimate = !!estimate,
group = !!group,
x_label = "Window Midpoint",
y_label = "Event Rate",
include_ribbon = include_ribbon,
include_rug = include_rug,
include_points = include_points,
is_tune_results = is_tune_results
)
}
#---------------------------------- Table --------------------------------------
#------------------------------- >> Methods ------------------------------------
#' @rdname cal_binary_tables
#' @export
#' @keywords internal
.cal_table_windowed <- function(.data,
truth = NULL,
estimate = NULL,
.by = NULL,
window_size = 0.1,
step_size = window_size / 2,
conf_level = 0.90,
event_level = c("auto", "first", "second"),
...) {
UseMethod(".cal_table_windowed")
}
#' @export
#' @keywords internal
.cal_table_windowed.data.frame <- function(.data,
truth = NULL,
estimate = NULL,
.by = NULL,
window_size = 0.1,
step_size = window_size / 2,
conf_level = 0.90,
event_level = c("auto", "first", "second"),
...) {
.cal_table_windowed_impl(
.data = .data,
truth = {{ truth }},
estimate = {{ estimate }},
group = {{ .by }},
window_size = window_size,
step_size = step_size,
conf_level = conf_level,
event_level = event_level
)
}
#' @export
#' @keywords internal
.cal_table_windowed.tune_results <- function(.data,
truth = NULL,
estimate = NULL,
.by = NULL,
window_size = 0.1,
step_size = window_size / 2,
conf_level = 0.90,
event_level = c("auto", "first", "second"),
...) {
tune_args <- tune_results_args(
.data = .data,
truth = {{ truth }},
estimate = {{ estimate }},
event_level = event_level,
...
)
.cal_table_windowed_impl(
.data = tune_args$predictions,
truth = !!tune_args$truth,
estimate = !!tune_args$estimate,
group = !!tune_args$group,
window_size = window_size,
step_size = step_size,
conf_level = conf_level,
event_level = event_level
)
}
#--------------------------- >> Implementation ---------------------------------
.cal_table_windowed_impl <- function(.data,
truth = NULL,
estimate = NULL,
group = NULL,
window_size = 0.1,
step_size = window_size / 2,
conf_level = 0.90,
event_level = c("auto", "first", "second"),
...) {
truth <- enquo(truth)
estimate <- enquo(estimate)
group <- enquo(group)
levels <- truth_estimate_map(
.data = .data,
truth = !!truth,
estimate = !!estimate
)
res <- .data %>%
dplyr::group_by(!!group, .add = TRUE) %>%
dplyr::group_map(~ {
grp <- .cal_table_windowed_grp(
.data = .x,
truth = !!truth,
window_size = window_size,
step_size = step_size,
conf_level = conf_level,
event_level = event_level,
levels = levels
)
dplyr::bind_cols(.y, grp)
}) %>%
dplyr::bind_rows()
if (length(levels) > 2) {
res <- dplyr::group_by(res, !!truth, .add = TRUE)
}
res
}
.cal_table_windowed_grp <- function(.data,
truth,
window_size = 0.1,
step_size = window_size / 2,
conf_level = 0.90,
event_level = c("auto", "first", "second"),
levels = levels,
...) {
steps <- seq(0, 1, by = step_size)
cuts <- list()
cuts$lower_cut <- steps - (window_size / 2)
cuts$lower_cut[cuts$lower_cut < 0] <- 0
cuts$upper_cut <- steps + (window_size / 2)
cuts$upper_cut[cuts$upper_cut > 1] <- 1
.cal_class_grps(
.data = .data,
truth = {{ truth }},
cuts = cuts,
levels = levels,
event_level = event_level,
conf_level = conf_level
)
}
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