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#-------------------------------- Plot -----------------------------------------
#------------------------------ >> Methods -------------------------------------
#' Probability calibration plots via binning
#'
#' @description
#' A plot is created to assess whether the observed rate of the event is about
#' the same as the predicted probability of the event from some model.
#'
#' A sequence of even, mutually exclusive 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 .data An ungrouped data frame object containing predictions and
#' probability columns.
#' @param truth The column identifier for the true class results
#' (that is a factor). This should be an unquoted column name.
#' @param estimate A vector of column identifiers, or one of `dplyr` selector
#' functions to choose which variables contains the class probabilities. It
#' defaults to the prefix used by tidymodels (`.pred_`). The order of the
#' identifiers will be considered the same as the order of the levels of the
#' `truth` variable.
#' @param .by The column identifier for the grouping variable. This should be
#' a single unquoted column name that selects a qualitative variable for
#' grouping. Default to `NULL`. When `.by = NULL` no grouping will take place.
#' @param event_level single string. Either "first" or "second" to specify which
#' level of truth to consider as the "event". Defaults to "auto", which allows
#' the function decide which one to use based on the type of model (binary,
#' multi-class or linear)
#' @param num_breaks The number of segments to group the probabilities. It
#' defaults to 10.
#' @param conf_level Confidence level to use in the visualization. It defaults
#' to 0.9.
#' @param include_ribbon Flag that indicates if the ribbon layer is to be
#' included. It defaults to `TRUE`.
#' @param include_rug Flag that indicates if the Rug layer is to be included.
#' It defaults to `TRUE`. In the plot, the top side shows the frequency the
#' event occurring, and the bottom the frequency of the event not occurring.
#' @param include_points Flag that indicates if the point layer is to be included.
#' @param ... Additional arguments passed to the `tune_results` object.
#' @return A ggplot object.
#' @seealso
#' \url{https://www.tidymodels.org/learn/models/calibration/},
#' [cal_plot_windowed()], [cal_plot_logistic()]
#' @examples
#'
#' library(ggplot2)
#' library(dplyr)
#'
#' cal_plot_breaks(
#' segment_logistic,
#' Class,
#' .pred_good
#' )
#'
#' cal_plot_logistic(
#' segment_logistic,
#' Class,
#' .pred_good
#' )
#'
#' cal_plot_windowed(
#' segment_logistic,
#' Class,
#' .pred_good
#' )
#'
#' # The functions support dplyr groups
#'
#' model <- glm(Class ~ .pred_good, segment_logistic, family = "binomial")
#'
#' preds <- predict(model, segment_logistic, type = "response")
#'
#' gl <- segment_logistic %>%
#' mutate(.pred_good = 1 - preds, source = "glm")
#'
#' combined <- bind_rows(mutate(segment_logistic, source = "original"), gl)
#'
#' combined %>%
#' cal_plot_logistic(Class, .pred_good, .by = source)
#'
#' # The grouping can be faceted in ggplot2
#' combined %>%
#' cal_plot_logistic(Class, .pred_good, .by = source) +
#' facet_wrap(~source) +
#' theme(legend.position = "")
#' @seealso [cal_plot_logistic()], [cal_plot_windowed()]
#' @export
cal_plot_breaks <- function(.data,
truth = NULL,
estimate = dplyr::starts_with(".pred"),
num_breaks = 10,
conf_level = 0.90,
include_ribbon = TRUE,
include_rug = TRUE,
include_points = TRUE,
event_level = c("auto", "first", "second"),
...) {
UseMethod("cal_plot_breaks")
}
#' @export
#' @rdname cal_plot_breaks
cal_plot_breaks.data.frame <- function(.data,
truth = NULL,
estimate = dplyr::starts_with(".pred"),
num_breaks = 10,
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_breaks_impl(
.data = .data,
truth = {{ truth }},
estimate = {{ estimate }},
group = {{ group }},
num_breaks = num_breaks,
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_breaks
cal_plot_breaks.tune_results <- function(.data,
truth = NULL,
estimate = dplyr::starts_with(".pred"),
num_breaks = 10,
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_breaks_impl(
.data = tune_args$predictions,
truth = !!tune_args$truth,
estimate = !!tune_args$estimate,
group = !!tune_args$group,
num_breaks = num_breaks,
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_breaks
cal_plot_breaks.grouped_df <- function(.data,
truth = NULL,
estimate = NULL,
num_breaks = 10,
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_breaks_impl <- function(.data,
truth = NULL,
estimate = dplyr::starts_with(".pred"),
group = NULL,
num_breaks = 10,
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_breaks(
.data = .data,
truth = !!truth,
estimate = !!estimate,
group = !!group,
num_breaks = num_breaks,
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 = "Bin 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 ------------------------------------
#' Probability Calibration table
#'
#' @description Calibration table functions. They require a data.frame that
#' contains the predictions and probability columns. The output is another
#' `tibble` with segmented data that compares the accuracy of the probability
#' to the actual outcome.
#'
#' @details
#' - `.cal_table_breaks()` - Splits the data into bins, based on the
#' number of breaks provided (`num_breaks`). The bins are even ranges, starting
#' at 0, and ending at 1.
#' - `.cal_table_logistic()` - Fits a logistic spline regression (GAM)
#' against the data. It then creates a table with the predictions based on 100
#' probabilities starting at 0, and ending at 1.
#' - `.cal_table_windowed()` - Creates a running percentage of the
#' probability that moves across the proportion of events.
#'
#' @inheritParams cal_plot_breaks
#'
#' @examples
#' .cal_table_breaks(
#' segment_logistic,
#' Class,
#' .pred_good
#' )
#'
#' .cal_table_logistic(
#' segment_logistic,
#' Class,
#' .pred_good
#' )
#'
#' .cal_table_windowed(
#' segment_logistic,
#' Class,
#' .pred_good
#' )
#' @rdname cal_binary_tables
#' @export
#' @keywords internal
.cal_table_breaks <- function(.data,
truth = NULL,
estimate = NULL,
.by = NULL,
num_breaks = 10,
conf_level = 0.90,
event_level = c("auto", "first", "second"),
...) {
UseMethod(".cal_table_breaks")
}
#' @export
#' @keywords internal
.cal_table_breaks.data.frame <- function(.data,
truth = NULL,
estimate = NULL,
.by = NULL,
num_breaks = 10,
conf_level = 0.90,
event_level = c("auto", "first", "second"),
...) {
.cal_table_breaks_impl(
.data = .data,
truth = {{ truth }},
estimate = {{ estimate }},
group = {{ .by }},
num_breaks = num_breaks,
conf_level = conf_level,
event_level = event_level
)
}
#' @export
#' @keywords internal
.cal_table_breaks.tune_results <- function(.data,
truth = NULL,
estimate = NULL,
.by = NULL,
num_breaks = 10,
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_breaks_impl(
.data = tune_args$predictions,
truth = !!tune_args$truth,
estimate = !!tune_args$estimate,
group = !!tune_args$group,
num_breaks = num_breaks,
conf_level = conf_level,
event_level = event_level
)
}
#--------------------------- >> Implementation ---------------------------------
.cal_table_breaks_impl <- function(.data,
truth,
estimate,
group,
num_breaks = 10,
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_breaks_grp(
.data = .x,
truth = !!truth,
num_breaks = num_breaks,
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_breaks_grp <- function(.data,
truth,
group,
num_breaks = 10,
conf_level = 0.90,
event_level = c("auto", "first", "second"),
levels,
...) {
side <- seq(0, 1, by = 1 / num_breaks)
cuts <- list(
lower_cut = side[1:length(side) - 1],
upper_cut = side[2:length(side)]
)
.cal_class_grps(
.data = .data,
truth = {{ truth }},
cuts = cuts,
levels = levels,
event_level = event_level,
conf_level = conf_level
)
}
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