# R/individual_plots.R In classifierplots: Generates a Visualization of Classifier Performance as a Grid of Diagnostic Plots

#### Documented in accuracy_plotpropensity_plotrecall_plotsigmoid

```check_predictions <- function(pred.prob) {

if(max(pred.prob) > 1) {
stop(paste("Pred.prob not in [0,1]. Max:", max(pred.prob),
". You can use the sigmoid(x) function in this package to map to [0,1]."))
}

if(min(pred.prob) < 0) {
stop(paste("Pred.prob not in [0,1]. Min:", min(pred.prob),
". You can use the sigmoid(x) function in this package to map to [0,1]."))
}
}

check_classifier_input_and_init <- function(test.y, pred.prob) {

if(length(test.y) != length(pred.prob)) {
stop(paste("Length of test.y:", length(test.y), "did not match pred.prob:", length(pred.prob)))
}
yvals <- unique(test.y)
if(length(yvals) != 2) {
stop(paste("test.y had more than 2 unique values:", length(yvals)))
}
if(sum(yvals == 1.0) != 1) {
stop(paste("This code expects test.y to be numerical, with the positive class indicated by '1'. There was no 1 in test.y!"))
}

check_predictions(pred.prob)
}

#' @title sigmoid
#' @description Logistic sigmoid function, that maps any real number to the [0,1] interval. Supports vectors of numeric.
#' @param x data
#' @export
sigmoid <- function(x) { 1.0/(1.0+exp(-x)) }

#' @title propensity_plot
#' @description Returns a ggplot2 plot object containing an propensity @@ percentile plot
#' @param test.y List of know labels on the test set
#' @param pred.prob List of probability predictions on the test set
#' @param granularity Default 0.02, probability step between points in plot.
#' @export
propensity_plot <- function(test.y, pred.prob, granularity=0.02) {
check_classifier_input_and_init(test.y, pred.prob)
step_array <- seq(0.0, 1.0, by=granularity)
thesh_steps <- round(quantile(pred.prob, step_array), digits=4)
pred.order <- order(pred.prob,  decreasing=T)

propensity_tbl_perc <- data.table(
part=1:length(step_array), percentage=100 - 100*step_array,
threshold=thesh_steps, step_array=step_array)
propensity_tbl_perc[, propensity :=
propensity_at_threshold(test.y, pred.prob, part, pred.order, thesh_steps), by=c("part")]

return(ggplot(propensity_tbl_perc, aes(x=percentage, y=100.0*propensity)) +
geom_line(color=green_str, size=1.5) + classifier_theme + classifier_colours +
scale_x_continuous(name="Instance decile (non-cumulative %)", breaks=seq(0.0, 100.0, 10.0)) +
scale_y_continuous(name="Smoothed positive (%)") +
ggtitle("Positive rate (rolling window)"))
}

#' @title accuracy_plot
#' @description Returns a ggplot2 plot object containing an accuracy @@ percentile plot
#' @param test.y List of know labels on the test set
#' @param pred.prob List of probability predictions on the test set
#' @param granularity Default 0.02, probability step between points in plot.
#' @param show_numbers Show values as numbers above the plot line
#' @export
accuracy_plot <- function(test.y, pred.prob, granularity=0.02, show_numbers=T) {
check_classifier_input_and_init(test.y, pred.prob)
step_array <- seq(0.0, 1.0, by=granularity)
thesh_steps <- round(quantile(pred.prob, step_array), digits=4)
accuracy_tbl_perc <- data.table(percentage=100 - 100*step_array, threshold=thesh_steps)
accuracy_tbl_perc[, accuracy := sapply(threshold, function(x) accuracy_at_threshold(x, test.y, pred.prob))]
accuracy_tbl_perc[, accuracy_lb := sapply(threshold, function(x) accuracy_at_threshold_p(0.025, x, test.y, pred.prob))]
accuracy_tbl_perc[, accuracy_ub := sapply(threshold, function(x) accuracy_at_threshold_p(0.975, x, test.y, pred.prob))]

if(show_numbers) {
deciles <- seq(0, 100, 10)
accuracy_tbl_perc[percentage %in% deciles, dec_lbl := paste0(format(100*accuracy, digits=2), "%")]
numbers <- geom_text(aes(x=percentage, y=102*accuracy, label=dec_lbl),
hjust=0.3, vjust=-1.0, size=4, color=I(blue_str))
} else {
numbers <- NULL
}

return(ggplot(accuracy_tbl_perc, aes(x=percentage, y=100.0*accuracy)) +
geom_ribbon(aes(ymin=100.0*accuracy_lb, ymax=100.0*accuracy_ub), fill=green_str, alpha=0.2) +
geom_line(color=green_str, size=1.5) + classifier_theme + classifier_colours +
scale_x_continuous(name="k% (thresholded to positive class)", breaks=seq(0.0, 100.0, 10.0)) +
scale_y_continuous(name="Accuracy (%)", limits=c(0,100), breaks=seq(0.0, 100.0, 10.0)) +
numbers +
ggtitle("Accuracy @ k"))
}

#' @title recall_plot
#' @description Returns a ggplot2 plot object containing an sensitivity @@ percentile plot
#' @param test.y List of know labels on the test set
#' @param pred.prob List of probability predictions on the test set
#' @param granularity Default 0.02, probability step between points in plot.
#' @param show_numbers Show numbers at deciles T/F default T.
#' @export
recall_plot <- function(test.y, pred.prob, granularity=0.02, show_numbers=T) {
check_classifier_input_and_init(test.y, pred.prob)
step_array <- seq(0.0, 1.0, by=granularity)
thesh_steps <- round(quantile(pred.prob, step_array), digits=4)
tbl <- data.table(percentage=100 - 100*step_array, threshold=thesh_steps)
tbl[, sensitivity := sapply(threshold, function(x) sensitivity_at_threshold(x, test.y, pred.prob))]
tbl[, sensitivity_lb := sapply(threshold, function(x) sensitivity_at_threshold_p(0.025, x, test.y, pred.prob))]
tbl[, sensitivity_ub := sapply(threshold, function(x) sensitivity_at_threshold_p(0.975, x, test.y, pred.prob))]

if(show_numbers) {
deciles <- seq(10, 100, 10)
tbl[percentage %in% deciles, dec_lbl := paste0(format(100*sensitivity, digits=2), "%")]
numbers <- geom_text(aes(x=percentage, y=100*sensitivity+2*sensitivity, label=dec_lbl),
hjust=0.3, vjust=3.0, size=4, color=I(blue_str))
} else {
numbers <- NULL
}

return(ggplot(tbl, aes(x=percentage, y=100.0*sensitivity)) +
geom_ribbon(aes(ymin=100.0*sensitivity_lb, ymax=100.0*sensitivity_ub), fill=green_str, alpha=0.2) +
geom_line(color=green_str, size=1.5) + classifier_theme + classifier_colours +
scale_x_continuous(name="k% (thresholded to positive class)", breaks=seq(0.0, 100.0, 10.0), limits=c(0,100), expand=c(0, 0.3)) +
scale_y_continuous(name="Recall (%)", breaks=seq(0.0, 100.0, 10.0), limits=c(0,100), expand=c(0, 0.3)) +
numbers +
ggtitle("Recall @ k"))
}

# Variables used in data.table expressions have to be defined here
utils::globalVariables(c(
"Prediction", "Ground Truth", "accuracy", "threshold",
"precision", "sensitivity", "percentage", "fpr", "tpr",
"propensity", "positive_perc", "bucket", "dec_lbl", "part",
"ymin", "ymax", "sensitivity_lb", "sensitivity_ub"))
```

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classifierplots documentation built on Jan. 13, 2021, 5:23 p.m.