R/utils.R

In BoShiangKe/InfluenceAUC: Identify Influential Observations in Binary Classification

#' Internal function: Build an AUC ggplot2 object
#' @keywords internal
#' @param df A data frame consists of `x`, `y`, `color`, `shape`, `label`
#' @param threshold A numeric inherits `threshold` in `IAUC` and `LAUC`
#' @param title A character represents the plot's title
#' @param show.legend Logical which controls the illustration of the legends
#' @param ylimit A vector for lower and upper limits of y-axis
#' @return A ggplot2 object
build_AUC_plot <-
  function(df,
           threshold,
           title = "",
           show.legend = FALSE,
           ylimit = c(-1, 1),
           yintercept = NULL,
           is_two_sided = TRUE) {
    if (is.null(yintercept))
      yintercept <- threshold

    # Set the default theme
    theme_set(
      theme_minimal() +
        theme(
          legend.position = "top",
          legend.title = element_blank(),
          plot.title = element_text(hjust = 0.5)
        )
    )
    # df consists of `Index`, `unitslope`, `Outcome`, `slopepoten`, `label`
    result <- ggplot(df,
                     aes_string(
                       x = "x",
                       y = "y",
                       color = "color",
                       shape = "color",
                       label = "label"
                     )) +
      geom_point() +
      scale_shape_manual(values = c(4, 16)) +
      scale_color_manual(values = c("#e63535", "#3d46eb")) +
      scale_y_continuous("Influence Value", limits = ylimit) +
      ggtitle(title) +
      geom_text_repel(show.legend = show.legend)

    if (is_two_sided)
      result <- result +
      geom_hline(yintercept = yintercept, linetype = "dashed") +
      geom_hline(yintercept = -yintercept, linetype = "dashed")
    else
      result <- result +
      geom_hline(yintercept = yintercept, linetype = "dashed")

    return(result)
  }

#' Internal function: Create lift-chart ggplot2 object
#' @keywords internal
#' @param score A vector inherits `score` in `ICLC`
#' @param binary A vector inherits `binary` in `ICLC`
#' @param prop A numeric inherits `prop` in `ICLC`
#' @param xlab A character represents plot's x axis label
#' @param ylab A character represents plot's y axis label
#' @param  title A character represents plot's title
#' @return A ggplot2 object
create_lift_chart <-
  function(score, binary, prop, xlab, ylab, title) {
    pred <- prediction(score, binary)
    perf <- performance(pred, "lift", "rpp")
    temp <- unique(data.frame(binary, score) %>%
                     arrange(desc(score)))

    data <-
      data.frame(x = perf@x.values[[1]][-1],  #  rate of positive predictions
                 y = perf@y.values[[1]][-1],  #  lift index
                 temp)

    npoten <- data %>%
      filter(x <= prop, binary == 0) %>%
      mutate(y_zero = 0)

    # customize the ggplot setting
    theme_set(
      theme_minimal() +
        theme(
          plot.title = element_text(hjust = 0.5),
          axis.title.x = element_text(size = rel(1.15)),
          axis.title.y = element_text(size = rel(1.15))
        )
    )

    # Create a cumulative lift chart
    clc <- ggplot(data, aes(x = .data$x, y = .data$y)) +
      geom_line() +
      labs(title = title,
           x = xlab,
           y = ylab) +
      geom_hline(yintercept = 1,
                 linetype = "solid",
                 col = "blue") +
      geom_segment(
        data = npoten,
        mapping = aes_string(
          x = "x",
          y = "y_zero",
          xend = "x",
          yend = "y"
        ),
        size = 0.3,
        color = "red",
        linetype = 3
      )

    return(clc)
  }

#' Internal function: Print output
#' @keywords internal
#' @param output A valid object (e.g., `IAUC`, `LAUC`)
#' @param attrs A vector of attribute names
print_output <- function(output, attrs) {
  if (sum(attrs %in% names(output)) == 0)
    cat("Wrong input object")
  else
    for (attr in attrs)
      if (attr %in% names(output) & !is.null(output[[attr]])) {
        cat(paste(attr, "is: \n", sep = " "))
        print(output[[attr]])
      }
}

#' Internal function: Plot sequentially
#' @keywords internal
#' @param objs A valid object (e.g., `IAUC`, `LAUC`)
plot_sequentially <- function(objs) {
  par(ask = TRUE)
  for (obj in objs)
    show(obj)
  par(ask = FALSE)
}


#' Internal function: Fetch indeces in an output object
#' @keywords internal
#' @param obj A valid object (e.g., `IAUC`, `LAUC`)
fetch_output_indeces <- function(obj) {
  output <- obj$output

  output_names <- names(output)
  attr_names <- output_names[output_names != "AUC"]

  indeces <- lapply(attr_names,
                    function(n)
                      do.call("c", lapply(output[[n]], function(x)
                        x[, 1])))
  return(unique(unlist(indeces)))
}