R/plots.R

In stacks: Tidy Model Stacking

#' @importFrom ggplot2 autoplot
#' @export
ggplot2::autoplot


#' Plot results of a stacked ensemble model.
#' 
#' @param object A `linear_stack` object outputted from [blend_predictions()]
#' or [fit_members()].
#' @param type A single character string for plot type with values "performance", 
#' "members", or "weights".
#' @param n An integer for how many members weights to plot when 
#' `type = "weights"`. With multi-class data, this is the total number of weights
#' across classes; otherwise this is equal to the number of members. 
#' @param ... Not currently used. 
#' @return A `ggplot` object. 
#' @details 
#' A "performance" plot shows the relationship between the lasso penalty and the
#' resampled performance metrics. The latter includes the average number of 
#' ensemble members. This plot can be helpful for understanding what penalty
#' values are reasonable. 
#' 
#' A "members" plot shows the relationship between the average number of 
#' ensemble members and the performance metrics. Each point is for a different
#' penalty value. 
#' 
#' Neither of the "performance" or "members" plots are helpful when a single
#' penalty is used. 
#' 
#' A "weights" plot shows the blending weights for the top ensemble members. The
#' results are for the final penalty value used to fit the ensemble. 
#' @export
autoplot.linear_stack <- function(object, type = "performance", n = Inf, ...) {
  type <- match.arg(type, c("performance", "members", "weights"))
  dat <- object$metrics
  if (type == "members") {
    p <- member_plot(object)
  } else if (type == "performance") {
    p <- performance_plot(object)
  } else {
    p <- weights_plot(object, penalty = object$penalty$penalty, n = n)
  }
  p
}

member_plot <- function(x) {
  dat <- x$metrics
  
  plot_dat <- 
    dat %>% 
    dplyr::select(penalty, mixture, .config, mean, .metric) 
  
  memb_data <- 
    dplyr::filter(plot_dat, .metric == "num_members") %>% 
    dplyr::rename(num_members = mean) %>% 
    dplyr::select(-.metric)
  
  other_metrics <- dplyr::filter(plot_dat, .metric != "num_members")
  
  plot_dat <- 
    dplyr::full_join(
      memb_data, 
      other_metrics, 
      by = c("penalty", "mixture", ".config"), 
      multiple = "all"
    )
  
  mult_mix <- length(unique(plot_dat$mixture)) > 1
  
  if (mult_mix) {
    plot_dat$mixture <- format(plot_dat$mixture)
    p <- ggplot2::ggplot(plot_dat, ggplot2::aes(x = num_members, y = mean, col = mixture)) 
  } else {
    p <- ggplot2::ggplot(plot_dat, ggplot2::aes(x = num_members, y = mean)) 
  }
  
  p <- 
    p + 
    ggplot2::geom_point() + 
    ggplot2::facet_wrap(
      ~.metric, 
      nrow = length(unique(other_metrics$.metric)), 
      scales = "free_y"
    ) + 
    ggplot2::xlab("Average number of members")
  
  p
}

performance_plot <- function(x) {
  dat <- x$metrics
  mult_mix <- length(unique(dat$mixture)) > 1
  
  if (mult_mix) {
    dat$mixture <- format(dat$mixture)
    p <- ggplot2::ggplot(dat, ggplot2::aes(x = penalty, y = mean, col = mixture)) 
  } else {
    p <- ggplot2::ggplot(dat, ggplot2::aes(x = penalty, y = mean)) 
  }
  p <- 
    p + 
    ggplot2::geom_vline(xintercept = x$penalty$penalty, lty = 2) +
    ggplot2::geom_point() + 
    ggplot2::geom_path() + 
    ggplot2::facet_wrap(~ .metric, scales = "free_y", ncol = 1) + 
    ggplot2::scale_x_log10()
  p
}

weights_plot <- function(x, penalty = x$penalty$penalty, n = Inf) {

  dat <- top_coefs(x, penalty = penalty, n = n) %>%
    dplyr::rename(terms = member, model = type)
  
  if (any(names(dat) == "class")) {
    dat_order <- 
      dat %>% 
      dplyr::group_by(model, terms) %>% 
      dplyr::summarize(mean = max(abs(weight), na.rm = TRUE)) %>% 
      dplyr::ungroup() %>% 
      dplyr::arrange(mean) %>% 
      dplyr::mutate(member = dplyr::row_number()) %>% 
      dplyr::select(-mean)
    dat <- dplyr::full_join(dat, dat_order, by = c("model", "terms"))
  } else {
    dat <- 
      dat %>% 
      dplyr::arrange(abs(weight)) %>% 
      dplyr::mutate(member = dplyr::row_number())
  }
  p <- 
    ggplot2::ggplot(dat, ggplot2::aes(x = weight, y = format(member), fill = model)) + 
    ggplot2::geom_bar(stat = "identity") + 
    ggplot2::ylab("Member") + 
    ggplot2::ggtitle(paste("penalty =", format(x$coefs$spec$args$penalty, digits = 3, scientific = FALSE))) + 
    ggplot2::geom_vline(xintercept = 0) + 
    ggplot2::xlab("Stacking Coefficient")
  
  if (any(names(dat) == "class")) {
   p <- p + ggplot2::facet_wrap(~ class) 
  }
  p
}