plot_MSEv_eval_crit: Plots of the MSEv Evaluation Criterion

In shapr: Prediction Explanation with Dependence-Aware Shapley Values

Plots of the MSEv Evaluation Criterion

Description

Make plots to visualize and compare the MSEv evaluation criterion for a list of explain() objects applied to the same data and model. The function creates bar plots and line plots with points to illustrate the overall MSEv evaluation criterion, but also for each observation/explicand and coalition by only averaging over the coalitions and observations/explicands, respectively.

Usage

plot_MSEv_eval_crit(
  explanation_list,
  index_x_explain = NULL,
  id_coalition = NULL,
  CI_level = if (length(explanation_list[[1]]$pred_explain) < 20) NULL else 0.95,
  geom_col_width = 0.9,
  plot_type = "overall"
)

Arguments

explanation_list	A list of explain() objects applied to the same data and model. If the entries in the list are named, then the function use these names. Otherwise, they default to the approach names (with integer suffix for duplicates) for the explanation objects in explanation_list.
index_x_explain	Integer vector. Which of the test observations to plot. E.g. if you have explained 10 observations using explain(), you can generate a plot for the first 5 observations by setting index_x_explain = 1:5.
id_coalition	Integer vector. Which of the coalitions to plot. E.g. if you used n_coalitions = 16 in explain(), you can generate a plot for the first 5 coalitions and the 10th by setting id_coalition = c(1:5, 10).
CI_level	Positive numeric between zero and one. Default is 0.95 if the number of observations to explain is larger than 20, otherwise CI_level = NULL, which removes the confidence intervals. The level of the approximate confidence intervals for the overall MSEv and the MSEv_coalition. The confidence intervals are based on that the MSEv scores are means over the observations/explicands, and that means are approximation normal. Since the standard deviations are estimated, we use the quantile t from the T distribution with N_explicands - 1 degrees of freedom corresponding to the provided level. Here, N_explicands is the number of observations/explicands. MSEv +/- tSD(MSEv)/sqrt(N_explicands). Note that the explain() function already scales the standard deviation by sqrt(N_explicands), thus, the CI are MSEv \/- tMSEv_sd, where the values MSEv and MSEv_sd are extracted from the MSEv data.tables in the objects in the explanation_list.
geom_col_width	Numeric. Bar width. By default, set to 90% of the ggplot2::resolution() of the data.
plot_type	Character vector. The possible options are "overall" (default), "comb", and "explicand". If plot_type = "overall", then the plot (one bar plot) associated with the overall MSEv evaluation criterion for each method is created, i.e., when averaging over both the coalitions and observations/explicands. If plot_type = "comb", then the plots (one line plot and one bar plot) associated with the MSEv evaluation criterion for each coalition are created, i.e., when we only average over the observations/explicands. If plot_type = "explicand", then the plots (one line plot and one bar plot) associated with the MSEv evaluation criterion for each observations/explicands are created, i.e., when we only average over the coalitions. If plot_type is a vector of one or several of "overall", "comb", and "explicand", then the associated plots are created.

Value

Either a single ggplot2::ggplot() object of the MSEv criterion when plot_type = "overall", or a list of ggplot2::ggplot() objects based on the plot_type parameter.

Author(s)

Lars Henry Berge Olsen

Examples

if (requireNamespace("xgboost", quietly = TRUE) && requireNamespace("ggplot2", quietly = TRUE)) {
  # Get the data
  data("airquality")
  data <- data.table::as.data.table(airquality)
  data <- data[complete.cases(data), ]

  #' Define the features and the response
  x_var <- c("Solar.R", "Wind", "Temp", "Month")
  y_var <- "Ozone"

  # Split data into test and training data set
  ind_x_explain <- 1:25
  x_train <- data[-ind_x_explain, ..x_var]
  y_train <- data[-ind_x_explain, get(y_var)]
  x_explain <- data[ind_x_explain, ..x_var]

  # Fitting a basic xgboost model to the training data
  model <- xgboost::xgboost(
    data = as.matrix(x_train),
    label = y_train,
    nround = 20,
    verbose = FALSE
  )

  # Specifying the phi_0, i.e. the expected prediction without any features
  phi0 <- mean(y_train)

  # Independence approach
  explanation_independence <- explain(
    model = model,
    x_explain = x_explain,
    x_train = x_train,
    approach = "independence",
    phi0 = phi0,
    n_MC_samples = 1e2
  )

  # Gaussian 1e1 approach
  explanation_gaussian_1e1 <- explain(
    model = model,
    x_explain = x_explain,
    x_train = x_train,
    approach = "gaussian",
    phi0 = phi0,
    n_MC_samples = 1e1
  )

  # Gaussian 1e2 approach
  explanation_gaussian_1e2 <- explain(
    model = model,
    x_explain = x_explain,
    x_train = x_train,
    approach = "gaussian",
    phi0 = phi0,
    n_MC_samples = 1e2
  )

  # ctree approach
  explanation_ctree <- explain(
    model = model,
    x_explain = x_explain,
    x_train = x_train,
    approach = "ctree",
    phi0 = phi0,
    n_MC_samples = 1e2
  )

  # Combined approach
  explanation_combined <- explain(
    model = model,
    x_explain = x_explain,
    x_train = x_train,
    approach = c("gaussian", "independence", "ctree"),
    phi0 = phi0,
    n_MC_samples = 1e2
  )

  # Create a list of explanations with names
  explanation_list_named <- list(
    "Ind." = explanation_independence,
    "Gaus. 1e1" = explanation_gaussian_1e1,
    "Gaus. 1e2" = explanation_gaussian_1e2,
    "Ctree" = explanation_ctree,
    "Combined" = explanation_combined
  )

  # Create the default MSEv plot where we average over both the coalitions and observations
  # with approximate 95% confidence intervals
  plot_MSEv_eval_crit(explanation_list_named, CI_level = 0.95, plot_type = "overall")

  # Can also create plots of the MSEv criterion averaged only over the coalitions or observations.
  MSEv_figures <- plot_MSEv_eval_crit(explanation_list_named,
    CI_level = 0.95,
    plot_type = c("overall", "comb", "explicand")
  )
  MSEv_figures$MSEv_bar
  MSEv_figures$MSEv_coalition_bar
  MSEv_figures$MSEv_explicand_bar

  # When there are many coalitions or observations, then it can be easier to look at line plots
  MSEv_figures$MSEv_coalition_line_point
  MSEv_figures$MSEv_explicand_line_point

  # We can specify which observations or coalitions to plot
  plot_MSEv_eval_crit(explanation_list_named,
    plot_type = "explicand",
    index_x_explain = c(1, 3:4, 6),
    CI_level = 0.95
  )$MSEv_explicand_bar
  plot_MSEv_eval_crit(explanation_list_named,
    plot_type = "comb",
    id_coalition = c(3, 4, 9, 13:15),
    CI_level = 0.95
  )$MSEv_coalition_bar

  # We can alter the figures if other palette schemes or design is wanted
  bar_text_n_decimals <- 1
  MSEv_figures$MSEv_bar +
    ggplot2::scale_x_discrete(limits = rev(levels(MSEv_figures$MSEv_bar$data$Method))) +
    ggplot2::coord_flip() +
    ggplot2::scale_fill_discrete() + #' Default ggplot2 palette
    ggplot2::theme_minimal() + #' This must be set before the other theme call
    ggplot2::theme(
      plot.title = ggplot2::element_text(size = 10),
      legend.position = "bottom"
    ) +
    ggplot2::guides(fill = ggplot2::guide_legend(nrow = 1, ncol = 6)) +
    ggplot2::geom_text(
      ggplot2::aes(label = sprintf(
        paste("%.", sprintf("%d", bar_text_n_decimals), "f", sep = ""),
        round(MSEv, bar_text_n_decimals)
      )),
      vjust = -1.1, # This value must be altered based on the plot dimension
      hjust = 1.1, # This value must be altered based on the plot dimension
      color = "black",
      position = ggplot2::position_dodge(0.9),
      size = 5
    )
}

shapr documentation built on April 4, 2025, 12:18 a.m.