plot_feature_importance: Plot feature importances.

In Yu-Group/simChef: Intensive Computational Experiments Made Easy

Plot feature importances.

Description

Plot the raw or summarized feature importances as a boxplot, scatter plot, line plot, or bar plot with or without 1 SD error bars.

Usage

plot_feature_importance(
  fit_results = NULL,
  eval_results = NULL,
  eval_name = NULL,
  eval_fun = "summarize_feature_importance",
  eval_fun_options = NULL,
  vary_params = NULL,
  feature_col,
  show_max_features = NULL,
  show = c("errorbar", "bar"),
  ...
)

Arguments

fit_results	A tibble, as returned by fit_experiment().
eval_results	A list of result tibbles, as returned by evaluate_experiment().
eval_name	Name of Evaluator containing results to plot. If NULL, the data used for plotting is computed from scratch via eval_fun.
eval_fun	Character string, specifying the function used to compute the data used for plotting if eval_name = NULL. If eval_name is not NULL, this argument is ignored.
eval_fun_options	List of named arguments to pass to eval_fun.
vary_params	A vector of DGP or Method parameter names that are varied across in the Experiment.
feature_col	A character string identifying the column in fit_results with the feature names or IDs.
show_max_features	Maximum number of features to plot.
show	Character vector with elements being one of "boxplot", "point", "line", "bar", "errorbar", "ribbon", "violin", indicating what plot layer(s) to construct.
...	Arguments passed on to plot_eval_constructor eval_id (Optional) Character string. ID used as the suffix for naming columns in evaluation results tibble. If eval_summary_constructor() was used to construct the Evaluator, this should be the same as the eval_id argument in eval_summary_constructor(). Only used to assign default (i.e., "auto") aesthetics in ggplot. x_str (Optional) Name of column in data frame to plot on the x-axis. Default "auto" chooses what to plot on the x-axis automatically. y_str (Optional) Name of column in data frame to plot on the y-axis if show is anything but "boxplot". Default "auto" chooses what to plot on the y-axis automatically. y_boxplot_str (Optional) Name of column in data frame to plot on the y-axis if show is "boxplot". Default "auto" chooses what to plot on the y-axis automatically. err_sd_str (Optional) Name of column in data frame containing the standard deviations of y_str. Used for plotting the errorbar and ribbon ggplot layers. Default "auto" chooses what column to use for the standard deviations automatically. color_str (Optional) Name of column in data frame to use for the color and fill aesthetics when plotting. Default "auto" chooses what to use for the color and fill aesthetics automatically. Use NULL to avoid adding any color and fill aesthetic. linetype_str (Optional) Name of column in data frame to use for the linetype aesthetic when plotting. Used only when show = "line". Default "auto" chooses what to use for the linetype aesthetic automatically. Use NULL to avoid adding any linetype aesthetic. facet_formula (Optional) Formula for ggplot2::facet_wrap() or ggplot2::facet_grid() if need be. facet_type One of "grid" or "wrap" specifying whether to use ggplot2::facet_wrap() or ggplot2::facet_grid() if need be. plot_by (Optional) Name of column in eval_tib to use for subsetting data and creating different plots for each unique value. Default "auto" chooses what column to use for the subsetting automatically. Use NULL to avoid creating multiple plots. add_ggplot_layers List of additional layers to add to a ggplot object via +. boxplot_args (Optional) Additional arguments to pass into ggplot2::geom_boxplot(). point_args (Optional) Additional arguments to pass into ggplot2::geom_point(). line_args (Optional) Additional arguments to pass into ggplot2::geom_line(). bar_args (Optional) Additional arguments to pass into ggplot2::geom_bar(). errorbar_args (Optional) Additional arguments to pass into ggplot2::geom_errorbar(). ribbon_args (Optional) Additional arguments to pass into ggplot2::geom_ribbon(). violin_args (Optional) Additional arguments to pass into ggplot2::geom_violin(). facet_args (Optional) Additional arguments to pass into ggplot2::facet_grid() or ggplot2::facet_wrap(). interactive Logical. If TRUE, returns interactive plotly plots. If FALSE, returns static ggplot plots.

Value

If interactive = TRUE, returns a plotly object if plot_by is NULL and a list of plotly objects if plot_by is not NULL. If interactive = FALSE, returns a ggplot object if plot_by is NULL and a list of ggplot objects if plot_by is not NULL.

See Also

Other feature_selection_funs: eval_feature_importance_funs, eval_feature_selection_curve_funs, eval_feature_selection_err_funs, plot_feature_selection_curve(), plot_feature_selection_err()

Examples

# generate example fit_results data
fit_results <- tibble::tibble(
  .rep = rep(1:2, times = 2),
  .dgp_name = c("DGP1", "DGP1", "DGP2", "DGP2"),
  .method_name = c("Method"),
  feature_info = lapply(
    1:4,
    FUN = function(i) {
      tibble::tibble(
        # feature names
        feature = c("featureA", "featureB", "featureC"),
        # estimated feature importance scores
        est_importance = c(10, runif(2, min = -2, max = 2))
      )
    }
  )
)

# generate example eval_results data
eval_results <- list(
  `Feature Importance` = summarize_feature_importance(
    fit_results,
    nested_cols = "feature_info",
    feature_col = "feature",
    imp_col = "est_importance"
  )
)

# create bar plot using pre-computed evaluation results
plt <- plot_feature_importance(eval_results = eval_results,
                               eval_name = "Feature Importance",
                               feature_col = "feature")
# or alternatively, create the same plot directly from fit results
plt <- plot_feature_importance(fit_results = fit_results,
                               feature_col = "feature",
                               eval_fun_options = list(
                                 nested_cols = "feature_info",
                                 imp_col = "est_importance"
                               ))

# can customize plot (see plot_eval_constructor() for possible arguments)
plt <- plot_feature_importance(eval_results = eval_results,
                               eval_name = "Feature Importance",
                               feature_col = "feature",
                               errorbar_args = list(width = .5, position = "dodge"),
                               bar_args = list(width = .5))

Yu-Group/simChef documentation built on March 25, 2024, 3:22 a.m.