codes.benchmark.bench_plots

Functions

get_custom_palette(num_colors)	Returns a list of colors sampled from a custom color palette.
inference_time_bar_plot(surrogates, means, ...)	Plot the mean inference time with standard deviation for different surrogate models.
int_ext_sparse(all_metrics, config[, show_title])	Function to make one comparative plot of the interpolation, extrapolation, and sparse training errors.
plot_MAE_comparison(MAEs, labels, config[, ...])	Plot the MAE for different surrogate models.
plot_all_generalization_errors(all_metrics, ...)	Function to make one comparative plot of the interpolation, extrapolation, sparse, and batch size errors.
plot_average_errors_over_time(surr_name, ...)	Plot the errors over time for different modes (interpolation, extrapolation, sparse, batchsize).
plot_average_uncertainty_over_time(...[, ...])	Plot the average uncertainty over time.
plot_comparative_dynamic_correlation_heatmaps(...)	Plot comparative heatmaps of correlation between gradient and prediction errors for multiple surrogate models.
plot_comparative_error_correlation_heatmaps(...)	Plot comparative heatmaps of correlation between predictive uncertainty and prediction errors for multiple surrogate models.
plot_dynamic_correlation(surr_name, conf, ...)	Plot the correlation between the gradients of the data and the prediction errors.
plot_dynamic_correlation_heatmap(surr_name, ...)	Plot the correlation between predictive uncertainty and prediction errors using a heatmap.
plot_dynamic_correlation_heatmap_old(...[, ...])	Plot the correlation between predictive uncertainty and prediction errors using a heatmap.
plot_error_correlation_heatmap(surr_name, ...)	Plot the correlation between predictive uncertainty and prediction errors using a heatmap.
plot_error_correlation_heatmap_old(...[, ...])	Plot the correlation between predictive uncertainty and prediction errors using a heatmap.
plot_error_distribution_comparative(errors, conf)	Plot the comparative distribution of errors for each surrogate model as a smoothed histogram plot.
plot_error_distribution_per_quantity(...[, ...])	Plot the distribution of errors for each quantity as a smoothed histogram plot.
plot_error_percentiles_over_time(surr_name, ...)	Plot mean, median, and percentiles (50th, 90th, 99th) over time.
plot_example_iterative_predictions(...[, ...])	Plot one sample's full iterative trajectory: ground truth vs.
plot_example_mode_predictions(surr_name, ...)	Plot example predictions from a single model alongside targets, and highlight either the training timesteps (interpolation mode) or the cutoff point (extrapolation mode).
plot_example_predictions_with_uncertainty(...)	Plot example predictions with uncertainty.
plot_generalization_error_comparison(...[, ...])	Plot the generalization errors of different surrogate models.
plot_generalization_errors(surr_name, conf, ...)	Plot the generalization errors of a model for various metrics.
plot_loss_comparison(train_losses, ...[, ...])	Plot the training and test losses for different surrogate models.
plot_loss_comparison_equal(train_losses, ...)	Plot the test loss trajectories for different surrogate models on a single plot, after log-transforming and normalizing each trajectory.
plot_loss_comparison_train_duration(...[, ...])	Plot the test loss trajectories for different surrogate models over training duration.
plot_losses(loss_histories, epochs, labels)	Plot the loss trajectories for the training of multiple models.
plot_losses_dual_axis(train_loss, test_loss)	Plot the training and test loss trajectories with dual y-axes.
plot_relative_errors(mean_errors, ...[, ...])	Plot the relative errors over time for different surrogate models.
plot_surr_losses(model, surr_name, conf, ...)	Plot the training and test losses for the surrogate model.
plot_uncertainty_confidence(weighted_diffs, conf)	Plot a comparative grouped bar chart of catastrophic confidence measures and return a metric quantifying the net skew of over- versus underconfidence.
plot_uncertainty_over_time_comparison(...[, ...])	Plot the uncertainty and true MAE over time for different surrogate models.
plot_uncertainty_vs_errors(surr_name, conf, ...)	Plot the correlation between predictive uncertainty and prediction errors.
rel_errors_and_uq(metrics, config[, save, ...])	Create a figure with two subplots: relative errors over time and uncertainty over time for different surrogate models.
save_plot(plt, filename, conf[, surr_name, ...])	Save the plot to a file, creating necessary directories if they don't exist.
save_plot_counter(filename, directory[, ...])	Save a plot with an incremented filename if a file with the same name already exists.