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)	Function to make one comparative plot of the interpolation, extrapolation, and sparse training errors.
plot_MAE_comparison(MAEs, labels, config[, save])	Plot the MAE for different surrogate models.
plot_MAE_comparison_train_duration(MAEs, ...)	Plot the MAE for different surrogate models.
plot_average_errors_over_time(surr_name, ...)	Plot the errors over time for different modes (interpolation, extrapolation, sparse, batchsize).
plot_average_uncertainty_over_time(...[, save])	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_error_correlation_heatmap(surr_name, ...)	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_chemical(...[, ...])	Plot the distribution of errors for each chemical as a smoothed histogram plot.
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, ...[, save])	Plot the training and test losses for different surrogate models.
plot_losses(loss_histories, labels[, title, ...])	Plot the loss trajectories for the training of multiple models.
plot_relative_errors(mean_errors, ...[, save])	Plot the relative errors over time for different surrogate models.
plot_relative_errors_over_time(surr_name, ...)	Plot the mean and median relative errors over time with shaded regions for the 50th, 90th, and 99th percentiles.
plot_surr_losses(model, surr_name, conf, ...)	Plot the training and test losses for the surrogate model.
plot_uncertainty_over_time_comparison(...[, ...])	Plot the uncertainty 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.