graphorge.gnn_base_model.train.training_plots.plot_kfold_cross_validation

plot_kfold_cross_validation(k_fold_loss_array, loss_type=None, loss_scale='linear', filename='kfold_cross_validation', save_dir=None, is_save_fig=False, is_stdout_display=False, is_latex=False)[source]

Plot k-fold cross-validation results.

Parameters:

  • k_fold_loss_array (numpy.ndarray(2d)) – k-fold cross-validation loss array. For the i-th fold, data_array[i, 0] stores the best training loss and data_array[i, 1] stores the average prediction loss per sample.

  • loss_type (str, default=None) – Loss type. If provided, then loss type is added to the y-axis label.

  • loss_scale ({'linear', 'log'}, default='linear') – Loss axis scale type.

  • filename (str, default='prediction_vs_groundtruth') – Figure name.

  • save_dir (str, default=None) – Directory where figure is saved. If None, then figure is saved in current working directory.

  • is_save_fig (bool, default=False) – Save figure.

  • is_stdout_display (bool, default=False) – True if displaying figure to standard output device, False otherwise.

  • is_latex (bool, default=False) – If True, then render all strings in LaTeX. If LaTex is not available, then this option is silently set to False and all input strings are processed to remove $(…)$ enclosure.