Beispiel #1
0
            best_dendro_corrs.append(max(dendro_corrs))
            """
            Plotting best & final validation losses
            """
            if len(validation_losses) > 0:
                dendro_best_loss.append(min(validation_losses[1:]))
                simple_best_loss.append(min(simple_validation_losses[1:]))
                dendro_final_loss.append(validation_losses[-1])
                simple_final_loss.append(simple_validation_losses[-1])
    if len(dendro_best_loss) > 0:
        names = ['dendro_best', 'dendro_final', 'simple_best', 'simple_final']
        loss_outputs = [
            dendro_best_loss, dendro_final_loss, simple_best_loss,
            simple_final_loss
        ]
        log_aucs(os.path.join(base_output_dir, 'loss_log.txt'), loss_outputs,
                 names)
    print('Done disentanglement analysis')

    disentanglement_metrics = {
        'simple_errors': best_simple_errors,
        'dendro_errors': best_dendro_errors,
        'simple_correlations': best_simple_corrs,
        'dendro_correlations': best_dendro_corrs,
        # 'simple_final_weights': simple_final_weights,
        # 'dendro_final_weights': dendro_final_weights
    }

    dump_dict(disentanglement_metrics,
              base_output_dir,
              name='disentanglement_metrics.csv')
Beispiel #2
0
            roc_metrics[ls]['targets'].extend(targets)
            roc_metrics[ls]['dendro_preds'].extend(dendro_predictions.numpy()[:, 1])
            roc_metrics[ls]['simple_preds'].extend(simple_predictions.numpy()[:, 1])
            if args.baselines:
                _, parsimony_predictions, one_hot_predictions = baseline_experiment.retrieve_predictions()
                roc_metrics[ls]['parsimony_preds'].extend(np.squeeze(parsimony_predictions.numpy())[:, 1])
                roc_metrics[ls]['one_hot_preds'].extend(one_hot_predictions.numpy()[:, 1])

    auc_list = list()
    log_auc_names = list()
    for ls in DIVERSE_TROPHIC_LEVELS:
        for name in basenames:
            auc_list.append(aucs[ls][name])
            log_auc_names.append(str(ls + '_' + name))
    if len(auc_list[0]) > 0:
        log_aucs(os.path.join(base_output_dir, 'auc_log.txt'), auc_list, log_auc_names)
    print('done')

    if PLOT_ROC:
        for ls in DIVERSE_TROPHIC_LEVELS:
            pd_name = ls +'_roc_metrics.csv'
            pd.DataFrame.from_dict(roc_metrics[ls]).to_csv(pd_name)
            targets = roc_metrics[ls]['targets']
            dendro_predictions = roc_metrics[ls]['dendro_preds']
            simple_predictions = roc_metrics[ls]['simple_preds']
            bin_targets = [t[1] for t in targets]

            dendro_fpr, dendro_tpr, _ = roc_curve(bin_targets, dendro_predictions)
            simple_fpr, simple_tpr, _ = roc_curve(bin_targets, simple_predictions)

            dendro_auc = auc(dendro_fpr, dendro_tpr)