(100 * auc, 100 * acc, 100 * sens, 100 * spec))
logger.info('\t\tResults here at optimal threshold:')
logger.info(
'\t\t\tAUC: %.3f\tAcc: %.3f\tSens: %.3f\tSpec: %.3f' %
(auc * 100, 100 * test_acc_opt, test_sens_opt, test_spec_opt))
# Store the results over each run for this adipose shell
adi_results[adi_id] = [
aucs_here, accs_here, sens_here, spec_here, accs_opt_thresh_here,
sens_opt_thresh_here, spec_opt_thresh_here
]
# Report the results to the logger
for adi_id in unique_adi_ids:
logger.info('CNN Gen-2 Results on Unseen Adi-ID: %s' % adi_id)
report_metrics(adi_results[adi_id][0], adi_results[adi_id][1],
adi_results[adi_id][2], adi_results[adi_id][3], logger)
# Save the results to a .pickle file
save_dir = os.path.join(get_proj_path(), 'output/g2-by-adi//')
verify_path(save_dir)
save_pickle(adi_results, os.path.join(save_dir,
'g2_by_adi_metrics.pickle'))
# Save the incorrect predictions
out_dir = os.path.join(get_proj_path(), 'output/by-adi-preds/')
verify_path(out_dir)
save_pickle(incor_preds, os.path.join(out_dir, 'byadi_incor_preds.pickle'))
save_pickle(cor_preds, os.path.join(out_dir, 'byadi_cor_preds.pickle'))
accs[run_idx] = 100 * get_acc(
preds=g1_preds[:, 1], labels=g1_labels[:, 1], threshold=opt_thresh)
sens[run_idx] = 100 * get_sens(
preds=g1_preds[:, 1], labels=g1_labels[:, 1], threshold=opt_thresh)
spec[run_idx] = 100 * get_spec(
preds=g1_preds[:, 1], labels=g1_labels[:, 1], threshold=opt_thresh)
# Plot ROC curve
plt_roc_curve(preds=g1_preds[:, 1],
labels=g1_labels[:, 1],
save_str='dnn_run_%d_roc' % run_idx,
save=True)
# Report AUC at this run
logger.info('\t\tAUC:\t%.2f' % g1_auc)
# Get the class predictions
class_preds = g1_preds * np.zeros_like(g1_preds)
class_preds[g1_preds >= opt_thresh] = 1
# Reset the model
clear_session()
# Report performance metrics to logger
logger.info('Average performance metrics')
logger.info('')
report_metrics(aucs=auc_scores,
accs=accs,
sens=sens,
spec=spec,
logger=logger)