auc50 = aucs[bg]['AUC50'][method] R.plot_roc_points(rocs, label=method, marker=markers[method], color=colors[method]) R.plot_random_classifier(label='Random') R.label_plot() P.legend(loc='lower right') P.title('%s - %s' % (fragment_name(fragment), bg)) P.savefig(os.path.join(options.results_dir, 'ROC-%s-%s.png' % (fragment, bg))) P.savefig(os.path.join(options.results_dir, 'ROC-%s-%s.eps' % (fragment, bg))) P.close() # precision-recall curves P.figure() for method in methods: rocs = [roc for roc, t in roc_thresholds[method]] R.plot_precision_versus_recall(rocs, label=method, marker=markers[method], color=colors[method]) R.label_precision_versus_recall() P.legend(loc='lower left') P.title('%s - %s' % (fragment_name(fragment), bg)) P.savefig(os.path.join(options.results_dir, 'Precision-Recall-%s-%s.png' % (fragment, bg))) P.savefig(os.path.join(options.results_dir, 'Precision-Recall-%s-%s.eps' % (fragment, bg))) P.close() # do AUC bar-chart #P.rcParams['xtick.direction'] = 'out' def x(a, b, m): return b*(len(methods)+1) + m P.figure(figsize=(14,6)) for a, auc in enumerate(('AUC', 'AUC50')): ax = P.subplot(1, 2, a+1) xlocs = []
P.figure() for method in methods: rocs = R.picked_rocs_from_thresholds( scores[(method,)], scores[(method, bg)], num_points=options.num_points ) auc = R.area_under_curve(rocs) R.plot_roc_points(rocs, label='%.2f %s'%(auc,name(method)), marker=markers[method]) R.plot_random_classifier(label='0.50 Random') R.label_plot() P.legend(loc='lower right') P.title('Full Sp1 - %s' % bg) P.savefig('ROC-Sp1-%s.png' % bg) P.savefig('ROC-Sp1-%s.eps' % bg) # precision-recall curves P.figure() for method in methods: rocs = R.picked_rocs_from_thresholds( scores[(method,)], scores[(method, bg)], num_points=options.num_points ) R.plot_precision_versus_recall(rocs, label=name(method), marker=markers[method]) R.label_precision_versus_recall() P.legend(loc='lower left') P.title('Full Sp1 - %s' % bg) P.savefig('Precision-Recall-Sp1-%s.png' % bg) P.savefig('Precision-Recall-Sp1-%s.eps' % bg)
P.legend(loc='lower right') P.title('%s - %s' % (fragment_name(fragment), bg)) P.savefig( os.path.join(options.results_dir, 'ROC-%s-%s.png' % (fragment, bg))) P.savefig( os.path.join(options.results_dir, 'ROC-%s-%s.eps' % (fragment, bg))) P.close() # precision-recall curves P.figure() for method in methods: rocs = [roc for roc, t in roc_thresholds[method]] R.plot_precision_versus_recall(rocs, label=method, marker=markers[method], color=colors[method]) R.label_precision_versus_recall() P.legend(loc='lower left') P.title('%s - %s' % (fragment_name(fragment), bg)) P.savefig( os.path.join(options.results_dir, 'Precision-Recall-%s-%s.png' % (fragment, bg))) P.savefig( os.path.join(options.results_dir, 'Precision-Recall-%s-%s.eps' % (fragment, bg))) P.close() # do AUC bar-chart #P.rcParams['xtick.direction'] = 'out' def x(a, b, m):
P.figure() for method in methods: rocs = R.picked_rocs_from_thresholds(scores[(method, )], scores[(method, bg)], num_points=options.num_points) auc = R.area_under_curve(rocs) R.plot_roc_points(rocs, label='%.2f %s' % (auc, name(method)), marker=markers[method]) R.plot_random_classifier(label='0.50 Random') R.label_plot() P.legend(loc='lower right') P.title('Full Sp1 - %s' % bg) P.savefig('ROC-Sp1-%s.png' % bg) P.savefig('ROC-Sp1-%s.eps' % bg) # precision-recall curves P.figure() for method in methods: rocs = R.picked_rocs_from_thresholds(scores[(method, )], scores[(method, bg)], num_points=options.num_points) R.plot_precision_versus_recall(rocs, label=name(method), marker=markers[method]) R.label_precision_versus_recall() P.legend(loc='lower left') P.title('Full Sp1 - %s' % bg) P.savefig('Precision-Recall-Sp1-%s.png' % bg) P.savefig('Precision-Recall-Sp1-%s.eps' % bg)