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)