) for method in methods
)
auc50s = dict(
(
method,
R.auc50(
scores[(method, fragment)],
scores[(method, fragment, bg)],
num_negative=num_negative,
num_points=options.num_points
)
) for method in methods
)
aucs[bg] = dict()
aucs[bg]['AUC'] = dict(
(method, R.area_under_curve([roc for roc, t in roc_thresholds[method]]))
for method in methods
)
aucs[bg]['AUC50'] = dict(
(method, auc50s[method][0])
for method in methods
)
# ROC curves
P.figure()
for method in methods:
rocs = [roc for roc, t in roc_thresholds[method]]
auc = aucs[bg]['AUC'][method]
auc50 = aucs[bg]['AUC50'][method]
R.plot_roc_points(rocs, label=method, marker=markers[method], color=colors[method])
return 'Ungapped'
if 'GLAM2-i7' == method:
return 'GLAM2'
return method
for bg in backgrounds:
# ROC curves
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
roc_thresholds = dict(
(method,
R.create_rocs_from_thresholds(scores[(method, fragment)],
scores[(method, fragment, bg)],
num_points=options.num_points))
for method in methods)
auc50s = dict((method,
R.auc50(scores[(method, fragment)],
scores[(method, fragment, bg)],
num_negative=num_negative,
num_points=options.num_points))
for method in methods)
aucs[bg] = dict()
aucs[bg]['AUC'] = dict(
(method,
R.area_under_curve([roc for roc, t in roc_thresholds[method]]))
for method in methods)
aucs[bg]['AUC50'] = dict(
(method, auc50s[method][0]) for method in methods)
# ROC curves
P.figure()
for method in methods:
rocs = [roc for roc, t in roc_thresholds[method]]
auc = aucs[bg]['AUC'][method]
auc50 = aucs[bg]['AUC50'][method]
R.plot_roc_points(rocs,
label=method,
marker=markers[method],
color=colors[method])
R.plot_random_classifier(label='Random')
if 'Ungapped-new' == method:
return 'Ungapped'
if 'GLAM2-i7' == method:
return 'GLAM2'
return method
for bg in backgrounds:
# ROC curves
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)],
