def run_pwm_forward_backward(tag, freqs, gaps, positive_seqs, negative_seqs): """ Run the PWM using forward-backward. """ logging.info('Running PWM: %s', tag) logo = L.pssm_as_image(freqs, size=None, transparencies=gaps) logo_filename = '%s-logo.png' % tag logo.save(logo_filename) logging.info('%s: Created logo: %s', tag, logo_filename) # build model model = build_hmm_model(freqs, gaps, .001) hmm.graph_as_svg(model, '%s-states' % tag, neato_properties={'-Elen': 1.4}) logging.debug('%s: Graphed model', tag) positive_scores = test_hmm_forward_backward(model, positive_seqs.values()) negative_scores = test_hmm_forward_backward(model, negative_seqs.values()) return roc.picked_rocs_from_thresholds(positive_scores, negative_scores)
def run_pwm_forward_backward(tag, freqs, gaps, positive_seqs, negative_seqs): """ Run the PWM using forward-backward. """ logging.info('Running PWM: %s', tag) logo = L.pssm_as_image(freqs, size=None, transparencies=gaps) logo_filename = '%s-logo.png' % tag logo.save(logo_filename) logging.info('%s: Created logo: %s', tag, logo_filename) # build model model = build_hmm_model(freqs, gaps, .001) hmm.graph_as_svg(model, '%s-states' % tag, neato_properties={'-Elen':1.4}) logging.debug('%s: Graphed model', tag) positive_scores = test_hmm_forward_backward(model, positive_seqs.values()) negative_scores = test_hmm_forward_backward(model, negative_seqs.values()) return roc.picked_rocs_from_thresholds(positive_scores, negative_scores)
if 'Gapped-new' == method: return 'Gapped' 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(
if 'Gapped-new' == method: return 'Gapped' 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: