acc = np.mean(accs)
print "CV accuracy %0.4f (std %0.4f)" % \
(acc, np.std(accs))
d['cv_acc'].append(acc)
aucs = sklearn.cross_validation.cross_val_score(
ensemble, X, Y, cv = 5, scoring='roc_auc')
auc = np.mean(aucs)
print "CV AUC %0.4f (std %0.4f)" % \
(auc, np.std(aucs))
d['cv_auc'].append(auc)
ensemble.fit(X, Y)
X_pos_test = vectorizer.transform(cancer_peptides)
Y_pos_pred = ensemble.predict(X_pos_test)
pos_acc = np.mean(Y_pos_pred)
print "Tumor antigen accuracy %0.4f" % (pos_acc,)
d['pos_acc'].append(pos_acc)
X_neg_test = vectorizer.transform(
non_immunogenic_hiv_peptides)
Y_neg_pred = ensemble.predict(X_neg_test)
neg_acc = 1.0 - np.mean(Y_neg_pred)
print "Non-immunogenic accuracy %0.4f" % (neg_acc,)
d['neg_acc'].append(neg_acc)
n_pos_pred = np.sum(Y_pos_pred)
n_neg_pred = np.sum(Y_neg_pred)
precision = n_pos_pred / float(n_pos_pred + n_neg_pred)
recall = pos_acc
(np.mean(accs), np.std(accs))
d['acc'].append(np.mean(accs))
aucs = sklearn.cross_validation.cross_val_score(
ensemble, X, Y, cv = 5, scoring='roc_auc')
print "CV AUC %0.4f (std %0.4f)" % \
(np.mean(aucs), np.std(aucs))
d['auc'].append(np.mean(aucs))
ensemble.fit(X, Y)
#X_self = vectorizer.transform(self_peptides)
#Y_pred = ensemble.predict(X_self)
#print "Self epitope accuracy %0.4f" % \
# (1.0 - np.mean(Y_pred))
X_test = vectorizer.transform(cancer_peptides)
Y_pred = ensemble.predict(X_test)
recall = np.mean(Y_pred)
print "Tumor antigen accuracy %0.4f" % (recall,)
d['recall'].append(recall)
print "---"
print
combined = (np.mean(aucs) + recall) / 2.0
d['combined'].append(combined)
df = pd.DataFrame(d)
print df.sort('combined', ascending=False)
