accs = sklearn.cross_validation.cross_val_score(
ensemble, X, Y, cv = 3)
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)
X -= ord('0')
return X
X = strings_to_array(X_combined)
Y = np.array(Y_combined)
W = np.array(W_combined)
print "# imm = %d, # non = %d" % (len(imm), len(non))
print "Data shape", X.shape, "n_true", np.sum(Y)
rf = BalancedEnsembleClassifier(n_estimators = 200)
#aucs = sklearn.cross_validation.cross_val_score(
# rf, X, Y, cv = 10, scoring='roc_auc')
#print "CV AUC %0.4f (std %0.4f)" % (np.mean(aucs), np.std(aucs))
#d['cv_auc'].append(np.mean(aucs))
#rf = RandomForestClassifier(n_estimators = 100)
rf.fit(X, Y, W)
def predict(peptides):
Y_pred = np.zeros(len(peptides), dtype=float)
counts = np.zeros(len(peptides), dtype=int)
X_test, _, Indices = expand(peptides)
X_test = strings_to_array(X_test)
#Y_pred_raw = rf.predict(X_test)
Y_pred_prob = rf.predict_proba(X_test)[:, 1]
Y_pred_rescaled = (2 * (Y_pred_prob - 0.5))
Y_pred_weight = np.sign(Y_pred_rescaled) * Y_pred_rescaled ** 2
# group outputs by the sample they came from,
# at the end we'll have the majority vote
#Y_pred = rf.predict(X_test)
for (y,i) in zip(Y_pred_weight, Indices):
Y_pred[i] += y
