def experiment_anomaly_detection(train, test, comb, num_train, anom_prob, labels): phi = calc_feature_vecs(comb.X) print phi.size # bayes classifier (DIMS, N) = phi.size w_bayes = co.matrix(1.0, (DIMS, 1)) pred = w_bayes.trans()*phi[:,num_train:] (fpr, tpr, thres) = metric.roc_curve(labels[num_train:], pred.trans()) bayes_auc = metric.auc(fpr, tpr) # train one-class svm kern = Kernel.get_kernel(phi[:,0:num_train], phi[:,0:num_train]) ocsvm = OCSVM(kern, C=1.0/(num_train*anom_prob)) ocsvm.train_dual() kern = Kernel.get_kernel(phi, phi) (oc_as, foo) = ocsvm.apply_dual(kern[num_train:,ocsvm.get_support_dual()]) (fpr, tpr, thres) = metric.roc_curve(labels[num_train:], oc_as) base_auc = metric.auc(fpr, tpr) if (base_auc<0.5): base_auc = 1.0-base_auc # train structured anomaly detection #sad = StructuredOCSVM(train, C=1.0/(num_train*anom_prob)) sad = StructuredOCSVM(train, C=1.0/(num_train*0.5)) (lsol, lats, thres) = sad.train_dc(max_iter=50) (pred_vals, pred_lats) = sad.apply(test) (fpr, tpr, thres) = metric.roc_curve(labels[num_train:], pred_vals) auc = metric.auc(fpr, tpr) if (auc<0.5): auc = 1.0-auc return (auc, base_auc, bayes_auc)
def test_ocsvm(phi, kern, train, test, num_train, anom_prob, labels):
auc = 0.5
ocsvm = OCSVM(kern[:num_train, :num_train], C=1.0 / (num_train * anom_prob))
msg = ocsvm.train_dual()
if not msg == OCSVM.MSG_ERROR:
(oc_as, foo) = ocsvm.apply_dual(kern[num_train:, ocsvm.get_support_dual()])
(fpr, tpr, thres) = metric.roc_curve(labels[num_train:], oc_as)
auc = metric.auc(fpr, tpr)
return auc
def perf_ocsvm(phi, marker, train, test, anom_prob, ord=1):
#phi = phi[phi_inds.tolist(),:]
print('(a) normalize features...')
phi = normalize_features(phi, ord=ord)
print('(b) Build kernel...')
kern = Kernel.get_kernel(phi, phi)
print('(c) Train OCSVM...')
ocsvm = OCSVM(kern[train, train],
C=1.0 / (float(len(train)) * (1.0 - anom_prob)))
ocsvm.train_dual()
print('(d) Apply OCSVM...')
(oc_as, foo) = ocsvm.apply_dual(kern[test,
train[ocsvm.get_support_dual()]])
(fpr, tpr, thres) = metric.roc_curve(co.matrix(marker)[test], oc_as)
auc = metric.auc(fpr, tpr)
print('(e) Return AUC={0}...'.format(auc))
return auc
def experiment_anomaly_detection(train, test, comb, num_train, anom_prob, labels): # train one-class svm phi = calc_feature_vecs(comb.X) kern = Kernel.get_kernel(phi[:,0:num_train], phi[:,0:num_train]) ocsvm = OCSVM(kern, C=1.0/(num_train*anom_prob)) ocsvm.train_dual() kern = Kernel.get_kernel(phi, phi) (oc_as, foo) = ocsvm.apply_dual(kern[num_train:,ocsvm.get_support_dual()]) (fpr, tpr, thres) = metric.roc_curve(labels[num_train:], oc_as) base_auc = metric.auc(fpr, tpr) # train structured anomaly detection sad = StructuredOCSVM(train, C=1.0/(num_train*anom_prob)) (lsol, lats, thres) = sad.train_dc(max_iter=40) (pred_vals, pred_lats) = sad.apply(test) (fpr, tpr, thres) = metric.roc_curve(labels[num_train:], pred_vals) auc = metric.auc(fpr, tpr) return (auc, base_auc)
def experiment_anomaly_detection(train, test, comb, num_train, anom_prob,
labels):
# train one-class svm
phi = calc_feature_vecs(comb.X)
kern = Kernel.get_kernel(phi[:, 0:num_train], phi[:, 0:num_train])
ocsvm = OCSVM(kern, C=1.0 / (num_train * anom_prob))
ocsvm.train_dual()
kern = Kernel.get_kernel(phi, phi)
(oc_as, foo) = ocsvm.apply_dual(kern[num_train:, ocsvm.get_support_dual()])
(fpr, tpr, thres) = metric.roc_curve(labels[num_train:], oc_as)
base_auc = metric.auc(fpr, tpr)
# train structured anomaly detection
sad = StructuredOCSVM(train, C=1.0 / (num_train * anom_prob))
(lsol, lats, thres) = sad.train_dc(max_iter=40)
(pred_vals, pred_lats) = sad.apply(test)
(fpr, tpr, thres) = metric.roc_curve(labels[num_train:], pred_vals)
auc = metric.auc(fpr, tpr)
return (auc, base_auc)
svm = OCSVM(kernel,0.1)
svm.train_dual()
delta = 0.07
x = np.arange(-4.0, 4.0, delta)
y = np.arange(-4.0, 4.0, delta)
X, Y = np.meshgrid(x, y)
(sx,sy) = X.shape
Xf = np.reshape(X,(1,sx*sy))
Yf = np.reshape(Y,(1,sx*sy))
Dtest = np.append(Xf,Yf,axis=0)
print(Dtest.shape)
print('halloooo')
foo = 3 * delta
# build test kernel
kernel = Kernel.get_kernel(co.matrix(Dtest),Dtrain[:,svm.get_support_dual()],ktype,kparam)
(res,state) = svm.apply_dual(kernel)
print(res.size)
Z = np.reshape(res,(sx,sy))
plt.contourf(X, Y, Z)
plt.contour(X, Y, Z, [np.array(svm.get_threshold())[0,0]])
plt.scatter(Dtrain[0,svm.get_support_dual()],Dtrain[1,svm.get_support_dual()],40,c='k')
plt.scatter(Dtrain[0,:],Dtrain[1,:],10)
plt.show()
print('finished')
plt.figure()
scores = []
for i in range(train.samples):
LENS = len(train.y[i])
(anom_score, scores_exm) = train.get_scores(lsol, i, lats[i])
scores.append(anom_score)
plt.plot(range(LENS),scores_exm.trans() + i*8,'-g')
plt.plot(range(LENS),train.y[i].trans() + i*8,'-b')
plt.plot(range(LENS),lats[i].trans() + i*8,'-r')
if i==0:
plt.plot(range(LENS),train.X[i][0,:].trans() + i*8,'-k')
(fpr, tpr, thres) = metric.roc_curve(label, co.matrix(scores))
auc = metric.auc(fpr, tpr)
print auc
# train one-class svm
kern = Kernel.get_kernel(phi, phi)
ocsvm = OCSVM(kern, C=1.0/(EXMS_TRAIN*0.1))
ocsvm.train_dual()
(oc_as, foo) = ocsvm.apply_dual(kern[:,ocsvm.get_support_dual()])
(fpr, tpr, thres) = metric.roc_curve(label[0:EXMS_TRAIN], oc_as)
base_auc = metric.auc(fpr, tpr)
print base_auc
plt.show()
print('finished')
x = np.arange(-4.0, 4.0, delta)
y = np.arange(-4.0, 4.0, delta)
X, Y = np.meshgrid(x, y)
(sx, sy) = X.shape
Xf = np.reshape(X, (1, sx * sy))
Yf = np.reshape(Y, (1, sx * sy))
Dtest = np.append(Xf, Yf, axis=0)
print(Dtest.shape)
print('halloooo')
foo = 3 * delta
# build test kernel
kernel = Kernel.get_kernel(co.matrix(Dtest),
Dtrain[:,
svm.get_support_dual()], ktype, kparam)
(res, state) = svm.apply_dual(kernel)
print(res.size)
Z = np.reshape(res, (sx, sy))
plt.contourf(X, Y, Z)
plt.contour(X, Y, Z, [np.array(svm.get_threshold())[0, 0]])
plt.scatter(Dtrain[0, svm.get_support_dual()],
Dtrain[1, svm.get_support_dual()],
40,
c='k')
plt.scatter(Dtrain[0, :], Dtrain[1, :], 10)
plt.show()
print('finished')