def test_hmad(phi, kern, train, test, num_train, anom_prob, labels, zero_shot=False):
auc = 0.5
# train structured anomaly detection
sad = LatentOCSVM(train, C=1.0 / (num_train * anom_prob))
(lsol, lats, thres) = sad.train_dc(max_iter=60, zero_shot=zero_shot)
(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
def experiment_anomaly_detection(train, test, comb, num_train, anom_prob, labels):
# train one-class svm
phi = calc_feature_vecs(comb.X)
kern = get_kernel(phi[:,0:num_train], phi[:,0:num_train])
ocsvm = OcSvmDualQP(kern, anom_prob)
ocsvm.fit()
kern = get_kernel(phi, phi)
oc_as = ocsvm.apply(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 = LatentOCSVM(train, anom_prob)
sad.fit(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 = get_kernel(phi[:,0:num_train], phi[:,0:num_train])
ocsvm = OcSvmDualQP(kern, anom_prob)
ocsvm.fit()
kern = get_kernel(phi, phi)
oc_as = ocsvm.apply(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 = LatentOCSVM(train, anom_prob)
sad.fit(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
for i in range(NUM_CLASSES):
mean = (np.random.rand(2) - 0.5) * 12.
Dtrain[i * NUM_DATA:(i + 1) *
NUM_DATA, :2] = np.random.multivariate_normal(
mean, 1. * np.random.rand() * np.eye(2), size=NUM_DATA)
Dy[i * NUM_DATA:(i + 1) * NUM_DATA] = i
# generate structured object
sobj = SOMultiClass(Dtrain.T, y=Dy, classes=NUM_CLASSES)
# unsupervised methods
lsvdd = LatentSVDD(sobj, 0.9)
lsvdd.fit()
spca = LatentPCA(sobj)
spca.fit()
socsvm = LatentOCSVM(sobj, .2)
socsvm.fit()
# supervised methods
ssvm = SSVM(sobj)
ssvm.train()
# generate test data grid
delta = 0.2
x = np.arange(-8.0, 8.0, delta)
y = np.arange(-8.0, 8.0, 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)
Dy = np.zeros(NUM_CLASSES*NUM_DATA, dtype=np.int)
Dtrain = np.ones((NUM_CLASSES*NUM_DATA, 3))
for i in range(NUM_CLASSES):
mean = (np.random.rand(2)-0.5)*12.
Dtrain[i*NUM_DATA:(i+1)*NUM_DATA, :2] = np.random.multivariate_normal(mean, 1.*np.random.rand()*np.eye(2), size=NUM_DATA)
Dy[i*NUM_DATA:(i+1)*NUM_DATA] = i
# generate structured object
sobj = SOMultiClass(Dtrain.T, y=Dy , classes=NUM_CLASSES)
# unsupervised methods
lsvdd = LatentSVDD(sobj, 0.9)
lsvdd.fit()
spca = LatentPCA(sobj)
spca.fit()
socsvm = LatentOCSVM(sobj, .2)
socsvm.fit()
# supervised methods
ssvm = SSVM(sobj)
ssvm.train()
# generate test data grid
delta = 0.2
x = np.arange(-8.0, 8.0, delta)
y = np.arange(-8.0, 8.0, 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)