def experiment_anomaly_segmentation(train, test, comb, num_train, anom_prob, labels): # transductive train/pred for structured anomaly detection sad = StructuredOCSVM(comb, C=1.0/(num_train*0.5)) (lsol, lats, thres) = sad.train_dc(max_iter=40) (cont, cont_exm) = test.evaluate(lats[num_train:]) # train structured svm ssvm = SSVM(train) (sol, slacks) = ssvm.train() (vals, preds) = ssvm.apply(test) (base_cont, base_cont_exm) = test.evaluate(preds) return (cont, base_cont)
def experiment_anomaly_segmentation(train, test, comb, num_train, anom_prob,
labels):
# transductive train/pred for structured anomaly detection
sad = StructuredOCSVM(comb, C=1.0 / (num_train * 0.5))
(lsol, lats, thres) = sad.train_dc(max_iter=40)
(cont, cont_exm) = test.evaluate(lats[num_train:])
# train structured svm
ssvm = SSVM(train)
(sol, slacks) = ssvm.train()
(vals, preds) = ssvm.apply(test)
(base_cont, base_cont_exm) = test.evaluate(preds)
return (cont, base_cont)
def perf_ssvm(test_inds, marker, train, test):
# SAD annotation
print('(a) Setup SSVM...')
ssvm = SSVM(train, C=10.0)
print('(b) Train SSVM...')
(lsol, slacks) = ssvm.train()
print('(c) Evaluate SSVM...')
(scores, lats) = ssvm.apply(test)
(err, err_exm) = test.evaluate(lats)
res = (err['fscore'], err['precision'], err['sensitivity'],
err['specificity'])
(fpr, tpr, thres) = metric.roc_curve(co.matrix(marker)[test_inds], -scores)
auc = metric.auc(fpr, tpr)
print('(d) Return AUC={0}...'.format(auc))
print res
return auc, res
(ws, slacks) = ssvm.train()
print(ws)
# print(slacks)
# generate test data grid
delta = 0.1
x = np.arange(-4.0, 8.0, delta)
y = np.arange(-4.0, 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)
Dtest = np.append(Dtest,
np.reshape([1.0] * (sx * sy), (1, sx * sy)),
axis=0)
print(Dtest.shape)
# generate structured object
predsobj = SOMultiClass(co.matrix(Dtest), NUM_CLASSES)
(res, cls) = ssvm.apply(predsobj)
print(res.size)
# nice visualization
Z = np.reshape(cls, (sx, sy))
plt.contourf(X, Y, Z)
plt.scatter(Dtrain[0, :], Dtrain[1, :], 10)
plt.show()
print('finished')
fig = plt.figure()
for i in xrange(4):
plt.subplot(2, 4, i + 1)
if i == 0:
plt.title("LatentSVDD")
scores, lats = lsvdd.apply(predsobj)
if i == 1:
plt.title("StructPCA")
(scores, lats) = spca.apply(predsobj)
if i == 2:
plt.title("StructOCSVM")
scores, lats = socsvm.apply(predsobj)
if i == 3:
plt.title("SSVM")
scores, lats = ssvm.apply(predsobj)
# plot scores
Z = np.reshape(scores, (sx, sy))
plt.contourf(X, Y, Z)
plt.grid()
plt.scatter(Dtrain[:, 0], Dtrain[:, 1], 10)
# plot latent variable
Z = np.reshape(lats, (sx, sy))
plt.subplot(2, 4, i + 4 + 1)
plt.contourf(X, Y, Z)
plt.grid()
plt.scatter(Dtrain[:, 0], Dtrain[:, 1], 10)
plt.show()
if i==0:
plt.title("LatentSVDD")
lsvdd.train_dc()
(scores,lats) = lsvdd.apply(predsobj)
if i==1:
plt.title("StructPCA")
spca.train_dc()
(scores,lats) = spca.apply(predsobj)
if i==2:
plt.title("StructOCSVM")
socsvm.train_dc()
(scores,lats) = socsvm.apply(predsobj)
if i==3:
plt.title("SSVM")
ssvm.train()
(scores,lats) = ssvm.apply(predsobj)
# plot scores
Z = np.reshape(scores,(sx,sy))
plt.contourf(X, Y, Z)
plt.scatter(Dtrain[0,:],Dtrain[1,:],10)
# plot latent variable
Z = np.reshape(lats,(sx,sy))
plt.subplot(2,4,i+4+1)
plt.contourf(X, Y, Z)
plt.scatter(Dtrain[0,:],Dtrain[1,:],10)
plt.show()
print('finished')
# train svdd
ssvm = SSVM(sobj,1.0)
(ws,slacks) = ssvm.train()
print(ws)
# print(slacks)
# generate test data grid
delta = 0.1
x = np.arange(-4.0, 8.0, delta)
y = np.arange(-4.0, 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)
Dtest = np.append(Dtest,np.reshape([1.0]*(sx*sy),(1,sx*sy)),axis=0)
print(Dtest.shape)
# generate structured object
predsobj = SOMultiClass(co.matrix(Dtest),NUM_CLASSES)
(res,cls) = ssvm.apply(predsobj)
print(res.size)
# nice visualization
Z = np.reshape(cls,(sx,sy))
plt.contourf(X, Y, Z)
plt.scatter(Dtrain[0,:],Dtrain[1,:],10)
plt.show()
print('finished')
