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
if keys_cv[j][0] not in poi_set:
good_partition = False
break
if good_partition is True:
poi_list = sorted(poi_set)
break
# train
ssvm = SSVM(inference_train=inference_method,
inference_pred=inference_method,
dat_obj=dat_obj,
share_params=SSVM_SHARE_PARAMS,
multi_label=SSVM_MULTI_LABEL,
C=ssvm_C,
poi_info=poi_info_i.loc[poi_list].copy())
if ssvm.train(sorted(trajid_set_train), n_jobs=N_JOBS) is True:
for j in test_ix: # test
ps_cv, L_cv = keys_cv[j]
y_hat_list = ssvm.predict(ps_cv, L_cv)
if y_hat_list is not None:
F1, pF1, tau = evaluate(dat_obj, keys_cv[j], y_hat_list)
F1_ssvm.append(F1)
pF1_ssvm.append(pF1)
Tau_ssvm.append(tau)
else:
for j in test_ix:
F1_ssvm.append(0)
pF1_ssvm.append(0)
Tau_ssvm.append(0)
mean_F1 = np.mean(F1_ssvm)
Dtrain4 = ToyData.get_gaussian(50,
dims=2,
means=[6.0, -3.0],
vars=[0.2, 0.1])
Dtrain = co.matrix([[Dtrain1], [Dtrain2], [Dtrain3], [Dtrain4]])
Dtrain = co.matrix([[Dtrain.trans()], [co.matrix(1.0, (1250, 1))]]).trans()
Dy = co.matrix([[co.matrix(0, (1, 1000))], [co.matrix(1, (1, 100))],
[co.matrix(2, (1, 100))], [co.matrix(3, (1, 50))]])
# generate structured object
sobj = SOMultiClass(Dtrain, NUM_CLASSES, Dy)
# 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)
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)
Dtest = np.append(Dtest, np.ones((1, sx * sy)), axis=0)
print(Dtest.shape)
# generate structured object
predsobj = SOMultiClass(Dtest, NUM_CLASSES)
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()
# generate raw training data Dtrain1 = ToyData.get_gaussian(1000,dims=2,means=[4.0,2.0],vars=[1.0,0.3]) Dtrain2 = ToyData.get_gaussian(100,dims=2,means=[-2.0,1.0],vars=[0.3,1.3]) Dtrain3 = ToyData.get_gaussian(100,dims=2,means=[3.0,-1.0],vars=[0.3,0.3]) Dtrain4 = ToyData.get_gaussian(50,dims=2,means=[6.0,-3.0],vars=[0.2,0.1]) Dtrain = co.matrix([[Dtrain1], [Dtrain2], [Dtrain3], [Dtrain4]]) Dtrain = co.matrix([[Dtrain.trans()],[co.matrix(1.0,(1250,1))]]).trans() Dy = co.matrix([[co.matrix(0,(1,1000))], [co.matrix(1,(1,100))], [co.matrix(2,(1,100))], [co.matrix(3,(1,50))]]) # generate structured object sobj = SOMultiClass(Dtrain,NUM_CLASSES,Dy) # 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)