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 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 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)
lens = len(X[i][0,:]) cnt += lens tst_mean += co.matrix(1.0, (1, lens))*X[i].trans() print tst_mean/float(cnt) return X if __name__ == '__main__': EXMS_TRAIN = 80 EXMS_TEST = 100 EXMS_COMB = EXMS_TRAIN+EXMS_TEST (train, test, comb, label, phi) = get_model(EXMS_COMB, EXMS_TRAIN) lsvm = StructuredOCSVM(train, C=1.0/(train.samples*0.5)) (lsol, lats, thres) = lsvm.train_dc(max_iter=100) (err, err_exm) = train.evaluate(lats) print err 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:
# for all methods
fig = plt.figure()
for i in xrange(4):
plt.subplot(2,4,i+1)
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)
# for all methods
fig = plt.figure()
for i in xrange(4):
plt.subplot(2, 4, i + 1)
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)
