X_train_, X_test_, X_train, X_test, y_train, y_test, y_org_train, y_org_test =\
train_test_split(X_, X, Y, y_org, test_size=.5)
# first, do it with a standard CRF / SVM
pbl = GraphCRF(n_features=64, n_states=2, inference_method='lp')
svm = StructuredSVM(pbl, verbose=1, check_constraints=True, C=1000, n_jobs=1,
batch_size=-1)
svm.fit(X_train_, y_train)
y_pred = np.vstack(svm.predict(X_test_))
print("Score with pystruct crf svm: %f" % np.mean(y_pred == y_test))
print(svm.score(X_train_, y_train))
print(svm.score(X_test_, y_test))
# now with latent CRF SVM
latent_pbl = LatentGraphCRF(n_features=64, n_labels=2, n_states_per_label=5,
inference_method='dai')
latent_svm = LatentSubgradientSSVM(model=latent_pbl, max_iter=5000, C=1,
verbose=2, n_jobs=1, learning_rate=0.1,
show_loss_every=10, momentum=0.0,
decay_exponent=0.5)
#latent_svm = LatentSSVM(latent_pbl, verbose=2, check_constraints=True, C=100,
#n_jobs=1, batch_size=-1, tol=.1, latent_iter=2)
latent_svm.fit(X_train_, y_train)
print(latent_svm.score(X_train_, y_train))
print(latent_svm.score(X_test_, y_test))
h_pred = np.hstack(latent_svm.predict_latent(X_test_))
print("Latent class counts: %s" % repr(np.bincount(h_pred)))
#inactive_window=0, learning_rate=0.01, momentum=0)
latent_svm = LatentSubgradientSSVM(model=latent_crf, max_iter=200, C=100,
verbose=1, n_jobs=1, show_loss_every=10,
learning_rate=0.01, momentum=0)
# make edges for hidden states:
edges = []
node_indices = np.arange(4 * 4).reshape(4, 4)
for i, (x, y) in enumerate(itertools.product([0, 2], repeat=2)):
for j in xrange(x, x + 2):
for k in xrange(y, y + 2):
edges.append([i + 4 * 4, node_indices[j, k]])
G = [np.vstack([make_grid_edges(x), edges]) for x in X]
#G = [make_grid_edges(x) for x in X]
#H_init = [np.hstack([y.ravel(), 2 + y[1: -1, 1: -1].ravel()]) for y in Y]
H_init = [np.hstack([y.ravel(), np.random.randint(2, 4, size=2 * 2)]) for y in
Y]
plot_boxes(H_init)
X_ = zip(X_flat, G, [2 * 2 for x in X_flat])
latent_svm.fit(X_, Y_flat, H_init)
print("Training score with latent nodes: %f " % latent_svm.score(X_, Y_flat))
H = latent_svm.predict_latent(X_)
plot_boxes(H)
plt.show()
