def test_latent_node_boxes_latent_subgradient():
# same as above, now with elementary subgradients
# learn the "easy" 2x2 boxes dataset.
# a 2x2 box is placed randomly in a 4x4 grid
# we add a latent variable for each 2x2 patch
# that should make the model fairly simple
X, Y = toy.make_simple_2x2(seed=1)
latent_crf = LatentNodeCRF(n_labels=2, inference_method='lp',
n_hidden_states=2, n_features=1)
latent_svm = LatentSubgradientSSVM(model=latent_crf, max_iter=250, C=10,
verbose=10, learning_rate=0.1,
momentum=0)
G = [make_grid_edges(x) for x in X]
# 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]
# reshape / flatten x and y
X_flat = [x.reshape(-1, 1) for x in X]
Y_flat = [y.ravel() for y in Y]
X_ = zip(X_flat, G, [4 * 4 for x in X_flat])
latent_svm.fit(X_, Y_flat)
assert_equal(latent_svm.score(X_, Y_flat), 1)
def test_latent_node_boxes_latent_subgradient():
# same as above, now with elementary subgradients
X, Y = toy.make_simple_2x2(seed=1)
latent_crf = LatentNodeCRF(n_labels=2, n_hidden_states=2, n_features=1)
latent_svm = LatentSubgradientSSVM(model=latent_crf, max_iter=250, C=10,
learning_rate=0.1, momentum=0)
G = [make_grid_edges(x) for x in X]
edges = make_edges_2x2()
G = [np.vstack([make_grid_edges(x), edges]) for x in X]
# reshape / flatten x and y
X_flat = [x.reshape(-1, 1) for x in X]
Y_flat = [y.ravel() for y in Y]
X_ = zip(X_flat, G, [4 * 4 for x in X_flat])
latent_svm.fit(X_, Y_flat)
assert_equal(latent_svm.score(X_, Y_flat), 1)
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()
