def test_joint_feature_continuous():
# FIXME
# first make perfect prediction, including pairwise part
X, Y = generate_blocks_multinomial(noise=2, n_samples=1, seed=1)
x, y = X[0], Y[0]
n_states = x.shape[-1]
pw_horz = -1 * np.eye(n_states)
xx, yy = np.indices(pw_horz.shape)
# linear ordering constraint horizontally
pw_horz[xx > yy] = 1
# high cost for unequal labels vertically
pw_vert = -1 * np.eye(n_states)
pw_vert[xx != yy] = 1
pw_vert *= 10
# create crf, assemble weight, make prediction
for inference_method in get_installed(["lp", "ad3"]):
crf = DirectionalGridCRF(inference_method=inference_method)
crf.initialize(X, Y)
w = np.hstack([np.eye(3).ravel(), -pw_horz.ravel(), -pw_vert.ravel()])
y_pred = crf.inference(x, w, relaxed=True)
# compute joint_feature for prediction
joint_feature_y = crf.joint_feature(x, y_pred)
assert_equal(joint_feature_y.shape, (crf.size_joint_feature,))
def test_inference():
# Test inference with different weights in different directions
X, Y = generate_blocks_multinomial(noise=2, n_samples=1, seed=1)
x, y = X[0], Y[0]
n_states = x.shape[-1]
edge_list = make_grid_edges(x, 4, return_lists=True)
edges = np.vstack(edge_list)
pw_horz = -1 * np.eye(n_states)
xx, yy = np.indices(pw_horz.shape)
# linear ordering constraint horizontally
pw_horz[xx > yy] = 1
# high cost for unequal labels vertically
pw_vert = -1 * np.eye(n_states)
pw_vert[xx != yy] = 1
pw_vert *= 10
# generate edge weights
edge_weights_horizontal = np.repeat(pw_horz[np.newaxis, :, :],
edge_list[0].shape[0],
axis=0)
edge_weights_vertical = np.repeat(pw_vert[np.newaxis, :, :],
edge_list[1].shape[0],
axis=0)
edge_weights = np.vstack([edge_weights_horizontal, edge_weights_vertical])
# do inference
res = lp_general_graph(-x.reshape(-1, n_states), edges, edge_weights)
for inference_method in get_installed(["lp", "ad3"]):
# same inference through CRF inferface
crf = DirectionalGridCRF(inference_method=inference_method)
crf.initialize(X, Y)
w = np.hstack([np.eye(3).ravel(), -pw_horz.ravel(), -pw_vert.ravel()])
y_pred = crf.inference(x, w, relaxed=True)
if isinstance(y_pred, tuple):
# ad3 produces an integer result if it found the exact solution
assert_array_almost_equal(res[0], y_pred[0].reshape(-1, n_states))
assert_array_almost_equal(res[1], y_pred[1])
assert_array_equal(y, np.argmax(y_pred[0], axis=-1))
for inference_method in get_installed(["lp", "ad3", "qpbo"]):
# again, this time discrete predictions only
crf = DirectionalGridCRF(inference_method=inference_method)
crf.initialize(X, Y)
w = np.hstack([np.eye(3).ravel(), -pw_horz.ravel(), -pw_vert.ravel()])
y_pred = crf.inference(x, w, relaxed=False)
assert_array_equal(y, y_pred)
def test_inference():
# Test inference with different weights in different directions
X, Y = generate_blocks_multinomial(noise=2, n_samples=1, seed=1)
x, y = X[0], Y[0]
n_states = x.shape[-1]
edge_list = make_grid_edges(x, 4, return_lists=True)
edges = np.vstack(edge_list)
pw_horz = -1 * np.eye(n_states)
xx, yy = np.indices(pw_horz.shape)
# linear ordering constraint horizontally
pw_horz[xx > yy] = 1
# high cost for unequal labels vertically
pw_vert = -1 * np.eye(n_states)
pw_vert[xx != yy] = 1
pw_vert *= 10
# generate edge weights
edge_weights_horizontal = np.repeat(pw_horz[np.newaxis, :, :],
edge_list[0].shape[0], axis=0)
edge_weights_vertical = np.repeat(pw_vert[np.newaxis, :, :],
edge_list[1].shape[0], axis=0)
edge_weights = np.vstack([edge_weights_horizontal, edge_weights_vertical])
# do inference
res = lp_general_graph(-x.reshape(-1, n_states), edges, edge_weights)
for inference_method in get_installed(["lp", "ad3"]):
# same inference through CRF inferface
crf = DirectionalGridCRF(inference_method=inference_method)
crf.initialize(X, Y)
w = np.hstack([np.eye(3).ravel(), -pw_horz.ravel(), -pw_vert.ravel()])
y_pred = crf.inference(x, w, relaxed=True)
if isinstance(y_pred, tuple):
# ad3 produces an integer result if it found the exact solution
assert_array_almost_equal(res[0], y_pred[0].reshape(-1, n_states))
assert_array_almost_equal(res[1], y_pred[1])
assert_array_equal(y, np.argmax(y_pred[0], axis=-1))
for inference_method in get_installed(["lp", "ad3", "qpbo"]):
# again, this time discrete predictions only
crf = DirectionalGridCRF(inference_method=inference_method)
crf.initialize(X, Y)
w = np.hstack([np.eye(3).ravel(), -pw_horz.ravel(), -pw_vert.ravel()])
y_pred = crf.inference(x, w, relaxed=False)
assert_array_equal(y, y_pred)
def test_joint_feature_discrete():
X, Y = generate_blocks_multinomial(noise=2, n_samples=1, seed=1)
x, y = X[0], Y[0]
for inference_method in get_installed(["lp", "ad3", "qpbo"]):
crf = DirectionalGridCRF(inference_method=inference_method)
crf.initialize(X, Y)
joint_feature_y = crf.joint_feature(x, y)
assert_equal(joint_feature_y.shape, (crf.size_joint_feature,))
# first horizontal, then vertical
# we trust the unaries ;)
pw_joint_feature_horz, pw_joint_feature_vert = joint_feature_y[crf.n_states *
crf.n_features:].reshape(
2, crf.n_states, crf.n_states)
xx, yy = np.indices(y.shape)
assert_array_equal(pw_joint_feature_vert, np.diag([9 * 4, 9 * 4, 9 * 4]))
vert_joint_feature = np.diag([10 * 3, 10 * 3, 10 * 3])
vert_joint_feature[0, 1] = 10
vert_joint_feature[1, 2] = 10
assert_array_equal(pw_joint_feature_horz, vert_joint_feature)
