def test_continuous_y():
# for inference_method in ["lp", "ad3"]:
for inference_method in ["lp"]:
X, Y = toy.generate_blocks(n_samples=1)
x, y = X[0], Y[0]
w = np.array([1, 1, 0, -4, 0])
crf = GridCRF(inference_method=inference_method)
psi = crf.psi(x, y)
y_cont = np.zeros_like(x)
gx, gy = np.indices(x.shape[:-1])
y_cont[gx, gy, y] = 1
# need to generate edge marginals
vert = np.dot(y_cont[1:, :, :].reshape(-1, 2).T, y_cont[:-1, :, :].reshape(-1, 2))
# horizontal edges
horz = np.dot(y_cont[:, 1:, :].reshape(-1, 2).T, y_cont[:, :-1, :].reshape(-1, 2))
pw = vert + horz
psi_cont = crf.psi(x, (y_cont, pw))
assert_array_almost_equal(psi, psi_cont)
const = find_constraint(crf, x, y, w, relaxed=False)
const_cont = find_constraint(crf, x, y, w, relaxed=True)
# dpsi and loss are equal:
assert_array_almost_equal(const[1], const_cont[1])
assert_almost_equal(const[2], const_cont[2])
# returned y_hat is one-hot version of other
assert_array_equal(const[0], np.argmax(const_cont[0][0], axis=-1))
# test loss:
assert_equal(crf.loss(y, const[0]), crf.continuous_loss(y, const_cont[0][0]))
def test_binary_blocks_crf_n8_lp():
X, Y = toy.generate_blocks(n_samples=1, noise=1)
x, y = X[0], Y[0]
w = np.array([1, 1, 1, -1.4, 1])
crf = GridCRF(inference_method="lp", neighborhood=8)
y_hat = crf.inference(x, w)
assert_array_equal(y, y_hat)
def test_binary_blocks_one_slack_graph():
#testing cutting plane ssvm on easy binary dataset
# generate graphs explicitly for each example
for inference_method in ["dai", "lp"]:
print("testing %s" % inference_method)
X, Y = toy.generate_blocks(n_samples=3)
crf = GraphCRF(inference_method=inference_method)
clf = OneSlackSSVM(problem=crf, max_iter=100, C=100, verbose=100,
check_constraints=True, break_on_bad=True,
n_jobs=1)
x1, x2, x3 = X
y1, y2, y3 = Y
n_states = len(np.unique(Y))
# delete some rows to make it more fun
x1, y1 = x1[:, :-1], y1[:, :-1]
x2, y2 = x2[:-1], y2[:-1]
# generate graphs
X_ = [x1, x2, x3]
G = [make_grid_edges(x) for x in X_]
# reshape / flatten x and y
X_ = [x.reshape(-1, n_states) for x in X_]
Y = [y.ravel() for y in [y1, y2, y3]]
X = zip(X_, G)
clf.fit(X, Y)
Y_pred = clf.predict(X)
for y, y_pred in zip(Y, Y_pred):
assert_array_equal(y, y_pred)
def test_binary_blocks_crf():
X, Y = toy.generate_blocks(n_samples=1)
x, y = X[0], Y[0]
w = np.array([1, 1, 0, -4, 0])
crf = GridCRF()
y_hat = crf.inference(x, w)
assert_array_equal(y, y_hat)
def test_blocks_crf_directional():
# test latent directional CRF on blocks
# test that all results are the same as equivalent LatentGridCRF
X, Y = toy.generate_blocks(n_samples=1)
x, y = X[0], Y[0]
pairwise_weights = np.array([0, 0, 0, -4, -4, 0, -4, -4, 0, 0])
unary_weights = np.repeat(np.eye(2), 2, axis=0)
w = np.hstack([unary_weights.ravel(), pairwise_weights])
pw_directional = np.array(
[0, 0, -4, -4, 0, 0, -4, -4, -4, -4, 0, 0, -4, -4, 0, 0, 0, 0, -4, -4, 0, 0, -4, -4, -4, -4, 0, 0, -4, -4, 0, 0]
)
w_directional = np.hstack([unary_weights.ravel(), pw_directional])
crf = LatentGridCRF(n_labels=2, n_states_per_label=2)
directional_crf = LatentDirectionalGridCRF(n_labels=2, n_states_per_label=2)
h_hat = crf.inference(x, w)
h_hat_d = directional_crf.inference(x, w_directional)
assert_array_equal(h_hat, h_hat_d)
h = crf.latent(x, y, w)
h_d = directional_crf.latent(x, y, w_directional)
assert_array_equal(h, h_d)
h_hat = crf.loss_augmented_inference(x, y, w)
h_hat_d = directional_crf.loss_augmented_inference(x, y, w_directional)
assert_array_equal(h_hat, h_hat_d)
psi = crf.psi(x, h_hat)
psi_d = directional_crf.psi(x, h_hat)
assert_array_equal(np.dot(psi, w), np.dot(psi_d, w_directional))
def test_binary_blocks():
X, Y = toy.generate_blocks(n_samples=10)
crf = GridCRF()
clf = StructuredPerceptron(problem=crf, max_iter=40)
clf.fit(X, Y)
Y_pred = clf.predict(X)
assert_array_equal(Y, Y_pred)
def test_blocks_crf_unaries():
X, Y = toy.generate_blocks(n_samples=1)
x, y = X[0], Y[0]
w = np.array([1, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
crf = LatentGridCRF(n_labels=2, n_states_per_label=2)
h_hat = crf.inference(x, w)
assert_array_equal(h_hat / 2, np.argmax(x, axis=-1))
def test_binary_blocks_subgradient():
#testing subgradient ssvm on easy binary dataset
X, Y = toy.generate_blocks(n_samples=10)
crf = GridCRF()
clf = SubgradientSSVM(model=crf, max_iter=200, C=100, learning_rate=0.1)
clf.fit(X, Y)
Y_pred = clf.predict(X)
assert_array_equal(Y, Y_pred)
def test_binary_blocks_perceptron_online():
#testing subgradient ssvm on easy binary dataset
X, Y = toy.generate_blocks(n_samples=10)
crf = GridCRF()
clf = StructuredPerceptron(model=crf, max_iter=20)
clf.fit(X, Y)
Y_pred = clf.predict(X)
assert_array_equal(Y, Y_pred)
def test_binary_blocks_batches_n_slack():
#testing cutting plane ssvm on easy binary dataset
X, Y = toy.generate_blocks(n_samples=5)
crf = GridCRF()
clf = NSlackSSVM(model=crf, max_iter=20, C=100, check_constraints=True,
break_on_bad=False, n_jobs=1, batch_size=1)
clf.fit(X, Y)
Y_pred = clf.predict(X)
assert_array_equal(Y, Y_pred)
def test_blocks_crf_unaries():
X, Y = toy.generate_blocks(n_samples=1)
x, y = X[0], Y[0]
unary_weights = np.repeat(np.eye(2), 2, axis=0)
pairwise_weights = np.array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])
w = np.hstack([unary_weights.ravel(), pairwise_weights])
crf = LatentGridCRF(n_labels=2, n_states_per_label=2)
h_hat = crf.inference(x, w)
assert_array_equal(h_hat / 2, np.argmax(x, axis=-1))
def test_binary_blocks_subgradient():
#testing subgradient ssvm on easy binary dataset
X, Y = toy.generate_blocks(n_samples=10)
crf = GridCRF()
clf = SubgradientStructuredSVM(problem=crf, max_iter=200, C=100,
verbose=10, learning_rate=0.1, n_jobs=-1)
clf.fit(X, Y)
Y_pred = clf.predict(X)
assert_array_equal(Y, Y_pred)
def test_blocks_crf():
X, Y = toy.generate_blocks(n_samples=1)
x, y = X[0], Y[0]
w = np.array([1, 1, 1, 1, 0, 0, 0, -4, -4, 0, -4, -4, 0, 0])
crf = LatentGridCRF(n_labels=2, n_states_per_label=2)
h_hat = crf.inference(x, w)
assert_array_equal(y, h_hat / 2)
h = crf.latent(x, y, w)
assert_equal(crf.loss(h, h_hat), 0)
def test_binary_ssvm_attractive_potentials():
# test that submodular SSVM can learn the block dataset
X, Y = toy.generate_blocks(n_samples=10)
crf = GridCRF()
submodular_clf = StructuredSVM(problem=crf, max_iter=200, C=100,
verbose=1, check_constraints=True,
positive_constraint=[3])
submodular_clf.fit(X, Y)
Y_pred = submodular_clf.predict(X)
assert_array_equal(Y, Y_pred)
def test_binary_blocks_cutting_plane():
#testing cutting plane ssvm on easy binary dataset
for inference_method in get_installed(["dai", "lp", "qpbo", "ad3"]):
X, Y = toy.generate_blocks(n_samples=5)
crf = GridCRF(inference_method=inference_method)
clf = NSlackSSVM(model=crf, max_iter=20, C=100,
check_constraints=True, break_on_bad=False)
clf.fit(X, Y)
Y_pred = clf.predict(X)
assert_array_equal(Y, Y_pred)
def test_binary_blocks_batches_n_slack():
#testing cutting plane ssvm on easy binary dataset
X, Y = toy.generate_blocks(n_samples=5)
crf = GridCRF(inference_method='lp')
clf = StructuredSVM(problem=crf, max_iter=20, C=100, verbose=0,
check_constraints=True, break_on_bad=False,
n_jobs=1, batch_size=1)
clf.fit(X, Y)
Y_pred = clf.predict(X)
assert_array_equal(Y, Y_pred)
def test_binary_blocks_crf():
X, Y = toy.generate_blocks(n_samples=1)
x, y = X[0], Y[0]
w = np.array([1, 0, # unary
0, 1,
0, # pairwise
-4, 0])
for inference_method in ['dai', 'qpbo', 'lp', 'ad3']:
crf = GridCRF(inference_method=inference_method)
y_hat = crf.inference(x, w)
assert_array_equal(y, y_hat)
def test_binary_ssvm_attractive_potentials():
# test that submodular SSVM can learn the block dataset
X, Y = toy.generate_blocks(n_samples=10)
crf = GridCRF()
submodular_clf = NSlackSSVM(model=crf, max_iter=200, C=100,
check_constraints=True,
positive_constraint=[5])
submodular_clf.fit(X, Y)
Y_pred = submodular_clf.predict(X)
assert_array_equal(Y, Y_pred)
assert_true(submodular_clf.w[5] < 0) # don't ask me about signs
def test_binary_blocks_cutting_plane():
#testing cutting plane ssvm on easy binary dataset
for inference_method in ["dai", "lp", "qpbo", "ad3"]:
X, Y = toy.generate_blocks(n_samples=5)
crf = GridCRF(inference_method=inference_method)
clf = StructuredSVM(problem=crf, max_iter=20, C=100, verbose=0,
check_constraints=True, break_on_bad=False,
n_jobs=-1)
clf.fit(X, Y)
Y_pred = clf.predict(X)
assert_array_equal(Y, Y_pred)
def test_blocks_crf():
X, Y = toy.generate_blocks(n_samples=1)
x, y = X[0], Y[0]
pairwise_weights = np.array([0, 0, 0, -4, -4, 0, -4, -4, 0, 0])
unary_weights = np.repeat(np.eye(2), 2, axis=0)
w = np.hstack([unary_weights.ravel(), pairwise_weights])
crf = LatentGridCRF(n_labels=2, n_states_per_label=2)
h_hat = crf.inference(x, w)
assert_array_equal(y, h_hat / 2)
h = crf.latent(x, y, w)
assert_equal(crf.loss(h, h_hat), 0)
def test_loss_augmentation():
X, Y = toy.generate_blocks(n_samples=1)
x, y = X[0], Y[0]
w = np.array([1, 0, # unary
0, 1,
0, # pairwise
-4, 0])
crf = GridCRF(inference_method='lp')
y_hat, energy = crf.loss_augmented_inference(x, y, w, return_energy=True)
assert_almost_equal(energy + crf.loss(y, y_hat),
-np.dot(w, crf.psi(x, y_hat)))
def test_binary_blocks_cutting_plane_latent_node():
#testing cutting plane ssvm on easy binary dataset
# we use the LatentNodeCRF without latent nodes and check that it does the
# same as GraphCRF
X, Y = toy.generate_blocks(n_samples=3)
crf = GraphCRF(inference_method='lp')
clf = StructuredSVM(model=crf, max_iter=20, C=100, verbose=0,
check_constraints=True, break_on_bad=False,
n_jobs=1)
x1, x2, x3 = X
y1, y2, y3 = Y
n_states = len(np.unique(Y))
# delete some rows to make it more fun
x1, y1 = x1[:, :-1], y1[:, :-1]
x2, y2 = x2[:-1], y2[:-1]
# generate graphs
X_ = [x1, x2, x3]
G = [make_grid_edges(x) for x in X_]
# reshape / flatten x and y
X_ = [x.reshape(-1, n_states) for x in X_]
Y = [y.ravel() for y in [y1, y2, y3]]
X = zip(X_, G)
clf.fit(X, Y)
Y_pred = clf.predict(X)
for y, y_pred in zip(Y, Y_pred):
assert_array_equal(y, y_pred)
latent_crf = LatentNodeCRF(n_labels=2, inference_method='lp',
n_hidden_states=0)
latent_svm = LatentSSVM(StructuredSVM(model=latent_crf, max_iter=20,
C=100, verbose=0,
check_constraints=True,
break_on_bad=False, n_jobs=1),
latent_iter=3)
X_latent = zip(X_, G, np.zeros(len(X_)))
latent_svm.fit(X_latent, Y, H_init=Y)
Y_pred = latent_svm.predict(X_latent)
for y, y_pred in zip(Y, Y_pred):
assert_array_equal(y, y_pred)
assert_array_almost_equal(latent_svm.w, clf.w)
def test_loss_augmentation():
X, Y = toy.generate_blocks(n_samples=1)
x, y = X[0], Y[0]
w = np.array([1.0, 1.0, 0.0, -4.0, 0.0])
unary_params = w[:2]
pairwise_flat = np.asarray(w[2:])
pairwise_params = np.zeros((2, 2))
pairwise_params[np.tri(2, dtype=np.bool)] = pairwise_flat
pairwise_params = pairwise_params + pairwise_params.T - np.diag(np.diag(pairwise_params))
crf = GridCRF()
x_loss_augmented = crf.loss_augment(x, y, w)
y_hat = crf.inference(x_loss_augmented, w)
# test that loss_augmented_inference does the same
y_hat_2 = crf.loss_augmented_inference(x, y, w)
assert_array_equal(y_hat_2, y_hat)
energy = compute_energy(x, y_hat, unary_params, pairwise_params)
energy_loss_augmented = compute_energy(x_loss_augmented, y_hat, unary_params, pairwise_params)
assert_almost_equal(energy + crf.loss(y, y_hat), energy_loss_augmented)
# with zero in w:
unary_params[1] = 0
assert_raises(ValueError, crf.loss_augment, x, y, w)
