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_blocks_crf_directional():
# test latent directional CRF on blocks
# test that all results are the same as equivalent LatentGridCRF
X, Y = 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_states_per_label=2, inference_method='lp')
crf.initialize(X, Y)
directional_crf = LatentDirectionalGridCRF(n_states_per_label=2,
inference_method='lp')
directional_crf.initialize(X, Y)
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)
joint_feature = crf.joint_feature(x, h_hat)
joint_feature_d = directional_crf.joint_feature(x, h_hat)
assert_array_equal(np.dot(joint_feature, w),
np.dot(joint_feature_d, w_directional))
def test_blocks_crf_unaries():
X, Y = 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_states_per_label=2, n_labels=2, n_features=2)
h_hat = crf.inference(x, w)
assert_array_equal(h_hat / 2, np.argmax(x, axis=-1))
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_blocks_crf():
X, Y = 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_states_per_label=2, n_labels=2, n_features=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_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 main():
X, Y = toy.generate_crosses(n_samples=20, noise=5, n_crosses=1,
total_size=8)
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=.5)
n_labels = len(np.unique(Y_train))
crf = LatentGridCRF(n_labels=n_labels, n_states_per_label=[1, 2],
inference_method='lp')
#clf = LatentSSVM(model=crf, max_iter=500, C=1000., verbose=2,
#check_constraints=True, n_jobs=-1, break_on_bad=True,
#base_svm='1-slack', inference_cache=20, tol=.1)
clf = LatentSubgradientSSVM(
model=crf, max_iter=500, C=1000., verbose=2,
n_jobs=-1, learning_rate=0.1, show_loss_every=10)
clf.fit(X_train, Y_train)
#for X_, Y_, H, name in [[X_train, Y_train, clf.H_init_, "train"],
#[X_test, Y_test, [None] * len(X_test), "test"]]:
for X_, Y_, H, name in [[X_train, Y_train, [None] * len(X_test), "train"],
[X_test, Y_test, [None] * len(X_test), "test"]]:
Y_pred = clf.predict(X_)
i = 0
loss = 0
for x, y, h_init, y_pred in zip(X_, Y_, H, Y_pred):
loss += np.sum(y != y_pred)
fig, ax = plt.subplots(3, 2)
ax[0, 0].matshow(y, vmin=0, vmax=crf.n_labels - 1)
ax[0, 0].set_title("ground truth")
ax[0, 1].matshow(np.argmax(x, axis=-1),
vmin=0, vmax=crf.n_labels - 1)
ax[0, 1].set_title("unaries only")
if h_init is None:
ax[1, 0].set_visible(False)
else:
ax[1, 0].matshow(h_init, vmin=0, vmax=crf.n_states - 1)
ax[1, 0].set_title("latent initial")
ax[1, 1].matshow(crf.latent(x, y, clf.w),
vmin=0, vmax=crf.n_states - 1)
ax[1, 1].set_title("latent final")
ax[2, 0].matshow(crf.inference(x, clf.w),
vmin=0, vmax=crf.n_states - 1)
ax[2, 0].set_title("prediction latent")
ax[2, 1].matshow(y_pred,
vmin=0, vmax=crf.n_labels - 1)
ax[2, 1].set_title("prediction")
for a in ax.ravel():
a.set_xticks(())
a.set_yticks(())
fig.savefig("data_%s_%03d.png" % (name, i), bbox_inches="tight")
i += 1
print("loss %s set: %f" % (name, loss))
print(clf.w)
tol=.1)
clf = LatentSSVM(base_ssvm=base_ssvm)
clf.fit(X_train, Y_train)
print("loss training set: %f" % clf.score(X_train, Y_train))
print("loss test set: %f" % clf.score(X_test, Y_test))
Y_pred = clf.predict(X_test)
x, y, y_pred = X_test[1], Y_test[1], Y_pred[1]
fig, ax = plt.subplots(3, 2)
ax[0, 0].matshow(y, vmin=0, vmax=crf.n_labels - 1)
ax[0, 0].set_title("ground truth")
ax[0, 1].matshow(np.argmax(x, axis=-1),
vmin=0, vmax=crf.n_labels - 1)
ax[0, 1].set_title("unaries only")
ax[1, 0].set_visible(False)
ax[1, 1].matshow(crf.latent(x, y, clf.w),
vmin=0, vmax=crf.n_states - 1)
ax[1, 1].set_title("latent final")
ax[2, 0].matshow(crf.inference(x, clf.w),
vmin=0, vmax=crf.n_states - 1)
ax[2, 0].set_title("prediction latent")
ax[2, 1].matshow(y_pred,
vmin=0, vmax=crf.n_labels - 1)
ax[2, 1].set_title("prediction")
for a in ax.ravel():
a.set_xticks(())
a.set_yticks(())
plt.show()
base_ssvm = OneSlackSSVM(model=crf,
C=10.,
n_jobs=-1,
inference_cache=20,
tol=.1)
clf = LatentSSVM(base_ssvm=base_ssvm)
clf.fit(X_train, Y_train)
print("Score training set: %f" % clf.score(X_train, Y_train))
print("Score test set: %f" % clf.score(X_test, Y_test))
Y_pred = clf.predict(X_test)
x, y, y_pred = X_test[1], Y_test[1], Y_pred[1]
fig, ax = plt.subplots(3, 2)
ax[0, 0].matshow(y, vmin=0, vmax=crf.n_labels - 1)
ax[0, 0].set_title("ground truth")
ax[0, 1].matshow(np.argmax(x, axis=-1), vmin=0, vmax=crf.n_labels - 1)
ax[0, 1].set_title("unaries only")
ax[1, 0].set_visible(False)
ax[1, 1].matshow(crf.latent(x, y, clf.w), vmin=0, vmax=crf.n_states - 1)
ax[1, 1].set_title("latent final")
ax[2, 0].matshow(crf.inference(x, clf.w), vmin=0, vmax=crf.n_states - 1)
ax[2, 0].set_title("prediction latent")
ax[2, 1].matshow(y_pred, vmin=0, vmax=crf.n_labels - 1)
ax[2, 1].set_title("prediction")
for a in ax.ravel():
a.set_xticks(())
a.set_yticks(())
plt.show()
tol=.1)
clf = LatentSSVM(base_ssvm=base_ssvm)
clf.fit(X_train, Y_train)
print("Score training set: %f" % clf.score(X_train, Y_train))
print("Score test set: %f" % clf.score(X_test, Y_test))
Y_pred = clf.predict(X_test)
x, y, y_pred = X_test[1], Y_test[1], Y_pred[1]
fig, ax = plt.subplots(3, 2)
ax[0, 0].matshow(y, vmin=0, vmax=crf.n_labels - 1)
ax[0, 0].set_title("ground truth")
ax[0, 1].matshow(np.argmax(x, axis=-1),
vmin=0, vmax=crf.n_labels - 1)
ax[0, 1].set_title("unaries only")
ax[1, 0].set_visible(False)
ax[1, 1].matshow(crf.latent(x, y, clf.w),
vmin=0, vmax=crf.n_states - 1)
ax[1, 1].set_title("latent final")
ax[2, 0].matshow(crf.inference(x, clf.w),
vmin=0, vmax=crf.n_states - 1)
ax[2, 0].set_title("prediction latent")
ax[2, 1].matshow(y_pred,
vmin=0, vmax=crf.n_labels - 1)
ax[2, 1].set_title("prediction")
for a in ax.ravel():
a.set_xticks(())
a.set_yticks(())
plt.show()
X, Y = generate_crosses(n_samples=20, noise=5, n_crosses=1, total_size=8)
X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.5)
crf = LatentGridCRF(n_states_per_label=[1, 2])
base_ssvm = OneSlackSSVM(model=crf, C=10.0, n_jobs=-1, inference_cache=20, tol=0.1)
clf = LatentSSVM(base_ssvm=base_ssvm)
clf.fit(X_train, Y_train)
print("loss training set: %f" % clf.score(X_train, Y_train))
print("loss test set: %f" % clf.score(X_test, Y_test))
Y_pred = clf.predict(X_test)
x, y, y_pred = X_test[1], Y_test[1], Y_pred[1]
fig, ax = plt.subplots(3, 2)
ax[0, 0].matshow(y, vmin=0, vmax=crf.n_labels - 1)
ax[0, 0].set_title("ground truth")
ax[0, 1].matshow(np.argmax(x, axis=-1), vmin=0, vmax=crf.n_labels - 1)
ax[0, 1].set_title("unaries only")
ax[1, 0].set_visible(False)
ax[1, 1].matshow(crf.latent(x, y, clf.w), vmin=0, vmax=crf.n_states - 1)
ax[1, 1].set_title("latent final")
ax[2, 0].matshow(crf.inference(x, clf.w), vmin=0, vmax=crf.n_states - 1)
ax[2, 0].set_title("prediction latent")
ax[2, 1].matshow(y_pred, vmin=0, vmax=crf.n_labels - 1)
ax[2, 1].set_title("prediction")
for a in ax.ravel():
a.set_xticks(())
a.set_yticks(())
plt.show()
