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)
def test_directional_bars():
for inference_method in ['lp']:
X, Y = toy.generate_easy(n_samples=10, noise=5, box_size=2,
total_size=6, seed=1)
n_labels = 2
crf = LatentDirectionalGridCRF(n_labels=n_labels,
n_states_per_label=[1, 4],
inference_method=inference_method)
clf = LatentSubgradientSSVM(model=crf, max_iter=500, C=10. ** 5,
verbose=2)
clf.fit(X, Y)
Y_pred = clf.predict(X)
assert_array_equal(np.array(Y_pred), Y)
def test_directional_bars():
# this test is very fragile :-/
X, Y = toy.generate_easy(n_samples=20, noise=2, box_size=2,
total_size=6, seed=2)
n_labels = 2
crf = LatentDirectionalGridCRF(n_labels=n_labels,
n_states_per_label=[1, 4])
clf = LatentSubgradientSSVM(model=crf, max_iter=75, C=10.,
learning_rate=1, momentum=0,
decay_exponent=0.5, decay_t0=10)
clf.fit(X, Y)
Y_pred = clf.predict(X)
assert_array_equal(np.array(Y_pred), Y)
def test_with_crosses():
# very simple dataset. k-means init is perfect
for n_states_per_label in [2, [1, 2]]:
# test with 2 states for both foreground and background,
# as well as with single background state
#for inference_method in ['ad3', 'qpbo', 'lp']:
for inference_method in ['lp']:
X, Y = toy.generate_crosses(n_samples=10, noise=5, n_crosses=1,
total_size=8)
n_labels = 2
crf = LatentGridCRF(n_labels=n_labels,
n_states_per_label=n_states_per_label,
inference_method=inference_method)
clf = LatentSubgradientSSVM(model=crf, max_iter=250, C=10. ** 5,
verbose=20, learning_rate=0.0001,
show_loss_every=10, momentum=0.98,
decay_exponent=0)
clf.fit(X, Y)
Y_pred = clf.predict(X)
assert_array_equal(np.array(Y_pred), Y)
