def main():
#X, Y = toy.generate_crosses_latent(n_samples=25, noise=10)
#X, Y = toy.generate_crosses(n_samples=50, noise=10)
X, Y = toy.generate_easy(n_samples=50, noise=5)
#X, Y = toy.generate_xs(n_samples=25, noise=5)
n_labels = 2
#crf = LatentGridCRF(n_labels=n_labels, n_states_per_label=2,
#inference_method='dai')
crf = LatentGridCRF(n_labels=n_labels, n_states_per_label=3,
inference_method='lp')
clf = StupidLatentSVM(problem=crf, max_iter=50, C=10. ** 5, verbose=2,
check_constraints=True, n_jobs=12, break_on_bad=True)
#clf = StupidLatentSVM(problem=crf, max_iter=50, C=1, verbose=2,
#check_constraints=True, n_jobs=12)
clf.fit(X, Y)
Y_pred = clf.predict(X)
i = 0
loss = 0
for x, y, h_init, y_pred in zip(X, Y, clf.H_init_, Y_pred):
y_pred = y_pred.reshape(x.shape[:2])
loss += np.sum(y != y_pred / 2)
if i > 100:
continue
fig, ax = plt.subplots(3, 2)
ax[0, 0].matshow(y * crf.n_states_per_label,
vmin=0, vmax=crf.n_states - 1)
ax[0, 0].set_title("ground truth")
w_unaries_only = np.array([1, 1, 1, 1,
0,
0, 0,
0, 0, 0,
0, 0, 0, 0])
unary_pred = crf.inference(x, w_unaries_only)
ax[0, 1].matshow(unary_pred, vmin=0, vmax=crf.n_states - 1)
ax[0, 1].set_title("unaries only")
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(y_pred, vmin=0, vmax=crf.n_states - 1)
ax[2, 0].set_title("prediction")
ax[2, 1].matshow((y_pred // crf.n_states_per_label)
* crf.n_states_per_label,
vmin=0, vmax=crf.n_states - 1)
ax[2, 1].set_title("prediction")
for a in ax.ravel():
a.set_xticks(())
a.set_yticks(())
fig.savefig("data_%03d.png" % i, bbox_inches="tight")
i += 1
print("loss: %f" % loss)
print(clf.w)
def main():
# X, Y = make_dataset_checker_multinomial()
# X, Y = make_dataset_big_checker_extended()
# X, Y = make_dataset_big_checker(n_samples=5)
X, Y = make_dataset_easy_latent_explicit(n_samples=50)
# X = X[:, :18, :18]
# Y = Y[:, :18, :18]
# X, Y = make_dataset_blocks_multinomial(n_samples=100)
size_y = Y[0].size
shape_y = Y[0].shape
inds = np.arange(size_y).reshape(shape_y)
horz = np.c_[inds[:, :-1].ravel(), inds[:, 1:].ravel()]
vert = np.c_[inds[:-1, :].ravel(), inds[1:, :].ravel()]
downleft = np.c_[inds[:-1, :-1].ravel(), inds[1:, 1:].ravel()]
downright = np.c_[inds[:-1, 1:].ravel(), inds[1:, :-1].ravel()]
edges = np.vstack([horz, vert, downleft, downright]).astype(np.int32)
graph = sparse.coo_matrix((np.ones(edges.shape[0]), (edges[:, 0], edges[:, 1])), shape=(size_y, size_y)).tocsr()
graph = graph + graph.T
# n_labels = len(np.unique(Y))
n_labels = 2
# crf = LatentFixedGraphCRF(n_labels=2, n_states_per_label=2, graph=graph)
crf = LatentFixedGraphCRF(n_labels=n_labels, n_states_per_label=2, graph=graph)
# clf = LatentStructuredPerceptron(problem=crf, max_iter=500)
clf = StupidLatentSVM(problem=crf, max_iter=10000, C=10000, verbose=2, check_constraints=True)
X_flat = [x.reshape(-1, n_labels) for x in X]
Y_flat = [y.ravel() for y in Y]
clf.fit(X_flat, Y_flat)
# clf.fit(X, Y)
Y_pred = clf.predict(X_flat)
# Y_pred = clf.predict(X)
i = 0
loss = 0
tracer()
for x, y, y_pred in zip(X, Y, Y_pred):
y_pred = y_pred.reshape(x.shape[:2])
loss += np.sum(y / 2 != y_pred / 2)
if i > 20:
continue
plt.subplot(131)
plt.imshow(y, interpolation="nearest")
plt.colorbar()
plt.subplot(132)
plt.imshow(np.argmin(x, axis=2), interpolation="nearest")
plt.colorbar()
plt.subplot(133)
plt.imshow(y_pred, interpolation="nearest")
plt.colorbar()
plt.savefig("data_%03d.png" % i)
plt.close()
i += 1
print("loss: %f" % loss)
def main():
#X, Y = make_dataset_checker_multinomial()
#X, Y = make_dataset_big_checker_extended()
#X, Y = make_dataset_big_checker(n_samples=5)
X, Y = make_dataset_easy_latent_explicit(n_samples=50)
#X = X[:, :18, :18]
#Y = Y[:, :18, :18]
#X, Y = make_dataset_blocks_multinomial(n_samples=100)
size_y = Y[0].size
shape_y = Y[0].shape
inds = np.arange(size_y).reshape(shape_y)
horz = np.c_[inds[:, :-1].ravel(), inds[:, 1:].ravel()]
vert = np.c_[inds[:-1, :].ravel(), inds[1:, :].ravel()]
downleft = np.c_[inds[:-1, :-1].ravel(), inds[1:, 1:].ravel()]
downright = np.c_[inds[:-1, 1:].ravel(), inds[1:, :-1].ravel()]
edges = np.vstack([horz, vert, downleft, downright]).astype(np.int32)
graph = sparse.coo_matrix(
(np.ones(edges.shape[0]), (edges[:, 0], edges[:, 1])),
shape=(size_y, size_y)).tocsr()
graph = graph + graph.T
#n_labels = len(np.unique(Y))
n_labels = 2
#crf = LatentFixedGraphCRF(n_labels=2, n_states_per_label=2, graph=graph)
crf = LatentFixedGraphCRF(n_labels=n_labels,
n_states_per_label=2,
graph=graph)
#clf = LatentStructuredPerceptron(problem=crf, max_iter=500)
clf = StupidLatentSVM(problem=crf,
max_iter=10000,
C=10000,
verbose=2,
check_constraints=True)
X_flat = [x.reshape(-1, n_labels) for x in X]
Y_flat = [y.ravel() for y in Y]
clf.fit(X_flat, Y_flat)
#clf.fit(X, Y)
Y_pred = clf.predict(X_flat)
#Y_pred = clf.predict(X)
i = 0
loss = 0
tracer()
for x, y, y_pred in zip(X, Y, Y_pred):
y_pred = y_pred.reshape(x.shape[:2])
loss += np.sum(y / 2 != y_pred / 2)
if i > 20:
continue
plt.subplot(131)
plt.imshow(y, interpolation='nearest')
plt.colorbar()
plt.subplot(132)
plt.imshow(np.argmin(x, axis=2), interpolation='nearest')
plt.colorbar()
plt.subplot(133)
plt.imshow(y_pred, interpolation='nearest')
plt.colorbar()
plt.savefig("data_%03d.png" % i)
plt.close()
i += 1
print("loss: %f" % loss)
