def main():
X, Y = generate_blocks(n_samples=1)
# X, Y = generate_checker()
# X, Y = generate_easy(n_samples=1)
# X, Y = generate_big_checker()
crf = BinaryGridCRF()
# clf = StructuredPerceptron(problem=crf, max_iter=100)
clf = StructuredSVM(problem=crf, max_iter=200, C=100, verbose=10, check_constraints=True)
# clf = SubgradientStructuredSVM(problem=crf, max_iter=2000, C=100)
clf.fit(X, Y)
Y_pred = clf.predict(X)
i = 0
loss = 0
for x, y, y_pred in zip(X, Y, Y_pred):
loss += np.sum(y != y_pred)
fig, plots = plt.subplots(2, 2)
plots[0, 0].set_title("x")
plots[0, 0].imshow(x[:, :, 0], interpolation="nearest")
plots[0, 1].set_title("y")
plots[0, 1].imshow(y, interpolation="nearest")
plots[1, 0].set_title("unaries")
w_unaries = np.zeros(4)
w_unaries[0] = 1.0
y_unaries = crf.inference(x, w_unaries)
plots[1, 0].imshow(y_unaries, interpolation="nearest")
plots[1, 1].set_title("full crf")
plots[1, 1].imshow(y_pred, interpolation="nearest")
for plot in plots.ravel():
plot.set_axis_off()
fig.savefig("data_%03d.png" % i)
plt.close(fig)
i += 1
print("loss: %f" % loss)
def main():
X, Y = make_dataset_big_checker(n_samples=1)
size_y = Y[0].size
shape_y = Y[0].shape
n_labels = len(np.unique(Y))
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
crf = MultinomialFixedGraphCRFNoBias(n_states=n_labels, graph=graph)
#crf = MultinomialGridCRF(n_labels=4)
#clf = StructuredPerceptron(problem=crf, max_iter=50)
clf = StructuredSVM(problem=crf, max_iter=20, C=1000000, verbose=20,
check_constraints=True,
positive_constraint=np.arange(crf.size_psi - 1))
#clf = SubgradientStructuredSVM(problem=crf, max_iter=100, C=10000)
X_flat = [x.reshape(-1, n_labels).copy("C") 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
for x, y, y_pred in zip(X, Y, Y_pred):
y_pred = y_pred.reshape(x.shape[:2])
#loss += np.sum(y != y_pred)
loss += np.sum(np.logical_xor(y, y_pred))
if i > 4:
continue
fig, plots = plt.subplots(1, 4)
plots[0].imshow(y, interpolation='nearest')
plots[0].set_title("gt")
pw_z = np.zeros((n_labels, n_labels), dtype=np.int32)
un = np.ascontiguousarray(
-1000 * x.reshape(-1, n_labels)).astype(np.int32)
unaries = alpha_expansion_graph(edges, un, pw_z)
plots[1].imshow(unaries.reshape(y.shape), interpolation='nearest')
plots[1].set_title("unaries only")
plots[2].imshow(y_pred, interpolation='nearest')
plots[2].set_title("prediction")
loss_augmented = clf.problem.loss_augmented_inference(
x.reshape(-1, n_labels), y.ravel(), clf.w)
loss_augmented = loss_augmented.reshape(y.shape)
plots[3].imshow(loss_augmented, interpolation='nearest')
plots[3].set_title("loss augmented")
fig.savefig("data_%03d.png" % i)
plt.close(fig)
i += 1
print("loss: %f" % loss)
def fit(self, X, Y):
w = np.ones(self.problem.size_psi) * 1e-5
subsvm = StructuredSVM(self.problem,
self.max_iter,
self.C,
self.check_constraints,
verbose=0)
objectives = []
ws = []
tracer()
H = Y
Y = [y / self.problem.n_states_per_label for y in Y]
for iteration in xrange(1):
print("LATENT SVM ITERATION %d" % iteration)
# find latent variables for ground truth:
if iteration == 0:
pass
else:
H = [self.problem.latent(x, y, w) for x, y in zip(X, Y)]
#X_wide = [np.repeat(x, self.problem.n_states_per_label, axis=1)
#for x in X]
subsvm.fit(X, H)
H_hat = [self.problem.inference(x, subsvm.w) for x in X]
inds = np.arange(len(H))
for i, h, h_hat in zip(inds, H, H_hat):
plt.matshow(h.reshape(18, 18))
plt.colorbar()
plt.savefig("figures/h_%03d_%03d.png" % (iteration, i))
plt.close()
plt.matshow(h_hat.reshape(18, 18))
plt.colorbar()
plt.savefig("figures/h_hat_%03d_%03d.png" % (iteration, i))
plt.close()
w = subsvm.w
ws.append(w)
objectives.append(subsvm.primal_objective_)
tracer()
self.w = w
plt.figure()
plt.plot(objectives)
plt.show()
tracer()
def fit(self, X, Y):
w = np.ones(self.problem.size_psi) * 1e-5
subsvm = StructuredSVM(self.problem, self.max_iter, self.C,
self.check_constraints, verbose=0)
objectives = []
ws = []
tracer()
H = Y
Y = [y / self.problem.n_states_per_label for y in Y]
for iteration in xrange(1):
print("LATENT SVM ITERATION %d" % iteration)
# find latent variables for ground truth:
if iteration == 0:
pass
else:
H = [self.problem.latent(x, y, w) for x, y in zip(X, Y)]
#X_wide = [np.repeat(x, self.problem.n_states_per_label, axis=1)
#for x in X]
subsvm.fit(X, H)
H_hat = [self.problem.inference(x, subsvm.w) for x in X]
inds = np.arange(len(H))
for i, h, h_hat in zip(inds, H, H_hat):
plt.matshow(h.reshape(18, 18))
plt.colorbar()
plt.savefig("figures/h_%03d_%03d.png" % (iteration, i))
plt.close()
plt.matshow(h_hat.reshape(18, 18))
plt.colorbar()
plt.savefig("figures/h_hat_%03d_%03d.png" % (iteration, i))
plt.close()
w = subsvm.w
ws.append(w)
objectives.append(subsvm.primal_objective_)
tracer()
self.w = w
plt.figure()
plt.plot(objectives)
plt.show()
tracer()
def main():
X, Y = generate_blocks(n_samples=1)
#X, Y = generate_checker()
#X, Y = generate_easy(n_samples=1)
#X, Y = generate_big_checker()
crf = BinaryGridCRF()
#clf = StructuredPerceptron(problem=crf, max_iter=100)
clf = StructuredSVM(problem=crf,
max_iter=200,
C=100,
verbose=10,
check_constraints=True)
#clf = SubgradientStructuredSVM(problem=crf, max_iter=2000, C=100)
clf.fit(X, Y)
Y_pred = clf.predict(X)
i = 0
loss = 0
for x, y, y_pred in zip(X, Y, Y_pred):
loss += np.sum(y != y_pred)
fig, plots = plt.subplots(2, 2)
plots[0, 0].set_title("x")
plots[0, 0].imshow(x[:, :, 0], interpolation='nearest')
plots[0, 1].set_title("y")
plots[0, 1].imshow(y, interpolation='nearest')
plots[1, 0].set_title("unaries")
w_unaries = np.zeros(4)
w_unaries[0] = 1.
y_unaries = crf.inference(x, w_unaries)
plots[1, 0].imshow(y_unaries, interpolation='nearest')
plots[1, 1].set_title("full crf")
plots[1, 1].imshow(y_pred, interpolation='nearest')
for plot in plots.ravel():
plot.set_axis_off()
fig.savefig("data_%03d.png" % i)
plt.close(fig)
i += 1
print("loss: %f" % loss)
def fit(self, X, Y):
w = np.ones(self.problem.size_psi) * 1e-5
subsvm = StructuredSVM(self.problem, self.max_iter, self.C,
self.check_constraints, verbose=self.verbose -
1, n_jobs=self.n_jobs,
break_on_bad=self.break_on_bad)
#objectives = []
constraints = None
ws = []
#Y = Y / self.problem.n_states_per_label
H = self.problem.init_latent(X, Y)
#kmeans_init(X, Y, edges, self.problem.n_states_per_label)
self.H_init_ = H
inds = np.arange(len(H))
for i, h in zip(inds, H):
plt.matshow(h, vmin=0, vmax=self.problem.n_states - 1)
plt.colorbar()
plt.savefig("figures/h_init_%03d.png" % i)
plt.close()
for iteration in xrange(10):
print("LATENT SVM ITERATION %d" % iteration)
# find latent variables for ground truth:
if iteration == 0:
pass
else:
H_new = np.array([self.problem.latent(x, y, w)
for x, y in zip(X, Y)])
if np.all(H_new == H):
print("no changes in latent variables of ground truth."
" stopping.")
break
print("changes in H: %d" % np.sum(H_new != H))
# update constraints:
constraints = [[] for i in xrange(len(X))]
for sample, h, i in zip(subsvm.constraints_, H_new,
np.arange(len(X))):
for constraint in sample:
const = find_constraint(self.problem, X[i], h, w,
constraint[0])
y_hat, dpsi, _, loss = const
constraints[i].append([y_hat, dpsi, loss])
H = H_new
#if initialization weak?
#if iteration == 0:
#subsvm.max_iter = 10
#subsvm.fit(X, H, constraints=new_constraints)
#constraints = subsvm.constraints_
subsvm.fit(X, H, constraints=constraints)
#if iteration == 0:
#subsvm.max_iter = self.max_iter
H_hat = Parallel(n_jobs=self.n_jobs)(delayed(inference)
(self.problem, x, subsvm.w)
for x in X)
inds = np.arange(len(H))
for i, h, h_hat in zip(inds, H, H_hat):
plt.matshow(h, vmin=0, vmax=self.problem.n_states - 1)
plt.colorbar()
plt.savefig("figures/h_%03d_%03d.png" % (iteration, i))
plt.close()
plt.matshow(h_hat, vmin=0, vmax=self.problem.n_states - 1)
plt.colorbar()
plt.savefig("figures/h_hat_%03d_%03d.png" % (iteration, i))
plt.close()
w = subsvm.w
ws.append(w)
#objectives.append(subsvm.primal_objective_)
self.w = w
def main():
X, Y = make_dataset_big_checker(n_samples=1)
size_y = Y[0].size
shape_y = Y[0].shape
n_labels = len(np.unique(Y))
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
crf = MultinomialFixedGraphCRFNoBias(n_states=n_labels, graph=graph)
#crf = MultinomialGridCRF(n_labels=4)
#clf = StructuredPerceptron(problem=crf, max_iter=50)
clf = StructuredSVM(problem=crf,
max_iter=20,
C=1000000,
verbose=20,
check_constraints=True,
positive_constraint=np.arange(crf.size_psi - 1))
#clf = SubgradientStructuredSVM(problem=crf, max_iter=100, C=10000)
X_flat = [x.reshape(-1, n_labels).copy("C") 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
for x, y, y_pred in zip(X, Y, Y_pred):
y_pred = y_pred.reshape(x.shape[:2])
#loss += np.sum(y != y_pred)
loss += np.sum(np.logical_xor(y, y_pred))
if i > 4:
continue
fig, plots = plt.subplots(1, 4)
plots[0].imshow(y, interpolation='nearest')
plots[0].set_title("gt")
pw_z = np.zeros((n_labels, n_labels), dtype=np.int32)
un = np.ascontiguousarray(-1000 * x.reshape(-1, n_labels)).astype(
np.int32)
unaries = alpha_expansion_graph(edges, un, pw_z)
plots[1].imshow(unaries.reshape(y.shape), interpolation='nearest')
plots[1].set_title("unaries only")
plots[2].imshow(y_pred, interpolation='nearest')
plots[2].set_title("prediction")
loss_augmented = clf.problem.loss_augmented_inference(
x.reshape(-1, n_labels), y.ravel(), clf.w)
loss_augmented = loss_augmented.reshape(y.shape)
plots[3].imshow(loss_augmented, interpolation='nearest')
plots[3].set_title("loss augmented")
fig.savefig("data_%03d.png" % i)
plt.close(fig)
i += 1
print("loss: %f" % loss)
