def test_constrained_viterbi_tags(self):
constraints = {(0, 0), (0, 1),
(1, 1), (1, 2),
(2, 2), (2, 3),
(3, 3), (3, 4),
(4, 4), (4, 0)}
# Add the transitions to the end tag
# and from the start tag.
for i in range(5):
constraints.add((5, i))
constraints.add((i, 6))
crf = ConditionalRandomField(num_tags=5, constraints=constraints)
crf.transitions = torch.nn.Parameter(self.transitions)
crf.start_transitions = torch.nn.Parameter(self.transitions_from_start)
crf.end_transitions = torch.nn.Parameter(self.transitions_to_end)
mask = torch.LongTensor([
[1, 1, 1],
[1, 1, 0]
])
viterbi_path = crf.viterbi_tags(self.logits, mask)
# Get just the tags from each tuple of (tags, score).
viterbi_tags = [x for x, y in viterbi_path]
# Now the tags should respect the constraints
assert viterbi_tags == [
[2, 3, 3],
[2, 3]
]
def test_constrained_viterbi_tags(self):
constraints = {(0, 0), (0, 1),
(1, 1), (1, 2),
(2, 2), (2, 3),
(3, 3), (3, 4),
(4, 4), (4, 0)}
# Add the transitions to the end tag
# and from the start tag.
for i in range(5):
constraints.add((5, i))
constraints.add((i, 6))
crf = ConditionalRandomField(num_tags=5, constraints=constraints)
crf.transitions = torch.nn.Parameter(self.transitions)
crf.start_transitions = torch.nn.Parameter(self.transitions_from_start)
crf.end_transitions = torch.nn.Parameter(self.transitions_to_end)
mask = torch.LongTensor([
[1, 1, 1],
[1, 1, 0]
])
viterbi_path = crf.viterbi_tags(self.logits, mask)
# Get just the tags from each tuple of (tags, score).
viterbi_tags = [x for x, y in viterbi_path]
# Now the tags should respect the constraints
assert viterbi_tags == [
[2, 3, 3],
[2, 3]
]
def test_constrained_viterbi_tags(self):
constraints = {(0, 0), (0, 1), (1, 1), (1, 2), (2, 2), (2, 3), (3, 3),
(3, 4), (4, 4), (4, 0)}
crf = ConditionalRandomField(num_tags=5, constraints=constraints)
crf.transitions = torch.nn.Parameter(self.transitions)
crf.start_transitions = torch.nn.Parameter(self.transitions_from_start)
crf.end_transitions = torch.nn.Parameter(self.transitions_to_end)
mask = Variable(torch.LongTensor([[1, 1, 1], [1, 1, 0]]))
viterbi_tags = crf.viterbi_tags(self.logits, mask)
# Now the tags should respect the constraints
assert viterbi_tags == [[2, 3, 3], [2, 3]]
def test_constrained_viterbi_tags(self):
constraints = {(0, 0), (0, 1),
(1, 1), (1, 2),
(2, 2), (2, 3),
(3, 3), (3, 4),
(4, 4), (4, 0)}
crf = ConditionalRandomField(num_tags=5, constraints=constraints)
crf.transitions = torch.nn.Parameter(self.transitions)
crf.start_transitions = torch.nn.Parameter(self.transitions_from_start)
crf.end_transitions = torch.nn.Parameter(self.transitions_to_end)
mask = Variable(torch.LongTensor([
[1, 1, 1],
[1, 1, 0]
]))
viterbi_tags = crf.viterbi_tags(self.logits, mask)
# Now the tags should respect the constraints
assert viterbi_tags == [
[2, 3, 3],
[2, 3]
]
def test_constrained_viterbi_tags(self):
constraints = {(0, 0), (0, 1), (1, 1), (1, 2), (2, 2), (2, 3), (3, 3),
(3, 4), (4, 4), (4, 0)}
# Add the transitions to the end tag
# and from the start tag.
for i in range(5):
constraints.add((5, i))
constraints.add((i, 6))
crf = ConditionalRandomField(num_tags=5, constraints=constraints)
crf.transitions = torch.nn.Parameter(self.transitions)
crf.start_transitions = torch.nn.Parameter(self.transitions_from_start)
crf.end_transitions = torch.nn.Parameter(self.transitions_to_end)
timestep_constrained_crf = TimestepConstrainedConditionalRandomField(
num_tags=5, constraints=constraints)
timestep_constrained_crf.transitions = torch.nn.Parameter(
self.transitions)
timestep_constrained_crf.start_transitions = torch.nn.Parameter(
self.transitions_from_start)
timestep_constrained_crf.end_transitions = torch.nn.Parameter(
self.transitions_to_end)
mask = torch.LongTensor([[1, 1, 1], [1, 1, 0]])
viterbi_path = crf.viterbi_tags(self.logits, mask)
# Get just the tags from each tuple of (tags, score).
viterbi_tags = [x for x, y in viterbi_path]
# Now the tags should respect the constraints
assert viterbi_tags == [[2, 3, 3], [2, 3]]
timestep_constrained_viterbi_path = timestep_constrained_crf.viterbi_tags(
self.logits, mask)
# Get just the tags from each tuple of (tags, score).
timestep_constrained_viterbi_tags = [
x for x, y in timestep_constrained_viterbi_path
]
# Now the tags should respect the constraints
assert timestep_constrained_viterbi_tags == viterbi_tags
timestep_constrained_crf_no_constraints = TimestepConstrainedConditionalRandomField(
num_tags=5)
timestep_constrained_crf_no_constraints.transitions = torch.nn.Parameter(
self.transitions)
timestep_constrained_crf_no_constraints.start_transitions = torch.nn.Parameter(
self.transitions_from_start)
timestep_constrained_crf_no_constraints.end_transitions = torch.nn.Parameter(
self.transitions_to_end)
constraint_mask = crf._constraint_mask.data
expanded_constraint_mask = constraint_mask.unsqueeze(0).expand(
2, -1, -1)
# Pass in the constraints at viterbi_tags
timestep_constrained_viterbi_path = timestep_constrained_crf_no_constraints.viterbi_tags(
self.logits, mask, expanded_constraint_mask)
# Get just the tags from each tuple of (tags, score).
timestep_constrained_viterbi_tags = [
x for x, y in timestep_constrained_viterbi_path
]
# Now the tags should respect the constraints
assert timestep_constrained_viterbi_tags == viterbi_tags
