def test_get_best_span(self):
# pylint: disable=protected-access
span_begin_probs = Variable(torch.FloatTensor([[0.1, 0.3, 0.05, 0.3, 0.25]])).log()
span_end_probs = Variable(torch.FloatTensor([[0.65, 0.05, 0.2, 0.05, 0.05]])).log()
begin_end_idxs = BidirectionalAttentionFlow._get_best_span(span_begin_probs, span_end_probs)
assert_almost_equal(begin_end_idxs.data.numpy(), [[0, 0]])
# When we were using exlcusive span ends, this was an edge case of the dynamic program.
# We're keeping the test to make sure we get it right now, after the switch in inclusive
# span end. The best answer is (1, 1).
span_begin_probs = Variable(torch.FloatTensor([[0.4, 0.5, 0.1]])).log()
span_end_probs = Variable(torch.FloatTensor([[0.3, 0.6, 0.1]])).log()
begin_end_idxs = BidirectionalAttentionFlow._get_best_span(span_begin_probs, span_end_probs)
assert_almost_equal(begin_end_idxs.data.numpy(), [[1, 1]])
# Another instance that used to be an edge case.
span_begin_probs = Variable(torch.FloatTensor([[0.8, 0.1, 0.1]])).log()
span_end_probs = Variable(torch.FloatTensor([[0.8, 0.1, 0.1]])).log()
begin_end_idxs = BidirectionalAttentionFlow._get_best_span(span_begin_probs, span_end_probs)
assert_almost_equal(begin_end_idxs.data.numpy(), [[0, 0]])
span_begin_probs = Variable(torch.FloatTensor([[0.1, 0.2, 0.05, 0.3, 0.25]])).log()
span_end_probs = Variable(torch.FloatTensor([[0.1, 0.2, 0.5, 0.05, 0.15]])).log()
begin_end_idxs = BidirectionalAttentionFlow._get_best_span(span_begin_probs, span_end_probs)
assert_almost_equal(begin_end_idxs.data.numpy(), [[1, 2]])
def test_get_best_span(self):
# pylint: disable=protected-access
span_begin_probs = Variable(
torch.FloatTensor([[0.1, 0.3, 0.05, 0.3, 0.25]])).log()
span_end_probs = Variable(
torch.FloatTensor([[0.65, 0.05, 0.2, 0.05, 0.05]])).log()
begin_end_idxs = BidirectionalAttentionFlow._get_best_span(
span_begin_probs, span_end_probs)
assert_almost_equal(begin_end_idxs.data.numpy(), [[0, 0]])
# When we were using exlcusive span ends, this was an edge case of the dynamic program.
# We're keeping the test to make sure we get it right now, after the switch in inclusive
# span end. The best answer is (1, 1).
span_begin_probs = Variable(torch.FloatTensor([[0.4, 0.5, 0.1]])).log()
span_end_probs = Variable(torch.FloatTensor([[0.3, 0.6, 0.1]])).log()
begin_end_idxs = BidirectionalAttentionFlow._get_best_span(
span_begin_probs, span_end_probs)
assert_almost_equal(begin_end_idxs.data.numpy(), [[1, 1]])
# Another instance that used to be an edge case.
span_begin_probs = Variable(torch.FloatTensor([[0.8, 0.1, 0.1]])).log()
span_end_probs = Variable(torch.FloatTensor([[0.8, 0.1, 0.1]])).log()
begin_end_idxs = BidirectionalAttentionFlow._get_best_span(
span_begin_probs, span_end_probs)
assert_almost_equal(begin_end_idxs.data.numpy(), [[0, 0]])
span_begin_probs = Variable(
torch.FloatTensor([[0.1, 0.2, 0.05, 0.3, 0.25]])).log()
span_end_probs = Variable(
torch.FloatTensor([[0.1, 0.2, 0.5, 0.05, 0.15]])).log()
begin_end_idxs = BidirectionalAttentionFlow._get_best_span(
span_begin_probs, span_end_probs)
assert_almost_equal(begin_end_idxs.data.numpy(), [[1, 2]])
def test_get_best_span(self):
# pylint: disable=protected-access
# Note that the best span cannot be (1, 0) since even though 0.3 * 0.5 is the greatest
# value, the end span index is constrained to occur after the begin span index.
span_begin_probs = Variable(
torch.FloatTensor([[0.1, 0.3, 0.05, 0.3, 0.25]])).log()
span_end_probs = Variable(
torch.FloatTensor([[0.5, 0.1, 0.2, 0.05, 0.15]])).log()
begin_end_idxs = BidirectionalAttentionFlow._get_best_span(
span_begin_probs, span_end_probs)
assert_almost_equal(begin_end_idxs.data.numpy(), [[1, 2]])
# Testing an edge case of the dynamic program here, for the order of when you update the
# best previous span position. We should not get (1, 1), because that's an empty span.
span_begin_probs = Variable(torch.FloatTensor([[0.4, 0.5, 0.1]])).log()
span_end_probs = Variable(torch.FloatTensor([[0.3, 0.6, 0.1]])).log()
begin_end_idxs = BidirectionalAttentionFlow._get_best_span(
span_begin_probs, span_end_probs)
assert_almost_equal(begin_end_idxs.data.numpy(), [[0, 1]])
# Testing another edge case of the dynamic program here, where (0, 0) is the best solution
# without constraints.
span_begin_probs = Variable(torch.FloatTensor([[0.8, 0.1, 0.1]])).log()
span_end_probs = Variable(torch.FloatTensor([[0.8, 0.1, 0.1]])).log()
begin_end_idxs = BidirectionalAttentionFlow._get_best_span(
span_begin_probs, span_end_probs)
assert_almost_equal(begin_end_idxs.data.numpy(), [[0, 1]])
# test higher-order input
# Note that the best span cannot be (1, 1) since even though 0.3 * 0.5 is the greatest
# value, the end span index is constrained to occur after the begin span index.
span_begin_probs = Variable(
torch.FloatTensor([[0.1, 0.3, 0.05, 0.3, 0.25]])).log()
span_end_probs = Variable(
torch.FloatTensor([[0.1, 0.5, 0.2, 0.05, 0.15]])).log()
begin_end_idxs = BidirectionalAttentionFlow._get_best_span(
span_begin_probs, span_end_probs)
assert_almost_equal(begin_end_idxs.data.numpy(), [[1, 2]])