def test_trellis_score():
global nb_weights, hmm_trans_weights, tag_to_ix, all_tags, vocab, word_to_ix
sentence = "they can can fish".split()
initial_vec = np.full((1, len(all_tags)), -np.inf)
initial_vec[0][
tag_to_ix[START_TAG]] = 0 #setting all the score to START_TAG
prev_scores = torch.autograd.Variable(
torch.from_numpy(initial_vec.astype(np.float32)))
emission_probs, tag_transition_probs = hmm.compute_weights_variables(nb_weights, hmm_trans_weights,\
vocab, word_to_ix, tag_to_ix)
path_score, best_path = viterbi.build_trellis(
all_tags, tag_to_ix, [emission_probs[word_to_ix[w]] for w in sentence],
tag_transition_probs)
eq_(path_score.data.numpy(), -17)
sentence = "they can can can can fish".split()
path_score, best_path = viterbi.build_trellis(
all_tags, tag_to_ix, [emission_probs[word_to_ix[w]] for w in sentence],
tag_transition_probs)
eq_(path_score.data.numpy(), -25.)
def test_compute_hmm_weights_variables():
global nb_weights, hmm_trans_weights, vocab, word_to_ix, tag_to_ix
emission_probs, tag_transition_probs = hmm.compute_weights_variables(nb_weights, hmm_trans_weights, \
vocab, word_to_ix, tag_to_ix)
eq_(emission_probs[0][0].data.numpy(), -np.inf)
eq_(emission_probs[0][1].data.numpy(), -1)
eq_(emission_probs[0][2].data.numpy(), -11)
eq_(emission_probs[2][1].data.numpy(), -3)
eq_(emission_probs[2][2].data.numpy(), -4)
eq_(tag_transition_probs[0][0].data.numpy(), -np.inf)
eq_(tag_transition_probs[1][1].data.numpy(), -5)
eq_(tag_transition_probs[2][2].data.numpy(), -3)
eq_(tag_transition_probs[2][3].data.numpy(), -np.inf)
eq_(tag_transition_probs[3][3].data.numpy(), -np.inf)
def test_hmm_on_example_sentence():
global nb_weights, hmm_trans_weights, all_tags
tag_to_ix = {}
for tag in list(all_tags):
tag_to_ix[tag] = len(tag_to_ix)
vocab, word_to_ix = most_common.get_word_to_ix(TRAIN_FILE)
emission_probs, tag_transition_probs = hmm.compute_weights_variables(nb_weights, hmm_trans_weights, \
vocab, word_to_ix, tag_to_ix)
score, pred_tags = viterbi.build_trellis(all_tags, tag_to_ix, [
emission_probs[word_to_ix[w]]
for w in ['they', 'can', 'can', 'fish', '.']
], tag_transition_probs)
assert_almost_equal(score.item(), -28.5981, places=2)
eq_(pred_tags, ['PRON', 'AUX', 'AUX', 'ADJ', 'PUNCT'])
def test_viterbi_step_init():
global nb_weights, hmm_trans_weights, tag_to_ix, all_tags, vocab, word_to_ix
sentence = "they can can fish".split()
initial_vec = np.full((1, len(all_tags)), -np.inf)
initial_vec[0][
tag_to_ix[START_TAG]] = 0 #setting all the score to START_TAG
prev_scores = torch.autograd.Variable(
torch.from_numpy(initial_vec.astype(np.float32)))
emission_probs, tag_transition_probs = hmm.compute_weights_variables(nb_weights, hmm_trans_weights, \
vocab, word_to_ix, tag_to_ix)
viterbivars, bptrs = viterbi.viterbi_step(all_tags, tag_to_ix,
emission_probs[0],
tag_transition_probs,
prev_scores)
eq_(viterbivars[1].data.numpy(), -2)
eq_(viterbivars[2].data.numpy(), -13)
eq_(bptrs[1], 0)
eq_(bptrs[2], 0)
eq_(bptrs[3], 0)
prev_scores = torch.autograd.Variable(
torch.from_numpy(
np.array([-np.inf, -2, -13,
-np.inf]).astype(np.float32))).view(1, -1)
viterbivars, bptrs = viterbi.viterbi_step(all_tags, tag_to_ix,
emission_probs[1],
tag_transition_probs,
prev_scores)
eq_(viterbivars[1].data.numpy(), -10)
eq_(viterbivars[2].data.numpy(), -6)
eq_(bptrs[1], 1)
eq_(bptrs[2], 1)
eq_(bptrs[3], 0)