def k_fold_cross_valid_known(k, parsed, known, discounts):
res = defaultdict(list)
for train, test in _fold(parsed, k):
for discount in discounts:
print 'train: ', len(train), 'test: ', len(test)
tag2id, word2id = build_dict(parsed)
id2tag = {v: k for k, v in tag2id.iteritems()}
id2word = {v: k for k, v in word2id.iteritems()}
emission, transition = _counter_known(parsed, train, known,
0.85, tag2id, word2id, discount)
count_ok, count_total = 0., 0.
for i, seq in enumerate(test):
out = viterbi(seq, transition, emission, word2id, tag2id)
ok, total = _compare(seq[1:-1], id_to_token(out, id2word, id2tag))
count_ok += ok; count_total += total
if DEBUG:
print 'evaluating', i, 'th sentence.', count_ok/count_total, 'so far.'
res[discount].append(count_ok/count_total)
print 'Fold accuracy: ', res[discount][-1], 'discount: ', discount
for d in res:
print 'discount:', d, '->', 'avg:', np.mean(res[d])
for j in xrange(1, len(seq)):
for i in xrange(len(transition)):
k_score = scores[:, j - 1] + np.log(transition[:, i]) + np.log(emission[i, seq[j]])
backpointer[i, j] = np.argmax(k_score)
scores[i, j] = k_score[backpointer[i, j]]
j = int(np.argmax(scores, axis=0)[-1])
sol = [j]
for i in xrange(len(seq) - 1, 0, -1):
j = backpointer[j, i]
sol.append(j)
sol.reverse()
return zip(seq[:-1], sol[:-1])
if __name__ == "__main__":
path = "../WSJ-2-12/*/*.POS"
docs = glob(path)
parsed = parse(docs)
np.random.shuffle(parsed)
parsed = trigramize(parsed)
tag2id, word2id = build_dict(parsed[:-10])
id2word = {v: k for k, v in word2id.iteritems()}
id2tag = {v: k for k, v in tag2id.iteritems()}
emission, transition = counter(parsed[:-10], tag2id, word2id)
print "test POS", parsed[-1][1:-1]
output = viterbi(parsed[-1], transition, emission, word2id, tag2id)
print "TAGGED", id_to_token(output, id2word, id2tag)