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* Try beam search for SGD
This commit is contained in:
Matthew Honnibal
parent
db87db87ea
commit
a76316ae7e
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@ -22,44 +22,20 @@ from spacy.de import German
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import spacy.util
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from spacy.syntax.util import Config
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from spacy.scorer import Scorer
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from spacy.tagger import Tagger
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from spacy.tagger import P2_orth, P2_shape, P2_prefix, P2_suffix, P2_pos, P2_flags
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from spacy.tagger import P1_orth, P1_shape, P1_prefix, P1_suffix, P1_pos, P1_flags
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from spacy.tagger import W_orth, W_shape, W_prefix, W_suffix, W_pos, W_flags
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from spacy.tagger import N1_orth, N1_shape, N1_prefix, N1_suffix, N1_pos, N1_flags
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from spacy.tagger import N2_orth, N2_shape, N2_prefix, N2_suffix, N2_pos, N2_flags
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class GoldSents(object):
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def __init__(self, tokenizer, sents, n=5000):
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self.tokenizer = tokenizer
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self.sents = sents
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self.n = n
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templates = {
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'de': [
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(W_orth,),
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(P1_orth, P1_pos),
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(P2_orth, P2_pos),
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(N1_orth,),
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(N2_orth,),
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(W_suffix,),
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(W_prefix,),
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(P1_pos,),
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(P2_pos,),
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(P1_pos, P2_pos),
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(P1_pos, W_orth),
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(P1_suffix,),
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(N1_suffix,),
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(W_shape,),
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(W_flags,),
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(N1_flags,),
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(N2_flags,),
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(P1_flags,),
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(P2_flags,)
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]
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}
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def __iter__(self):
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random.shuffle(self.sents)
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for words, gold in self.sents[:self.n]:
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tokens = self.tokenizer.tokens_from_list(words)
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yield tokens, gold
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def read_conll(file_):
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@ -86,20 +62,63 @@ def _parse_line(line):
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return id_, word, pos
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def score_model(nlp, gold_tuples, verbose=False):
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def beam_sgd(tagger, train_data, check_data):
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print(tagger.model.widths)
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print("Itn.\tTrain\tPrev\tNew")
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queue = [(score_model(check_data, tagger), 0, tagger)]
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workers = [None] * 100
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limit = 4
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while True:
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for prev_score, i, tagger in list(queue):
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#prev_score, i, tagger = max(queue)
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train_acc, new_model = get_new_model(train_data, tagger)
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new_score = score_model(check_data, new_model)
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queue.append((new_score, i+1, new_model))
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print('%d:\t%.3f\t%.3f\t%.3f\t%.4f' % (i, train_acc, prev_score, new_score,
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tagger.model.eta))
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queue.sort(reverse=True)
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queue = queue[:limit]
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return max(queue)
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def score_model(gold_sents, tagger):
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correct = 0.0
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total = 0.0
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for words, gold_tags in gold_tuples:
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tokens = nlp.tokenizer.tokens_from_list(words)
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nlp.tagger(tokens)
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for tokens, gold_tags in gold_sents:
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tagger(tokens)
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for token, gold in zip(tokens, gold_tags):
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correct += token.tag_ == gold
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total += 1
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return (correct / total) * 100
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def get_new_model(gold_sents, tagger):
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learn_rate = numpy.random.normal(loc=tagger.model.learn_rate, scale=0.001)
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if learn_rate < 0.0001:
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learn_rate = 0.0001
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new_model = Tagger.blank(tagger.vocab, [],
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learn_rate=learn_rate,
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depth=tagger.model.depth,
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hidden_width=tagger.model.hidden_width,
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chars_width=tagger.model.chars_width,
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tags_width=tagger.model.tags_width,
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left_window=tagger.model.left_window,
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right_window=tagger.model.right_window,
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tags_window=tagger.model.tags_window,
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chars_per_word=tagger.model.chars_per_word)
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new_model.model.embeddings = tagger.model.embeddings
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new_model.model.weights = tagger.model.weights
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correct = 0.0
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total = 0.0
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for tokens, gold in gold_sents:
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correct += new_model.train(tokens, gold)
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total += len(tokens)
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return (correct / total), new_model
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def train(Language, train_sents, dev_sents, model_dir, n_iter=15, seed=0,
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gold_preproc=False, eta=0.005):
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gold_preproc=False, **model_args):
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pos_model_dir = path.join(model_dir, 'pos')
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if path.exists(pos_model_dir):
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shutil.rmtree(pos_model_dir)
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@ -109,59 +128,75 @@ def train(Language, train_sents, dev_sents, model_dir, n_iter=15, seed=0,
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for words, tags in train_sents:
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for word in words:
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_ = nlp.vocab[word]
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nlp.tagger = Tagger.blank(nlp.vocab, templates['de'], learn_rate=eta)
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print(nlp.tagger.model.widths)
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print("Itn.\tTrain\tCheck\tDev")
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nr_train = len(train_sents)
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random.shuffle(train_sents)
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heldout_sents = train_sents[:int(nr_train * 0.1)]
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train_sents = train_sents[len(heldout_sents):]
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prev_score = 0.0
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variance = 0.001
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last_good_learn_rate = nlp.tagger.model.eta
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n = 0
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total = 0
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acc = 0
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while True:
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words, gold_tags = random.choice(train_sents)
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tokens = nlp.tokenizer.tokens_from_list(words)
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acc += nlp.tagger.train(tokens, gold_tags)
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total += len(tokens)
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n += 1
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if n and n % 20000 == 0:
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dev_score = score_model(nlp, heldout_sents)
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eval_score = score_model(nlp, dev_sents)
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if dev_score >= prev_score:
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nlp.tagger.model.keep_update()
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prev_score = dev_score
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variance = 0.001
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last_good_learn_rate = nlp.tagger.model.eta
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nlp.tagger.model.eta *= 1.01
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print('%d:\t%.3f\t%.3f\t%.3f\t%.4f' % (n, acc/total, dev_score, eval_score, nlp.tagger.model.eta))
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else:
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nlp.tagger.model.backtrack()
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new_eta = numpy.random.normal(loc=last_good_learn_rate, scale=variance)
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if new_eta >= 0.0001:
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nlp.tagger.model.eta = new_eta
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else:
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nlp.tagger.model.eta = 0.0001
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print('X:\t%.3f\t%.3f\t%.3f\t%.4f' % (acc/total, dev_score, eval_score, nlp.tagger.model.eta))
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variance *= 1.1
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prev_score *= 0.9999
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acc = 0.0
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total = 0.0
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nlp.end_training(data_dir=model_dir)
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return nlp
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train_sents = GoldSents(nlp.tokenizer, train_sents)
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heldout_sents = GoldSents(nlp.tokenizer, heldout_sents)
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tagger = Tagger.blank(nlp.vocab, [], **model_args)
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return beam_sgd(tagger, train_sents, heldout_sents)
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#prev_score = 0.0
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#variance = 0.001
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#last_good_learn_rate = nlp.tagger.model.eta
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#n = 0
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#total = 0
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#acc = 0
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#last_model = (nlp.tagger.model.weights, nlp.tagger.model.embeddings)
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#while True:
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# words, gold_tags = random.choice(train_sents)
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# tokens = nlp.tokenizer.tokens_from_list(words)
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# acc += nlp.tagger.train(tokens, gold_tags)
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# total += len(tokens)
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# n += 1
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# if n and n % 20000 == 0:
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# dev_score = score_model(nlp, heldout_sents)
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# eval_score = score_model(nlp, dev_sents)
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# if dev_score >= prev_score:
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# last_model = (nlp.tagger.model.weights, nlp.tagger.model.embeddings)
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# prev_score = dev_score
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# variance = 0.001
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# last_good_learn_rate = nlp.tagger.model.eta
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# nlp.tagger.model.eta *= 1.01
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#
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# else:
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# nlp.tagger.model.weights = last_model[0]
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# nlp.tagger.model.embeddings = last_model[1]
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# new_eta = numpy.random.normal(loc=last_good_learn_rate, scale=variance)
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# if new_eta >= 0.0001:
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# nlp.tagger.model.eta = new_eta
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# else:
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# nlp.tagger.model.eta = 0.0001
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# print('X:\t%.3f\t%.3f\t%.3f\t%.4f' % (acc/total, dev_score, eval_score, nlp.tagger.model.eta))
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# variance *= 1.1
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# prev_score *= 0.9999
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# acc = 0.0
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# total = 0.0
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#nlp.end_training(data_dir=model_dir)
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#return nlp
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@plac.annotations(
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train_loc=("Location of training file or directory"),
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dev_loc=("Location of development file or directory"),
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model_dir=("Location of output model directory",),
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eta=("Learning rate for Adagrad optimizer", "option", "e", float),
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learn_rate=("Learning rate for SGD", "option", "e", float),
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n_iter=("Number of training iterations", "option", "i", int),
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depth=("Number of hidden layers", "option", "d", int),
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hidden_width=("Number of neurons in each hidden layers", "option", "H", int),
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chars_width=("Width of character embedding", "option", "C", int),
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tags_width=("Width of tag embedding", "option", "T", int),
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left_window=("Number of words of left context", "option", "l", int),
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right_window=("Number of words of right context", "option", "r", int),
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tags_window=("Number of tags in history", "option", "t", int),
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chars_per_word=("Number of characters per word", "option", "c", int),
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)
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def main(lang_id, train_loc, dev_loc, model_dir, n_iter=5, eta=0.005):
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def main(lang_id, train_loc, dev_loc, model_dir, n_iter=5, learn_rate=0.005,
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depth=3, hidden_width=100, chars_width=5, tags_width=10, left_window=2,
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right_window=2, tags_window=2, chars_per_word=8):
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if lang_id == 'en':
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Language = English
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elif lang_id == 'de':
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@ -173,11 +208,11 @@ def main(lang_id, train_loc, dev_loc, model_dir, n_iter=5, eta=0.005):
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with codecs.open(train_loc, 'r', 'utf8') as file_:
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train_sents = read_conll(file_)
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dev_sents = read_conll(codecs.open(dev_loc, 'r', 'utf8'))
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nlp = train(Language, train_sents, dev_sents, model_dir, n_iter=n_iter, eta=eta)
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#nlp = Language(data_dir=model_dir)
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scorer = score_model(nlp, dev_sents)
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print('TOK', 100-scorer.token_acc)
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print('POS', scorer.tags_acc)
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nlp = train(Language, train_sents, dev_sents, model_dir,
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n_iter=n_iter, learn_rate=learn_rate,
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depth=depth, hidden_width=hidden_width, chars_width=chars_width, tags_width=tags_width,
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left_window=left_window, right_window=right_window, tags_window=tags_window,
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chars_per_word=chars_per_word)
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if __name__ == '__main__':
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