mirror of
https://github.com/explosion/spaCy.git
synced 2024-12-25 17:36:30 +03:00
Refactor training, with new spacy.train module. Defaults still a little awkward.
This commit is contained in:
Matthew Honnibal
parent
53fbd3dd1c
commit
ea23b64cc8
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@ -79,82 +79,23 @@ def _merge_sents(sents):
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return [(m_deps, m_brackets)]
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def train(Language, gold_tuples, model_dir, tagger_cfg, parser_cfg, entity_cfg,
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def train(Language, train_data, dev_data, model_dir, tagger_cfg, parser_cfg, entity_cfg,
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n_iter=15, seed=0, gold_preproc=False, n_sents=0, corruption_level=0):
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dep_model_dir = path.join(model_dir, 'deps')
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ner_model_dir = path.join(model_dir, 'ner')
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pos_model_dir = path.join(model_dir, 'pos')
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if path.exists(dep_model_dir):
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shutil.rmtree(dep_model_dir)
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if path.exists(ner_model_dir):
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shutil.rmtree(ner_model_dir)
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if path.exists(pos_model_dir):
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shutil.rmtree(pos_model_dir)
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os.mkdir(dep_model_dir)
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os.mkdir(ner_model_dir)
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os.mkdir(pos_model_dir)
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if parser_cfg['pseudoprojective']:
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# preprocess training data here before ArcEager.get_labels() is called
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gold_tuples = PseudoProjectivity.preprocess_training_data(gold_tuples)
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parser_cfg['labels'] = ArcEager.get_labels(gold_tuples)
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entity_cfg['labels'] = BiluoPushDown.get_labels(gold_tuples)
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with (dep_model_dir / 'config.json').open('w') as file_:
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json.dump(file_, parser_config)
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with (ner_model_dir / 'config.json').open('w') as file_:
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json.dump(file_, entity_config)
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with (pos_model_dir / 'config.json').open('w') as file_:
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json.dump(file_, tagger_config)
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if n_sents > 0:
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gold_tuples = gold_tuples[:n_sents]
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nlp = Language(
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data_dir=model_dir,
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tagger=Tagger.blank(nlp.vocab, **tagger_cfg),
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parser=Parser.blank(nlp.vocab, ArcEager, **parser_cfg),
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entity=Parser.blank(nlp.vocab, BiluoPushDown, **entity_cfg))
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print("Itn.\tP.Loss\tUAS\tNER F.\tTag %\tToken %")
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for itn in range(n_iter):
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scorer = Scorer()
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format_str = '{:d}\t{:d}\t{uas:.3f}\t{ents_f:.3f}\t{tags_acc:.3f}\t{token_acc:.3f}'
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with Language.train(model_dir, train_data,
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tagger_cfg, parser_cfg, entity_cfg) as trainer:
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loss = 0
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for raw_text, sents in gold_tuples:
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if gold_preproc:
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raw_text = None
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else:
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sents = _merge_sents(sents)
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for annot_tuples, ctnt in sents:
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if len(annot_tuples[1]) == 1:
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continue
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score_model(scorer, nlp, raw_text, annot_tuples,
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verbose=verbose if itn >= 2 else False)
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if raw_text is None:
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words = add_noise(annot_tuples[1], corruption_level)
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tokens = nlp.tokenizer.tokens_from_list(words)
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else:
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raw_text = add_noise(raw_text, corruption_level)
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tokens = nlp.tokenizer(raw_text)
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nlp.tagger(tokens)
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gold = GoldParse(tokens, annot_tuples)
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if not gold.is_projective:
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raise Exception("Non-projective sentence in training: %s" % annot_tuples[1])
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loss += nlp.parser.train(tokens, gold)
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nlp.entity.train(tokens, gold)
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nlp.tagger.train(tokens, gold.tags)
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random.shuffle(gold_tuples)
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print('%d:\t%d\t%.3f\t%.3f\t%.3f\t%.3f' % (itn, loss, scorer.uas, scorer.ents_f,
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scorer.tags_acc,
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scorer.token_acc))
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print('end training')
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nlp.end_training(model_dir)
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print('done')
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for itn, epoch in enumerate(trainer.epochs(n_iter, augment_data=None)):
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for doc, gold in epoch:
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trainer.update(doc, gold)
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dev_scores = trainer.evaluate(dev_data)
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print(format_str.format(itn, loss, **dev_scores.scores))
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def evaluate(Language, gold_tuples, model_dir, gold_preproc=False, verbose=False,
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beam_width=None, cand_preproc=None):
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nlp = Language(data_dir=model_dir)
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nlp = Language(path=model_dir)
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if nlp.lang == 'de':
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nlp.vocab.morphology.lemmatizer = lambda string,pos: set([string])
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if beam_width is not None:
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@ -226,10 +167,14 @@ def main(language, train_loc, dev_loc, model_dir, n_sents=0, n_iter=15, out_loc=
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entity_cfg = dict(locals())
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lang = spacy.util.get_lang_class(language)
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parser_cfg['features'] = lang.Defaults.parser_features
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entity_cfg['features'] = lang.Defaults.entity_features
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if not eval_only:
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gold_train = list(read_json_file(train_loc))
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train(lang, gold_train, model_dir, tagger_cfg, parser_cfg, entity_cfg,
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gold_dev = list(read_json_file(dev_loc))
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train(lang, gold_train, gold_dev, model_dir, tagger_cfg, parser_cfg, entity_cfg,
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n_sents=n_sents, gold_preproc=gold_preproc, corruption_level=corruption_level,
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n_iter=n_iter)
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if out_loc:
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@ -27,4 +27,3 @@ class English(Language):
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tag_map = dict(language_data.TAG_MAP)
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stop_words = set(language_data.STOP_WORDS)
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@ -1,3 +1,5 @@
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from __future__ import unicode_literals, print_function
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import numpy
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import io
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import json
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@ -128,7 +130,6 @@ def _min_edit_path(cand_words, gold_words):
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def read_json_file(loc, docs_filter=None):
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print loc
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if path.isdir(loc):
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for filename in os.listdir(loc):
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yield from read_json_file(path.join(loc, filename))
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@ -199,7 +200,7 @@ def _consume_ent(tags):
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cdef class GoldParse:
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def __init__(self, tokens, annot_tuples, brackets=tuple(), make_projective=False):
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def __init__(self, tokens, annot_tuples, make_projective=False):
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self.mem = Pool()
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self.loss = 0
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self.length = len(tokens)
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@ -209,9 +210,6 @@ cdef class GoldParse:
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self.c.heads = <int*>self.mem.alloc(len(tokens), sizeof(int))
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self.c.labels = <int*>self.mem.alloc(len(tokens), sizeof(int))
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self.c.ner = <Transition*>self.mem.alloc(len(tokens), sizeof(Transition))
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self.c.brackets = <int**>self.mem.alloc(len(tokens), sizeof(int*))
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for i in range(len(tokens)):
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self.c.brackets[i] = <int*>self.mem.alloc(len(tokens), sizeof(int))
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self.tags = [None] * len(tokens)
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self.heads = [None] * len(tokens)
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@ -246,14 +244,6 @@ cdef class GoldParse:
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proj_heads,_ = nonproj.PseudoProjectivity.projectivize(self.heads,self.labels)
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self.heads = proj_heads
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self.brackets = {}
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for (gold_start, gold_end, label_str) in brackets:
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start = self.gold_to_cand[gold_start]
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end = self.gold_to_cand[gold_end]
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if start is not None and end is not None:
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self.brackets.setdefault(start, {}).setdefault(end, set())
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self.brackets[end][start].add(label_str)
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def __len__(self):
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return self.length
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@ -2,6 +2,8 @@ from __future__ import absolute_import
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from __future__ import unicode_literals
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from warnings import warn
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import pathlib
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from contextlib import contextmanager
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import shutil
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try:
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import ujson as json
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@ -15,7 +17,6 @@ except NameError:
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basestring = str
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from .tokenizer import Tokenizer
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from .vocab import Vocab
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from .syntax.parser import Parser
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@ -27,9 +28,12 @@ from .syntax.ner import BiluoPushDown
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from .syntax.arc_eager import ArcEager
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from . import util
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from .lemmatizer import Lemmatizer
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from .train import Trainer
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from .attrs import TAG, DEP, ENT_IOB, ENT_TYPE, HEAD, PROB, LANG, IS_STOP
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from .syntax.parser import get_templates
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from .syntax.nonproj import PseudoProjectivity
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class BaseDefaults(object):
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@ -84,47 +88,63 @@ class BaseDefaults(object):
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suffix_search=suffix_search,
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infix_finditer=infix_finditer)
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else:
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return Tokenizer(vocab, rules=rules,
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tokenizer = Tokenizer(vocab, rules=rules,
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prefix_search=prefix_search, suffix_search=suffix_search,
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infix_finditer=infix_finditer)
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return tokenizer
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def Tagger(self, vocab):
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def Tagger(self, vocab, **cfg):
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if self.path:
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return Tagger.load(self.path / 'pos', vocab)
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else:
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return Tagger.blank(vocab, Tagger.default_templates())
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def Parser(self, vocab, blank=False):
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if blank:
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return Parser.blank(vocab, ArcEager,
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features=self.parser_features, labels=self.parser_labels)
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elif self.path and (self.path / 'deps').exists():
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return Parser.load(self.path / 'deps', vocab, ArcEager)
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def Parser(self, vocab, **cfg):
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if self.path and (self.path / 'dep').exists():
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return Parser.load(self.path / 'dep', vocab, ArcEager)
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else:
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return None
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if 'features' not in cfg:
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cfg['features'] = self.parser_features
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if 'labels' not in cfg:
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cfg['labels'] = self.parser_labels
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return Parser.blank(vocab, ArcEager, **cfg)
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def Entity(self, vocab, blank=False):
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if blank:
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return Parser.blank(vocab, BiluoPushDown,
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features=self.entity_features, labels=self.entity_labels)
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elif self.path and (self.path / 'ner').exists():
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def Entity(self, vocab, **cfg):
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if self.path and (self.path / 'ner').exists():
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return Parser.load(self.path / 'ner', vocab, BiluoPushDown)
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else:
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return None
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if 'features' not in cfg:
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cfg['features'] = self.entity_features
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if 'labels' not in cfg:
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cfg['labels'] = self.entity_labels
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return Parser.blank(vocab, BiluoPushDown, **cfg)
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def Matcher(self, vocab):
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def Matcher(self, vocab, **cfg):
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if self.path:
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return Matcher.load(self.path, vocab)
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else:
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return Matcher(vocab)
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def Pipeline(self, nlp):
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return [
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nlp.tokenizer,
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nlp.tagger,
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nlp.parser,
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nlp.entity]
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def Pipeline(self, nlp, **cfg):
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pipeline = [nlp.tokenizer]
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if nlp.tagger:
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pipeline.append(nlp.tagger)
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if nlp.parser:
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pipeline.append(nlp.parser)
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if nlp.entity:
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pipeline.append(nlp.entity)
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return pipeline
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prefixes = tuple()
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suffixes = tuple()
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infixes = tuple()
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tag_map = {}
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tokenizer_exceptions = {}
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parser_labels = {0: {'ROOT': True}}
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entity_labels = {0: {'PER': True, 'LOC': True, 'ORG': True, 'MISC': True}}
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@ -169,6 +189,58 @@ class Language(object):
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Defaults = BaseDefaults
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lang = None
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@classmethod
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def blank(cls):
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return cls(path=False, vocab=False, tokenizer=False, tagger=False,
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parser=False, entity=False, matcher=False, serializer=False,
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vectors=False, pipeline=False)
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@classmethod
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@contextmanager
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def train(cls, path, gold_tuples, *configs):
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if isinstance(path, basestring):
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path = pathlib.Path(path)
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tagger_cfg, parser_cfg, entity_cfg = configs
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dep_model_dir = path / 'dep'
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ner_model_dir = path / 'ner'
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pos_model_dir = path / 'pos'
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if dep_model_dir.exists():
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shutil.rmtree(str(dep_model_dir))
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if ner_model_dir.exists():
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shutil.rmtree(str(ner_model_dir))
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if pos_model_dir.exists():
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shutil.rmtree(str(pos_model_dir))
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dep_model_dir.mkdir()
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ner_model_dir.mkdir()
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pos_model_dir.mkdir()
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if parser_cfg['pseudoprojective']:
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# preprocess training data here before ArcEager.get_labels() is called
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gold_tuples = PseudoProjectivity.preprocess_training_data(gold_tuples)
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parser_cfg['labels'] = ArcEager.get_labels(gold_tuples)
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entity_cfg['labels'] = BiluoPushDown.get_labels(gold_tuples)
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with (dep_model_dir / 'config.json').open('wb') as file_:
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json.dump(parser_cfg, file_)
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with (ner_model_dir / 'config.json').open('wb') as file_:
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json.dump(entity_cfg, file_)
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with (pos_model_dir / 'config.json').open('wb') as file_:
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json.dump(tagger_cfg, file_)
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self = cls.blank()
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self.path = path
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self.vocab = self.defaults.Vocab()
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self.defaults.parser_labels = parser_cfg['labels']
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self.defaults.entity_labels = entity_cfg['labels']
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self.tokenizer = self.defaults.Tokenizer(self.vocab)
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self.tagger = self.defaults.Tagger(self.vocab, **tagger_cfg)
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self.parser = self.defaults.Parser(self.vocab, **parser_cfg)
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self.entity = self.defaults.Entity(self.vocab, **entity_cfg)
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self.pipeline = self.defaults.Pipeline(self)
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yield Trainer(self, gold_tuples)
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self.end_training()
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def __init__(self,
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path=None,
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vocab=True,
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@ -210,13 +282,19 @@ class Language(object):
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self.path = path
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defaults = defaults if defaults is not True else self.get_defaults(self.path)
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self.defaults = defaults
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self.vocab = vocab if vocab is not True else defaults.Vocab(vectors=vectors)
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self.tokenizer = tokenizer if tokenizer is not True else defaults.Tokenizer(self.vocab)
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self.tagger = tagger if tagger is not True else defaults.Tagger(self.vocab)
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self.entity = entity if entity is not True else defaults.Entity(self.vocab)
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self.parser = parser if parser is not True else defaults.Parser(self.vocab)
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self.matcher = matcher if matcher is not True else defaults.Matcher(self.vocab)
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self.pipeline = pipeline(self) if pipeline is not True else defaults.Pipeline(self)
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if pipeline in (None, False):
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self.pipeline = []
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elif pipeline is True:
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self.pipeline = defaults.Pipeline(self)
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else:
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self.pipeline = pipeline(self)
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def __reduce__(self):
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args = (
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@ -276,16 +354,19 @@ class Language(object):
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def end_training(self, path=None):
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if path is None:
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path = self.path
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if self.parser:
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self.parser.model.end_training()
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self.parser.model.dump(path / 'deps' / 'model')
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if self.entity:
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self.entity.model.end_training()
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self.entity.model.dump(path / 'ner' / 'model')
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elif isinstance(path, basestring):
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path = pathlib.Path(path)
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if self.tagger:
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self.tagger.model.end_training()
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self.tagger.model.dump(path / 'pos' / 'model')
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self.tagger.model.dump(str(path / 'pos' / 'model'))
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if self.parser:
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self.parser.model.end_training()
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self.parser.model.dump(str(path / 'dep' / 'model'))
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if self.entity:
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self.entity.model.end_training()
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self.entity.model.dump(str(path / 'ner' / 'model'))
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strings_loc = path / 'vocab' / 'strings.json'
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with strings_loc.open('w', encoding='utf8') as file_:
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self.vocab.strings.dump(file_)
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@ -307,7 +388,7 @@ class Language(object):
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else:
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entity_iob_freqs = []
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entity_type_freqs = []
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with (path / 'vocab' / 'serializer.json').open('w') as file_:
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with (path / 'vocab' / 'serializer.json').open('wb') as file_:
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file_.write(
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json.dumps([
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(TAG, tagger_freqs),
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@ -70,6 +70,15 @@ class Scorer(object):
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def ents_f(self):
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return self.ner.fscore * 100
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@property
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def scores(self):
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return {
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'uas': self.uas, 'las': self.las,
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'ents_p': self.ents_p, 'ents_r': self.ents_r, 'ents_f': self.ents_f,
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'tags_acc': self.tags_acc,
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'token_acc': self.token_acc
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}
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def score(self, tokens, gold, verbose=False, punct_labels=('p', 'punct')):
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assert len(tokens) == len(gold)
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|||
|
|
@ -1,11 +1,11 @@
|
|||
from libc.stdint cimport int64_t
|
||||
|
||||
from cymem.cymem cimport Pool
|
||||
from preshed.maps cimport PreshMap
|
||||
from murmurhash.mrmr cimport hash64
|
||||
from .typedefs cimport attr_t
|
||||
|
||||
from libc.stdint cimport int64_t
|
||||
from .typedefs cimport attr_t, hash_t
|
||||
|
||||
from .typedefs cimport hash_t
|
||||
|
||||
cpdef hash_t hash_string(unicode string) except 0
|
||||
|
||||
|
|
|
|||
|
|
@ -312,12 +312,6 @@ cdef class ArcEager(TransitionSystem):
|
|||
# Count frequencies, for use in encoder
|
||||
self.freqs[HEAD][gold.c.heads[i] - i] += 1
|
||||
self.freqs[DEP][gold.c.labels[i]] += 1
|
||||
for end, brackets in gold.brackets.items():
|
||||
for start, label_strs in brackets.items():
|
||||
gold.c.brackets[start][end] = 1
|
||||
for label_str in label_strs:
|
||||
# Add the encoded label to the set
|
||||
gold.brackets[end][start].add(self.strings[label_str])
|
||||
|
||||
cdef Transition lookup_transition(self, object name) except *:
|
||||
if '-' in name:
|
||||
|
|
|
|||
|
|
@ -83,8 +83,7 @@ cdef class Parser:
|
|||
with (path / 'config.json').open() as file_:
|
||||
cfg = json.load(file_)
|
||||
moves = moves_class(vocab.strings, cfg['labels'])
|
||||
templates = get_templates(cfg['features'])
|
||||
model = ParserModel(templates)
|
||||
model = ParserModel(cfg['features'])
|
||||
if (path / 'model').exists():
|
||||
model.load(str(path / 'model'))
|
||||
return cls(vocab, moves, model, **cfg)
|
||||
|
|
@ -96,7 +95,6 @@ cdef class Parser:
|
|||
model = ParserModel(templates)
|
||||
return cls(vocab, moves, model, **cfg)
|
||||
|
||||
|
||||
def __init__(self, Vocab vocab, transition_system, ParserModel model, **cfg):
|
||||
self.moves = transition_system
|
||||
self.model = model
|
||||
|
|
@ -191,7 +189,7 @@ cdef class Parser:
|
|||
free(eg.is_valid)
|
||||
return 0
|
||||
|
||||
def train(self, Doc tokens, GoldParse gold):
|
||||
def update(self, Doc tokens, GoldParse gold):
|
||||
self.moves.preprocess_gold(gold)
|
||||
cdef StateClass stcls = StateClass.init(tokens.c, tokens.length)
|
||||
self.moves.initialize_state(stcls.c)
|
||||
|
|
|
|||
|
|
@ -154,7 +154,7 @@ cdef class Tagger:
|
|||
model.load(str(path / 'model'))
|
||||
return cls(vocab, model)
|
||||
|
||||
def __init__(self, Vocab vocab, TaggerModel model):
|
||||
def __init__(self, Vocab vocab, TaggerModel model, **cfg):
|
||||
self.vocab = vocab
|
||||
self.model = model
|
||||
# TODO: Move this to tag map
|
||||
|
|
@ -208,11 +208,13 @@ cdef class Tagger:
|
|||
self(doc)
|
||||
yield doc
|
||||
|
||||
def train(self, Doc tokens, object gold_tag_strs):
|
||||
def update(self, Doc tokens, object gold):
|
||||
if hasattr(gold, 'tags'):
|
||||
gold_tag_strs = list(gold.tags)
|
||||
assert len(tokens) == len(gold_tag_strs)
|
||||
for tag in gold_tag_strs:
|
||||
if tag != None and tag not in self.tag_names:
|
||||
msg = ("Unrecognized gold tag: %s. tag_map.json must contain all"
|
||||
msg = ("Unrecognized gold tag: %s. tag_map.json must contain all "
|
||||
"gold tags, to maintain coarse-grained mapping.")
|
||||
raise ValueError(msg % tag)
|
||||
golds = [self.tag_names.index(g) if g is not None else -1 for g in gold_tag_strs]
|
||||
|
|
|
|||
59
spacy/train.py
Normal file
59
spacy/train.py
Normal file
|
|
@ -0,0 +1,59 @@
|
|||
from __future__ import absolute_import
|
||||
from __future__ import unicode_literals
|
||||
|
||||
import random
|
||||
from .gold import GoldParse
|
||||
from .scorer import Scorer
|
||||
|
||||
|
||||
class Trainer(object):
|
||||
def __init__(self, nlp, gold_tuples):
|
||||
self.nlp = nlp
|
||||
self.gold_tuples = gold_tuples
|
||||
|
||||
def epochs(self, nr_epoch, augment_data=None):
|
||||
def _epoch():
|
||||
for raw_text, paragraph_tuples in self.gold_tuples:
|
||||
if augment_data is not None:
|
||||
raw_text, paragraph_tuples = augment_data(raw_text, paragraph_tuples)
|
||||
docs = self.make_docs(raw_text, paragraph_tuples)
|
||||
golds = self.make_golds(docs, paragraph_tuples)
|
||||
for doc, gold in zip(docs, golds):
|
||||
yield doc, gold
|
||||
|
||||
for itn in range(nr_epoch):
|
||||
random.shuffle(self.gold_tuples)
|
||||
yield _epoch()
|
||||
|
||||
def update(self, doc, gold):
|
||||
for process in self.nlp.pipeline[1:]:
|
||||
if hasattr(process, 'update'):
|
||||
process.update(doc, gold)
|
||||
process(doc)
|
||||
return doc
|
||||
|
||||
def evaluate(self, dev_sents):
|
||||
scorer = Scorer()
|
||||
for raw_text, paragraph_tuples in dev_sents:
|
||||
docs = self.make_docs(raw_text, paragraph_tuples)
|
||||
golds = self.make_golds(docs, paragraph_tuples)
|
||||
for doc, gold in zip(docs, golds):
|
||||
for process in self.nlp.pipeline[1:]:
|
||||
process(doc)
|
||||
scorer.score(doc, gold)
|
||||
return scorer
|
||||
|
||||
def make_docs(self, raw_text, paragraph_tuples):
|
||||
if raw_text is not None:
|
||||
return [self.nlp.tokenizer(raw_text)]
|
||||
else:
|
||||
return [self.nlp.tokenizer.tokens_from_list(sent_tuples[0][1])
|
||||
for sent_tuples in paragraph_tuples]
|
||||
|
||||
def make_golds(self, docs, paragraph_tuples):
|
||||
if len(docs) == 1:
|
||||
return [GoldParse(docs[0], sent_tuples[0])
|
||||
for sent_tuples in paragraph_tuples]
|
||||
else:
|
||||
return [GoldParse(doc, sent_tuples[0])
|
||||
for doc, sent_tuples in zip(docs, paragraph_tuples)]
|
||||
|
|
@ -13,6 +13,7 @@ try:
|
|||
except NameError:
|
||||
basestring = str
|
||||
|
||||
|
||||
LANGUAGES = {}
|
||||
_data_path = pathlib.Path(__file__).parent / 'data'
|
||||
|
||||
|
|
|
|||
|
|
@ -177,7 +177,7 @@ cdef class Vocab:
|
|||
value = self.strings[value]
|
||||
if attr == PROB:
|
||||
lex.prob = value
|
||||
else:
|
||||
elif value is not None:
|
||||
Lexeme.set_struct_attr(lex, attr, value)
|
||||
if is_oov:
|
||||
lex.id = 0
|
||||
|
|
|
|||
Loading…
Reference in New Issue
Block a user