def test_load_careful(self): config_data = {"labels": {"0": {"": True}, "1": {"": True}, "2": {"cc": True, "agent": True, "ccomp": True, "prt": True, "meta": True, "nsubjpass": True, "csubj": True, "conj": True, "dobj": True, "neg": True, "csubjpass": True, "mark": True, "auxpass": True, "advcl": True, "aux": True, "ROOT": True, "prep": True, "parataxis": True, "xcomp": True, "nsubj": True, "nummod": True, "advmod": True, "punct": True, "relcl": True, "quantmod": True, "acomp": True, "compound": True, "pcomp": True, "intj": True, "poss": True, "npadvmod": True, "case": True, "attr": True, "dep": True, "appos": True, "det": True, "nmod": True, "amod": True, "dative": True, "pobj": True, "expl": True, "predet": True, "preconj": True, "oprd": True, "acl": True}, "3": {"cc": True, "agent": True, "ccomp": True, "prt": True, "meta": True, "nsubjpass": True, "csubj": True, "conj": True, "acl": True, "poss": True, "neg": True, "mark": True, "auxpass": True, "advcl": True, "aux": True, "amod": True, "ROOT": True, "prep": True, "parataxis": True, "xcomp": True, "nsubj": True, "nummod": True, "advmod": True, "punct": True, "quantmod": True, "acomp": True, "pcomp": True, "intj": True, "relcl": True, "npadvmod": True, "case": True, "attr": True, "dep": True, "appos": True, "det": True, "nmod": True, "dobj": True, "dative": True, "pobj": True, "iobj": True, "expl": True, "predet": True, "preconj": True, "oprd": True}, "4": {"ROOT": True}}, "seed": 0, "features": "basic", "beam_width": 1} data_dir = English.default_data_dir() vocab = Vocab.from_dir(path.join(data_dir, 'vocab')) moves = ArcEager(vocab.strings, config_data['labels']) templates = get_templates(config_data['features']) model = Model(moves.n_moves, templates, path.join(data_dir, 'deps')) parser = Parser(vocab.strings, moves, model)
def main(train_loc, dev_loc, model_dir, tag_map_loc=None): if tag_map_loc: with open(tag_map_loc) as file_: tag_map = json.loads(file_.read()) else: tag_map = DEFAULT_TAG_MAP train_sents = list(read_conllx(train_loc)) train_sents = PseudoProjectivity.preprocess_training_data(train_sents) actions = ArcEager.get_actions(gold_parses=train_sents) features = get_templates('basic') model_dir = pathlib.Path(model_dir) if not (model_dir / 'deps').exists(): (model_dir / 'deps').mkdir() with (model_dir / 'deps' / 'config.json').open('wb') as file_: file_.write( json.dumps( {'pseudoprojective': True, 'labels': actions, 'features': features}).encode('utf8')) vocab = Vocab(lex_attr_getters=Language.Defaults.lex_attr_getters, tag_map=tag_map) # Populate vocab for _, doc_sents in train_sents: for (ids, words, tags, heads, deps, ner), _ in doc_sents: for word in words: _ = vocab[word] for dep in deps: _ = vocab[dep] for tag in tags: _ = vocab[tag] if tag_map: for tag in tags: assert tag in tag_map, repr(tag) tagger = Tagger(vocab, tag_map=tag_map) parser = DependencyParser(vocab, actions=actions, features=features, L1=0.0) for itn in range(15): loss = 0. for _, doc_sents in train_sents: for (ids, words, tags, heads, deps, ner), _ in doc_sents: doc = Doc(vocab, words=words) gold = GoldParse(doc, tags=tags, heads=heads, deps=deps) tagger(doc) loss += parser.update(doc, gold, itn=itn) doc = Doc(vocab, words=words) tagger.update(doc, gold) random.shuffle(train_sents) scorer = score_model(vocab, tagger, parser, read_conllx(dev_loc)) print('%d:\t%.3f\t%.3f\t%.3f' % (itn, loss, scorer.uas, scorer.tags_acc)) nlp = Language(vocab=vocab, tagger=tagger, parser=parser) nlp.end_training(model_dir) scorer = score_model(vocab, tagger, parser, read_conllx(dev_loc)) print('%d:\t%.3f\t%.3f\t%.3f' % (itn, scorer.uas, scorer.las, scorer.tags_acc))
def main(train_loc, dev_loc, model_dir, tag_map_loc): with open(tag_map_loc) as file_: tag_map = json.loads(file_.read()) train_sents = list(read_conllx(train_loc)) train_sents = PseudoProjectivity.preprocess_training_data(train_sents) actions = ArcEager.get_actions(gold_parses=train_sents) features = get_templates('basic') model_dir = pathlib.Path(model_dir) with (model_dir / 'deps' / 'config.json').open('w') as file_: json.dump({'pseudoprojective': True, 'labels': actions, 'features': features}, file_) vocab = Vocab(lex_attr_getters=Language.Defaults.lex_attr_getters, tag_map=tag_map) # Populate vocab for _, doc_sents in train_sents: for (ids, words, tags, heads, deps, ner), _ in doc_sents: for word in words: _ = vocab[word] for dep in deps: _ = vocab[dep] for tag in tags: _ = vocab[tag] for tag in tags: assert tag in tag_map, repr(tag) tagger = Tagger(vocab, tag_map=tag_map) parser = DependencyParser(vocab, actions=actions, features=features) for itn in range(15): for _, doc_sents in train_sents: for (ids, words, tags, heads, deps, ner), _ in doc_sents: doc = Doc(vocab, words=words) gold = GoldParse(doc, tags=tags, heads=heads, deps=deps) tagger(doc) parser.update(doc, gold) doc = Doc(vocab, words=words) tagger.update(doc, gold) random.shuffle(train_sents) scorer = score_model(vocab, tagger, parser, read_conllx(dev_loc)) print('%d:\t%.3f\t%.3f' % (itn, scorer.uas, scorer.tags_acc)) nlp = Language(vocab=vocab, tagger=tagger, parser=parser) nlp.end_training(model_dir) scorer = score_model(vocab, tagger, parser, read_conllx(dev_loc)) print('%d:\t%.3f\t%.3f\t%.3f' % (itn, scorer.uas, scorer.las, scorer.tags_acc))
def main(train_loc, dev_loc, model_dir, tag_map_loc): with open(tag_map_loc) as file_: tag_map = json.loads(file_.read()) train_sents = list(read_conllx(train_loc)) labels = ArcEager.get_labels(train_sents) templates = get_templates('basic') TreebankParser.setup_model_dir(model_dir, labels, templates) nlp = TreebankParser.from_dir(tag_map, model_dir) for itn in range(15): for _, doc_sents in train_sents: for (ids, words, tags, heads, deps, ner), _ in doc_sents: nlp.train(words, tags, heads, deps) random.shuffle(train_sents) scorer = score_model(nlp, read_conllx(dev_loc)) print('%d:\t%.3f\t%.3f' % (itn, scorer.uas, scorer.tags_acc)) nlp.end_training(model_dir) scorer = score_model(nlp, read_conllx(dev_loc)) print('%d:\t%.3f\t%.3f\t%.3f' % (itn, scorer.uas, scorer.las, scorer.tags_acc))
def main(lang_name, train_loc, dev_loc, model_dir, clusters_loc=None): LangClass = spacy.util.get_lang_class(lang_name) train_sents = list(read_conllx(train_loc)) train_sents = PseudoProjectivity.preprocess_training_data(train_sents) actions = ArcEager.get_actions(gold_parses=train_sents) features = get_templates('basic') model_dir = pathlib.Path(model_dir) if not model_dir.exists(): model_dir.mkdir() if not (model_dir / 'deps').exists(): (model_dir / 'deps').mkdir() if not (model_dir / 'pos').exists(): (model_dir / 'pos').mkdir() with (model_dir / 'deps' / 'config.json').open('wb') as file_: file_.write( json.dumps( {'pseudoprojective': True, 'labels': actions, 'features': features}).encode('utf8')) vocab = LangClass.Defaults.create_vocab() if not (model_dir / 'vocab').exists(): (model_dir / 'vocab').mkdir() else: if (model_dir / 'vocab' / 'strings.json').exists(): with (model_dir / 'vocab' / 'strings.json').open() as file_: vocab.strings.load(file_) if (model_dir / 'vocab' / 'lexemes.bin').exists(): vocab.load_lexemes(model_dir / 'vocab' / 'lexemes.bin') if clusters_loc is not None: clusters_loc = pathlib.Path(clusters_loc) with clusters_loc.open() as file_: for line in file_: try: cluster, word, freq = line.split() except ValueError: continue lex = vocab[word] lex.cluster = int(cluster[::-1], 2) # Populate vocab for _, doc_sents in train_sents: for (ids, words, tags, heads, deps, ner), _ in doc_sents: for word in words: _ = vocab[word] for dep in deps: _ = vocab[dep] for tag in tags: _ = vocab[tag] if vocab.morphology.tag_map: for tag in tags: assert tag in vocab.morphology.tag_map, repr(tag) tagger = Tagger(vocab) parser = DependencyParser(vocab, actions=actions, features=features, L1=0.0) for itn in range(30): loss = 0. for _, doc_sents in train_sents: for (ids, words, tags, heads, deps, ner), _ in doc_sents: doc = Doc(vocab, words=words) gold = GoldParse(doc, tags=tags, heads=heads, deps=deps) tagger(doc) loss += parser.update(doc, gold, itn=itn) doc = Doc(vocab, words=words) tagger.update(doc, gold) random.shuffle(train_sents) scorer = score_model(vocab, tagger, parser, read_conllx(dev_loc)) print('%d:\t%.3f\t%.3f\t%.3f' % (itn, loss, scorer.uas, scorer.tags_acc)) nlp = LangClass(vocab=vocab, tagger=tagger, parser=parser) nlp.end_training(model_dir) scorer = score_model(vocab, tagger, parser, read_conllx(dev_loc)) print('%d:\t%.3f\t%.3f\t%.3f' % (itn, scorer.uas, scorer.las, scorer.tags_acc))
def main(lang_name, train_loc, dev_loc, model_dir, clusters_loc=None): LangClass = spacy.util.get_lang_class(lang_name) train_sents = list(read_conllx(train_loc)) train_sents = PseudoProjectivity.preprocess_training_data(train_sents) actions = ArcEager.get_actions(gold_parses=train_sents) features = get_templates('basic') model_dir = pathlib.Path(model_dir) if not model_dir.exists(): model_dir.mkdir() if not (model_dir / 'deps').exists(): (model_dir / 'deps').mkdir() if not (model_dir / 'pos').exists(): (model_dir / 'pos').mkdir() with (model_dir / 'deps' / 'config.json').open('wb') as file_: file_.write( json.dumps( {'pseudoprojective': True, 'labels': actions, 'features': features}).encode('utf8')) vocab = LangClass.Defaults.create_vocab() if not (model_dir / 'vocab').exists(): (model_dir / 'vocab').mkdir() else: if (model_dir / 'vocab' / 'strings.json').exists(): with (model_dir / 'vocab' / 'strings.json').open() as file_: vocab.strings.load(file_) if (model_dir / 'vocab' / 'lexemes.bin').exists(): vocab.load_lexemes(model_dir / 'vocab' / 'lexemes.bin') if clusters_loc is not None: clusters_loc = pathlib.Path(clusters_loc) with clusters_loc.open() as file_: for line in file_: try: cluster, word, freq = line.split() except ValueError: continue lex = vocab[word] lex.cluster = int(cluster[::-1], 2) # Populate vocab for _, doc_sents in train_sents: for (ids, words, tags, heads, deps, ner), _ in doc_sents: for word in words: _ = vocab[word] for dep in deps: _ = vocab[dep] for tag in tags: _ = vocab[tag] if vocab.morphology.tag_map: for tag in tags: assert tag in vocab.morphology.tag_map, repr(tag) tagger = Tagger(vocab) parser = DependencyParser(vocab, actions=actions, features=features, L1=0.0) for itn in range(30): loss = 0. for _, doc_sents in train_sents: for (ids, words, tags, heads, deps, ner), _ in doc_sents: doc = Doc(vocab, words=words) gold = GoldParse(doc, tags=tags, heads=heads, deps=deps) tagger(doc) loss += parser.update(doc, gold, itn=itn) doc = Doc(vocab, words=words) tagger.update(doc, gold) random.shuffle(train_sents) scorer = score_model(vocab, tagger, parser, read_conllx(dev_loc)) print('%d:\t%.3f\t%.3f\t%.3f' % (itn, loss, scorer.uas, scorer.tags_acc)) nlp = Language(vocab=vocab, tagger=tagger, parser=parser) nlp.end_training(model_dir) scorer = score_model(vocab, tagger, parser, read_conllx(dev_loc)) print('%d:\t%.3f\t%.3f\t%.3f' % (itn, scorer.uas, scorer.las, scorer.tags_acc))
def test_load_careful(self): config_data = { "labels": { "0": { "": True }, "1": { "": True }, "2": { "cc": True, "agent": True, "ccomp": True, "prt": True, "meta": True, "nsubjpass": True, "csubj": True, "conj": True, "dobj": True, "neg": True, "csubjpass": True, "mark": True, "auxpass": True, "advcl": True, "aux": True, "ROOT": True, "prep": True, "parataxis": True, "xcomp": True, "nsubj": True, "nummod": True, "advmod": True, "punct": True, "relcl": True, "quantmod": True, "acomp": True, "compound": True, "pcomp": True, "intj": True, "poss": True, "npadvmod": True, "case": True, "attr": True, "dep": True, "appos": True, "det": True, "nmod": True, "amod": True, "dative": True, "pobj": True, "expl": True, "predet": True, "preconj": True, "oprd": True, "acl": True }, "3": { "cc": True, "agent": True, "ccomp": True, "prt": True, "meta": True, "nsubjpass": True, "csubj": True, "conj": True, "acl": True, "poss": True, "neg": True, "mark": True, "auxpass": True, "advcl": True, "aux": True, "amod": True, "ROOT": True, "prep": True, "parataxis": True, "xcomp": True, "nsubj": True, "nummod": True, "advmod": True, "punct": True, "quantmod": True, "acomp": True, "pcomp": True, "intj": True, "relcl": True, "npadvmod": True, "case": True, "attr": True, "dep": True, "appos": True, "det": True, "nmod": True, "dobj": True, "dative": True, "pobj": True, "iobj": True, "expl": True, "predet": True, "preconj": True, "oprd": True }, "4": { "ROOT": True } }, "seed": 0, "features": "basic", "beam_width": 1 } data_dir = English.default_data_dir() vocab = Vocab.from_dir(path.join(data_dir, 'vocab')) moves = ArcEager(vocab.strings, config_data['labels']) templates = get_templates(config_data['features']) model = Model(moves.n_moves, templates, path.join(data_dir, 'deps')) parser = Parser(vocab.strings, moves, model)