mirror of
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99 lines
3.6 KiB
Python
99 lines
3.6 KiB
Python
# coding: utf8
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from __future__ import unicode_literals, division, print_function
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import json
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from pathlib import Path
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from ..scorer import Scorer
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from ..tagger import Tagger
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from ..syntax.parser import Parser
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from ..gold import GoldParse, merge_sents
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from ..gold import read_json_file as read_gold_json
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from .. import util
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def train(language, output_dir, train_data, dev_data, n_iter, tagger, parser, ner):
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output_path = Path(output_dir)
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train_path = Path(train_data)
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dev_path = Path(dev_data)
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check_dirs(output_path, data_path, dev_path)
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lang = util.get_lang_class(language)
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parser_cfg = dict(locals())
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tagger_cfg = dict(locals())
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entity_cfg = dict(locals())
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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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gold_train = list(read_gold_json(train_path))
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gold_dev = list(read_gold_json(dev_path))
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train_model(lang, gold_train, gold_dev, output_path, tagger_cfg, parser_cfg,
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entity_cfg, n_iter)
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scorer = evaluate(lang, list(read_gold_json(dev_loc)), output_path)
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print_results(scorer)
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def train_config(config):
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config_path = Path(config)
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if not config_path.is_file():
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util.sys_exit(config_path.as_posix(), title="Config file not found")
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config = json.load(config_path)
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for setting in []:
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if setting not in config.keys():
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util.sys_exit("{s} not found in config file.".format(s=setting),
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title="Missing setting")
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def train_model(Language, train_data, dev_data, output_path, tagger_cfg, parser_cfg,
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entity_cfg, n_iter):
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print("Itn.\tN weight\tN feats\tUAS\tNER F.\tTag %\tToken %")
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with Language.train(output_path, train_data, tagger_cfg, parser_cfg, entity_cfg) as trainer:
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loss = 0
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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_progress(itn, trainer.nlp.parser.model.nr_weight,
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trainer.nlp.parser.model.nr_active_feat,
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**dev_scores.scores)
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def evaluate(Language, gold_tuples, output_path):
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print("Load parser", output_path)
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nlp = Language(path=output_path)
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scorer = Scorer()
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for raw_text, sents in gold_tuples:
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sents = merge_sents(sents)
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for annot_tuples, brackets in sents:
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if raw_text is None:
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tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1])
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nlp.tagger(tokens)
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nlp.parser(tokens)
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nlp.entity(tokens)
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else:
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tokens = nlp(raw_text)
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gold = GoldParse.from_annot_tuples(tokens, annot_tuples)
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scorer.score(tokens, gold)
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return scorer
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def check_dirs(input_path, train_path, dev_path):
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if not output_path.exists():
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util.sys_exit(output_path.as_posix(), title="Output directory not found")
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if not train_path.exists() and train_path.is_file():
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util.sys_exit(train_path.as_posix(), title="Training data not found")
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def print_progress(itn, nr_weight, nr_active_feat, **scores):
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tpl = '{:d}\t{:d}\t{:d}\t{uas:.3f}\t{ents_f:.3f}\t{tags_acc:.3f}\t{token_acc:.3f}'
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print(tpl.format(itn, nr_weight, nr_active_feat, **scores))
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def print_results(scorer):
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results = {'TOK': scorer.token_acc, 'POS': scorer.tags_acc, 'UAS': scorer.uas,
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'LAS': scorer.las, 'NER P': scorer.ents_p, 'NER R': scorer.ents_r,
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'NER F': scorer.ents_f}
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util.print_table(results, title="Results")
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