# coding: utf8 from __future__ import unicode_literals, division, print_function import json from collections import defaultdict from ..scorer import Scorer from ..gold import GoldParse, merge_sents from ..gold import read_json_file as read_gold_json from ..util import prints from .. import util def train(language, output_dir, train_data, dev_data, n_iter, tagger, parser, ner, parser_L1): output_path = util.ensure_path(output_dir) train_path = util.ensure_path(train_data) dev_path = util.ensure_path(dev_data) if not output_path.exists(): prints(output_path, title="Output directory not found", exits=True) if not train_path.exists(): prints(train_path, title="Training data not found", exits=True) if dev_path and not dev_path.exists(): prints(dev_path, title="Development data not found", exits=True) lang = util.get_lang_class(language) parser_cfg = { 'pseudoprojective': True, 'L1': parser_L1, 'n_iter': n_iter, 'lang': language, 'features': lang.Defaults.parser_features} entity_cfg = { 'n_iter': n_iter, 'lang': language, 'features': lang.Defaults.entity_features} tagger_cfg = { 'n_iter': n_iter, 'lang': language, 'features': lang.Defaults.tagger_features} gold_train = list(read_gold_json(train_path)) gold_dev = list(read_gold_json(dev_path)) if dev_path else None train_model(lang, gold_train, gold_dev, output_path, tagger_cfg, parser_cfg, entity_cfg, n_iter) if gold_dev: scorer = evaluate(lang, gold_dev, output_path) print_results(scorer) def train_config(config): config_path = util.ensure_path(config) if not config_path.is_file(): prints(config_path, title="Config file not found", exits=True) config = json.load(config_path) for setting in []: if setting not in config.keys(): prints("%s not found in config file." % setting, title="Missing setting") def train_model(Language, train_data, dev_data, output_path, tagger_cfg, parser_cfg, entity_cfg, n_iter): print("Itn.\tN weight\tN feats\tUAS\tNER F.\tTag %\tToken %") with Language.train(output_path, train_data, pos=tagger_cfg, deps=parser_cfg, ner=entity_cfg) as trainer: for itn, epoch in enumerate(trainer.epochs(n_iter, augment_data=None)): for doc, gold in epoch: trainer.update(doc, gold) dev_scores = trainer.evaluate(dev_data).scores if dev_data else defaultdict(float) print_progress(itn, trainer.nlp.parser.model.nr_weight, trainer.nlp.parser.model.nr_active_feat, **dev_scores) def evaluate(Language, gold_tuples, output_path): print("Load parser", output_path) nlp = Language(path=output_path) scorer = Scorer() for raw_text, sents in gold_tuples: sents = merge_sents(sents) for annot_tuples, brackets in sents: if raw_text is None: tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1]) nlp.tagger(tokens) nlp.parser(tokens) nlp.entity(tokens) else: tokens = nlp(raw_text) gold = GoldParse.from_annot_tuples(tokens, annot_tuples) scorer.score(tokens, gold) return scorer def print_progress(itn, nr_weight, nr_active_feat, **scores): # TODO: Fix! tpl = '{:d}\t{:d}\t{:d}\t{uas:.3f}\t{ents_f:.3f}\t{tags_acc:.3f}\t{token_acc:.3f}' print(tpl.format(itn, nr_weight, nr_active_feat, **scores)) def print_results(scorer): results = { 'TOK': '%.2f' % scorer.token_acc, 'POS': '%.2f' % scorer.tags_acc, 'UAS': '%.2f' % scorer.uas, 'LAS': '%.2f' % scorer.las, 'NER P': '%.2f' % scorer.ents_p, 'NER R': '%.2f' % scorer.ents_r, 'NER F': '%.2f' % scorer.ents_f} util.print_table(results, title="Results")