mirror of
https://github.com/explosion/spaCy.git
synced 2025-11-04 01:48:04 +03:00
113 lines
4.0 KiB
Python
113 lines
4.0 KiB
Python
# coding: utf8
|
|
from __future__ import unicode_literals, division, print_function
|
|
|
|
import json
|
|
from pathlib import Path
|
|
|
|
from ..scorer import Scorer
|
|
from ..gold import GoldParse, merge_sents
|
|
from ..gold import read_json_file as read_gold_json
|
|
from .. import util
|
|
|
|
|
|
def train(language, output_dir, train_data, dev_data, n_iter, tagger, parser, ner,
|
|
parser_L1):
|
|
output_path = Path(output_dir)
|
|
train_path = Path(train_data)
|
|
dev_path = Path(dev_data)
|
|
check_dirs(output_path, train_path, dev_path)
|
|
|
|
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 = Path(config)
|
|
if not config_path.is_file():
|
|
util.sys_exit(config_path.as_posix(), title="Config file not found")
|
|
config = json.load(config_path)
|
|
for setting in []:
|
|
if setting not in config.keys():
|
|
util.sys_exit("{s} not found in config file.".format(s=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, tagger_cfg, parser_cfg, 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) if dev_data else []
|
|
print_progress(itn, trainer.nlp.parser.model.nr_weight,
|
|
trainer.nlp.parser.model.nr_active_feat,
|
|
**dev_scores.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 check_dirs(output_path, train_path, dev_path):
|
|
if not output_path.exists():
|
|
util.sys_exit(output_path.as_posix(), title="Output directory not found")
|
|
if not train_path.exists():
|
|
util.sys_exit(train_path.as_posix(), title="Training data not found")
|
|
if dev_path and not dev_path.exists():
|
|
util.sys_exit(dev_path.as_posix(), title="Development data not found")
|
|
|
|
|
|
def print_progress(itn, nr_weight, nr_active_feat, **scores):
|
|
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")
|