explosion/spaCy
1
1
Fork 0
mirror of https://github.com/explosion/spaCy.git synced 2024-12-26 01:46:28 +03:00
Code Issues Packages Projects Releases Wiki Activity

* Use JSON docs for training and evaluation. Currently a bug that is costing 0.6 acc

Browse Source
This commit is contained in:
Matthew Honnibal 2015-05-06 16:38:54 +02:00
parent 69840d8cc3
commit e167355505
1 changed files with 30 additions and 14 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

44
bin/parser/train.py
View File

@ -19,13 +19,13 @@ from spacy.en.pos import POS_TEMPLATES, POS_TAGS, setup_model_dir
from spacy.syntax.parser import GreedyParser
from spacy.syntax.parser import OracleError
from spacy.syntax.util import Config
from spacy.syntax.conll import read_docparse_file
from spacy.syntax.conll import read_docparse_file, read_json_file
from spacy.syntax.conll import GoldParse
from spacy.scorer import Scorer
def train(Language, train_loc, model_dir, n_iter=15, feat_set=u'basic', seed=0,
def train(Language, gold_tuples, model_dir, n_iter=15, feat_set=u'basic', seed=0,
gold_preproc=False, n_sents=0):
dep_model_dir = path.join(model_dir, 'deps')
pos_model_dir = path.join(model_dir, 'pos')
@ -42,8 +42,6 @@ def train(Language, train_loc, model_dir, n_iter=15, feat_set=u'basic', seed=0,
setup_model_dir(sorted(POS_TAGS.keys()), POS_TAGS, POS_TEMPLATES, pos_model_dir)
gold_tuples = read_docparse_file(train_loc)
Config.write(dep_model_dir, 'config', features=feat_set, seed=seed,
labels=Language.ParserTransitionSystem.get_labels(gold_tuples))
Config.write(ner_model_dir, 'config', features='ner', seed=seed,
@ -56,9 +54,12 @@ def train(Language, train_loc, model_dir, n_iter=15, feat_set=u'basic', seed=0,
print "Itn.\tUAS\tNER F.\tTag %"
for itn in range(n_iter):
scorer = Scorer()
for raw_text, segmented_text, annot_tuples in gold_tuples:
for raw_text, segmented_text, annot_tuples, ctnt in gold_tuples:
# Eval before train
tokens = nlp(raw_text, merge_mwes=False)
#print segmented_text
#for annot in zip(*annot_tuples):
# print annot
gold = GoldParse(tokens, annot_tuples)
scorer.score(tokens, gold, verbose=False)
@ -75,19 +76,18 @@ def train(Language, train_loc, model_dir, n_iter=15, feat_set=u'basic', seed=0,
nlp.tagger.train(tokens, gold.tags)
print '%d:\t%.3f\t%.3f\t%.3f' % (itn, scorer.uas, scorer.ents_f, scorer.tags_acc)
random.shuffle(gold_tuples)
#random.shuffle(gold_tuples)
nlp.parser.model.end_training()
nlp.entity.model.end_training()
nlp.tagger.model.end_training()
nlp.vocab.strings.dump(path.join(model_dir, 'vocab', 'strings.txt'))
def evaluate(Language, dev_loc, model_dir, gold_preproc=False, verbose=True):
def evaluate(Language, gold_tuples, model_dir, gold_preproc=False, verbose=True):
assert not gold_preproc
nlp = Language(data_dir=model_dir)
gold_tuples = read_docparse_file(dev_loc)
scorer = Scorer()
for raw_text, segmented_text, annot_tuples in gold_tuples:
for raw_text, segmented_text, annot_tuples, brackets in gold_tuples:
tokens = nlp(raw_text, merge_mwes=False)
gold = GoldParse(tokens, annot_tuples)
scorer.score(tokens, gold, verbose=verbose)
@ -108,22 +108,38 @@ def write_parses(Language, dev_loc, model_dir, out_loc):
return scorer
def get_sents(json_dir, section):
if section == 'train':
file_range = range(2, 22)
elif section == 'dev':
file_range = range(22, 23)
for i in file_range:
sec = str(i)
if len(sec) == 1:
sec = '0' + sec
loc = path.join(json_dir, sec + '.json')
for sent in read_json_file(loc):
yield sent
@plac.annotations(
train_loc=("Training file location",),
dev_loc=("Dev. file location",),
json_dir=("Annotated JSON files directory",),
model_dir=("Location of output model directory",),
out_loc=("Out location", "option", "o", str),
n_sents=("Number of training sentences", "option", "n", int),
verbose=("Verbose error reporting", "flag", "v", bool),
debug=("Debug mode", "flag", "d", bool)
)
def main(train_loc, dev_loc, model_dir, n_sents=0, out_loc="", verbose=False,
def main(json_dir, model_dir, n_sents=0, out_loc="", verbose=False,
debug=False):
train(English, train_loc, model_dir, feat_set='basic' if not debug else 'debug',
train(English, list(get_sents(json_dir, 'train')), model_dir,
feat_set='basic' if not debug else 'debug',
gold_preproc=False, n_sents=n_sents)
if out_loc:
write_parses(English, dev_loc, model_dir, out_loc)
scorer = evaluate(English, dev_loc, model_dir, gold_preproc=False, verbose=verbose)
scorer = evaluate(English, list(get_sents(json_dir, 'dev')),
model_dir, gold_preproc=False, verbose=verbose)
print 'TOK', scorer.mistokened
print 'POS', scorer.tags_acc
print 'UAS', scorer.uas