mirror of
https://github.com/explosion/spaCy.git
synced 2024-09-21 19:39:13 +03:00
118 lines
4.2 KiB
Python
118 lines
4.2 KiB
Python
# coding: utf8
|
|
from __future__ import unicode_literals, division, print_function
|
|
|
|
import json
|
|
from collections import defaultdict
|
|
import cytoolz
|
|
from pathlib import Path
|
|
import dill
|
|
import tqdm
|
|
|
|
from ..tokens.doc import Doc
|
|
from ..scorer import Scorer
|
|
from ..gold import GoldParse, merge_sents
|
|
from ..gold import GoldCorpus
|
|
from ..util import prints
|
|
from .. import util
|
|
from .. import displacy
|
|
|
|
|
|
def train(lang_id, output_dir, train_data, dev_data, n_iter, n_sents,
|
|
use_gpu, no_tagger, no_parser, no_entities):
|
|
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_class = util.get_lang_class(lang_id)
|
|
|
|
pipeline = ['token_vectors', 'tags', 'dependencies', 'entities']
|
|
if no_tagger and 'tags' in pipeline: pipeline.remove('tags')
|
|
if no_parser and 'dependencies' in pipeline: pipeline.remove('dependencies')
|
|
if no_entities and 'entities' in pipeline: pipeline.remove('entities')
|
|
|
|
nlp = lang_class(pipeline=pipeline)
|
|
corpus = GoldCorpus(train_path, dev_path)
|
|
|
|
dropout = util.env_opt('dropout', 0.0)
|
|
|
|
optimizer = nlp.begin_training(lambda: corpus.train_tuples, use_gpu=use_gpu)
|
|
n_train_docs = corpus.count_train()
|
|
print("Itn.\tDep. Loss\tUAS\tNER F.\tTag %\tToken %")
|
|
for i in range(n_iter):
|
|
with tqdm.tqdm(total=n_train_docs) as pbar:
|
|
train_docs = corpus.train_docs(nlp, shuffle=i)
|
|
for batch in cytoolz.partition_all(20, train_docs):
|
|
docs, golds = zip(*batch)
|
|
docs = list(docs)
|
|
golds = list(golds)
|
|
nlp.update(docs, golds, drop=dropout, sgd=optimizer)
|
|
pbar.update(len(docs))
|
|
with nlp.use_params(optimizer.averages):
|
|
scorer = nlp.evaluate(corpus.dev_docs(nlp))
|
|
print_progress(i, {}, scorer.scores)
|
|
with (output_path / 'model.bin').open('wb') as file_:
|
|
dill.dump(nlp, file_, -1)
|
|
|
|
|
|
def _render_parses(i, to_render):
|
|
to_render[0].user_data['title'] = "Batch %d" % i
|
|
with Path('/tmp/entities.html').open('w') as file_:
|
|
html = displacy.render(to_render[:5], style='ent', page=True)
|
|
file_.write(html)
|
|
with Path('/tmp/parses.html').open('w') as file_:
|
|
html = displacy.render(to_render[:5], style='dep', page=True)
|
|
file_.write(html)
|
|
|
|
|
|
def evaluate(Language, gold_tuples, path):
|
|
with (path / 'model.bin').open('rb') as file_:
|
|
nlp = dill.load(file_)
|
|
# TODO:
|
|
# 1. This code is duplicate with spacy.train.Trainer.evaluate
|
|
# 2. There's currently a semantic difference between pipe and
|
|
# not pipe! It matters whether we batch the inputs. Must fix!
|
|
all_docs = []
|
|
all_golds = []
|
|
for raw_text, paragraph_tuples in dev_sents:
|
|
if gold_preproc:
|
|
raw_text = None
|
|
else:
|
|
paragraph_tuples = merge_sents(paragraph_tuples)
|
|
docs = self.make_docs(raw_text, paragraph_tuples)
|
|
golds = self.make_golds(docs, paragraph_tuples)
|
|
all_docs.extend(docs)
|
|
all_golds.extend(golds)
|
|
scorer = Scorer()
|
|
for doc, gold in zip(self.nlp.pipe(all_docs), all_golds):
|
|
scorer.score(doc, gold)
|
|
return scorer
|
|
|
|
|
|
def print_progress(itn, losses, dev_scores):
|
|
# TODO: Fix!
|
|
scores = {}
|
|
for col in ['dep_loss', 'tag_loss', 'uas', 'tags_acc', 'token_acc', 'ents_f']:
|
|
scores[col] = 0.0
|
|
scores.update(losses)
|
|
scores.update(dev_scores)
|
|
tpl = '{:d}\t{dep_loss:.3f}\t{tag_loss:.3f}\t{uas:.3f}\t{ents_f:.3f}\t{tags_acc:.3f}\t{token_acc:.3f}'
|
|
print(tpl.format(itn, **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")
|