mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-10 19:57:17 +03:00
Remove old, outdated files in /bin
This commit is contained in:
ines
parent
9c89e2cdef
commit
5025d709e0
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#!/usr/bin/env python
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from __future__ import unicode_literals, print_function
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import plac
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import joblib
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from os import path
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import os
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import bz2
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import ujson
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from preshed.counter import PreshCounter
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from joblib import Parallel, delayed
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import io
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from spacy.en import English
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from spacy.strings import StringStore
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from spacy.attrs import ORTH
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from spacy.tokenizer import Tokenizer
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from spacy.vocab import Vocab
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def iter_comments(loc):
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with bz2.BZ2File(loc) as file_:
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for line in file_:
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yield ujson.loads(line)
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def count_freqs(input_loc, output_loc):
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print(output_loc)
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vocab = English.default_vocab(get_lex_attr=None)
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tokenizer = Tokenizer.from_dir(vocab,
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path.join(English.default_data_dir(), 'tokenizer'))
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counts = PreshCounter()
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for json_comment in iter_comments(input_loc):
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doc = tokenizer(json_comment['body'])
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doc.count_by(ORTH, counts=counts)
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with io.open(output_loc, 'w', 'utf8') as file_:
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for orth, freq in counts:
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string = tokenizer.vocab.strings[orth]
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if not string.isspace():
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file_.write('%d\t%s\n' % (freq, string))
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def parallelize(func, iterator, n_jobs):
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Parallel(n_jobs=n_jobs)(delayed(func)(*item) for item in iterator)
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def merge_counts(locs, out_loc):
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string_map = StringStore()
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counts = PreshCounter()
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for loc in locs:
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with io.open(loc, 'r', encoding='utf8') as file_:
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for line in file_:
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freq, word = line.strip().split('\t', 1)
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orth = string_map[word]
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counts.inc(orth, int(freq))
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with io.open(out_loc, 'w', encoding='utf8') as file_:
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for orth, count in counts:
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string = string_map[orth]
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file_.write('%d\t%s\n' % (count, string))
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@plac.annotations(
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input_loc=("Location of input file list"),
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freqs_dir=("Directory for frequency files"),
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output_loc=("Location for output file"),
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n_jobs=("Number of workers", "option", "n", int),
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skip_existing=("Skip inputs where an output file exists", "flag", "s", bool),
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)
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def main(input_loc, freqs_dir, output_loc, n_jobs=2, skip_existing=False):
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tasks = []
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outputs = []
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for input_path in open(input_loc):
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input_path = input_path.strip()
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if not input_path:
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continue
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filename = input_path.split('/')[-1]
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output_path = path.join(freqs_dir, filename.replace('bz2', 'freq'))
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outputs.append(output_path)
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if not path.exists(output_path) or not skip_existing:
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tasks.append((input_path, output_path))
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if tasks:
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parallelize(count_freqs, tasks, n_jobs)
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print("Merge")
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merge_counts(outputs, output_loc)
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if __name__ == '__main__':
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plac.call(main)
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@ -1,89 +0,0 @@
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#!/usr/bin/env python
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from __future__ import unicode_literals
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from xml.etree import cElementTree as ElementTree
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import json
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import re
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import plac
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from pathlib import Path
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from os import path
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escaped_tokens = {
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'-LRB-': '(',
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'-RRB-': ')',
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'-LSB-': '[',
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'-RSB-': ']',
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'-LCB-': '{',
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'-RCB-': '}',
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}
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def read_parses(parse_loc):
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offset = 0
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doc = []
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for parse in open(str(parse_loc) + '.dep').read().strip().split('\n\n'):
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parse = _adjust_token_ids(parse, offset)
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offset += len(parse.split('\n'))
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doc.append(parse)
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return doc
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def _adjust_token_ids(parse, offset):
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output = []
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for line in parse.split('\n'):
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pieces = line.split()
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pieces[0] = str(int(pieces[0]) + offset)
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pieces[5] = str(int(pieces[5]) + offset) if pieces[5] != '0' else '0'
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output.append('\t'.join(pieces))
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return '\n'.join(output)
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def _fmt_doc(filename, paras):
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return {'id': filename, 'paragraphs': [_fmt_para(*para) for para in paras]}
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def _fmt_para(raw, sents):
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return {'raw': raw, 'sentences': [_fmt_sent(sent) for sent in sents]}
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def _fmt_sent(sent):
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return {
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'tokens': [_fmt_token(*t.split()) for t in sent.strip().split('\n')],
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'brackets': []}
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def _fmt_token(id_, word, hyph, pos, ner, head, dep, blank1, blank2, blank3):
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head = int(head) - 1
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id_ = int(id_) - 1
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head = (head - id_) if head != -1 else 0
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return {'id': id_, 'orth': word, 'tag': pos, 'dep': dep, 'head': head}
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tags_re = re.compile(r'<[\w\?/][^>]+>')
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def main(out_dir, ewtb_dir='/usr/local/data/eng_web_tbk'):
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ewtb_dir = Path(ewtb_dir)
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out_dir = Path(out_dir)
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if not out_dir.exists():
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out_dir.mkdir()
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for genre_dir in ewtb_dir.joinpath('data').iterdir():
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#if 'answers' in str(genre_dir): continue
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parse_dir = genre_dir.joinpath('penntree')
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docs = []
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for source_loc in genre_dir.joinpath('source').joinpath('source_original').iterdir():
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filename = source_loc.parts[-1].replace('.sgm.sgm', '')
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filename = filename.replace('.xml', '')
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filename = filename.replace('.txt', '')
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parse_loc = parse_dir.joinpath(filename + '.xml.tree')
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parses = read_parses(parse_loc)
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source = source_loc.open().read().strip()
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if 'answers' in str(genre_dir):
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source = tags_re.sub('', source).strip()
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docs.append(_fmt_doc(filename, [[source, parses]]))
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out_loc = out_dir.joinpath(genre_dir.parts[-1] + '.json')
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with open(str(out_loc), 'w') as out_file:
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out_file.write(json.dumps(docs, indent=4))
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if __name__ == '__main__':
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plac.call(main)
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import io
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import plac
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from spacy.en import English
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def main(text_loc):
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with io.open(text_loc, 'r', encoding='utf8') as file_:
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text = file_.read()
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NLU = English()
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for paragraph in text.split('\n\n'):
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tokens = NLU(paragraph)
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ent_starts = {}
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ent_ends = {}
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for span in tokens.ents:
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ent_starts[span.start] = span.label_
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ent_ends[span.end] = span.label_
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output = []
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for token in tokens:
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if token.i in ent_starts:
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output.append('<%s>' % ent_starts[token.i])
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output.append(token.orth_)
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if (token.i+1) in ent_ends:
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output.append('</%s>' % ent_ends[token.i+1])
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output.append('\n\n')
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print ' '.join(output)
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if __name__ == '__main__':
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plac.call(main)
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#!/usr/bin/env python
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from __future__ import division
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from __future__ import unicode_literals
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import os
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from os import path
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import shutil
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import io
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import random
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import time
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import gzip
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import plac
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import cProfile
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import pstats
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import spacy.util
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from spacy.en import English
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from spacy.gold import GoldParse
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from spacy.syntax.util import Config
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from spacy.syntax.arc_eager import ArcEager
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from spacy.syntax.parser import Parser
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from spacy.scorer import Scorer
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from spacy.tagger import Tagger
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# Last updated for spaCy v0.97
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def read_conll(file_):
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"""Read a standard CoNLL/MALT-style format"""
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sents = []
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for sent_str in file_.read().strip().split('\n\n'):
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ids = []
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words = []
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heads = []
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labels = []
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tags = []
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for i, line in enumerate(sent_str.split('\n')):
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word, pos_string, head_idx, label = _parse_line(line)
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words.append(word)
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if head_idx < 0:
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head_idx = i
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ids.append(i)
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heads.append(head_idx)
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labels.append(label)
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tags.append(pos_string)
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text = ' '.join(words)
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annot = (ids, words, tags, heads, labels, ['O'] * len(ids))
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sents.append((None, [(annot, [])]))
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return sents
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def _parse_line(line):
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pieces = line.split()
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if len(pieces) == 4:
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word, pos, head_idx, label = pieces
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head_idx = int(head_idx)
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elif len(pieces) == 15:
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id_ = int(pieces[0].split('_')[-1])
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word = pieces[1]
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pos = pieces[4]
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head_idx = int(pieces[8])-1
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label = pieces[10]
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else:
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id_ = int(pieces[0].split('_')[-1])
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word = pieces[1]
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pos = pieces[4]
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head_idx = int(pieces[6])-1
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label = pieces[7]
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if head_idx == 0:
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label = 'ROOT'
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return word, pos, head_idx, label
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def score_model(scorer, nlp, raw_text, annot_tuples, verbose=False):
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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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gold = GoldParse(tokens, annot_tuples, make_projective=False)
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scorer.score(tokens, gold, verbose=verbose, punct_labels=('--', 'p', 'punct'))
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def train(Language, gold_tuples, model_dir, n_iter=15, feat_set=u'basic', seed=0,
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gold_preproc=False, force_gold=False):
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dep_model_dir = path.join(model_dir, 'deps')
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pos_model_dir = path.join(model_dir, 'pos')
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if path.exists(dep_model_dir):
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shutil.rmtree(dep_model_dir)
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if path.exists(pos_model_dir):
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shutil.rmtree(pos_model_dir)
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os.mkdir(dep_model_dir)
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os.mkdir(pos_model_dir)
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Config.write(dep_model_dir, 'config', features=feat_set, seed=seed,
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labels=ArcEager.get_labels(gold_tuples))
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nlp = Language(data_dir=model_dir, tagger=False, parser=False, entity=False)
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nlp.tagger = Tagger.blank(nlp.vocab, Tagger.default_templates())
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nlp.parser = Parser.from_dir(dep_model_dir, nlp.vocab.strings, ArcEager)
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print("Itn.\tP.Loss\tUAS\tNER F.\tTag %\tToken %")
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for itn in range(n_iter):
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scorer = Scorer()
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loss = 0
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for _, sents in gold_tuples:
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for annot_tuples, _ in sents:
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if len(annot_tuples[1]) == 1:
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continue
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score_model(scorer, nlp, None, annot_tuples, verbose=False)
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tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1])
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nlp.tagger(tokens)
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gold = GoldParse(tokens, annot_tuples, make_projective=True)
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if not gold.is_projective:
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raise Exception(
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"Non-projective sentence in training, after we should "
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"have enforced projectivity: %s" % annot_tuples
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)
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loss += nlp.parser.train(tokens, gold)
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nlp.tagger.train(tokens, gold.tags)
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random.shuffle(gold_tuples)
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print('%d:\t%d\t%.3f\t%.3f\t%.3f' % (itn, loss, scorer.uas,
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scorer.tags_acc, scorer.token_acc))
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print('end training')
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nlp.end_training(model_dir)
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print('done')
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@plac.annotations(
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train_loc=("Location of CoNLL 09 formatted training file"),
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dev_loc=("Location of CoNLL 09 formatted development file"),
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model_dir=("Location of output model directory"),
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eval_only=("Skip training, and only evaluate", "flag", "e", bool),
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n_iter=("Number of training iterations", "option", "i", int),
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)
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def main(train_loc, dev_loc, model_dir, n_iter=15):
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with io.open(train_loc, 'r', encoding='utf8') as file_:
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train_sents = read_conll(file_)
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if not eval_only:
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train(English, train_sents, model_dir, n_iter=n_iter)
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nlp = English(data_dir=model_dir)
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dev_sents = read_conll(io.open(dev_loc, 'r', encoding='utf8'))
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scorer = Scorer()
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for _, sents in dev_sents:
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for annot_tuples, _ in sents:
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score_model(scorer, nlp, None, annot_tuples)
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print('TOK', 100-scorer.token_acc)
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print('POS', scorer.tags_acc)
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print('UAS', scorer.uas)
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print('LAS', scorer.las)
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if __name__ == '__main__':
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plac.call(main)
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@ -1,187 +0,0 @@
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#!/usr/bin/env python
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from __future__ import division
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from __future__ import unicode_literals
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from __future__ import print_function
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import os
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from os import path
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import shutil
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import io
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import random
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import plac
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import re
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import spacy.util
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from spacy.syntax.util import Config
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from spacy.gold import read_json_file
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from spacy.gold import GoldParse
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from spacy.gold import merge_sents
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from spacy.scorer import Scorer
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from spacy.syntax.arc_eager import ArcEager
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from spacy.syntax.ner import BiluoPushDown
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from spacy.tagger import Tagger
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from spacy.syntax.parser import Parser
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from spacy.syntax.nonproj import PseudoProjectivity
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def _corrupt(c, noise_level):
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if random.random() >= noise_level:
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return c
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elif c == ' ':
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return '\n'
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elif c == '\n':
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return ' '
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elif c in ['.', "'", "!", "?"]:
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return ''
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else:
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return c.lower()
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def add_noise(orig, noise_level):
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if random.random() >= noise_level:
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return orig
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elif type(orig) == list:
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corrupted = [_corrupt(word, noise_level) for word in orig]
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corrupted = [w for w in corrupted if w]
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return corrupted
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else:
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return ''.join(_corrupt(c, noise_level) for c in orig)
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def score_model(scorer, nlp, raw_text, annot_tuples, verbose=False):
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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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else:
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tokens = nlp.tokenizer(raw_text)
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nlp.tagger(tokens)
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nlp.entity(tokens)
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nlp.parser(tokens)
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gold = GoldParse(tokens, annot_tuples)
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scorer.score(tokens, gold, verbose=verbose)
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def train(Language, train_data, dev_data, model_dir, tagger_cfg, parser_cfg, entity_cfg,
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n_iter=15, seed=0, gold_preproc=False, n_sents=0, corruption_level=0):
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print("Itn.\tN weight\tN feats\tUAS\tNER F.\tTag %\tToken %")
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format_str = '{:d}\t{:d}\t{:d}\t{uas:.3f}\t{ents_f:.3f}\t{tags_acc:.3f}\t{token_acc:.3f}'
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with Language.train(model_dir, train_data,
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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, gold_preproc=gold_preproc,
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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, gold_preproc=gold_preproc)
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print(format_str.format(itn, trainer.nlp.parser.model.nr_weight,
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trainer.nlp.parser.model.nr_active_feat, **dev_scores.scores))
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def evaluate(Language, gold_tuples, model_dir, gold_preproc=False, verbose=False,
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beam_width=None, cand_preproc=None):
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print("Load parser", model_dir)
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nlp = Language(path=model_dir)
|
||||
if nlp.lang == 'de':
|
||||
nlp.vocab.morphology.lemmatizer = lambda string,pos: set([string])
|
||||
if beam_width is not None:
|
||||
nlp.parser.cfg.beam_width = beam_width
|
||||
scorer = Scorer()
|
||||
for raw_text, sents in gold_tuples:
|
||||
if gold_preproc:
|
||||
raw_text = None
|
||||
else:
|
||||
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, verbose=verbose)
|
||||
return scorer
|
||||
|
||||
|
||||
def write_parses(Language, dev_loc, model_dir, out_loc):
|
||||
nlp = Language(data_dir=model_dir)
|
||||
gold_tuples = read_json_file(dev_loc)
|
||||
scorer = Scorer()
|
||||
out_file = io.open(out_loc, 'w', 'utf8')
|
||||
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.entity(tokens)
|
||||
nlp.parser(tokens)
|
||||
else:
|
||||
tokens = nlp(raw_text)
|
||||
#gold = GoldParse(tokens, annot_tuples)
|
||||
#scorer.score(tokens, gold, verbose=False)
|
||||
for sent in tokens.sents:
|
||||
for t in sent:
|
||||
if not t.is_space:
|
||||
out_file.write(
|
||||
'%d\t%s\t%s\t%s\t%s\n' % (t.i, t.orth_, t.tag_, t.head.orth_, t.dep_)
|
||||
)
|
||||
out_file.write('\n')
|
||||
|
||||
|
||||
@plac.annotations(
|
||||
language=("The language to train", "positional", None, str, ['en','de', 'zh']),
|
||||
train_loc=("Location of training file or directory"),
|
||||
dev_loc=("Location of development file or directory"),
|
||||
model_dir=("Location of output model directory",),
|
||||
eval_only=("Skip training, and only evaluate", "flag", "e", bool),
|
||||
corruption_level=("Amount of noise to add to training data", "option", "c", float),
|
||||
gold_preproc=("Use gold-standard sentence boundaries in training?", "flag", "g", bool),
|
||||
out_loc=("Out location", "option", "o", str),
|
||||
n_sents=("Number of training sentences", "option", "n", int),
|
||||
n_iter=("Number of training iterations", "option", "i", int),
|
||||
verbose=("Verbose error reporting", "flag", "v", bool),
|
||||
debug=("Debug mode", "flag", "d", bool),
|
||||
pseudoprojective=("Use pseudo-projective parsing", "flag", "p", bool),
|
||||
L1=("L1 regularization penalty", "option", "L", float),
|
||||
)
|
||||
def main(language, train_loc, dev_loc, model_dir, n_sents=0, n_iter=15, out_loc="", verbose=False,
|
||||
debug=False, corruption_level=0.0, gold_preproc=False, eval_only=False, pseudoprojective=False,
|
||||
L1=1e-6):
|
||||
parser_cfg = dict(locals())
|
||||
tagger_cfg = dict(locals())
|
||||
entity_cfg = dict(locals())
|
||||
|
||||
lang = spacy.util.get_lang_class(language)
|
||||
|
||||
parser_cfg['features'] = lang.Defaults.parser_features
|
||||
entity_cfg['features'] = lang.Defaults.entity_features
|
||||
|
||||
if not eval_only:
|
||||
gold_train = list(read_json_file(train_loc))
|
||||
gold_dev = list(read_json_file(dev_loc))
|
||||
if n_sents > 0:
|
||||
gold_train = gold_train[:n_sents]
|
||||
train(lang, gold_train, gold_dev, model_dir, tagger_cfg, parser_cfg, entity_cfg,
|
||||
n_sents=n_sents, gold_preproc=gold_preproc, corruption_level=corruption_level,
|
||||
n_iter=n_iter)
|
||||
if out_loc:
|
||||
write_parses(lang, dev_loc, model_dir, out_loc)
|
||||
scorer = evaluate(lang, list(read_json_file(dev_loc)),
|
||||
model_dir, gold_preproc=gold_preproc, verbose=verbose)
|
||||
print('TOK', scorer.token_acc)
|
||||
print('POS', scorer.tags_acc)
|
||||
print('UAS', scorer.uas)
|
||||
print('LAS', scorer.las)
|
||||
|
||||
print('NER P', scorer.ents_p)
|
||||
print('NER R', scorer.ents_r)
|
||||
print('NER F', scorer.ents_f)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
plac.call(main)
|
||||
|
|
@ -1,201 +0,0 @@
|
|||
from __future__ import unicode_literals, print_function
|
||||
import plac
|
||||
import json
|
||||
import random
|
||||
import pathlib
|
||||
|
||||
from spacy.tokens import Doc
|
||||
from spacy.syntax.nonproj import PseudoProjectivity
|
||||
from spacy.language import Language
|
||||
from spacy.gold import GoldParse
|
||||
from spacy.tagger import Tagger
|
||||
from spacy.pipeline import DependencyParser, TokenVectorEncoder
|
||||
from spacy.syntax.parser import get_templates
|
||||
from spacy.syntax.arc_eager import ArcEager
|
||||
from spacy.scorer import Scorer
|
||||
from spacy.language_data.tag_map import TAG_MAP as DEFAULT_TAG_MAP
|
||||
import spacy.attrs
|
||||
import io
|
||||
from thinc.neural.ops import CupyOps
|
||||
from thinc.neural import Model
|
||||
from spacy.es import Spanish
|
||||
from spacy.attrs import POS
|
||||
|
||||
|
||||
from thinc.neural import Model
|
||||
|
||||
|
||||
try:
|
||||
import cupy
|
||||
from thinc.neural.ops import CupyOps
|
||||
except:
|
||||
cupy = None
|
||||
|
||||
|
||||
def read_conllx(loc, n=0):
|
||||
with io.open(loc, 'r', encoding='utf8') as file_:
|
||||
text = file_.read()
|
||||
i = 0
|
||||
for sent in text.strip().split('\n\n'):
|
||||
lines = sent.strip().split('\n')
|
||||
if lines:
|
||||
while lines[0].startswith('#'):
|
||||
lines.pop(0)
|
||||
tokens = []
|
||||
for line in lines:
|
||||
id_, word, lemma, pos, tag, morph, head, dep, _1, \
|
||||
_2 = line.split('\t')
|
||||
if '-' in id_ or '.' in id_:
|
||||
continue
|
||||
try:
|
||||
id_ = int(id_) - 1
|
||||
head = (int(head) - 1) if head != '0' else id_
|
||||
dep = 'ROOT' if dep == 'root' else dep #'unlabelled'
|
||||
tag = pos+'__'+dep+'__'+morph
|
||||
Spanish.Defaults.tag_map[tag] = {POS: pos}
|
||||
tokens.append((id_, word, tag, head, dep, 'O'))
|
||||
except:
|
||||
raise
|
||||
tuples = [list(t) for t in zip(*tokens)]
|
||||
yield (None, [[tuples, []]])
|
||||
i += 1
|
||||
if n >= 1 and i >= n:
|
||||
break
|
||||
|
||||
|
||||
def score_model(vocab, encoder, parser, Xs, ys, verbose=False):
|
||||
scorer = Scorer()
|
||||
correct = 0.
|
||||
total = 0.
|
||||
for doc, gold in zip(Xs, ys):
|
||||
doc = Doc(vocab, words=[w.text for w in doc])
|
||||
encoder(doc)
|
||||
parser(doc)
|
||||
PseudoProjectivity.deprojectivize(doc)
|
||||
scorer.score(doc, gold, verbose=verbose)
|
||||
for token, tag in zip(doc, gold.tags):
|
||||
if '_' in token.tag_:
|
||||
univ_guess, _ = token.tag_.split('_', 1)
|
||||
else:
|
||||
univ_guess = ''
|
||||
univ_truth, _ = tag.split('_', 1)
|
||||
correct += univ_guess == univ_truth
|
||||
total += 1
|
||||
return scorer
|
||||
|
||||
|
||||
def organize_data(vocab, train_sents):
|
||||
Xs = []
|
||||
ys = []
|
||||
for _, doc_sents in train_sents:
|
||||
for (ids, words, tags, heads, deps, ner), _ in doc_sents:
|
||||
doc = Doc(vocab, words=words)
|
||||
gold = GoldParse(doc, tags=tags, heads=heads, deps=deps)
|
||||
Xs.append(doc)
|
||||
ys.append(gold)
|
||||
return Xs, ys
|
||||
|
||||
|
||||
def main(lang_name, train_loc, dev_loc, model_dir, clusters_loc=None):
|
||||
LangClass = spacy.util.get_lang_class(lang_name)
|
||||
train_sents = list(read_conllx(train_loc))
|
||||
dev_sents = list(read_conllx(dev_loc))
|
||||
train_sents = PseudoProjectivity.preprocess_training_data(train_sents)
|
||||
|
||||
actions = ArcEager.get_actions(gold_parses=train_sents)
|
||||
features = get_templates('basic')
|
||||
|
||||
model_dir = pathlib.Path(model_dir)
|
||||
if not model_dir.exists():
|
||||
model_dir.mkdir()
|
||||
if not (model_dir / 'deps').exists():
|
||||
(model_dir / 'deps').mkdir()
|
||||
if not (model_dir / 'pos').exists():
|
||||
(model_dir / 'pos').mkdir()
|
||||
with (model_dir / 'deps' / 'config.json').open('wb') as file_:
|
||||
file_.write(
|
||||
json.dumps(
|
||||
{'pseudoprojective': True, 'labels': actions, 'features': features}).encode('utf8'))
|
||||
|
||||
vocab = LangClass.Defaults.create_vocab()
|
||||
if not (model_dir / 'vocab').exists():
|
||||
(model_dir / 'vocab').mkdir()
|
||||
else:
|
||||
if (model_dir / 'vocab' / 'strings.json').exists():
|
||||
with (model_dir / 'vocab' / 'strings.json').open() as file_:
|
||||
vocab.strings.load(file_)
|
||||
if (model_dir / 'vocab' / 'lexemes.bin').exists():
|
||||
vocab.load_lexemes(model_dir / 'vocab' / 'lexemes.bin')
|
||||
|
||||
if clusters_loc is not None:
|
||||
clusters_loc = pathlib.Path(clusters_loc)
|
||||
with clusters_loc.open() as file_:
|
||||
for line in file_:
|
||||
try:
|
||||
cluster, word, freq = line.split()
|
||||
except ValueError:
|
||||
continue
|
||||
lex = vocab[word]
|
||||
lex.cluster = int(cluster[::-1], 2)
|
||||
# Populate vocab
|
||||
for _, doc_sents in train_sents:
|
||||
for (ids, words, tags, heads, deps, ner), _ in doc_sents:
|
||||
for word in words:
|
||||
_ = vocab[word]
|
||||
for dep in deps:
|
||||
_ = vocab[dep]
|
||||
for tag in tags:
|
||||
_ = vocab[tag]
|
||||
if vocab.morphology.tag_map:
|
||||
for tag in tags:
|
||||
vocab.morphology.tag_map[tag] = {POS: tag.split('__', 1)[0]}
|
||||
tagger = Tagger(vocab)
|
||||
encoder = TokenVectorEncoder(vocab, width=64)
|
||||
parser = DependencyParser(vocab, actions=actions, features=features, L1=0.0)
|
||||
|
||||
Xs, ys = organize_data(vocab, train_sents)
|
||||
dev_Xs, dev_ys = organize_data(vocab, dev_sents)
|
||||
with encoder.model.begin_training(Xs[:100], ys[:100]) as (trainer, optimizer):
|
||||
docs = list(Xs)
|
||||
for doc in docs:
|
||||
encoder(doc)
|
||||
nn_loss = [0.]
|
||||
def track_progress():
|
||||
with encoder.tagger.use_params(optimizer.averages):
|
||||
with parser.model.use_params(optimizer.averages):
|
||||
scorer = score_model(vocab, encoder, parser, dev_Xs, dev_ys)
|
||||
itn = len(nn_loss)
|
||||
print('%d:\t%.3f\t%.3f\t%.3f' % (itn, nn_loss[-1], scorer.uas, scorer.tags_acc))
|
||||
nn_loss.append(0.)
|
||||
track_progress()
|
||||
trainer.each_epoch.append(track_progress)
|
||||
trainer.batch_size = 24
|
||||
trainer.nb_epoch = 40
|
||||
for docs, golds in trainer.iterate(Xs, ys, progress_bar=True):
|
||||
docs = [Doc(vocab, words=[w.text for w in doc]) for doc in docs]
|
||||
tokvecs, upd_tokvecs = encoder.begin_update(docs)
|
||||
for doc, tokvec in zip(docs, tokvecs):
|
||||
doc.tensor = tokvec
|
||||
d_tokvecs = parser.update(docs, golds, sgd=optimizer)
|
||||
upd_tokvecs(d_tokvecs, sgd=optimizer)
|
||||
encoder.update(docs, golds, sgd=optimizer)
|
||||
nlp = LangClass(vocab=vocab, parser=parser)
|
||||
scorer = score_model(vocab, encoder, parser, read_conllx(dev_loc))
|
||||
print('%d:\t%.3f\t%.3f\t%.3f' % (itn, scorer.uas, scorer.las, scorer.tags_acc))
|
||||
#nlp.end_training(model_dir)
|
||||
#scorer = score_model(vocab, tagger, parser, read_conllx(dev_loc))
|
||||
#print('%d:\t%.3f\t%.3f\t%.3f' % (itn, scorer.uas, scorer.las, scorer.tags_acc))
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
import cProfile
|
||||
import pstats
|
||||
if 1:
|
||||
plac.call(main)
|
||||
else:
|
||||
cProfile.runctx("plac.call(main)", globals(), locals(), "Profile.prof")
|
||||
s = pstats.Stats("Profile.prof")
|
||||
s.strip_dirs().sort_stats("time").print_stats()
|
||||
|
||||
|
||||
plac.call(main)
|
||||
|
|
@ -1,194 +0,0 @@
|
|||
"""Convert OntoNotes into a json format.
|
||||
|
||||
doc: {
|
||||
id: string,
|
||||
paragraphs: [{
|
||||
raw: string,
|
||||
sents: [int],
|
||||
tokens: [{
|
||||
start: int,
|
||||
tag: string,
|
||||
head: int,
|
||||
dep: string}],
|
||||
ner: [{
|
||||
start: int,
|
||||
end: int,
|
||||
label: string}],
|
||||
brackets: [{
|
||||
start: int,
|
||||
end: int,
|
||||
label: string}]}]}
|
||||
|
||||
Consumes output of spacy/munge/align_raw.py
|
||||
"""
|
||||
from __future__ import unicode_literals
|
||||
import plac
|
||||
import json
|
||||
from os import path
|
||||
import os
|
||||
import re
|
||||
import io
|
||||
from collections import defaultdict
|
||||
|
||||
from spacy.munge import read_ptb
|
||||
from spacy.munge import read_conll
|
||||
from spacy.munge import read_ner
|
||||
|
||||
|
||||
def _iter_raw_files(raw_loc):
|
||||
files = json.load(open(raw_loc))
|
||||
for f in files:
|
||||
yield f
|
||||
|
||||
|
||||
def format_doc(file_id, raw_paras, ptb_text, dep_text, ner_text):
|
||||
ptb_sents = read_ptb.split(ptb_text)
|
||||
dep_sents = read_conll.split(dep_text)
|
||||
if len(ptb_sents) != len(dep_sents):
|
||||
return None
|
||||
if ner_text is not None:
|
||||
ner_sents = read_ner.split(ner_text)
|
||||
else:
|
||||
ner_sents = [None] * len(ptb_sents)
|
||||
|
||||
i = 0
|
||||
doc = {'id': file_id}
|
||||
if raw_paras is None:
|
||||
doc['paragraphs'] = [format_para(None, ptb_sents, dep_sents, ner_sents)]
|
||||
#for ptb_sent, dep_sent, ner_sent in zip(ptb_sents, dep_sents, ner_sents):
|
||||
# doc['paragraphs'].append(format_para(None, [ptb_sent], [dep_sent], [ner_sent]))
|
||||
else:
|
||||
doc['paragraphs'] = []
|
||||
for raw_sents in raw_paras:
|
||||
para = format_para(
|
||||
' '.join(raw_sents).replace('<SEP>', ''),
|
||||
ptb_sents[i:i+len(raw_sents)],
|
||||
dep_sents[i:i+len(raw_sents)],
|
||||
ner_sents[i:i+len(raw_sents)])
|
||||
if para['sentences']:
|
||||
doc['paragraphs'].append(para)
|
||||
i += len(raw_sents)
|
||||
return doc
|
||||
|
||||
|
||||
def format_para(raw_text, ptb_sents, dep_sents, ner_sents):
|
||||
para = {'raw': raw_text, 'sentences': []}
|
||||
offset = 0
|
||||
assert len(ptb_sents) == len(dep_sents) == len(ner_sents)
|
||||
for ptb_text, dep_text, ner_text in zip(ptb_sents, dep_sents, ner_sents):
|
||||
_, deps = read_conll.parse(dep_text, strip_bad_periods=True)
|
||||
if deps and 'VERB' in [t['tag'] for t in deps]:
|
||||
continue
|
||||
if ner_text is not None:
|
||||
_, ner = read_ner.parse(ner_text, strip_bad_periods=True)
|
||||
else:
|
||||
ner = ['-' for _ in deps]
|
||||
_, brackets = read_ptb.parse(ptb_text, strip_bad_periods=True)
|
||||
# Necessary because the ClearNLP converter deletes EDITED words.
|
||||
if len(ner) != len(deps):
|
||||
ner = ['-' for _ in deps]
|
||||
para['sentences'].append(format_sentence(deps, ner, brackets))
|
||||
return para
|
||||
|
||||
|
||||
def format_sentence(deps, ner, brackets):
|
||||
sent = {'tokens': [], 'brackets': []}
|
||||
for token_id, (token, token_ent) in enumerate(zip(deps, ner)):
|
||||
sent['tokens'].append(format_token(token_id, token, token_ent))
|
||||
|
||||
for label, start, end in brackets:
|
||||
if start != end:
|
||||
sent['brackets'].append({
|
||||
'label': label,
|
||||
'first': start,
|
||||
'last': (end-1)})
|
||||
return sent
|
||||
|
||||
|
||||
def format_token(token_id, token, ner):
|
||||
assert token_id == token['id']
|
||||
head = (token['head'] - token_id) if token['head'] != -1 else 0
|
||||
return {
|
||||
'id': token_id,
|
||||
'orth': token['word'],
|
||||
'tag': token['tag'],
|
||||
'head': head,
|
||||
'dep': token['dep'],
|
||||
'ner': ner}
|
||||
|
||||
|
||||
def read_file(*pieces):
|
||||
loc = path.join(*pieces)
|
||||
if not path.exists(loc):
|
||||
return None
|
||||
else:
|
||||
return io.open(loc, 'r', encoding='utf8').read().strip()
|
||||
|
||||
|
||||
def get_file_names(section_dir, subsection):
|
||||
filenames = []
|
||||
for fn in os.listdir(path.join(section_dir, subsection)):
|
||||
filenames.append(fn.rsplit('.', 1)[0])
|
||||
return list(sorted(set(filenames)))
|
||||
|
||||
|
||||
def read_wsj_with_source(onto_dir, raw_dir):
|
||||
# Now do WSJ, with source alignment
|
||||
onto_dir = path.join(onto_dir, 'data', 'english', 'annotations', 'nw', 'wsj')
|
||||
docs = {}
|
||||
for i in range(25):
|
||||
section = str(i) if i >= 10 else ('0' + str(i))
|
||||
raw_loc = path.join(raw_dir, 'wsj%s.json' % section)
|
||||
for j, (filename, raw_paras) in enumerate(_iter_raw_files(raw_loc)):
|
||||
if section == '00':
|
||||
j += 1
|
||||
if section == '04' and filename == '55':
|
||||
continue
|
||||
ptb = read_file(onto_dir, section, '%s.parse' % filename)
|
||||
dep = read_file(onto_dir, section, '%s.parse.dep' % filename)
|
||||
ner = read_file(onto_dir, section, '%s.name' % filename)
|
||||
if ptb is not None and dep is not None:
|
||||
docs[filename] = format_doc(filename, raw_paras, ptb, dep, ner)
|
||||
return docs
|
||||
|
||||
|
||||
def get_doc(onto_dir, file_path, wsj_docs):
|
||||
filename = file_path.rsplit('/', 1)[1]
|
||||
if filename in wsj_docs:
|
||||
return wsj_docs[filename]
|
||||
else:
|
||||
ptb = read_file(onto_dir, file_path + '.parse')
|
||||
dep = read_file(onto_dir, file_path + '.parse.dep')
|
||||
ner = read_file(onto_dir, file_path + '.name')
|
||||
if ptb is not None and dep is not None:
|
||||
return format_doc(filename, None, ptb, dep, ner)
|
||||
else:
|
||||
return None
|
||||
|
||||
|
||||
def read_ids(loc):
|
||||
return open(loc).read().strip().split('\n')
|
||||
|
||||
|
||||
def main(onto_dir, raw_dir, out_dir):
|
||||
wsj_docs = read_wsj_with_source(onto_dir, raw_dir)
|
||||
|
||||
for partition in ('train', 'test', 'development'):
|
||||
ids = read_ids(path.join(onto_dir, '%s.id' % partition))
|
||||
docs_by_genre = defaultdict(list)
|
||||
for file_path in ids:
|
||||
doc = get_doc(onto_dir, file_path, wsj_docs)
|
||||
if doc is not None:
|
||||
genre = file_path.split('/')[3]
|
||||
docs_by_genre[genre].append(doc)
|
||||
part_dir = path.join(out_dir, partition)
|
||||
if not path.exists(part_dir):
|
||||
os.mkdir(part_dir)
|
||||
for genre, docs in sorted(docs_by_genre.items()):
|
||||
out_loc = path.join(part_dir, genre + '.json')
|
||||
with open(out_loc, 'w') as file_:
|
||||
json.dump(docs, file_, indent=4)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
plac.call(main)
|
||||
|
|
@ -1,13 +0,0 @@
|
|||
"""Read a vector file, and prepare it as binary data, for easy consumption"""
|
||||
|
||||
import plac
|
||||
|
||||
from spacy.vocab import write_binary_vectors
|
||||
|
||||
|
||||
def main(in_loc, out_loc):
|
||||
write_binary_vectors(in_loc, out_loc)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
plac.call(main)
|
||||
|
|
@ -1,175 +0,0 @@
|
|||
#!/usr/bin/env python
|
||||
from __future__ import division
|
||||
from __future__ import unicode_literals
|
||||
from __future__ import print_function
|
||||
|
||||
import os
|
||||
from os import path
|
||||
import shutil
|
||||
import codecs
|
||||
import random
|
||||
|
||||
import plac
|
||||
import re
|
||||
|
||||
import spacy.util
|
||||
from spacy.en import English
|
||||
|
||||
from spacy.tagger import Tagger
|
||||
|
||||
from spacy.syntax.util import Config
|
||||
from spacy.gold import read_json_file
|
||||
from spacy.gold import GoldParse
|
||||
|
||||
from spacy.scorer import Scorer
|
||||
|
||||
|
||||
def score_model(scorer, nlp, raw_text, annot_tuples):
|
||||
if raw_text is None:
|
||||
tokens = nlp.tokenizer.tokens_from_list(annot_tuples[1])
|
||||
else:
|
||||
tokens = nlp.tokenizer(raw_text)
|
||||
nlp.tagger(tokens)
|
||||
gold = GoldParse(tokens, annot_tuples)
|
||||
scorer.score(tokens, gold)
|
||||
|
||||
|
||||
def _merge_sents(sents):
|
||||
m_deps = [[], [], [], [], [], []]
|
||||
m_brackets = []
|
||||
i = 0
|
||||
for (ids, words, tags, heads, labels, ner), brackets in sents:
|
||||
m_deps[0].extend(id_ + i for id_ in ids)
|
||||
m_deps[1].extend(words)
|
||||
m_deps[2].extend(tags)
|
||||
m_deps[3].extend(head + i for head in heads)
|
||||
m_deps[4].extend(labels)
|
||||
m_deps[5].extend(ner)
|
||||
m_brackets.extend((b['first'] + i, b['last'] + i, b['label']) for b in brackets)
|
||||
i += len(ids)
|
||||
return [(m_deps, m_brackets)]
|
||||
|
||||
|
||||
def train(Language, gold_tuples, model_dir, n_iter=15, feat_set=u'basic',
|
||||
seed=0, gold_preproc=False, n_sents=0, corruption_level=0,
|
||||
beam_width=1, verbose=False,
|
||||
use_orig_arc_eager=False):
|
||||
if n_sents > 0:
|
||||
gold_tuples = gold_tuples[:n_sents]
|
||||
|
||||
templates = Tagger.default_templates()
|
||||
nlp = Language(data_dir=model_dir, tagger=False)
|
||||
nlp.tagger = Tagger.blank(nlp.vocab, templates)
|
||||
|
||||
print("Itn.\tP.Loss\tUAS\tNER F.\tTag %\tToken %")
|
||||
for itn in range(n_iter):
|
||||
scorer = Scorer()
|
||||
loss = 0
|
||||
for raw_text, sents in gold_tuples:
|
||||
if gold_preproc:
|
||||
raw_text = None
|
||||
else:
|
||||
sents = _merge_sents(sents)
|
||||
for annot_tuples, ctnt in sents:
|
||||
words = annot_tuples[1]
|
||||
gold_tags = annot_tuples[2]
|
||||
score_model(scorer, nlp, raw_text, annot_tuples)
|
||||
if raw_text is None:
|
||||
tokens = nlp.tokenizer.tokens_from_list(words)
|
||||
else:
|
||||
tokens = nlp.tokenizer(raw_text)
|
||||
loss += nlp.tagger.train(tokens, gold_tags)
|
||||
random.shuffle(gold_tuples)
|
||||
print('%d:\t%d\t%.3f\t%.3f\t%.3f\t%.3f' % (itn, loss, scorer.uas, scorer.ents_f,
|
||||
scorer.tags_acc,
|
||||
scorer.token_acc))
|
||||
nlp.end_training(model_dir)
|
||||
|
||||
def evaluate(Language, gold_tuples, model_dir, gold_preproc=False, verbose=False,
|
||||
beam_width=None):
|
||||
nlp = Language(data_dir=model_dir)
|
||||
if beam_width is not None:
|
||||
nlp.parser.cfg.beam_width = beam_width
|
||||
scorer = Scorer()
|
||||
for raw_text, sents in gold_tuples:
|
||||
if gold_preproc:
|
||||
raw_text = None
|
||||
else:
|
||||
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.entity(tokens)
|
||||
nlp.parser(tokens)
|
||||
else:
|
||||
tokens = nlp(raw_text, merge_mwes=False)
|
||||
gold = GoldParse(tokens, annot_tuples)
|
||||
scorer.score(tokens, gold, verbose=verbose)
|
||||
return scorer
|
||||
|
||||
|
||||
def write_parses(Language, dev_loc, model_dir, out_loc, beam_width=None):
|
||||
nlp = Language(data_dir=model_dir)
|
||||
if beam_width is not None:
|
||||
nlp.parser.cfg.beam_width = beam_width
|
||||
gold_tuples = read_json_file(dev_loc)
|
||||
scorer = Scorer()
|
||||
out_file = codecs.open(out_loc, 'w', 'utf8')
|
||||
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.entity(tokens)
|
||||
nlp.parser(tokens)
|
||||
else:
|
||||
tokens = nlp(raw_text, merge_mwes=False)
|
||||
gold = GoldParse(tokens, annot_tuples)
|
||||
scorer.score(tokens, gold, verbose=False)
|
||||
for t in tokens:
|
||||
out_file.write(
|
||||
'%s\t%s\t%s\t%s\n' % (t.orth_, t.tag_, t.head.orth_, t.dep_)
|
||||
)
|
||||
return scorer
|
||||
|
||||
|
||||
@plac.annotations(
|
||||
train_loc=("Location of training file or directory"),
|
||||
dev_loc=("Location of development file or directory"),
|
||||
model_dir=("Location of output model directory",),
|
||||
eval_only=("Skip training, and only evaluate", "flag", "e", bool),
|
||||
corruption_level=("Amount of noise to add to training data", "option", "c", float),
|
||||
gold_preproc=("Use gold-standard sentence boundaries in training?", "flag", "g", bool),
|
||||
out_loc=("Out location", "option", "o", str),
|
||||
n_sents=("Number of training sentences", "option", "n", int),
|
||||
n_iter=("Number of training iterations", "option", "i", 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, n_iter=15, out_loc="", verbose=False,
|
||||
debug=False, corruption_level=0.0, gold_preproc=False, eval_only=False):
|
||||
if not eval_only:
|
||||
gold_train = list(read_json_file(train_loc))
|
||||
train(English, gold_train, model_dir,
|
||||
feat_set='basic' if not debug else 'debug',
|
||||
gold_preproc=gold_preproc, n_sents=n_sents,
|
||||
corruption_level=corruption_level, n_iter=n_iter,
|
||||
verbose=verbose)
|
||||
#if out_loc:
|
||||
# write_parses(English, dev_loc, model_dir, out_loc, beam_width=beam_width)
|
||||
scorer = evaluate(English, list(read_json_file(dev_loc)),
|
||||
model_dir, gold_preproc=gold_preproc, verbose=verbose)
|
||||
print('TOK', scorer.token_acc)
|
||||
print('POS', scorer.tags_acc)
|
||||
print('UAS', scorer.uas)
|
||||
print('LAS', scorer.las)
|
||||
|
||||
print('NER P', scorer.ents_p)
|
||||
print('NER R', scorer.ents_r)
|
||||
print('NER F', scorer.ents_f)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
plac.call(main)
|
||||
|
|
@ -1,160 +0,0 @@
|
|||
#!/usr/bin/env python
|
||||
from __future__ import division
|
||||
from __future__ import unicode_literals
|
||||
|
||||
import os
|
||||
from os import path
|
||||
import shutil
|
||||
import io
|
||||
import random
|
||||
import time
|
||||
import gzip
|
||||
import ujson
|
||||
|
||||
import plac
|
||||
import cProfile
|
||||
import pstats
|
||||
|
||||
import spacy.util
|
||||
from spacy.de import German
|
||||
from spacy.gold import GoldParse
|
||||
from spacy.tagger import Tagger
|
||||
from spacy.scorer import PRFScore
|
||||
|
||||
from spacy.tagger import P2_orth, P2_cluster, P2_shape, P2_prefix, P2_suffix, P2_pos, P2_lemma, P2_flags
|
||||
from spacy.tagger import P1_orth, P1_cluster, P1_shape, P1_prefix, P1_suffix, P1_pos, P1_lemma, P1_flags
|
||||
from spacy.tagger import W_orth, W_cluster, W_shape, W_prefix, W_suffix, W_pos, W_lemma, W_flags
|
||||
from spacy.tagger import N1_orth, N1_cluster, N1_shape, N1_prefix, N1_suffix, N1_pos, N1_lemma, N1_flags
|
||||
from spacy.tagger import N2_orth, N2_cluster, N2_shape, N2_prefix, N2_suffix, N2_pos, N2_lemma, N2_flags, N_CONTEXT_FIELDS
|
||||
|
||||
|
||||
def default_templates():
|
||||
return spacy.tagger.Tagger.default_templates()
|
||||
|
||||
def default_templates_without_clusters():
|
||||
return (
|
||||
(W_orth,),
|
||||
(P1_lemma, P1_pos),
|
||||
(P2_lemma, P2_pos),
|
||||
(N1_orth,),
|
||||
(N2_orth,),
|
||||
|
||||
(W_suffix,),
|
||||
(W_prefix,),
|
||||
|
||||
(P1_pos,),
|
||||
(P2_pos,),
|
||||
(P1_pos, P2_pos),
|
||||
(P1_pos, W_orth),
|
||||
(P1_suffix,),
|
||||
(N1_suffix,),
|
||||
|
||||
(W_shape,),
|
||||
|
||||
(W_flags,),
|
||||
(N1_flags,),
|
||||
(N2_flags,),
|
||||
(P1_flags,),
|
||||
(P2_flags,),
|
||||
)
|
||||
|
||||
|
||||
def make_tagger(vocab, templates):
|
||||
model = spacy.tagger.TaggerModel(templates)
|
||||
return spacy.tagger.Tagger(vocab,model)
|
||||
|
||||
|
||||
def read_conll(file_):
|
||||
def sentences():
|
||||
words, tags = [], []
|
||||
for line in file_:
|
||||
line = line.strip()
|
||||
if line:
|
||||
word, tag = line.split('\t')[1::3][:2] # get column 1 and 4 (CoNLL09)
|
||||
words.append(word)
|
||||
tags.append(tag)
|
||||
elif words:
|
||||
yield words, tags
|
||||
words, tags = [], []
|
||||
if words:
|
||||
yield words, tags
|
||||
return [ s for s in sentences() ]
|
||||
|
||||
|
||||
def score_model(score, nlp, words, gold_tags):
|
||||
tokens = nlp.tokenizer.tokens_from_list(words)
|
||||
assert(len(tokens) == len(gold_tags))
|
||||
nlp.tagger(tokens)
|
||||
|
||||
for token, gold_tag in zip(tokens,gold_tags):
|
||||
score.score_set(set([token.tag_]),set([gold_tag]))
|
||||
|
||||
|
||||
def train(Language, train_sents, dev_sents, model_dir, n_iter=15, seed=21):
|
||||
# make shuffling deterministic
|
||||
random.seed(seed)
|
||||
|
||||
# set up directory for model
|
||||
pos_model_dir = path.join(model_dir, 'pos')
|
||||
if path.exists(pos_model_dir):
|
||||
shutil.rmtree(pos_model_dir)
|
||||
os.mkdir(pos_model_dir)
|
||||
|
||||
nlp = Language(data_dir=model_dir, tagger=False, parser=False, entity=False)
|
||||
nlp.tagger = make_tagger(nlp.vocab,default_templates())
|
||||
|
||||
print("Itn.\ttrain acc %\tdev acc %")
|
||||
for itn in range(n_iter):
|
||||
# train on train set
|
||||
#train_acc = PRFScore()
|
||||
correct, total = 0., 0.
|
||||
for words, gold_tags in train_sents:
|
||||
tokens = nlp.tokenizer.tokens_from_list(words)
|
||||
correct += nlp.tagger.train(tokens, gold_tags)
|
||||
total += len(words)
|
||||
train_acc = correct/total
|
||||
|
||||
# test on dev set
|
||||
dev_acc = PRFScore()
|
||||
for words, gold_tags in dev_sents:
|
||||
score_model(dev_acc, nlp, words, gold_tags)
|
||||
|
||||
random.shuffle(train_sents)
|
||||
print('%d:\t%6.2f\t%6.2f' % (itn, 100*train_acc, 100*dev_acc.precision))
|
||||
|
||||
|
||||
print('end training')
|
||||
nlp.end_training(model_dir)
|
||||
print('done')
|
||||
|
||||
|
||||
@plac.annotations(
|
||||
train_loc=("Location of CoNLL 09 formatted training file"),
|
||||
dev_loc=("Location of CoNLL 09 formatted development file"),
|
||||
model_dir=("Location of output model directory"),
|
||||
eval_only=("Skip training, and only evaluate", "flag", "e", bool),
|
||||
n_iter=("Number of training iterations", "option", "i", int),
|
||||
)
|
||||
def main(train_loc, dev_loc, model_dir, eval_only=False, n_iter=15):
|
||||
# training
|
||||
if not eval_only:
|
||||
with io.open(train_loc, 'r', encoding='utf8') as trainfile_, \
|
||||
io.open(dev_loc, 'r', encoding='utf8') as devfile_:
|
||||
train_sents = read_conll(trainfile_)
|
||||
dev_sents = read_conll(devfile_)
|
||||
train(German, train_sents, dev_sents, model_dir, n_iter=n_iter)
|
||||
|
||||
# testing
|
||||
with io.open(dev_loc, 'r', encoding='utf8') as file_:
|
||||
dev_sents = read_conll(file_)
|
||||
nlp = German(data_dir=model_dir)
|
||||
|
||||
dev_acc = PRFScore()
|
||||
for words, gold_tags in dev_sents:
|
||||
score_model(dev_acc, nlp, words, gold_tags)
|
||||
|
||||
print('POS: %6.2f %%' % (100*dev_acc.precision))
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
plac.call(main)
|
||||
Loading…
Reference in New Issue
Block a user