spaCy/bin/init_model.py
2016-04-24 18:44:24 +02:00

229 lines
7.2 KiB
Python

"""Set up a model directory.
Requires:
lang_data --- Rules for the tokenizer
* prefix.txt
* suffix.txt
* infix.txt
* morphs.json
* specials.json
corpora --- Data files
* WordNet
* words.sgt.prob --- Smoothed unigram probabilities
* clusters.txt --- Output of hierarchical clustering, e.g. Brown clusters
* vectors.bz2 --- output of something like word2vec, compressed with bzip
"""
from __future__ import unicode_literals
from ast import literal_eval
import math
import gzip
import json
import plac
from pathlib import Path
from shutil import copyfile
from shutil import copytree
from collections import defaultdict
import io
from spacy.vocab import Vocab
from spacy.vocab import write_binary_vectors
from spacy.strings import hash_string
from preshed.counter import PreshCounter
from spacy.parts_of_speech import NOUN, VERB, ADJ
from spacy.util import get_lang_class
try:
unicode
except NameError:
unicode = str
def setup_tokenizer(lang_data_dir, tok_dir):
if not tok_dir.exists():
tok_dir.mkdir()
for filename in ('infix.txt', 'morphs.json', 'prefix.txt', 'specials.json',
'suffix.txt'):
src = lang_data_dir / filename
dst = tok_dir / filename
copyfile(str(src), str(dst))
def _read_clusters(loc):
if not loc.exists():
print("Warning: Clusters file not found")
return {}
clusters = {}
for line in io.open(str(loc), 'r', encoding='utf8'):
try:
cluster, word, freq = line.split()
except ValueError:
continue
# If the clusterer has only seen the word a few times, its cluster is
# unreliable.
if int(freq) >= 3:
clusters[word] = cluster
else:
clusters[word] = '0'
# Expand clusters with re-casing
for word, cluster in list(clusters.items()):
if word.lower() not in clusters:
clusters[word.lower()] = cluster
if word.title() not in clusters:
clusters[word.title()] = cluster
if word.upper() not in clusters:
clusters[word.upper()] = cluster
return clusters
def _read_probs(loc):
if not loc.exists():
print("Probabilities file not found. Trying freqs.")
return {}, 0.0
probs = {}
for i, line in enumerate(io.open(str(loc), 'r', encoding='utf8')):
prob, word = line.split()
prob = float(prob)
probs[word] = prob
return probs, probs['-OOV-']
def _read_freqs(loc, max_length=100, min_doc_freq=5, min_freq=200):
if not loc.exists():
print("Warning: Frequencies file not found")
return {}, 0.0
counts = PreshCounter()
total = 0
if str(loc).endswith('gz'):
file_ = gzip.open(str(loc))
else:
file_ = loc.open()
for i, line in enumerate(file_):
freq, doc_freq, key = line.rstrip().split('\t', 2)
freq = int(freq)
counts.inc(i+1, freq)
total += freq
counts.smooth()
log_total = math.log(total)
if str(loc).endswith('gz'):
file_ = gzip.open(str(loc))
else:
file_ = loc.open()
probs = {}
for line in file_:
freq, doc_freq, key = line.rstrip().split('\t', 2)
doc_freq = int(doc_freq)
freq = int(freq)
if doc_freq >= min_doc_freq and freq >= min_freq and len(key) < max_length:
word = literal_eval(key)
smooth_count = counts.smoother(int(freq))
probs[word] = math.log(smooth_count) - log_total
oov_prob = math.log(counts.smoother(0)) - log_total
return probs, oov_prob
def _read_senses(loc):
lexicon = defaultdict(lambda: defaultdict(list))
if not loc.exists():
print("Warning: WordNet senses not found")
return lexicon
sense_names = dict((s, i) for i, s in enumerate(spacy.senses.STRINGS))
pos_ids = {'noun': NOUN, 'verb': VERB, 'adjective': ADJ}
for line in codecs.open(str(loc), 'r', 'utf8'):
sense_strings = line.split()
word = sense_strings.pop(0)
for sense in sense_strings:
pos, sense = sense[3:].split('.')
sense_name = '%s_%s' % (pos[0].upper(), sense.lower())
if sense_name != 'N_tops':
sense_id = sense_names[sense_name]
lexicon[word][pos_ids[pos]].append(sense_id)
return lexicon
def setup_vocab(get_lex_attr, tag_map, src_dir, dst_dir):
if not dst_dir.exists():
dst_dir.mkdir()
vectors_src = src_dir / 'vectors.bz2'
if vectors_src.exists():
write_binary_vectors(str(vectors_src), str(dst_dir / 'vec.bin'))
else:
print("Warning: Word vectors file not found")
vocab = Vocab(get_lex_attr=get_lex_attr, tag_map=tag_map)
clusters = _read_clusters(src_dir / 'clusters.txt')
probs, oov_prob = _read_probs(src_dir / 'words.sgt.prob')
if not probs:
probs, oov_prob = _read_freqs(src_dir / 'freqs.txt.gz')
if not probs:
oov_prob = -20
else:
oov_prob = min(probs.values())
for word in clusters:
if word not in probs:
probs[word] = oov_prob
lexicon = []
for word, prob in reversed(sorted(list(probs.items()), key=lambda item: item[1])):
# First encode the strings into the StringStore. This way, we can map
# the orth IDs to frequency ranks
orth = vocab.strings[word]
# Now actually load the vocab
for word, prob in reversed(sorted(list(probs.items()), key=lambda item: item[1])):
lexeme = vocab[word]
lexeme.prob = prob
lexeme.is_oov = False
# Decode as a little-endian string, so that we can do & 15 to get
# the first 4 bits. See _parse_features.pyx
if word in clusters:
lexeme.cluster = int(clusters[word][::-1], 2)
else:
lexeme.cluster = 0
vocab.dump(str(dst_dir / 'lexemes.bin'))
with (dst_dir / 'strings.json').open('w') as file_:
vocab.strings.dump(file_)
with (dst_dir / 'oov_prob').open('w') as file_:
file_.write('%f' % oov_prob)
def main(lang_id, lang_data_dir, corpora_dir, model_dir):
model_dir = Path(model_dir)
lang_data_dir = Path(lang_data_dir) / lang_id
corpora_dir = Path(corpora_dir) / lang_id
assert corpora_dir.exists()
assert lang_data_dir.exists()
if not model_dir.exists():
model_dir.mkdir()
tag_map = json.load((lang_data_dir / 'tag_map.json').open())
setup_tokenizer(lang_data_dir, model_dir / 'tokenizer')
setup_vocab(get_lang_class(lang_id).default_lex_attrs(), tag_map, corpora_dir,
model_dir / 'vocab')
if (lang_data_dir / 'gazetteer.json').exists():
copyfile(str(lang_data_dir / 'gazetteer.json'),
str(model_dir / 'vocab' / 'gazetteer.json'))
copyfile(str(lang_data_dir / 'tag_map.json'),
str(model_dir / 'vocab' / 'tag_map.json'))
if (lang_data_dir / 'lemma_rules.json').exists():
copyfile(str(lang_data_dir / 'lemma_rules.json'),
str(model_dir / 'vocab' / 'lemma_rules.json'))
if not (model_dir / 'wordnet').exists() and (corpora_dir / 'wordnet').exists():
copytree(str(corpora_dir / 'wordnet' / 'dict'), str(model_dir / 'wordnet'))
if __name__ == '__main__':
plac.call(main)