spaCy/bin/init_model.py

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"""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.tgz --- output of something like word2vec
"""
from __future__ import unicode_literals
from ast import literal_eval
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import math
import gzip
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import json
import plac
from pathlib import Path
from shutil import copyfile
from shutil import copytree
import codecs
from collections import defaultdict
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
import spacy.en
import spacy.de
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import spacy.fi
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import spacy.it
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 codecs.open(str(loc), 'r', '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'
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# Expand clusters with re-casing
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for word, cluster in list(clusters.items()):
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if word.lower() not in clusters:
clusters[word.lower()] = cluster
if word.title() not in clusters:
clusters[word.title()] = cluster
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if word.upper() not in clusters:
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clusters[word.upper()] = cluster
return clusters
def _read_probs(loc):
if not loc.exists():
print("Probabilities file not found. Trying freqs.")
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return {}, 0.0
probs = {}
for i, line in enumerate(codecs.open(str(loc), 'r', 'utf8')):
prob, word = line.split()
prob = float(prob)
probs[word] = prob
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return probs, probs['-OOV-']
def _read_freqs(loc, max_length=100, min_doc_freq=5, min_freq=200):
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if not loc.exists():
print("Warning: Frequencies file not found")
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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.split('\t', 2)
freq = int(freq)
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counts.inc(i+1, freq)
total += freq
counts.smooth()
log_total = math.log(total)
probs = {}
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for line in loc.open():
freq, doc_freq, key = line.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))
log_smooth_count = math.log(smooth_count)
probs[word] = math.log(smooth_count) - log_total
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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
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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.tgz'
if vectors_src.exists():
write_binary_vectors(str(vectors_src), str(dst_dir / 'vec.bin'))
else:
print("Warning: Word vectors file not found")
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vocab = Vocab(get_lex_attr=get_lex_attr, tag_map=tag_map)
clusters = _read_clusters(src_dir / 'clusters.txt')
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probs, oov_prob = _read_probs(src_dir / 'words.sgt.prob')
if not probs:
probs, oov_prob = _read_freqs(src_dir / 'freqs.txt')
if not probs:
oov_prob = -20
else:
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oov_prob = min(probs.values())
for word in clusters:
if word not in probs:
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probs[word] = oov_prob
lexicon = []
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for word, prob in reversed(sorted(list(probs.items()), key=lambda item: item[1])):
lexeme = vocab[word]
lexeme.prob = prob
lexeme.is_oov = False
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# 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'))
vocab.strings.dump(str(dst_dir / 'strings.txt'))
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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):
languages = {
'en': spacy.en.English.default_lex_attrs(),
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'de': spacy.de.German.default_lex_attrs(),
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'fi': spacy.fi.Finnish.default_lex_attrs(),
'it': spacy.it.Italian.default_lex_attrs(),
}
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()
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tag_map = json.load((lang_data_dir / 'tag_map.json').open())
setup_tokenizer(lang_data_dir, model_dir / 'tokenizer')
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setup_vocab(languages[lang_id], tag_map, corpora_dir, model_dir / 'vocab')
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if (lang_data_dir / 'gazetteer.json').exists():
copyfile(str(lang_data_dir / 'gazetteer.json'),
str(model_dir / 'vocab' / 'gazetteer.json'))
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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)