mirror of
https://github.com/explosion/spaCy.git
synced 2024-12-29 11:26:28 +03:00
128 lines
4.4 KiB
Python
128 lines
4.4 KiB
Python
# coding: utf8
|
|
from __future__ import unicode_literals
|
|
|
|
import gzip
|
|
import math
|
|
from ast import literal_eval
|
|
from pathlib import Path
|
|
from preshed.counter import PreshCounter
|
|
|
|
from ..vocab import write_binary_vectors
|
|
from ..compat import fix_text
|
|
from .. import util
|
|
|
|
|
|
def model(lang, model_dir, freqs_data, clusters_data, vectors_data):
|
|
model_path = Path(model_dir)
|
|
freqs_path = Path(freqs_data)
|
|
clusters_path = Path(clusters_data) if clusters_data else None
|
|
vectors_path = Path(vectors_data) if vectors_data else None
|
|
|
|
check_dirs(freqs_path, clusters_path, vectors_path)
|
|
vocab = util.get_lang_class(lang).Defaults.create_vocab()
|
|
probs, oov_prob = read_probs(freqs_path)
|
|
clusters = read_clusters(clusters_path) if clusters_path else {}
|
|
populate_vocab(vocab, clusters, probs, oov_prob)
|
|
create_model(model_path, vectors_path, vocab, oov_prob)
|
|
|
|
|
|
def create_model(model_path, vectors_path, vocab, oov_prob):
|
|
vocab_path = model_path / 'vocab'
|
|
lexemes_path = vocab_path / 'lexemes.bin'
|
|
strings_path = vocab_path / 'strings.json'
|
|
oov_path = vocab_path / 'oov_prob'
|
|
|
|
if not model_path.exists():
|
|
model_path.mkdir()
|
|
if not vocab_path.exists():
|
|
vocab_path.mkdir()
|
|
vocab.dump(lexemes_path.as_posix())
|
|
with strings_path.open('w') as f:
|
|
vocab.strings.dump(f)
|
|
with oov_path.open('w') as f:
|
|
f.write('%f' % oov_prob)
|
|
if vectors_path:
|
|
vectors_dest = vocab_path / 'vec.bin'
|
|
write_binary_vectors(vectors_path.as_posix(), vectors_dest.as_posix())
|
|
|
|
|
|
def read_probs(freqs_path, max_length=100, min_doc_freq=5, min_freq=200):
|
|
counts = PreshCounter()
|
|
total = 0
|
|
freqs_file = check_unzip(freqs_path)
|
|
for i, line in enumerate(freqs_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)
|
|
freqs_file = check_unzip(freqs_path)
|
|
probs = {}
|
|
for line in freqs_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_clusters(clusters_path):
|
|
clusters = {}
|
|
with clusters_path.open() as f:
|
|
for line in f:
|
|
try:
|
|
cluster, word, freq = line.split()
|
|
word = fix_text(word)
|
|
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 populate_vocab(vocab, clusters, probs, oov_prob):
|
|
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
|
|
|
|
|
|
def check_unzip(file_path):
|
|
file_path_str = file_path.as_posix()
|
|
if file_path_str.endswith('gz'):
|
|
return gzip.open(file_path_str)
|
|
else:
|
|
return file_path.open()
|
|
|
|
|
|
def check_dirs(freqs_data, clusters_data, vectors_data):
|
|
if not freqs_data.is_file():
|
|
util.sys_exit(freqs_data.as_posix(), title="No frequencies file found")
|
|
if clusters_data and not clusters_data.is_file():
|
|
util.sys_exit(clusters_data.as_posix(), title="No Brown clusters file found")
|
|
if vectors_data and not vectors_data.is_file():
|
|
util.sys_exit(vectors_data.as_posix(), title="No word vectors file found")
|