spaCy/spacy/cli/init_model.py

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# coding: utf8
from __future__ import unicode_literals
import plac
import math
from tqdm import tqdm
import numpy
from ast import literal_eval
from pathlib import Path
from preshed.counter import PreshCounter
import tarfile
import gzip
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from ..compat import fix_text
from ..vectors import Vectors
from ..util import prints, ensure_path, get_lang_class
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try:
import ftfy
except ImportError:
ftfy = None
@plac.annotations(
lang=("model language", "positional", None, str),
output_dir=("model output directory", "positional", None, Path),
freqs_loc=("location of words frequencies file", "positional", None, Path),
clusters_loc=("optional: location of brown clusters data",
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"option", "c", str),
vectors_loc=("optional: location of vectors file in GenSim text format",
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"option", "v", str),
prune_vectors=("optional: number of vectors to prune to",
"option", "V", int)
)
def init_model(lang, output_dir, freqs_loc=None, clusters_loc=None, vectors_loc=None, prune_vectors=-1):
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"""
Create a new model from raw data, like word frequencies, Brown clusters
and word vectors.
"""
if freqs_loc is not None and not freqs_loc.exists():
prints(freqs_loc, title="Can't find words frequencies file", exits=1)
clusters_loc = ensure_path(clusters_loc)
vectors_loc = ensure_path(vectors_loc)
probs, oov_prob = read_freqs(freqs_loc) if freqs_loc is not None else ({}, -20)
vectors_data, vector_keys = read_vectors(vectors_loc) if vectors_loc else (None, None)
clusters = read_clusters(clusters_loc) if clusters_loc else {}
nlp = create_model(lang, probs, oov_prob, clusters, vectors_data, vector_keys, prune_vectors)
if not output_dir.exists():
output_dir.mkdir()
nlp.to_disk(output_dir)
return nlp
def open_file(loc):
'''Handle .gz, .tar.gz or unzipped files'''
loc = ensure_path(loc)
if tarfile.is_tarfile(str(loc)):
return tarfile.open(str(loc), 'r:gz')
elif loc.parts[-1].endswith('gz'):
return (line.decode('utf8') for line in gzip.open(str(loc), 'r'))
else:
return loc.open('r', encoding='utf8')
def create_model(lang, probs, oov_prob, clusters, vectors_data, vector_keys, prune_vectors):
print("Creating model...")
lang_class = get_lang_class(lang)
nlp = lang_class()
for lexeme in nlp.vocab:
lexeme.rank = 0
lex_added = 0
for i, (word, prob) in enumerate(tqdm(sorted(probs.items(), key=lambda item: item[1], reverse=True))):
lexeme = nlp.vocab[word]
lexeme.rank = i
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
lex_added += 1
nlp.vocab.cfg.update({'oov_prob': oov_prob})
for word in vector_keys:
if word not in nlp.vocab:
lexeme = nlp.vocab[word]
lexeme.is_oov = False
lex_added += 1
if len(vectors_data):
nlp.vocab.vectors = Vectors(data=vectors_data, keys=vector_keys)
if prune_vectors >= 1:
nlp.vocab.prune_vectors(prune_vectors)
vec_added = len(nlp.vocab.vectors)
prints("{} entries, {} vectors".format(lex_added, vec_added),
title="Sucessfully compiled vocab")
return nlp
def read_vectors(vectors_loc):
print("Reading vectors from %s" % vectors_loc)
f = open_file(vectors_loc)
shape = tuple(int(size) for size in next(f).split())
vectors_data = numpy.zeros(shape=shape, dtype='f')
vectors_keys = []
for i, line in enumerate(tqdm(f)):
pieces = line.split()
word = pieces.pop(0)
vectors_data[i] = numpy.asarray(pieces, dtype='f')
vectors_keys.append(word)
return vectors_data, vectors_keys
def read_freqs(freqs_loc, max_length=100, min_doc_freq=5, min_freq=50):
print("Counting frequencies...")
counts = PreshCounter()
total = 0
with freqs_loc.open() as f:
for i, line in enumerate(f):
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)
probs = {}
with freqs_loc.open() as f:
for line in tqdm(f):
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_loc):
print("Reading clusters...")
clusters = {}
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if ftfy is None:
print("Warning: No text fixing. Run pip install ftfy if necessary")
with clusters_loc.open() as f:
for line in tqdm(f):
try:
cluster, word, freq = line.split()
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if ftfy is not None:
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