mirror of
https://github.com/explosion/spaCy.git
synced 2025-11-04 09:57:26 +03:00
361 lines
13 KiB
Python
361 lines
13 KiB
Python
from typing import Optional, List, Dict, Any, Union, IO
|
|
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
|
|
import zipfile
|
|
import srsly
|
|
import warnings
|
|
from wasabi import msg, Printer
|
|
import typer
|
|
|
|
from ._util import app, init_cli, Arg, Opt
|
|
from ..vectors import Vectors
|
|
from ..errors import Errors, Warnings
|
|
from ..language import Language
|
|
from ..util import ensure_path, get_lang_class, load_model, OOV_RANK
|
|
|
|
try:
|
|
import ftfy
|
|
except ImportError:
|
|
ftfy = None
|
|
|
|
|
|
DEFAULT_OOV_PROB = -20
|
|
|
|
|
|
@init_cli.command("vocab")
|
|
@app.command(
|
|
"init-model",
|
|
context_settings={"allow_extra_args": True, "ignore_unknown_options": True},
|
|
hidden=True, # hide this from main CLI help but still allow it to work with warning
|
|
)
|
|
def init_model_cli(
|
|
# fmt: off
|
|
ctx: typer.Context, # This is only used to read additional arguments
|
|
lang: str = Arg(..., help="Pipeline language"),
|
|
output_dir: Path = Arg(..., help="Pipeline output directory"),
|
|
freqs_loc: Optional[Path] = Arg(None, help="Location of words frequencies file", exists=True),
|
|
clusters_loc: Optional[Path] = Opt(None, "--clusters-loc", "-c", help="Optional location of brown clusters data", exists=True),
|
|
jsonl_loc: Optional[Path] = Opt(None, "--jsonl-loc", "-j", help="Location of JSONL-formatted attributes file", exists=True),
|
|
vectors_loc: Optional[Path] = Opt(None, "--vectors-loc", "-v", help="Optional vectors file in Word2Vec format", exists=True),
|
|
prune_vectors: int = Opt(-1, "--prune-vectors", "-V", help="Optional number of vectors to prune to"),
|
|
truncate_vectors: int = Opt(0, "--truncate-vectors", "-t", help="Optional number of vectors to truncate to when reading in vectors file"),
|
|
vectors_name: Optional[str] = Opt(None, "--vectors-name", "-vn", help="Optional name for the word vectors, e.g. en_core_web_lg.vectors"),
|
|
model_name: Optional[str] = Opt(None, "--meta-name", "-mn", help="Optional name of the package for the pipeline meta"),
|
|
base_model: Optional[str] = Opt(None, "--base", "-b", help="Name of or path to base pipeline to start with (mostly relevant for pipelines with custom tokenizers)")
|
|
# fmt: on
|
|
):
|
|
"""
|
|
Create a new blank pipeline directory with vocab and vectors from raw data.
|
|
If vectors are provided in Word2Vec format, they can be either a .txt or
|
|
zipped as a .zip or .tar.gz.
|
|
|
|
DOCS: https://nightly.spacy.io/api/cli#init-vocab
|
|
"""
|
|
if ctx.command.name == "init-model":
|
|
msg.warn(
|
|
"The init-model command is now called 'init vocab'. You can run "
|
|
"'python -m spacy init --help' for an overview of the other "
|
|
"available initialization commands."
|
|
)
|
|
init_model(
|
|
lang,
|
|
output_dir,
|
|
freqs_loc=freqs_loc,
|
|
clusters_loc=clusters_loc,
|
|
jsonl_loc=jsonl_loc,
|
|
vectors_loc=vectors_loc,
|
|
prune_vectors=prune_vectors,
|
|
truncate_vectors=truncate_vectors,
|
|
vectors_name=vectors_name,
|
|
model_name=model_name,
|
|
base_model=base_model,
|
|
silent=False,
|
|
)
|
|
|
|
|
|
def init_model(
|
|
lang: str,
|
|
output_dir: Path,
|
|
freqs_loc: Optional[Path] = None,
|
|
clusters_loc: Optional[Path] = None,
|
|
jsonl_loc: Optional[Path] = None,
|
|
vectors_loc: Optional[Path] = None,
|
|
prune_vectors: int = -1,
|
|
truncate_vectors: int = 0,
|
|
vectors_name: Optional[str] = None,
|
|
model_name: Optional[str] = None,
|
|
base_model: Optional[str] = None,
|
|
silent: bool = True,
|
|
) -> Language:
|
|
msg = Printer(no_print=silent, pretty=not silent)
|
|
if jsonl_loc is not None:
|
|
if freqs_loc is not None or clusters_loc is not None:
|
|
settings = ["-j"]
|
|
if freqs_loc:
|
|
settings.append("-f")
|
|
if clusters_loc:
|
|
settings.append("-c")
|
|
msg.warn(
|
|
"Incompatible arguments",
|
|
"The -f and -c arguments are deprecated, and not compatible "
|
|
"with the -j argument, which should specify the same "
|
|
"information. Either merge the frequencies and clusters data "
|
|
"into the JSONL-formatted file (recommended), or use only the "
|
|
"-f and -c files, without the other lexical attributes.",
|
|
)
|
|
jsonl_loc = ensure_path(jsonl_loc)
|
|
lex_attrs = srsly.read_jsonl(jsonl_loc)
|
|
else:
|
|
clusters_loc = ensure_path(clusters_loc)
|
|
freqs_loc = ensure_path(freqs_loc)
|
|
if freqs_loc is not None and not freqs_loc.exists():
|
|
msg.fail("Can't find words frequencies file", freqs_loc, exits=1)
|
|
lex_attrs = read_attrs_from_deprecated(msg, freqs_loc, clusters_loc)
|
|
|
|
with msg.loading("Creating blank pipeline..."):
|
|
nlp = create_model(lang, lex_attrs, name=model_name, base_model=base_model)
|
|
|
|
msg.good("Successfully created blank pipeline")
|
|
if vectors_loc is not None:
|
|
add_vectors(
|
|
msg, nlp, vectors_loc, truncate_vectors, prune_vectors, vectors_name
|
|
)
|
|
vec_added = len(nlp.vocab.vectors)
|
|
lex_added = len(nlp.vocab)
|
|
msg.good(
|
|
"Sucessfully compiled vocab", f"{lex_added} entries, {vec_added} vectors",
|
|
)
|
|
if not output_dir.exists():
|
|
output_dir.mkdir()
|
|
nlp.to_disk(output_dir)
|
|
return nlp
|
|
|
|
|
|
def open_file(loc: Union[str, Path]) -> IO:
|
|
"""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"))
|
|
elif loc.parts[-1].endswith("zip"):
|
|
zip_file = zipfile.ZipFile(str(loc))
|
|
names = zip_file.namelist()
|
|
file_ = zip_file.open(names[0])
|
|
return (line.decode("utf8") for line in file_)
|
|
else:
|
|
return loc.open("r", encoding="utf8")
|
|
|
|
|
|
def read_attrs_from_deprecated(
|
|
msg: Printer, freqs_loc: Optional[Path], clusters_loc: Optional[Path]
|
|
) -> List[Dict[str, Any]]:
|
|
if freqs_loc is not None:
|
|
with msg.loading("Counting frequencies..."):
|
|
probs, _ = read_freqs(freqs_loc)
|
|
msg.good("Counted frequencies")
|
|
else:
|
|
probs, _ = ({}, DEFAULT_OOV_PROB) # noqa: F841
|
|
if clusters_loc:
|
|
with msg.loading("Reading clusters..."):
|
|
clusters = read_clusters(clusters_loc)
|
|
msg.good("Read clusters")
|
|
else:
|
|
clusters = {}
|
|
lex_attrs = []
|
|
sorted_probs = sorted(probs.items(), key=lambda item: item[1], reverse=True)
|
|
if len(sorted_probs):
|
|
for i, (word, prob) in tqdm(enumerate(sorted_probs)):
|
|
attrs = {"orth": word, "id": i, "prob": prob}
|
|
# 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:
|
|
attrs["cluster"] = int(clusters[word][::-1], 2)
|
|
else:
|
|
attrs["cluster"] = 0
|
|
lex_attrs.append(attrs)
|
|
return lex_attrs
|
|
|
|
|
|
def create_model(
|
|
lang: str,
|
|
lex_attrs: List[Dict[str, Any]],
|
|
name: Optional[str] = None,
|
|
base_model: Optional[Union[str, Path]] = None,
|
|
) -> Language:
|
|
if base_model:
|
|
nlp = load_model(base_model)
|
|
# keep the tokenizer but remove any existing pipeline components due to
|
|
# potentially conflicting vectors
|
|
for pipe in nlp.pipe_names:
|
|
nlp.remove_pipe(pipe)
|
|
else:
|
|
lang_class = get_lang_class(lang)
|
|
nlp = lang_class()
|
|
for lexeme in nlp.vocab:
|
|
lexeme.rank = OOV_RANK
|
|
for attrs in lex_attrs:
|
|
if "settings" in attrs:
|
|
continue
|
|
lexeme = nlp.vocab[attrs["orth"]]
|
|
lexeme.set_attrs(**attrs)
|
|
if len(nlp.vocab):
|
|
oov_prob = min(lex.prob for lex in nlp.vocab) - 1
|
|
else:
|
|
oov_prob = DEFAULT_OOV_PROB
|
|
nlp.vocab.cfg.update({"oov_prob": oov_prob})
|
|
if name:
|
|
nlp.meta["name"] = name
|
|
return nlp
|
|
|
|
|
|
def add_vectors(
|
|
msg: Printer,
|
|
nlp: Language,
|
|
vectors_loc: Optional[Path],
|
|
truncate_vectors: int,
|
|
prune_vectors: int,
|
|
name: Optional[str] = None,
|
|
) -> None:
|
|
vectors_loc = ensure_path(vectors_loc)
|
|
if vectors_loc and vectors_loc.parts[-1].endswith(".npz"):
|
|
nlp.vocab.vectors = Vectors(data=numpy.load(vectors_loc.open("rb")))
|
|
for lex in nlp.vocab:
|
|
if lex.rank and lex.rank != OOV_RANK:
|
|
nlp.vocab.vectors.add(lex.orth, row=lex.rank)
|
|
else:
|
|
if vectors_loc:
|
|
with msg.loading(f"Reading vectors from {vectors_loc}"):
|
|
vectors_data, vector_keys = read_vectors(
|
|
msg, vectors_loc, truncate_vectors
|
|
)
|
|
msg.good(f"Loaded vectors from {vectors_loc}")
|
|
else:
|
|
vectors_data, vector_keys = (None, None)
|
|
if vector_keys is not None:
|
|
for word in vector_keys:
|
|
if word not in nlp.vocab:
|
|
nlp.vocab[word]
|
|
if vectors_data is not None:
|
|
nlp.vocab.vectors = Vectors(data=vectors_data, keys=vector_keys)
|
|
if name is None:
|
|
# TODO: Is this correct? Does this matter?
|
|
nlp.vocab.vectors.name = f"{nlp.meta['lang']}_{nlp.meta['name']}.vectors"
|
|
else:
|
|
nlp.vocab.vectors.name = name
|
|
nlp.meta["vectors"]["name"] = nlp.vocab.vectors.name
|
|
if prune_vectors >= 1:
|
|
nlp.vocab.prune_vectors(prune_vectors)
|
|
|
|
|
|
def read_vectors(msg: Printer, vectors_loc: Path, truncate_vectors: int):
|
|
f = open_file(vectors_loc)
|
|
f = ensure_shape(f)
|
|
shape = tuple(int(size) for size in next(f).split())
|
|
if truncate_vectors >= 1:
|
|
shape = (truncate_vectors, shape[1])
|
|
vectors_data = numpy.zeros(shape=shape, dtype="f")
|
|
vectors_keys = []
|
|
for i, line in enumerate(tqdm(f)):
|
|
line = line.rstrip()
|
|
pieces = line.rsplit(" ", vectors_data.shape[1])
|
|
word = pieces.pop(0)
|
|
if len(pieces) != vectors_data.shape[1]:
|
|
msg.fail(Errors.E094.format(line_num=i, loc=vectors_loc), exits=1)
|
|
vectors_data[i] = numpy.asarray(pieces, dtype="f")
|
|
vectors_keys.append(word)
|
|
if i == truncate_vectors - 1:
|
|
break
|
|
return vectors_data, vectors_keys
|
|
|
|
|
|
def ensure_shape(lines):
|
|
"""Ensure that the first line of the data is the vectors shape.
|
|
|
|
If it's not, we read in the data and output the shape as the first result,
|
|
so that the reader doesn't have to deal with the problem.
|
|
"""
|
|
first_line = next(lines)
|
|
try:
|
|
shape = tuple(int(size) for size in first_line.split())
|
|
except ValueError:
|
|
shape = None
|
|
if shape is not None:
|
|
# All good, give the data
|
|
yield first_line
|
|
yield from lines
|
|
else:
|
|
# Figure out the shape, make it the first value, and then give the
|
|
# rest of the data.
|
|
width = len(first_line.split()) - 1
|
|
captured = [first_line] + list(lines)
|
|
length = len(captured)
|
|
yield f"{length} {width}"
|
|
yield from captured
|
|
|
|
|
|
def read_freqs(
|
|
freqs_loc: Path, max_length: int = 100, min_doc_freq: int = 5, min_freq: int = 50
|
|
):
|
|
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:
|
|
try:
|
|
word = literal_eval(key)
|
|
except SyntaxError:
|
|
# Take odd strings literally.
|
|
word = literal_eval(f"'{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: Path) -> dict:
|
|
clusters = {}
|
|
if ftfy is None:
|
|
warnings.warn(Warnings.W004)
|
|
with clusters_loc.open() as f:
|
|
for line in tqdm(f):
|
|
try:
|
|
cluster, word, freq = line.split()
|
|
if ftfy is not None:
|
|
word = ftfy.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
|