mirror of https://github.com/explosion/spaCy.git
854 lines
28 KiB
Python
854 lines
28 KiB
Python
# coding: utf8
|
||
from __future__ import unicode_literals, print_function
|
||
|
||
import os
|
||
import importlib
|
||
import re
|
||
from pathlib import Path
|
||
import random
|
||
from collections import OrderedDict
|
||
from thinc.neural._classes.model import Model
|
||
from thinc.neural.ops import NumpyOps
|
||
import functools
|
||
import itertools
|
||
import numpy.random
|
||
import numpy
|
||
import srsly
|
||
import catalogue
|
||
import sys
|
||
import warnings
|
||
from . import about
|
||
|
||
try:
|
||
import jsonschema
|
||
except ImportError:
|
||
jsonschema = None
|
||
|
||
try:
|
||
import cupy.random
|
||
except ImportError:
|
||
cupy = None
|
||
|
||
from .symbols import ORTH
|
||
from .compat import cupy, CudaStream, path2str, basestring_, unicode_
|
||
from .compat import import_file
|
||
from .errors import Errors, Warnings
|
||
|
||
|
||
_data_path = Path(__file__).parent / "data"
|
||
_PRINT_ENV = False
|
||
OOV_RANK = numpy.iinfo(numpy.uint64).max
|
||
|
||
|
||
class registry(object):
|
||
languages = catalogue.create("spacy", "languages", entry_points=True)
|
||
architectures = catalogue.create("spacy", "architectures", entry_points=True)
|
||
lookups = catalogue.create("spacy", "lookups", entry_points=True)
|
||
factories = catalogue.create("spacy", "factories", entry_points=True)
|
||
displacy_colors = catalogue.create("spacy", "displacy_colors", entry_points=True)
|
||
|
||
|
||
def set_env_log(value):
|
||
global _PRINT_ENV
|
||
_PRINT_ENV = value
|
||
|
||
|
||
def lang_class_is_loaded(lang):
|
||
"""Check whether a Language class is already loaded. Language classes are
|
||
loaded lazily, to avoid expensive setup code associated with the language
|
||
data.
|
||
|
||
lang (unicode): Two-letter language code, e.g. 'en'.
|
||
RETURNS (bool): Whether a Language class has been loaded.
|
||
"""
|
||
return lang in registry.languages
|
||
|
||
|
||
def get_lang_class(lang):
|
||
"""Import and load a Language class.
|
||
|
||
lang (unicode): Two-letter language code, e.g. 'en'.
|
||
RETURNS (Language): Language class.
|
||
"""
|
||
# Check if language is registered / entry point is available
|
||
if lang in registry.languages:
|
||
return registry.languages.get(lang)
|
||
else:
|
||
try:
|
||
module = importlib.import_module(".lang.%s" % lang, "spacy")
|
||
except ImportError as err:
|
||
raise ImportError(Errors.E048.format(lang=lang, err=err))
|
||
set_lang_class(lang, getattr(module, module.__all__[0]))
|
||
return registry.languages.get(lang)
|
||
|
||
|
||
def set_lang_class(name, cls):
|
||
"""Set a custom Language class name that can be loaded via get_lang_class.
|
||
|
||
name (unicode): Name of Language class.
|
||
cls (Language): Language class.
|
||
"""
|
||
registry.languages.register(name, func=cls)
|
||
|
||
|
||
def get_data_path(require_exists=True):
|
||
"""Get path to spaCy data directory.
|
||
|
||
require_exists (bool): Only return path if it exists, otherwise None.
|
||
RETURNS (Path or None): Data path or None.
|
||
"""
|
||
if not require_exists:
|
||
return _data_path
|
||
else:
|
||
return _data_path if _data_path.exists() else None
|
||
|
||
|
||
def set_data_path(path):
|
||
"""Set path to spaCy data directory.
|
||
|
||
path (unicode or Path): Path to new data directory.
|
||
"""
|
||
global _data_path
|
||
_data_path = ensure_path(path)
|
||
|
||
|
||
def make_layer(arch_config):
|
||
arch_func = registry.architectures.get(arch_config["arch"])
|
||
return arch_func(arch_config["config"])
|
||
|
||
|
||
def ensure_path(path):
|
||
"""Ensure string is converted to a Path.
|
||
|
||
path: Anything. If string, it's converted to Path.
|
||
RETURNS: Path or original argument.
|
||
"""
|
||
if isinstance(path, basestring_):
|
||
return Path(path)
|
||
else:
|
||
return path
|
||
|
||
|
||
def load_language_data(path):
|
||
"""Load JSON language data using the given path as a base. If the provided
|
||
path isn't present, will attempt to load a gzipped version before giving up.
|
||
|
||
path (unicode / Path): The data to load.
|
||
RETURNS: The loaded data.
|
||
"""
|
||
path = ensure_path(path)
|
||
if path.exists():
|
||
return srsly.read_json(path)
|
||
path = path.with_suffix(path.suffix + ".gz")
|
||
if path.exists():
|
||
return srsly.read_gzip_json(path)
|
||
raise ValueError(Errors.E160.format(path=path2str(path)))
|
||
|
||
|
||
def get_module_path(module):
|
||
if not hasattr(module, "__module__"):
|
||
raise ValueError(Errors.E169.format(module=repr(module)))
|
||
return Path(sys.modules[module.__module__].__file__).parent
|
||
|
||
|
||
def load_model(name, **overrides):
|
||
"""Load a model from a shortcut link, package or data path.
|
||
|
||
name (unicode): Package name, shortcut link or model path.
|
||
**overrides: Specific overrides, like pipeline components to disable.
|
||
RETURNS (Language): `Language` class with the loaded model.
|
||
"""
|
||
data_path = get_data_path()
|
||
if not data_path or not data_path.exists():
|
||
raise IOError(Errors.E049.format(path=path2str(data_path)))
|
||
if isinstance(name, basestring_): # in data dir / shortcut
|
||
if name.startswith("blank:"): # shortcut for blank model
|
||
return get_lang_class(name.replace("blank:", ""))()
|
||
if name in set([d.name for d in data_path.iterdir()]):
|
||
return load_model_from_link(name, **overrides)
|
||
if is_package(name): # installed as package
|
||
return load_model_from_package(name, **overrides)
|
||
if Path(name).exists(): # path to model data directory
|
||
return load_model_from_path(Path(name), **overrides)
|
||
elif hasattr(name, "exists"): # Path or Path-like to model data
|
||
return load_model_from_path(name, **overrides)
|
||
raise IOError(Errors.E050.format(name=name))
|
||
|
||
|
||
def load_model_from_link(name, **overrides):
|
||
"""Load a model from a shortcut link, or directory in spaCy data path."""
|
||
path = get_data_path() / name / "__init__.py"
|
||
try:
|
||
cls = import_file(name, path)
|
||
except AttributeError:
|
||
raise IOError(Errors.E051.format(name=name))
|
||
return cls.load(**overrides)
|
||
|
||
|
||
def load_model_from_package(name, **overrides):
|
||
"""Load a model from an installed package."""
|
||
cls = importlib.import_module(name)
|
||
return cls.load(**overrides)
|
||
|
||
|
||
def load_model_from_path(model_path, meta=False, **overrides):
|
||
"""Load a model from a data directory path. Creates Language class with
|
||
pipeline from meta.json and then calls from_disk() with path."""
|
||
if not meta:
|
||
meta = get_model_meta(model_path)
|
||
# Support language factories registered via entry points (e.g. custom
|
||
# language subclass) while keeping top-level language identifier "lang"
|
||
lang = meta.get("lang_factory", meta["lang"])
|
||
cls = get_lang_class(lang)
|
||
nlp = cls(meta=meta, **overrides)
|
||
pipeline = meta.get("pipeline", [])
|
||
factories = meta.get("factories", {})
|
||
disable = overrides.get("disable", [])
|
||
if pipeline is True:
|
||
pipeline = nlp.Defaults.pipe_names
|
||
elif pipeline in (False, None):
|
||
pipeline = []
|
||
# skip "vocab" from overrides in component initialization since vocab is
|
||
# already configured from overrides when nlp is initialized above
|
||
if "vocab" in overrides:
|
||
del overrides["vocab"]
|
||
for name in pipeline:
|
||
if name not in disable:
|
||
config = meta.get("pipeline_args", {}).get(name, {})
|
||
config.update(overrides)
|
||
factory = factories.get(name, name)
|
||
component = nlp.create_pipe(factory, config=config)
|
||
nlp.add_pipe(component, name=name)
|
||
return nlp.from_disk(model_path, exclude=disable)
|
||
|
||
|
||
def load_model_from_init_py(init_file, **overrides):
|
||
"""Helper function to use in the `load()` method of a model package's
|
||
__init__.py.
|
||
|
||
init_file (unicode): Path to model's __init__.py, i.e. `__file__`.
|
||
**overrides: Specific overrides, like pipeline components to disable.
|
||
RETURNS (Language): `Language` class with loaded model.
|
||
"""
|
||
model_path = Path(init_file).parent
|
||
meta = get_model_meta(model_path)
|
||
data_dir = "%s_%s-%s" % (meta["lang"], meta["name"], meta["version"])
|
||
data_path = model_path / data_dir
|
||
if not model_path.exists():
|
||
raise IOError(Errors.E052.format(path=path2str(data_path)))
|
||
return load_model_from_path(data_path, meta, **overrides)
|
||
|
||
|
||
def get_model_meta(path):
|
||
"""Get model meta.json from a directory path and validate its contents.
|
||
|
||
path (unicode or Path): Path to model directory.
|
||
RETURNS (dict): The model's meta data.
|
||
"""
|
||
model_path = ensure_path(path)
|
||
if not model_path.exists():
|
||
raise IOError(Errors.E052.format(path=path2str(model_path)))
|
||
meta_path = model_path / "meta.json"
|
||
if not meta_path.is_file():
|
||
raise IOError(Errors.E053.format(path=meta_path))
|
||
meta = srsly.read_json(meta_path)
|
||
for setting in ["lang", "name", "version"]:
|
||
if setting not in meta or not meta[setting]:
|
||
raise ValueError(Errors.E054.format(setting=setting))
|
||
if "spacy_version" in meta:
|
||
about_major_minor = ".".join(about.__version__.split(".")[:2])
|
||
if not meta["spacy_version"].startswith(">=" + about_major_minor):
|
||
# try to simplify version requirements from model meta to vx.x
|
||
# for warning message
|
||
meta_spacy_version = "v" + ".".join(
|
||
meta["spacy_version"].replace(">=", "").split(".")[:2]
|
||
)
|
||
# if the format is unexpected, supply the full version
|
||
if not re.match(r"v\d+\.\d+", meta_spacy_version):
|
||
meta_spacy_version = meta["spacy_version"]
|
||
warn_msg = Warnings.W031.format(
|
||
model=meta["lang"] + "_" + meta["name"],
|
||
model_version=meta["version"],
|
||
version=meta_spacy_version,
|
||
current=about.__version__,
|
||
)
|
||
warnings.warn(warn_msg)
|
||
else:
|
||
warn_msg = Warnings.W032.format(
|
||
model=meta["lang"] + "_" + meta["name"],
|
||
model_version=meta["version"],
|
||
current=about.__version__,
|
||
)
|
||
warnings.warn(warn_msg)
|
||
return meta
|
||
|
||
|
||
def is_package(name):
|
||
"""Check if string maps to a package installed via pip.
|
||
|
||
name (unicode): Name of package.
|
||
RETURNS (bool): True if installed package, False if not.
|
||
"""
|
||
import pkg_resources
|
||
|
||
name = name.lower() # compare package name against lowercase name
|
||
packages = pkg_resources.working_set.by_key.keys()
|
||
for package in packages:
|
||
if package.lower().replace("-", "_") == name:
|
||
return True
|
||
return False
|
||
|
||
|
||
def get_package_path(name):
|
||
"""Get the path to an installed package.
|
||
|
||
name (unicode): Package name.
|
||
RETURNS (Path): Path to installed package.
|
||
"""
|
||
name = name.lower() # use lowercase version to be safe
|
||
# Here we're importing the module just to find it. This is worryingly
|
||
# indirect, but it's otherwise very difficult to find the package.
|
||
pkg = importlib.import_module(name)
|
||
return Path(pkg.__file__).parent
|
||
|
||
|
||
def is_in_jupyter():
|
||
"""Check if user is running spaCy from a Jupyter notebook by detecting the
|
||
IPython kernel. Mainly used for the displaCy visualizer.
|
||
RETURNS (bool): True if in Jupyter, False if not.
|
||
"""
|
||
# https://stackoverflow.com/a/39662359/6400719
|
||
try:
|
||
shell = get_ipython().__class__.__name__
|
||
if shell == "ZMQInteractiveShell":
|
||
return True # Jupyter notebook or qtconsole
|
||
except NameError:
|
||
return False # Probably standard Python interpreter
|
||
return False
|
||
|
||
|
||
def get_component_name(component):
|
||
if hasattr(component, "name"):
|
||
return component.name
|
||
if hasattr(component, "__name__"):
|
||
return component.__name__
|
||
if hasattr(component, "__class__") and hasattr(component.__class__, "__name__"):
|
||
return component.__class__.__name__
|
||
return repr(component)
|
||
|
||
|
||
def get_cuda_stream(require=False, non_blocking=True):
|
||
if CudaStream is None:
|
||
return None
|
||
elif isinstance(Model.ops, NumpyOps):
|
||
return None
|
||
else:
|
||
return CudaStream(non_blocking=non_blocking)
|
||
|
||
|
||
def get_async(stream, numpy_array):
|
||
if cupy is None:
|
||
return numpy_array
|
||
else:
|
||
array = cupy.ndarray(numpy_array.shape, order="C", dtype=numpy_array.dtype)
|
||
array.set(numpy_array, stream=stream)
|
||
return array
|
||
|
||
|
||
def env_opt(name, default=None):
|
||
if type(default) is float:
|
||
type_convert = float
|
||
else:
|
||
type_convert = int
|
||
if "SPACY_" + name.upper() in os.environ:
|
||
value = type_convert(os.environ["SPACY_" + name.upper()])
|
||
if _PRINT_ENV:
|
||
print(name, "=", repr(value), "via", "$SPACY_" + name.upper())
|
||
return value
|
||
elif name in os.environ:
|
||
value = type_convert(os.environ[name])
|
||
if _PRINT_ENV:
|
||
print(name, "=", repr(value), "via", "$" + name)
|
||
return value
|
||
else:
|
||
if _PRINT_ENV:
|
||
print(name, "=", repr(default), "by default")
|
||
return default
|
||
|
||
|
||
def read_regex(path):
|
||
path = ensure_path(path)
|
||
with path.open(encoding="utf8") as file_:
|
||
entries = file_.read().split("\n")
|
||
expression = "|".join(
|
||
["^" + re.escape(piece) for piece in entries if piece.strip()]
|
||
)
|
||
return re.compile(expression)
|
||
|
||
|
||
def compile_prefix_regex(entries):
|
||
"""Compile a sequence of prefix rules into a regex object.
|
||
|
||
entries (tuple): The prefix rules, e.g. spacy.lang.punctuation.TOKENIZER_PREFIXES.
|
||
RETURNS (regex object): The regex object. to be used for Tokenizer.prefix_search.
|
||
"""
|
||
if "(" in entries:
|
||
# Handle deprecated data
|
||
expression = "|".join(
|
||
["^" + re.escape(piece) for piece in entries if piece.strip()]
|
||
)
|
||
return re.compile(expression)
|
||
else:
|
||
expression = "|".join(["^" + piece for piece in entries if piece.strip()])
|
||
return re.compile(expression)
|
||
|
||
|
||
def compile_suffix_regex(entries):
|
||
"""Compile a sequence of suffix rules into a regex object.
|
||
|
||
entries (tuple): The suffix rules, e.g. spacy.lang.punctuation.TOKENIZER_SUFFIXES.
|
||
RETURNS (regex object): The regex object. to be used for Tokenizer.suffix_search.
|
||
"""
|
||
expression = "|".join([piece + "$" for piece in entries if piece.strip()])
|
||
return re.compile(expression)
|
||
|
||
|
||
def compile_infix_regex(entries):
|
||
"""Compile a sequence of infix rules into a regex object.
|
||
|
||
entries (tuple): The infix rules, e.g. spacy.lang.punctuation.TOKENIZER_INFIXES.
|
||
RETURNS (regex object): The regex object. to be used for Tokenizer.infix_finditer.
|
||
"""
|
||
expression = "|".join([piece for piece in entries if piece.strip()])
|
||
return re.compile(expression)
|
||
|
||
|
||
def add_lookups(default_func, *lookups):
|
||
"""Extend an attribute function with special cases. If a word is in the
|
||
lookups, the value is returned. Otherwise the previous function is used.
|
||
|
||
default_func (callable): The default function to execute.
|
||
*lookups (dict): Lookup dictionary mapping string to attribute value.
|
||
RETURNS (callable): Lexical attribute getter.
|
||
"""
|
||
# This is implemented as functools.partial instead of a closure, to allow
|
||
# pickle to work.
|
||
return functools.partial(_get_attr_unless_lookup, default_func, lookups)
|
||
|
||
|
||
def _get_attr_unless_lookup(default_func, lookups, string):
|
||
for lookup in lookups:
|
||
if string in lookup:
|
||
return lookup[string]
|
||
return default_func(string)
|
||
|
||
|
||
def update_exc(base_exceptions, *addition_dicts):
|
||
"""Update and validate tokenizer exceptions. Will overwrite exceptions.
|
||
|
||
base_exceptions (dict): Base exceptions.
|
||
*addition_dicts (dict): Exceptions to add to the base dict, in order.
|
||
RETURNS (dict): Combined tokenizer exceptions.
|
||
"""
|
||
exc = dict(base_exceptions)
|
||
for additions in addition_dicts:
|
||
for orth, token_attrs in additions.items():
|
||
if not all(isinstance(attr[ORTH], unicode_) for attr in token_attrs):
|
||
raise ValueError(Errors.E055.format(key=orth, orths=token_attrs))
|
||
described_orth = "".join(attr[ORTH] for attr in token_attrs)
|
||
if orth != described_orth:
|
||
raise ValueError(Errors.E056.format(key=orth, orths=described_orth))
|
||
exc.update(additions)
|
||
exc = expand_exc(exc, "'", "’")
|
||
return exc
|
||
|
||
|
||
def expand_exc(excs, search, replace):
|
||
"""Find string in tokenizer exceptions, duplicate entry and replace string.
|
||
For example, to add additional versions with typographic apostrophes.
|
||
|
||
excs (dict): Tokenizer exceptions.
|
||
search (unicode): String to find and replace.
|
||
replace (unicode): Replacement.
|
||
RETURNS (dict): Combined tokenizer exceptions.
|
||
"""
|
||
|
||
def _fix_token(token, search, replace):
|
||
fixed = dict(token)
|
||
fixed[ORTH] = fixed[ORTH].replace(search, replace)
|
||
return fixed
|
||
|
||
new_excs = dict(excs)
|
||
for token_string, tokens in excs.items():
|
||
if search in token_string:
|
||
new_key = token_string.replace(search, replace)
|
||
new_value = [_fix_token(t, search, replace) for t in tokens]
|
||
new_excs[new_key] = new_value
|
||
return new_excs
|
||
|
||
|
||
def normalize_slice(length, start, stop, step=None):
|
||
if not (step is None or step == 1):
|
||
raise ValueError(Errors.E057)
|
||
if start is None:
|
||
start = 0
|
||
elif start < 0:
|
||
start += length
|
||
start = min(length, max(0, start))
|
||
if stop is None:
|
||
stop = length
|
||
elif stop < 0:
|
||
stop += length
|
||
stop = min(length, max(start, stop))
|
||
return start, stop
|
||
|
||
|
||
def minibatch(items, size=8):
|
||
"""Iterate over batches of items. `size` may be an iterator,
|
||
so that batch-size can vary on each step.
|
||
"""
|
||
if isinstance(size, int):
|
||
size_ = itertools.repeat(size)
|
||
else:
|
||
size_ = size
|
||
items = iter(items)
|
||
while True:
|
||
batch_size = next(size_)
|
||
batch = list(itertools.islice(items, int(batch_size)))
|
||
if len(batch) == 0:
|
||
break
|
||
yield list(batch)
|
||
|
||
|
||
def compounding(start, stop, compound):
|
||
"""Yield an infinite series of compounding values. Each time the
|
||
generator is called, a value is produced by multiplying the previous
|
||
value by the compound rate.
|
||
|
||
EXAMPLE:
|
||
>>> sizes = compounding(1., 10., 1.5)
|
||
>>> assert next(sizes) == 1.
|
||
>>> assert next(sizes) == 1 * 1.5
|
||
>>> assert next(sizes) == 1.5 * 1.5
|
||
"""
|
||
|
||
def clip(value):
|
||
return max(value, stop) if (start > stop) else min(value, stop)
|
||
|
||
curr = float(start)
|
||
while True:
|
||
yield clip(curr)
|
||
curr *= compound
|
||
|
||
|
||
def stepping(start, stop, steps):
|
||
"""Yield an infinite series of values that step from a start value to a
|
||
final value over some number of steps. Each step is (stop-start)/steps.
|
||
|
||
After the final value is reached, the generator continues yielding that
|
||
value.
|
||
|
||
EXAMPLE:
|
||
>>> sizes = stepping(1., 200., 100)
|
||
>>> assert next(sizes) == 1.
|
||
>>> assert next(sizes) == 1 * (200.-1.) / 100
|
||
>>> assert next(sizes) == 1 + (200.-1.) / 100 + (200.-1.) / 100
|
||
"""
|
||
|
||
def clip(value):
|
||
return max(value, stop) if (start > stop) else min(value, stop)
|
||
|
||
curr = float(start)
|
||
while True:
|
||
yield clip(curr)
|
||
curr += (stop - start) / steps
|
||
|
||
|
||
def decaying(start, stop, decay):
|
||
"""Yield an infinite series of linearly decaying values."""
|
||
|
||
curr = float(start)
|
||
while True:
|
||
yield max(curr, stop)
|
||
curr -= decay
|
||
|
||
|
||
def minibatch_by_words(items, size, tuples=True, count_words=len):
|
||
"""Create minibatches of a given number of words."""
|
||
if isinstance(size, int):
|
||
size_ = itertools.repeat(size)
|
||
else:
|
||
size_ = size
|
||
items = iter(items)
|
||
while True:
|
||
batch_size = next(size_)
|
||
batch = []
|
||
while batch_size >= 0:
|
||
try:
|
||
if tuples:
|
||
doc, gold = next(items)
|
||
else:
|
||
doc = next(items)
|
||
except StopIteration:
|
||
if batch:
|
||
yield batch
|
||
return
|
||
batch_size -= count_words(doc)
|
||
if tuples:
|
||
batch.append((doc, gold))
|
||
else:
|
||
batch.append(doc)
|
||
if batch:
|
||
yield batch
|
||
|
||
|
||
def itershuffle(iterable, bufsize=1000):
|
||
"""Shuffle an iterator. This works by holding `bufsize` items back
|
||
and yielding them sometime later. Obviously, this is not unbiased –
|
||
but should be good enough for batching. Larger bufsize means less bias.
|
||
From https://gist.github.com/andres-erbsen/1307752
|
||
|
||
iterable (iterable): Iterator to shuffle.
|
||
bufsize (int): Items to hold back.
|
||
YIELDS (iterable): The shuffled iterator.
|
||
"""
|
||
iterable = iter(iterable)
|
||
buf = []
|
||
try:
|
||
while True:
|
||
for i in range(random.randint(1, bufsize - len(buf))):
|
||
buf.append(next(iterable))
|
||
random.shuffle(buf)
|
||
for i in range(random.randint(1, bufsize)):
|
||
if buf:
|
||
yield buf.pop()
|
||
else:
|
||
break
|
||
except StopIteration:
|
||
random.shuffle(buf)
|
||
while buf:
|
||
yield buf.pop()
|
||
raise StopIteration
|
||
|
||
|
||
def filter_spans(spans):
|
||
"""Filter a sequence of spans and remove duplicates or overlaps. Useful for
|
||
creating named entities (where one token can only be part of one entity) or
|
||
when merging spans with `Retokenizer.merge`. When spans overlap, the (first)
|
||
longest span is preferred over shorter spans.
|
||
|
||
spans (iterable): The spans to filter.
|
||
RETURNS (list): The filtered spans.
|
||
"""
|
||
get_sort_key = lambda span: (span.end - span.start, -span.start)
|
||
sorted_spans = sorted(spans, key=get_sort_key, reverse=True)
|
||
result = []
|
||
seen_tokens = set()
|
||
for span in sorted_spans:
|
||
# Check for end - 1 here because boundaries are inclusive
|
||
if span.start not in seen_tokens and span.end - 1 not in seen_tokens:
|
||
result.append(span)
|
||
seen_tokens.update(range(span.start, span.end))
|
||
result = sorted(result, key=lambda span: span.start)
|
||
return result
|
||
|
||
|
||
def to_bytes(getters, exclude):
|
||
serialized = OrderedDict()
|
||
for key, getter in getters.items():
|
||
# Split to support file names like meta.json
|
||
if key.split(".")[0] not in exclude:
|
||
serialized[key] = getter()
|
||
return srsly.msgpack_dumps(serialized)
|
||
|
||
|
||
def from_bytes(bytes_data, setters, exclude):
|
||
msg = srsly.msgpack_loads(bytes_data)
|
||
for key, setter in setters.items():
|
||
# Split to support file names like meta.json
|
||
if key.split(".")[0] not in exclude and key in msg:
|
||
setter(msg[key])
|
||
return msg
|
||
|
||
|
||
def to_disk(path, writers, exclude):
|
||
path = ensure_path(path)
|
||
if not path.exists():
|
||
path.mkdir()
|
||
for key, writer in writers.items():
|
||
# Split to support file names like meta.json
|
||
if key.split(".")[0] not in exclude:
|
||
writer(path / key)
|
||
return path
|
||
|
||
|
||
def from_disk(path, readers, exclude):
|
||
path = ensure_path(path)
|
||
for key, reader in readers.items():
|
||
# Split to support file names like meta.json
|
||
if key.split(".")[0] not in exclude:
|
||
reader(path / key)
|
||
return path
|
||
|
||
|
||
def minify_html(html):
|
||
"""Perform a template-specific, rudimentary HTML minification for displaCy.
|
||
Disclaimer: NOT a general-purpose solution, only removes indentation and
|
||
newlines.
|
||
|
||
html (unicode): Markup to minify.
|
||
RETURNS (unicode): "Minified" HTML.
|
||
"""
|
||
return html.strip().replace(" ", "").replace("\n", "")
|
||
|
||
|
||
def escape_html(text):
|
||
"""Replace <, >, &, " with their HTML encoded representation. Intended to
|
||
prevent HTML errors in rendered displaCy markup.
|
||
|
||
text (unicode): The original text.
|
||
RETURNS (unicode): Equivalent text to be safely used within HTML.
|
||
"""
|
||
text = text.replace("&", "&")
|
||
text = text.replace("<", "<")
|
||
text = text.replace(">", ">")
|
||
text = text.replace('"', """)
|
||
return text
|
||
|
||
|
||
def use_gpu(gpu_id):
|
||
try:
|
||
import cupy.cuda.device
|
||
except ImportError:
|
||
return None
|
||
from thinc.neural.ops import CupyOps
|
||
|
||
device = cupy.cuda.device.Device(gpu_id)
|
||
device.use()
|
||
Model.ops = CupyOps()
|
||
Model.Ops = CupyOps
|
||
return device
|
||
|
||
|
||
def fix_random_seed(seed=0):
|
||
random.seed(seed)
|
||
numpy.random.seed(seed)
|
||
if cupy is not None:
|
||
cupy.random.seed(seed)
|
||
|
||
|
||
def get_json_validator(schema):
|
||
# We're using a helper function here to make it easier to change the
|
||
# validator that's used (e.g. different draft implementation), without
|
||
# having to change it all across the codebase.
|
||
# TODO: replace with (stable) Draft6Validator, if available
|
||
if jsonschema is None:
|
||
raise ValueError(Errors.E136)
|
||
return jsonschema.Draft4Validator(schema)
|
||
|
||
|
||
def validate_schema(schema):
|
||
"""Validate a given schema. This just checks if the schema itself is valid."""
|
||
validator = get_json_validator(schema)
|
||
validator.check_schema(schema)
|
||
|
||
|
||
def validate_json(data, validator):
|
||
"""Validate data against a given JSON schema (see https://json-schema.org).
|
||
|
||
data: JSON-serializable data to validate.
|
||
validator (jsonschema.DraftXValidator): The validator.
|
||
RETURNS (list): A list of error messages, if available.
|
||
"""
|
||
errors = []
|
||
for err in sorted(validator.iter_errors(data), key=lambda e: e.path):
|
||
if err.path:
|
||
err_path = "[{}]".format(" -> ".join([str(p) for p in err.path]))
|
||
else:
|
||
err_path = ""
|
||
msg = err.message + " " + err_path
|
||
if err.context: # Error has suberrors, e.g. if schema uses anyOf
|
||
suberrs = [" - {}".format(suberr.message) for suberr in err.context]
|
||
msg += ":\n{}".format("".join(suberrs))
|
||
errors.append(msg)
|
||
return errors
|
||
|
||
|
||
def get_serialization_exclude(serializers, exclude, kwargs):
|
||
"""Helper function to validate serialization args and manage transition from
|
||
keyword arguments (pre v2.1) to exclude argument.
|
||
"""
|
||
exclude = list(exclude)
|
||
# Split to support file names like meta.json
|
||
options = [name.split(".")[0] for name in serializers]
|
||
for key, value in kwargs.items():
|
||
if key in ("vocab",) and value is False:
|
||
warnings.warn(Warnings.W015.format(arg=key), DeprecationWarning)
|
||
exclude.append(key)
|
||
elif key.split(".")[0] in options:
|
||
raise ValueError(Errors.E128.format(arg=key))
|
||
# TODO: user warning?
|
||
return exclude
|
||
|
||
|
||
def get_words_and_spaces(words, text):
|
||
if "".join("".join(words).split()) != "".join(text.split()):
|
||
raise ValueError(Errors.E194.format(text=text, words=words))
|
||
text_words = []
|
||
text_spaces = []
|
||
text_pos = 0
|
||
# normalize words to remove all whitespace tokens
|
||
norm_words = [word for word in words if not word.isspace()]
|
||
# align words with text
|
||
for word in norm_words:
|
||
try:
|
||
word_start = text[text_pos:].index(word)
|
||
except ValueError:
|
||
raise ValueError(Errors.E194.format(text=text, words=words))
|
||
if word_start > 0:
|
||
text_words.append(text[text_pos : text_pos + word_start])
|
||
text_spaces.append(False)
|
||
text_pos += word_start
|
||
text_words.append(word)
|
||
text_spaces.append(False)
|
||
text_pos += len(word)
|
||
if text_pos < len(text) and text[text_pos] == " ":
|
||
text_spaces[-1] = True
|
||
text_pos += 1
|
||
if text_pos < len(text):
|
||
text_words.append(text[text_pos:])
|
||
text_spaces.append(False)
|
||
return (text_words, text_spaces)
|
||
|
||
|
||
class SimpleFrozenDict(dict):
|
||
"""Simplified implementation of a frozen dict, mainly used as default
|
||
function or method argument (for arguments that should default to empty
|
||
dictionary). Will raise an error if user or spaCy attempts to add to dict.
|
||
"""
|
||
|
||
def __setitem__(self, key, value):
|
||
raise NotImplementedError(Errors.E095)
|
||
|
||
def pop(self, key, default=None):
|
||
raise NotImplementedError(Errors.E095)
|
||
|
||
def update(self, other):
|
||
raise NotImplementedError(Errors.E095)
|
||
|
||
|
||
class DummyTokenizer(object):
|
||
# add dummy methods for to_bytes, from_bytes, to_disk and from_disk to
|
||
# allow serialization (see #1557)
|
||
def to_bytes(self, **kwargs):
|
||
return b""
|
||
|
||
def from_bytes(self, _bytes_data, **kwargs):
|
||
return self
|
||
|
||
def to_disk(self, _path, **kwargs):
|
||
return None
|
||
|
||
def from_disk(self, _path, **kwargs):
|
||
return self
|