mirror of
https://github.com/explosion/spaCy.git
synced 2024-12-26 09:56:28 +03:00
897 lines
29 KiB
Python
897 lines
29 KiB
Python
import os
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import importlib
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import importlib.util
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import re
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from pathlib import Path
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import random
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from typing import List
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import thinc
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from thinc.api import NumpyOps, get_current_ops, Adam, require_gpu, Config
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import functools
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import itertools
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import numpy.random
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import srsly
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import catalogue
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import sys
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import warnings
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try:
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import cupy.random
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except ImportError:
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cupy = None
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try: # Python 3.8
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import importlib.metadata as importlib_metadata
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except ImportError:
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import importlib_metadata
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from .symbols import ORTH
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from .compat import cupy, CudaStream
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from .errors import Errors, Warnings
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from . import about
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_PRINT_ENV = False
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class registry(thinc.registry):
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languages = catalogue.create("spacy", "languages", entry_points=True)
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architectures = catalogue.create("spacy", "architectures", entry_points=True)
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lookups = catalogue.create("spacy", "lookups", entry_points=True)
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factories = catalogue.create("spacy", "factories", entry_points=True)
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displacy_colors = catalogue.create("spacy", "displacy_colors", entry_points=True)
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assets = catalogue.create("spacy", "assets", entry_points=True)
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# This is mostly used to get a list of all installed models in the current
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# environment. spaCy models packaged with `spacy package` will "advertise"
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# themselves via entry points.
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models = catalogue.create("spacy", "models", entry_points=True)
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def set_env_log(value):
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global _PRINT_ENV
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_PRINT_ENV = value
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def lang_class_is_loaded(lang):
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"""Check whether a Language class is already loaded. Language classes are
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loaded lazily, to avoid expensive setup code associated with the language
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data.
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lang (unicode): Two-letter language code, e.g. 'en'.
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RETURNS (bool): Whether a Language class has been loaded.
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"""
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return lang in registry.languages
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def get_lang_class(lang):
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"""Import and load a Language class.
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lang (unicode): Two-letter language code, e.g. 'en'.
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RETURNS (Language): Language class.
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"""
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# Check if language is registered / entry point is available
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if lang in registry.languages:
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return registry.languages.get(lang)
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else:
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try:
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module = importlib.import_module(f".lang.{lang}", "spacy")
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except ImportError as err:
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raise ImportError(Errors.E048.format(lang=lang, err=err))
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set_lang_class(lang, getattr(module, module.__all__[0]))
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return registry.languages.get(lang)
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def set_lang_class(name, cls):
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"""Set a custom Language class name that can be loaded via get_lang_class.
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name (unicode): Name of Language class.
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cls (Language): Language class.
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"""
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registry.languages.register(name, func=cls)
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def ensure_path(path):
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"""Ensure string is converted to a Path.
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path: Anything. If string, it's converted to Path.
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RETURNS: Path or original argument.
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"""
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if isinstance(path, str):
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return Path(path)
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else:
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return path
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def load_language_data(path):
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"""Load JSON language data using the given path as a base. If the provided
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path isn't present, will attempt to load a gzipped version before giving up.
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path (unicode / Path): The data to load.
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RETURNS: The loaded data.
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"""
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path = ensure_path(path)
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if path.exists():
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return srsly.read_json(path)
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path = path.with_suffix(path.suffix + ".gz")
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if path.exists():
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return srsly.read_gzip_json(path)
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raise ValueError(Errors.E160.format(path=path))
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def get_module_path(module):
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if not hasattr(module, "__module__"):
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raise ValueError(Errors.E169.format(module=repr(module)))
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return Path(sys.modules[module.__module__].__file__).parent
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def load_model(name, **overrides):
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"""Load a model from a package or data path.
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name (unicode): Package name or model path.
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**overrides: Specific overrides, like pipeline components to disable.
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RETURNS (Language): `Language` class with the loaded model.
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"""
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if isinstance(name, str): # name or string path
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if is_package(name): # installed as package
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return load_model_from_package(name, **overrides)
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if Path(name).exists(): # path to model data directory
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return load_model_from_path(Path(name), **overrides)
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elif hasattr(name, "exists"): # Path or Path-like to model data
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return load_model_from_path(name, **overrides)
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raise IOError(Errors.E050.format(name=name))
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def load_model_from_package(name, **overrides):
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"""Load a model from an installed package."""
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cls = importlib.import_module(name)
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return cls.load(**overrides)
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def load_model_from_path(model_path, meta=False, **overrides):
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"""Load a model from a data directory path. Creates Language class with
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pipeline from meta.json and then calls from_disk() with path."""
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if not meta:
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meta = get_model_meta(model_path)
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nlp_config = get_model_config(model_path)
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if nlp_config.get("nlp", None):
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return load_model_from_config(nlp_config["nlp"])
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# Support language factories registered via entry points (e.g. custom
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# language subclass) while keeping top-level language identifier "lang"
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lang = meta.get("lang_factory", meta["lang"])
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cls = get_lang_class(lang)
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nlp = cls(meta=meta, **overrides)
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pipeline = meta.get("pipeline", [])
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factories = meta.get("factories", {})
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disable = overrides.get("disable", [])
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if pipeline is True:
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pipeline = nlp.Defaults.pipe_names
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elif pipeline in (False, None):
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pipeline = []
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for name in pipeline:
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if name not in disable:
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config = meta.get("pipeline_args", {}).get(name, {})
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config.update(overrides)
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factory = factories.get(name, name)
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if nlp_config.get(name, None):
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model_config = nlp_config[name]["model"]
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config["model"] = model_config
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component = nlp.create_pipe(factory, config=config)
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nlp.add_pipe(component, name=name)
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return nlp.from_disk(model_path, exclude=disable)
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def load_model_from_config(nlp_config):
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if "name" in nlp_config:
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nlp = load_model(**nlp_config)
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elif "lang" in nlp_config:
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lang_class = get_lang_class(nlp_config["lang"])
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nlp = lang_class()
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else:
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raise ValueError(Errors.E993)
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if "pipeline" in nlp_config:
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for name, component_cfg in nlp_config["pipeline"].items():
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factory = component_cfg.pop("factory")
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component = nlp.create_pipe(factory, config=component_cfg)
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nlp.add_pipe(component, name=name)
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return nlp
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def load_model_from_init_py(init_file, **overrides):
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"""Helper function to use in the `load()` method of a model package's
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__init__.py.
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init_file (unicode): Path to model's __init__.py, i.e. `__file__`.
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**overrides: Specific overrides, like pipeline components to disable.
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RETURNS (Language): `Language` class with loaded model.
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"""
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model_path = Path(init_file).parent
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meta = get_model_meta(model_path)
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data_dir = f"{meta['lang']}_{meta['name']}-{meta['version']}"
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data_path = model_path / data_dir
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if not model_path.exists():
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raise IOError(Errors.E052.format(path=data_path))
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return load_model_from_path(data_path, meta, **overrides)
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def get_installed_models():
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"""List all model packages currently installed in the environment.
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RETURNS (list): The string names of the models.
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"""
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return list(registry.models.get_all().keys())
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def get_package_version(name):
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"""Get the version of an installed package. Typically used to get model
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package versions.
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name (unicode): The name of the installed Python package.
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RETURNS (unicode / None): The version or None if package not installed.
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"""
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try:
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return importlib_metadata.version(name)
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except importlib_metadata.PackageNotFoundError:
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return None
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def split_version(version):
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"""RETURNS (tuple): Two integers, the major and minor spaCy version."""
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pieces = version.split(".", 3)
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return int(pieces[0]), int(pieces[1])
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def is_compatible_model(meta):
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"""Check if a model is compatible with the current version of spaCy, based
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on its meta.json. We compare the version of spaCy the model was created with
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with the current version. If the minor version is different, it's considered
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incompatible.
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meta (dict): The model's meta.
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RETURNS (bool / None): Whether the model is compatible with the current
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spaCy or None if we don't have enough info.
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"""
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cur_v = about.__version__
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pkg_v = meta.get("spacy_version")
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if not pkg_v or not isinstance(pkg_v, str):
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return None
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# Handle spacy_version values like >=x,<y, just in case
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pkg_v = re.sub(r"[^0-9.]", "", pkg_v.split(",")[0])
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cur_major, cur_minor = split_version(cur_v)
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pkg_major, pkg_minor = split_version(pkg_v)
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if cur_major != pkg_major or cur_minor != pkg_minor:
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return False
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return True
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def load_config(path, create_objects=False):
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"""Load a Thinc-formatted config file, optionally filling in objects where
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the config references registry entries. See "Thinc config files" for details.
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path (unicode or Path): Path to the config file
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create_objects (bool): Whether to automatically create objects when the config
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references registry entries. Defaults to False.
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RETURNS (dict): The objects from the config file.
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"""
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config = thinc.config.Config().from_disk(path)
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if create_objects:
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return registry.make_from_config(config, validate=True)
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else:
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return config
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def load_config_from_str(string, create_objects=False):
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"""Load a Thinc-formatted config, optionally filling in objects where
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the config references registry entries. See "Thinc config files" for details.
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string (unicode or Path): Text contents of the config file.
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create_objects (bool): Whether to automatically create objects when the config
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references registry entries. Defaults to False.
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RETURNS (dict): The objects from the config file.
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"""
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config = thinc.config.Config().from_str(string)
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if create_objects:
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return registry.make_from_config(config, validate=True)
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else:
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return config
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def get_model_meta(path):
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"""Get model meta.json from a directory path and validate its contents.
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path (unicode or Path): Path to model directory.
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RETURNS (dict): The model's meta data.
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"""
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model_path = ensure_path(path)
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if not model_path.exists():
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raise IOError(Errors.E052.format(path=model_path))
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meta_path = model_path / "meta.json"
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if not meta_path.is_file():
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raise IOError(Errors.E053.format(path=meta_path, name="meta.json"))
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meta = srsly.read_json(meta_path)
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for setting in ["lang", "name", "version"]:
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if setting not in meta or not meta[setting]:
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raise ValueError(Errors.E054.format(setting=setting))
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return meta
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def get_model_config(path):
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"""Get the model's config from a directory path.
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path (unicode or Path): Path to model directory.
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RETURNS (Config): The model's config data.
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"""
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model_path = ensure_path(path)
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if not model_path.exists():
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raise IOError(Errors.E052.format(path=model_path))
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config_path = model_path / "config.cfg"
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# model directories are allowed not to have config files ?
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if not config_path.is_file():
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return Config({})
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# raise IOError(Errors.E053.format(path=config_path, name="config.cfg"))
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return Config().from_disk(config_path)
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def is_package(name):
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"""Check if string maps to a package installed via pip.
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name (unicode): Name of package.
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RETURNS (bool): True if installed package, False if not.
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"""
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import pkg_resources
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name = name.lower() # compare package name against lowercase name
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packages = pkg_resources.working_set.by_key.keys()
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for package in packages:
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if package.lower().replace("-", "_") == name:
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return True
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return False
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def get_package_path(name):
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"""Get the path to an installed package.
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name (unicode): Package name.
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RETURNS (Path): Path to installed package.
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"""
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name = name.lower() # use lowercase version to be safe
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# Here we're importing the module just to find it. This is worryingly
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# indirect, but it's otherwise very difficult to find the package.
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pkg = importlib.import_module(name)
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return Path(pkg.__file__).parent
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def is_in_jupyter():
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"""Check if user is running spaCy from a Jupyter notebook by detecting the
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IPython kernel. Mainly used for the displaCy visualizer.
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RETURNS (bool): True if in Jupyter, False if not.
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"""
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# https://stackoverflow.com/a/39662359/6400719
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try:
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shell = get_ipython().__class__.__name__
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if shell == "ZMQInteractiveShell":
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return True # Jupyter notebook or qtconsole
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except NameError:
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return False # Probably standard Python interpreter
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return False
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def get_component_name(component):
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if hasattr(component, "name"):
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return component.name
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if hasattr(component, "__name__"):
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return component.__name__
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if hasattr(component, "__class__") and hasattr(component.__class__, "__name__"):
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return component.__class__.__name__
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return repr(component)
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def get_cuda_stream(require=False, non_blocking=True):
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ops = get_current_ops()
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if CudaStream is None:
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return None
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elif isinstance(ops, NumpyOps):
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return None
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else:
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return CudaStream(non_blocking=non_blocking)
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def get_async(stream, numpy_array):
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if cupy is None:
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return numpy_array
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else:
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array = cupy.ndarray(numpy_array.shape, order="C", dtype=numpy_array.dtype)
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array.set(numpy_array, stream=stream)
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return array
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def eg2doc(example):
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"""Get a Doc object from an Example (or if it's a Doc, use it directly)"""
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# Put the import here to avoid circular import problems
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from .tokens.doc import Doc
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return example if isinstance(example, Doc) else example.doc
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def env_opt(name, default=None):
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if type(default) is float:
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type_convert = float
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else:
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type_convert = int
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if "SPACY_" + name.upper() in os.environ:
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value = type_convert(os.environ["SPACY_" + name.upper()])
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if _PRINT_ENV:
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print(name, "=", repr(value), "via", "$SPACY_" + name.upper())
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return value
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elif name in os.environ:
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value = type_convert(os.environ[name])
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if _PRINT_ENV:
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print(name, "=", repr(value), "via", "$" + name)
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return value
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else:
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if _PRINT_ENV:
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print(name, "=", repr(default), "by default")
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return default
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def read_regex(path):
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path = ensure_path(path)
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with path.open(encoding="utf8") as file_:
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entries = file_.read().split("\n")
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expression = "|".join(
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["^" + re.escape(piece) for piece in entries if piece.strip()]
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)
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return re.compile(expression)
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def compile_prefix_regex(entries):
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"""Compile a sequence of prefix rules into a regex object.
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entries (tuple): The prefix rules, e.g. spacy.lang.punctuation.TOKENIZER_PREFIXES.
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RETURNS (regex object): The regex object. to be used for Tokenizer.prefix_search.
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"""
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if "(" in entries:
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# Handle deprecated data
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expression = "|".join(
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["^" + re.escape(piece) for piece in entries if piece.strip()]
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)
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return re.compile(expression)
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else:
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expression = "|".join(["^" + piece for piece in entries if piece.strip()])
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return re.compile(expression)
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def compile_suffix_regex(entries):
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"""Compile a sequence of suffix rules into a regex object.
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entries (tuple): The suffix rules, e.g. spacy.lang.punctuation.TOKENIZER_SUFFIXES.
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RETURNS (regex object): The regex object. to be used for Tokenizer.suffix_search.
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"""
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expression = "|".join([piece + "$" for piece in entries if piece.strip()])
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return re.compile(expression)
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def compile_infix_regex(entries):
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"""Compile a sequence of infix rules into a regex object.
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entries (tuple): The infix rules, e.g. spacy.lang.punctuation.TOKENIZER_INFIXES.
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RETURNS (regex object): The regex object. to be used for Tokenizer.infix_finditer.
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"""
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expression = "|".join([piece for piece in entries if piece.strip()])
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return re.compile(expression)
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def add_lookups(default_func, *lookups):
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"""Extend an attribute function with special cases. If a word is in the
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lookups, the value is returned. Otherwise the previous function is used.
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default_func (callable): The default function to execute.
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*lookups (dict): Lookup dictionary mapping string to attribute value.
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RETURNS (callable): Lexical attribute getter.
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"""
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# This is implemented as functools.partial instead of a closure, to allow
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# pickle to work.
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return functools.partial(_get_attr_unless_lookup, default_func, lookups)
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def _get_attr_unless_lookup(default_func, lookups, string):
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for lookup in lookups:
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if string in lookup:
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return lookup[string]
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return default_func(string)
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def update_exc(base_exceptions, *addition_dicts):
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"""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], str) 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(examples, size, tuples=True, count_words=len, tolerance=0.2):
|
||
"""Create minibatches of roughly a given number of words. If any examples
|
||
are longer than the specified batch length, they will appear in a batch by
|
||
themselves."""
|
||
if isinstance(size, int):
|
||
size_ = itertools.repeat(size)
|
||
elif isinstance(size, List):
|
||
size_ = iter(size)
|
||
else:
|
||
size_ = size
|
||
examples = iter(examples)
|
||
oversize = []
|
||
while True:
|
||
batch_size = next(size_)
|
||
tol_size = batch_size * 0.2
|
||
batch = []
|
||
if oversize:
|
||
example = oversize.pop(0)
|
||
n_words = count_words(example.doc)
|
||
batch.append(example)
|
||
batch_size -= n_words
|
||
while batch_size >= 1:
|
||
try:
|
||
example = next(examples)
|
||
except StopIteration:
|
||
if batch:
|
||
yield batch
|
||
return
|
||
n_words = count_words(example.doc)
|
||
if n_words < (batch_size + tol_size):
|
||
batch_size -= n_words
|
||
batch.append(example)
|
||
else:
|
||
oversize.append(example)
|
||
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 = {}
|
||
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 import_file(name, loc):
|
||
"""Import module from a file. Used to load models from a directory.
|
||
|
||
name (unicode): Name of module to load.
|
||
loc (unicode / Path): Path to the file.
|
||
RETURNS: The loaded module.
|
||
"""
|
||
loc = str(loc)
|
||
spec = importlib.util.spec_from_file_location(name, str(loc))
|
||
module = importlib.util.module_from_spec(spec)
|
||
spec.loader.exec_module(module)
|
||
return module
|
||
|
||
|
||
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):
|
||
return require_gpu(gpu_id)
|
||
|
||
|
||
def fix_random_seed(seed=0):
|
||
random.seed(seed)
|
||
numpy.random.seed(seed)
|
||
if cupy is not None:
|
||
cupy.random.seed(seed)
|
||
|
||
|
||
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
|
||
|
||
|
||
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
|
||
|
||
|
||
def link_vectors_to_models(vocab):
|
||
vectors = vocab.vectors
|
||
if vectors.name is None:
|
||
vectors.name = VECTORS_KEY
|
||
if vectors.data.size != 0:
|
||
warnings.warn(Warnings.W020.format(shape=vectors.data.shape))
|
||
for word in vocab:
|
||
if word.orth in vectors.key2row:
|
||
word.rank = vectors.key2row[word.orth]
|
||
else:
|
||
word.rank = 0
|
||
|
||
|
||
VECTORS_KEY = "spacy_pretrained_vectors"
|
||
|
||
|
||
def create_default_optimizer():
|
||
learn_rate = env_opt("learn_rate", 0.001)
|
||
beta1 = env_opt("optimizer_B1", 0.9)
|
||
beta2 = env_opt("optimizer_B2", 0.999)
|
||
eps = env_opt("optimizer_eps", 1e-8)
|
||
L2 = env_opt("L2_penalty", 1e-6)
|
||
grad_clip = env_opt("grad_norm_clip", 10.0)
|
||
L2_is_weight_decay = env_opt("L2_is_weight_decay", False)
|
||
optimizer = Adam(
|
||
learn_rate,
|
||
L2=L2,
|
||
beta1=beta1,
|
||
beta2=beta2,
|
||
eps=eps,
|
||
grad_clip=grad_clip,
|
||
L2_is_weight_decay=L2_is_weight_decay,
|
||
)
|
||
return optimizer
|