explosion/spaCy
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Merge branch 'master' into spacy.io

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Adriane Boyd 2023-10-02 10:43:52 +02:00
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12
.github/workflows/tests.yml vendored
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@ -45,6 +45,12 @@ jobs:
run: |
python -m pip install flake8==5.0.4
python -m flake8 spacy --count --select=E901,E999,F821,F822,F823,W605 --show-source --statistics
- name: cython-lint
run: |
python -m pip install cython-lint -c requirements.txt
# E501: line too log, W291: trailing whitespace, E266: too many leading '#' for block comment
cython-lint spacy --ignore E501,W291,E266
tests:
name: Test
needs: Validate
@ -52,10 +58,8 @@ jobs:
fail-fast: true
matrix:
os: [ubuntu-latest, windows-latest, macos-latest]
python_version: ["3.11"]
python_version: ["3.11", "3.12.0-rc.2"]
include:
- os: ubuntu-20.04
python_version: "3.6"
- os: windows-latest
python_version: "3.7"
- os: macos-latest
@ -89,7 +93,7 @@ jobs:
- name: Run mypy
run: |
python -m mypy spacy
if: matrix.python_version != '3.6'
if: matrix.python_version != '3.7'
- name: Delete source directory and .egg-info
run: |

74
README.md
View File

@ -6,23 +6,20 @@ spaCy is a library for **advanced Natural Language Processing** in Python and
Cython. It's built on the very latest research, and was designed from day one to
be used in real products.
spaCy comes with
[pretrained pipelines](https://spacy.io/models) and
currently supports tokenization and training for **70+ languages**. It features
state-of-the-art speed and **neural network models** for tagging,
parsing, **named entity recognition**, **text classification** and more,
multi-task learning with pretrained **transformers** like BERT, as well as a
spaCy comes with [pretrained pipelines](https://spacy.io/models) and currently
supports tokenization and training for **70+ languages**. It features
state-of-the-art speed and **neural network models** for tagging, parsing,
**named entity recognition**, **text classification** and more, multi-task
learning with pretrained **transformers** like BERT, as well as a
production-ready [**training system**](https://spacy.io/usage/training) and easy
model packaging, deployment and workflow management. spaCy is commercial
open-source software, released under the [MIT license](https://github.com/explosion/spaCy/blob/master/LICENSE).
open-source software, released under the
[MIT license](https://github.com/explosion/spaCy/blob/master/LICENSE).
💥 **We'd love to hear more about your experience with spaCy!**
[Fill out our survey here.](https://form.typeform.com/to/aMel9q9f)
💫 **Version 3.5 out now!**
💫 **Version 3.7 out now!**
[Check out the release notes here.](https://github.com/explosion/spaCy/releases)
[![Azure Pipelines](https://img.shields.io/azure-devops/build/explosion-ai/public/8/master.svg?logo=azure-pipelines&style=flat-square&label=build)](https://dev.azure.com/explosion-ai/public/_build?definitionId=8)
[![tests](https://github.com/explosion/spaCy/actions/workflows/tests.yml/badge.svg)](https://github.com/explosion/spaCy/actions/workflows/tests.yml)
[![Current Release Version](https://img.shields.io/github/release/explosion/spacy.svg?style=flat-square&logo=github)](https://github.com/explosion/spaCy/releases)
[![pypi Version](https://img.shields.io/pypi/v/spacy.svg?style=flat-square&logo=pypi&logoColor=white)](https://pypi.org/project/spacy/)
[![conda Version](https://img.shields.io/conda/vn/conda-forge/spacy.svg?style=flat-square&logo=conda-forge&logoColor=white)](https://anaconda.org/conda-forge/spacy)
@ -35,22 +32,22 @@ open-source software, released under the [MIT license](https://github.com/explos
## 📖 Documentation
| Documentation | |
| ----------------------------- | ---------------------------------------------------------------------- |
| ⭐️ **[spaCy 101]** | New to spaCy? Here's everything you need to know! |
| 📚 **[Usage Guides]** | How to use spaCy and its features. |
| 🚀 **[New in v3.0]** | New features, backwards incompatibilities and migration guide. |
| 🪐 **[Project Templates]** | End-to-end workflows you can clone, modify and run. |
| 🎛 **[API Reference]** | The detailed reference for spaCy's API. |
| 📦 **[Models]** | Download trained pipelines for spaCy. |
| 🌌 **[Universe]** | Plugins, extensions, demos and books from the spaCy ecosystem. |
| ⚙️ **[spaCy VS Code Extension]** | Additional tooling and features for working with spaCy's config files. |
| 👩‍🏫 **[Online Course]** | Learn spaCy in this free and interactive online course. |
| 📺 **[Videos]** | Our YouTube channel with video tutorials, talks and more. |
| 🛠 **[Changelog]** | Changes and version history. |
| 💝 **[Contribute]** | How to contribute to the spaCy project and code base. |
| <a href="https://explosion.ai/spacy-tailored-pipelines"><img src="https://user-images.githubusercontent.com/13643239/152853098-1c761611-ccb0-4ec6-9066-b234552831fe.png" width="125" alt="spaCy Tailored Pipelines"/></a> | Get a custom spaCy pipeline, tailor-made for your NLP problem by spaCy's core developers. Streamlined, production-ready, predictable and maintainable. Start by completing our 5-minute questionnaire to tell us what you need and we'll be in touch! **[Learn more &rarr;](https://explosion.ai/spacy-tailored-pipelines)** |
| <a href="https://explosion.ai/spacy-tailored-analysis"><img src="https://user-images.githubusercontent.com/1019791/206151300-b00cd189-e503-4797-aa1e-1bb6344062c5.png" width="125" alt="spaCy Tailored Pipelines"/></a> | Bespoke advice for problem solving, strategy and analysis for applied NLP projects. Services include data strategy, code reviews, pipeline design and annotation coaching. Curious? Fill in our 5-minute questionnaire to tell us what you need and we'll be in touch! **[Learn more &rarr;](https://explosion.ai/spacy-tailored-analysis)** |
| Documentation | |
| ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| ⭐️ **[spaCy 101]** | New to spaCy? Here's everything you need to know! |
| 📚 **[Usage Guides]** | How to use spaCy and its features. |
| 🚀 **[New in v3.0]** | New features, backwards incompatibilities and migration guide. |
| 🪐 **[Project Templates]** | End-to-end workflows you can clone, modify and run. |
| 🎛 **[API Reference]** | The detailed reference for spaCy's API. |
| 📦 **[Models]** | Download trained pipelines for spaCy. |
| 🌌 **[Universe]** | Plugins, extensions, demos and books from the spaCy ecosystem. |
| ⚙️ **[spaCy VS Code Extension]** | Additional tooling and features for working with spaCy's config files. |
| 👩‍🏫 **[Online Course]** | Learn spaCy in this free and interactive online course. |
| 📺 **[Videos]** | Our YouTube channel with video tutorials, talks and more. |
| 🛠 **[Changelog]** | Changes and version history. |
| 💝 **[Contribute]** | How to contribute to the spaCy project and code base. |
| <a href="https://explosion.ai/spacy-tailored-pipelines"><img src="https://user-images.githubusercontent.com/13643239/152853098-1c761611-ccb0-4ec6-9066-b234552831fe.png" width="125" alt="spaCy Tailored Pipelines"/></a> | Get a custom spaCy pipeline, tailor-made for your NLP problem by spaCy's core developers. Streamlined, production-ready, predictable and maintainable. Start by completing our 5-minute questionnaire to tell us what you need and we'll be in touch! **[Learn more &rarr;](https://explosion.ai/spacy-tailored-pipelines)** |
| <a href="https://explosion.ai/spacy-tailored-analysis"><img src="https://user-images.githubusercontent.com/1019791/206151300-b00cd189-e503-4797-aa1e-1bb6344062c5.png" width="125" alt="spaCy Tailored Pipelines"/></a> | Bespoke advice for problem solving, strategy and analysis for applied NLP projects. Services include data strategy, code reviews, pipeline design and annotation coaching. Curious? Fill in our 5-minute questionnaire to tell us what you need and we'll be in touch! **[Learn more &rarr;](https://explosion.ai/spacy-tailored-analysis)** |
[spacy 101]: https://spacy.io/usage/spacy-101
[new in v3.0]: https://spacy.io/usage/v3
@ -58,7 +55,7 @@ open-source software, released under the [MIT license](https://github.com/explos
[api reference]: https://spacy.io/api/
[models]: https://spacy.io/models
[universe]: https://spacy.io/universe
[spaCy VS Code Extension]: https://github.com/explosion/spacy-vscode
[spacy vs code extension]: https://github.com/explosion/spacy-vscode
[videos]: https://www.youtube.com/c/ExplosionAI
[online course]: https://course.spacy.io
[project templates]: https://github.com/explosion/projects
@ -92,7 +89,9 @@ more people can benefit from it.
- State-of-the-art speed
- Production-ready **training system**
- Linguistically-motivated **tokenization**
- Components for named **entity recognition**, part-of-speech-tagging, dependency parsing, sentence segmentation, **text classification**, lemmatization, morphological analysis, entity linking and more
- Components for named **entity recognition**, part-of-speech-tagging,
dependency parsing, sentence segmentation, **text classification**,
lemmatization, morphological analysis, entity linking and more
- Easily extensible with **custom components** and attributes
- Support for custom models in **PyTorch**, **TensorFlow** and other frameworks
- Built in **visualizers** for syntax and NER
@ -109,7 +108,7 @@ For detailed installation instructions, see the
- **Operating system**: macOS / OS X · Linux · Windows (Cygwin, MinGW, Visual
Studio)
- **Python version**: Python 3.6+ (only 64 bit)
- **Python version**: Python 3.7+ (only 64 bit)
- **Package managers**: [pip] · [conda] (via `conda-forge`)
[pip]: https://pypi.org/project/spacy/
@ -118,8 +117,8 @@ For detailed installation instructions, see the
### pip
Using pip, spaCy releases are available as source packages and binary wheels.
Before you install spaCy and its dependencies, make sure that
your `pip`, `setuptools` and `wheel` are up to date.
Before you install spaCy and its dependencies, make sure that your `pip`,
`setuptools` and `wheel` are up to date.
```bash
pip install -U pip setuptools wheel
@ -174,9 +173,9 @@ with the new version.
## 📦 Download model packages
Trained pipelines for spaCy can be installed as **Python packages**. This
means that they're a component of your application, just like any other module.
Models can be installed using spaCy's [`download`](https://spacy.io/api/cli#download)
Trained pipelines for spaCy can be installed as **Python packages**. This means
that they're a component of your application, just like any other module. Models
can be installed using spaCy's [`download`](https://spacy.io/api/cli#download)
command, or manually by pointing pip to a path or URL.
| Documentation | |
@ -242,8 +241,7 @@ do that depends on your system.
| **Mac** | Install a recent version of [XCode](https://developer.apple.com/xcode/), including the so-called "Command Line Tools". macOS and OS X ship with Python and git preinstalled. |
| **Windows** | Install a version of the [Visual C++ Build Tools](https://visualstudio.microsoft.com/visual-cpp-build-tools/) or [Visual Studio Express](https://visualstudio.microsoft.com/vs/express/) that matches the version that was used to compile your Python interpreter. |
For more details
and instructions, see the documentation on
For more details and instructions, see the documentation on
[compiling spaCy from source](https://spacy.io/usage#source) and the
[quickstart widget](https://spacy.io/usage#section-quickstart) to get the right
commands for your platform and Python version.

11
build-constraints.txt
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@ -1,9 +1,6 @@
# build version constraints for use with wheelwright + multibuild
numpy==1.15.0; python_version<='3.7' and platform_machine!='aarch64'
numpy==1.19.2; python_version<='3.7' and platform_machine=='aarch64'
# build version constraints for use with wheelwright
numpy==1.15.0; python_version=='3.7' and platform_machine!='aarch64'
numpy==1.19.2; python_version=='3.7' and platform_machine=='aarch64'
numpy==1.17.3; python_version=='3.8' and platform_machine!='aarch64'
numpy==1.19.2; python_version=='3.8' and platform_machine=='aarch64'
numpy==1.19.3; python_version=='3.9'
numpy==1.21.3; python_version=='3.10'
numpy==1.23.2; python_version=='3.11'
numpy; python_version>='3.12'
numpy>=1.25.0; python_version>='3.9'

39
extra/DEVELOPER_DOCS/Listeners.md
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@ -1,14 +1,17 @@
# Listeners
1. [Overview](#1-overview)
2. [Initialization](#2-initialization)
- [A. Linking listeners to the embedding component](#2a-linking-listeners-to-the-embedding-component)
- [B. Shape inference](#2b-shape-inference)
3. [Internal communication](#3-internal-communication)
- [A. During prediction](#3a-during-prediction)
- [B. During training](#3b-during-training)
- [C. Frozen components](#3c-frozen-components)
4. [Replacing listener with standalone](#4-replacing-listener-with-standalone)
- [1. Overview](#1-overview)
- [2. Initialization](#2-initialization)
- [2A. Linking listeners to the embedding component](#2a-linking-listeners-to-the-embedding-component)
- [2B. Shape inference](#2b-shape-inference)
- [3. Internal communication](#3-internal-communication)
- [3A. During prediction](#3a-during-prediction)
- [3B. During training](#3b-during-training)
- [Training with multiple listeners](#training-with-multiple-listeners)
- [3C. Frozen components](#3c-frozen-components)
- [The Tok2Vec or Transformer is frozen](#the-tok2vec-or-transformer-is-frozen)
- [The upstream component is frozen](#the-upstream-component-is-frozen)
- [4. Replacing listener with standalone](#4-replacing-listener-with-standalone)
## 1. Overview
@ -62,7 +65,7 @@ of this `find_listener()` method will specifically identify sublayers of a model
If it's a Transformer-based pipeline, a
[`transformer` component](https://github.com/explosion/spacy-transformers/blob/master/spacy_transformers/pipeline_component.py)
has a similar implementation but its `find_listener()` function will specifically look for `TransformerListener`
has a similar implementation but its `find_listener()` function will specifically look for `TransformerListener`
sublayers of downstream components.
### 2B. Shape inference
@ -154,7 +157,7 @@ as a tagger or a parser. This used to be impossible before 3.1, but has become s
embedding component in the [`annotating_components`](https://spacy.io/usage/training#annotating-components)
list of the config. This works like any other "annotating component" because it relies on the `Doc` attributes.
However, if the `Tok2Vec` or `Transformer` is frozen, and not present in `annotating_components`, and a related
However, if the `Tok2Vec` or `Transformer` is frozen, and not present in `annotating_components`, and a related
listener isn't frozen, then a `W086` warning is shown and further training of the pipeline will likely end with `E954`.
#### The upstream component is frozen
@ -216,5 +219,17 @@ new_model = tok2vec_model.attrs["replace_listener"](new_model)
```
The new config and model are then properly stored on the `nlp` object.
Note that this functionality (running the replacement for a transformer listener) was broken prior to
Note that this functionality (running the replacement for a transformer listener) was broken prior to
`spacy-transformers` 1.0.5.
In spaCy 3.7, `Language.replace_listeners` was updated to pass the following additional arguments to the `replace_listener` callback:
the listener to be replaced and the `tok2vec`/`transformer` pipe from which the new model was copied. To maintain backwards-compatiblity,
the method only passes these extra arguments for callbacks that support them:
```
def replace_listener_pre_37(copied_tok2vec_model):
...
def replace_listener_post_37(copied_tok2vec_model, replaced_listener, tok2vec_pipe):
...
```

5
pyproject.toml
View File

@ -5,8 +5,9 @@ requires = [
"cymem>=2.0.2,<2.1.0",
"preshed>=3.0.2,<3.1.0",
"murmurhash>=0.28.0,<1.1.0",
"thinc>=8.1.8,<8.2.0",
"numpy>=1.15.0",
"thinc>=8.1.8,<8.3.0",
"numpy>=1.15.0; python_version < '3.9'",
"numpy>=1.25.0; python_version >= '3.9'",
]
build-backend = "setuptools.build_meta"

12
requirements.txt
View File

@ -3,7 +3,7 @@ spacy-legacy>=3.0.11,<3.1.0
spacy-loggers>=1.0.0,<2.0.0
cymem>=2.0.2,<2.1.0
preshed>=3.0.2,<3.1.0
thinc>=8.1.8,<8.2.0
thinc>=8.1.8,<8.3.0
ml_datasets>=0.2.0,<0.3.0
murmurhash>=0.28.0,<1.1.0
wasabi>=0.9.1,<1.2.0
@ -12,11 +12,13 @@ catalogue>=2.0.6,<2.1.0
typer>=0.3.0,<0.10.0
pathy>=0.10.0
smart-open>=5.2.1,<7.0.0
weasel>=0.1.0,<0.4.0
# Third party dependencies
numpy>=1.15.0
numpy>=1.15.0; python_version < "3.9"
numpy>=1.19.0; python_version >= "3.9"
requests>=2.13.0,<3.0.0
tqdm>=4.38.0,<5.0.0
pydantic>=1.7.4,!=1.8,!=1.8.1,<1.11.0
pydantic>=1.7.4,!=1.8,!=1.8.1,<3.0.0
jinja2
langcodes>=3.2.0,<4.0.0
# Official Python utilities
@ -31,11 +33,11 @@ pytest-timeout>=1.3.0,<2.0.0
mock>=2.0.0,<3.0.0
flake8>=3.8.0,<6.0.0
hypothesis>=3.27.0,<7.0.0
mypy>=0.990,<1.1.0; platform_machine != "aarch64" and python_version >= "3.7"
types-dataclasses>=0.1.3; python_version < "3.7"
mypy>=1.5.0,<1.6.0; platform_machine != "aarch64" and python_version >= "3.8"
types-mock>=0.1.1
types-setuptools>=57.0.0
types-requests
types-setuptools>=57.0.0
black==22.3.0
cython-lint>=0.15.0
isort>=5.0,<6.0

24
setup.cfg
View File

@ -17,7 +17,6 @@ classifiers =
Operating System :: Microsoft :: Windows
Programming Language :: Cython
Programming Language :: Python :: 3
Programming Language :: Python :: 3.6
Programming Language :: Python :: 3.7
Programming Language :: Python :: 3.8
Programming Language :: Python :: 3.9
@ -31,15 +30,18 @@ project_urls =
[options]
zip_safe = false
include_package_data = true
python_requires = >=3.6
python_requires = >=3.7
# NOTE: This section is superseded by pyproject.toml and will be removed in
# spaCy v4
setup_requires =
cython>=0.25,<3.0
numpy>=1.15.0
numpy>=1.15.0; python_version < "3.9"
numpy>=1.19.0; python_version >= "3.9"
# We also need our Cython packages here to compile against
cymem>=2.0.2,<2.1.0
preshed>=3.0.2,<3.1.0
murmurhash>=0.28.0,<1.1.0
thinc>=8.1.8,<8.2.0
thinc>=8.1.8,<8.3.0
install_requires =
# Our libraries
spacy-legacy>=3.0.11,<3.1.0
@ -47,18 +49,20 @@ install_requires =
murmurhash>=0.28.0,<1.1.0
cymem>=2.0.2,<2.1.0
preshed>=3.0.2,<3.1.0
thinc>=8.1.8,<8.2.0
thinc>=8.1.8,<8.3.0
wasabi>=0.9.1,<1.2.0
srsly>=2.4.3,<3.0.0
catalogue>=2.0.6,<2.1.0
weasel>=0.1.0,<0.4.0
# Third-party dependencies
typer>=0.3.0,<0.10.0
pathy>=0.10.0
smart-open>=5.2.1,<7.0.0
tqdm>=4.38.0,<5.0.0
numpy>=1.15.0
numpy>=1.15.0; python_version < "3.9"
numpy>=1.19.0; python_version >= "3.9"
requests>=2.13.0,<3.0.0
pydantic>=1.7.4,!=1.8,!=1.8.1,<1.11.0
pydantic>=1.7.4,!=1.8,!=1.8.1,<3.0.0
jinja2
# Official Python utilities
setuptools
@ -74,9 +78,7 @@ console_scripts =
lookups =
spacy_lookups_data>=1.0.3,<1.1.0
transformers =
spacy_transformers>=1.1.2,<1.3.0
ray =
spacy_ray>=0.1.0,<1.0.0
spacy_transformers>=1.1.2,<1.4.0
cuda =
cupy>=5.0.0b4,<13.0.0
cuda80 =
@ -111,6 +113,8 @@ cuda117 =
cupy-cuda117>=5.0.0b4,<13.0.0
cuda11x =
cupy-cuda11x>=11.0.0,<13.0.0
cuda12x =
cupy-cuda12x>=11.5.0,<13.0.0
cuda-autodetect =
cupy-wheel>=11.0.0,<13.0.0
apple =

32
setup.py
View File

@ -1,10 +1,9 @@
#!/usr/bin/env python
from setuptools import Extension, setup, find_packages
import sys
import platform
import numpy
from distutils.command.build_ext import build_ext
from distutils.sysconfig import get_python_inc
from setuptools.command.build_ext import build_ext
from sysconfig import get_path
from pathlib import Path
import shutil
from Cython.Build import cythonize
@ -79,6 +78,7 @@ COMPILER_DIRECTIVES = {
"language_level": -3,
"embedsignature": True,
"annotation_typing": False,
"profile": sys.version_info < (3, 12),
}
# Files to copy into the package that are otherwise not included
COPY_FILES = {
@ -88,30 +88,6 @@ COPY_FILES = {
}
def is_new_osx():
"""Check whether we're on OSX >= 10.7"""
if sys.platform != "darwin":
return False
mac_ver = platform.mac_ver()[0]
if mac_ver.startswith("10"):
minor_version = int(mac_ver.split(".")[1])
if minor_version >= 7:
return True
else:
return False
return False
if is_new_osx():
# On Mac, use libc++ because Apple deprecated use of
# libstdc
COMPILE_OPTIONS["other"].append("-stdlib=libc++")
LINK_OPTIONS["other"].append("-lc++")
# g++ (used by unix compiler on mac) links to libstdc++ as a default lib.
# See: https://stackoverflow.com/questions/1653047/avoid-linking-to-libstdc
LINK_OPTIONS["other"].append("-nodefaultlibs")
# By subclassing build_extensions we have the actual compiler that will be used which is really known only after finalize_options
# http://stackoverflow.com/questions/724664/python-distutils-how-to-get-a-compiler-that-is-going-to-be-used
class build_ext_options:
@ -204,7 +180,7 @@ def setup_package():
include_dirs = [
numpy.get_include(),
get_python_inc(plat_specific=True),
get_path("include"),
]
ext_modules = []
ext_modules.append(

7
spacy/__init__.py
View File

@ -13,7 +13,6 @@ from thinc.api import Config, prefer_gpu, require_cpu, require_gpu # noqa: F401
from . import pipeline # noqa: F401
from . import util
from .about import __version__ # noqa: F401
from .cli.info import info # noqa: F401
from .errors import Errors
from .glossary import explain # noqa: F401
from .language import Language
@ -77,3 +76,9 @@ def blank(
# We should accept both dot notation and nested dict here for consistency
config = util.dot_to_dict(config)
return LangClass.from_config(config, vocab=vocab, meta=meta)
def info(*args, **kwargs):
from .cli.info import info as cli_info
return cli_info(*args, **kwargs)

4
spacy/about.py
View File

@ -1,7 +1,5 @@
# fmt: off
__title__ = "spacy"
__version__ = "3.6.0"
__version__ = "3.7.0"
__download_url__ = "https://github.com/explosion/spacy-models/releases/download"
__compatibility__ = "https://raw.githubusercontent.com/explosion/spacy-models/master/compatibility.json"
__projects__ = "https://github.com/explosion/projects"
__projects_branch__ = "v3"

2
spacy/attrs.pxd
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@ -96,4 +96,4 @@ cdef enum attr_id_t:
ENT_ID = symbols.ENT_ID
IDX
SENT_END
SENT_END

3
spacy/attrs.pyx
View File

@ -1,3 +1,4 @@
# cython: profile=False
from .errors import Errors
IOB_STRINGS = ("", "I", "O", "B")
@ -117,7 +118,7 @@ def intify_attrs(stringy_attrs, strings_map=None, _do_deprecated=False):
if "pos" in stringy_attrs:
stringy_attrs["TAG"] = stringy_attrs.pop("pos")
if "morph" in stringy_attrs:
morphs = stringy_attrs.pop("morph")
morphs = stringy_attrs.pop("morph") # no-cython-lint
if "number" in stringy_attrs:
stringy_attrs.pop("number")
if "tenspect" in stringy_attrs:

8
spacy/cli/__init__.py
View File

@ -14,6 +14,7 @@ from .debug_diff import debug_diff # noqa: F401
from .debug_model import debug_model # noqa: F401
from .download import download # noqa: F401
from .evaluate import evaluate # noqa: F401
from .find_function import find_function # noqa: F401
from .find_threshold import find_threshold # noqa: F401
from .info import info # noqa: F401
from .init_config import fill_config, init_config # noqa: F401
@ -21,13 +22,6 @@ from .init_pipeline import init_pipeline_cli # noqa: F401
from .package import package # noqa: F401
from .pretrain import pretrain # noqa: F401
from .profile import profile # noqa: F401
from .project.assets import project_assets # noqa: F401
from .project.clone import project_clone # noqa: F401
from .project.document import project_document # noqa: F401
from .project.dvc import project_update_dvc # noqa: F401
from .project.pull import project_pull # noqa: F401
from .project.push import project_push # noqa: F401
from .project.run import project_run # noqa: F401
from .train import train_cli # noqa: F401
from .validate import validate # noqa: F401

329
spacy/cli/_util.py
View File

@ -25,10 +25,11 @@ from thinc.api import Config, ConfigValidationError, require_gpu
from thinc.util import gpu_is_available
from typer.main import get_command
from wasabi import Printer, msg
from weasel import app as project_cli
from .. import about
from ..compat import Literal
from ..schemas import ProjectConfigSchema, validate
from ..schemas import validate
from ..util import (
ENV_VARS,
SimpleFrozenDict,
@ -48,7 +49,6 @@ SDIST_SUFFIX = ".tar.gz"
WHEEL_SUFFIX = "-py3-none-any.whl"
PROJECT_FILE = "project.yml"
PROJECT_LOCK = "project.lock"
COMMAND = "python -m spacy"
NAME = "spacy"
HELP = """spaCy Command-line Interface
@ -74,11 +74,10 @@ Opt = typer.Option
app = typer.Typer(name=NAME, help=HELP)
benchmark_cli = typer.Typer(name="benchmark", help=BENCHMARK_HELP, no_args_is_help=True)
project_cli = typer.Typer(name="project", help=PROJECT_HELP, no_args_is_help=True)
debug_cli = typer.Typer(name="debug", help=DEBUG_HELP, no_args_is_help=True)
init_cli = typer.Typer(name="init", help=INIT_HELP, no_args_is_help=True)
app.add_typer(project_cli)
app.add_typer(project_cli, name="project", help=PROJECT_HELP, no_args_is_help=True)
app.add_typer(debug_cli)
app.add_typer(benchmark_cli)
app.add_typer(init_cli)
@ -153,148 +152,6 @@ def _parse_override(value: Any) -> Any:
return str(value)
def load_project_config(
path: Path, interpolate: bool = True, overrides: Dict[str, Any] = SimpleFrozenDict()
) -> Dict[str, Any]:
"""Load the project.yml file from a directory and validate it. Also make
sure that all directories defined in the config exist.
path (Path): The path to the project directory.
interpolate (bool): Whether to substitute project variables.
overrides (Dict[str, Any]): Optional config overrides.
RETURNS (Dict[str, Any]): The loaded project.yml.
"""
config_path = path / PROJECT_FILE
if not config_path.exists():
msg.fail(f"Can't find {PROJECT_FILE}", config_path, exits=1)
invalid_err = f"Invalid {PROJECT_FILE}. Double-check that the YAML is correct."
try:
config = srsly.read_yaml(config_path)
except ValueError as e:
msg.fail(invalid_err, e, exits=1)
errors = validate(ProjectConfigSchema, config)
if errors:
msg.fail(invalid_err)
print("\n".join(errors))
sys.exit(1)
validate_project_version(config)
validate_project_commands(config)
if interpolate:
err = f"{PROJECT_FILE} validation error"
with show_validation_error(title=err, hint_fill=False):
config = substitute_project_variables(config, overrides)
# Make sure directories defined in config exist
for subdir in config.get("directories", []):
dir_path = path / subdir
if not dir_path.exists():
dir_path.mkdir(parents=True)
return config
def substitute_project_variables(
config: Dict[str, Any],
overrides: Dict[str, Any] = SimpleFrozenDict(),
key: str = "vars",
env_key: str = "env",
) -> Dict[str, Any]:
"""Interpolate variables in the project file using the config system.
config (Dict[str, Any]): The project config.
overrides (Dict[str, Any]): Optional config overrides.
key (str): Key containing variables in project config.
env_key (str): Key containing environment variable mapping in project config.
RETURNS (Dict[str, Any]): The interpolated project config.
"""
config.setdefault(key, {})
config.setdefault(env_key, {})
# Substitute references to env vars with their values
for config_var, env_var in config[env_key].items():
config[env_key][config_var] = _parse_override(os.environ.get(env_var, ""))
# Need to put variables in the top scope again so we can have a top-level
# section "project" (otherwise, a list of commands in the top scope wouldn't)
# be allowed by Thinc's config system
cfg = Config({"project": config, key: config[key], env_key: config[env_key]})
cfg = Config().from_str(cfg.to_str(), overrides=overrides)
interpolated = cfg.interpolate()
return dict(interpolated["project"])
def validate_project_version(config: Dict[str, Any]) -> None:
"""If the project defines a compatible spaCy version range, chec that it's
compatible with the current version of spaCy.
config (Dict[str, Any]): The loaded config.
"""
spacy_version = config.get("spacy_version", None)
if spacy_version and not is_compatible_version(about.__version__, spacy_version):
err = (
f"The {PROJECT_FILE} specifies a spaCy version range ({spacy_version}) "
f"that's not compatible with the version of spaCy you're running "
f"({about.__version__}). You can edit version requirement in the "
f"{PROJECT_FILE} to load it, but the project may not run as expected."
)
msg.fail(err, exits=1)
def validate_project_commands(config: Dict[str, Any]) -> None:
"""Check that project commands and workflows are valid, don't contain
duplicates, don't clash and only refer to commands that exist.
config (Dict[str, Any]): The loaded config.
"""
command_names = [cmd["name"] for cmd in config.get("commands", [])]
workflows = config.get("workflows", {})
duplicates = set([cmd for cmd in command_names if command_names.count(cmd) > 1])
if duplicates:
err = f"Duplicate commands defined in {PROJECT_FILE}: {', '.join(duplicates)}"
msg.fail(err, exits=1)
for workflow_name, workflow_steps in workflows.items():
if workflow_name in command_names:
err = f"Can't use workflow name '{workflow_name}': name already exists as a command"
msg.fail(err, exits=1)
for step in workflow_steps:
if step not in command_names:
msg.fail(
f"Unknown command specified in workflow '{workflow_name}': {step}",
f"Workflows can only refer to commands defined in the 'commands' "
f"section of the {PROJECT_FILE}.",
exits=1,
)
def get_hash(data, exclude: Iterable[str] = tuple()) -> str:
"""Get the hash for a JSON-serializable object.
data: The data to hash.
exclude (Iterable[str]): Top-level keys to exclude if data is a dict.
RETURNS (str): The hash.
"""
if isinstance(data, dict):
data = {k: v for k, v in data.items() if k not in exclude}
data_str = srsly.json_dumps(data, sort_keys=True).encode("utf8")
return hashlib.md5(data_str).hexdigest()
def get_checksum(path: Union[Path, str]) -> str:
"""Get the checksum for a file or directory given its file path. If a
directory path is provided, this uses all files in that directory.
path (Union[Path, str]): The file or directory path.
RETURNS (str): The checksum.
"""
path = Path(path)
if not (path.is_file() or path.is_dir()):
msg.fail(f"Can't get checksum for {path}: not a file or directory", exits=1)
if path.is_file():
return hashlib.md5(Path(path).read_bytes()).hexdigest()
else:
# TODO: this is currently pretty slow
dir_checksum = hashlib.md5()
for sub_file in sorted(fp for fp in path.rglob("*") if fp.is_file()):
dir_checksum.update(sub_file.read_bytes())
return dir_checksum.hexdigest()
@contextmanager
def show_validation_error(
file_path: Optional[Union[str, Path]] = None,
@ -352,166 +209,10 @@ def import_code(code_path: Optional[Union[Path, str]]) -> None:
msg.fail(f"Couldn't load Python code: {code_path}", e, exits=1)
def upload_file(src: Path, dest: Union[str, "FluidPath"]) -> None:
"""Upload a file.
src (Path): The source path.
url (str): The destination URL to upload to.
"""
import smart_open
# Create parent directories for local paths
if isinstance(dest, Path):
if not dest.parent.exists():
dest.parent.mkdir(parents=True)
dest = str(dest)
with smart_open.open(dest, mode="wb") as output_file:
with src.open(mode="rb") as input_file:
output_file.write(input_file.read())
def download_file(
src: Union[str, "FluidPath"], dest: Path, *, force: bool = False
) -> None:
"""Download a file using smart_open.
url (str): The URL of the file.
dest (Path): The destination path.
force (bool): Whether to force download even if file exists.
If False, the download will be skipped.
"""
import smart_open
if dest.exists() and not force:
return None
src = str(src)
with smart_open.open(src, mode="rb", compression="disable") as input_file:
with dest.open(mode="wb") as output_file:
shutil.copyfileobj(input_file, output_file)
def ensure_pathy(path):
"""Temporary helper to prevent importing Pathy globally (which can cause
slow and annoying Google Cloud warning)."""
from pathy import Pathy # noqa: F811
return Pathy.fluid(path)
def git_checkout(
repo: str, subpath: str, dest: Path, *, branch: str = "master", sparse: bool = False
):
git_version = get_git_version()
if dest.exists():
msg.fail("Destination of checkout must not exist", exits=1)
if not dest.parent.exists():
msg.fail("Parent of destination of checkout must exist", exits=1)
if sparse and git_version >= (2, 22):
return git_sparse_checkout(repo, subpath, dest, branch)
elif sparse:
# Only show warnings if the user explicitly wants sparse checkout but
# the Git version doesn't support it
err_old = (
f"You're running an old version of Git (v{git_version[0]}.{git_version[1]}) "
f"that doesn't fully support sparse checkout yet."
)
err_unk = "You're running an unknown version of Git, so sparse checkout has been disabled."
msg.warn(
f"{err_unk if git_version == (0, 0) else err_old} "
f"This means that more files than necessary may be downloaded "
f"temporarily. To only download the files needed, make sure "
f"you're using Git v2.22 or above."
)
with make_tempdir() as tmp_dir:
cmd = f"git -C {tmp_dir} clone {repo} . -b {branch}"
run_command(cmd, capture=True)
# We need Path(name) to make sure we also support subdirectories
try:
source_path = tmp_dir / Path(subpath)
if not is_subpath_of(tmp_dir, source_path):
err = f"'{subpath}' is a path outside of the cloned repository."
msg.fail(err, repo, exits=1)
shutil.copytree(str(source_path), str(dest))
except FileNotFoundError:
err = f"Can't clone {subpath}. Make sure the directory exists in the repo (branch '{branch}')"
msg.fail(err, repo, exits=1)
def git_sparse_checkout(repo, subpath, dest, branch):
# We're using Git, partial clone and sparse checkout to
# only clone the files we need
# This ends up being RIDICULOUS. omg.
# So, every tutorial and SO post talks about 'sparse checkout'...But they
# go and *clone* the whole repo. Worthless. And cloning part of a repo
# turns out to be completely broken. The only way to specify a "path" is..
# a path *on the server*? The contents of which, specifies the paths. Wat.
# Obviously this is hopelessly broken and insecure, because you can query
# arbitrary paths on the server! So nobody enables this.
# What we have to do is disable *all* files. We could then just checkout
# the path, and it'd "work", but be hopelessly slow...Because it goes and
# transfers every missing object one-by-one. So the final piece is that we
# need to use some weird git internals to fetch the missings in bulk, and
# *that* we can do by path.
# We're using Git and sparse checkout to only clone the files we need
with make_tempdir() as tmp_dir:
# This is the "clone, but don't download anything" part.
cmd = (
f"git clone {repo} {tmp_dir} --no-checkout --depth 1 "
f"-b {branch} --filter=blob:none"
)
run_command(cmd)
# Now we need to find the missing filenames for the subpath we want.
# Looking for this 'rev-list' command in the git --help? Hah.
cmd = f"git -C {tmp_dir} rev-list --objects --all --missing=print -- {subpath}"
ret = run_command(cmd, capture=True)
git_repo = _http_to_git(repo)
# Now pass those missings into another bit of git internals
missings = " ".join([x[1:] for x in ret.stdout.split() if x.startswith("?")])
if not missings:
err = (
f"Could not find any relevant files for '{subpath}'. "
f"Did you specify a correct and complete path within repo '{repo}' "
f"and branch {branch}?"
)
msg.fail(err, exits=1)
cmd = f"git -C {tmp_dir} fetch-pack {git_repo} {missings}"
run_command(cmd, capture=True)
# And finally, we can checkout our subpath
cmd = f"git -C {tmp_dir} checkout {branch} {subpath}"
run_command(cmd, capture=True)
# Get a subdirectory of the cloned path, if appropriate
source_path = tmp_dir / Path(subpath)
if not is_subpath_of(tmp_dir, source_path):
err = f"'{subpath}' is a path outside of the cloned repository."
msg.fail(err, repo, exits=1)
shutil.move(str(source_path), str(dest))
def git_repo_branch_exists(repo: str, branch: str) -> bool:
"""Uses 'git ls-remote' to check if a repository and branch exists
repo (str): URL to get repo.
branch (str): Branch on repo to check.
RETURNS (bool): True if repo:branch exists.
"""
get_git_version()
cmd = f"git ls-remote {repo} {branch}"
# We might be tempted to use `--exit-code` with `git ls-remote`, but
# `run_command` handles the `returncode` for us, so we'll rely on
# the fact that stdout returns '' if the requested branch doesn't exist
ret = run_command(cmd, capture=True)
exists = ret.stdout != ""
return exists
def get_git_version(
error: str = "Could not run 'git'. Make sure it's installed and the executable is available.",
) -> Tuple[int, int]:
"""Get the version of git and raise an error if calling 'git --version' fails.
error (str): The error message to show.
RETURNS (Tuple[int, int]): The version as a (major, minor) tuple. Returns
(0, 0) if the version couldn't be determined.
@ -527,30 +228,6 @@ def get_git_version(
return int(version[0]), int(version[1])
def _http_to_git(repo: str) -> str:
if repo.startswith("http://"):
repo = repo.replace(r"http://", r"https://")
if repo.startswith(r"https://"):
repo = repo.replace("https://", "git@").replace("/", ":", 1)
if repo.endswith("/"):
repo = repo[:-1]
repo = f"{repo}.git"
return repo
def is_subpath_of(parent, child):
"""
Check whether `child` is a path contained within `parent`.
"""
# Based on https://stackoverflow.com/a/37095733 .
# In Python 3.9, the `Path.is_relative_to()` method will supplant this, so
# we can stop using crusty old os.path functions.
parent_realpath = os.path.realpath(parent)
child_realpath = os.path.realpath(child)
return os.path.commonpath([parent_realpath, child_realpath]) == parent_realpath
@overload
def string_to_list(value: str, intify: Literal[False] = ...) -> List[str]:
...

4
spacy/cli/apply.py
View File

@ -133,7 +133,9 @@ def apply(
if len(text_files) > 0:
streams.append(_stream_texts(text_files))
datagen = cast(DocOrStrStream, chain(*streams))
for doc in tqdm.tqdm(nlp.pipe(datagen, batch_size=batch_size, n_process=n_process)):
for doc in tqdm.tqdm(
nlp.pipe(datagen, batch_size=batch_size, n_process=n_process), disable=None
):
docbin.add(doc)
if output_file.suffix == "":
output_file = output_file.with_suffix(".spacy")

3
spacy/cli/assemble.py
View File

@ -40,7 +40,8 @@ def assemble_cli(
DOCS: https://spacy.io/api/cli#assemble
"""
util.logger.setLevel(logging.DEBUG if verbose else logging.INFO)
if verbose:
util.logger.setLevel(logging.DEBUG)
# Make sure all files and paths exists if they are needed
if not config_path or (str(config_path) != "-" and not config_path.exists()):
msg.fail("Config file not found", config_path, exits=1)

2
spacy/cli/benchmark_speed.py
View File

@ -89,7 +89,7 @@ class Quartiles:
def annotate(
nlp: Language, docs: List[Doc], batch_size: Optional[int]
) -> numpy.ndarray:
docs = nlp.pipe(tqdm(docs, unit="doc"), batch_size=batch_size)
docs = nlp.pipe(tqdm(docs, unit="doc", disable=None), batch_size=batch_size)
wps = []
while True:
with time_context() as elapsed:

2
spacy/cli/evaluate.py
View File

@ -28,6 +28,7 @@ def evaluate_cli(
displacy_path: Optional[Path] = Opt(None, "--displacy-path", "-dp", help="Directory to output rendered parses as HTML", exists=True, file_okay=False),
displacy_limit: int = Opt(25, "--displacy-limit", "-dl", help="Limit of parses to render as HTML"),
per_component: bool = Opt(False, "--per-component", "-P", help="Return scores per component, only applicable when an output JSON file is specified."),
spans_key: str = Opt("sc", "--spans-key", "-sk", help="Spans key to use when evaluating Doc.spans"),
# fmt: on
):
"""
@ -53,6 +54,7 @@ def evaluate_cli(
displacy_limit=displacy_limit,
per_component=per_component,
silent=False,
spans_key=spans_key,
)

69
spacy/cli/find_function.py Normal file
View File

@ -0,0 +1,69 @@
from typing import Optional, Tuple
from catalogue import RegistryError
from wasabi import msg
from ..util import registry
from ._util import Arg, Opt, app
@app.command("find-function")
def find_function_cli(
# fmt: off
func_name: str = Arg(..., help="Name of the registered function."),
registry_name: Optional[str] = Opt(None, "--registry", "-r", help="Name of the catalogue registry."),
# fmt: on
):
"""
Find the module, path and line number to the file the registered
function is defined in, if available.
func_name (str): Name of the registered function.
registry_name (Optional[str]): Name of the catalogue registry.
DOCS: https://spacy.io/api/cli#find-function
"""
if not registry_name:
registry_names = registry.get_registry_names()
for name in registry_names:
if registry.has(name, func_name):
registry_name = name
break
if not registry_name:
msg.fail(
f"Couldn't find registered function: '{func_name}'",
exits=1,
)
assert registry_name is not None
find_function(func_name, registry_name)
def find_function(func_name: str, registry_name: str) -> Tuple[str, int]:
registry_desc = None
try:
registry_desc = registry.find(registry_name, func_name)
except RegistryError as e:
msg.fail(
f"Couldn't find registered function: '{func_name}' in registry '{registry_name}'",
)
msg.fail(f"{e}", exits=1)
assert registry_desc is not None
registry_path = None
line_no = None
if registry_desc["file"]:
registry_path = registry_desc["file"]
line_no = registry_desc["line_no"]
if not registry_path or not line_no:
msg.fail(
f"Couldn't find path to registered function: '{func_name}' in registry '{registry_name}'",
exits=1,
)
assert registry_path is not None
assert line_no is not None
msg.good(f"Found registered function '{func_name}' at {registry_path}:{line_no}")
return str(registry_path), int(line_no)

4
spacy/cli/find_threshold.py
View File

@ -52,8 +52,8 @@ def find_threshold_cli(
DOCS: https://spacy.io/api/cli#find-threshold
"""
util.logger.setLevel(logging.DEBUG if verbose else logging.INFO)
if verbose:
util.logger.setLevel(logging.DEBUG)
import_code(code_path)
find_threshold(
model=model,

9
spacy/cli/init_pipeline.py
View File

@ -39,7 +39,8 @@ def init_vectors_cli(
you can use in the [initialize] block of your config to initialize
a model with vectors.
"""
util.logger.setLevel(logging.DEBUG if verbose else logging.INFO)
if verbose:
util.logger.setLevel(logging.DEBUG)
msg.info(f"Creating blank nlp object for language '{lang}'")
nlp = util.get_lang_class(lang)()
if jsonl_loc is not None:
@ -87,7 +88,8 @@ def init_pipeline_cli(
use_gpu: int = Opt(-1, "--gpu-id", "-g", help="GPU ID or -1 for CPU")
# fmt: on
):
util.logger.setLevel(logging.DEBUG if verbose else logging.INFO)
if verbose:
util.logger.setLevel(logging.DEBUG)
overrides = parse_config_overrides(ctx.args)
import_code(code_path)
setup_gpu(use_gpu)
@ -116,7 +118,8 @@ def init_labels_cli(
"""Generate JSON files for the labels in the data. This helps speed up the
training process, since spaCy won't have to preprocess the data to
extract the labels."""
util.logger.setLevel(logging.DEBUG if verbose else logging.INFO)
if verbose:
util.logger.setLevel(logging.DEBUG)
if not output_path.exists():
output_path.mkdir(parents=True)
overrides = parse_config_overrides(ctx.args)

2
spacy/cli/package.py
View File

@ -403,7 +403,7 @@ def _format_sources(data: Any) -> str:
if author:
result += " ({})".format(author)
sources.append(result)
return "<br />".join(sources)
return "<br>".join(sources)
def _format_accuracy(data: Dict[str, Any], exclude: List[str] = ["speed"]) -> str:

2
spacy/cli/profile.py
View File

@ -71,7 +71,7 @@ def profile(model: str, inputs: Optional[Path] = None, n_texts: int = 10000) ->
def parse_texts(nlp: Language, texts: Sequence[str]) -> None:
for doc in nlp.pipe(tqdm.tqdm(texts), batch_size=16):
for doc in nlp.pipe(tqdm.tqdm(texts, disable=None), batch_size=16):
pass

0
spacy/cli/project/__init__.py
View File

217
spacy/cli/project/assets.py
View File

@ -1,217 +0,0 @@
import os
import re
import shutil
from pathlib import Path
from typing import Any, Dict, Optional
import requests
import typer
from wasabi import msg
from ...util import ensure_path, working_dir
from .._util import (
PROJECT_FILE,
Arg,
Opt,
SimpleFrozenDict,
download_file,
get_checksum,
get_git_version,
git_checkout,
load_project_config,
parse_config_overrides,
project_cli,
)
# Whether assets are extra if `extra` is not set.
EXTRA_DEFAULT = False
@project_cli.command(
"assets",
context_settings={"allow_extra_args": True, "ignore_unknown_options": True},
)
def project_assets_cli(
# fmt: off
ctx: typer.Context, # This is only used to read additional arguments
project_dir: Path = Arg(Path.cwd(), help="Path to cloned project. Defaults to current working directory.", exists=True, file_okay=False),
sparse_checkout: bool = Opt(False, "--sparse", "-S", help="Use sparse checkout for assets provided via Git, to only check out and clone the files needed. Requires Git v22.2+."),
extra: bool = Opt(False, "--extra", "-e", help="Download all assets, including those marked as 'extra'.")
# fmt: on
):
"""Fetch project assets like datasets and pretrained weights. Assets are
defined in the "assets" section of the project.yml. If a checksum is
provided in the project.yml, the file is only downloaded if no local file
with the same checksum exists.
DOCS: https://spacy.io/api/cli#project-assets
"""
overrides = parse_config_overrides(ctx.args)
project_assets(
project_dir,
overrides=overrides,
sparse_checkout=sparse_checkout,
extra=extra,
)
def project_assets(
project_dir: Path,
*,
overrides: Dict[str, Any] = SimpleFrozenDict(),
sparse_checkout: bool = False,
extra: bool = False,
) -> None:
"""Fetch assets for a project using DVC if possible.
project_dir (Path): Path to project directory.
sparse_checkout (bool): Use sparse checkout for assets provided via Git, to only check out and clone the files
needed.
extra (bool): Whether to download all assets, including those marked as 'extra'.
"""
project_path = ensure_path(project_dir)
config = load_project_config(project_path, overrides=overrides)
assets = [
asset
for asset in config.get("assets", [])
if extra or not asset.get("extra", EXTRA_DEFAULT)
]
if not assets:
msg.warn(
f"No assets specified in {PROJECT_FILE} (if assets are marked as extra, download them with --extra)",
exits=0,
)
msg.info(f"Fetching {len(assets)} asset(s)")
for asset in assets:
dest = (project_dir / asset["dest"]).resolve()
checksum = asset.get("checksum")
if "git" in asset:
git_err = (
f"Cloning spaCy project templates requires Git and the 'git' command. "
f"Make sure it's installed and that the executable is available."
)
get_git_version(error=git_err)
if dest.exists():
# If there's already a file, check for checksum
if checksum and checksum == get_checksum(dest):
msg.good(
f"Skipping download with matching checksum: {asset['dest']}"
)
continue
else:
if dest.is_dir():
shutil.rmtree(dest)
else:
dest.unlink()
if "repo" not in asset["git"] or asset["git"]["repo"] is None:
msg.fail(
"A git asset must include 'repo', the repository address.", exits=1
)
if "path" not in asset["git"] or asset["git"]["path"] is None:
msg.fail(
"A git asset must include 'path' - use \"\" to get the entire repository.",
exits=1,
)
git_checkout(
asset["git"]["repo"],
asset["git"]["path"],
dest,
branch=asset["git"].get("branch"),
sparse=sparse_checkout,
)
msg.good(f"Downloaded asset {dest}")
else:
url = asset.get("url")
if not url:
# project.yml defines asset without URL that the user has to place
check_private_asset(dest, checksum)
continue
fetch_asset(project_path, url, dest, checksum)
def check_private_asset(dest: Path, checksum: Optional[str] = None) -> None:
"""Check and validate assets without a URL (private assets that the user
has to provide themselves) and give feedback about the checksum.
dest (Path): Destination path of the asset.
checksum (Optional[str]): Optional checksum of the expected file.
"""
if not Path(dest).exists():
err = f"No URL provided for asset. You need to add this file yourself: {dest}"
msg.warn(err)
else:
if not checksum:
msg.good(f"Asset already exists: {dest}")
elif checksum == get_checksum(dest):
msg.good(f"Asset exists with matching checksum: {dest}")
else:
msg.fail(f"Asset available but with incorrect checksum: {dest}")
def fetch_asset(
project_path: Path, url: str, dest: Path, checksum: Optional[str] = None
) -> None:
"""Fetch an asset from a given URL or path. If a checksum is provided and a
local file exists, it's only re-downloaded if the checksum doesn't match.
project_path (Path): Path to project directory.
url (str): URL or path to asset.
checksum (Optional[str]): Optional expected checksum of local file.
RETURNS (Optional[Path]): The path to the fetched asset or None if fetching
the asset failed.
"""
dest_path = (project_path / dest).resolve()
if dest_path.exists():
# If there's already a file, check for checksum
if checksum:
if checksum == get_checksum(dest_path):
msg.good(f"Skipping download with matching checksum: {dest}")
return
else:
# If there's not a checksum, make sure the file is a possibly valid size
if os.path.getsize(dest_path) == 0:
msg.warn(f"Asset exists but with size of 0 bytes, deleting: {dest}")
os.remove(dest_path)
# We might as well support the user here and create parent directories in
# case the asset dir isn't listed as a dir to create in the project.yml
if not dest_path.parent.exists():
dest_path.parent.mkdir(parents=True)
with working_dir(project_path):
url = convert_asset_url(url)
try:
download_file(url, dest_path)
msg.good(f"Downloaded asset {dest}")
except requests.exceptions.RequestException as e:
if Path(url).exists() and Path(url).is_file():
# If it's a local file, copy to destination
shutil.copy(url, str(dest_path))
msg.good(f"Copied local asset {dest}")
else:
msg.fail(f"Download failed: {dest}", e)
if checksum and checksum != get_checksum(dest_path):
msg.fail(f"Checksum doesn't match value defined in {PROJECT_FILE}: {dest}")
def convert_asset_url(url: str) -> str:
"""Check and convert the asset URL if needed.
url (str): The asset URL.
RETURNS (str): The converted URL.
"""
# If the asset URL is a regular GitHub URL it's likely a mistake
if (
re.match(r"(http(s?)):\/\/github.com", url)
and "releases/download" not in url
and "/raw/" not in url
):
converted = url.replace("github.com", "raw.githubusercontent.com")
converted = re.sub(r"/(tree|blob)/", "/", converted)
msg.warn(
"Downloading from a regular GitHub URL. This will only download "
"the source of the page, not the actual file. Converting the URL "
"to a raw URL.",
converted,
)
return converted
return url

124
spacy/cli/project/clone.py
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@ -1,124 +0,0 @@
import re
import subprocess
from pathlib import Path
from typing import Optional
from wasabi import msg
from ... import about
from ...util import ensure_path
from .._util import (
COMMAND,
PROJECT_FILE,
Arg,
Opt,
get_git_version,
git_checkout,
git_repo_branch_exists,
project_cli,
)
DEFAULT_REPO = about.__projects__
DEFAULT_PROJECTS_BRANCH = about.__projects_branch__
DEFAULT_BRANCHES = ["main", "master"]
@project_cli.command("clone")
def project_clone_cli(
# fmt: off
name: str = Arg(..., help="The name of the template to clone"),
dest: Optional[Path] = Arg(None, help="Where to clone the project. Defaults to current working directory", exists=False),
repo: str = Opt(DEFAULT_REPO, "--repo", "-r", help="The repository to clone from"),
branch: Optional[str] = Opt(None, "--branch", "-b", help=f"The branch to clone from. If not provided, will attempt {', '.join(DEFAULT_BRANCHES)}"),
sparse_checkout: bool = Opt(False, "--sparse", "-S", help="Use sparse Git checkout to only check out and clone the files needed. Requires Git v22.2+.")
# fmt: on
):
"""Clone a project template from a repository. Calls into "git" and will
only download the files from the given subdirectory. The GitHub repo
defaults to the official spaCy template repo, but can be customized
(including using a private repo).
DOCS: https://spacy.io/api/cli#project-clone
"""
if dest is None:
dest = Path.cwd() / Path(name).parts[-1]
if repo == DEFAULT_REPO and branch is None:
branch = DEFAULT_PROJECTS_BRANCH
if branch is None:
for default_branch in DEFAULT_BRANCHES:
if git_repo_branch_exists(repo, default_branch):
branch = default_branch
break
if branch is None:
default_branches_msg = ", ".join(f"'{b}'" for b in DEFAULT_BRANCHES)
msg.fail(
"No branch provided and attempted default "
f"branches {default_branches_msg} do not exist.",
exits=1,
)
else:
if not git_repo_branch_exists(repo, branch):
msg.fail(f"repo: {repo} (branch: {branch}) does not exist.", exits=1)
assert isinstance(branch, str)
project_clone(name, dest, repo=repo, branch=branch, sparse_checkout=sparse_checkout)
def project_clone(
name: str,
dest: Path,
*,
repo: str = about.__projects__,
branch: str = about.__projects_branch__,
sparse_checkout: bool = False,
) -> None:
"""Clone a project template from a repository.
name (str): Name of subdirectory to clone.
dest (Path): Destination path of cloned project.
repo (str): URL of Git repo containing project templates.
branch (str): The branch to clone from
"""
dest = ensure_path(dest)
check_clone(name, dest, repo)
project_dir = dest.resolve()
repo_name = re.sub(r"(http(s?)):\/\/github.com/", "", repo)
try:
git_checkout(repo, name, dest, branch=branch, sparse=sparse_checkout)
except subprocess.CalledProcessError:
err = f"Could not clone '{name}' from repo '{repo_name}' (branch '{branch}')"
msg.fail(err, exits=1)
msg.good(f"Cloned '{name}' from '{repo_name}' (branch '{branch}')", project_dir)
if not (project_dir / PROJECT_FILE).exists():
msg.warn(f"No {PROJECT_FILE} found in directory")
else:
msg.good(f"Your project is now ready!")
print(f"To fetch the assets, run:\n{COMMAND} project assets {dest}")
def check_clone(name: str, dest: Path, repo: str) -> None:
"""Check and validate that the destination path can be used to clone. Will
check that Git is available and that the destination path is suitable.
name (str): Name of the directory to clone from the repo.
dest (Path): Local destination of cloned directory.
repo (str): URL of the repo to clone from.
"""
git_err = (
f"Cloning spaCy project templates requires Git and the 'git' command. "
f"To clone a project without Git, copy the files from the '{name}' "
f"directory in the {repo} to {dest} manually."
)
get_git_version(error=git_err)
if not dest:
msg.fail(f"Not a valid directory to clone project: {dest}", exits=1)
if dest.exists():
# Directory already exists (not allowed, clone needs to create it)
msg.fail(f"Can't clone project, directory already exists: {dest}", exits=1)
if not dest.parent.exists():
# We're not creating parents, parent dir should exist
msg.fail(
f"Can't clone project, parent directory doesn't exist: {dest.parent}. "
f"Create the necessary folder(s) first before continuing.",
exits=1,
)

115
spacy/cli/project/document.py
View File

@ -1,115 +0,0 @@
from pathlib import Path
from wasabi import MarkdownRenderer, msg
from ...util import working_dir
from .._util import PROJECT_FILE, Arg, Opt, load_project_config, project_cli
DOCS_URL = "https://spacy.io"
INTRO_PROJECT = f"""The [`{PROJECT_FILE}`]({PROJECT_FILE}) defines the data assets required by the
project, as well as the available commands and workflows. For details, see the
[spaCy projects documentation]({DOCS_URL}/usage/projects)."""
INTRO_COMMANDS = f"""The following commands are defined by the project. They
can be executed using [`spacy project run [name]`]({DOCS_URL}/api/cli#project-run).
Commands are only re-run if their inputs have changed."""
INTRO_WORKFLOWS = f"""The following workflows are defined by the project. They
can be executed using [`spacy project run [name]`]({DOCS_URL}/api/cli#project-run)
and will run the specified commands in order. Commands are only re-run if their
inputs have changed."""
INTRO_ASSETS = f"""The following assets are defined by the project. They can
be fetched by running [`spacy project assets`]({DOCS_URL}/api/cli#project-assets)
in the project directory."""
# These markers are added to the Markdown and can be used to update the file in
# place if it already exists. Only the auto-generated part will be replaced.
MARKER_START = "<!-- SPACY PROJECT: AUTO-GENERATED DOCS START (do not remove) -->"
MARKER_END = "<!-- SPACY PROJECT: AUTO-GENERATED DOCS END (do not remove) -->"
# If this marker is used in an existing README, it's ignored and not replaced
MARKER_IGNORE = "<!-- SPACY PROJECT: IGNORE -->"
@project_cli.command("document")
def project_document_cli(
# fmt: off
project_dir: Path = Arg(Path.cwd(), help="Path to cloned project. Defaults to current working directory.", exists=True, file_okay=False),
output_file: Path = Opt("-", "--output", "-o", help="Path to output Markdown file for output. Defaults to - for standard output"),
no_emoji: bool = Opt(False, "--no-emoji", "-NE", help="Don't use emoji")
# fmt: on
):
"""
Auto-generate a README.md for a project. If the content is saved to a file,
hidden markers are added so you can add custom content before or after the
auto-generated section and only the auto-generated docs will be replaced
when you re-run the command.
DOCS: https://spacy.io/api/cli#project-document
"""
project_document(project_dir, output_file, no_emoji=no_emoji)
def project_document(
project_dir: Path, output_file: Path, *, no_emoji: bool = False
) -> None:
is_stdout = str(output_file) == "-"
config = load_project_config(project_dir)
md = MarkdownRenderer(no_emoji=no_emoji)
md.add(MARKER_START)
title = config.get("title")
description = config.get("description")
md.add(md.title(1, f"spaCy Project{f': {title}' if title else ''}", "🪐"))
if description:
md.add(description)
md.add(md.title(2, PROJECT_FILE, "📋"))
md.add(INTRO_PROJECT)
# Commands
cmds = config.get("commands", [])
data = [(md.code(cmd["name"]), cmd.get("help", "")) for cmd in cmds]
if data:
md.add(md.title(3, "Commands", "⏯"))
md.add(INTRO_COMMANDS)
md.add(md.table(data, ["Command", "Description"]))
# Workflows
wfs = config.get("workflows", {}).items()
data = [(md.code(n), " &rarr; ".join(md.code(w) for w in stp)) for n, stp in wfs]
if data:
md.add(md.title(3, "Workflows", "⏭"))
md.add(INTRO_WORKFLOWS)
md.add(md.table(data, ["Workflow", "Steps"]))
# Assets
assets = config.get("assets", [])
data = []
for a in assets:
source = "Git" if a.get("git") else "URL" if a.get("url") else "Local"
dest_path = a["dest"]
dest = md.code(dest_path)
if source == "Local":
# Only link assets if they're in the repo
with working_dir(project_dir) as p:
if (p / dest_path).exists():
dest = md.link(dest, dest_path)
data.append((dest, source, a.get("description", "")))
if data:
md.add(md.title(3, "Assets", "🗂"))
md.add(INTRO_ASSETS)
md.add(md.table(data, ["File", "Source", "Description"]))
md.add(MARKER_END)
# Output result
if is_stdout:
print(md.text)
else:
content = md.text
if output_file.exists():
with output_file.open("r", encoding="utf8") as f:
existing = f.read()
if MARKER_IGNORE in existing:
msg.warn("Found ignore marker in existing file: skipping", output_file)
return
if MARKER_START in existing and MARKER_END in existing:
msg.info("Found existing file: only replacing auto-generated docs")
before = existing.split(MARKER_START)[0]
after = existing.split(MARKER_END)[1]
content = f"{before}{content}{after}"
else:
msg.warn("Replacing existing file")
with output_file.open("w", encoding="utf8") as f:
f.write(content)
msg.good("Saved project documentation", output_file)

220
spacy/cli/project/dvc.py
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@ -1,220 +0,0 @@
"""This module contains helpers and subcommands for integrating spaCy projects
with Data Version Controk (DVC). https://dvc.org"""
import subprocess
from pathlib import Path
from typing import Any, Dict, Iterable, List, Optional
from wasabi import msg
from ...util import (
SimpleFrozenList,
join_command,
run_command,
split_command,
working_dir,
)
from .._util import (
COMMAND,
NAME,
PROJECT_FILE,
Arg,
Opt,
get_hash,
load_project_config,
project_cli,
)
DVC_CONFIG = "dvc.yaml"
DVC_DIR = ".dvc"
UPDATE_COMMAND = "dvc"
DVC_CONFIG_COMMENT = f"""# This file is auto-generated by spaCy based on your {PROJECT_FILE}. If you've
# edited your {PROJECT_FILE}, you can regenerate this file by running:
# {COMMAND} project {UPDATE_COMMAND}"""
@project_cli.command(UPDATE_COMMAND)
def project_update_dvc_cli(
# fmt: off
project_dir: Path = Arg(Path.cwd(), help="Location of project directory. Defaults to current working directory.", exists=True, file_okay=False),
workflow: Optional[str] = Arg(None, help=f"Name of workflow defined in {PROJECT_FILE}. Defaults to first workflow if not set."),
verbose: bool = Opt(False, "--verbose", "-V", help="Print more info"),
quiet: bool = Opt(False, "--quiet", "-q", help="Print less info"),
force: bool = Opt(False, "--force", "-F", help="Force update DVC config"),
# fmt: on
):
"""Auto-generate Data Version Control (DVC) config. A DVC
project can only define one pipeline, so you need to specify one workflow
defined in the project.yml. If no workflow is specified, the first defined
workflow is used. The DVC config will only be updated if the project.yml
changed.
DOCS: https://spacy.io/api/cli#project-dvc
"""
project_update_dvc(project_dir, workflow, verbose=verbose, quiet=quiet, force=force)
def project_update_dvc(
project_dir: Path,
workflow: Optional[str] = None,
*,
verbose: bool = False,
quiet: bool = False,
force: bool = False,
) -> None:
"""Update the auto-generated Data Version Control (DVC) config file. A DVC
project can only define one pipeline, so you need to specify one workflow
defined in the project.yml. Will only update the file if the checksum changed.
project_dir (Path): The project directory.
workflow (Optional[str]): Optional name of workflow defined in project.yml.
If not set, the first workflow will be used.
verbose (bool): Print more info.
quiet (bool): Print less info.
force (bool): Force update DVC config.
"""
config = load_project_config(project_dir)
updated = update_dvc_config(
project_dir, config, workflow, verbose=verbose, quiet=quiet, force=force
)
help_msg = "To execute the workflow with DVC, run: dvc repro"
if updated:
msg.good(f"Updated DVC config from {PROJECT_FILE}", help_msg)
else:
msg.info(f"No changes found in {PROJECT_FILE}, no update needed", help_msg)
def update_dvc_config(
path: Path,
config: Dict[str, Any],
workflow: Optional[str] = None,
verbose: bool = False,
quiet: bool = False,
force: bool = False,
) -> bool:
"""Re-run the DVC commands in dry mode and update dvc.yaml file in the
project directory. The file is auto-generated based on the config. The
first line of the auto-generated file specifies the hash of the config
dict, so if any of the config values change, the DVC config is regenerated.
path (Path): The path to the project directory.
config (Dict[str, Any]): The loaded project.yml.
verbose (bool): Whether to print additional info (via DVC).
quiet (bool): Don't output anything (via DVC).
force (bool): Force update, even if hashes match.
RETURNS (bool): Whether the DVC config file was updated.
"""
ensure_dvc(path)
workflows = config.get("workflows", {})
workflow_names = list(workflows.keys())
check_workflows(workflow_names, workflow)
if not workflow:
workflow = workflow_names[0]
config_hash = get_hash(config)
path = path.resolve()
dvc_config_path = path / DVC_CONFIG
if dvc_config_path.exists():
# Check if the file was generated using the current config, if not, redo
with dvc_config_path.open("r", encoding="utf8") as f:
ref_hash = f.readline().strip().replace("# ", "")
if ref_hash == config_hash and not force:
return False # Nothing has changed in project.yml, don't need to update
dvc_config_path.unlink()
dvc_commands = []
config_commands = {cmd["name"]: cmd for cmd in config.get("commands", [])}
# some flags that apply to every command
flags = []
if verbose:
flags.append("--verbose")
if quiet:
flags.append("--quiet")
for name in workflows[workflow]:
command = config_commands[name]
deps = command.get("deps", [])
outputs = command.get("outputs", [])
outputs_no_cache = command.get("outputs_no_cache", [])
if not deps and not outputs and not outputs_no_cache:
continue
# Default to the working dir as the project path since dvc.yaml is auto-generated
# and we don't want arbitrary paths in there
project_cmd = ["python", "-m", NAME, "project", "run", name]
deps_cmd = [c for cl in [["-d", p] for p in deps] for c in cl]
outputs_cmd = [c for cl in [["-o", p] for p in outputs] for c in cl]
outputs_nc_cmd = [c for cl in [["-O", p] for p in outputs_no_cache] for c in cl]
dvc_cmd = ["run", *flags, "-n", name, "-w", str(path), "--no-exec"]
if command.get("no_skip"):
dvc_cmd.append("--always-changed")
full_cmd = [*dvc_cmd, *deps_cmd, *outputs_cmd, *outputs_nc_cmd, *project_cmd]
dvc_commands.append(join_command(full_cmd))
if not dvc_commands:
# If we don't check for this, then there will be an error when reading the
# config, since DVC wouldn't create it.
msg.fail(
"No usable commands for DVC found. This can happen if none of your "
"commands have dependencies or outputs.",
exits=1,
)
with working_dir(path):
for c in dvc_commands:
dvc_command = "dvc " + c
run_command(dvc_command)
with dvc_config_path.open("r+", encoding="utf8") as f:
content = f.read()
f.seek(0, 0)
f.write(f"# {config_hash}\n{DVC_CONFIG_COMMENT}\n{content}")
return True
def check_workflows(workflows: List[str], workflow: Optional[str] = None) -> None:
"""Validate workflows provided in project.yml and check that a given
workflow can be used to generate a DVC config.
workflows (List[str]): Names of the available workflows.
workflow (Optional[str]): The name of the workflow to convert.
"""
if not workflows:
msg.fail(
f"No workflows defined in {PROJECT_FILE}. To generate a DVC config, "
f"define at least one list of commands.",
exits=1,
)
if workflow is not None and workflow not in workflows:
msg.fail(
f"Workflow '{workflow}' not defined in {PROJECT_FILE}. "
f"Available workflows: {', '.join(workflows)}",
exits=1,
)
if not workflow:
msg.warn(
f"No workflow specified for DVC pipeline. Using the first workflow "
f"defined in {PROJECT_FILE}: '{workflows[0]}'"
)
def ensure_dvc(project_dir: Path) -> None:
"""Ensure that the "dvc" command is available and that the current project
directory is an initialized DVC project.
"""
try:
subprocess.run(["dvc", "--version"], stdout=subprocess.DEVNULL)
except Exception:
msg.fail(
"To use spaCy projects with DVC (Data Version Control), DVC needs "
"to be installed and the 'dvc' command needs to be available",
"You can install the Python package from pip (pip install dvc) or "
"conda (conda install -c conda-forge dvc). For more details, see the "
"documentation: https://dvc.org/doc/install",
exits=1,
)
if not (project_dir / ".dvc").exists():
msg.fail(
"Project not initialized as a DVC project",
"To initialize a DVC project, you can run 'dvc init' in the project "
"directory. For more details, see the documentation: "
"https://dvc.org/doc/command-reference/init",
exits=1,
)

67
spacy/cli/project/pull.py
View File

@ -1,67 +0,0 @@
from pathlib import Path
from wasabi import msg
from .._util import Arg, load_project_config, logger, project_cli
from .remote_storage import RemoteStorage, get_command_hash
from .run import update_lockfile
@project_cli.command("pull")
def project_pull_cli(
# fmt: off
remote: str = Arg("default", help="Name or path of remote storage"),
project_dir: Path = Arg(Path.cwd(), help="Location of project directory. Defaults to current working directory.", exists=True, file_okay=False),
# fmt: on
):
"""Retrieve available precomputed outputs from a remote storage.
You can alias remotes in your project.yml by mapping them to storage paths.
A storage can be anything that the smart-open library can upload to, e.g.
AWS, Google Cloud Storage, SSH, local directories etc.
DOCS: https://spacy.io/api/cli#project-pull
"""
for url, output_path in project_pull(project_dir, remote):
if url is not None:
msg.good(f"Pulled {output_path} from {url}")
def project_pull(project_dir: Path, remote: str, *, verbose: bool = False):
# TODO: We don't have tests for this :(. It would take a bit of mockery to
# set up. I guess see if it breaks first?
config = load_project_config(project_dir)
if remote in config.get("remotes", {}):
remote = config["remotes"][remote]
storage = RemoteStorage(project_dir, remote)
commands = list(config.get("commands", []))
# We use a while loop here because we don't know how the commands
# will be ordered. A command might need dependencies from one that's later
# in the list.
while commands:
for i, cmd in enumerate(list(commands)):
logger.debug("CMD: %s.", cmd["name"])
deps = [project_dir / dep for dep in cmd.get("deps", [])]
if all(dep.exists() for dep in deps):
cmd_hash = get_command_hash("", "", deps, cmd["script"])
for output_path in cmd.get("outputs", []):
url = storage.pull(output_path, command_hash=cmd_hash)
logger.debug(
"URL: %s for %s with command hash %s",
url,
output_path,
cmd_hash,
)
yield url, output_path
out_locs = [project_dir / out for out in cmd.get("outputs", [])]
if all(loc.exists() for loc in out_locs):
update_lockfile(project_dir, cmd)
# We remove the command from the list here, and break, so that
# we iterate over the loop again.
commands.pop(i)
break
else:
logger.debug("Dependency missing. Skipping %s outputs.", cmd["name"])
else:
# If we didn't break the for loop, break the while loop.
break

69
spacy/cli/project/push.py
View File

@ -1,69 +0,0 @@
from pathlib import Path
from wasabi import msg
from .._util import Arg, load_project_config, logger, project_cli
from .remote_storage import RemoteStorage, get_command_hash, get_content_hash
@project_cli.command("push")
def project_push_cli(
# fmt: off
remote: str = Arg("default", help="Name or path of remote storage"),
project_dir: Path = Arg(Path.cwd(), help="Location of project directory. Defaults to current working directory.", exists=True, file_okay=False),
# fmt: on
):
"""Persist outputs to a remote storage. You can alias remotes in your
project.yml by mapping them to storage paths. A storage can be anything that
the smart-open library can upload to, e.g. AWS, Google Cloud Storage, SSH,
local directories etc.
DOCS: https://spacy.io/api/cli#project-push
"""
for output_path, url in project_push(project_dir, remote):
if url is None:
msg.info(f"Skipping {output_path}")
else:
msg.good(f"Pushed {output_path} to {url}")
def project_push(project_dir: Path, remote: str):
"""Persist outputs to a remote storage. You can alias remotes in your project.yml
by mapping them to storage paths. A storage can be anything that the smart-open
library can upload to, e.g. gcs, aws, ssh, local directories etc
"""
config = load_project_config(project_dir)
if remote in config.get("remotes", {}):
remote = config["remotes"][remote]
storage = RemoteStorage(project_dir, remote)
for cmd in config.get("commands", []):
logger.debug("CMD: %s", cmd["name"])
deps = [project_dir / dep for dep in cmd.get("deps", [])]
if any(not dep.exists() for dep in deps):
logger.debug("Dependency missing. Skipping %s outputs", cmd["name"])
continue
cmd_hash = get_command_hash(
"", "", [project_dir / dep for dep in cmd.get("deps", [])], cmd["script"]
)
logger.debug("CMD_HASH: %s", cmd_hash)
for output_path in cmd.get("outputs", []):
output_loc = project_dir / output_path
if output_loc.exists() and _is_not_empty_dir(output_loc):
url = storage.push(
output_path,
command_hash=cmd_hash,
content_hash=get_content_hash(output_loc),
)
logger.debug(
"URL: %s for output %s with cmd_hash %s", url, output_path, cmd_hash
)
yield output_path, url
def _is_not_empty_dir(loc: Path):
if not loc.is_dir():
return True
elif any(_is_not_empty_dir(child) for child in loc.iterdir()):
return True
else:
return False

212
spacy/cli/project/remote_storage.py
View File

@ -1,212 +0,0 @@
import hashlib
import os
import site
import tarfile
import urllib.parse
from pathlib import Path
from typing import TYPE_CHECKING, Dict, List, Optional
from wasabi import msg
from ... import about
from ...errors import Errors
from ...git_info import GIT_VERSION
from ...util import ENV_VARS, check_bool_env_var, get_minor_version
from .._util import (
download_file,
ensure_pathy,
get_checksum,
get_hash,
make_tempdir,
upload_file,
)
if TYPE_CHECKING:
from pathy import FluidPath # noqa: F401
class RemoteStorage:
"""Push and pull outputs to and from a remote file storage.
Remotes can be anything that `smart-open` can support: AWS, GCS, file system,
ssh, etc.
"""
def __init__(self, project_root: Path, url: str, *, compression="gz"):
self.root = project_root
self.url = ensure_pathy(url)
self.compression = compression
def push(self, path: Path, command_hash: str, content_hash: str) -> "FluidPath":
"""Compress a file or directory within a project and upload it to a remote
storage. If an object exists at the full URL, nothing is done.
Within the remote storage, files are addressed by their project path
(url encoded) and two user-supplied hashes, representing their creation
context and their file contents. If the URL already exists, the data is
not uploaded. Paths are archived and compressed prior to upload.
"""
loc = self.root / path
if not loc.exists():
raise IOError(f"Cannot push {loc}: does not exist.")
url = self.make_url(path, command_hash, content_hash)
if url.exists():
return url
tmp: Path
with make_tempdir() as tmp:
tar_loc = tmp / self.encode_name(str(path))
mode_string = f"w:{self.compression}" if self.compression else "w"
with tarfile.open(tar_loc, mode=mode_string) as tar_file:
tar_file.add(str(loc), arcname=str(path))
upload_file(tar_loc, url)
return url
def pull(
self,
path: Path,
*,
command_hash: Optional[str] = None,
content_hash: Optional[str] = None,
) -> Optional["FluidPath"]:
"""Retrieve a file from the remote cache. If the file already exists,
nothing is done.
If the command_hash and/or content_hash are specified, only matching
results are returned. If no results are available, an error is raised.
"""
dest = self.root / path
if dest.exists():
return None
url = self.find(path, command_hash=command_hash, content_hash=content_hash)
if url is None:
return url
else:
# Make sure the destination exists
if not dest.parent.exists():
dest.parent.mkdir(parents=True)
tmp: Path
with make_tempdir() as tmp:
tar_loc = tmp / url.parts[-1]
download_file(url, tar_loc)
mode_string = f"r:{self.compression}" if self.compression else "r"
with tarfile.open(tar_loc, mode=mode_string) as tar_file:
# This requires that the path is added correctly, relative
# to root. This is how we set things up in push()
# Disallow paths outside the current directory for the tar
# file (CVE-2007-4559, directory traversal vulnerability)
def is_within_directory(directory, target):
abs_directory = os.path.abspath(directory)
abs_target = os.path.abspath(target)
prefix = os.path.commonprefix([abs_directory, abs_target])
return prefix == abs_directory
def safe_extract(tar, path):
for member in tar.getmembers():
member_path = os.path.join(path, member.name)
if not is_within_directory(path, member_path):
raise ValueError(Errors.E852)
tar.extractall(path)
safe_extract(tar_file, self.root)
return url
def find(
self,
path: Path,
*,
command_hash: Optional[str] = None,
content_hash: Optional[str] = None,
) -> Optional["FluidPath"]:
"""Find the best matching version of a file within the storage,
or `None` if no match can be found. If both the creation and content hash
are specified, only exact matches will be returned. Otherwise, the most
recent matching file is preferred.
"""
name = self.encode_name(str(path))
urls = []
if command_hash is not None and content_hash is not None:
url = self.url / name / command_hash / content_hash
urls = [url] if url.exists() else []
elif command_hash is not None:
if (self.url / name / command_hash).exists():
urls = list((self.url / name / command_hash).iterdir())
else:
if (self.url / name).exists():
for sub_dir in (self.url / name).iterdir():
urls.extend(sub_dir.iterdir())
if content_hash is not None:
urls = [url for url in urls if url.parts[-1] == content_hash]
if len(urls) >= 2:
try:
urls.sort(key=lambda x: x.stat().last_modified) # type: ignore
except Exception:
msg.warn(
"Unable to sort remote files by last modified. The file(s) "
"pulled from the cache may not be the most recent."
)
return urls[-1] if urls else None
def make_url(self, path: Path, command_hash: str, content_hash: str) -> "FluidPath":
"""Construct a URL from a subpath, a creation hash and a content hash."""
return self.url / self.encode_name(str(path)) / command_hash / content_hash
def encode_name(self, name: str) -> str:
"""Encode a subpath into a URL-safe name."""
return urllib.parse.quote_plus(name)
def get_content_hash(loc: Path) -> str:
return get_checksum(loc)
def get_command_hash(
site_hash: str, env_hash: str, deps: List[Path], cmd: List[str]
) -> str:
"""Create a hash representing the execution of a command. This includes the
currently installed packages, whatever environment variables have been marked
as relevant, and the command.
"""
if check_bool_env_var(ENV_VARS.PROJECT_USE_GIT_VERSION):
spacy_v = GIT_VERSION
else:
spacy_v = str(get_minor_version(about.__version__) or "")
dep_checksums = [get_checksum(dep) for dep in sorted(deps)]
hashes = [spacy_v, site_hash, env_hash] + dep_checksums
hashes.extend(cmd)
creation_bytes = "".join(hashes).encode("utf8")
return hashlib.md5(creation_bytes).hexdigest()
def get_site_hash():
"""Hash the current Python environment's site-packages contents, including
the name and version of the libraries. The list we're hashing is what
`pip freeze` would output.
"""
site_dirs = site.getsitepackages()
if site.ENABLE_USER_SITE:
site_dirs.extend(site.getusersitepackages())
packages = set()
for site_dir in site_dirs:
site_dir = Path(site_dir)
for subpath in site_dir.iterdir():
if subpath.parts[-1].endswith("dist-info"):
packages.add(subpath.parts[-1].replace(".dist-info", ""))
package_bytes = "".join(sorted(packages)).encode("utf8")
return hashlib.md5sum(package_bytes).hexdigest()
def get_env_hash(env: Dict[str, str]) -> str:
"""Construct a hash of the environment variables that will be passed into
the commands.
Values in the env dict may be references to the current os.environ, using
the syntax $ENV_VAR to mean os.environ[ENV_VAR]
"""
env_vars = {}
for key, value in env.items():
if value.startswith("$"):
env_vars[key] = os.environ.get(value[1:], "")
else:
env_vars[key] = value
return get_hash(env_vars)

379
spacy/cli/project/run.py
View File

@ -1,379 +0,0 @@
import os.path
import sys
from pathlib import Path
from typing import Any, Dict, Iterable, List, Optional, Sequence, Tuple
import srsly
import typer
from wasabi import msg
from wasabi.util import locale_escape
from ... import about
from ...git_info import GIT_VERSION
from ...util import (
ENV_VARS,
SimpleFrozenDict,
SimpleFrozenList,
check_bool_env_var,
is_cwd,
is_minor_version_match,
join_command,
run_command,
split_command,
working_dir,
)
from .._util import (
COMMAND,
PROJECT_FILE,
PROJECT_LOCK,
Arg,
Opt,
get_checksum,
get_hash,
load_project_config,
parse_config_overrides,
project_cli,
)
@project_cli.command(
"run", context_settings={"allow_extra_args": True, "ignore_unknown_options": True}
)
def project_run_cli(
# fmt: off
ctx: typer.Context, # This is only used to read additional arguments
subcommand: str = Arg(None, help=f"Name of command defined in the {PROJECT_FILE}"),
project_dir: Path = Arg(Path.cwd(), help="Location of project directory. Defaults to current working directory.", exists=True, file_okay=False),
force: bool = Opt(False, "--force", "-F", help="Force re-running steps, even if nothing changed"),
dry: bool = Opt(False, "--dry", "-D", help="Perform a dry run and don't execute scripts"),
show_help: bool = Opt(False, "--help", help="Show help message and available subcommands")
# fmt: on
):
"""Run a named command or workflow defined in the project.yml. If a workflow
name is specified, all commands in the workflow are run, in order. If
commands define dependencies and/or outputs, they will only be re-run if
state has changed.
DOCS: https://spacy.io/api/cli#project-run
"""
if show_help or not subcommand:
print_run_help(project_dir, subcommand)
else:
overrides = parse_config_overrides(ctx.args)
project_run(project_dir, subcommand, overrides=overrides, force=force, dry=dry)
def project_run(
project_dir: Path,
subcommand: str,
*,
overrides: Dict[str, Any] = SimpleFrozenDict(),
force: bool = False,
dry: bool = False,
capture: bool = False,
skip_requirements_check: bool = False,
) -> None:
"""Run a named script defined in the project.yml. If the script is part
of the default pipeline (defined in the "run" section), DVC is used to
execute the command, so it can determine whether to rerun it. It then
calls into "exec" to execute it.
project_dir (Path): Path to project directory.
subcommand (str): Name of command to run.
overrides (Dict[str, Any]): Optional config overrides.
force (bool): Force re-running, even if nothing changed.
dry (bool): Perform a dry run and don't execute commands.
capture (bool): Whether to capture the output and errors of individual commands.
If False, the stdout and stderr will not be redirected, and if there's an error,
sys.exit will be called with the return code. You should use capture=False
when you want to turn over execution to the command, and capture=True
when you want to run the command more like a function.
skip_requirements_check (bool): Whether to skip the requirements check.
"""
config = load_project_config(project_dir, overrides=overrides)
commands = {cmd["name"]: cmd for cmd in config.get("commands", [])}
workflows = config.get("workflows", {})
validate_subcommand(list(commands.keys()), list(workflows.keys()), subcommand)
req_path = project_dir / "requirements.txt"
if not skip_requirements_check:
if config.get("check_requirements", True) and os.path.exists(req_path):
with req_path.open() as requirements_file:
_check_requirements([req.strip() for req in requirements_file])
if subcommand in workflows:
msg.info(f"Running workflow '{subcommand}'")
for cmd in workflows[subcommand]:
project_run(
project_dir,
cmd,
overrides=overrides,
force=force,
dry=dry,
capture=capture,
skip_requirements_check=True,
)
else:
cmd = commands[subcommand]
for dep in cmd.get("deps", []):
if not (project_dir / dep).exists():
err = f"Missing dependency specified by command '{subcommand}': {dep}"
err_help = "Maybe you forgot to run the 'project assets' command or a previous step?"
err_exits = 1 if not dry else None
msg.fail(err, err_help, exits=err_exits)
check_spacy_commit = check_bool_env_var(ENV_VARS.PROJECT_USE_GIT_VERSION)
with working_dir(project_dir) as current_dir:
msg.divider(subcommand)
rerun = check_rerun(current_dir, cmd, check_spacy_commit=check_spacy_commit)
if not rerun and not force:
msg.info(f"Skipping '{cmd['name']}': nothing changed")
else:
run_commands(cmd["script"], dry=dry, capture=capture)
if not dry:
update_lockfile(current_dir, cmd)
def print_run_help(project_dir: Path, subcommand: Optional[str] = None) -> None:
"""Simulate a CLI help prompt using the info available in the project.yml.
project_dir (Path): The project directory.
subcommand (Optional[str]): The subcommand or None. If a subcommand is
provided, the subcommand help is shown. Otherwise, the top-level help
and a list of available commands is printed.
"""
config = load_project_config(project_dir)
config_commands = config.get("commands", [])
commands = {cmd["name"]: cmd for cmd in config_commands}
workflows = config.get("workflows", {})
project_loc = "" if is_cwd(project_dir) else project_dir
if subcommand:
validate_subcommand(list(commands.keys()), list(workflows.keys()), subcommand)
print(f"Usage: {COMMAND} project run {subcommand} {project_loc}")
if subcommand in commands:
help_text = commands[subcommand].get("help")
if help_text:
print(f"\n{help_text}\n")
elif subcommand in workflows:
steps = workflows[subcommand]
print(f"\nWorkflow consisting of {len(steps)} commands:")
steps_data = [
(f"{i + 1}. {step}", commands[step].get("help", ""))
for i, step in enumerate(steps)
]
msg.table(steps_data)
help_cmd = f"{COMMAND} project run [COMMAND] {project_loc} --help"
print(f"For command details, run: {help_cmd}")
else:
print("")
title = config.get("title")
if title:
print(f"{locale_escape(title)}\n")
if config_commands:
print(f"Available commands in {PROJECT_FILE}")
print(f"Usage: {COMMAND} project run [COMMAND] {project_loc}")
msg.table([(cmd["name"], cmd.get("help", "")) for cmd in config_commands])
if workflows:
print(f"Available workflows in {PROJECT_FILE}")
print(f"Usage: {COMMAND} project run [WORKFLOW] {project_loc}")
msg.table([(name, " -> ".join(steps)) for name, steps in workflows.items()])
def run_commands(
commands: Iterable[str] = SimpleFrozenList(),
silent: bool = False,
dry: bool = False,
capture: bool = False,
) -> None:
"""Run a sequence of commands in a subprocess, in order.
commands (List[str]): The string commands.
silent (bool): Don't print the commands.
dry (bool): Perform a dry run and don't execut anything.
capture (bool): Whether to capture the output and errors of individual commands.
If False, the stdout and stderr will not be redirected, and if there's an error,
sys.exit will be called with the return code. You should use capture=False
when you want to turn over execution to the command, and capture=True
when you want to run the command more like a function.
"""
for c in commands:
command = split_command(c)
# Not sure if this is needed or a good idea. Motivation: users may often
# use commands in their config that reference "python" and we want to
# make sure that it's always executing the same Python that spaCy is
# executed with and the pip in the same env, not some other Python/pip.
# Also ensures cross-compatibility if user 1 writes "python3" (because
# that's how it's set up on their system), and user 2 without the
# shortcut tries to re-run the command.
if len(command) and command[0] in ("python", "python3"):
command[0] = sys.executable
elif len(command) and command[0] in ("pip", "pip3"):
command = [sys.executable, "-m", "pip", *command[1:]]
if not silent:
print(f"Running command: {join_command(command)}")
if not dry:
run_command(command, capture=capture)
def validate_subcommand(
commands: Sequence[str], workflows: Sequence[str], subcommand: str
) -> None:
"""Check that a subcommand is valid and defined. Raises an error otherwise.
commands (Sequence[str]): The available commands.
subcommand (str): The subcommand.
"""
if not commands and not workflows:
msg.fail(f"No commands or workflows defined in {PROJECT_FILE}", exits=1)
if subcommand not in commands and subcommand not in workflows:
help_msg = []
if subcommand in ["assets", "asset"]:
help_msg.append("Did you mean to run: python -m spacy project assets?")
if commands:
help_msg.append(f"Available commands: {', '.join(commands)}")
if workflows:
help_msg.append(f"Available workflows: {', '.join(workflows)}")
msg.fail(
f"Can't find command or workflow '{subcommand}' in {PROJECT_FILE}",
". ".join(help_msg),
exits=1,
)
def check_rerun(
project_dir: Path,
command: Dict[str, Any],
*,
check_spacy_version: bool = True,
check_spacy_commit: bool = False,
) -> bool:
"""Check if a command should be rerun because its settings or inputs/outputs
changed.
project_dir (Path): The current project directory.
command (Dict[str, Any]): The command, as defined in the project.yml.
strict_version (bool):
RETURNS (bool): Whether to re-run the command.
"""
# Always rerun if no-skip is set
if command.get("no_skip", False):
return True
lock_path = project_dir / PROJECT_LOCK
if not lock_path.exists(): # We don't have a lockfile, run command
return True
data = srsly.read_yaml(lock_path)
if command["name"] not in data: # We don't have info about this command
return True
entry = data[command["name"]]
# Always run commands with no outputs (otherwise they'd always be skipped)
if not entry.get("outs", []):
return True
# Always rerun if spaCy version or commit hash changed
spacy_v = entry.get("spacy_version")
commit = entry.get("spacy_git_version")
if check_spacy_version and not is_minor_version_match(spacy_v, about.__version__):
info = f"({spacy_v} in {PROJECT_LOCK}, {about.__version__} current)"
msg.info(f"Re-running '{command['name']}': spaCy minor version changed {info}")
return True
if check_spacy_commit and commit != GIT_VERSION:
info = f"({commit} in {PROJECT_LOCK}, {GIT_VERSION} current)"
msg.info(f"Re-running '{command['name']}': spaCy commit changed {info}")
return True
# If the entry in the lockfile matches the lockfile entry that would be
# generated from the current command, we don't rerun because it means that
# all inputs/outputs, hashes and scripts are the same and nothing changed
lock_entry = get_lock_entry(project_dir, command)
exclude = ["spacy_version", "spacy_git_version"]
return get_hash(lock_entry, exclude=exclude) != get_hash(entry, exclude=exclude)
def update_lockfile(project_dir: Path, command: Dict[str, Any]) -> None:
"""Update the lockfile after running a command. Will create a lockfile if
it doesn't yet exist and will add an entry for the current command, its
script and dependencies/outputs.
project_dir (Path): The current project directory.
command (Dict[str, Any]): The command, as defined in the project.yml.
"""
lock_path = project_dir / PROJECT_LOCK
if not lock_path.exists():
srsly.write_yaml(lock_path, {})
data = {}
else:
data = srsly.read_yaml(lock_path)
data[command["name"]] = get_lock_entry(project_dir, command)
srsly.write_yaml(lock_path, data)
def get_lock_entry(project_dir: Path, command: Dict[str, Any]) -> Dict[str, Any]:
"""Get a lockfile entry for a given command. An entry includes the command,
the script (command steps) and a list of dependencies and outputs with
their paths and file hashes, if available. The format is based on the
dvc.lock files, to keep things consistent.
project_dir (Path): The current project directory.
command (Dict[str, Any]): The command, as defined in the project.yml.
RETURNS (Dict[str, Any]): The lockfile entry.
"""
deps = get_fileinfo(project_dir, command.get("deps", []))
outs = get_fileinfo(project_dir, command.get("outputs", []))
outs_nc = get_fileinfo(project_dir, command.get("outputs_no_cache", []))
return {
"cmd": f"{COMMAND} run {command['name']}",
"script": command["script"],
"deps": deps,
"outs": [*outs, *outs_nc],
"spacy_version": about.__version__,
"spacy_git_version": GIT_VERSION,
}
def get_fileinfo(project_dir: Path, paths: List[str]) -> List[Dict[str, Optional[str]]]:
"""Generate the file information for a list of paths (dependencies, outputs).
Includes the file path and the file's checksum.
project_dir (Path): The current project directory.
paths (List[str]): The file paths.
RETURNS (List[Dict[str, str]]): The lockfile entry for a file.
"""
data = []
for path in paths:
file_path = project_dir / path
md5 = get_checksum(file_path) if file_path.exists() else None
data.append({"path": path, "md5": md5})
return data
def _check_requirements(requirements: List[str]) -> Tuple[bool, bool]:
"""Checks whether requirements are installed and free of version conflicts.
requirements (List[str]): List of requirements.
RETURNS (Tuple[bool, bool]): Whether (1) any packages couldn't be imported, (2) any packages with version conflicts
exist.
"""
import pkg_resources
failed_pkgs_msgs: List[str] = []
conflicting_pkgs_msgs: List[str] = []
for req in requirements:
try:
pkg_resources.require(req)
except pkg_resources.DistributionNotFound as dnf:
failed_pkgs_msgs.append(dnf.report())
except pkg_resources.VersionConflict as vc:
conflicting_pkgs_msgs.append(vc.report())
except Exception:
msg.warn(
f"Unable to check requirement: {req} "
"Checks are currently limited to requirement specifiers "
"(PEP 508)"
)
if len(failed_pkgs_msgs) or len(conflicting_pkgs_msgs):
msg.warn(
title="Missing requirements or requirement conflicts detected. Make sure your Python environment is set up "
"correctly and you installed all requirements specified in your project's requirements.txt: "
)
for pgk_msg in failed_pkgs_msgs + conflicting_pkgs_msgs:
msg.text(pgk_msg)
return len(failed_pkgs_msgs) > 0, len(conflicting_pkgs_msgs) > 0

3
spacy/cli/train.py
View File

@ -47,7 +47,8 @@ def train_cli(
DOCS: https://spacy.io/api/cli#train
"""
util.logger.setLevel(logging.DEBUG if verbose else logging.INFO)
if verbose:
util.logger.setLevel(logging.DEBUG)
overrides = parse_config_overrides(ctx.args)
import_code(code_path)
train(config_path, output_path, use_gpu=use_gpu, overrides=overrides)

3
spacy/default_config.cfg
View File

@ -26,6 +26,9 @@ batch_size = 1000
[nlp.tokenizer]
@tokenizers = "spacy.Tokenizer.v1"
[nlp.vectors]
@vectors = "spacy.Vectors.v1"
# The pipeline components and their models
[components]

9
spacy/displacy/render.py
View File

@ -1,4 +1,3 @@
import itertools
import uuid
from typing import Any, Dict, List, Optional, Tuple, Union
@ -218,7 +217,7 @@ class SpanRenderer:
+ (self.offset_step * (len(entities) - 1))
)
markup += self.span_template.format(
text=token["text"],
text=escape_html(token["text"]),
span_slices=slices,
span_starts=starts,
total_height=total_height,
@ -314,6 +313,8 @@ class DependencyRenderer:
self.lang = settings.get("lang", DEFAULT_LANG)
render_id = f"{id_prefix}-{i}"
svg = self.render_svg(render_id, p["words"], p["arcs"])
if p.get("title"):
svg = TPL_TITLE.format(title=p.get("title")) + svg
rendered.append(svg)
if page:
content = "".join([TPL_FIGURE.format(content=svg) for svg in rendered])
@ -566,7 +567,7 @@ class EntityRenderer:
for i, fragment in enumerate(fragments):
markup += escape_html(fragment)
if len(fragments) > 1 and i != len(fragments) - 1:
markup += "</br>"
markup += "<br>"
if self.ents is None or label.upper() in self.ents:
color = self.colors.get(label.upper(), self.default_color)
ent_settings = {
@ -584,7 +585,7 @@ class EntityRenderer:
for i, fragment in enumerate(fragments):
markup += escape_html(fragment)
if len(fragments) > 1 and i != len(fragments) - 1:
markup += "</br>"
markup += "<br>"
markup = TPL_ENTS.format(content=markup, dir=self.direction)
if title:
markup = TPL_TITLE.format(title=title) + markup

7
spacy/errors.py
View File

@ -219,6 +219,7 @@ class Warnings(metaclass=ErrorsWithCodes):
W125 = ("The StaticVectors key_attr is no longer used. To set a custom "
"key attribute for vectors, configure it through Vectors(attr=) or "
"'spacy init vectors --attr'")
W126 = ("These keys are unsupported: {unsupported}")
class Errors(metaclass=ErrorsWithCodes):
@ -553,12 +554,12 @@ class Errors(metaclass=ErrorsWithCodes):
"during training, make sure to include it in 'annotating components'")
# New errors added in v3.x
E849 = ("The vocab only supports {method} for vectors of type "
"spacy.vectors.Vectors, not {vectors_type}.")
E850 = ("The PretrainVectors objective currently only supports default or "
"floret vectors, not {mode} vectors.")
E851 = ("The 'textcat' component labels should only have values of 0 or 1, "
"but found value of '{val}'.")
E852 = ("The tar file pulled from the remote attempted an unsafe path "
"traversal.")
E853 = ("Unsupported component factory name '{name}'. The character '.' is "
"not permitted in factory names.")
E854 = ("Unable to set doc.ents. Check that the 'ents_filter' does not "
@ -981,6 +982,8 @@ class Errors(metaclass=ErrorsWithCodes):
" 'min_length': {min_length}, 'max_length': {max_length}")
E1054 = ("The text, including whitespace, must match between reference and "
"predicted docs when training {component}.")
E1055 = ("The 'replace_listener' callback expects {num_params} parameters, "
"but only callbacks with one or three parameters are supported")
# Deprecated model shortcuts, only used in errors and warnings

3
spacy/kb/candidate.pxd
View File

@ -4,7 +4,8 @@ from ..typedefs cimport hash_t
from .kb cimport KnowledgeBase
# Object used by the Entity Linker that summarizes one entity-alias candidate combination.
# Object used by the Entity Linker that summarizes one entity-alias candidate
# combination.
cdef class Candidate:
cdef readonly KnowledgeBase kb
cdef hash_t entity_hash

29
spacy/kb/candidate.pyx
View File

@ -1,4 +1,4 @@
# cython: infer_types=True, profile=True
# cython: infer_types=True
from typing import Iterable
@ -8,15 +8,24 @@ from ..tokens import Span
cdef class Candidate:
"""A `Candidate` object refers to a textual mention (`alias`) that may or may not be resolved
to a specific `entity` from a Knowledge Base. This will be used as input for the entity linking
algorithm which will disambiguate the various candidates to the correct one.
"""A `Candidate` object refers to a textual mention (`alias`) that may or
may not be resolved to a specific `entity` from a Knowledge Base. This
will be used as input for the entity linking algorithm which will
disambiguate the various candidates to the correct one.
Each candidate (alias, entity) pair is assigned a certain prior probability.
DOCS: https://spacy.io/api/kb/#candidate-init
"""
def __init__(self, KnowledgeBase kb, entity_hash, entity_freq, entity_vector, alias_hash, prior_prob):
def __init__(
self,
KnowledgeBase kb,
entity_hash,
entity_freq,
entity_vector,
alias_hash,
prior_prob
):
self.kb = kb
self.entity_hash = entity_hash
self.entity_freq = entity_freq
@ -59,7 +68,8 @@ cdef class Candidate:
def get_candidates(kb: KnowledgeBase, mention: Span) -> Iterable[Candidate]:
"""
Return candidate entities for a given mention and fetching appropriate entries from the index.
Return candidate entities for a given mention and fetching appropriate
entries from the index.
kb (KnowledgeBase): Knowledge base to query.
mention (Span): Entity mention for which to identify candidates.
RETURNS (Iterable[Candidate]): Identified candidates.
@ -67,9 +77,12 @@ def get_candidates(kb: KnowledgeBase, mention: Span) -> Iterable[Candidate]:
return kb.get_candidates(mention)
def get_candidates_batch(kb: KnowledgeBase, mentions: Iterable[Span]) -> Iterable[Iterable[Candidate]]:
def get_candidates_batch(
kb: KnowledgeBase, mentions: Iterable[Span]
) -> Iterable[Iterable[Candidate]]:
"""
Return candidate entities for the given mentions and fetching appropriate entries from the index.
Return candidate entities for the given mentions and fetching appropriate entries
from the index.
kb (KnowledgeBase): Knowledge base to query.
mention (Iterable[Span]): Entity mentions for which to identify candidates.
RETURNS (Iterable[Iterable[Candidate]]): Identified candidates.

51
spacy/kb/kb.pyx
View File

@ -1,4 +1,4 @@
# cython: infer_types=True, profile=True
# cython: infer_types=True
from pathlib import Path
from typing import Iterable, Tuple, Union
@ -12,8 +12,9 @@ from .candidate import Candidate
cdef class KnowledgeBase:
"""A `KnowledgeBase` instance stores unique identifiers for entities and their textual aliases,
to support entity linking of named entities to real-world concepts.
"""A `KnowledgeBase` instance stores unique identifiers for entities and
their textual aliases, to support entity linking of named entities to
real-world concepts.
This is an abstract class and requires its operations to be implemented.
DOCS: https://spacy.io/api/kb
@ -31,10 +32,13 @@ cdef class KnowledgeBase:
self.entity_vector_length = entity_vector_length
self.mem = Pool()
def get_candidates_batch(self, mentions: Iterable[Span]) -> Iterable[Iterable[Candidate]]:
def get_candidates_batch(
self, mentions: Iterable[Span]
) -> Iterable[Iterable[Candidate]]:
"""
Return candidate entities for specified texts. Each candidate defines the entity, the original alias,
and the prior probability of that alias resolving to that entity.
Return candidate entities for specified texts. Each candidate defines
the entity, the original alias, and the prior probability of that
alias resolving to that entity.
If no candidate is found for a given text, an empty list is returned.
mentions (Iterable[Span]): Mentions for which to get candidates.
RETURNS (Iterable[Iterable[Candidate]]): Identified candidates.
@ -43,14 +47,17 @@ cdef class KnowledgeBase:
def get_candidates(self, mention: Span) -> Iterable[Candidate]:
"""
Return candidate entities for specified text. Each candidate defines the entity, the original alias,
Return candidate entities for specified text. Each candidate defines
the entity, the original alias,
and the prior probability of that alias resolving to that entity.
If the no candidate is found for a given text, an empty list is returned.
mention (Span): Mention for which to get candidates.
RETURNS (Iterable[Candidate]): Identified candidates.
"""
raise NotImplementedError(
Errors.E1045.format(parent="KnowledgeBase", method="get_candidates", name=self.__name__)
Errors.E1045.format(
parent="KnowledgeBase", method="get_candidates", name=self.__name__
)
)
def get_vectors(self, entities: Iterable[str]) -> Iterable[Iterable[float]]:
@ -68,7 +75,9 @@ cdef class KnowledgeBase:
RETURNS (Iterable[float]): Vector for specified entity.
"""
raise NotImplementedError(
Errors.E1045.format(parent="KnowledgeBase", method="get_vector", name=self.__name__)
Errors.E1045.format(
parent="KnowledgeBase", method="get_vector", name=self.__name__
)
)
def to_bytes(self, **kwargs) -> bytes:
@ -76,7 +85,9 @@ cdef class KnowledgeBase:
RETURNS (bytes): Current state as binary string.
"""
raise NotImplementedError(
Errors.E1045.format(parent="KnowledgeBase", method="to_bytes", name=self.__name__)
Errors.E1045.format(
parent="KnowledgeBase", method="to_bytes", name=self.__name__
)
)
def from_bytes(self, bytes_data: bytes, *, exclude: Tuple[str] = tuple()):
@ -85,25 +96,35 @@ cdef class KnowledgeBase:
exclude (Tuple[str]): Properties to exclude when restoring KB.
"""
raise NotImplementedError(
Errors.E1045.format(parent="KnowledgeBase", method="from_bytes", name=self.__name__)
Errors.E1045.format(
parent="KnowledgeBase", method="from_bytes", name=self.__name__
)
)
def to_disk(self, path: Union[str, Path], exclude: Iterable[str] = SimpleFrozenList()) -> None:
def to_disk(
self, path: Union[str, Path], exclude: Iterable[str] = SimpleFrozenList()
) -> None:
"""
Write KnowledgeBase content to disk.
path (Union[str, Path]): Target file path.
exclude (Iterable[str]): List of components to exclude.
"""
raise NotImplementedError(
Errors.E1045.format(parent="KnowledgeBase", method="to_disk", name=self.__name__)
Errors.E1045.format(
parent="KnowledgeBase", method="to_disk", name=self.__name__
)
)
def from_disk(self, path: Union[str, Path], exclude: Iterable[str] = SimpleFrozenList()) -> None:
def from_disk(
self, path: Union[str, Path], exclude: Iterable[str] = SimpleFrozenList()
) -> None:
"""
Load KnowledgeBase content from disk.
path (Union[str, Path]): Target file path.
exclude (Iterable[str]): List of components to exclude.
"""
raise NotImplementedError(
Errors.E1045.format(parent="KnowledgeBase", method="from_disk", name=self.__name__)
Errors.E1045.format(
parent="KnowledgeBase", method="from_disk", name=self.__name__
)
)

62
spacy/kb/kb_in_memory.pxd
View File

@ -55,23 +55,28 @@ cdef class InMemoryLookupKB(KnowledgeBase):
# optional data, we can let users configure a DB as the backend for this.
cdef object _features_table
cdef inline int64_t c_add_vector(self, vector[float] entity_vector) nogil:
"""Add an entity vector to the vectors table."""
cdef int64_t new_index = self._vectors_table.size()
self._vectors_table.push_back(entity_vector)
return new_index
cdef inline int64_t c_add_entity(self, hash_t entity_hash, float freq,
int32_t vector_index, int feats_row) nogil:
cdef inline int64_t c_add_entity(
self,
hash_t entity_hash,
float freq,
int32_t vector_index,
int feats_row
) nogil:
"""Add an entry to the vector of entries.
After calling this method, make sure to update also the _entry_index using the return value"""
After calling this method, make sure to update also the _entry_index
using the return value"""
# This is what we'll map the entity hash key to. It's where the entry will sit
# in the vector of entries, so we can get it later.
cdef int64_t new_index = self._entries.size()
# Avoid struct initializer to enable nogil, cf https://github.com/cython/cython/issues/1642
# Avoid struct initializer to enable nogil, cf.
# https://github.com/cython/cython/issues/1642
cdef KBEntryC entry
entry.entity_hash = entity_hash
entry.vector_index = vector_index
@ -81,11 +86,17 @@ cdef class InMemoryLookupKB(KnowledgeBase):
self._entries.push_back(entry)
return new_index
cdef inline int64_t c_add_aliases(self, hash_t alias_hash, vector[int64_t] entry_indices, vector[float] probs) nogil:
"""Connect a mention to a list of potential entities with their prior probabilities .
After calling this method, make sure to update also the _alias_index using the return value"""
# This is what we'll map the alias hash key to. It's where the alias will be defined
# in the vector of aliases.
cdef inline int64_t c_add_aliases(
self,
hash_t alias_hash,
vector[int64_t] entry_indices,
vector[float] probs
) nogil:
"""Connect a mention to a list of potential entities with their prior
probabilities. After calling this method, make sure to update also the
_alias_index using the return value"""
# This is what we'll map the alias hash key to. It's where the alias will be
# defined in the vector of aliases.
cdef int64_t new_index = self._aliases_table.size()
# Avoid struct initializer to enable nogil
@ -98,8 +109,9 @@ cdef class InMemoryLookupKB(KnowledgeBase):
cdef inline void _create_empty_vectors(self, hash_t dummy_hash) nogil:
"""
Initializing the vectors and making sure the first element of each vector is a dummy,
because the PreshMap maps pointing to indices in these vectors can not contain 0 as value
Initializing the vectors and making sure the first element of each vector is a
dummy, because the PreshMap maps pointing to indices in these vectors can not
contain 0 as value.
cf. https://github.com/explosion/preshed/issues/17
"""
cdef int32_t dummy_value = 0
@ -130,12 +142,18 @@ cdef class InMemoryLookupKB(KnowledgeBase):
cdef class Writer:
cdef FILE* _fp
cdef int write_header(self, int64_t nr_entries, int64_t entity_vector_length) except -1
cdef int write_header(
self, int64_t nr_entries, int64_t entity_vector_length
) except -1
cdef int write_vector_element(self, float element) except -1
cdef int write_entry(self, hash_t entry_hash, float entry_freq, int32_t vector_index) except -1
cdef int write_entry(
self, hash_t entry_hash, float entry_freq, int32_t vector_index
) except -1
cdef int write_alias_length(self, int64_t alias_length) except -1
cdef int write_alias_header(self, hash_t alias_hash, int64_t candidate_length) except -1
cdef int write_alias_header(
self, hash_t alias_hash, int64_t candidate_length
) except -1
cdef int write_alias(self, int64_t entry_index, float prob) except -1
cdef int _write(self, void* value, size_t size) except -1
@ -143,12 +161,18 @@ cdef class Writer:
cdef class Reader:
cdef FILE* _fp
cdef int read_header(self, int64_t* nr_entries, int64_t* entity_vector_length) except -1
cdef int read_header(
self, int64_t* nr_entries, int64_t* entity_vector_length
) except -1
cdef int read_vector_element(self, float* element) except -1
cdef int read_entry(self, hash_t* entity_hash, float* freq, int32_t* vector_index) except -1
cdef int read_entry(
self, hash_t* entity_hash, float* freq, int32_t* vector_index
) except -1
cdef int read_alias_length(self, int64_t* alias_length) except -1
cdef int read_alias_header(self, hash_t* alias_hash, int64_t* candidate_length) except -1
cdef int read_alias_header(
self, hash_t* alias_hash, int64_t* candidate_length
) except -1
cdef int read_alias(self, int64_t* entry_index, float* prob) except -1
cdef int _read(self, void* value, size_t size) except -1

156
spacy/kb/kb_in_memory.pyx
View File

@ -1,5 +1,5 @@
# cython: infer_types=True, profile=True
from typing import Any, Callable, Dict, Iterable, Union
# cython: infer_types=True
from typing import Any, Callable, Dict, Iterable
import srsly
@ -27,8 +27,9 @@ from .candidate import Candidate as Candidate
cdef class InMemoryLookupKB(KnowledgeBase):
"""An `InMemoryLookupKB` instance stores unique identifiers for entities and their textual aliases,
to support entity linking of named entities to real-world concepts.
"""An `InMemoryLookupKB` instance stores unique identifiers for entities
and their textual aliases, to support entity linking of named entities to
real-world concepts.
DOCS: https://spacy.io/api/inmemorylookupkb
"""
@ -71,7 +72,8 @@ cdef class InMemoryLookupKB(KnowledgeBase):
def add_entity(self, str entity, float freq, vector[float] entity_vector):
"""
Add an entity to the KB, optionally specifying its log probability based on corpus frequency
Add an entity to the KB, optionally specifying its log probability
based on corpus frequency.
Return the hash of the entity ID/name at the end.
"""
cdef hash_t entity_hash = self.vocab.strings.add(entity)
@ -83,14 +85,20 @@ cdef class InMemoryLookupKB(KnowledgeBase):
# Raise an error if the provided entity vector is not of the correct length
if len(entity_vector) != self.entity_vector_length:
raise ValueError(Errors.E141.format(found=len(entity_vector), required=self.entity_vector_length))
raise ValueError(
Errors.E141.format(
found=len(entity_vector), required=self.entity_vector_length
)
)
vector_index = self.c_add_vector(entity_vector=entity_vector)
new_index = self.c_add_entity(entity_hash=entity_hash,
freq=freq,
vector_index=vector_index,
feats_row=-1) # Features table currently not implemented
new_index = self.c_add_entity(
entity_hash=entity_hash,
freq=freq,
vector_index=vector_index,
feats_row=-1
) # Features table currently not implemented
self._entry_index[entity_hash] = new_index
return entity_hash
@ -115,7 +123,12 @@ cdef class InMemoryLookupKB(KnowledgeBase):
else:
entity_vector = vector_list[i]
if len(entity_vector) != self.entity_vector_length:
raise ValueError(Errors.E141.format(found=len(entity_vector), required=self.entity_vector_length))
raise ValueError(
Errors.E141.format(
found=len(entity_vector),
required=self.entity_vector_length
)
)
entry.entity_hash = entity_hash
entry.freq = freq_list[i]
@ -149,11 +162,15 @@ cdef class InMemoryLookupKB(KnowledgeBase):
previous_alias_nr = self.get_size_aliases()
# Throw an error if the length of entities and probabilities are not the same
if not len(entities) == len(probabilities):
raise ValueError(Errors.E132.format(alias=alias,
entities_length=len(entities),
probabilities_length=len(probabilities)))
raise ValueError(
Errors.E132.format(
alias=alias,
entities_length=len(entities),
probabilities_length=len(probabilities))
)
# Throw an error if the probabilities sum up to more than 1 (allow for some rounding errors)
# Throw an error if the probabilities sum up to more than 1 (allow for
# some rounding errors)
prob_sum = sum(probabilities)
if prob_sum > 1.00001:
raise ValueError(Errors.E133.format(alias=alias, sum=prob_sum))
@ -170,40 +187,47 @@ cdef class InMemoryLookupKB(KnowledgeBase):
for entity, prob in zip(entities, probabilities):
entity_hash = self.vocab.strings[entity]
if not entity_hash in self._entry_index:
if entity_hash not in self._entry_index:
raise ValueError(Errors.E134.format(entity=entity))
entry_index = <int64_t>self._entry_index.get(entity_hash)
entry_indices.push_back(int(entry_index))
probs.push_back(float(prob))
new_index = self.c_add_aliases(alias_hash=alias_hash, entry_indices=entry_indices, probs=probs)
new_index = self.c_add_aliases(
alias_hash=alias_hash, entry_indices=entry_indices, probs=probs
)
self._alias_index[alias_hash] = new_index
if previous_alias_nr + 1 != self.get_size_aliases():
raise RuntimeError(Errors.E891.format(alias=alias))
return alias_hash
def append_alias(self, str alias, str entity, float prior_prob, ignore_warnings=False):
def append_alias(
self, str alias, str entity, float prior_prob, ignore_warnings=False
):
"""
For an alias already existing in the KB, extend its potential entities with one more.
For an alias already existing in the KB, extend its potential entities
with one more.
Throw a warning if either the alias or the entity is unknown,
or when the combination is already previously recorded.
Throw an error if this entity+prior prob would exceed the sum of 1.
For efficiency, it's best to use the method `add_alias` as much as possible instead of this one.
For efficiency, it's best to use the method `add_alias` as much as
possible instead of this one.
"""
# Check if the alias exists in the KB
cdef hash_t alias_hash = self.vocab.strings[alias]
if not alias_hash in self._alias_index:
if alias_hash not in self._alias_index:
raise ValueError(Errors.E176.format(alias=alias))
# Check if the entity exists in the KB
cdef hash_t entity_hash = self.vocab.strings[entity]
if not entity_hash in self._entry_index:
if entity_hash not in self._entry_index:
raise ValueError(Errors.E134.format(entity=entity))
entry_index = <int64_t>self._entry_index.get(entity_hash)
# Throw an error if the prior probabilities (including the new one) sum up to more than 1
# Throw an error if the prior probabilities (including the new one)
# sum up to more than 1
alias_index = <int64_t>self._alias_index.get(alias_hash)
alias_entry = self._aliases_table[alias_index]
current_sum = sum([p for p in alias_entry.probs])
@ -236,12 +260,13 @@ cdef class InMemoryLookupKB(KnowledgeBase):
def get_alias_candidates(self, str alias) -> Iterable[Candidate]:
"""
Return candidate entities for an alias. Each candidate defines the entity, the original alias,
and the prior probability of that alias resolving to that entity.
Return candidate entities for an alias. Each candidate defines the
entity, the original alias, and the prior probability of that alias
resolving to that entity.
If the alias is not known in the KB, and empty list is returned.
"""
cdef hash_t alias_hash = self.vocab.strings[alias]
if not alias_hash in self._alias_index:
if alias_hash not in self._alias_index:
return []
alias_index = <int64_t>self._alias_index.get(alias_hash)
alias_entry = self._aliases_table[alias_index]
@ -249,10 +274,14 @@ cdef class InMemoryLookupKB(KnowledgeBase):
return [Candidate(kb=self,
entity_hash=self._entries[entry_index].entity_hash,
entity_freq=self._entries[entry_index].freq,
entity_vector=self._vectors_table[self._entries[entry_index].vector_index],
entity_vector=self._vectors_table[
self._entries[entry_index].vector_index
],
alias_hash=alias_hash,
prior_prob=prior_prob)
for (entry_index, prior_prob) in zip(alias_entry.entry_indices, alias_entry.probs)
for (entry_index, prior_prob) in zip(
alias_entry.entry_indices, alias_entry.probs
)
if entry_index != 0]
def get_vector(self, str entity):
@ -266,8 +295,9 @@ cdef class InMemoryLookupKB(KnowledgeBase):
return self._vectors_table[self._entries[entry_index].vector_index]
def get_prior_prob(self, str entity, str alias):
""" Return the prior probability of a given alias being linked to a given entity,
or return 0.0 when this combination is not known in the knowledge base"""
""" Return the prior probability of a given alias being linked to a
given entity, or return 0.0 when this combination is not known in the
knowledge base."""
cdef hash_t alias_hash = self.vocab.strings[alias]
cdef hash_t entity_hash = self.vocab.strings[entity]
@ -278,7 +308,9 @@ cdef class InMemoryLookupKB(KnowledgeBase):
entry_index = self._entry_index[entity_hash]
alias_entry = self._aliases_table[alias_index]
for (entry_index, prior_prob) in zip(alias_entry.entry_indices, alias_entry.probs):
for (entry_index, prior_prob) in zip(
alias_entry.entry_indices, alias_entry.probs
):
if self._entries[entry_index].entity_hash == entity_hash:
return prior_prob
@ -288,13 +320,19 @@ cdef class InMemoryLookupKB(KnowledgeBase):
"""Serialize the current state to a binary string.
"""
def serialize_header():
header = (self.get_size_entities(), self.get_size_aliases(), self.entity_vector_length)
header = (
self.get_size_entities(),
self.get_size_aliases(),
self.entity_vector_length
)
return srsly.json_dumps(header)
def serialize_entries():
i = 1
tuples = []
for entry_hash, entry_index in sorted(self._entry_index.items(), key=lambda x: x[1]):
for entry_hash, entry_index in sorted(
self._entry_index.items(), key=lambda x: x[1]
):
entry = self._entries[entry_index]
assert entry.entity_hash == entry_hash
assert entry_index == i
@ -307,7 +345,9 @@ cdef class InMemoryLookupKB(KnowledgeBase):
headers = []
indices_lists = []
probs_lists = []
for alias_hash, alias_index in sorted(self._alias_index.items(), key=lambda x: x[1]):
for alias_hash, alias_index in sorted(
self._alias_index.items(), key=lambda x: x[1]
):
alias = self._aliases_table[alias_index]
assert alias_index == i
candidate_length = len(alias.entry_indices)
@ -365,7 +405,7 @@ cdef class InMemoryLookupKB(KnowledgeBase):
indices = srsly.json_loads(all_data[1])
probs = srsly.json_loads(all_data[2])
for header, indices, probs in zip(headers, indices, probs):
alias_hash, candidate_length = header
alias_hash, _candidate_length = header
alias.entry_indices = indices
alias.probs = probs
self._aliases_table[i] = alias
@ -414,10 +454,14 @@ cdef class InMemoryLookupKB(KnowledgeBase):
writer.write_vector_element(element)
i = i+1
# dumping the entry records in the order in which they are in the _entries vector.
# index 0 is a dummy object not stored in the _entry_index and can be ignored.
# dumping the entry records in the order in which they are in the
# _entries vector.
# index 0 is a dummy object not stored in the _entry_index and can
# be ignored.
i = 1
for entry_hash, entry_index in sorted(self._entry_index.items(), key=lambda x: x[1]):
for entry_hash, entry_index in sorted(
self._entry_index.items(), key=lambda x: x[1]
):
entry = self._entries[entry_index]
assert entry.entity_hash == entry_hash
assert entry_index == i
@ -429,7 +473,9 @@ cdef class InMemoryLookupKB(KnowledgeBase):
# dumping the aliases in the order in which they are in the _alias_index vector.
# index 0 is a dummy object not stored in the _aliases_table and can be ignored.
i = 1
for alias_hash, alias_index in sorted(self._alias_index.items(), key=lambda x: x[1]):
for alias_hash, alias_index in sorted(
self._alias_index.items(), key=lambda x: x[1]
):
alias = self._aliases_table[alias_index]
assert alias_index == i
@ -535,7 +581,8 @@ cdef class Writer:
def __init__(self, path):
assert isinstance(path, Path)
content = bytes(path)
cdef bytes bytes_loc = content.encode('utf8') if type(content) == str else content
cdef bytes bytes_loc = content.encode('utf8') \
if type(content) == str else content
self._fp = fopen(<char*>bytes_loc, 'wb')
if not self._fp:
raise IOError(Errors.E146.format(path=path))
@ -545,14 +592,18 @@ cdef class Writer:
cdef size_t status = fclose(self._fp)
assert status == 0
cdef int write_header(self, int64_t nr_entries, int64_t entity_vector_length) except -1:
cdef int write_header(
self, int64_t nr_entries, int64_t entity_vector_length
) except -1:
self._write(&nr_entries, sizeof(nr_entries))
self._write(&entity_vector_length, sizeof(entity_vector_length))
cdef int write_vector_element(self, float element) except -1:
self._write(&element, sizeof(element))
cdef int write_entry(self, hash_t entry_hash, float entry_freq, int32_t vector_index) except -1:
cdef int write_entry(
self, hash_t entry_hash, float entry_freq, int32_t vector_index
) except -1:
self._write(&entry_hash, sizeof(entry_hash))
self._write(&entry_freq, sizeof(entry_freq))
self._write(&vector_index, sizeof(vector_index))
@ -561,7 +612,9 @@ cdef class Writer:
cdef int write_alias_length(self, int64_t alias_length) except -1:
self._write(&alias_length, sizeof(alias_length))
cdef int write_alias_header(self, hash_t alias_hash, int64_t candidate_length) except -1:
cdef int write_alias_header(
self, hash_t alias_hash, int64_t candidate_length
) except -1:
self._write(&alias_hash, sizeof(alias_hash))
self._write(&candidate_length, sizeof(candidate_length))
@ -577,16 +630,19 @@ cdef class Writer:
cdef class Reader:
def __init__(self, path):
content = bytes(path)
cdef bytes bytes_loc = content.encode('utf8') if type(content) == str else content
cdef bytes bytes_loc = content.encode('utf8') \
if type(content) == str else content
self._fp = fopen(<char*>bytes_loc, 'rb')
if not self._fp:
PyErr_SetFromErrno(IOError)
status = fseek(self._fp, 0, 0) # this can be 0 if there is no header
fseek(self._fp, 0, 0) # this can be 0 if there is no header
def __dealloc__(self):
fclose(self._fp)
cdef int read_header(self, int64_t* nr_entries, int64_t* entity_vector_length) except -1:
cdef int read_header(
self, int64_t* nr_entries, int64_t* entity_vector_length
) except -1:
status = self._read(nr_entries, sizeof(int64_t))
if status < 1:
if feof(self._fp):
@ -606,7 +662,9 @@ cdef class Reader:
return 0 # end of file
raise IOError(Errors.E145.format(param="vector element"))
cdef int read_entry(self, hash_t* entity_hash, float* freq, int32_t* vector_index) except -1:
cdef int read_entry(
self, hash_t* entity_hash, float* freq, int32_t* vector_index
) except -1:
status = self._read(entity_hash, sizeof(hash_t))
if status < 1:
if feof(self._fp):
@ -637,7 +695,9 @@ cdef class Reader:
return 0 # end of file
raise IOError(Errors.E145.format(param="alias length"))
cdef int read_alias_header(self, hash_t* alias_hash, int64_t* candidate_length) except -1:
cdef int read_alias_header(
self, hash_t* alias_hash, int64_t* candidate_length
) except -1:
status = self._read(alias_hash, sizeof(hash_t))
if status < 1:
if feof(self._fp):

4
spacy/lang/es/lemmatizer.py
View File

@ -163,7 +163,7 @@ class SpanishLemmatizer(Lemmatizer):
for old, new in self.lookups.get_table("lemma_rules").get("det", []):
if word == old:
return [new]
# If none of the specfic rules apply, search in the common rules for
# If none of the specific rules apply, search in the common rules for
# determiners and pronouns that follow a unique pattern for
# lemmatization. If the word is in the list, return the corresponding
# lemma.
@ -291,7 +291,7 @@ class SpanishLemmatizer(Lemmatizer):
for old, new in self.lookups.get_table("lemma_rules").get("pron", []):
if word == old:
return [new]
# If none of the specfic rules apply, search in the common rules for
# If none of the specific rules apply, search in the common rules for
# determiners and pronouns that follow a unique pattern for
# lemmatization. If the word is in the list, return the corresponding
# lemma.

2
spacy/lang/grc/punctuation.py
View File

@ -15,6 +15,7 @@ _prefixes = (
[
"†",
"⸏",
"〈",
]
+ LIST_PUNCT
+ LIST_ELLIPSES
@ -31,6 +32,7 @@ _suffixes = (
+ [
"†",
"⸎",
"〉",
r"(?<=[\u1F00-\u1FFF\u0370-\u03FF])[\-\.⸏]",
]
)

3
spacy/lang/tr/examples.py
View File

@ -15,4 +15,7 @@ sentences = [
"Türkiye'nin başkenti neresi?",
"Bakanlar Kurulu 180 günlük eylem planını açıkladı.",
"Merkez Bankası, beklentiler doğrultusunda faizlerde değişikliğe gitmedi.",
"Cemal Sureya kimdir?",
"Bunlari Biliyor muydunuz?",
"Altinoluk Turkiye haritasinin neresinde yer alir?",
]

38
spacy/language.py
View File

@ -1,4 +1,5 @@
import functools
import inspect
import itertools
import multiprocessing as mp
import random
@ -64,6 +65,7 @@ from .util import (
registry,
warn_if_jupyter_cupy,
)
from .vectors import BaseVectors
from .vocab import Vocab, create_vocab
PipeCallable = Callable[[Doc], Doc]
@ -157,6 +159,7 @@ class Language:
max_length: int = 10**6,
meta: Dict[str, Any] = {},
create_tokenizer: Optional[Callable[["Language"], Callable[[str], Doc]]] = None,
create_vectors: Optional[Callable[["Vocab"], BaseVectors]] = None,
batch_size: int = 1000,
**kwargs,
) -> None:
@ -197,6 +200,10 @@ class Language:
if vocab is True:
vectors_name = meta.get("vectors", {}).get("name")
vocab = create_vocab(self.lang, self.Defaults, vectors_name=vectors_name)
if not create_vectors:
vectors_cfg = {"vectors": self._config["nlp"]["vectors"]}
create_vectors = registry.resolve(vectors_cfg)["vectors"]
vocab.vectors = create_vectors(vocab)
else:
if (self.lang and vocab.lang) and (self.lang != vocab.lang):
raise ValueError(Errors.E150.format(nlp=self.lang, vocab=vocab.lang))
@ -1764,6 +1771,10 @@ class Language:
).merge(config)
if "nlp" not in config:
raise ValueError(Errors.E985.format(config=config))
# fill in [nlp.vectors] if not present (as a narrower alternative to
# auto-filling [nlp] from the default config)
if "vectors" not in config["nlp"]:
config["nlp"]["vectors"] = {"@vectors": "spacy.Vectors.v1"}
config_lang = config["nlp"].get("lang")
if config_lang is not None and config_lang != cls.lang:
raise ValueError(
@ -1795,6 +1806,7 @@ class Language:
filled["nlp"], validate=validate, schema=ConfigSchemaNlp
)
create_tokenizer = resolved_nlp["tokenizer"]
create_vectors = resolved_nlp["vectors"]
before_creation = resolved_nlp["before_creation"]
after_creation = resolved_nlp["after_creation"]
after_pipeline_creation = resolved_nlp["after_pipeline_creation"]
@ -1815,7 +1827,12 @@ class Language:
# inside stuff like the spacy train function. If we loaded them here,
# then we would load them twice at runtime: once when we make from config,
# and then again when we load from disk.
nlp = lang_cls(vocab=vocab, create_tokenizer=create_tokenizer, meta=meta)
nlp = lang_cls(
vocab=vocab,
create_tokenizer=create_tokenizer,
create_vectors=create_vectors,
meta=meta,
)
if after_creation is not None:
nlp = after_creation(nlp)
if not isinstance(nlp, cls):
@ -1825,7 +1842,6 @@ class Language:
# Later we replace the component config with the raw config again.
interpolated = filled.interpolate() if not filled.is_interpolated else filled
pipeline = interpolated.get("components", {})
sourced = util.get_sourced_components(interpolated)
# If components are loaded from a source (existing models), we cache
# them here so they're only loaded once
source_nlps = {}
@ -1958,7 +1974,7 @@ class Language:
useful when training a pipeline with components sourced from an existing
pipeline: if multiple components (e.g. tagger, parser, NER) listen to
the same tok2vec component, but some of them are frozen and not updated,
their performance may degrade significally as the tok2vec component is
their performance may degrade significantly as the tok2vec component is
updated with new data. To prevent this, listeners can be replaced with
a standalone tok2vec layer that is owned by the component and doesn't
change if the component isn't updated.
@ -2033,8 +2049,20 @@ class Language:
# Go over the listener layers and replace them
for listener in pipe_listeners:
new_model = tok2vec_model.copy()
if "replace_listener" in tok2vec_model.attrs:
new_model = tok2vec_model.attrs["replace_listener"](new_model)
replace_listener_func = tok2vec_model.attrs.get("replace_listener")
if replace_listener_func is not None:
# Pass the extra args to the callback without breaking compatibility with
# old library versions that only expect a single parameter.
num_params = len(
inspect.signature(replace_listener_func).parameters
)
if num_params == 1:
new_model = replace_listener_func(new_model)
elif num_params == 3:
new_model = replace_listener_func(new_model, listener, tok2vec)
else:
raise ValueError(Errors.E1055.format(num_params=num_params))
util.replace_model_node(pipe.model, listener, new_model) # type: ignore[attr-defined]
tok2vec.remove_listener(listener, pipe_name)

18
spacy/lexeme.pyx
View File

@ -1,7 +1,7 @@
# cython: embedsignature=True
# cython: profile=False
# Compiler crashes on memory view coercion without this. Should report bug.
cimport numpy as np
from cython.view cimport array as cvarray
from libc.string cimport memset
np.import_array()
@ -35,7 +35,7 @@ from .typedefs cimport attr_t, flags_t
from .attrs import intify_attrs
from .errors import Errors, Warnings
OOV_RANK = 0xffffffffffffffff # UINT64_MAX
OOV_RANK = 0xffffffffffffffff # UINT64_MAX
memset(&EMPTY_LEXEME, 0, sizeof(LexemeC))
EMPTY_LEXEME.id = OOV_RANK
@ -105,7 +105,7 @@ cdef class Lexeme:
if isinstance(value, float):
continue
elif isinstance(value, (int, long)):
Lexeme.set_struct_attr(self.c, attr, value)
Lexeme.set_struct_attr(self.c, attr, value)
else:
Lexeme.set_struct_attr(self.c, attr, self.vocab.strings.add(value))
@ -137,10 +137,12 @@ cdef class Lexeme:
if hasattr(other, "orth"):
if self.c.orth == other.orth:
return 1.0
elif hasattr(other, "__len__") and len(other) == 1 \
and hasattr(other[0], "orth"):
if self.c.orth == other[0].orth:
return 1.0
elif (
hasattr(other, "__len__") and len(other) == 1
and hasattr(other[0], "orth")
and self.c.orth == other[0].orth
):
return 1.0
if self.vector_norm == 0 or other.vector_norm == 0:
warnings.warn(Warnings.W008.format(obj="Lexeme"))
return 0.0
@ -149,7 +151,7 @@ cdef class Lexeme:
result = xp.dot(vector, other.vector) / (self.vector_norm * other.vector_norm)
# ensure we get a scalar back (numpy does this automatically but cupy doesn't)
return result.item()
@property
def has_vector(self):
"""RETURNS (bool): Whether a word vector is associated with the object.

20
spacy/matcher/dependencymatcher.pyx
View File

@ -1,4 +1,4 @@
# cython: infer_types=True, profile=True
# cython: infer_types=True
import warnings
from collections import defaultdict
from itertools import product
@ -108,7 +108,7 @@ cdef class DependencyMatcher:
key (str): The match ID.
RETURNS (bool): Whether the matcher contains rules for this match ID.
"""
return self.has_key(key)
return self.has_key(key) # no-cython-lint: W601
def _validate_input(self, pattern, key):
idx = 0
@ -129,6 +129,7 @@ cdef class DependencyMatcher:
else:
required_keys = {"RIGHT_ID", "RIGHT_ATTRS", "REL_OP", "LEFT_ID"}
relation_keys = set(relation.keys())
# Identify required keys that have not been specified
missing = required_keys - relation_keys
if missing:
missing_txt = ", ".join(list(missing))
@ -136,6 +137,13 @@ cdef class DependencyMatcher:
required=required_keys,
missing=missing_txt
))
# Identify additional, unsupported keys
unsupported = relation_keys - required_keys
if unsupported:
unsupported_txt = ", ".join(list(unsupported))
warnings.warn(Warnings.W126.format(
unsupported=unsupported_txt
))
if (
relation["RIGHT_ID"] in visited_nodes
or relation["LEFT_ID"] not in visited_nodes
@ -264,7 +272,7 @@ cdef class DependencyMatcher:
def remove(self, key):
key = self._normalize_key(key)
if not key in self._patterns:
if key not in self._patterns:
raise ValueError(Errors.E175.format(key=key))
self._patterns.pop(key)
self._raw_patterns.pop(key)
@ -382,7 +390,7 @@ cdef class DependencyMatcher:
return []
return [doc[node].head]
def _gov(self,doc,node):
def _gov(self, doc, node):
return list(doc[node].children)
def _dep_chain(self, doc, node):
@ -443,7 +451,7 @@ cdef class DependencyMatcher:
def _right_child(self, doc, node):
return [child for child in doc[node].rights]
def _left_child(self, doc, node):
return [child for child in doc[node].lefts]
@ -461,7 +469,7 @@ cdef class DependencyMatcher:
if doc[node].head.i > node:
return [doc[node].head]
return []
def _left_parent(self, doc, node):
if doc[node].head.i < node:
return [doc[node].head]

2
spacy/matcher/levenshtein.pyx
View File

@ -1,4 +1,4 @@
# cython: profile=True, binding=True, infer_types=True
# cython: binding=True, infer_types=True
from cpython.object cimport PyObject
from libc.stdint cimport int64_t

248
spacy/matcher/matcher.pyx
View File

@ -1,4 +1,4 @@
# cython: binding=True, infer_types=True, profile=True
# cython: binding=True, infer_types=True
from typing import Iterable, List
from cymem.cymem cimport Pool
@ -12,31 +12,18 @@ import warnings
import srsly
from ..attrs cimport (
DEP,
ENT_IOB,
ID,
LEMMA,
MORPH,
NULL_ATTR,
ORTH,
POS,
TAG,
attr_id_t,
)
from ..attrs cimport DEP, ENT_IOB, ID, LEMMA, MORPH, NULL_ATTR, POS, TAG
from ..structs cimport TokenC
from ..tokens.doc cimport Doc, get_token_attr_for_matcher
from ..tokens.morphanalysis cimport MorphAnalysis
from ..tokens.span cimport Span
from ..tokens.token cimport Token
from ..typedefs cimport attr_t
from ..vocab cimport Vocab
from ..attrs import IDS
from ..errors import Errors, MatchPatternError, Warnings
from ..schemas import validate_token_pattern
from ..strings import get_string_id
from ..util import registry
from .levenshtein import levenshtein_compare
DEF PADDING = 5
@ -87,9 +74,9 @@ cdef class Matcher:
key (str): The match ID.
RETURNS (bool): Whether the matcher contains rules for this match ID.
"""
return self.has_key(key)
return self.has_key(key) # no-cython-lint: W601
def add(self, key, patterns, *, on_match=None, greedy: str=None):
def add(self, key, patterns, *, on_match=None, greedy: str = None):
"""Add a match-rule to the matcher. A match-rule consists of: an ID
key, an on_match callback, and one or more patterns.
@ -143,8 +130,13 @@ cdef class Matcher:
key = self._normalize_key(key)
for pattern in patterns:
try:
specs = _preprocess_pattern(pattern, self.vocab,
self._extensions, self._extra_predicates, self._fuzzy_compare)
specs = _preprocess_pattern(
pattern,
self.vocab,
self._extensions,
self._extra_predicates,
self._fuzzy_compare
)
self.patterns.push_back(init_pattern(self.mem, key, specs))
for spec in specs:
for attr, _ in spec[1]:
@ -168,7 +160,7 @@ cdef class Matcher:
key (str): The ID of the match rule.
"""
norm_key = self._normalize_key(key)
if not norm_key in self._patterns:
if norm_key not in self._patterns:
raise ValueError(Errors.E175.format(key=key))
self._patterns.pop(norm_key)
self._callbacks.pop(norm_key)
@ -268,8 +260,15 @@ cdef class Matcher:
if self.patterns.empty():
matches = []
else:
matches = find_matches(&self.patterns[0], self.patterns.size(), doclike, length,
extensions=self._extensions, predicates=self._extra_predicates, with_alignments=with_alignments)
matches = find_matches(
&self.patterns[0],
self.patterns.size(),
doclike,
length,
extensions=self._extensions,
predicates=self._extra_predicates,
with_alignments=with_alignments
)
final_matches = []
pairs_by_id = {}
# For each key, either add all matches, or only the filtered,
@ -289,9 +288,9 @@ cdef class Matcher:
memset(matched, 0, length * sizeof(matched[0]))
span_filter = self._filter.get(key)
if span_filter == "FIRST":
sorted_pairs = sorted(pairs, key=lambda x: (x[0], -x[1]), reverse=False) # sort by start
sorted_pairs = sorted(pairs, key=lambda x: (x[0], -x[1]), reverse=False) # sort by start
elif span_filter == "LONGEST":
sorted_pairs = sorted(pairs, key=lambda x: (x[1]-x[0], -x[0]), reverse=True) # reverse sort by length
sorted_pairs = sorted(pairs, key=lambda x: (x[1]-x[0], -x[0]), reverse=True) # reverse sort by length
else:
raise ValueError(Errors.E947.format(expected=["FIRST", "LONGEST"], arg=span_filter))
for match in sorted_pairs:
@ -366,7 +365,6 @@ cdef find_matches(TokenPatternC** patterns, int n, object doclike, int length, e
cdef vector[MatchC] matches
cdef vector[vector[MatchAlignmentC]] align_states
cdef vector[vector[MatchAlignmentC]] align_matches
cdef PatternStateC state
cdef int i, j, nr_extra_attr
cdef Pool mem = Pool()
output = []
@ -388,14 +386,22 @@ cdef find_matches(TokenPatternC** patterns, int n, object doclike, int length, e
value = token.vocab.strings[value]
extra_attr_values[i * nr_extra_attr + index] = value
# Main loop
cdef int nr_predicate = len(predicates)
for i in range(length):
for j in range(n):
states.push_back(PatternStateC(patterns[j], i, 0))
if with_alignments != 0:
align_states.resize(states.size())
transition_states(states, matches, align_states, align_matches, predicate_cache,
doclike[i], extra_attr_values, predicates, with_alignments)
transition_states(
states,
matches,
align_states,
align_matches,
predicate_cache,
doclike[i],
extra_attr_values,
predicates,
with_alignments
)
extra_attr_values += nr_extra_attr
predicate_cache += len(predicates)
# Handle matches that end in 0-width patterns
@ -421,18 +427,28 @@ cdef find_matches(TokenPatternC** patterns, int n, object doclike, int length, e
return output
cdef void transition_states(vector[PatternStateC]& states, vector[MatchC]& matches,
vector[vector[MatchAlignmentC]]& align_states, vector[vector[MatchAlignmentC]]& align_matches,
int8_t* cached_py_predicates,
Token token, const attr_t* extra_attrs, py_predicates, bint with_alignments) except *:
cdef void transition_states(
vector[PatternStateC]& states,
vector[MatchC]& matches,
vector[vector[MatchAlignmentC]]& align_states,
vector[vector[MatchAlignmentC]]& align_matches,
int8_t* cached_py_predicates,
Token token,
const attr_t* extra_attrs,
py_predicates,
bint with_alignments
) except *:
cdef int q = 0
cdef vector[PatternStateC] new_states
cdef vector[vector[MatchAlignmentC]] align_new_states
cdef int nr_predicate = len(py_predicates)
for i in range(states.size()):
if states[i].pattern.nr_py >= 1:
update_predicate_cache(cached_py_predicates,
states[i].pattern, token, py_predicates)
update_predicate_cache(
cached_py_predicates,
states[i].pattern,
token,
py_predicates
)
action = get_action(states[i], token.c, extra_attrs,
cached_py_predicates)
if action == REJECT:
@ -468,8 +484,12 @@ cdef void transition_states(vector[PatternStateC]& states, vector[MatchC]& match
align_new_states.push_back(align_states[q])
states[q].pattern += 1
if states[q].pattern.nr_py != 0:
update_predicate_cache(cached_py_predicates,
states[q].pattern, token, py_predicates)
update_predicate_cache(
cached_py_predicates,
states[q].pattern,
token,
py_predicates
)
action = get_action(states[q], token.c, extra_attrs,
cached_py_predicates)
# Update alignment before the transition of current state
@ -485,8 +505,12 @@ cdef void transition_states(vector[PatternStateC]& states, vector[MatchC]& match
ent_id = get_ent_id(state.pattern)
if action == MATCH:
matches.push_back(
MatchC(pattern_id=ent_id, start=state.start,
length=state.length+1))
MatchC(
pattern_id=ent_id,
start=state.start,
length=state.length+1
)
)
# `align_matches` always corresponds to `matches` 1:1
if with_alignments != 0:
align_matches.push_back(align_states[q])
@ -494,23 +518,35 @@ cdef void transition_states(vector[PatternStateC]& states, vector[MatchC]& match
# push match without last token if length > 0
if state.length > 0:
matches.push_back(
MatchC(pattern_id=ent_id, start=state.start,
length=state.length))
MatchC(
pattern_id=ent_id,
start=state.start,
length=state.length
)
)
# MATCH_DOUBLE emits matches twice,
# add one more to align_matches in order to keep 1:1 relationship
if with_alignments != 0:
align_matches.push_back(align_states[q])
# push match with last token
matches.push_back(
MatchC(pattern_id=ent_id, start=state.start,
length=state.length+1))
MatchC(
pattern_id=ent_id,
start=state.start,
length=state.length + 1
)
)
# `align_matches` always corresponds to `matches` 1:1
if with_alignments != 0:
align_matches.push_back(align_states[q])
elif action == MATCH_REJECT:
matches.push_back(
MatchC(pattern_id=ent_id, start=state.start,
length=state.length))
MatchC(
pattern_id=ent_id,
start=state.start,
length=state.length
)
)
# `align_matches` always corresponds to `matches` 1:1
if with_alignments != 0:
align_matches.push_back(align_states[q])
@ -533,8 +569,12 @@ cdef void transition_states(vector[PatternStateC]& states, vector[MatchC]& match
align_states.push_back(align_new_states[i])
cdef int update_predicate_cache(int8_t* cache,
const TokenPatternC* pattern, Token token, predicates) except -1:
cdef int update_predicate_cache(
int8_t* cache,
const TokenPatternC* pattern,
Token token,
predicates
) except -1:
# If the state references any extra predicates, check whether they match.
# These are cached, so that we don't call these potentially expensive
# Python functions more than we need to.
@ -580,10 +620,12 @@ cdef void finish_states(vector[MatchC]& matches, vector[PatternStateC]& states,
else:
state.pattern += 1
cdef action_t get_action(PatternStateC state,
const TokenC* token, const attr_t* extra_attrs,
const int8_t* predicate_matches) nogil:
cdef action_t get_action(
PatternStateC state,
const TokenC * token,
const attr_t * extra_attrs,
const int8_t * predicate_matches
) nogil:
"""We need to consider:
a) Does the token match the specification? [Yes, No]
b) What's the quantifier? [1, 0+, ?]
@ -649,53 +691,56 @@ cdef action_t get_action(PatternStateC state,
is_match = not is_match
quantifier = ONE
if quantifier == ONE:
if is_match and is_final:
# Yes, final: 1000
return MATCH
elif is_match and not is_final:
# Yes, non-final: 0100
return ADVANCE
elif not is_match and is_final:
# No, final: 0000
return REJECT
else:
return REJECT
if is_match and is_final:
# Yes, final: 1000
return MATCH
elif is_match and not is_final:
# Yes, non-final: 0100
return ADVANCE
elif not is_match and is_final:
# No, final: 0000
return REJECT
else:
return REJECT
elif quantifier == ZERO_PLUS:
if is_match and is_final:
# Yes, final: 1001
return MATCH_EXTEND
elif is_match and not is_final:
# Yes, non-final: 0011
return RETRY_EXTEND
elif not is_match and is_final:
# No, final 2000 (note: Don't include last token!)
return MATCH_REJECT
else:
# No, non-final 0010
return RETRY
if is_match and is_final:
# Yes, final: 1001
return MATCH_EXTEND
elif is_match and not is_final:
# Yes, non-final: 0011
return RETRY_EXTEND
elif not is_match and is_final:
# No, final 2000 (note: Don't include last token!)
return MATCH_REJECT
else:
# No, non-final 0010
return RETRY
elif quantifier == ZERO_ONE:
if is_match and is_final:
# Yes, final: 3000
# To cater for a pattern ending in "?", we need to add
# a match both with and without the last token
return MATCH_DOUBLE
elif is_match and not is_final:
# Yes, non-final: 0110
# We need both branches here, consider a pair like:
# pattern: .?b string: b
# If we 'ADVANCE' on the .?, we miss the match.
return RETRY_ADVANCE
elif not is_match and is_final:
# No, final 2000 (note: Don't include last token!)
return MATCH_REJECT
else:
# No, non-final 0010
return RETRY
if is_match and is_final:
# Yes, final: 3000
# To cater for a pattern ending in "?", we need to add
# a match both with and without the last token
return MATCH_DOUBLE
elif is_match and not is_final:
# Yes, non-final: 0110
# We need both branches here, consider a pair like:
# pattern: .?b string: b
# If we 'ADVANCE' on the .?, we miss the match.
return RETRY_ADVANCE
elif not is_match and is_final:
# No, final 2000 (note: Don't include last token!)
return MATCH_REJECT
else:
# No, non-final 0010
return RETRY
cdef int8_t get_is_match(PatternStateC state,
const TokenC* token, const attr_t* extra_attrs,
const int8_t* predicate_matches) nogil:
cdef int8_t get_is_match(
PatternStateC state,
const TokenC* token,
const attr_t* extra_attrs,
const int8_t* predicate_matches
) nogil:
for i in range(state.pattern.nr_py):
if predicate_matches[state.pattern.py_predicates[i]] == -1:
return 0
@ -860,7 +905,7 @@ class _FuzzyPredicate:
self.is_extension = is_extension
if self.predicate not in self.operators:
raise ValueError(Errors.E126.format(good=self.operators, bad=self.predicate))
fuzz = self.predicate[len("FUZZY"):] # number after prefix
fuzz = self.predicate[len("FUZZY"):] # number after prefix
self.fuzzy = int(fuzz) if fuzz else -1
self.fuzzy_compare = fuzzy_compare
self.key = _predicate_cache_key(self.attr, self.predicate, value, fuzzy=self.fuzzy)
@ -1082,7 +1127,7 @@ def _get_extra_predicates_dict(attr, value_dict, vocab, predicate_types,
elif cls == _FuzzyPredicate:
if isinstance(value, dict):
# add predicates inside fuzzy operator
fuzz = type_[len("FUZZY"):] # number after prefix
fuzz = type_[len("FUZZY"):] # number after prefix
fuzzy_val = int(fuzz) if fuzz else -1
output.extend(_get_extra_predicates_dict(attr, value, vocab, predicate_types,
extra_predicates, seen_predicates,
@ -1101,8 +1146,9 @@ def _get_extra_predicates_dict(attr, value_dict, vocab, predicate_types,
return output
def _get_extension_extra_predicates(spec, extra_predicates, predicate_types,
seen_predicates):
def _get_extension_extra_predicates(
spec, extra_predicates, predicate_types, seen_predicates
):
output = []
for attr, value in spec.items():
if isinstance(value, dict):
@ -1131,7 +1177,7 @@ def _get_operators(spec):
return (ONE,)
elif spec["OP"] in lookup:
return lookup[spec["OP"]]
#Min_max {n,m}
# Min_max {n,m}
elif spec["OP"].startswith("{") and spec["OP"].endswith("}"):
# {n} --> {n,n} exactly n ONE,(n)
# {n,m}--> {n,m} min of n, max of m ONE,(n),ZERO_ONE,(m)
@ -1142,8 +1188,8 @@ def _get_operators(spec):
min_max = min_max if "," in min_max else f"{min_max},{min_max}"
n, m = min_max.split(",")
#1. Either n or m is a blank string and the other is numeric -->isdigit
#2. Both are numeric and n <= m
# 1. Either n or m is a blank string and the other is numeric -->isdigit
# 2. Both are numeric and n <= m
if (not n.isdecimal() and not m.isdecimal()) or (n.isdecimal() and m.isdecimal() and int(n) > int(m)):
keys = ", ".join(lookup.keys()) + ", {n}, {n,m}, {n,}, {,m} where n and m are integers and n <= m "
raise ValueError(Errors.E011.format(op=spec["OP"], opts=keys))

6
spacy/matcher/phrasematcher.pyx
View File

@ -1,14 +1,12 @@
# cython: infer_types=True, profile=True
from libc.stdint cimport uintptr_t
# cython: infer_types=True
from preshed.maps cimport map_clear, map_get, map_init, map_iter, map_set
import warnings
from ..attrs cimport DEP, LEMMA, MORPH, ORTH, POS, TAG
from ..attrs cimport DEP, LEMMA, MORPH, POS, TAG
from ..attrs import IDS
from ..structs cimport TokenC
from ..tokens.span cimport Span
from ..tokens.token cimport Token
from ..typedefs cimport attr_t

17
spacy/ml/parser_model.pxd
View File

@ -40,11 +40,16 @@ cdef ActivationsC alloc_activations(SizesC n) nogil
cdef void free_activations(const ActivationsC* A) nogil
cdef void predict_states(CBlas cblas, ActivationsC* A, StateC** states,
const WeightsC* W, SizesC n) nogil
cdef void predict_states(
CBlas cblas, ActivationsC* A, StateC** states, const WeightsC* W, SizesC n
) nogil
cdef int arg_max_if_valid(const weight_t* scores, const int* is_valid, int n) nogil
cdef void cpu_log_loss(float* d_scores,
const float* costs, const int* is_valid, const float* scores, int O) nogil
cdef void cpu_log_loss(
float* d_scores,
const float* costs,
const int* is_valid,
const float* scores,
int O
) nogil

130
spacy/ml/parser_model.pyx
View File

@ -1,4 +1,5 @@
# cython: infer_types=True, cdivision=True, boundscheck=False
# cython: profile=False
cimport numpy as np
from libc.math cimport exp
from libc.stdlib cimport calloc, free, realloc
@ -8,13 +9,13 @@ from thinc.backends.linalg cimport Vec, VecVec
import numpy
import numpy.random
from thinc.api import CupyOps, Model, NumpyOps, get_ops
from thinc.api import CupyOps, Model, NumpyOps
from .. import util
from ..errors import Errors
from ..pipeline._parser_internals.stateclass cimport StateClass
from ..typedefs cimport class_t, hash_t, weight_t
from ..typedefs cimport weight_t
cdef WeightsC get_c_weights(model) except *:
@ -78,33 +79,48 @@ cdef void resize_activations(ActivationsC* A, SizesC n) nogil:
A.is_valid = <int*>calloc(n.states * n.classes, sizeof(A.is_valid[0]))
A._max_size = n.states
else:
A.token_ids = <int*>realloc(A.token_ids,
n.states * n.feats * sizeof(A.token_ids[0]))
A.scores = <float*>realloc(A.scores,
n.states * n.classes * sizeof(A.scores[0]))
A.unmaxed = <float*>realloc(A.unmaxed,
n.states * n.hiddens * n.pieces * sizeof(A.unmaxed[0]))
A.hiddens = <float*>realloc(A.hiddens,
n.states * n.hiddens * sizeof(A.hiddens[0]))
A.is_valid = <int*>realloc(A.is_valid,
n.states * n.classes * sizeof(A.is_valid[0]))
A.token_ids = <int*>realloc(
A.token_ids, n.states * n.feats * sizeof(A.token_ids[0])
)
A.scores = <float*>realloc(
A.scores, n.states * n.classes * sizeof(A.scores[0])
)
A.unmaxed = <float*>realloc(
A.unmaxed, n.states * n.hiddens * n.pieces * sizeof(A.unmaxed[0])
)
A.hiddens = <float*>realloc(
A.hiddens, n.states * n.hiddens * sizeof(A.hiddens[0])
)
A.is_valid = <int*>realloc(
A.is_valid, n.states * n.classes * sizeof(A.is_valid[0])
)
A._max_size = n.states
A._curr_size = n.states
cdef void predict_states(CBlas cblas, ActivationsC* A, StateC** states,
const WeightsC* W, SizesC n) nogil:
cdef double one = 1.0
cdef void predict_states(
CBlas cblas, ActivationsC* A, StateC** states, const WeightsC* W, SizesC n
) nogil:
resize_activations(A, n)
for i in range(n.states):
states[i].set_context_tokens(&A.token_ids[i*n.feats], n.feats)
memset(A.unmaxed, 0, n.states * n.hiddens * n.pieces * sizeof(float))
memset(A.hiddens, 0, n.states * n.hiddens * sizeof(float))
sum_state_features(cblas, A.unmaxed,
W.feat_weights, A.token_ids, n.states, n.feats, n.hiddens * n.pieces)
sum_state_features(
cblas,
A.unmaxed,
W.feat_weights,
A.token_ids,
n.states,
n.feats,
n.hiddens * n.pieces
)
for i in range(n.states):
VecVec.add_i(&A.unmaxed[i*n.hiddens*n.pieces],
W.feat_bias, 1., n.hiddens * n.pieces)
VecVec.add_i(
&A.unmaxed[i*n.hiddens*n.pieces],
W.feat_bias, 1.,
n.hiddens * n.pieces
)
for j in range(n.hiddens):
index = i * n.hiddens * n.pieces + j * n.pieces
which = Vec.arg_max(&A.unmaxed[index], n.pieces)
@ -114,14 +130,15 @@ cdef void predict_states(CBlas cblas, ActivationsC* A, StateC** states,
memcpy(A.scores, A.hiddens, n.states * n.classes * sizeof(float))
else:
# Compute hidden-to-output
sgemm(cblas)(False, True, n.states, n.classes, n.hiddens,
sgemm(cblas)(
False, True, n.states, n.classes, n.hiddens,
1.0, <const float *>A.hiddens, n.hiddens,
<const float *>W.hidden_weights, n.hiddens,
0.0, A.scores, n.classes)
0.0, A.scores, n.classes
)
# Add bias
for i in range(n.states):
VecVec.add_i(&A.scores[i*n.classes],
W.hidden_bias, 1., n.classes)
VecVec.add_i(&A.scores[i*n.classes], W.hidden_bias, 1., n.classes)
# Set unseen classes to minimum value
i = 0
min_ = A.scores[0]
@ -134,9 +151,16 @@ cdef void predict_states(CBlas cblas, ActivationsC* A, StateC** states,
A.scores[i*n.classes+j] = min_
cdef void sum_state_features(CBlas cblas, float* output,
const float* cached, const int* token_ids, int B, int F, int O) nogil:
cdef int idx, b, f, i
cdef void sum_state_features(
CBlas cblas,
float* output,
const float* cached,
const int* token_ids,
int B,
int F,
int O
) nogil:
cdef int idx, b, f
cdef const float* feature
padding = cached
cached += F * O
@ -153,9 +177,13 @@ cdef void sum_state_features(CBlas cblas, float* output,
token_ids += F
cdef void cpu_log_loss(float* d_scores,
const float* costs, const int* is_valid, const float* scores,
int O) nogil:
cdef void cpu_log_loss(
float* d_scores,
const float* costs,
const int* is_valid,
const float* scores,
int O
) nogil:
"""Do multi-label log loss"""
cdef double max_, gmax, Z, gZ
best = arg_max_if_gold(scores, costs, is_valid, O)
@ -179,8 +207,9 @@ cdef void cpu_log_loss(float* d_scores,
d_scores[i] = exp(scores[i]-max_) / Z
cdef int arg_max_if_gold(const weight_t* scores, const weight_t* costs,
const int* is_valid, int n) nogil:
cdef int arg_max_if_gold(
const weight_t* scores, const weight_t* costs, const int* is_valid, int n
) nogil:
# Find minimum cost
cdef float cost = 1
for i in range(n):
@ -204,10 +233,17 @@ cdef int arg_max_if_valid(const weight_t* scores, const int* is_valid, int n) no
return best
class ParserStepModel(Model):
def __init__(self, docs, layers, *, has_upper, unseen_classes=None, train=True,
dropout=0.1):
def __init__(
self,
docs,
layers,
*,
has_upper,
unseen_classes=None,
train=True,
dropout=0.1
):
Model.__init__(self, name="parser_step_model", forward=step_forward)
self.attrs["has_upper"] = has_upper
self.attrs["dropout_rate"] = dropout
@ -268,8 +304,10 @@ class ParserStepModel(Model):
return ids
def backprop_step(self, token_ids, d_vector, get_d_tokvecs):
if isinstance(self.state2vec.ops, CupyOps) \
and not isinstance(token_ids, self.state2vec.ops.xp.ndarray):
if (
isinstance(self.state2vec.ops, CupyOps)
and not isinstance(token_ids, self.state2vec.ops.xp.ndarray)
):
# Move token_ids and d_vector to GPU, asynchronously
self.backprops.append((
util.get_async(self.cuda_stream, token_ids),
@ -279,7 +317,6 @@ class ParserStepModel(Model):
else:
self.backprops.append((token_ids, d_vector, get_d_tokvecs))
def finish_steps(self, golds):
# Add a padding vector to the d_tokvecs gradient, so that missing
# values don't affect the real gradient.
@ -292,14 +329,15 @@ class ParserStepModel(Model):
ids = ids.flatten()
d_state_features = d_state_features.reshape(
(ids.size, d_state_features.shape[2]))
self.ops.scatter_add(d_tokvecs, ids,
d_state_features)
self.ops.scatter_add(d_tokvecs, ids, d_state_features)
# Padded -- see update()
self.bp_tokvecs(d_tokvecs[:-1])
return d_tokvecs
NUMPY_OPS = NumpyOps()
def step_forward(model: ParserStepModel, states, is_train):
token_ids = model.get_token_ids(states)
vector, get_d_tokvecs = model.state2vec(token_ids, is_train)
@ -312,7 +350,7 @@ def step_forward(model: ParserStepModel, states, is_train):
scores, get_d_vector = model.vec2scores(vector, is_train)
else:
scores = NumpyOps().asarray(vector)
get_d_vector = lambda d_scores: d_scores
get_d_vector = lambda d_scores: d_scores # no-cython-lint: E731
# If the class is unseen, make sure its score is minimum
scores[:, model._class_mask == 0] = numpy.nanmin(scores)
@ -448,9 +486,11 @@ cdef class precompute_hiddens:
feat_weights = self.get_feat_weights()
cdef int[:, ::1] ids = token_ids
sum_state_features(cblas, <float*>state_vector.data,
feat_weights, &ids[0,0],
token_ids.shape[0], self.nF, self.nO*self.nP)
sum_state_features(
cblas, <float*>state_vector.data,
feat_weights, &ids[0, 0],
token_ids.shape[0], self.nF, self.nO*self.nP
)
state_vector += self.bias
state_vector, bp_nonlinearity = self._nonlinearity(state_vector)
@ -475,7 +515,7 @@ cdef class precompute_hiddens:
def backprop_maxout(d_best):
return self.ops.backprop_maxout(d_best, mask, self.nP)
return state_vector, backprop_maxout
def _relu_nonlinearity(self, state_vector):
@ -489,5 +529,5 @@ cdef class precompute_hiddens:
def backprop_relu(d_best):
d_best *= mask
return d_best.reshape((d_best.shape + (1,)))
return state_vector, backprop_relu

11
spacy/ml/staticvectors.py
View File

@ -9,7 +9,7 @@ from thinc.util import partial
from ..attrs import ORTH
from ..errors import Errors, Warnings
from ..tokens import Doc
from ..vectors import Mode
from ..vectors import Mode, Vectors
from ..vocab import Vocab
@ -48,11 +48,14 @@ def forward(
key_attr: int = getattr(vocab.vectors, "attr", ORTH)
keys = model.ops.flatten([cast(Ints1d, doc.to_array(key_attr)) for doc in docs])
W = cast(Floats2d, model.ops.as_contig(model.get_param("W")))
if vocab.vectors.mode == Mode.default:
if isinstance(vocab.vectors, Vectors) and vocab.vectors.mode == Mode.default:
V = model.ops.asarray(vocab.vectors.data)
rows = vocab.vectors.find(keys=keys)
V = model.ops.as_contig(V[rows])
elif vocab.vectors.mode == Mode.floret:
elif isinstance(vocab.vectors, Vectors) and vocab.vectors.mode == Mode.floret:
V = vocab.vectors.get_batch(keys)
V = model.ops.as_contig(V)
elif hasattr(vocab.vectors, "get_batch"):
V = vocab.vectors.get_batch(keys)
V = model.ops.as_contig(V)
else:
@ -61,7 +64,7 @@ def forward(
vectors_data = model.ops.gemm(V, W, trans2=True)
except ValueError:
raise RuntimeError(Errors.E896)
if vocab.vectors.mode == Mode.default:
if isinstance(vocab.vectors, Vectors) and vocab.vectors.mode == Mode.default:
# Convert negative indices to 0-vectors
# TODO: more options for UNK tokens
vectors_data[rows < 0] = 0

8
spacy/morphology.pxd
View File

@ -11,7 +11,7 @@ from .typedefs cimport attr_t, hash_t
cdef class Morphology:
cdef readonly Pool mem
cdef readonly StringStore strings
cdef PreshMap tags # Keyed by hash, value is pointer to tag
cdef PreshMap tags # Keyed by hash, value is pointer to tag
cdef MorphAnalysisC create_morph_tag(self, field_feature_pairs) except *
cdef int insert(self, MorphAnalysisC tag) except -1
@ -20,4 +20,8 @@ cdef class Morphology:
cdef int check_feature(const MorphAnalysisC* morph, attr_t feature) nogil
cdef list list_features(const MorphAnalysisC* morph)
cdef np.ndarray get_by_field(const MorphAnalysisC* morph, attr_t field)
cdef int get_n_by_field(attr_t* results, const MorphAnalysisC* morph, attr_t field) nogil
cdef int get_n_by_field(
attr_t* results,
const MorphAnalysisC* morph,
attr_t field,
) nogil

11
spacy/morphology.pyx
View File

@ -1,4 +1,5 @@
# cython: infer_types
# cython: profile=False
import warnings
import numpy
@ -83,10 +84,11 @@ cdef class Morphology:
features = self.normalize_attrs(features)
string_features = {self.strings.as_string(field): self.strings.as_string(values) for field, values in features.items()}
# normalized UFEATS string with sorted fields and values
norm_feats_string = self.FEATURE_SEP.join(sorted([
self.FIELD_SEP.join([field, values])
for field, values in string_features.items()
]))
norm_feats_string = self.FEATURE_SEP.join(
sorted(
[self.FIELD_SEP.join([field, values]) for field, values in string_features.items()]
)
)
return norm_feats_string or self.EMPTY_MORPH
def normalize_attrs(self, attrs):
@ -192,6 +194,7 @@ cdef int get_n_by_field(attr_t* results, const MorphAnalysisC* morph, attr_t fie
n_results += 1
return n_results
def unpickle_morphology(strings, tags):
cdef Morphology morphology = Morphology(strings)
for tag in tags:

2
spacy/parts_of_speech.pxd
View File

@ -8,7 +8,7 @@ cpdef enum univ_pos_t:
ADV
AUX
CONJ
CCONJ # U20
CCONJ # U20
DET
INTJ
NOUN

2
spacy/parts_of_speech.pyx
View File

@ -1,4 +1,4 @@
# cython: profile=False
IDS = {
"": NO_TAG,
"ADJ": ADJ,

17
spacy/pipeline/_edit_tree_internals/edit_trees.pxd
View File

@ -46,11 +46,18 @@ cdef struct EditTreeC:
bint is_match_node
NodeC inner
cdef inline EditTreeC edittree_new_match(len_t prefix_len, len_t suffix_len,
uint32_t prefix_tree, uint32_t suffix_tree):
cdef MatchNodeC match_node = MatchNodeC(prefix_len=prefix_len,
suffix_len=suffix_len, prefix_tree=prefix_tree,
suffix_tree=suffix_tree)
cdef inline EditTreeC edittree_new_match(
len_t prefix_len,
len_t suffix_len,
uint32_t prefix_tree,
uint32_t suffix_tree
):
cdef MatchNodeC match_node = MatchNodeC(
prefix_len=prefix_len,
suffix_len=suffix_len,
prefix_tree=prefix_tree,
suffix_tree=suffix_tree
)
cdef NodeC inner = NodeC(match_node=match_node)
return EditTreeC(is_match_node=True, inner=inner)

15
spacy/pipeline/_edit_tree_internals/edit_trees.pyx
View File

@ -1,12 +1,11 @@
# cython: infer_types=True, binding=True
# cython: profile=False
from cython.operator cimport dereference as deref
from libc.stdint cimport UINT32_MAX, uint32_t
from libc.string cimport memset
from libcpp.pair cimport pair
from libcpp.vector cimport vector
from pathlib import Path
from ...typedefs cimport hash_t
from ... import util
@ -25,17 +24,16 @@ cdef LCS find_lcs(str source, str target):
target (str): The second string.
RETURNS (LCS): The spans of the longest common subsequences.
"""
cdef Py_ssize_t source_len = len(source)
cdef Py_ssize_t target_len = len(target)
cdef size_t longest_align = 0;
cdef size_t longest_align = 0
cdef int source_idx, target_idx
cdef LCS lcs
cdef Py_UCS4 source_cp, target_cp
memset(&lcs, 0, sizeof(lcs))
cdef vector[size_t] prev_aligns = vector[size_t](target_len);
cdef vector[size_t] cur_aligns = vector[size_t](target_len);
cdef vector[size_t] prev_aligns = vector[size_t](target_len)
cdef vector[size_t] cur_aligns = vector[size_t](target_len)
for (source_idx, source_cp) in enumerate(source):
for (target_idx, target_cp) in enumerate(target):
@ -89,7 +87,7 @@ cdef class EditTrees:
cdef LCS lcs = find_lcs(form, lemma)
cdef EditTreeC tree
cdef uint32_t tree_id, prefix_tree, suffix_tree
cdef uint32_t prefix_tree, suffix_tree
if lcs_is_empty(lcs):
tree = edittree_new_subst(self.strings.add(form), self.strings.add(lemma))
else:
@ -108,7 +106,7 @@ cdef class EditTrees:
return self._tree_id(tree)
cdef uint32_t _tree_id(self, EditTreeC tree):
# If this tree has been constructed before, return its identifier.
# If this tree has been constructed before, return its identifier.
cdef hash_t hash = edittree_hash(tree)
cdef unordered_map[hash_t, uint32_t].iterator iter = self.map.find(hash)
if iter != self.map.end():
@ -289,6 +287,7 @@ def _tree2dict(tree):
tree = tree["inner"]["subst_node"]
return(dict(tree))
def _dict2tree(tree):
errors = validate_edit_tree(tree)
if errors:

8
spacy/pipeline/_edit_tree_internals/schemas.py
View File

@ -1,8 +1,12 @@
from collections import defaultdict
from typing import Any, Dict, List, Union
from pydantic import BaseModel, Field, ValidationError
from pydantic.types import StrictBool, StrictInt, StrictStr
try:
from pydantic.v1 import BaseModel, Field, ValidationError
from pydantic.v1.types import StrictBool, StrictInt, StrictStr
except ImportError:
from pydantic import BaseModel, Field, ValidationError # type: ignore
from pydantic.types import StrictBool, StrictInt, StrictStr # type: ignore
class MatchNodeSchema(BaseModel):

7
spacy/pipeline/_parser_internals/_beam_utils.pyx
View File

@ -1,17 +1,13 @@
# cython: infer_types=True
# cython: profile=True
cimport numpy as np
import numpy
from cpython.ref cimport Py_XDECREF, PyObject
from thinc.extra.search cimport Beam
from thinc.extra.search import MaxViolation
from thinc.extra.search cimport MaxViolation
from ...typedefs cimport class_t, hash_t
from ...typedefs cimport class_t
from .transition_system cimport Transition, TransitionSystem
from ...errors import Errors
@ -146,7 +142,6 @@ def update_beam(TransitionSystem moves, states, golds, model, int width, beam_de
cdef MaxViolation violn
pbeam = BeamBatch(moves, states, golds, width=width, density=beam_density)
gbeam = BeamBatch(moves, states, golds, width=width, density=0.0)
cdef StateClass state
beam_maps = []
backprops = []
violns = [MaxViolation() for _ in range(len(states))]

1
spacy/pipeline/_parser_internals/_state.pxd
View File

@ -277,7 +277,6 @@ cdef cppclass StateC:
return n
int n_L(int head) nogil const:
return n_arcs(this._left_arcs, head)

1
spacy/pipeline/_parser_internals/_state.pyx
View File

@ -0,0 +1 @@
# cython: profile=False

20
spacy/pipeline/_parser_internals/arc_eager.pyx
View File

@ -1,4 +1,4 @@
# cython: profile=True, cdivision=True, infer_types=True
# cython: cdivision=True, infer_types=True
from cymem.cymem cimport Address, Pool
from libc.stdint cimport int32_t
from libcpp.vector cimport vector
@ -9,7 +9,7 @@ from ...strings cimport hash_string
from ...structs cimport TokenC
from ...tokens.doc cimport Doc, set_children_from_heads
from ...tokens.token cimport MISSING_DEP
from ...typedefs cimport attr_t, hash_t
from ...typedefs cimport attr_t
from ...training import split_bilu_label
@ -68,8 +68,9 @@ cdef struct GoldParseStateC:
weight_t pop_cost
cdef GoldParseStateC create_gold_state(Pool mem, const StateC* state,
heads, labels, sent_starts) except *:
cdef GoldParseStateC create_gold_state(
Pool mem, const StateC* state, heads, labels, sent_starts
) except *:
cdef GoldParseStateC gs
gs.length = len(heads)
gs.stride = 1
@ -82,7 +83,7 @@ cdef GoldParseStateC create_gold_state(Pool mem, const StateC* state,
gs.n_kids_in_stack = <int32_t*>mem.alloc(gs.length, sizeof(gs.n_kids_in_stack[0]))
for i, is_sent_start in enumerate(sent_starts):
if is_sent_start == True:
if is_sent_start is True:
gs.state_bits[i] = set_state_flag(
gs.state_bits[i],
IS_SENT_START,
@ -210,6 +211,7 @@ cdef class ArcEagerGold:
def update(self, StateClass stcls):
update_gold_state(&self.c, stcls.c)
def _get_aligned_sent_starts(example):
"""Get list of SENT_START attributes aligned to the predicted tokenization.
If the reference has not sentence starts, return a list of None values.
@ -524,7 +526,6 @@ cdef class Break:
"""
@staticmethod
cdef bint is_valid(const StateC* st, attr_t label) nogil:
cdef int i
if st.buffer_length() < 2:
return False
elif st.B(1) != st.B(0) + 1:
@ -556,8 +557,8 @@ cdef class Break:
cost -= 1
if gold.heads[si] == b0:
cost -= 1
if not is_sent_start(gold, state.B(1)) \
and not is_sent_start_unknown(gold, state.B(1)):
if not is_sent_start(gold, state.B(1)) and\
not is_sent_start_unknown(gold, state.B(1)):
cost += 1
return cost
@ -803,7 +804,6 @@ cdef class ArcEager(TransitionSystem):
raise TypeError(Errors.E909.format(name="ArcEagerGold"))
cdef ArcEagerGold gold_ = gold
gold_state = gold_.c
n_gold = 0
if self.c[i].is_valid(stcls.c, self.c[i].label):
cost = self.c[i].get_cost(stcls.c, &gold_state, self.c[i].label)
else:
@ -875,7 +875,7 @@ cdef class ArcEager(TransitionSystem):
print("Gold")
for token in example.y:
print(token.i, token.text, token.dep_, token.head.text)
aligned_heads, aligned_labels = example.get_aligned_parse()
aligned_heads, _aligned_labels = example.get_aligned_parse()
print("Aligned heads")
for i, head in enumerate(aligned_heads):
print(example.x[i], example.x[head] if head is not None else "__")

18
spacy/pipeline/_parser_internals/ner.pyx
View File

@ -1,6 +1,4 @@
import os
import random
# cython: profile=False
from cymem.cymem cimport Pool
from libc.stdint cimport int32_t
@ -14,7 +12,7 @@ from ...tokens.span import Span
from ...attrs cimport IS_SPACE
from ...lexeme cimport Lexeme
from ...structs cimport SpanC, TokenC
from ...structs cimport SpanC
from ...tokens.span cimport Span
from ...typedefs cimport attr_t, weight_t
@ -141,11 +139,10 @@ cdef class BiluoPushDown(TransitionSystem):
OUT: Counter()
}
actions[OUT][''] = 1 # Represents a token predicted to be outside of any entity
actions[UNIT][''] = 1 # Represents a token prohibited to be in an entity
actions[UNIT][''] = 1 # Represents a token prohibited to be in an entity
for entity_type in kwargs.get('entity_types', []):
for action in (BEGIN, IN, LAST, UNIT):
actions[action][entity_type] = 1
moves = ('M', 'B', 'I', 'L', 'U')
for example in kwargs.get('examples', []):
for token in example.y:
ent_type = token.ent_type_
@ -164,7 +161,7 @@ cdef class BiluoPushDown(TransitionSystem):
if token.ent_type:
labels.add(token.ent_type_)
return labels
def move_name(self, int move, attr_t label):
if move == OUT:
return 'O'
@ -325,7 +322,6 @@ cdef class BiluoPushDown(TransitionSystem):
raise TypeError(Errors.E909.format(name="BiluoGold"))
cdef BiluoGold gold_ = gold
gold_state = gold_.c
n_gold = 0
if self.c[i].is_valid(stcls.c, self.c[i].label):
cost = self.c[i].get_cost(stcls.c, &gold_state, self.c[i].label)
else:
@ -486,10 +482,8 @@ cdef class In:
@staticmethod
cdef weight_t cost(const StateC* s, const void* _gold, attr_t label) nogil:
gold = <GoldNERStateC*>_gold
move = IN
cdef int next_act = gold.ner[s.B(1)].move if s.B(1) >= 0 else OUT
cdef int g_act = gold.ner[s.B(0)].move
cdef attr_t g_tag = gold.ner[s.B(0)].label
cdef bint is_sunk = _entity_is_sunk(s, gold.ner)
if g_act == MISSING:
@ -549,12 +543,10 @@ cdef class Last:
@staticmethod
cdef weight_t cost(const StateC* s, const void* _gold, attr_t label) nogil:
gold = <GoldNERStateC*>_gold
move = LAST
b0 = s.B(0)
ent_start = s.E(0)
cdef int g_act = gold.ner[b0].move
cdef attr_t g_tag = gold.ner[b0].label
cdef int cost = 0
@ -650,7 +642,6 @@ cdef class Unit:
cost += 1
break
return cost
cdef class Out:
@ -675,7 +666,6 @@ cdef class Out:
cdef weight_t cost(const StateC* s, const void* _gold, attr_t label) nogil:
gold = <GoldNERStateC*>_gold
cdef int g_act = gold.ner[s.B(0)].move
cdef attr_t g_tag = gold.ner[s.B(0)].label
cdef weight_t cost = 0
if g_act == MISSING:
pass

14
spacy/pipeline/_parser_internals/nonproj.pyx
View File

@ -1,4 +1,4 @@
# cython: profile=True, infer_types=True
# cython: infer_types=True
"""Implements the projectivize/deprojectivize mechanism in Nivre & Nilsson 2005
for doing pseudo-projective parsing implementation uses the HEAD decoration
scheme.
@ -125,14 +125,17 @@ def decompose(label):
def is_decorated(label):
return DELIMITER in label
def count_decorated_labels(gold_data):
freqs = {}
for example in gold_data:
proj_heads, deco_deps = projectivize(example.get_aligned("HEAD"),
example.get_aligned("DEP"))
# set the label to ROOT for each root dependent
deco_deps = ['ROOT' if head == i else deco_deps[i]
for i, head in enumerate(proj_heads)]
deco_deps = [
'ROOT' if head == i else deco_deps[i]
for i, head in enumerate(proj_heads)
]
# count label frequencies
for label in deco_deps:
if is_decorated(label):
@ -160,9 +163,9 @@ def projectivize(heads, labels):
cdef vector[int] _heads_to_c(heads):
cdef vector[int] c_heads;
cdef vector[int] c_heads
for head in heads:
if head == None:
if head is None:
c_heads.push_back(-1)
else:
assert head < len(heads)
@ -199,6 +202,7 @@ def _decorate(heads, proj_heads, labels):
deco_labels.append(labels[tokenid])
return deco_labels
def get_smallest_nonproj_arc_slow(heads):
cdef vector[int] c_heads = _heads_to_c(heads)
return _get_smallest_nonproj_arc(c_heads)

25
spacy/pipeline/_parser_internals/stateclass.pyx
View File

@ -1,6 +1,5 @@
# cython: infer_types=True
import numpy
# cython: profile=False
from libcpp.vector cimport vector
from ...tokens.doc cimport Doc
@ -38,11 +37,11 @@ cdef class StateClass:
cdef vector[ArcC] arcs
self.c.get_arcs(&arcs)
return list(arcs)
#py_arcs = []
#for arc in arcs:
# if arc.head != -1 and arc.child != -1:
# py_arcs.append((arc.head, arc.child, arc.label))
#return arcs
# py_arcs = []
# for arc in arcs:
# if arc.head != -1 and arc.child != -1:
# py_arcs.append((arc.head, arc.child, arc.label))
# return arcs
def add_arc(self, int head, int child, int label):
self.c.add_arc(head, child, label)
@ -52,10 +51,10 @@ cdef class StateClass:
def H(self, int child):
return self.c.H(child)
def L(self, int head, int idx):
return self.c.L(head, idx)
def R(self, int head, int idx):
return self.c.R(head, idx)
@ -98,7 +97,7 @@ cdef class StateClass:
def H(self, int i):
return self.c.H(i)
def E(self, int i):
return self.c.E(i)
@ -116,7 +115,7 @@ cdef class StateClass:
def H_(self, int i):
return self.doc[self.c.H(i)]
def E_(self, int i):
return self.doc[self.c.E(i)]
@ -125,7 +124,7 @@ cdef class StateClass:
def R_(self, int i, int idx):
return self.doc[self.c.R(i, idx)]
def empty(self):
return self.c.empty()
@ -134,7 +133,7 @@ cdef class StateClass:
def at_break(self):
return False
#return self.c.at_break()
# return self.c.at_break()
def has_head(self, int i):
return self.c.has_head(i)

14
spacy/pipeline/_parser_internals/transition_system.pxd
View File

@ -20,11 +20,15 @@ cdef struct Transition:
int (*do)(StateC* state, attr_t label) nogil
ctypedef weight_t (*get_cost_func_t)(const StateC* state, const void* gold,
attr_tlabel) nogil
ctypedef weight_t (*move_cost_func_t)(const StateC* state, const void* gold) nogil
ctypedef weight_t (*label_cost_func_t)(const StateC* state, const void*
gold, attr_t label) nogil
ctypedef weight_t (*get_cost_func_t)(
const StateC* state, const void* gold, attr_tlabel
) nogil
ctypedef weight_t (*move_cost_func_t)(
const StateC* state, const void* gold
) nogil
ctypedef weight_t (*label_cost_func_t)(
const StateC* state, const void* gold, attr_t label
) nogil
ctypedef int (*do_func_t)(StateC* state, attr_t label) nogil

4
spacy/pipeline/_parser_internals/transition_system.pyx
View File

@ -1,4 +1,5 @@
# cython: infer_types=True
# cython: profile=False
from __future__ import print_function
from cymem.cymem cimport Pool
@ -8,9 +9,7 @@ from collections import Counter
import srsly
from ...structs cimport TokenC
from ...tokens.doc cimport Doc
from ...typedefs cimport attr_t, weight_t
from . cimport _beam_utils
from .stateclass cimport StateClass
from ... import util
@ -231,7 +230,6 @@ cdef class TransitionSystem:
return self
def to_bytes(self, exclude=tuple()):
transitions = []
serializers = {
'moves': lambda: srsly.json_dumps(self.labels),
'strings': lambda: self.strings.to_bytes(),

5
spacy/pipeline/dep_parser.pyx
View File

@ -1,6 +1,6 @@
# cython: infer_types=True, profile=True, binding=True
# cython: infer_types=True, binding=True
from collections import defaultdict
from typing import Callable, Iterable, Optional
from typing import Callable, Optional
from thinc.api import Config, Model
@ -124,6 +124,7 @@ def make_parser(
scorer=scorer,
)
@Language.factory(
"beam_parser",
assigns=["token.dep", "token.head", "token.is_sent_start", "doc.sents"],

13
spacy/pipeline/morphologizer.pyx
View File

@ -1,8 +1,7 @@
# cython: infer_types=True, profile=True, binding=True
# cython: infer_types=True, binding=True
from itertools import islice
from typing import Callable, Dict, Optional, Union
import srsly
from thinc.api import Config, Model, SequenceCategoricalCrossentropy
from ..morphology cimport Morphology
@ -14,10 +13,8 @@ from ..errors import Errors
from ..language import Language
from ..parts_of_speech import IDS as POS_IDS
from ..scorer import Scorer
from ..symbols import POS
from ..training import validate_examples, validate_get_examples
from ..util import registry
from .pipe import deserialize_config
from .tagger import Tagger
# See #9050
@ -76,8 +73,11 @@ def morphologizer_score(examples, **kwargs):
results = {}
results.update(Scorer.score_token_attr(examples, "pos", **kwargs))
results.update(Scorer.score_token_attr(examples, "morph", getter=morph_key_getter, **kwargs))
results.update(Scorer.score_token_attr_per_feat(examples,
"morph", getter=morph_key_getter, **kwargs))
results.update(
Scorer.score_token_attr_per_feat(
examples, "morph", getter=morph_key_getter, **kwargs
)
)
return results
@ -233,7 +233,6 @@ class Morphologizer(Tagger):
if isinstance(docs, Doc):
docs = [docs]
cdef Doc doc
cdef Vocab vocab = self.vocab
cdef bint overwrite = self.cfg["overwrite"]
cdef bint extend = self.cfg["extend"]
labels = self.labels

11
spacy/pipeline/multitask.pyx
View File

@ -1,16 +1,13 @@
# cython: infer_types=True, profile=True, binding=True
# cython: infer_types=True, binding=True
from typing import Optional
import numpy
from thinc.api import Config, CosineDistance, Model, set_dropout_rate, to_categorical
from ..tokens.doc cimport Doc
from ..attrs import ID, POS
from ..attrs import ID
from ..errors import Errors
from ..language import Language
from ..training import validate_examples
from ._parser_internals import nonproj
from .tagger import Tagger
from .trainable_pipe import TrainablePipe
@ -103,10 +100,9 @@ class MultitaskObjective(Tagger):
cdef int idx = 0
correct = numpy.zeros((scores.shape[0],), dtype="i")
guesses = scores.argmax(axis=1)
docs = [eg.predicted for eg in examples]
for i, eg in enumerate(examples):
# Handles alignment for tokenization differences
doc_annots = eg.get_aligned() # TODO
_doc_annots = eg.get_aligned() # TODO
for j in range(len(eg.predicted)):
tok_annots = {key: values[j] for key, values in tok_annots.items()}
label = self.make_label(j, tok_annots)
@ -206,7 +202,6 @@ class ClozeMultitask(TrainablePipe):
losses[self.name] = 0.
set_dropout_rate(self.model, drop)
validate_examples(examples, "ClozeMultitask.rehearse")
docs = [eg.predicted for eg in examples]
predictions, bp_predictions = self.model.begin_update()
loss, d_predictions = self.get_loss(examples, self.vocab.vectors.data, predictions)
bp_predictions(d_predictions)

7
spacy/pipeline/ner.pyx
View File

@ -1,6 +1,6 @@
# cython: infer_types=True, profile=True, binding=True
# cython: infer_types=True, binding=True
from collections import defaultdict
from typing import Callable, Iterable, Optional
from typing import Callable, Optional
from thinc.api import Config, Model
@ -10,7 +10,7 @@ from ._parser_internals.ner cimport BiluoPushDown
from .transition_parser cimport Parser
from ..language import Language
from ..scorer import PRFScore, get_ner_prf
from ..scorer import get_ner_prf
from ..training import remove_bilu_prefix
from ..util import registry
@ -100,6 +100,7 @@ def make_ner(
scorer=scorer,
)
@Language.factory(
"beam_ner",
assigns=["doc.ents", "token.ent_iob", "token.ent_type"],

8
spacy/pipeline/pipe.pyx
View File

@ -1,6 +1,6 @@
# cython: infer_types=True, profile=True, binding=True
# cython: infer_types=True, binding=True
import warnings
from typing import Callable, Dict, Iterable, Iterator, Optional, Tuple, Union
from typing import Callable, Dict, Iterable, Iterator, Tuple, Union
import srsly
@ -40,7 +40,7 @@ cdef class Pipe:
"""
raise NotImplementedError(Errors.E931.format(parent="Pipe", method="__call__", name=self.name))
def pipe(self, stream: Iterable[Doc], *, batch_size: int=128) -> Iterator[Doc]:
def pipe(self, stream: Iterable[Doc], *, batch_size: int = 128) -> Iterator[Doc]:
"""Apply the pipe to a stream of documents. This usually happens under
the hood when the nlp object is called on a text and all components are
applied to the Doc.
@ -59,7 +59,7 @@ cdef class Pipe:
except Exception as e:
error_handler(self.name, self, [doc], e)
def initialize(self, get_examples: Callable[[], Iterable[Example]], *, nlp: Language=None):
def initialize(self, get_examples: Callable[[], Iterable[Example]], *, nlp: Language = None):
"""Initialize the pipe. For non-trainable components, this method
is optional. For trainable components, which should inherit
from the subclass TrainablePipe, the provided data examples

30
spacy/pipeline/sentencizer.pyx
View File

@ -1,4 +1,4 @@
# cython: infer_types=True, profile=True, binding=True
# cython: infer_types=True, binding=True
from typing import Callable, List, Optional
import srsly
@ -7,13 +7,13 @@ from ..tokens.doc cimport Doc
from .. import util
from ..language import Language
from ..scorer import Scorer
from .pipe import Pipe
from .senter import senter_score
# see #9050
BACKWARD_OVERWRITE = False
@Language.factory(
"sentencizer",
assigns=["token.is_sent_start", "doc.sents"],
@ -36,17 +36,19 @@ class Sentencizer(Pipe):
DOCS: https://spacy.io/api/sentencizer
"""
default_punct_chars = ['!', '.', '?', '։', '؟', '۔', '܀', '܁', '܂', '߹',
'।', '॥', '၊', '။', '።', '፧', '፨', '᙮', '᜵', '᜶', '᠃', '᠉', '᥄',
'᥅', '᪨', '᪩', '᪪', '᪫', '᭚', '᭛', '᭞', '᭟', '᰻', '᰼', '᱾', '᱿',
'‼', '‽', '⁇', '⁈', '⁉', '⸮', '⸼', '꓿', '꘎', '꘏', '꛳', '꛷', '꡶',
'꡷', '꣎', '꣏', '꤯', '꧈', '꧉', '꩝', '꩞', '꩟', '꫰', '꫱', '꯫', '﹒',
'﹖', '﹗', '！', '．', '？', '𐩖', '𐩗', '𑁇', '𑁈', '𑂾', '𑂿', '𑃀',
'𑃁', '𑅁', '𑅂', '𑅃', '𑇅', '𑇆', '𑇍', '𑇞', '𑇟', '𑈸', '𑈹', '𑈻', '𑈼',
'𑊩', '𑑋', '𑑌', '𑗂', '𑗃', '𑗉', '𑗊', '𑗋', '𑗌', '𑗍', '𑗎', '𑗏', '𑗐',
'𑗑', '𑗒', '𑗓', '𑗔', '𑗕', '𑗖', '𑗗', '𑙁', '𑙂', '𑜼', '𑜽', '𑜾', '𑩂',
'𑩃', '𑪛', '𑪜', '𑱁', '𑱂', '𖩮', '𖩯', '𖫵', '𖬷', '𖬸', '𖭄', '𛲟', '𝪈',
'｡', '。']
default_punct_chars = [
'!', '.', '?', '։', '؟', '۔', '܀', '܁', '܂', '߹',
'।', '॥', '၊', '။', '።', '፧', '፨', '᙮', '᜵', '᜶', '᠃', '᠉', '᥄',
'᥅', '᪨', '᪩', '᪪', '᪫', '᭚', '᭛', '᭞', '᭟', '᰻', '᰼', '᱾', '᱿',
'‼', '‽', '⁇', '⁈', '⁉', '⸮', '⸼', '꓿', '꘎', '꘏', '꛳', '꛷', '꡶',
'꡷', '꣎', '꣏', '꤯', '꧈', '꧉', '꩝', '꩞', '꩟', '꫰', '꫱', '꯫', '﹒',
'﹖', '﹗', '！', '．', '？', '𐩖', '𐩗', '𑁇', '𑁈', '𑂾', '𑂿', '𑃀',
'𑃁', '𑅁', '𑅂', '𑅃', '𑇅', '𑇆', '𑇍', '𑇞', '𑇟', '𑈸', '𑈹', '𑈻', '𑈼',
'𑊩', '𑑋', '𑑌', '𑗂', '𑗃', '𑗉', '𑗊', '𑗋', '𑗌', '𑗍', '𑗎', '𑗏', '𑗐',
'𑗑', '𑗒', '𑗓', '𑗔', '𑗕', '𑗖', '𑗗', '𑙁', '𑙂', '𑜼', '𑜽', '𑜾', '𑩂',
'𑩃', '𑪛', '𑪜', '𑱁', '𑱂', '𖩮', '𖩯', '𖫵', '𖬷', '𖬸', '𖭄', '𛲟', '𝪈',
'｡', '。'
]
def __init__(
self,
@ -128,7 +130,6 @@ class Sentencizer(Pipe):
if isinstance(docs, Doc):
docs = [docs]
cdef Doc doc
cdef int idx = 0
for i, doc in enumerate(docs):
doc_tag_ids = batch_tag_ids[i]
for j, tag_id in enumerate(doc_tag_ids):
@ -169,7 +170,6 @@ class Sentencizer(Pipe):
path = path.with_suffix(".json")
srsly.write_json(path, {"punct_chars": list(self.punct_chars), "overwrite": self.overwrite})
def from_disk(self, path, *, exclude=tuple()):
"""Load the sentencizer from disk.

3
spacy/pipeline/senter.pyx
View File

@ -1,8 +1,7 @@
# cython: infer_types=True, profile=True, binding=True
# cython: infer_types=True, binding=True
from itertools import islice
from typing import Callable, Optional
import srsly
from thinc.api import Config, Model, SequenceCategoricalCrossentropy
from ..tokens.doc cimport Doc

13
spacy/pipeline/tagger.pyx
View File

@ -1,26 +1,18 @@
# cython: infer_types=True, profile=True, binding=True
import warnings
# cython: infer_types=True, binding=True
from itertools import islice
from typing import Callable, Optional
import numpy
import srsly
from thinc.api import Config, Model, SequenceCategoricalCrossentropy, set_dropout_rate
from thinc.types import Floats2d
from ..morphology cimport Morphology
from ..tokens.doc cimport Doc
from ..vocab cimport Vocab
from .. import util
from ..attrs import ID, POS
from ..errors import Errors, Warnings
from ..errors import Errors
from ..language import Language
from ..parts_of_speech import X
from ..scorer import Scorer
from ..training import validate_examples, validate_get_examples
from ..util import registry
from .pipe import deserialize_config
from .trainable_pipe import TrainablePipe
# See #9050
@ -169,7 +161,6 @@ class Tagger(TrainablePipe):
if isinstance(docs, Doc):
docs = [docs]
cdef Doc doc
cdef Vocab vocab = self.vocab
cdef bint overwrite = self.cfg["overwrite"]
labels = self.labels
for i, doc in enumerate(docs):

16
spacy/pipeline/trainable_pipe.pyx
View File

@ -1,4 +1,4 @@
# cython: infer_types=True, profile=True, binding=True
# cython: infer_types=True, binding=True
from typing import Callable, Dict, Iterable, Iterator, Optional, Tuple
import srsly
@ -55,7 +55,7 @@ cdef class TrainablePipe(Pipe):
except Exception as e:
error_handler(self.name, self, [doc], e)
def pipe(self, stream: Iterable[Doc], *, batch_size: int=128) -> Iterator[Doc]:
def pipe(self, stream: Iterable[Doc], *, batch_size: int = 128) -> Iterator[Doc]:
"""Apply the pipe to a stream of documents. This usually happens under
the hood when the nlp object is called on a text and all components are
applied to the Doc.
@ -102,9 +102,9 @@ cdef class TrainablePipe(Pipe):
def update(self,
examples: Iterable["Example"],
*,
drop: float=0.0,
sgd: Optimizer=None,
losses: Optional[Dict[str, float]]=None) -> Dict[str, float]:
drop: float = 0.0,
sgd: Optimizer = None,
losses: Optional[Dict[str, float]] = None) -> Dict[str, float]:
"""Learn from a batch of documents and gold-standard information,
updating the pipe's model. Delegates to predict and get_loss.
@ -138,8 +138,8 @@ cdef class TrainablePipe(Pipe):
def rehearse(self,
examples: Iterable[Example],
*,
sgd: Optimizer=None,
losses: Dict[str, float]=None,
sgd: Optimizer = None,
losses: Dict[str, float] = None,
**config) -> Dict[str, float]:
"""Perform a "rehearsal" update from a batch of data. Rehearsal updates
teach the current model to make predictions similar to an initial model,
@ -177,7 +177,7 @@ cdef class TrainablePipe(Pipe):
"""
return util.create_default_optimizer()
def initialize(self, get_examples: Callable[[], Iterable[Example]], *, nlp: Language=None):
def initialize(self, get_examples: Callable[[], Iterable[Example]], *, nlp: Language = None):
"""Initialize the pipe for training, using data examples if available.
This method needs to be implemented by each TrainablePipe component,
ensuring the internal model (if available) is initialized properly

18
spacy/pipeline/transition_parser.pxd
View File

@ -13,8 +13,18 @@ cdef class Parser(TrainablePipe):
cdef readonly TransitionSystem moves
cdef public object _multitasks
cdef void _parseC(self, CBlas cblas, StateC** states,
WeightsC weights, SizesC sizes) nogil
cdef void _parseC(
self,
CBlas cblas,
StateC** states,
WeightsC weights,
SizesC sizes
) nogil
cdef void c_transition_batch(self, StateC** states, const float* scores,
int nr_class, int batch_size) nogil
cdef void c_transition_batch(
self,
StateC** states,
const float* scores,
int nr_class,
int batch_size
) nogil

65
spacy/pipeline/transition_parser.pyx
View File

@ -1,4 +1,5 @@
# cython: infer_types=True, cdivision=True, boundscheck=False, binding=True
# cython: profile=False
from __future__ import print_function
cimport numpy as np
@ -7,20 +8,15 @@ from cymem.cymem cimport Pool
from itertools import islice
from libc.stdlib cimport calloc, free
from libc.string cimport memcpy, memset
from libc.string cimport memset
from libcpp.vector cimport vector
import random
import srsly
from thinc.api import CupyOps, NumpyOps, get_ops, set_dropout_rate
from thinc.extra.search cimport Beam
import warnings
import numpy
import numpy.random
import srsly
from thinc.api import CupyOps, NumpyOps, set_dropout_rate
from ..ml.parser_model cimport (
ActivationsC,
@ -42,7 +38,7 @@ from .trainable_pipe import TrainablePipe
from ._parser_internals cimport _beam_utils
from .. import util
from ..errors import Errors, Warnings
from ..errors import Errors
from ..training import validate_examples, validate_get_examples
from ._parser_internals import _beam_utils
@ -258,7 +254,6 @@ cdef class Parser(TrainablePipe):
except Exception as e:
error_handler(self.name, self, batch_in_order, e)
def predict(self, docs):
if isinstance(docs, Doc):
docs = [docs]
@ -300,8 +295,6 @@ cdef class Parser(TrainablePipe):
return batch
def beam_parse(self, docs, int beam_width, float drop=0., beam_density=0.):
cdef Beam beam
cdef Doc doc
self._ensure_labels_are_added(docs)
batch = _beam_utils.BeamBatch(
self.moves,
@ -321,16 +314,18 @@ cdef class Parser(TrainablePipe):
del model
return list(batch)
cdef void _parseC(self, CBlas cblas, StateC** states,
WeightsC weights, SizesC sizes) nogil:
cdef int i, j
cdef void _parseC(
self, CBlas cblas, StateC** states, WeightsC weights, SizesC sizes
) nogil:
cdef int i
cdef vector[StateC*] unfinished
cdef ActivationsC activations = alloc_activations(sizes)
while sizes.states >= 1:
predict_states(cblas, &activations, states, &weights, sizes)
# Validate actions, argmax, take action.
self.c_transition_batch(states,
activations.scores, sizes.classes, sizes.states)
self.c_transition_batch(
states, activations.scores, sizes.classes, sizes.states
)
for i in range(sizes.states):
if not states[i].is_final():
unfinished.push_back(states[i])
@ -342,7 +337,6 @@ cdef class Parser(TrainablePipe):
def set_annotations(self, docs, states_or_beams):
cdef StateClass state
cdef Beam beam
cdef Doc doc
states = _beam_utils.collect_states(states_or_beams, docs)
for i, (state, doc) in enumerate(zip(states, docs)):
@ -359,8 +353,13 @@ cdef class Parser(TrainablePipe):
self.c_transition_batch(&c_states[0], c_scores, scores.shape[1], scores.shape[0])
return [state for state in states if not state.c.is_final()]
cdef void c_transition_batch(self, StateC** states, const float* scores,
int nr_class, int batch_size) nogil:
cdef void c_transition_batch(
self,
StateC** states,
const float* scores,
int nr_class,
int batch_size
) nogil:
# n_moves should not be zero at this point, but make sure to avoid zero-length mem alloc
with gil:
assert self.moves.n_moves > 0, Errors.E924.format(name=self.name)
@ -380,7 +379,6 @@ cdef class Parser(TrainablePipe):
free(is_valid)
def update(self, examples, *, drop=0., sgd=None, losses=None):
cdef StateClass state
if losses is None:
losses = {}
losses.setdefault(self.name, 0.)
@ -419,8 +417,7 @@ cdef class Parser(TrainablePipe):
if not states:
return losses
model, backprop_tok2vec = self.model.begin_update([eg.x for eg in examples])
all_states = list(states)
states_golds = list(zip(states, golds))
n_moves = 0
while states_golds:
@ -500,8 +497,16 @@ cdef class Parser(TrainablePipe):
del tutor
return losses
def update_beam(self, examples, *, beam_width,
drop=0., sgd=None, losses=None, beam_density=0.0):
def update_beam(
self,
examples,
*,
beam_width,
drop=0.,
sgd=None,
losses=None,
beam_density=0.0
):
states, golds, _ = self.moves.init_gold_batch(examples)
if not states:
return losses
@ -531,8 +536,9 @@ cdef class Parser(TrainablePipe):
is_valid = <int*>mem.alloc(self.moves.n_moves, sizeof(int))
costs = <float*>mem.alloc(self.moves.n_moves, sizeof(float))
cdef np.ndarray d_scores = numpy.zeros((len(states), self.moves.n_moves),
dtype='f', order='C')
cdef np.ndarray d_scores = numpy.zeros(
(len(states), self.moves.n_moves), dtype='f', order='C'
)
c_d_scores = <float*>d_scores.data
unseen_classes = self.model.attrs["unseen_classes"]
for i, (state, gold) in enumerate(zip(states, golds)):
@ -542,8 +548,9 @@ cdef class Parser(TrainablePipe):
for j in range(self.moves.n_moves):
if costs[j] <= 0.0 and j in unseen_classes:
unseen_classes.remove(j)
cpu_log_loss(c_d_scores,
costs, is_valid, &scores[i, 0], d_scores.shape[1])
cpu_log_loss(
c_d_scores, costs, is_valid, &scores[i, 0], d_scores.shape[1]
)
c_d_scores += d_scores.shape[1]
# Note that we don't normalize this. See comment in update() for why.
if losses is not None:

102
spacy/schemas.py
View File

@ -16,19 +16,34 @@ from typing import (
Union,
)
from pydantic import (
BaseModel,
ConstrainedStr,
Field,
StrictBool,
StrictFloat,
StrictInt,
StrictStr,
ValidationError,
create_model,
validator,
)
from pydantic.main import ModelMetaclass
try:
from pydantic.v1 import (
BaseModel,
ConstrainedStr,
Field,
StrictBool,
StrictFloat,
StrictInt,
StrictStr,
ValidationError,
create_model,
validator,
)
from pydantic.v1.main import ModelMetaclass
except ImportError:
from pydantic import ( # type: ignore
BaseModel,
ConstrainedStr,
Field,
StrictBool,
StrictFloat,
StrictInt,
StrictStr,
ValidationError,
create_model,
validator,
)
from pydantic.main import ModelMetaclass # type: ignore
from thinc.api import ConfigValidationError, Model, Optimizer
from thinc.config import Promise
@ -397,6 +412,7 @@ class ConfigSchemaNlp(BaseModel):
after_creation: Optional[Callable[["Language"], "Language"]] = Field(..., title="Optional callback to modify nlp object after creation and before the pipeline is constructed")
after_pipeline_creation: Optional[Callable[["Language"], "Language"]] = Field(..., title="Optional callback to modify nlp object after the pipeline is constructed")
batch_size: Optional[int] = Field(..., title="Default batch size")
vectors: Callable = Field(..., title="Vectors implementation")
# fmt: on
class Config:
@ -465,66 +481,6 @@ CONFIG_SCHEMAS = {
"initialize": ConfigSchemaInit,
}
# Project config Schema
class ProjectConfigAssetGitItem(BaseModel):
# fmt: off
repo: StrictStr = Field(..., title="URL of Git repo to download from")
path: StrictStr = Field(..., title="File path or sub-directory to download (used for sparse checkout)")
branch: StrictStr = Field("master", title="Branch to clone from")
# fmt: on
class ProjectConfigAssetURL(BaseModel):
# fmt: off
dest: StrictStr = Field(..., title="Destination of downloaded asset")
url: Optional[StrictStr] = Field(None, title="URL of asset")
checksum: Optional[str] = Field(None, title="MD5 hash of file", regex=r"([a-fA-F\d]{32})")
description: StrictStr = Field("", title="Description of asset")
# fmt: on
class ProjectConfigAssetGit(BaseModel):
# fmt: off
git: ProjectConfigAssetGitItem = Field(..., title="Git repo information")
checksum: Optional[str] = Field(None, title="MD5 hash of file", regex=r"([a-fA-F\d]{32})")
description: Optional[StrictStr] = Field(None, title="Description of asset")
# fmt: on
class ProjectConfigCommand(BaseModel):
# fmt: off
name: StrictStr = Field(..., title="Name of command")
help: Optional[StrictStr] = Field(None, title="Command description")
script: List[StrictStr] = Field([], title="List of CLI commands to run, in order")
deps: List[StrictStr] = Field([], title="File dependencies required by this command")
outputs: List[StrictStr] = Field([], title="Outputs produced by this command")
outputs_no_cache: List[StrictStr] = Field([], title="Outputs not tracked by DVC (DVC only)")
no_skip: bool = Field(False, title="Never skip this command, even if nothing changed")
# fmt: on
class Config:
title = "A single named command specified in a project config"
extra = "forbid"
class ProjectConfigSchema(BaseModel):
# fmt: off
vars: Dict[StrictStr, Any] = Field({}, title="Optional variables to substitute in commands")
env: Dict[StrictStr, Any] = Field({}, title="Optional variable names to substitute in commands, mapped to environment variable names")
assets: List[Union[ProjectConfigAssetURL, ProjectConfigAssetGit]] = Field([], title="Data assets")
workflows: Dict[StrictStr, List[StrictStr]] = Field({}, title="Named workflows, mapped to list of project commands to run in order")
commands: List[ProjectConfigCommand] = Field([], title="Project command shortucts")
title: Optional[str] = Field(None, title="Project title")
spacy_version: Optional[StrictStr] = Field(None, title="spaCy version range that the project is compatible with")
# fmt: on
class Config:
title = "Schema for project configuration file"
# Recommendations for init config workflows

6
spacy/strings.pyx
View File

@ -1,8 +1,8 @@
# cython: infer_types=True
# cython: profile=False
cimport cython
from libc.stdint cimport uint32_t
from libc.string cimport memcpy
from libcpp.set cimport set
from murmurhash.mrmr cimport hash32, hash64
import srsly
@ -20,9 +20,10 @@ cdef inline bint _try_coerce_to_hash(object key, hash_t* out_hash):
try:
out_hash[0] = key
return True
except:
except: # no-cython-lint
return False
def get_string_id(key):
"""Get a string ID, handling the reserved symbols correctly. If the key is
already an ID, return it.
@ -87,7 +88,6 @@ cdef Utf8Str* _allocate(Pool mem, const unsigned char* chars, uint32_t length) e
cdef int n_length_bytes
cdef int i
cdef Utf8Str* string = <Utf8Str*>mem.alloc(1, sizeof(Utf8Str))
cdef uint32_t ulength = length
if length < sizeof(string.s):
string.s[0] = <unsigned char>length
memcpy(&string.s[1], chars, length)

2
spacy/structs.pxd
View File

@ -52,7 +52,7 @@ cdef struct TokenC:
int sent_start
int ent_iob
attr_t ent_type # TODO: Is there a better way to do this? Multiple sources of truth..
attr_t ent_type # TODO: Is there a better way to do this? Multiple sources of truth..
attr_t ent_kb_id
hash_t ent_id

8
spacy/symbols.pxd
View File

@ -92,7 +92,7 @@ cdef enum symbol_t:
ADV
AUX
CONJ
CCONJ # U20
CCONJ # U20
DET
INTJ
NOUN
@ -418,7 +418,7 @@ cdef enum symbol_t:
ccomp
complm
conj
cop # U20
cop # U20
csubj
csubjpass
dep
@ -441,8 +441,8 @@ cdef enum symbol_t:
num
number
oprd
obj # U20
obl # U20
obj # U20
obl # U20
parataxis
partmod
pcomp

9
spacy/symbols.pyx
View File

@ -1,4 +1,5 @@
# cython: optimize.unpack_method_calls=False
# cython: profile=False
IDS = {
"": NIL,
"IS_ALPHA": IS_ALPHA,
@ -96,7 +97,7 @@ IDS = {
"ADV": ADV,
"AUX": AUX,
"CONJ": CONJ,
"CCONJ": CCONJ, # U20
"CCONJ": CCONJ, # U20
"DET": DET,
"INTJ": INTJ,
"NOUN": NOUN,
@ -421,7 +422,7 @@ IDS = {
"ccomp": ccomp,
"complm": complm,
"conj": conj,
"cop": cop, # U20
"cop": cop, # U20
"csubj": csubj,
"csubjpass": csubjpass,
"dep": dep,
@ -444,8 +445,8 @@ IDS = {
"num": num,
"number": number,
"oprd": oprd,
"obj": obj, # U20
"obl": obl, # U20
"obj": obj, # U20
"obl": obl, # U20
"parataxis": parataxis,
"partmod": partmod,
"pcomp": pcomp,

5
spacy/tests/matcher/test_dependency_matcher.py
View File

@ -216,6 +216,11 @@ def test_dependency_matcher_pattern_validation(en_vocab):
pattern2 = copy.deepcopy(pattern)
pattern2[1]["RIGHT_ID"] = "fox"
matcher.add("FOUNDED", [pattern2])
# invalid key
with pytest.warns(UserWarning):
pattern2 = copy.deepcopy(pattern)
pattern2[1]["FOO"] = "BAR"
matcher.add("FOUNDED", [pattern2])
def test_dependency_matcher_callback(en_vocab, doc):

3
spacy/tests/matcher/test_pattern_validation.py
View File

@ -52,7 +52,8 @@ TEST_PATTERNS = [
@pytest.mark.parametrize(
"pattern", [[{"XX": "y"}, {"LENGTH": "2"}, {"TEXT": {"IN": 5}}]]
"pattern",
[[{"XX": "y"}], [{"LENGTH": "2"}], [{"TEXT": {"IN": 5}}], [{"text": {"in": 6}}]],
)
def test_matcher_pattern_validation(en_vocab, pattern):
matcher = Matcher(en_vocab, validate=True)

4
spacy/tests/package/test_requirements.py
View File

@ -4,14 +4,15 @@ from pathlib import Path
def test_build_dependencies():
# Check that library requirements are pinned exactly the same across different setup files.
# TODO: correct checks for numpy rather than ignoring
libs_ignore_requirements = [
"numpy",
"pytest",
"pytest-timeout",
"mock",
"flake8",
"hypothesis",
"pre-commit",
"cython-lint",
"black",
"isort",
"mypy",
@ -22,6 +23,7 @@ def test_build_dependencies():
]
# ignore language-specific packages that shouldn't be installed by all
libs_ignore_setup = [
"numpy",
"fugashi",
"natto-py",
"pythainlp",

7
spacy/tests/pipeline/test_initialize.py
View File

@ -1,5 +1,10 @@
import pytest
from pydantic import StrictBool
try:
from pydantic.v1 import StrictBool
except ImportError:
from pydantic import StrictBool # type: ignore
from thinc.api import ConfigValidationError
from spacy.lang.en import English

7
spacy/tests/pipeline/test_pipe_factories.py
View File

@ -1,5 +1,10 @@
import pytest
from pydantic import StrictInt, StrictStr
try:
from pydantic.v1 import StrictInt, StrictStr
except ImportError:
from pydantic import StrictInt, StrictStr # type: ignore
from thinc.api import ConfigValidationError, Linear, Model
import spacy

299
spacy/tests/test_cli.py
View File

@ -1,31 +1,20 @@
import math
import os
import time
from collections import Counter
from pathlib import Path
from typing import Any, Dict, List, Tuple
import numpy
import pytest
import srsly
from click import NoSuchOption
from packaging.specifiers import SpecifierSet
from thinc.api import Config, ConfigValidationError
from thinc.api import Config
import spacy
from spacy import about
from spacy import info as spacy_info
from spacy.cli import info
from spacy.cli._util import (
download_file,
is_subpath_of,
load_project_config,
parse_config_overrides,
string_to_list,
substitute_project_variables,
upload_file,
validate_project_commands,
walk_directory,
)
from spacy.cli._util import parse_config_overrides, string_to_list, walk_directory
from spacy.cli.apply import apply
from spacy.cli.debug_data import (
_compile_gold,
@ -43,13 +32,11 @@ from spacy.cli.find_threshold import find_threshold
from spacy.cli.init_config import RECOMMENDATIONS, fill_config, init_config
from spacy.cli.init_pipeline import _init_labels
from spacy.cli.package import _is_permitted_package_name, get_third_party_dependencies
from spacy.cli.project.remote_storage import RemoteStorage
from spacy.cli.project.run import _check_requirements
from spacy.cli.validate import get_model_pkgs
from spacy.lang.en import English
from spacy.lang.nl import Dutch
from spacy.language import Language
from spacy.schemas import ProjectConfigSchema, RecommendationSchema, validate
from spacy.schemas import RecommendationSchema
from spacy.tokens import Doc, DocBin
from spacy.tokens.span import Span
from spacy.training import Example, docs_to_json, offsets_to_biluo_tags
@ -134,25 +121,6 @@ def test_issue7055():
assert "model" in filled_cfg["components"]["ner"]
@pytest.mark.issue(11235)
def test_issue11235():
"""
Test that the cli handles interpolation in the directory names correctly when loading project config.
"""
lang_var = "en"
variables = {"lang": lang_var}
commands = [{"name": "x", "script": ["hello ${vars.lang}"]}]
directories = ["cfg", "${vars.lang}_model"]
project = {"commands": commands, "vars": variables, "directories": directories}
with make_tempdir() as d:
srsly.write_yaml(d / "project.yml", project)
cfg = load_project_config(d)
# Check that the directories are interpolated and created correctly
assert os.path.exists(d / "cfg")
assert os.path.exists(d / f"{lang_var}_model")
assert cfg["commands"][0]["script"][0] == f"hello {lang_var}"
@pytest.mark.issue(12566)
@pytest.mark.parametrize(
"factory,output_file",
@ -225,6 +193,9 @@ def test_cli_info():
raw_data = info(tmp_dir, exclude=[""])
assert raw_data["lang"] == "nl"
assert raw_data["components"] == ["textcat"]
raw_data = spacy_info(tmp_dir, exclude=[""])
assert raw_data["lang"] == "nl"
assert raw_data["components"] == ["textcat"]
def test_cli_converters_conllu_to_docs():
@ -443,136 +414,6 @@ def test_cli_converters_conll_ner_to_docs():
assert ent.text in ["New York City", "London"]
def test_project_config_validation_full():
config = {
"vars": {"some_var": 20},
"directories": ["assets", "configs", "corpus", "scripts", "training"],
"assets": [
{
"dest": "x",
"extra": True,
"url": "https://example.com",
"checksum": "63373dd656daa1fd3043ce166a59474c",
},
{
"dest": "y",
"git": {
"repo": "https://github.com/example/repo",
"branch": "develop",
"path": "y",
},
},
{
"dest": "z",
"extra": False,
"url": "https://example.com",
"checksum": "63373dd656daa1fd3043ce166a59474c",
},
],
"commands": [
{
"name": "train",
"help": "Train a model",
"script": ["python -m spacy train config.cfg -o training"],
"deps": ["config.cfg", "corpus/training.spcy"],
"outputs": ["training/model-best"],
},
{"name": "test", "script": ["pytest", "custom.py"], "no_skip": True},
],
"workflows": {"all": ["train", "test"], "train": ["train"]},
}
errors = validate(ProjectConfigSchema, config)
assert not errors
@pytest.mark.parametrize(
"config",
[
{"commands": [{"name": "a"}, {"name": "a"}]},
{"commands": [{"name": "a"}], "workflows": {"a": []}},
{"commands": [{"name": "a"}], "workflows": {"b": ["c"]}},
],
)
def test_project_config_validation1(config):
with pytest.raises(SystemExit):
validate_project_commands(config)
@pytest.mark.parametrize(
"config,n_errors",
[
({"commands": {"a": []}}, 1),
({"commands": [{"help": "..."}]}, 1),
({"commands": [{"name": "a", "extra": "b"}]}, 1),
({"commands": [{"extra": "b"}]}, 2),
({"commands": [{"name": "a", "deps": [123]}]}, 1),
],
)
def test_project_config_validation2(config, n_errors):
errors = validate(ProjectConfigSchema, config)
assert len(errors) == n_errors
@pytest.mark.parametrize(
"int_value",
[10, pytest.param("10", marks=pytest.mark.xfail)],
)
def test_project_config_interpolation(int_value):
variables = {"a": int_value, "b": {"c": "foo", "d": True}}
commands = [
{"name": "x", "script": ["hello ${vars.a} ${vars.b.c}"]},
{"name": "y", "script": ["${vars.b.c} ${vars.b.d}"]},
]
project = {"commands": commands, "vars": variables}
with make_tempdir() as d:
srsly.write_yaml(d / "project.yml", project)
cfg = load_project_config(d)
assert type(cfg) == dict
assert type(cfg["commands"]) == list
assert cfg["commands"][0]["script"][0] == "hello 10 foo"
assert cfg["commands"][1]["script"][0] == "foo true"
commands = [{"name": "x", "script": ["hello ${vars.a} ${vars.b.e}"]}]
project = {"commands": commands, "vars": variables}
with pytest.raises(ConfigValidationError):
substitute_project_variables(project)
@pytest.mark.parametrize(
"greeting",
[342, "everyone", "tout le monde", pytest.param("42", marks=pytest.mark.xfail)],
)
def test_project_config_interpolation_override(greeting):
variables = {"a": "world"}
commands = [
{"name": "x", "script": ["hello ${vars.a}"]},
]
overrides = {"vars.a": greeting}
project = {"commands": commands, "vars": variables}
with make_tempdir() as d:
srsly.write_yaml(d / "project.yml", project)
cfg = load_project_config(d, overrides=overrides)
assert type(cfg) == dict
assert type(cfg["commands"]) == list
assert cfg["commands"][0]["script"][0] == f"hello {greeting}"
def test_project_config_interpolation_env():
variables = {"a": 10}
env_var = "SPACY_TEST_FOO"
env_vars = {"foo": env_var}
commands = [{"name": "x", "script": ["hello ${vars.a} ${env.foo}"]}]
project = {"commands": commands, "vars": variables, "env": env_vars}
with make_tempdir() as d:
srsly.write_yaml(d / "project.yml", project)
cfg = load_project_config(d)
assert cfg["commands"][0]["script"][0] == "hello 10 "
os.environ[env_var] = "123"
with make_tempdir() as d:
srsly.write_yaml(d / "project.yml", project)
cfg = load_project_config(d)
assert cfg["commands"][0]["script"][0] == "hello 10 123"
@pytest.mark.parametrize(
"args,expected",
[
@ -782,21 +623,6 @@ def test_get_third_party_dependencies():
get_third_party_dependencies(nlp.config)
@pytest.mark.parametrize(
"parent,child,expected",
[
("/tmp", "/tmp", True),
("/tmp", "/", False),
("/tmp", "/tmp/subdir", True),
("/tmp", "/tmpdir", False),
("/tmp", "/tmp/subdir/..", True),
("/tmp", "/tmp/..", False),
],
)
def test_is_subpath_of(parent, child, expected):
assert is_subpath_of(parent, child) == expected
@pytest.mark.slow
@pytest.mark.parametrize(
"factory_name,pipe_name",
@ -1042,60 +868,6 @@ def test_applycli_user_data():
assert result[0]._.ext == val
def test_local_remote_storage():
with make_tempdir() as d:
filename = "a.txt"
content_hashes = ("aaaa", "cccc", "bbbb")
for i, content_hash in enumerate(content_hashes):
# make sure that each subsequent file has a later timestamp
if i > 0:
time.sleep(1)
content = f"{content_hash} content"
loc_file = d / "root" / filename
if not loc_file.parent.exists():
loc_file.parent.mkdir(parents=True)
with loc_file.open(mode="w") as file_:
file_.write(content)
# push first version to remote storage
remote = RemoteStorage(d / "root", str(d / "remote"))
remote.push(filename, "aaaa", content_hash)
# retrieve with full hashes
loc_file.unlink()
remote.pull(filename, command_hash="aaaa", content_hash=content_hash)
with loc_file.open(mode="r") as file_:
assert file_.read() == content
# retrieve with command hash
loc_file.unlink()
remote.pull(filename, command_hash="aaaa")
with loc_file.open(mode="r") as file_:
assert file_.read() == content
# retrieve with content hash
loc_file.unlink()
remote.pull(filename, content_hash=content_hash)
with loc_file.open(mode="r") as file_:
assert file_.read() == content
# retrieve with no hashes
loc_file.unlink()
remote.pull(filename)
with loc_file.open(mode="r") as file_:
assert file_.read() == content
def test_local_remote_storage_pull_missing():
# pulling from a non-existent remote pulls nothing gracefully
with make_tempdir() as d:
filename = "a.txt"
remote = RemoteStorage(d / "root", str(d / "remote"))
assert remote.pull(filename, command_hash="aaaa") is None
assert remote.pull(filename) is None
def test_cli_find_threshold(capsys):
def make_examples(nlp: Language) -> List[Example]:
docs: List[Example] = []
@ -1206,63 +978,6 @@ def test_cli_find_threshold(capsys):
)
@pytest.mark.filterwarnings("ignore::DeprecationWarning")
@pytest.mark.parametrize(
"reqs,output",
[
[
"""
spacy
# comment
thinc""",
(False, False),
],
[
"""# comment
--some-flag
spacy""",
(False, False),
],
[
"""# comment
--some-flag
spacy; python_version >= '3.6'""",
(False, False),
],
[
"""# comment
spacyunknowndoesnotexist12345""",
(True, False),
],
],
)
def test_project_check_requirements(reqs, output):
import pkg_resources
# excessive guard against unlikely package name
try:
pkg_resources.require("spacyunknowndoesnotexist12345")
except pkg_resources.DistributionNotFound:
assert output == _check_requirements([req.strip() for req in reqs.split("\n")])
def test_upload_download_local_file():
with make_tempdir() as d1, make_tempdir() as d2:
filename = "f.txt"
content = "content"
local_file = d1 / filename
remote_file = d2 / filename
with local_file.open(mode="w") as file_:
file_.write(content)
upload_file(local_file, remote_file)
local_file.unlink()
download_file(remote_file, local_file)
with local_file.open(mode="r") as file_:
assert file_.read() == content
def test_walk_directory():
with make_tempdir() as d:
files = [

199
spacy/tests/test_cli_app.py
View File

@ -1,4 +1,5 @@
import os
import sys
from pathlib import Path
import pytest
@ -6,7 +7,7 @@ import srsly
from typer.testing import CliRunner
from spacy.cli._util import app, get_git_version
from spacy.tokens import Doc, DocBin
from spacy.tokens import Doc, DocBin, Span
from .util import make_tempdir, normalize_whitespace
@ -213,6 +214,9 @@ def test_project_clone(options):
assert (out / "README.md").is_file()
@pytest.mark.skipif(
sys.version_info >= (3, 12), reason="Python 3.12+ not supported for remotes"
)
def test_project_push_pull(project_dir):
proj = dict(SAMPLE_PROJECT)
remote = "xyz"
@ -233,3 +237,196 @@ def test_project_push_pull(project_dir):
result = CliRunner().invoke(app, ["project", "pull", remote, str(project_dir)])
assert result.exit_code == 0
assert test_file.is_file()
def test_find_function_valid():
# example of architecture in main code base
function = "spacy.TextCatBOW.v2"
result = CliRunner().invoke(app, ["find-function", function, "-r", "architectures"])
assert f"Found registered function '{function}'" in result.stdout
assert "textcat.py" in result.stdout
result = CliRunner().invoke(app, ["find-function", function])
assert f"Found registered function '{function}'" in result.stdout
assert "textcat.py" in result.stdout
# example of architecture in spacy-legacy
function = "spacy.TextCatBOW.v1"
result = CliRunner().invoke(app, ["find-function", function])
assert f"Found registered function '{function}'" in result.stdout
assert "spacy_legacy" in result.stdout
assert "textcat.py" in result.stdout
def test_find_function_invalid():
# invalid registry
function = "spacy.TextCatBOW.v2"
registry = "foobar"
result = CliRunner().invoke(
app, ["find-function", function, "--registry", registry]
)
assert f"Unknown function registry: '{registry}'" in result.stdout
# invalid function
function = "spacy.TextCatBOW.v666"
result = CliRunner().invoke(app, ["find-function", function])
assert f"Couldn't find registered function: '{function}'" in result.stdout
example_words_1 = ["I", "like", "cats"]
example_words_2 = ["I", "like", "dogs"]
example_lemmas_1 = ["I", "like", "cat"]
example_lemmas_2 = ["I", "like", "dog"]
example_tags = ["PRP", "VBP", "NNS"]
example_morphs = [
"Case=Nom|Number=Sing|Person=1|PronType=Prs",
"Tense=Pres|VerbForm=Fin",
"Number=Plur",
]
example_deps = ["nsubj", "ROOT", "dobj"]
example_pos = ["PRON", "VERB", "NOUN"]
example_ents = ["O", "O", "I-ANIMAL"]
example_spans = [(2, 3, "ANIMAL")]
TRAIN_EXAMPLE_1 = dict(
words=example_words_1,
lemmas=example_lemmas_1,
tags=example_tags,
morphs=example_morphs,
deps=example_deps,
heads=[1, 1, 1],
pos=example_pos,
ents=example_ents,
spans=example_spans,
cats={"CAT": 1.0, "DOG": 0.0},
)
TRAIN_EXAMPLE_2 = dict(
words=example_words_2,
lemmas=example_lemmas_2,
tags=example_tags,
morphs=example_morphs,
deps=example_deps,
heads=[1, 1, 1],
pos=example_pos,
ents=example_ents,
spans=example_spans,
cats={"CAT": 0.0, "DOG": 1.0},
)
@pytest.mark.slow
@pytest.mark.parametrize(
"component,examples",
[
("tagger", [TRAIN_EXAMPLE_1, TRAIN_EXAMPLE_2]),
("morphologizer", [TRAIN_EXAMPLE_1, TRAIN_EXAMPLE_2]),
("trainable_lemmatizer", [TRAIN_EXAMPLE_1, TRAIN_EXAMPLE_2]),
("parser", [TRAIN_EXAMPLE_1] * 30),
("ner", [TRAIN_EXAMPLE_1, TRAIN_EXAMPLE_2]),
("spancat", [TRAIN_EXAMPLE_1, TRAIN_EXAMPLE_2]),
("textcat", [TRAIN_EXAMPLE_1, TRAIN_EXAMPLE_2]),
],
)
def test_init_config_trainable(component, examples, en_vocab):
if component == "textcat":
train_docs = []
for example in examples:
doc = Doc(en_vocab, words=example["words"])
doc.cats = example["cats"]
train_docs.append(doc)
elif component == "spancat":
train_docs = []
for example in examples:
doc = Doc(en_vocab, words=example["words"])
doc.spans["sc"] = [
Span(doc, start, end, label) for start, end, label in example["spans"]
]
train_docs.append(doc)
else:
train_docs = []
for example in examples:
# cats, spans are not valid kwargs for instantiating a Doc
example = {k: v for k, v in example.items() if k not in ("cats", "spans")}
doc = Doc(en_vocab, **example)
train_docs.append(doc)
with make_tempdir() as d_in:
train_bin = DocBin(docs=train_docs)
train_bin.to_disk(d_in / "train.spacy")
dev_bin = DocBin(docs=train_docs)
dev_bin.to_disk(d_in / "dev.spacy")
init_config_result = CliRunner().invoke(
app,
[
"init",
"config",
f"{d_in}/config.cfg",
"--lang",
"en",
"--pipeline",
component,
],
)
assert init_config_result.exit_code == 0
train_result = CliRunner().invoke(
app,
[
"train",
f"{d_in}/config.cfg",
"--paths.train",
f"{d_in}/train.spacy",
"--paths.dev",
f"{d_in}/dev.spacy",
"--output",
f"{d_in}/model",
],
)
assert train_result.exit_code == 0
assert Path(d_in / "model" / "model-last").exists()
@pytest.mark.slow
@pytest.mark.parametrize(
"component,examples",
[("tagger,parser,morphologizer", [TRAIN_EXAMPLE_1, TRAIN_EXAMPLE_2] * 15)],
)
def test_init_config_trainable_multiple(component, examples, en_vocab):
train_docs = []
for example in examples:
example = {k: v for k, v in example.items() if k not in ("cats", "spans")}
doc = Doc(en_vocab, **example)
train_docs.append(doc)
with make_tempdir() as d_in:
train_bin = DocBin(docs=train_docs)
train_bin.to_disk(d_in / "train.spacy")
dev_bin = DocBin(docs=train_docs)
dev_bin.to_disk(d_in / "dev.spacy")
init_config_result = CliRunner().invoke(
app,
[
"init",
"config",
f"{d_in}/config.cfg",
"--lang",
"en",
"--pipeline",
component,
],
)
assert init_config_result.exit_code == 0
train_result = CliRunner().invoke(
app,
[
"train",
f"{d_in}/config.cfg",
"--paths.train",
f"{d_in}/train.spacy",
"--paths.dev",
f"{d_in}/dev.spacy",
"--output",
f"{d_in}/model",
],
)
assert train_result.exit_code == 0
assert Path(d_in / "model" / "model-last").exists()

93
spacy/tests/test_displacy.py
View File

@ -113,7 +113,7 @@ def test_issue5838():
doc = nlp(sample_text)
doc.ents = [Span(doc, 7, 8, label="test")]
html = displacy.render(doc, style="ent")
found = html.count("</br>")
found = html.count("<br>")
assert found == 4
@ -350,6 +350,78 @@ def test_displacy_render_wrapper(en_vocab):
displacy.set_render_wrapper(lambda html: html)
def test_displacy_render_manual_dep():
"""Test displacy.render with manual data for dep style"""
parsed_dep = {
"words": [
{"text": "This", "tag": "DT"},
{"text": "is", "tag": "VBZ"},
{"text": "a", "tag": "DT"},
{"text": "sentence", "tag": "NN"},
],
"arcs": [
{"start": 0, "end": 1, "label": "nsubj", "dir": "left"},
{"start": 2, "end": 3, "label": "det", "dir": "left"},
{"start": 1, "end": 3, "label": "attr", "dir": "right"},
],
"title": "Title",
}
html = displacy.render([parsed_dep], style="dep", manual=True)
for word in parsed_dep["words"]:
assert word["text"] in html
assert word["tag"] in html
def test_displacy_render_manual_ent():
"""Test displacy.render with manual data for ent style"""
parsed_ents = [
{
"text": "But Google is starting from behind.",
"ents": [{"start": 4, "end": 10, "label": "ORG"}],
},
{
"text": "But Google is starting from behind.",
"ents": [{"start": -100, "end": 100, "label": "COMPANY"}],
"title": "Title",
},
]
html = displacy.render(parsed_ents, style="ent", manual=True)
for parsed_ent in parsed_ents:
assert parsed_ent["ents"][0]["label"] in html
if "title" in parsed_ent:
assert parsed_ent["title"] in html
def test_displacy_render_manual_span():
"""Test displacy.render with manual data for span style"""
parsed_spans = [
{
"text": "Welcome to the Bank of China.",
"spans": [
{"start_token": 3, "end_token": 6, "label": "ORG"},
{"start_token": 5, "end_token": 6, "label": "GPE"},
],
"tokens": ["Welcome", "to", "the", "Bank", "of", "China", "."],
},
{
"text": "Welcome to the Bank of China.",
"spans": [
{"start_token": 3, "end_token": 6, "label": "ORG"},
{"start_token": 5, "end_token": 6, "label": "GPE"},
],
"tokens": ["Welcome", "to", "the", "Bank", "of", "China", "."],
"title": "Title",
},
]
html = displacy.render(parsed_spans, style="span", manual=True)
for parsed_span in parsed_spans:
assert parsed_span["spans"][0]["label"] in html
if "title" in parsed_span:
assert parsed_span["title"] in html
def test_displacy_options_case():
ents = ["foo", "BAR"]
colors = {"FOO": "red", "bar": "green"}
@ -377,3 +449,22 @@ def test_displacy_manual_sorted_entities():
html = displacy.render(doc, style="ent", manual=True)
assert html.find("FIRST") < html.find("SECOND")
@pytest.mark.issue(12816)
def test_issue12816(en_vocab) -> None:
"""Test that displaCy's span visualizer escapes annotated HTML tags correctly."""
# Create a doc containing an annotated word and an unannotated HTML tag
doc = Doc(en_vocab, words=["test", "<TEST>"])
doc.spans["sc"] = [Span(doc, 0, 1, label="test")]
# Verify that the HTML tag is escaped when unannotated
html = displacy.render(doc, style="span")
assert "&lt;TEST&gt;" in html
# Annotate the HTML tag
doc.spans["sc"].append(Span(doc, 1, 2, label="test"))
# Verify that the HTML tag is still escaped
html = displacy.render(doc, style="span")
assert "&lt;TEST&gt;" in html

7
spacy/tests/test_misc.py
View File

@ -3,7 +3,12 @@ import os
from pathlib import Path
import pytest
from pydantic import ValidationError
try:
from pydantic.v1 import ValidationError
except ImportError:
from pydantic import ValidationError # type: ignore
from thinc.api import (
Config,
ConfigValidationError,

76
spacy/tokenizer.pxd
View File

@ -31,24 +31,58 @@ cdef class Tokenizer:
cdef Doc _tokenize_affixes(self, str string, bint with_special_cases)
cdef int _apply_special_cases(self, Doc doc) except -1
cdef void _filter_special_spans(self, vector[SpanC] &original,
vector[SpanC] &filtered, int doc_len) nogil
cdef object _prepare_special_spans(self, Doc doc,
vector[SpanC] &filtered)
cdef int _retokenize_special_spans(self, Doc doc, TokenC* tokens,
object span_data)
cdef int _try_specials_and_cache(self, hash_t key, Doc tokens,
int* has_special,
bint with_special_cases) except -1
cdef int _tokenize(self, Doc tokens, str span, hash_t key,
int* has_special, bint with_special_cases) except -1
cdef str _split_affixes(self, Pool mem, str string,
vector[LexemeC*] *prefixes,
vector[LexemeC*] *suffixes, int* has_special,
bint with_special_cases)
cdef int _attach_tokens(self, Doc tokens, str string,
vector[LexemeC*] *prefixes,
vector[LexemeC*] *suffixes, int* has_special,
bint with_special_cases) except -1
cdef int _save_cached(self, const TokenC* tokens, hash_t key,
int* has_special, int n) except -1
cdef void _filter_special_spans(
self,
vector[SpanC] &original,
vector[SpanC] &filtered,
int doc_len,
) nogil
cdef object _prepare_special_spans(
self,
Doc doc,
vector[SpanC] &filtered,
)
cdef int _retokenize_special_spans(
self,
Doc doc,
TokenC* tokens,
object span_data,
)
cdef int _try_specials_and_cache(
self,
hash_t key,
Doc tokens,
int* has_special,
bint with_special_cases,
) except -1
cdef int _tokenize(
self,
Doc tokens,
str span,
hash_t key,
int* has_special,
bint with_special_cases,
) except -1
cdef str _split_affixes(
self,
Pool mem,
str string,
vector[LexemeC*] *prefixes,
vector[LexemeC*] *suffixes, int* has_special,
bint with_special_cases,
)
cdef int _attach_tokens(
self,
Doc tokens,
str string,
vector[LexemeC*] *prefixes,
vector[LexemeC*] *suffixes, int* has_special,
bint with_special_cases,
) except -1
cdef int _save_cached(
self,
const TokenC* tokens,
hash_t key,
int* has_special,
int n,
) except -1

39
spacy/tokenizer.pyx
View File

@ -1,4 +1,4 @@
# cython: embedsignature=True, profile=True, binding=True
# cython: embedsignature=True, binding=True
cimport cython
from cymem.cymem cimport Pool
from cython.operator cimport dereference as deref
@ -8,20 +8,18 @@ from libcpp.set cimport set as stdset
from preshed.maps cimport PreshMap
import re
import warnings
from .lexeme cimport EMPTY_LEXEME
from .strings cimport hash_string
from .tokens.doc cimport Doc
from . import util
from .attrs import intify_attrs
from .errors import Errors, Warnings
from .errors import Errors
from .scorer import Scorer
from .symbols import NORM, ORTH
from .tokens import Span
from .training import validate_examples
from .util import get_words_and_spaces, registry
from .util import get_words_and_spaces
cdef class Tokenizer:
@ -324,7 +322,7 @@ cdef class Tokenizer:
cdef int span_start
cdef int span_end
while i < doc.length:
if not i in span_data:
if i not in span_data:
tokens[i + offset] = doc.c[i]
i += 1
else:
@ -395,12 +393,15 @@ cdef class Tokenizer:
self._save_cached(&tokens.c[orig_size], orig_key, has_special,
tokens.length - orig_size)
cdef str _split_affixes(self, Pool mem, str string,
vector[const LexemeC*] *prefixes,
vector[const LexemeC*] *suffixes,
int* has_special,
bint with_special_cases):
cdef size_t i
cdef str _split_affixes(
self,
Pool mem,
str string,
vector[const LexemeC*] *prefixes,
vector[const LexemeC*] *suffixes,
int* has_special,
bint with_special_cases
):
cdef str prefix
cdef str suffix
cdef str minus_pre
@ -445,10 +446,6 @@ cdef class Tokenizer:
vector[const LexemeC*] *suffixes,
int* has_special,
bint with_special_cases) except -1:
cdef bint specials_hit = 0
cdef bint cache_hit = 0
cdef int split, end
cdef const LexemeC* const* lexemes
cdef const LexemeC* lexeme
cdef str span
cdef int i
@ -458,9 +455,11 @@ cdef class Tokenizer:
if string:
if self._try_specials_and_cache(hash_string(string), tokens, has_special, with_special_cases):
pass
elif (self.token_match and self.token_match(string)) or \
(self.url_match and \
self.url_match(string)):
elif (
(self.token_match and self.token_match(string)) or
(self.url_match and self.url_match(string))
):
# We're always saying 'no' to spaces here -- the caller will
# fix up the outermost one, with reference to the original.
# See Issue #859
@ -821,7 +820,7 @@ cdef class Tokenizer:
self.infix_finditer = None
self.token_match = None
self.url_match = None
msg = util.from_bytes(bytes_data, deserializers, exclude)
util.from_bytes(bytes_data, deserializers, exclude)
if "prefix_search" in data and isinstance(data["prefix_search"], str):
self.prefix_search = re.compile(data["prefix_search"]).search
if "suffix_search" in data and isinstance(data["suffix_search"], str):

29
spacy/tokens/_retokenize.pyx
View File

@ -1,7 +1,6 @@
# cython: infer_types=True, bounds_check=False, profile=True
# cython: infer_types=True, bounds_check=False
from cymem.cymem cimport Pool
from libc.stdlib cimport free, malloc
from libc.string cimport memcpy, memset
from libc.string cimport memset
import numpy
from thinc.api import get_array_module
@ -10,7 +9,7 @@ from ..attrs cimport MORPH, NORM
from ..lexeme cimport EMPTY_LEXEME, Lexeme
from ..structs cimport LexemeC, TokenC
from ..vocab cimport Vocab
from .doc cimport Doc, set_children_from_heads, token_by_end, token_by_start
from .doc cimport Doc, set_children_from_heads, token_by_start
from .span cimport Span
from .token cimport Token
@ -147,7 +146,7 @@ def _merge(Doc doc, merges):
syntactic root of the span.
RETURNS (Token): The first newly merged token.
"""
cdef int i, merge_index, start, end, token_index, current_span_index, current_offset, offset, span_index
cdef int i, merge_index, start, token_index, current_span_index, current_offset, offset, span_index
cdef Span span
cdef const LexemeC* lex
cdef TokenC* token
@ -165,7 +164,6 @@ def _merge(Doc doc, merges):
merges.sort(key=_get_start)
for merge_index, (span, attributes) in enumerate(merges):
start = span.start
end = span.end
spans.append(span)
# House the new merged token where it starts
token = &doc.c[start]
@ -203,8 +201,9 @@ def _merge(Doc doc, merges):
# for the merged region. To do this, we create a boolean array indicating
# whether the row is to be deleted, then use numpy.delete
if doc.tensor is not None and doc.tensor.size != 0:
doc.tensor = _resize_tensor(doc.tensor,
[(m[0].start, m[0].end) for m in merges])
doc.tensor = _resize_tensor(
doc.tensor, [(m[0].start, m[0].end) for m in merges]
)
# Memorize span roots and sets dependencies of the newly merged
# tokens to the dependencies of their roots.
span_roots = []
@ -267,11 +266,11 @@ def _merge(Doc doc, merges):
span_index += 1
if span_index < len(spans) and i == spans[span_index].start:
# First token in a span
doc.c[i - offset] = doc.c[i] # move token to its place
doc.c[i - offset] = doc.c[i] # move token to its place
offset += (spans[span_index].end - spans[span_index].start) - 1
in_span = True
if not in_span:
doc.c[i - offset] = doc.c[i] # move token to its place
doc.c[i - offset] = doc.c[i] # move token to its place
for i in range(doc.length - offset, doc.length):
memset(&doc.c[i], 0, sizeof(TokenC))
@ -345,7 +344,11 @@ def _split(Doc doc, int token_index, orths, heads, attrs):
if to_process_tensor:
xp = get_array_module(doc.tensor)
if xp is numpy:
doc.tensor = xp.append(doc.tensor, xp.zeros((nb_subtokens,doc.tensor.shape[1]), dtype="float32"), axis=0)
doc.tensor = xp.append(
doc.tensor,
xp.zeros((nb_subtokens, doc.tensor.shape[1]), dtype="float32"),
axis=0
)
else:
shape = (doc.tensor.shape[0] + nb_subtokens, doc.tensor.shape[1])
resized_array = xp.zeros(shape, dtype="float32")
@ -367,7 +370,8 @@ def _split(Doc doc, int token_index, orths, heads, attrs):
token.norm = 0 # reset norm
if to_process_tensor:
# setting the tensors of the split tokens to array of zeros
doc.tensor[token_index + i:token_index + i + 1] = xp.zeros((1,doc.tensor.shape[1]), dtype="float32")
doc.tensor[token_index + i:token_index + i + 1] = \
xp.zeros((1, doc.tensor.shape[1]), dtype="float32")
# Update the character offset of the subtokens
if i != 0:
token.idx = orig_token.idx + idx_offset
@ -455,7 +459,6 @@ def normalize_token_attrs(Vocab vocab, attrs):
def set_token_attrs(Token py_token, attrs):
cdef TokenC* token = py_token.c
cdef const LexemeC* lex = token.lex
cdef Doc doc = py_token.doc
# Assign attributes
for attr_name, attr_value in attrs.items():
if attr_name == "_": # Set extension attributes

3
spacy/tokens/doc.pxd
View File

@ -31,7 +31,7 @@ cdef int token_by_start(const TokenC* tokens, int length, int start_char) except
cdef int token_by_end(const TokenC* tokens, int length, int end_char) except -2
cdef int [:,:] _get_lca_matrix(Doc, int start, int end)
cdef int [:, :] _get_lca_matrix(Doc, int start, int end)
cdef class Doc:
@ -61,7 +61,6 @@ cdef class Doc:
cdef int length
cdef int max_length
cdef public object noun_chunks_iterator
cdef object __weakref__

8
spacy/tokens/doc.pyi
View File

@ -8,6 +8,7 @@ from typing import (
List,
Optional,
Protocol,
Sequence,
Tuple,
Union,
overload,
@ -134,7 +135,12 @@ class Doc:
def text(self) -> str: ...
@property
def text_with_ws(self) -> str: ...
ents: Tuple[Span]
# Ideally the getter would output Tuple[Span]
# see https://github.com/python/mypy/issues/3004
@property
def ents(self) -> Sequence[Span]: ...
@ents.setter
def ents(self, value: Sequence[Span]) -> None: ...
def set_ents(
self,
entities: List[Span],

34
spacy/tokens/doc.pyx
View File

@ -1,4 +1,4 @@
# cython: infer_types=True, bounds_check=False, profile=True
# cython: infer_types=True, bounds_check=False
from typing import Set
cimport cython
@ -43,14 +43,13 @@ from ..attrs cimport (
attr_id_t,
)
from ..lexeme cimport EMPTY_LEXEME, Lexeme
from ..typedefs cimport attr_t, flags_t
from ..typedefs cimport attr_t
from .token cimport Token
from .. import parts_of_speech, schemas, util
from ..attrs import IDS, intify_attr
from ..compat import copy_reg, pickle
from ..compat import copy_reg
from ..errors import Errors, Warnings
from ..morphology import Morphology
from ..util import get_words_and_spaces
from ._retokenize import Retokenizer
from .underscore import Underscore, get_ext_args
@ -784,7 +783,7 @@ cdef class Doc:
# TODO:
# 1. Test basic data-driven ORTH gazetteer
# 2. Test more nuanced date and currency regex
cdef attr_t entity_type, kb_id, ent_id
cdef attr_t kb_id, ent_id
cdef int ent_start, ent_end
ent_spans = []
for ent_info in ents:
@ -987,7 +986,6 @@ cdef class Doc:
>>> np_array = doc.to_array([LOWER, POS, ENT_TYPE, IS_ALPHA])
"""
cdef int i, j
cdef attr_id_t feature
cdef np.ndarray[attr_t, ndim=2] output
# Handle scalar/list inputs of strings/ints for py_attr_ids
# See also #3064
@ -999,8 +997,10 @@ cdef class Doc:
py_attr_ids = [py_attr_ids]
# Allow strings, e.g. 'lemma' or 'LEMMA'
try:
py_attr_ids = [(IDS[id_.upper()] if hasattr(id_, "upper") else id_)
for id_ in py_attr_ids]
py_attr_ids = [
(IDS[id_.upper()] if hasattr(id_, "upper") else id_)
for id_ in py_attr_ids
]
except KeyError as msg:
keys = [k for k in IDS.keys() if not k.startswith("FLAG")]
raise KeyError(Errors.E983.format(dict="IDS", key=msg, keys=keys)) from None
@ -1030,8 +1030,6 @@ cdef class Doc:
DOCS: https://spacy.io/api/doc#count_by
"""
cdef int i
cdef attr_t attr
cdef size_t count
if counts is None:
counts = Counter()
@ -1093,7 +1091,6 @@ cdef class Doc:
cdef int i, col
cdef int32_t abs_head_index
cdef attr_id_t attr_id
cdef TokenC* tokens = self.c
cdef int length = len(array)
if length != len(self):
raise ValueError(Errors.E971.format(array_length=length, doc_length=len(self)))
@ -1225,7 +1222,7 @@ cdef class Doc:
span.label,
span.kb_id,
span.id,
span.text, # included as a check
span.text, # included as a check
))
char_offset += len(doc.text)
if len(doc) > 0 and ensure_whitespace and not doc[-1].is_space and not bool(doc[-1].whitespace_):
@ -1508,7 +1505,6 @@ cdef class Doc:
attributes are inherited from the syntactic root of the span.
RETURNS (Token): The first newly merged token.
"""
cdef str tag, lemma, ent_type
attr_len = len(attributes)
span_len = len(spans)
if not attr_len == span_len:
@ -1624,7 +1620,6 @@ cdef class Doc:
for token in char_span[1:]:
token.is_sent_start = False
for span_group in doc_json.get("spans", {}):
spans = []
for span in doc_json["spans"][span_group]:
@ -1656,7 +1651,7 @@ cdef class Doc:
start = token_by_char(self.c, self.length, token_data["start"])
value = token_data["value"]
self[start]._.set(token_attr, value)
for span_attr in doc_json.get("underscore_span", {}):
if not Span.has_extension(span_attr):
Span.set_extension(span_attr)
@ -1698,7 +1693,7 @@ cdef class Doc:
token_data["dep"] = token.dep_
token_data["head"] = token.head.i
data["tokens"].append(token_data)
if self.spans:
data["spans"] = {}
for span_group in self.spans:
@ -1769,7 +1764,6 @@ cdef class Doc:
output.fill(255)
cdef int i, j, start_idx, end_idx
cdef bytes byte_string
cdef unsigned char utf8_char
for i, byte_string in enumerate(byte_strings):
j = 0
start_idx = 0
@ -1822,8 +1816,6 @@ cdef int token_by_char(const TokenC* tokens, int length, int char_idx) except -2
cdef int set_children_from_heads(TokenC* tokens, int start, int end) except -1:
# note: end is exclusive
cdef TokenC* head
cdef TokenC* child
cdef int i
# Set number of left/right children to 0. We'll increment it in the loops.
for i in range(start, end):
@ -1923,7 +1915,7 @@ cdef int _get_tokens_lca(Token token_j, Token token_k):
return -1
cdef int [:,:] _get_lca_matrix(Doc doc, int start, int end):
cdef int [:, :] _get_lca_matrix(Doc doc, int start, int end):
"""Given a doc and a start and end position defining a set of contiguous
tokens within it, returns a matrix of Lowest Common Ancestors (LCA), where
LCA[i, j] is the index of the lowest common ancestor among token i and j.
@ -1936,7 +1928,7 @@ cdef int [:,:] _get_lca_matrix(Doc doc, int start, int end):
RETURNS (int [:, :]): memoryview of numpy.array[ndim=2, dtype=numpy.int32],
with shape (n, n), where n = len(doc).
"""
cdef int [:,:] lca_matrix
cdef int [:, :] lca_matrix
cdef int j, k
n_tokens= end - start
lca_mat = numpy.empty((n_tokens, n_tokens), dtype=numpy.int32)

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