explosion/spaCy
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Merge branch 'master' into feature/projects-multiprocessing

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richardpaulhudson 2023-01-23 20:03:14 +01:00
commit cfaa90203e
364 changed files with 32945 additions and 36920 deletions
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76
.github/azure-steps.yml vendored
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@ -1,31 +1,30 @@
parameters:
python_version: ''
architecture: ''
prefix: ''
gpu: false
num_build_jobs: 1
architecture: 'x64'
num_build_jobs: 2
steps:
- task: UsePythonVersion@0
inputs:
versionSpec: ${{ parameters.python_version }}
architecture: ${{ parameters.architecture }}
allowUnstable: true
- bash: |
echo "##vso[task.setvariable variable=python_version]${{ parameters.python_version }}"
displayName: 'Set variables'
- script: |
${{ parameters.prefix }} python -m pip install -U pip setuptools
${{ parameters.prefix }} python -m pip install -U -r requirements.txt
python -m pip install -U build pip setuptools
python -m pip install -U -r requirements.txt
displayName: "Install dependencies"
- script: |
${{ parameters.prefix }} python setup.py build_ext --inplace -j ${{ parameters.num_build_jobs }}
${{ parameters.prefix }} python setup.py sdist --formats=gztar
displayName: "Compile and build sdist"
python -m build --sdist
displayName: "Build sdist"
- script: python -m mypy spacy
- script: |
python -m mypy spacy
displayName: 'Run mypy'
condition: ne(variables['python_version'], '3.6')
@ -34,35 +33,24 @@ steps:
contents: "spacy"
displayName: "Delete source directory"
- task: DeleteFiles@1
inputs:
contents: "*.egg-info"
displayName: "Delete egg-info directory"
- script: |
${{ parameters.prefix }} python -m pip freeze --exclude torch --exclude cupy-cuda110 > installed.txt
${{ parameters.prefix }} python -m pip uninstall -y -r installed.txt
python -m pip freeze > installed.txt
python -m pip uninstall -y -r installed.txt
displayName: "Uninstall all packages"
- bash: |
${{ parameters.prefix }} SDIST=$(python -c "import os;print(os.listdir('./dist')[-1])" 2>&1)
${{ parameters.prefix }} SPACY_NUM_BUILD_JOBS=2 python -m pip install dist/$SDIST
SDIST=$(python -c "import os;print(os.listdir('./dist')[-1])" 2>&1)
SPACY_NUM_BUILD_JOBS=${{ parameters.num_build_jobs }} python -m pip install dist/$SDIST
displayName: "Install from sdist"
- script: |
${{ parameters.prefix }} python -m pip install -U -r requirements.txt
displayName: "Install test requirements"
- script: |
${{ parameters.prefix }} python -m pip install -U cupy-cuda110 -f https://github.com/cupy/cupy/releases/v9.0.0
${{ parameters.prefix }} python -m pip install "torch==1.7.1+cu110" -f https://download.pytorch.org/whl/torch_stable.html
displayName: "Install GPU requirements"
condition: eq(${{ parameters.gpu }}, true)
- script: |
${{ parameters.prefix }} python -m pytest --pyargs spacy -W error
displayName: "Run CPU tests"
condition: eq(${{ parameters.gpu }}, false)
- script: |
${{ parameters.prefix }} python -m pytest --pyargs spacy -W error -p spacy.tests.enable_gpu
displayName: "Run GPU tests"
condition: eq(${{ parameters.gpu }}, true)
python -W error -c "import spacy"
displayName: "Test import"
- script: |
python -m spacy download ca_core_news_sm
@ -71,6 +59,11 @@ steps:
displayName: 'Test download CLI'
condition: eq(variables['python_version'], '3.8')
- script: |
python -W error -c "import ca_core_news_sm; nlp = ca_core_news_sm.load(); doc=nlp('test')"
displayName: 'Test no warnings on load (#11713)'
condition: eq(variables['python_version'], '3.8')
- script: |
python -m spacy convert extra/example_data/ner_example_data/ner-token-per-line-conll2003.json .
displayName: 'Test convert CLI'
@ -105,13 +98,22 @@ steps:
displayName: 'Test assemble CLI vectors warning'
condition: eq(variables['python_version'], '3.8')
- script: |
python -m pip install -U -r requirements.txt
displayName: "Install test requirements"
- script: |
python -m pytest --pyargs spacy -W error
displayName: "Run CPU tests"
- script: |
python -m pip install 'spacy[apple]'
python -m pytest --pyargs spacy
displayName: "Run CPU tests with thinc-apple-ops"
condition: and(startsWith(variables['imageName'], 'macos'), eq(variables['python.version'], '3.11'))
- script: |
python .github/validate_universe_json.py website/meta/universe.json
displayName: 'Test website/meta/universe.json'
condition: eq(variables['python_version'], '3.8')
- script: |
${{ parameters.prefix }} python -m pip install --pre thinc-apple-ops
${{ parameters.prefix }} python -m pytest --pyargs spacy
displayName: "Run CPU tests with thinc-apple-ops"
condition: and(startsWith(variables['imageName'], 'macos'), eq(variables['python.version'], '3.10'))

9
.github/workflows/autoblack.yml vendored
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@ -12,10 +12,10 @@ jobs:
if: github.repository_owner == 'explosion'
runs-on: ubuntu-latest
steps:
- uses: actions/checkout@v2
- uses: actions/checkout@v3
with:
ref: ${{ github.head_ref }}
- uses: actions/setup-python@v2
- uses: actions/setup-python@v4
- run: pip install black
- name: Auto-format code if needed
run: black spacy
@ -23,10 +23,11 @@ jobs:
# code and makes GitHub think the action failed
- name: Check for modified files
id: git-check
run: echo ::set-output name=modified::$(if git diff-index --quiet HEAD --; then echo "false"; else echo "true"; fi)
run: echo modified=$(if git diff-index --quiet HEAD --; then echo "false"; else echo "true"; fi) >> $GITHUB_OUTPUT
- name: Create Pull Request
if: steps.git-check.outputs.modified == 'true'
uses: peter-evans/create-pull-request@v3
uses: peter-evans/create-pull-request@v4
with:
title: Auto-format code with black
labels: meta

6
.github/workflows/explosionbot.yml vendored
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@ -8,14 +8,14 @@ on:
jobs:
explosion-bot:
runs-on: ubuntu-18.04
runs-on: ubuntu-latest
steps:
- name: Dump GitHub context
env:
GITHUB_CONTEXT: ${{ toJson(github) }}
run: echo "$GITHUB_CONTEXT"
- uses: actions/checkout@v1
- uses: actions/setup-python@v1
- uses: actions/checkout@v3
- uses: actions/setup-python@v4
- name: Install and run explosion-bot
run: |
pip install git+https://${{ secrets.EXPLOSIONBOT_TOKEN }}@github.com/explosion/explosion-bot

8
.github/workflows/lock.yml vendored
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@ -15,11 +15,11 @@ jobs:
action:
runs-on: ubuntu-latest
steps:
- uses: dessant/lock-threads@v3
- uses: dessant/lock-threads@v4
with:
process-only: 'issues'
issue-inactive-days: '30'
issue-comment: >
This thread has been automatically locked since there
has not been any recent activity after it was closed.
issue-comment: >
This thread has been automatically locked since there
has not been any recent activity after it was closed.
Please open a new issue for related bugs.

6
.github/workflows/slowtests.yml vendored
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@ -14,7 +14,7 @@ jobs:
runs-on: ubuntu-latest
steps:
- name: Checkout
uses: actions/checkout@v1
uses: actions/checkout@v3
with:
ref: ${{ matrix.branch }}
- name: Get commits from past 24 hours
@ -23,9 +23,9 @@ jobs:
today=$(date '+%Y-%m-%d %H:%M:%S')
yesterday=$(date -d "yesterday" '+%Y-%m-%d %H:%M:%S')
if git log --after="$yesterday" --before="$today" | grep commit ; then
echo "::set-output name=run_tests::true"
echo run_tests=true >> $GITHUB_OUTPUT
else
echo "::set-output name=run_tests::false"
echo run_tests=false >> $GITHUB_OUTPUT
fi
- name: Trigger buildkite build

6
.github/workflows/spacy_universe_alert.yml vendored
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@ -17,8 +17,10 @@ jobs:
run: |
echo "$GITHUB_CONTEXT"
- uses: actions/checkout@v1
- uses: actions/setup-python@v1
- uses: actions/checkout@v3
- uses: actions/setup-python@v4
with:
python-version: '3.10'
- name: Install Bernadette app dependency and send an alert
env:
SLACK_BOT_TOKEN: ${{ secrets.SLACK_BOT_TOKEN }}

10
.gitignore vendored
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@ -10,16 +10,6 @@ spacy/tests/package/setup.cfg
spacy/tests/package/pyproject.toml
spacy/tests/package/requirements.txt
# Website
website/.cache/
website/public/
website/node_modules
website/.npm
website/logs
*.log
npm-debug.log*
quickstart-training-generator.js
# Cython / C extensions
cythonize.json
spacy/*.html

2
.pre-commit-config.yaml
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@ -5,7 +5,7 @@ repos:
- id: black
language_version: python3.7
additional_dependencies: ['click==8.0.4']
- repo: https://gitlab.com/pycqa/flake8
- repo: https://github.com/pycqa/flake8
rev: 5.0.4
hooks:
- id: flake8

10
README.md
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@ -8,15 +8,15 @@ be used in real products.
spaCy comes with
[pretrained pipelines](https://spacy.io/models) and
currently supports tokenization and training for **60+ languages**. It features
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.
open-source software, released under the [MIT license](https://github.com/explosion/spaCy/blob/master/LICENSE).
💫 **Version 3.4.0 out now!**
💫 **Version 3.5 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)
@ -46,6 +46,7 @@ open-source software, released under the MIT license.
| 🛠 **[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
@ -59,6 +60,7 @@ open-source software, released under the MIT license.
[changelog]: https://spacy.io/usage#changelog
[contribute]: https://github.com/explosion/spaCy/blob/master/CONTRIBUTING.md
## 💬 Where to ask questions
The spaCy project is maintained by the [spaCy team](https://explosion.ai/about).
@ -79,7 +81,7 @@ more people can benefit from it.
## Features
- Support for **60+ languages**
- Support for **70+ languages**
- **Trained pipelines** for different languages and tasks
- Multi-task learning with pretrained **transformers** like BERT
- Support for pretrained **word vectors** and embeddings

42
azure-pipelines.yml
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@ -41,7 +41,7 @@ jobs:
matrix:
# We're only running one platform per Python version to speed up builds
Python36Linux:
imageName: "ubuntu-latest"
imageName: "ubuntu-20.04"
python.version: "3.6"
# Python36Windows:
# imageName: "windows-latest"
@ -50,7 +50,7 @@ jobs:
# imageName: "macos-latest"
# python.version: "3.6"
# Python37Linux:
# imageName: "ubuntu-latest"
# imageName: "ubuntu-20.04"
# python.version: "3.7"
Python37Windows:
imageName: "windows-latest"
@ -76,15 +76,24 @@ jobs:
# Python39Mac:
# imageName: "macos-latest"
# python.version: "3.9"
Python310Linux:
imageName: "ubuntu-latest"
python.version: "3.10"
# Python310Linux:
# imageName: "ubuntu-latest"
# python.version: "3.10"
Python310Windows:
imageName: "windows-latest"
python.version: "3.10"
Python310Mac:
imageName: "macos-latest"
python.version: "3.10"
# Python310Mac:
# imageName: "macos-latest"
# python.version: "3.10"
Python311Linux:
imageName: 'ubuntu-latest'
python.version: '3.11'
Python311Windows:
imageName: 'windows-latest'
python.version: '3.11'
Python311Mac:
imageName: 'macos-latest'
python.version: '3.11'
maxParallel: 4
pool:
vmImage: $(imageName)
@ -92,20 +101,3 @@ jobs:
- template: .github/azure-steps.yml
parameters:
python_version: '$(python.version)'
architecture: 'x64'
# - job: "TestGPU"
# dependsOn: "Validate"
# strategy:
# matrix:
# Python38LinuxX64_GPU:
# python.version: '3.8'
# pool:
# name: "LinuxX64_GPU"
# steps:
# - template: .github/azure-steps.yml
# parameters:
# python_version: '$(python.version)'
# architecture: 'x64'
# gpu: true
# num_build_jobs: 24

3
build-constraints.txt
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@ -5,4 +5,5 @@ 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; python_version>='3.11'
numpy==1.23.2; python_version=='3.11'
numpy; python_version>='3.12'

15
requirements.txt
View File

@ -1,27 +1,28 @@
# Our libraries
spacy-legacy>=3.0.10,<3.1.0
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>=8.1.0,<8.2.0
ml_datasets>=0.2.0,<0.3.0
murmurhash>=0.28.0,<1.1.0
wasabi>=0.9.1,<1.1.0
wasabi>=0.9.1,<1.2.0
srsly>=2.4.3,<3.0.0
catalogue>=2.0.6,<2.1.0
typer>=0.3.0,<0.5.0
pathy>=0.3.5
typer>=0.3.0,<0.8.0
pathy>=0.10.0
smart-open>=5.2.1,<7.0.0
# Third party dependencies
numpy>=1.15.0
requests>=2.13.0,<3.0.0
tqdm>=4.38.0,<5.0.0
pydantic>=1.7.4,!=1.8,!=1.8.1,<1.10.0
pydantic>=1.7.4,!=1.8,!=1.8.1,<1.11.0
jinja2
langcodes>=3.2.0,<4.0.0
# Official Python utilities
setuptools
packaging>=20.0
typing_extensions>=3.7.4.1,<4.2.0; python_version < "3.8"
typing_extensions>=3.7.4.1,<4.5.0; python_version < "3.8"
# Development dependencies
pre-commit>=2.13.0
cython>=0.25,<3.0
@ -30,7 +31,7 @@ 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>=0.980,<0.990; platform_machine != "aarch64" and python_version >= "3.7"
mypy>=0.990,<0.1000; platform_machine != "aarch64" and python_version >= "3.7"
types-dataclasses>=0.1.3; python_version < "3.7"
types-mock>=0.1.1
types-setuptools>=57.0.0

16
setup.cfg
View File

@ -22,6 +22,7 @@ classifiers =
Programming Language :: Python :: 3.8
Programming Language :: Python :: 3.9
Programming Language :: Python :: 3.10
Programming Language :: Python :: 3.11
Topic :: Scientific/Engineering
project_urls =
Release notes = https://github.com/explosion/spaCy/releases
@ -41,27 +42,28 @@ setup_requires =
thinc>=8.1.0,<8.2.0
install_requires =
# Our libraries
spacy-legacy>=3.0.10,<3.1.0
spacy-legacy>=3.0.11,<3.1.0
spacy-loggers>=1.0.0,<2.0.0
murmurhash>=0.28.0,<1.1.0
cymem>=2.0.2,<2.1.0
preshed>=3.0.2,<3.1.0
thinc>=8.1.0,<8.2.0
wasabi>=0.9.1,<1.1.0
wasabi>=0.9.1,<1.2.0
srsly>=2.4.3,<3.0.0
catalogue>=2.0.6,<2.1.0
# Third-party dependencies
typer>=0.3.0,<0.5.0
pathy>=0.3.5
typer>=0.3.0,<0.8.0
pathy>=0.10.0
smart-open>=5.2.1,<7.0.0
tqdm>=4.38.0,<5.0.0
numpy>=1.15.0
requests>=2.13.0,<3.0.0
pydantic>=1.7.4,!=1.8,!=1.8.1,<1.10.0
pydantic>=1.7.4,!=1.8,!=1.8.1,<1.11.0
jinja2
# Official Python utilities
setuptools
packaging>=20.0
typing_extensions>=3.7.4,<4.2.0; python_version < "3.8"
typing_extensions>=3.7.4.1,<4.5.0; python_version < "3.8"
langcodes>=3.2.0,<4.0.0
[options.entry_points]
@ -72,7 +74,7 @@ console_scripts =
lookups =
spacy_lookups_data>=1.0.3,<1.1.0
transformers =
spacy_transformers>=1.1.2,<1.2.0
spacy_transformers>=1.1.2,<1.3.0
ray =
spacy_ray>=0.1.0,<1.0.0
cuda =

4
setup.py
View File

@ -30,7 +30,9 @@ MOD_NAMES = [
"spacy.lexeme",
"spacy.vocab",
"spacy.attrs",
"spacy.kb",
"spacy.kb.candidate",
"spacy.kb.kb",
"spacy.kb.kb_in_memory",
"spacy.ml.parser_model",
"spacy.morphology",
"spacy.pipeline.dep_parser",

2
spacy/about.py
View File

@ -1,6 +1,6 @@
# fmt: off
__title__ = "spacy"
__version__ = "3.4.1"
__version__ = "3.5.0"
__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"

3
spacy/cli/__init__.py
View File

@ -4,6 +4,7 @@ from ._util import app, setup_cli # noqa: F401
# These are the actual functions, NOT the wrapped CLI commands. The CLI commands
# are registered automatically and won't have to be imported here.
from .benchmark_speed import benchmark_speed_cli # noqa: F401
from .download import download # noqa: F401
from .info import info # noqa: F401
from .package import package # noqa: F401
@ -16,6 +17,7 @@ from .debug_config import debug_config # noqa: F401
from .debug_model import debug_model # noqa: F401
from .debug_diff import debug_diff # noqa: F401
from .evaluate import evaluate # noqa: F401
from .apply import apply # noqa: F401
from .convert import convert # noqa: F401
from .init_pipeline import init_pipeline_cli # noqa: F401
from .init_config import init_config, fill_config # noqa: F401
@ -27,6 +29,7 @@ from .project.dvc import project_update_dvc # noqa: F401
from .project.push import project_push # noqa: F401
from .project.pull import project_pull # noqa: F401
from .project.document import project_document # noqa: F401
from .find_threshold import find_threshold # noqa: F401
@app.command("link", no_args_is_help=True, deprecated=True, hidden=True)

59
spacy/cli/_util.py
View File

@ -27,7 +27,7 @@ from ..util import is_minor_version_match
from .. import about
if TYPE_CHECKING:
from pathy import Pathy # noqa: F401
from pathy import FluidPath # noqa: F401
SDIST_SUFFIX = ".tar.gz"
@ -50,6 +50,7 @@ DEBUG_HELP = """Suite of helpful commands for debugging and profiling. Includes
commands to check and validate your config files, training and evaluation data,
and custom model implementations.
"""
BENCHMARK_HELP = """Commands for benchmarking pipelines."""
INIT_HELP = """Commands for initializing configs and pipeline packages."""
# Wrappers for Typer's annotations. Initially created to set defaults and to
@ -58,12 +59,14 @@ Arg = typer.Argument
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(debug_cli)
app.add_typer(benchmark_cli)
app.add_typer(init_cli)
@ -163,15 +166,15 @@ def load_project_config(
validate_project_version(config)
validate_max_parallel_processes(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)
if interpolate:
err = f"{PROJECT_FILE} validation error"
with show_validation_error(title=err, hint_fill=False):
config = substitute_project_variables(config, overrides)
return config
@ -373,7 +376,7 @@ 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, "Pathy"]) -> None:
def upload_file(src: Path, dest: Union[str, "FluidPath"]) -> None:
"""Upload a file.
src (Path): The source path.
@ -381,13 +384,20 @@ def upload_file(src: Path, dest: Union[str, "Pathy"]) -> None:
"""
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, "Pathy"], dest: Path, *, force: bool = False) -> None:
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.
@ -400,7 +410,7 @@ def download_file(src: Union[str, "Pathy"], dest: Path, *, force: bool = False)
if dest.exists() and not force:
return None
src = str(src)
with smart_open.open(src, mode="rb", ignore_ext=True) as input_file:
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)
@ -410,7 +420,7 @@ def ensure_pathy(path):
slow and annoying Google Cloud warning)."""
from pathy import Pathy # noqa: F811
return Pathy(path)
return Pathy.fluid(path)
def git_checkout(
@ -690,8 +700,8 @@ def check_deps(cmd: Dict, cmd_name: str, project_dir: Path, dry: bool):
if not (project_dir / dep).exists():
err = f"Missing dependency specified by command '{cmd_name}': {dep}"
err_help = "Maybe you forgot to run the 'project assets' command or a previous step?"
err_kwargs = {"exits": 1} if not dry else {}
msg.fail(err, err_help, **err_kwargs)
err_exits = 1 if not dry else None
msg.fail(err, err_help, exits=err_exits)
def _get_lock_entry(project_dir: Path, command: Dict[str, Any]) -> Dict[str, Any]:
@ -735,6 +745,33 @@ def _get_fileinfo(
return data
def walk_directory(path: Path, suffix: Optional[str] = None) -> List[Path]:
"""Given a directory and a suffix, recursively find all files matching the suffix.
Directories or files with names beginning with a . are ignored, but hidden flags on
filesystems are not checked.
When provided with a suffix `None`, there is no suffix-based filtering."""
if not path.is_dir():
return [path]
paths = [path]
locs = []
seen = set()
for path in paths:
if str(path) in seen:
continue
seen.add(str(path))
if path.parts[-1].startswith("."):
continue
elif path.is_dir():
paths.extend(path.iterdir())
elif suffix is not None and not path.parts[-1].endswith(suffix):
continue
else:
locs.append(path)
# It's good to sort these, in case the ordering messes up cache.
locs.sort()
return locs
def _format_number(number: Union[int, float], ndigits: int = 2) -> str:
"""Formats a number (float or int) rounding to `ndigits`, without truncating trailing 0s,
as happens with `round(number, ndigits)`"""

143
spacy/cli/apply.py Normal file
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@ -0,0 +1,143 @@
import tqdm
import srsly
from itertools import chain
from pathlib import Path
from typing import Optional, List, Iterable, cast, Union
from wasabi import msg
from ._util import app, Arg, Opt, setup_gpu, import_code, walk_directory
from ..tokens import Doc, DocBin
from ..vocab import Vocab
from ..util import ensure_path, load_model
path_help = """Location of the documents to predict on.
Can be a single file in .spacy format or a .jsonl file.
Files with other extensions are treated as single plain text documents.
If a directory is provided it is traversed recursively to grab
all files to be processed.
The files can be a mixture of .spacy, .jsonl and text files.
If .jsonl is provided the specified field is going
to be grabbed ("text" by default)."""
out_help = "Path to save the resulting .spacy file"
code_help = (
"Path to Python file with additional " "code (registered functions) to be imported"
)
gold_help = "Use gold preprocessing provided in the .spacy files"
force_msg = (
"The provided output file already exists. "
"To force overwriting the output file, set the --force or -F flag."
)
DocOrStrStream = Union[Iterable[str], Iterable[Doc]]
def _stream_docbin(path: Path, vocab: Vocab) -> Iterable[Doc]:
"""
Stream Doc objects from DocBin.
"""
docbin = DocBin().from_disk(path)
for doc in docbin.get_docs(vocab):
yield doc
def _stream_jsonl(path: Path, field: str) -> Iterable[str]:
"""
Stream "text" field from JSONL. If the field "text" is
not found it raises error.
"""
for entry in srsly.read_jsonl(path):
if field not in entry:
msg.fail(f"{path} does not contain the required '{field}' field.", exits=1)
else:
yield entry[field]
def _stream_texts(paths: Iterable[Path]) -> Iterable[str]:
"""
Yields strings from text files in paths.
"""
for path in paths:
with open(path, "r") as fin:
text = fin.read()
yield text
@app.command("apply")
def apply_cli(
# fmt: off
model: str = Arg(..., help="Model name or path"),
data_path: Path = Arg(..., help=path_help, exists=True),
output_file: Path = Arg(..., help=out_help, dir_okay=False),
code_path: Optional[Path] = Opt(None, "--code", "-c", help=code_help),
text_key: str = Opt("text", "--text-key", "-tk", help="Key containing text string for JSONL"),
force_overwrite: bool = Opt(False, "--force", "-F", help="Force overwriting the output file"),
use_gpu: int = Opt(-1, "--gpu-id", "-g", help="GPU ID or -1 for CPU."),
batch_size: int = Opt(1, "--batch-size", "-b", help="Batch size."),
n_process: int = Opt(1, "--n-process", "-n", help="number of processors to use.")
):
"""
Apply a trained pipeline to documents to get predictions.
Expects a loadable spaCy pipeline and path to the data, which
can be a directory or a file.
The data files can be provided in multiple formats:
1. .spacy files
2. .jsonl files with a specified "field" to read the text from.
3. Files with any other extension are assumed to be containing
a single document.
DOCS: https://spacy.io/api/cli#apply
"""
data_path = ensure_path(data_path)
output_file = ensure_path(output_file)
code_path = ensure_path(code_path)
if output_file.exists() and not force_overwrite:
msg.fail(force_msg, exits=1)
if not data_path.exists():
msg.fail(f"Couldn't find data path: {data_path}", exits=1)
import_code(code_path)
setup_gpu(use_gpu)
apply(data_path, output_file, model, text_key, batch_size, n_process)
def apply(
data_path: Path,
output_file: Path,
model: str,
json_field: str,
batch_size: int,
n_process: int,
):
docbin = DocBin(store_user_data=True)
paths = walk_directory(data_path)
if len(paths) == 0:
docbin.to_disk(output_file)
msg.warn(
"Did not find data to process,"
f" {data_path} seems to be an empty directory."
)
return
nlp = load_model(model)
msg.good(f"Loaded model {model}")
vocab = nlp.vocab
streams: List[DocOrStrStream] = []
text_files = []
for path in paths:
if path.suffix == ".spacy":
streams.append(_stream_docbin(path, vocab))
elif path.suffix == ".jsonl":
streams.append(_stream_jsonl(path, json_field))
else:
text_files.append(path)
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)):
docbin.add(doc)
if output_file.suffix == "":
output_file = output_file.with_suffix(".spacy")
docbin.to_disk(output_file)

174
spacy/cli/benchmark_speed.py Normal file
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@ -0,0 +1,174 @@
from typing import Iterable, List, Optional
import random
from itertools import islice
import numpy
from pathlib import Path
import time
from tqdm import tqdm
import typer
from wasabi import msg
from .. import util
from ..language import Language
from ..tokens import Doc
from ..training import Corpus
from ._util import Arg, Opt, benchmark_cli, setup_gpu
@benchmark_cli.command(
"speed",
context_settings={"allow_extra_args": True, "ignore_unknown_options": True},
)
def benchmark_speed_cli(
# fmt: off
ctx: typer.Context,
model: str = Arg(..., help="Model name or path"),
data_path: Path = Arg(..., help="Location of binary evaluation data in .spacy format", exists=True),
batch_size: Optional[int] = Opt(None, "--batch-size", "-b", min=1, help="Override the pipeline batch size"),
no_shuffle: bool = Opt(False, "--no-shuffle", help="Do not shuffle benchmark data"),
use_gpu: int = Opt(-1, "--gpu-id", "-g", help="GPU ID or -1 for CPU"),
n_batches: int = Opt(50, "--batches", help="Minimum number of batches to benchmark", min=30,),
warmup_epochs: int = Opt(3, "--warmup", "-w", min=0, help="Number of iterations over the data for warmup"),
# fmt: on
):
"""
Benchmark a pipeline. Expects a loadable spaCy pipeline and benchmark
data in the binary .spacy format.
"""
setup_gpu(use_gpu=use_gpu, silent=False)
nlp = util.load_model(model)
batch_size = batch_size if batch_size is not None else nlp.batch_size
corpus = Corpus(data_path)
docs = [eg.predicted for eg in corpus(nlp)]
if len(docs) == 0:
msg.fail("Cannot benchmark speed using an empty corpus.", exits=1)
print(f"Warming up for {warmup_epochs} epochs...")
warmup(nlp, docs, warmup_epochs, batch_size)
print()
print(f"Benchmarking {n_batches} batches...")
wps = benchmark(nlp, docs, n_batches, batch_size, not no_shuffle)
print()
print_outliers(wps)
print_mean_with_ci(wps)
# Lowercased, behaves as a context manager function.
class time_context:
"""Register the running time of a context."""
def __enter__(self):
self.start = time.perf_counter()
return self
def __exit__(self, type, value, traceback):
self.elapsed = time.perf_counter() - self.start
class Quartiles:
"""Calculate the q1, q2, q3 quartiles and the inter-quartile range (iqr)
of a sample."""
q1: float
q2: float
q3: float
iqr: float
def __init__(self, sample: numpy.ndarray) -> None:
self.q1 = numpy.quantile(sample, 0.25)
self.q2 = numpy.quantile(sample, 0.5)
self.q3 = numpy.quantile(sample, 0.75)
self.iqr = self.q3 - self.q1
def annotate(
nlp: Language, docs: List[Doc], batch_size: Optional[int]
) -> numpy.ndarray:
docs = nlp.pipe(tqdm(docs, unit="doc"), batch_size=batch_size)
wps = []
while True:
with time_context() as elapsed:
batch_docs = list(
islice(docs, batch_size if batch_size else nlp.batch_size)
)
if len(batch_docs) == 0:
break
n_tokens = count_tokens(batch_docs)
wps.append(n_tokens / elapsed.elapsed)
return numpy.array(wps)
def benchmark(
nlp: Language,
docs: List[Doc],
n_batches: int,
batch_size: int,
shuffle: bool,
) -> numpy.ndarray:
if shuffle:
bench_docs = [
nlp.make_doc(random.choice(docs).text)
for _ in range(n_batches * batch_size)
]
else:
bench_docs = [
nlp.make_doc(docs[i % len(docs)].text)
for i in range(n_batches * batch_size)
]
return annotate(nlp, bench_docs, batch_size)
def bootstrap(x, statistic=numpy.mean, iterations=10000) -> numpy.ndarray:
"""Apply a statistic to repeated random samples of an array."""
return numpy.fromiter(
(
statistic(numpy.random.choice(x, len(x), replace=True))
for _ in range(iterations)
),
numpy.float64,
)
def count_tokens(docs: Iterable[Doc]) -> int:
return sum(len(doc) for doc in docs)
def print_mean_with_ci(sample: numpy.ndarray):
mean = numpy.mean(sample)
bootstrap_means = bootstrap(sample)
bootstrap_means.sort()
# 95% confidence interval
low = bootstrap_means[int(len(bootstrap_means) * 0.025)]
high = bootstrap_means[int(len(bootstrap_means) * 0.975)]
print(f"Mean: {mean:.1f} words/s (95% CI: {low-mean:.1f} +{high-mean:.1f})")
def print_outliers(sample: numpy.ndarray):
quartiles = Quartiles(sample)
n_outliers = numpy.sum(
(sample < (quartiles.q1 - 1.5 * quartiles.iqr))
| (sample > (quartiles.q3 + 1.5 * quartiles.iqr))
)
n_extreme_outliers = numpy.sum(
(sample < (quartiles.q1 - 3.0 * quartiles.iqr))
| (sample > (quartiles.q3 + 3.0 * quartiles.iqr))
)
print(
f"Outliers: {(100 * n_outliers) / len(sample):.1f}%, extreme outliers: {(100 * n_extreme_outliers) / len(sample)}%"
)
def warmup(
nlp: Language, docs: List[Doc], warmup_epochs: int, batch_size: Optional[int]
) -> numpy.ndarray:
docs = warmup_epochs * docs
return annotate(nlp, docs, batch_size)

57
spacy/cli/convert.py
View File

@ -1,4 +1,4 @@
from typing import Callable, Iterable, Mapping, Optional, Any, List, Union
from typing import Callable, Iterable, Mapping, Optional, Any, Union
from enum import Enum
from pathlib import Path
from wasabi import Printer
@ -7,7 +7,7 @@ import re
import sys
import itertools
from ._util import app, Arg, Opt
from ._util import app, Arg, Opt, walk_directory
from ..training import docs_to_json
from ..tokens import Doc, DocBin
from ..training.converters import iob_to_docs, conll_ner_to_docs, json_to_docs
@ -28,6 +28,8 @@ CONVERTERS: Mapping[str, Callable[..., Iterable[Doc]]] = {
"json": json_to_docs,
}
AUTO = "auto"
# File types that can be written to stdout
FILE_TYPES_STDOUT = ("json",)
@ -49,7 +51,7 @@ def convert_cli(
model: Optional[str] = Opt(None, "--model", "--base", "-b", help="Trained spaCy pipeline for sentence segmentation to use as base (for --seg-sents)"),
morphology: bool = Opt(False, "--morphology", "-m", help="Enable appending morphology to tags"),
merge_subtokens: bool = Opt(False, "--merge-subtokens", "-T", help="Merge CoNLL-U subtokens"),
converter: str = Opt("auto", "--converter", "-c", help=f"Converter: {tuple(CONVERTERS.keys())}"),
converter: str = Opt(AUTO, "--converter", "-c", help=f"Converter: {tuple(CONVERTERS.keys())}"),
ner_map: Optional[Path] = Opt(None, "--ner-map", "-nm", help="NER tag mapping (as JSON-encoded dict of entity types)", exists=True),
lang: Optional[str] = Opt(None, "--lang", "-l", help="Language (if tokenizer required)"),
concatenate: bool = Opt(None, "--concatenate", "-C", help="Concatenate output to a single file"),
@ -70,8 +72,8 @@ def convert_cli(
output_dir: Union[str, Path] = "-" if output_dir == Path("-") else output_dir
silent = output_dir == "-"
msg = Printer(no_print=silent)
verify_cli_args(msg, input_path, output_dir, file_type.value, converter, ner_map)
converter = _get_converter(msg, converter, input_path)
verify_cli_args(msg, input_path, output_dir, file_type.value, converter, ner_map)
convert(
input_path,
output_dir,
@ -100,7 +102,7 @@ def convert(
model: Optional[str] = None,
morphology: bool = False,
merge_subtokens: bool = False,
converter: str = "auto",
converter: str,
ner_map: Optional[Path] = None,
lang: Optional[str] = None,
concatenate: bool = False,
@ -189,33 +191,6 @@ def autodetect_ner_format(input_data: str) -> Optional[str]:
return None
def walk_directory(path: Path, converter: str) -> List[Path]:
if not path.is_dir():
return [path]
paths = [path]
locs = []
seen = set()
for path in paths:
if str(path) in seen:
continue
seen.add(str(path))
if path.parts[-1].startswith("."):
continue
elif path.is_dir():
paths.extend(path.iterdir())
elif converter == "json" and not path.parts[-1].endswith("json"):
continue
elif converter == "conll" and not path.parts[-1].endswith("conll"):
continue
elif converter == "iob" and not path.parts[-1].endswith("iob"):
continue
else:
locs.append(path)
# It's good to sort these, in case the ordering messes up cache.
locs.sort()
return locs
def verify_cli_args(
msg: Printer,
input_path: Path,
@ -239,18 +214,22 @@ def verify_cli_args(
input_locs = walk_directory(input_path, converter)
if len(input_locs) == 0:
msg.fail("No input files in directory", input_path, exits=1)
file_types = list(set([loc.suffix[1:] for loc in input_locs]))
if converter == "auto" and len(file_types) >= 2:
file_types_str = ",".join(file_types)
msg.fail("All input files must be same type", file_types_str, exits=1)
if converter != "auto" and converter not in CONVERTERS:
if converter not in CONVERTERS:
msg.fail(f"Can't find converter for {converter}", exits=1)
def _get_converter(msg, converter, input_path: Path):
if input_path.is_dir():
input_path = walk_directory(input_path, converter)[0]
if converter == "auto":
if converter == AUTO:
input_locs = walk_directory(input_path, suffix=None)
file_types = list(set([loc.suffix[1:] for loc in input_locs]))
if len(file_types) >= 2:
file_types_str = ",".join(file_types)
msg.fail("All input files must be same type", file_types_str, exits=1)
input_path = input_locs[0]
else:
input_path = walk_directory(input_path, suffix=converter)[0]
if converter == AUTO:
converter = input_path.suffix[1:]
if converter == "ner" or converter == "iob":
with input_path.open(encoding="utf8") as file_:

2
spacy/cli/debug_data.py
View File

@ -13,6 +13,7 @@ from ._util import import_code, debug_cli, _format_number
from ..training import Example, remove_bilu_prefix
from ..training.initialize import get_sourced_components
from ..schemas import ConfigSchemaTraining
from ..pipeline import TrainablePipe
from ..pipeline._parser_internals import nonproj
from ..pipeline._parser_internals.nonproj import DELIMITER
from ..pipeline import Morphologizer, SpanCategorizer
@ -934,6 +935,7 @@ def _get_labels_from_model(nlp: Language, factory_name: str) -> Set[str]:
labels: Set[str] = set()
for pipe_name in pipe_names:
pipe = nlp.get_pipe(pipe_name)
assert isinstance(pipe, TrainablePipe)
labels.update(pipe.labels)
return labels

7
spacy/cli/evaluate.py
View File

@ -7,12 +7,15 @@ from thinc.api import fix_random_seed
from ..training import Corpus
from ..tokens import Doc
from ._util import app, Arg, Opt, setup_gpu, import_code
from ._util import app, Arg, Opt, setup_gpu, import_code, benchmark_cli
from ..scorer import Scorer
from .. import util
from .. import displacy
@benchmark_cli.command(
"accuracy",
)
@app.command("evaluate")
def evaluate_cli(
# fmt: off
@ -36,7 +39,7 @@ def evaluate_cli(
dependency parses in a HTML file, set as output directory as the
displacy_path argument.
DOCS: https://spacy.io/api/cli#evaluate
DOCS: https://spacy.io/api/cli#benchmark-accuracy
"""
import_code(code_path)
evaluate(

233
spacy/cli/find_threshold.py Normal file
View File

@ -0,0 +1,233 @@
import functools
import operator
from pathlib import Path
import logging
from typing import Optional, Tuple, Any, Dict, List
import numpy
import wasabi.tables
from ..pipeline import TextCategorizer, MultiLabel_TextCategorizer
from ..errors import Errors
from ..training import Corpus
from ._util import app, Arg, Opt, import_code, setup_gpu
from .. import util
_DEFAULTS = {
"n_trials": 11,
"use_gpu": -1,
"gold_preproc": False,
}
@app.command(
"find-threshold",
context_settings={"allow_extra_args": False, "ignore_unknown_options": True},
)
def find_threshold_cli(
# fmt: off
model: str = Arg(..., help="Model name or path"),
data_path: Path = Arg(..., help="Location of binary evaluation data in .spacy format", exists=True),
pipe_name: str = Arg(..., help="Name of pipe to examine thresholds for"),
threshold_key: str = Arg(..., help="Key of threshold attribute in component's configuration"),
scores_key: str = Arg(..., help="Metric to optimize"),
n_trials: int = Opt(_DEFAULTS["n_trials"], "--n_trials", "-n", help="Number of trials to determine optimal thresholds"),
code_path: Optional[Path] = Opt(None, "--code", "-c", help="Path to Python file with additional code (registered functions) to be imported"),
use_gpu: int = Opt(_DEFAULTS["use_gpu"], "--gpu-id", "-g", help="GPU ID or -1 for CPU"),
gold_preproc: bool = Opt(_DEFAULTS["gold_preproc"], "--gold-preproc", "-G", help="Use gold preprocessing"),
verbose: bool = Opt(False, "--silent", "-V", "-VV", help="Display more information for debugging purposes"),
# fmt: on
):
"""
Runs prediction trials for a trained model with varying tresholds to maximize
the specified metric. The search space for the threshold is traversed linearly
from 0 to 1 in `n_trials` steps. Results are displayed in a table on `stdout`
(the corresponding API call to `spacy.cli.find_threshold.find_threshold()`
returns all results).
This is applicable only for components whose predictions are influenced by
thresholds - e.g. `textcat_multilabel` and `spancat`, but not `textcat`. Note
that the full path to the corresponding threshold attribute in the config has to
be provided.
DOCS: https://spacy.io/api/cli#find-threshold
"""
util.logger.setLevel(logging.DEBUG if verbose else logging.INFO)
import_code(code_path)
find_threshold(
model=model,
data_path=data_path,
pipe_name=pipe_name,
threshold_key=threshold_key,
scores_key=scores_key,
n_trials=n_trials,
use_gpu=use_gpu,
gold_preproc=gold_preproc,
silent=False,
)
def find_threshold(
model: str,
data_path: Path,
pipe_name: str,
threshold_key: str,
scores_key: str,
*,
n_trials: int = _DEFAULTS["n_trials"], # type: ignore
use_gpu: int = _DEFAULTS["use_gpu"], # type: ignore
gold_preproc: bool = _DEFAULTS["gold_preproc"], # type: ignore
silent: bool = True,
) -> Tuple[float, float, Dict[float, float]]:
"""
Runs prediction trials for models with varying tresholds to maximize the specified metric.
model (Union[str, Path]): Pipeline to evaluate. Can be a package or a path to a data directory.
data_path (Path): Path to file with DocBin with docs to use for threshold search.
pipe_name (str): Name of pipe to examine thresholds for.
threshold_key (str): Key of threshold attribute in component's configuration.
scores_key (str): Name of score to metric to optimize.
n_trials (int): Number of trials to determine optimal thresholds.
use_gpu (int): GPU ID or -1 for CPU.
gold_preproc (bool): Whether to use gold preprocessing. Gold preprocessing helps the annotations align to the
tokenization, and may result in sequences of more consistent length. However, it may reduce runtime accuracy due
to train/test skew.
silent (bool): Whether to print non-error-related output to stdout.
RETURNS (Tuple[float, float, Dict[float, float]]): Best found threshold, the corresponding score, scores for all
evaluated thresholds.
"""
setup_gpu(use_gpu, silent=silent)
data_path = util.ensure_path(data_path)
if not data_path.exists():
wasabi.msg.fail("Evaluation data not found", data_path, exits=1)
nlp = util.load_model(model)
if pipe_name not in nlp.component_names:
raise AttributeError(
Errors.E001.format(name=pipe_name, opts=nlp.component_names)
)
pipe = nlp.get_pipe(pipe_name)
if not hasattr(pipe, "scorer"):
raise AttributeError(Errors.E1045)
if type(pipe) == TextCategorizer:
wasabi.msg.warn(
"The `textcat` component doesn't use a threshold as it's not applicable to the concept of "
"exclusive classes. All thresholds will yield the same results."
)
if not silent:
wasabi.msg.info(
title=f"Optimizing for {scores_key} for component '{pipe_name}' with {n_trials} "
f"trials."
)
# Load evaluation corpus.
corpus = Corpus(data_path, gold_preproc=gold_preproc)
dev_dataset = list(corpus(nlp))
config_keys = threshold_key.split(".")
def set_nested_item(
config: Dict[str, Any], keys: List[str], value: float
) -> Dict[str, Any]:
"""Set item in nested dictionary. Adapted from https://stackoverflow.com/a/54138200.
config (Dict[str, Any]): Configuration dictionary.
keys (List[Any]): Path to value to set.
value (float): Value to set.
RETURNS (Dict[str, Any]): Updated dictionary.
"""
functools.reduce(operator.getitem, keys[:-1], config)[keys[-1]] = value
return config
def filter_config(
config: Dict[str, Any], keys: List[str], full_key: str
) -> Dict[str, Any]:
"""Filters provided config dictionary so that only the specified keys path remains.
config (Dict[str, Any]): Configuration dictionary.
keys (List[Any]): Path to value to set.
full_key (str): Full user-specified key.
RETURNS (Dict[str, Any]): Filtered dictionary.
"""
if keys[0] not in config:
wasabi.msg.fail(
title=f"Failed to look up `{full_key}` in config: sub-key {[keys[0]]} not found.",
text=f"Make sure you specified {[keys[0]]} correctly. The following sub-keys are available instead: "
f"{list(config.keys())}",
exits=1,
)
return {
keys[0]: filter_config(config[keys[0]], keys[1:], full_key)
if len(keys) > 1
else config[keys[0]]
}
# Evaluate with varying threshold values.
scores: Dict[float, float] = {}
config_keys_full = ["components", pipe_name, *config_keys]
table_col_widths = (10, 10)
thresholds = numpy.linspace(0, 1, n_trials)
print(wasabi.tables.row(["Threshold", f"{scores_key}"], widths=table_col_widths))
for threshold in thresholds:
# Reload pipeline with overrides specifying the new threshold.
nlp = util.load_model(
model,
config=set_nested_item(
filter_config(
nlp.config, config_keys_full, ".".join(config_keys_full)
).copy(),
config_keys_full,
threshold,
),
)
if hasattr(pipe, "cfg"):
setattr(
nlp.get_pipe(pipe_name),
"cfg",
set_nested_item(getattr(pipe, "cfg"), config_keys, threshold),
)
eval_scores = nlp.evaluate(dev_dataset)
if scores_key not in eval_scores:
wasabi.msg.fail(
title=f"Failed to look up score `{scores_key}` in evaluation results.",
text=f"Make sure you specified the correct value for `scores_key`. The following scores are "
f"available: {list(eval_scores.keys())}",
exits=1,
)
scores[threshold] = eval_scores[scores_key]
if not isinstance(scores[threshold], (float, int)):
wasabi.msg.fail(
f"Returned score for key '{scores_key}' is not numeric. Threshold optimization only works for numeric "
f"scores.",
exits=1,
)
print(
wasabi.row(
[round(threshold, 3), round(scores[threshold], 3)],
widths=table_col_widths,
)
)
best_threshold = max(scores.keys(), key=(lambda key: scores[key]))
# If all scores are identical, emit warning.
if len(set(scores.values())) == 1:
wasabi.msg.warn(
title="All scores are identical. Verify that all settings are correct.",
text=""
if (
not isinstance(pipe, MultiLabel_TextCategorizer)
or scores_key in ("cats_macro_f", "cats_micro_f")
)
else "Use `cats_macro_f` or `cats_micro_f` when optimizing the threshold for `textcat_multilabel`.",
)
else:
if not silent:
print(
f"\nBest threshold: {round(best_threshold, ndigits=4)} with {scores_key} value of {scores[best_threshold]}."
)
return best_threshold, scores[best_threshold], scores

6
spacy/cli/project/assets.py
View File

@ -189,7 +189,11 @@ def convert_asset_url(url: str) -> str:
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:
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(

56
spacy/cli/project/dvc.py
View File

@ -25,6 +25,7 @@ def project_update_dvc_cli(
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
):
@ -36,7 +37,7 @@ def project_update_dvc_cli(
DOCS: https://spacy.io/api/cli#project-dvc
"""
project_update_dvc(project_dir, workflow, verbose=verbose, force=force)
project_update_dvc(project_dir, workflow, verbose=verbose, quiet=quiet, force=force)
def project_update_dvc(
@ -44,6 +45,7 @@ def project_update_dvc(
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
@ -54,11 +56,12 @@ def project_update_dvc(
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, force=force
project_dir, config, workflow, verbose=verbose, quiet=quiet, force=force
)
help_msg = "To execute the workflow with DVC, run: dvc repro"
if updated:
@ -72,7 +75,7 @@ def update_dvc_config(
config: Dict[str, Any],
workflow: Optional[str] = None,
verbose: bool = False,
silent: bool = False,
quiet: bool = False,
force: bool = False,
) -> bool:
"""Re-run the DVC commands in dry mode and update dvc.yaml file in the
@ -83,7 +86,7 @@ def update_dvc_config(
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).
silent (bool): Don't output anything (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.
"""
@ -105,6 +108,14 @@ def update_dvc_config(
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]:
if isinstance(name, dict) and "parallel" in name:
msg.fail(
@ -123,19 +134,26 @@ def update_dvc_config(
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", "-n", name, "-w", str(path), "--no-exec"]
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 len(dvc_commands) == 0:
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(
f"A DVC workflow must have at least one dependency or output",
"No usable commands for DVC found. This can happen if none of your "
"commands have dependencies or outputs.",
exits=1,
)
with working_dir(path):
dvc_flags = {"--verbose": verbose, "--quiet": silent}
run_dvc_commands(dvc_commands, flags=dvc_flags)
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)
@ -143,26 +161,6 @@ def update_dvc_config(
return True
def run_dvc_commands(
commands: Iterable[str] = SimpleFrozenList(), flags: Dict[str, bool] = {}
) -> None:
"""Run a sequence of DVC commands in a subprocess, in order.
commands (List[str]): The string commands without the leading "dvc".
flags (Dict[str, bool]): Conditional flags to be added to command. Makes it
easier to pass flags like --quiet that depend on a variable or
command-line setting while avoiding lots of nested conditionals.
"""
for c in commands:
command = split_command(c)
dvc_command = ["dvc", *command]
# Add the flags if they are set to True
for flag, is_active in flags.items():
if is_active:
dvc_command.append(flag)
run_command(dvc_command)
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.

2
spacy/cli/project/parallel.py
View File

@ -178,7 +178,7 @@ class _ParallelCommand:
def change_state(self, new_state: str) -> None:
if new_state not in self.state.transitions:
raise RuntimeError(
Errors.E1044.format(old_state=self.state.name, new_state=new_state)
Errors.E1051.format(old_state=self.state.name, new_state=new_state)
)
self.state = _ParallelCommand.state_dict[new_state]

61
spacy/cli/project/remote_storage.py
View File

@ -5,14 +5,17 @@ import hashlib
import urllib.parse
import tarfile
from pathlib import Path
from wasabi import msg
from .._util import get_hash, get_checksum, download_file, ensure_pathy
from ...util import make_tempdir, get_minor_version, ENV_VARS, check_bool_env_var
from .._util import get_hash, get_checksum, upload_file, download_file
from .._util import ensure_pathy, make_tempdir
from ...util import get_minor_version, ENV_VARS, check_bool_env_var
from ...git_info import GIT_VERSION
from ... import about
from ...errors import Errors
if TYPE_CHECKING:
from pathy import Pathy # noqa: F401
from pathy import FluidPath # noqa: F401
class RemoteStorage:
@ -27,7 +30,7 @@ class RemoteStorage:
self.url = ensure_pathy(url)
self.compression = compression
def push(self, path: Path, command_hash: str, content_hash: str) -> "Pathy":
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.
@ -48,9 +51,7 @@ class RemoteStorage:
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))
with tar_loc.open(mode="rb") as input_file:
with url.open(mode="wb") as output_file:
output_file.write(input_file.read())
upload_file(tar_loc, url)
return url
def pull(
@ -59,7 +60,7 @@ class RemoteStorage:
*,
command_hash: Optional[str] = None,
content_hash: Optional[str] = None,
) -> Optional["Pathy"]:
) -> Optional["FluidPath"]:
"""Retrieve a file from the remote cache. If the file already exists,
nothing is done.
@ -84,7 +85,23 @@ class RemoteStorage:
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()
tar_file.extractall(self.root)
# 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(
@ -93,25 +110,37 @@ class RemoteStorage:
*,
command_hash: Optional[str] = None,
content_hash: Optional[str] = None,
) -> Optional["Pathy"]:
) -> 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.make_url(path, command_hash, content_hash)
url = self.url / name / command_hash / content_hash
urls = [url] if url.exists() else []
elif command_hash is not None:
urls = list((self.url / name / command_hash).iterdir())
if (self.url / name / command_hash).exists():
urls = list((self.url / name / command_hash).iterdir())
else:
urls = list((self.url / name).iterdir())
if content_hash is not None:
urls = [url for url in urls if url.parts[-1] == content_hash]
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) -> "Pathy":
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

28
spacy/cli/project/run.py
View File

@ -51,6 +51,7 @@ def project_run(
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
@ -67,6 +68,7 @@ def project_run(
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", [])}
@ -74,26 +76,28 @@ def project_run(
validate_subcommand(list(commands.keys()), list(workflows.keys()), subcommand)
req_path = project_dir / "requirements.txt"
if config.get("check_requirements", True) and os.path.exists(req_path):
with req_path.open() as requirements_file:
_check_requirements([req.replace("\n", "") for req in requirements_file])
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 workflow_item in workflows[subcommand]:
if isinstance(workflow_item, str):
for cmd in workflows[subcommand]:
if isinstance(cmd, str):
project_run(
project_dir,
workflow_item,
cmd,
overrides=overrides,
force=force,
dry=dry,
capture=capture,
skip_requirements_check=True,
)
else:
assert isinstance(workflow_item, dict)
assert len(workflow_item) == 1
cmds = workflow_item["parallel"]
assert isinstance(cmd, dict)
assert len(cmd) == 1
cmds = cmd["parallel"]
assert isinstance(cmds[0], str)
project_run_parallel_group(
project_dir,
@ -289,6 +293,12 @@ def _check_requirements(requirements: List[str]) -> Tuple[bool, bool]:
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(

2
spacy/cli/templates/quickstart_training.jinja
View File

@ -1,7 +1,7 @@
{# This is a template for training configs used for the quickstart widget in
the docs and the init config command. It encodes various best practices and
can help generate the best possible configuration, given a user's requirements. #}
{%- set use_transformer = hardware != "cpu" -%}
{%- set use_transformer = hardware != "cpu" and transformer_data -%}
{%- set transformer = transformer_data[optimize] if use_transformer else {} -%}
{%- set listener_components = ["tagger", "morphologizer", "parser", "ner", "textcat", "textcat_multilabel", "entity_linker", "spancat", "trainable_lemmatizer"] -%}
[paths]

9
spacy/cli/templates/quickstart_training_recommendations.yml
View File

@ -37,6 +37,15 @@ bn:
accuracy:
name: sagorsarker/bangla-bert-base
size_factor: 3
ca:
word_vectors: null
transformer:
efficiency:
name: projecte-aina/roberta-base-ca-v2
size_factor: 3
accuracy:
name: projecte-aina/roberta-base-ca-v2
size_factor: 3
da:
word_vectors: da_core_news_lg
transformer:

2
spacy/default_config.cfg
View File

@ -90,6 +90,8 @@ dev_corpus = "corpora.dev"
train_corpus = "corpora.train"
# Optional callback before nlp object is saved to disk after training
before_to_disk = null
# Optional callback that is invoked at the start of each training step
before_update = null
[training.logger]
@loggers = "spacy.ConsoleLogger.v1"

12
spacy/displacy/__init__.py
View File

@ -11,6 +11,7 @@ from .render import DependencyRenderer, EntityRenderer, SpanRenderer
from ..tokens import Doc, Span
from ..errors import Errors, Warnings
from ..util import is_in_jupyter
from ..util import find_available_port
_html = {}
@ -36,7 +37,7 @@ def render(
jupyter (bool): Override Jupyter auto-detection.
options (dict): Visualiser-specific options, e.g. colors.
manual (bool): Don't parse `Doc` and instead expect a dict/list of dicts.
RETURNS (str): Rendered HTML markup.
RETURNS (str): Rendered SVG or HTML markup.
DOCS: https://spacy.io/api/top-level#displacy.render
USAGE: https://spacy.io/usage/visualizers
@ -82,6 +83,7 @@ def serve(
manual: bool = False,
port: int = 5000,
host: str = "0.0.0.0",
auto_select_port: bool = False,
) -> None:
"""Serve displaCy visualisation.
@ -93,12 +95,15 @@ def serve(
manual (bool): Don't parse `Doc` and instead expect a dict/list of dicts.
port (int): Port to serve visualisation.
host (str): Host to serve visualisation.
auto_select_port (bool): Automatically select a port if the specified port is in use.
DOCS: https://spacy.io/api/top-level#displacy.serve
USAGE: https://spacy.io/usage/visualizers
"""
from wsgiref import simple_server
port = find_available_port(port, host, auto_select_port)
if is_in_jupyter():
warnings.warn(Warnings.W011)
render(docs, style=style, page=page, minify=minify, options=options, manual=manual)
@ -228,12 +233,13 @@ def parse_spans(doc: Doc, options: Dict[str, Any] = {}) -> Dict[str, Any]:
"kb_id": span.kb_id_ if span.kb_id_ else "",
"kb_url": kb_url_template.format(span.kb_id_) if kb_url_template else "#",
}
for span in doc.spans[spans_key]
for span in doc.spans.get(spans_key, [])
]
tokens = [token.text for token in doc]
if not spans:
warnings.warn(Warnings.W117.format(spans_key=spans_key))
keys = list(doc.spans.keys())
warnings.warn(Warnings.W117.format(spans_key=spans_key, keys=keys))
title = doc.user_data.get("title", None) if hasattr(doc, "user_data") else None
settings = get_doc_settings(doc)
return {

4
spacy/displacy/render.py
View File

@ -94,7 +94,7 @@ class SpanRenderer:
parsed (list): Dependency parses to render.
page (bool): Render parses wrapped as full HTML page.
minify (bool): Minify HTML markup.
RETURNS (str): Rendered HTML markup.
RETURNS (str): Rendered SVG or HTML markup.
"""
rendered = []
for i, p in enumerate(parsed):
@ -510,7 +510,7 @@ class EntityRenderer:
parsed (list): Dependency parses to render.
page (bool): Render parses wrapped as full HTML page.
minify (bool): Minify HTML markup.
RETURNS (str): Rendered HTML markup.
RETURNS (str): Rendered SVG or HTML markup.
"""
rendered = []
for i, p in enumerate(parsed):

38
spacy/errors.py
View File

@ -199,7 +199,7 @@ class Warnings(metaclass=ErrorsWithCodes):
W117 = ("No spans to visualize found in Doc object with spans_key: '{spans_key}'. If this is "
"surprising to you, make sure the Doc was processed using a model "
"that supports span categorization, and check the `doc.spans[spans_key]` "
"property manually if necessary.")
"property manually if necessary.\n\nAvailable keys: {keys}")
W118 = ("Term '{term}' not found in glossary. It may however be explained in documentation "
"for the corpora used to train the language. Please check "
"`nlp.meta[\"sources\"]` for any relevant links.")
@ -212,8 +212,9 @@ class Warnings(metaclass=ErrorsWithCodes):
W121 = ("Attempting to trace non-existent method '{method}' in pipe '{pipe}'")
W122 = ("Couldn't trace method '{method}' in pipe '{pipe}'. This can happen if the pipe class "
"is a Cython extension type.")
W123 = ("Argument {arg} with value {arg_value} is used instead of {config_value} as specified in the config. Be "
"aware that this might affect other components in your pipeline.")
W123 = ("Argument `enable` with value {enable} does not contain all values specified in the config option "
"`enabled` ({enabled}). Be aware that this might affect other components in your pipeline.")
W124 = ("{host}:{port} is already in use, using the nearest available port {serve_port} as an alternative.")
class Errors(metaclass=ErrorsWithCodes):
@ -345,6 +346,11 @@ class Errors(metaclass=ErrorsWithCodes):
"clear the existing vectors and resize the table.")
E074 = ("Error interpreting compiled match pattern: patterns are expected "
"to end with the attribute {attr}. Got: {bad_attr}.")
E079 = ("Error computing states in beam: number of predicted beams "
"({pbeams}) does not equal number of gold beams ({gbeams}).")
E080 = ("Duplicate state found in beam: {key}.")
E081 = ("Error getting gradient in beam: number of histories ({n_hist}) "
"does not equal number of losses ({losses}).")
E082 = ("Error deprojectivizing parse: number of heads ({n_heads}), "
"projective heads ({n_proj_heads}) and labels ({n_labels}) do not "
"match.")
@ -540,8 +546,14 @@ class Errors(metaclass=ErrorsWithCodes):
E199 = ("Unable to merge 0-length span at `doc[{start}:{end}]`.")
E200 = ("Can't set {attr} from Span.")
E202 = ("Unsupported {name} mode '{mode}'. Supported modes: {modes}.")
E203 = ("If the {name} embedding layer is not updated "
"during training, make sure to include it in 'annotating components'")
# New errors added in v3.x
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 "
@ -711,9 +723,9 @@ class Errors(metaclass=ErrorsWithCodes):
"`nlp.enable_pipe` instead.")
E927 = ("Can't write to frozen list. Maybe you're trying to modify a computed "
"property or default function argument?")
E928 = ("A KnowledgeBase can only be serialized to/from from a directory, "
E928 = ("An InMemoryLookupKB can only be serialized to/from from a directory, "
"but the provided argument {loc} points to a file.")
E929 = ("Couldn't read KnowledgeBase from {loc}. The path does not seem to exist.")
E929 = ("Couldn't read InMemoryLookupKB from {loc}. The path does not seem to exist.")
E930 = ("Received invalid get_examples callback in `{method}`. "
"Expected function that returns an iterable of Example objects but "
"got: {obj}")
@ -944,8 +956,20 @@ class Errors(metaclass=ErrorsWithCodes):
"case pass an empty list for the previously not specified argument to avoid this error.")
E1043 = ("Expected None or a value in range [{range_start}, {range_end}] for entity linker threshold, but got "
"{value}.")
E1044 = ("Illegal transition from {old_state} to {new_state}.")
E1044 = ("Expected `candidates_batch_size` to be >= 1, but got: {value}")
E1045 = ("Encountered {parent} subclass without `{parent}.{method}` "
"method in '{name}'. If you want to use this method, make "
"sure it's overwritten on the subclass.")
E1046 = ("{cls_name} is an abstract class and cannot be instantiated. If you are looking for spaCy's default "
"knowledge base, use `InMemoryLookupKB`.")
E1047 = ("`find_threshold()` only supports components with a `scorer` attribute.")
E1048 = ("Got '{unexpected}' as console progress bar type, but expected one of the following: {expected}")
E1049 = ("No available port found for displaCy on host {host}. Please specify an available port "
"with `displacy.serve(doc, port=port)`")
E1050 = ("Port {port} is already in use. Please specify an available port with `displacy.serve(doc, port=port)` "
"or use `auto_switch_port=True` to pick an available port automatically.")
E1051 = ("Illegal transition from {old_state} to {new_state}.")
# Deprecated model shortcuts, only used in errors and warnings
OLD_MODEL_SHORTCUTS = {

3
spacy/kb/__init__.py Normal file
View File

@ -0,0 +1,3 @@
from .kb import KnowledgeBase
from .kb_in_memory import InMemoryLookupKB
from .candidate import Candidate, get_candidates, get_candidates_batch

12
spacy/kb/candidate.pxd Normal file
View File

@ -0,0 +1,12 @@
from .kb cimport KnowledgeBase
from libcpp.vector cimport vector
from ..typedefs cimport hash_t
# 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
cdef float entity_freq
cdef vector[float] entity_vector
cdef hash_t alias_hash
cdef float prior_prob

74
spacy/kb/candidate.pyx Normal file
View File

@ -0,0 +1,74 @@
# cython: infer_types=True, profile=True
from typing import Iterable
from .kb cimport KnowledgeBase
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.
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):
self.kb = kb
self.entity_hash = entity_hash
self.entity_freq = entity_freq
self.entity_vector = entity_vector
self.alias_hash = alias_hash
self.prior_prob = prior_prob
@property
def entity(self) -> int:
"""RETURNS (uint64): hash of the entity's KB ID/name"""
return self.entity_hash
@property
def entity_(self) -> str:
"""RETURNS (str): ID/name of this entity in the KB"""
return self.kb.vocab.strings[self.entity_hash]
@property
def alias(self) -> int:
"""RETURNS (uint64): hash of the alias"""
return self.alias_hash
@property
def alias_(self) -> str:
"""RETURNS (str): ID of the original alias"""
return self.kb.vocab.strings[self.alias_hash]
@property
def entity_freq(self) -> float:
return self.entity_freq
@property
def entity_vector(self) -> Iterable[float]:
return self.entity_vector
@property
def prior_prob(self) -> float:
return self.prior_prob
def get_candidates(kb: KnowledgeBase, mention: Span) -> Iterable[Candidate]:
"""
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.
"""
return kb.get_candidates(mention)
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.
kb (KnowledgeBase): Knowledge base to query.
mention (Iterable[Span]): Entity mentions for which to identify candidates.
RETURNS (Iterable[Iterable[Candidate]]): Identified candidates.
"""
return kb.get_candidates_batch(mentions)

10
spacy/kb/kb.pxd Normal file
View File

@ -0,0 +1,10 @@
"""Knowledge-base for entity or concept linking."""
from cymem.cymem cimport Pool
from libc.stdint cimport int64_t
from ..vocab cimport Vocab
cdef class KnowledgeBase:
cdef Pool mem
cdef readonly Vocab vocab
cdef readonly int64_t entity_vector_length

108
spacy/kb/kb.pyx Normal file
View File

@ -0,0 +1,108 @@
# cython: infer_types=True, profile=True
from pathlib import Path
from typing import Iterable, Tuple, Union
from cymem.cymem cimport Pool
from .candidate import Candidate
from ..tokens import Span
from ..util import SimpleFrozenList
from ..errors import Errors
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.
This is an abstract class and requires its operations to be implemented.
DOCS: https://spacy.io/api/kb
"""
def __init__(self, vocab: Vocab, entity_vector_length: int):
"""Create a KnowledgeBase."""
# Make sure abstract KB is not instantiated.
if self.__class__ == KnowledgeBase:
raise TypeError(
Errors.E1046.format(cls_name=self.__class__.__name__)
)
self.vocab = vocab
self.entity_vector_length = entity_vector_length
self.mem = Pool()
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.
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.
"""
return [self.get_candidates(span) for span in mentions]
def get_candidates(self, mention: Span) -> Iterable[Candidate]:
"""
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__)
)
def get_vectors(self, entities: Iterable[str]) -> Iterable[Iterable[float]]:
"""
Return vectors for entities.
entity (str): Entity name/ID.
RETURNS (Iterable[Iterable[float]]): Vectors for specified entities.
"""
return [self.get_vector(entity) for entity in entities]
def get_vector(self, str entity) -> Iterable[float]:
"""
Return vector for entity.
entity (str): Entity name/ID.
RETURNS (Iterable[float]): Vector for specified entity.
"""
raise NotImplementedError(
Errors.E1045.format(parent="KnowledgeBase", method="get_vector", name=self.__name__)
)
def to_bytes(self, **kwargs) -> bytes:
"""Serialize the current state to a binary string.
RETURNS (bytes): Current state as binary string.
"""
raise NotImplementedError(
Errors.E1045.format(parent="KnowledgeBase", method="to_bytes", name=self.__name__)
)
def from_bytes(self, bytes_data: bytes, *, exclude: Tuple[str] = tuple()):
"""Load state from a binary string.
bytes_data (bytes): KB state.
exclude (Tuple[str]): Properties to exclude when restoring KB.
"""
raise NotImplementedError(
Errors.E1045.format(parent="KnowledgeBase", method="from_bytes", name=self.__name__)
)
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__)
)
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__)
)

24
spacy/kb.pxd → spacy/kb/kb_in_memory.pxd
View File

@ -1,14 +1,12 @@
"""Knowledge-base for entity or concept linking."""
from cymem.cymem cimport Pool
from preshed.maps cimport PreshMap
from libcpp.vector cimport vector
from libc.stdint cimport int32_t, int64_t
from libc.stdio cimport FILE
from .vocab cimport Vocab
from .typedefs cimport hash_t
from .structs cimport KBEntryC, AliasC
from ..typedefs cimport hash_t
from ..structs cimport KBEntryC, AliasC
from .kb cimport KnowledgeBase
ctypedef vector[KBEntryC] entry_vec
ctypedef vector[AliasC] alias_vec
@ -16,21 +14,7 @@ ctypedef vector[float] float_vec
ctypedef vector[float_vec] float_matrix
# 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
cdef float entity_freq
cdef vector[float] entity_vector
cdef hash_t alias_hash
cdef float prior_prob
cdef class KnowledgeBase:
cdef Pool mem
cdef readonly Vocab vocab
cdef int64_t entity_vector_length
cdef class InMemoryLookupKB(KnowledgeBase):
# This maps 64bit keys (hash of unique entity string)
# to 64bit values (position of the _KBEntryC struct in the _entries vector).
# The PreshMap is pretty space efficient, as it uses open addressing. So

96
spacy/kb.pyx → spacy/kb/kb_in_memory.pyx
View File

@ -1,8 +1,7 @@
# cython: infer_types=True, profile=True
from typing import Iterator, Iterable, Callable, Dict, Any
from typing import Iterable, Callable, Dict, Any, Union
import srsly
from cymem.cymem cimport Pool
from preshed.maps cimport PreshMap
from cpython.exc cimport PyErr_SetFromErrno
from libc.stdio cimport fopen, fclose, fread, fwrite, feof, fseek
@ -12,85 +11,28 @@ from libcpp.vector cimport vector
from pathlib import Path
import warnings
from .typedefs cimport hash_t
from .errors import Errors, Warnings
from . import util
from .util import SimpleFrozenList, ensure_path
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.
Each candidate (alias, entity) pair is assigned to 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):
self.kb = kb
self.entity_hash = entity_hash
self.entity_freq = entity_freq
self.entity_vector = entity_vector
self.alias_hash = alias_hash
self.prior_prob = prior_prob
@property
def entity(self):
"""RETURNS (uint64): hash of the entity's KB ID/name"""
return self.entity_hash
@property
def entity_(self):
"""RETURNS (str): ID/name of this entity in the KB"""
return self.kb.vocab.strings[self.entity_hash]
@property
def alias(self):
"""RETURNS (uint64): hash of the alias"""
return self.alias_hash
@property
def alias_(self):
"""RETURNS (str): ID of the original alias"""
return self.kb.vocab.strings[self.alias_hash]
@property
def entity_freq(self):
return self.entity_freq
@property
def entity_vector(self):
return self.entity_vector
@property
def prior_prob(self):
return self.prior_prob
from ..tokens import Span
from ..typedefs cimport hash_t
from ..errors import Errors, Warnings
from .. import util
from ..util import SimpleFrozenList, ensure_path
from ..vocab cimport Vocab
from .kb cimport KnowledgeBase
from .candidate import Candidate as Candidate
def get_candidates(KnowledgeBase kb, span) -> Iterator[Candidate]:
"""
Return candidate entities for a given span by using the text of the span as the alias
and fetching appropriate entries from the index.
This particular function is optimized to work with the built-in KB functionality,
but any other custom candidate generation method can be used in combination with the KB as well.
"""
return kb.get_alias_candidates(span.text)
cdef class KnowledgeBase:
"""A `KnowledgeBase` instance stores unique identifiers for entities and their textual aliases,
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.
DOCS: https://spacy.io/api/kb
DOCS: https://spacy.io/api/inmemorylookupkb
"""
def __init__(self, Vocab vocab, entity_vector_length):
"""Create a KnowledgeBase."""
self.mem = Pool()
self.entity_vector_length = entity_vector_length
"""Create an InMemoryLookupKB."""
super().__init__(vocab, entity_vector_length)
self._entry_index = PreshMap()
self._alias_index = PreshMap()
self.vocab = vocab
self._create_empty_vectors(dummy_hash=self.vocab.strings[""])
def _initialize_entities(self, int64_t nr_entities):
@ -104,11 +46,6 @@ cdef class KnowledgeBase:
self._alias_index = PreshMap(nr_aliases + 1)
self._aliases_table = alias_vec(nr_aliases + 1)
@property
def entity_vector_length(self):
"""RETURNS (uint64): length of the entity vectors"""
return self.entity_vector_length
def __len__(self):
return self.get_size_entities()
@ -286,7 +223,10 @@ cdef class KnowledgeBase:
alias_entry.probs = probs
self._aliases_table[alias_index] = alias_entry
def get_alias_candidates(self, str alias) -> Iterator[Candidate]:
def get_candidates(self, mention: Span) -> Iterable[Candidate]:
return self.get_alias_candidates(mention.text) # type: ignore
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.

6
spacy/lang/ca/lemmatizer.py
View File

@ -72,10 +72,10 @@ class CatalanLemmatizer(Lemmatizer):
oov_forms.append(form)
if not forms:
forms.extend(oov_forms)
if not forms and string in lookup_table.keys():
forms.append(self.lookup_lemmatize(token)[0])
# use lookups, and fall back to the token itself
if not forms:
forms.append(string)
forms.append(lookup_table.get(string, [string])[0])
forms = list(dict.fromkeys(forms))
self.cache[cache_key] = forms
return forms

2
spacy/lang/char_classes.py
View File

@ -280,7 +280,7 @@ _currency = (
_punct = (
r"… …… , : ; \! \? ¿ ؟ ¡ \( \) \[ \] \{ \} < > _ # \* & 。 ？ ！ ， 、 ； ： ～ · । ، ۔ ؛ ٪"
)
_quotes = r'\' " ” “ ` ‘ ´ ’ ‚ , „ » « 「 」 『 』 （ ） 〔 〕 【 】 《 》 〈 〉'
_quotes = r'\' " ” “ ` ‘ ´ ’ ‚ , „ » « 「 」 『 』 （ ） 〔 〕 【 】 《 》 〈 〉 〈 〉 ⟦ ⟧'
_hyphens = "- – — -- --- —— ~"
# Various symbols like dingbats, but also emoji

13
spacy/lang/fr/lemmatizer.py
View File

@ -53,11 +53,16 @@ class FrenchLemmatizer(Lemmatizer):
rules = rules_table.get(univ_pos, [])
string = string.lower()
forms = []
# first try lookup in table based on upos
if string in index:
forms.append(string)
self.cache[cache_key] = forms
return forms
# then add anything in the exceptions table
forms.extend(exceptions.get(string, []))
# if nothing found yet, use the rules
oov_forms = []
if not forms:
for old, new in rules:
@ -69,12 +74,14 @@ class FrenchLemmatizer(Lemmatizer):
forms.append(form)
else:
oov_forms.append(form)
# if still nothing, add the oov forms from rules
if not forms:
forms.extend(oov_forms)
if not forms and string in lookup_table.keys():
forms.append(self.lookup_lemmatize(token)[0])
# use lookups, which fall back to the token itself
if not forms:
forms.append(string)
forms.append(lookup_table.get(string, [string])[0])
forms = list(dict.fromkeys(forms))
self.cache[cache_key] = forms
return forms

4
spacy/lang/grc/__init__.py
View File

@ -1,11 +1,15 @@
from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
from .stop_words import STOP_WORDS
from .lex_attrs import LEX_ATTRS
from .punctuation import TOKENIZER_PREFIXES, TOKENIZER_SUFFIXES, TOKENIZER_INFIXES
from ...language import Language, BaseDefaults
class AncientGreekDefaults(BaseDefaults):
tokenizer_exceptions = TOKENIZER_EXCEPTIONS
prefixes = TOKENIZER_PREFIXES
suffixes = TOKENIZER_SUFFIXES
infixes = TOKENIZER_INFIXES
lex_attr_getters = LEX_ATTRS
stop_words = STOP_WORDS

46
spacy/lang/grc/punctuation.py Normal file
View File

@ -0,0 +1,46 @@
from ..char_classes import LIST_PUNCT, LIST_ELLIPSES, LIST_QUOTES, LIST_CURRENCY
from ..char_classes import LIST_ICONS, ALPHA_LOWER, ALPHA_UPPER, ALPHA, HYPHENS
from ..char_classes import CONCAT_QUOTES
_prefixes = (
[
"†",
"⸏",
]
+ LIST_PUNCT
+ LIST_ELLIPSES
+ LIST_QUOTES
+ LIST_CURRENCY
+ LIST_ICONS
)
_suffixes = (
LIST_PUNCT
+ LIST_ELLIPSES
+ LIST_QUOTES
+ LIST_ICONS
+ [
"†",
"⸎",
r"(?<=[\u1F00-\u1FFF\u0370-\u03FF])[\-\.⸏]",
]
)
_infixes = (
LIST_ELLIPSES
+ LIST_ICONS
+ [
r"(?<=[0-9])[+\-\*^](?=[0-9-])",
r"(?<=[{al}{q}])\.(?=[{au}{q}])".format(
al=ALPHA_LOWER, au=ALPHA_UPPER, q=CONCAT_QUOTES
),
r"(?<=[{a}]),(?=[{a}])".format(a=ALPHA),
r"(?<=[{a}0-9])(?:{h})(?=[{a}])".format(a=ALPHA, h=HYPHENS),
r"(?<=[{a}0-9])[:<>=/](?=[{a}])".format(a=ALPHA),
r"(?<=[\u1F00-\u1FFF\u0370-\u03FF])—",
]
)
TOKENIZER_PREFIXES = _prefixes
TOKENIZER_SUFFIXES = _suffixes
TOKENIZER_INFIXES = _infixes

2
spacy/lang/nl/stop_words.py
View File

@ -15,7 +15,7 @@
STOP_WORDS = set(
"""
aan af al alle alles allebei alleen allen als altijd ander anders andere anderen aangaangde aangezien achter achterna
aan af al alle alles allebei alleen allen als altijd ander anders andere anderen aangaande aangezien achter achterna
afgelopen aldus alhoewel anderzijds
ben bij bijna bijvoorbeeld behalve beide beiden beneden bent bepaald beter betere betreffende binnen binnenin boven

2
spacy/lang/ru/__init__.py
View File

@ -28,7 +28,7 @@ class Russian(Language):
assigns=["token.lemma"],
default_config={
"model": None,
"mode": "pymorphy2",
"mode": "pymorphy3",
"overwrite": False,
"scorer": {"@scorers": "spacy.lemmatizer_scorer.v1"},
},

58
spacy/lang/ru/lemmatizer.py
View File

@ -19,33 +19,48 @@ class RussianLemmatizer(Lemmatizer):
model: Optional[Model],
name: str = "lemmatizer",
*,
mode: str = "pymorphy2",
mode: str = "pymorphy3",
overwrite: bool = False,
scorer: Optional[Callable] = lemmatizer_score,
) -> None:
if mode == "pymorphy2":
if mode in {"pymorphy2", "pymorphy2_lookup"}:
try:
from pymorphy2 import MorphAnalyzer
except ImportError:
raise ImportError(
"The Russian lemmatizer mode 'pymorphy2' requires the "
"pymorphy2 library. Install it with: pip install pymorphy2"
"The lemmatizer mode 'pymorphy2' requires the "
"pymorphy2 library and dictionaries. Install them with: "
"pip install pymorphy2"
"# for Ukrainian dictionaries:"
"pip install pymorphy2-dicts-uk"
) from None
if getattr(self, "_morph", None) is None:
self._morph = MorphAnalyzer()
self._morph = MorphAnalyzer(lang="ru")
elif mode in {"pymorphy3", "pymorphy3_lookup"}:
try:
from pymorphy3 import MorphAnalyzer
except ImportError:
raise ImportError(
"The lemmatizer mode 'pymorphy3' requires the "
"pymorphy3 library and dictionaries. Install them with: "
"pip install pymorphy3"
"# for Ukrainian dictionaries:"
"pip install pymorphy3-dicts-uk"
) from None
if getattr(self, "_morph", None) is None:
self._morph = MorphAnalyzer(lang="ru")
super().__init__(
vocab, model, name, mode=mode, overwrite=overwrite, scorer=scorer
)
def pymorphy2_lemmatize(self, token: Token) -> List[str]:
def _pymorphy_lemmatize(self, token: Token) -> List[str]:
string = token.text
univ_pos = token.pos_
morphology = token.morph.to_dict()
if univ_pos == "PUNCT":
return [PUNCT_RULES.get(string, string)]
if univ_pos not in ("ADJ", "DET", "NOUN", "NUM", "PRON", "PROPN", "VERB"):
# Skip unchangeable pos
return [string.lower()]
return self._pymorphy_lookup_lemmatize(token)
analyses = self._morph.parse(string)
filtered_analyses = []
for analysis in analyses:
@ -53,8 +68,10 @@ class RussianLemmatizer(Lemmatizer):
# Skip suggested parse variant for unknown word for pymorphy
continue
analysis_pos, _ = oc2ud(str(analysis.tag))
if analysis_pos == univ_pos or (
analysis_pos in ("NOUN", "PROPN") and univ_pos in ("NOUN", "PROPN")
if (
analysis_pos == univ_pos
or (analysis_pos in ("NOUN", "PROPN") and univ_pos in ("NOUN", "PROPN"))
or ((analysis_pos == "PRON") and (univ_pos == "DET"))
):
filtered_analyses.append(analysis)
if not len(filtered_analyses):
@ -97,13 +114,28 @@ class RussianLemmatizer(Lemmatizer):
dict.fromkeys([analysis.normal_form for analysis in filtered_analyses])
)
def pymorphy2_lookup_lemmatize(self, token: Token) -> List[str]:
def _pymorphy_lookup_lemmatize(self, token: Token) -> List[str]:
string = token.text
analyses = self._morph.parse(string)
if len(analyses) == 1:
return [analyses[0].normal_form]
# often multiple forms would derive from the same normal form
# thus check _unique_ normal forms
normal_forms = set([an.normal_form for an in analyses])
if len(normal_forms) == 1:
return [next(iter(normal_forms))]
return [string]
def pymorphy2_lemmatize(self, token: Token) -> List[str]:
return self._pymorphy_lemmatize(token)
def pymorphy2_lookup_lemmatize(self, token: Token) -> List[str]:
return self._pymorphy_lookup_lemmatize(token)
def pymorphy3_lemmatize(self, token: Token) -> List[str]:
return self._pymorphy_lemmatize(token)
def pymorphy3_lookup_lemmatize(self, token: Token) -> List[str]:
return self._pymorphy_lookup_lemmatize(token)
def oc2ud(oc_tag: str) -> Tuple[str, Dict[str, str]]:
gram_map = {

18
spacy/lang/ru/tokenizer_exceptions.py
View File

@ -61,6 +61,11 @@ for abbr in [
{ORTH: "2к23", NORM: "2023"},
{ORTH: "2к24", NORM: "2024"},
{ORTH: "2к25", NORM: "2025"},
{ORTH: "2к26", NORM: "2026"},
{ORTH: "2к27", NORM: "2027"},
{ORTH: "2к28", NORM: "2028"},
{ORTH: "2к29", NORM: "2029"},
{ORTH: "2к30", NORM: "2030"},
]:
_exc[abbr[ORTH]] = [abbr]
@ -268,8 +273,8 @@ for abbr in [
{ORTH: "з-ка", NORM: "заимка"},
{ORTH: "п-к", NORM: "починок"},
{ORTH: "киш.", NORM: "кишлак"},
{ORTH: "п. ст. ", NORM: "поселок станция"},
{ORTH: "п. ж/д ст. ", NORM: "поселок при железнодорожной станции"},
{ORTH: "п. ст.", NORM: "поселок станция"},
{ORTH: "п. ж/д ст.", NORM: "поселок при железнодорожной станции"},
{ORTH: "ж/д бл-ст", NORM: "железнодорожный блокпост"},
{ORTH: "ж/д б-ка", NORM: "железнодорожная будка"},
{ORTH: "ж/д в-ка", NORM: "железнодорожная ветка"},
@ -280,12 +285,12 @@ for abbr in [
{ORTH: "ж/д п.п.", NORM: "железнодорожный путевой пост"},
{ORTH: "ж/д о.п.", NORM: "железнодорожный остановочный пункт"},
{ORTH: "ж/д рзд.", NORM: "железнодорожный разъезд"},
{ORTH: "ж/д ст. ", NORM: "железнодорожная станция"},
{ORTH: "ж/д ст.", NORM: "железнодорожная станция"},
{ORTH: "м-ко", NORM: "местечко"},
{ORTH: "д.", NORM: "деревня"},
{ORTH: "с.", NORM: "село"},
{ORTH: "сл.", NORM: "слобода"},
{ORTH: "ст. ", NORM: "станция"},
{ORTH: "ст.", NORM: "станция"},
{ORTH: "ст-ца", NORM: "станица"},
{ORTH: "у.", NORM: "улус"},
{ORTH: "х.", NORM: "хутор"},
@ -388,8 +393,9 @@ for abbr in [
{ORTH: "прим.", NORM: "примечание"},
{ORTH: "прим.ред.", NORM: "примечание редакции"},
{ORTH: "см. также", NORM: "смотри также"},
{ORTH: "кв.м.", NORM: "квадрантный метр"},
{ORTH: "м2", NORM: "квадрантный метр"},
{ORTH: "см.", NORM: "смотри"},
{ORTH: "кв.м.", NORM: "квадратный метр"},
{ORTH: "м2", NORM: "квадратный метр"},
{ORTH: "б/у", NORM: "бывший в употреблении"},
{ORTH: "сокр.", NORM: "сокращение"},
{ORTH: "чел.", NORM: "человек"},

8
spacy/lang/sl/__init__.py
View File

@ -1,9 +1,17 @@
from .lex_attrs import LEX_ATTRS
from .punctuation import TOKENIZER_INFIXES, TOKENIZER_SUFFIXES, TOKENIZER_PREFIXES
from .stop_words import STOP_WORDS
from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS
from ...language import Language, BaseDefaults
class SlovenianDefaults(BaseDefaults):
stop_words = STOP_WORDS
tokenizer_exceptions = TOKENIZER_EXCEPTIONS
prefixes = TOKENIZER_PREFIXES
infixes = TOKENIZER_INFIXES
suffixes = TOKENIZER_SUFFIXES
lex_attr_getters = LEX_ATTRS
class Slovenian(Language):

145
spacy/lang/sl/lex_attrs.py Normal file
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@ -0,0 +1,145 @@
from ...attrs import LIKE_NUM
from ...attrs import IS_CURRENCY
import unicodedata
_num_words = set(
"""
nula ničla nič ena dva tri štiri pet šest sedem osem
devet deset enajst dvanajst trinajst štirinajst petnajst
šestnajst sedemnajst osemnajst devetnajst dvajset trideset štirideset
petdeset šestdest sedemdeset osemdeset devedeset sto tisoč
milijon bilijon trilijon kvadrilijon nešteto
en eden enega enemu ennem enim enih enima enimi ene eni eno
dveh dvema dvem dvoje trije treh trem tremi troje štirje štirih štirim štirimi
petih petim petimi šestih šestim šestimi sedmih sedmim sedmimi osmih osmim osmimi
devetih devetim devetimi desetih desetim desetimi enajstih enajstim enajstimi
dvanajstih dvanajstim dvanajstimi trinajstih trinajstim trinajstimi
šestnajstih šestnajstim šestnajstimi petnajstih petnajstim petnajstimi
sedemnajstih sedemnajstim sedemnajstimi osemnajstih osemnajstim osemnajstimi
devetnajstih devetnajstim devetnajstimi dvajsetih dvajsetim dvajsetimi
""".split()
)
_ordinal_words = set(
"""
prvi drugi tretji četrti peti šesti sedmi osmi
deveti deseti enajsti dvanajsti trinajsti štirinajsti
petnajsti šestnajsti sedemnajsti osemnajsti devetnajsti
dvajseti trideseti štirideseti petdeseti šestdeseti sedemdeseti
osemdeseti devetdeseti stoti tisoči milijonti bilijonti
trilijonti kvadrilijonti nešteti
prva druga tretja četrta peta šesta sedma osma
deveta deseta enajsta dvanajsta trinajsta štirnajsta
petnajsta šestnajsta sedemnajsta osemnajsta devetnajsta
dvajseta trideseta štirideseta petdeseta šestdeseta sedemdeseta
osemdeseta devetdeseta stota tisoča milijonta bilijonta
trilijonta kvadrilijonta nešteta
prvo drugo tretje četrto peto šestro sedmo osmo
deveto deseto enajsto dvanajsto trinajsto štirnajsto
petnajsto šestnajsto sedemnajsto osemnajsto devetnajsto
dvajseto trideseto štirideseto petdeseto šestdeseto sedemdeseto
osemdeseto devetdeseto stoto tisočo milijonto bilijonto
trilijonto kvadrilijonto nešteto
prvega drugega tretjega četrtega petega šestega sedmega osmega
devega desetega enajstega dvanajstega trinajstega štirnajstega
petnajstega šestnajstega sedemnajstega osemnajstega devetnajstega
dvajsetega tridesetega štiridesetega petdesetega šestdesetega sedemdesetega
osemdesetega devetdesetega stotega tisočega milijontega bilijontega
trilijontega kvadrilijontega neštetega
prvemu drugemu tretjemu četrtemu petemu šestemu sedmemu osmemu devetemu desetemu
enajstemu dvanajstemu trinajstemu štirnajstemu petnajstemu šestnajstemu sedemnajstemu
osemnajstemu devetnajstemu dvajsetemu tridesetemu štiridesetemu petdesetemu šestdesetemu
sedemdesetemu osemdesetemu devetdesetemu stotemu tisočemu milijontemu bilijontemu
trilijontemu kvadrilijontemu neštetemu
prvem drugem tretjem četrtem petem šestem sedmem osmem devetem desetem
enajstem dvanajstem trinajstem štirnajstem petnajstem šestnajstem sedemnajstem
osemnajstem devetnajstem dvajsetem tridesetem štiridesetem petdesetem šestdesetem
sedemdesetem osemdesetem devetdesetem stotem tisočem milijontem bilijontem
trilijontem kvadrilijontem neštetem
prvim drugim tretjim četrtim petim šestim sedtim osmim devetim desetim
enajstim dvanajstim trinajstim štirnajstim petnajstim šestnajstim sedemnajstim
osemnajstim devetnajstim dvajsetim tridesetim štiridesetim petdesetim šestdesetim
sedemdesetim osemdesetim devetdesetim stotim tisočim milijontim bilijontim
trilijontim kvadrilijontim neštetim
prvih drugih tretjih četrthih petih šestih sedmih osmih deveth desetih
enajstih dvanajstih trinajstih štirnajstih petnajstih šestnajstih sedemnajstih
osemnajstih devetnajstih dvajsetih tridesetih štiridesetih petdesetih šestdesetih
sedemdesetih osemdesetih devetdesetih stotih tisočih milijontih bilijontih
trilijontih kvadrilijontih nešteth
prvima drugima tretjima četrtima petima šestima sedmima osmima devetima desetima
enajstima dvanajstima trinajstima štirnajstima petnajstima šestnajstima sedemnajstima
osemnajstima devetnajstima dvajsetima tridesetima štiridesetima petdesetima šestdesetima
sedemdesetima osemdesetima devetdesetima stotima tisočima milijontima bilijontima
trilijontima kvadrilijontima neštetima
prve druge četrte pete šeste sedme osme devete desete
enajste dvanajste trinajste štirnajste petnajste šestnajste sedemnajste
osemnajste devetnajste dvajsete tridesete štiridesete petdesete šestdesete
sedemdesete osemdesete devetdesete stote tisoče milijonte bilijonte
trilijonte kvadrilijonte neštete
prvimi drugimi tretjimi četrtimi petimi šestimi sedtimi osmimi devetimi desetimi
enajstimi dvanajstimi trinajstimi štirnajstimi petnajstimi šestnajstimi sedemnajstimi
osemnajstimi devetnajstimi dvajsetimi tridesetimi štiridesetimi petdesetimi šestdesetimi
sedemdesetimi osemdesetimi devetdesetimi stotimi tisočimi milijontimi bilijontimi
trilijontimi kvadrilijontimi neštetimi
""".split()
)
_currency_words = set(
"""
evro evra evru evrom evrov evroma evrih evrom evre evri evr eur
cent centa centu cenom centov centoma centih centom cente centi
dolar dolarja dolarji dolarju dolarjem dolarjev dolarjema dolarjih dolarje usd
tolar tolarja tolarji tolarju tolarjem tolarjev tolarjema tolarjih tolarje tol
dinar dinarja dinarji dinarju dinarjem dinarjev dinarjema dinarjih dinarje din
funt funta funti funtu funtom funtov funtoma funtih funte gpb
forint forinta forinti forintu forintom forintov forintoma forintih forinte
zlot zlota zloti zlotu zlotom zlotov zlotoma zlotih zlote
rupij rupija rupiji rupiju rupijem rupijev rupijema rupijih rupije
jen jena jeni jenu jenom jenov jenoma jenih jene
kuna kuni kune kuno kun kunama kunah kunam kunami
marka marki marke markama markah markami
""".split()
)
def like_num(text):
if text.startswith(("+", "-", "±", "~")):
text = text[1:]
text = text.replace(",", "").replace(".", "")
if text.isdigit():
return True
if text.count("/") == 1:
num, denom = text.split("/")
if num.isdigit() and denom.isdigit():
return True
text_lower = text.lower()
if text_lower in _num_words:
return True
if text_lower in _ordinal_words:
return True
return False
def is_currency(text):
text_lower = text.lower()
if text in _currency_words:
return True
for char in text:
if unicodedata.category(char) != "Sc":
return False
return True
LEX_ATTRS = {LIKE_NUM: like_num, IS_CURRENCY: is_currency}

84
spacy/lang/sl/punctuation.py Normal file
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@ -0,0 +1,84 @@
from ..char_classes import (
LIST_ELLIPSES,
LIST_ICONS,
HYPHENS,
LIST_PUNCT,
LIST_QUOTES,
CURRENCY,
UNITS,
PUNCT,
LIST_CURRENCY,
CONCAT_QUOTES,
)
from ..char_classes import CONCAT_QUOTES, ALPHA_LOWER, ALPHA_UPPER, ALPHA
from ..char_classes import merge_chars
from ..punctuation import TOKENIZER_PREFIXES as BASE_TOKENIZER_PREFIXES
INCLUDE_SPECIAL = ["\\+", "\\/", "\\•", "\\¯", "\\=", "\\×"] + HYPHENS.split("|")
_prefixes = INCLUDE_SPECIAL + BASE_TOKENIZER_PREFIXES
_suffixes = (
INCLUDE_SPECIAL
+ LIST_PUNCT
+ LIST_ELLIPSES
+ LIST_QUOTES
+ LIST_ICONS
+ [
r"(?<=°[FfCcKk])\.",
r"(?<=[0-9])(?:{c})".format(c=CURRENCY),
r"(?<=[0-9])(?:{u})".format(u=UNITS),
r"(?<=[{al}{e}{p}(?:{q})])\.".format(
al=ALPHA_LOWER, e=r"%²\-\+", q=CONCAT_QUOTES, p=PUNCT
),
r"(?<=[{au}][{au}])\.".format(au=ALPHA_UPPER),
# split initials like J.K. Rowling
r"(?<=[A-Z]\.)(?:[A-Z].)",
]
)
# a list of all suffixes following a hyphen that are shouldn't split (eg. BTC-jev)
# source: Obeliks tokenizer - https://github.com/clarinsi/obeliks/blob/master/obeliks/res/TokRulesPart1.txt
CONCAT_QUOTES = CONCAT_QUOTES.replace("'", "")
HYPHENS_PERMITTED = (
"((a)|(evemu)|(evskega)|(i)|(jevega)|(jevska)|(jevskimi)|(jinemu)|(oma)|(ovim)|"
"(ovski)|(e)|(evi)|(evskem)|(ih)|(jevem)|(jevske)|(jevsko)|(jini)|(ov)|(ovima)|"
"(ovskih)|(em)|(evih)|(evskemu)|(ja)|(jevemu)|(jevskega)|(ji)|(jinih)|(ova)|"
"(ovimi)|(ovskim)|(ema)|(evim)|(evski)|(je)|(jevi)|(jevskem)|(jih)|(jinim)|"
"(ove)|(ovo)|(ovskima)|(ev)|(evima)|(evskih)|(jem)|(jevih)|(jevskemu)|(jin)|"
"(jinima)|(ovega)|(ovska)|(ovskimi)|(eva)|(evimi)|(evskim)|(jema)|(jevim)|"
"(jevski)|(jina)|(jinimi)|(ovem)|(ovske)|(ovsko)|(eve)|(evo)|(evskima)|(jev)|"
"(jevima)|(jevskih)|(jine)|(jino)|(ovemu)|(ovskega)|(u)|(evega)|(evska)|"
"(evskimi)|(jeva)|(jevimi)|(jevskim)|(jinega)|(ju)|(ovi)|(ovskem)|(evem)|"
"(evske)|(evsko)|(jeve)|(jevo)|(jevskima)|(jinem)|(om)|(ovih)|(ovskemu)|"
"(ovec)|(ovca)|(ovcu)|(ovcem)|(ovcev)|(ovcema)|(ovcih)|(ovci)|(ovce)|(ovcimi)|"
"(evec)|(evca)|(evcu)|(evcem)|(evcev)|(evcema)|(evcih)|(evci)|(evce)|(evcimi)|"
"(jevec)|(jevca)|(jevcu)|(jevcem)|(jevcev)|(jevcema)|(jevcih)|(jevci)|(jevce)|"
"(jevcimi)|(ovka)|(ovke)|(ovki)|(ovko)|(ovk)|(ovkama)|(ovkah)|(ovkam)|(ovkami)|"
"(evka)|(evke)|(evki)|(evko)|(evk)|(evkama)|(evkah)|(evkam)|(evkami)|(jevka)|"
"(jevke)|(jevki)|(jevko)|(jevk)|(jevkama)|(jevkah)|(jevkam)|(jevkami)|(timi)|"
"(im)|(ima)|(a)|(imi)|(e)|(o)|(ega)|(ti)|(em)|(tih)|(emu)|(tim)|(i)|(tima)|"
"(ih)|(ta)|(te)|(to)|(tega)|(tem)|(temu))"
)
_infixes = (
LIST_ELLIPSES
+ LIST_ICONS
+ [
r"(?<=[0-9])[+\-\*^](?=[0-9-])",
r"(?<=[{al}{q}])\.(?=[{au}{q}])".format(
al=ALPHA_LOWER, au=ALPHA_UPPER, q=CONCAT_QUOTES
),
r"(?<=[{a}]),(?=[{a}])".format(a=ALPHA),
r"(?<=[{a}0-9])(?:{h})(?!{hp}$)(?=[{a}])".format(
a=ALPHA, h=HYPHENS, hp=HYPHENS_PERMITTED
),
r"(?<=[{a}0-9])[:<>=/](?=[{a}])".format(a=ALPHA),
]
)
TOKENIZER_PREFIXES = _prefixes
TOKENIZER_SUFFIXES = _suffixes
TOKENIZER_INFIXES = _infixes

394
spacy/lang/sl/stop_words.py
View File

@ -1,326 +1,84 @@
# Source: https://github.com/stopwords-iso/stopwords-sl
# Removed various words that are not normally considered stop words, such as months.
STOP_WORDS = set(
"""
a
ali
b
bi
bil
bila
bile
bili
bilo
biti
blizu
bo
bodo
bolj
bom
bomo
boste
bova
boš
brez
c
cel
cela
celi
celo
d
da
daleč
dan
danes
do
dober
dobra
dobri
dobro
dokler
dol
dovolj
e
eden
en
ena
ene
eni
enkrat
eno
etc.
a ali
b bi bil bila bile bili bilo biti blizu bo bodo bojo bolj bom bomo
boste bova boš brez
c cel cela celi celo
č če često četrta četrtek četrti četrto čez čigav
d da daleč dan danes datum deset deseta deseti deseto devet
deveta deveti deveto do dober dobra dobri dobro dokler dol dolg
dolga dolgi dovolj drug druga drugi drugo dva dve
e eden en ena ene eni enkrat eno etc.
f
g
g.
ga
ga.
gor
gospa
gospod
h
halo
i
idr.
ii
iii
in
iv
ix
iz
j
jaz
je
ji
jih
jim
jo
k
kadarkoli
kaj
kajti
kako
kakor
kamor
kamorkoli
kar
karkoli
katerikoli
kdaj
kdo
kdorkoli
ker
ki
kje
kjer
kjerkoli
ko
koderkoli
koga
komu
kot
l
le
lep
lepa
lepe
lepi
lepo
m
manj
me
med
medtem
mene
mi
midva
midve
mnogo
moj
moja
moje
mora
morajo
moram
moramo
morate
moraš
morem
mu
n
na
nad
naj
najina
najino
najmanj
naju
največ
nam
nas
nato
nazaj
naš
naša
naše
ne
nedavno
nek
neka
nekaj
nekatere
nekateri
nekatero
nekdo
neke
nekega
neki
nekje
neko
nekoga
nekoč
ni
nikamor
nikdar
nikjer
nikoli
nič
nje
njega
njegov
njegova
njegovo
njej
njemu
njen
njena
njeno
nji
njih
njihov
njihova
njihovo
njiju
njim
njo
njun
njuna
njuno
no
nocoj
npr.
o
ob
oba
obe
oboje
od
okoli
on
onadva
one
oni
onidve
oz.
p
pa
po
pod
pogosto
poleg
ponavadi
ponovno
potem
povsod
prbl.
precej
pred
prej
preko
pri
pribl.
približno
proti
r
redko
res
s
saj
sam
sama
same
sami
samo
se
sebe
sebi
sedaj
sem
seveda
si
sicer
skoraj
skozi
smo
so
spet
sta
ste
sva
t
ta
tak
taka
take
taki
tako
takoj
tam
te
tebe
tebi
tega
ti
tista
tiste
tisti
tisto
tj.
tja
to
toda
tu
tudi
tukaj
tvoj
tvoja
tvoje
g g. ga ga. gor gospa gospod
h halo
i idr. ii iii in iv ix iz
j jaz je ji jih jim jo jutri
k kadarkoli kaj kajti kako kakor kamor kamorkoli kar karkoli
katerikoli kdaj kdo kdorkoli ker ki kje kjer kjerkoli
ko koder koderkoli koga komu kot kratek kratka kratke kratki
l lahka lahke lahki lahko le lep lepa lepe lepi lepo leto
m majhen majhna majhni malce malo manj me med medtem mene
mesec mi midva midve mnogo moj moja moje mora morajo moram
moramo morate moraš morem mu
n na nad naj najina najino najmanj naju največ nam narobe
nas nato nazaj naš naša naše ne nedavno nedelja nek neka
nekaj nekatere nekateri nekatero nekdo neke nekega neki
nekje neko nekoga nekoč ni nikamor nikdar nikjer nikoli
nič nje njega njegov njegova njegovo njej njemu njen
njena njeno nji njih njihov njihova njihovo njiju njim
njo njun njuna njuno no nocoj npr.
o ob oba obe oboje od odprt odprta odprti okoli on
onadva one oni onidve osem osma osmi osmo oz.
p pa pet peta petek peti peto po pod pogosto poleg poln
polna polni polno ponavadi ponedeljek ponovno potem
povsod pozdravljen pozdravljeni prav prava prave pravi
pravo prazen prazna prazno prbl. precej pred prej preko
pri pribl. približno primer pripravljen pripravljena
pripravljeni proti prva prvi prvo
r ravno redko res reč
s saj sam sama same sami samo se sebe sebi sedaj sedem
sedma sedmi sedmo sem seveda si sicer skoraj skozi slab sm
so sobota spet sreda srednja srednji sta ste stran stvar sva
š šest šesta šesti šesto štiri
t ta tak taka take taki tako takoj tam te tebe tebi tega
težak težka težki težko ti tista tiste tisti tisto tj.
tja to toda torek tretja tretje tretji tri tu tudi tukaj
tvoj tvoja tvoje
u
v
vaju
vam
vas
vaš
vaša
vaše
ve
vedno
vendar
ves
več
vi
vidva
vii
viii
vsa
vsaj
vsak
vsaka
vsakdo
vsake
vsaki
vsakomur
vse
vsega
vsi
vso
včasih
x
z
za
zadaj
zadnji
zakaj
zdaj
zelo
zunaj
č
če
često
čez
čigav
š
ž
že
v vaju vam vas vaš vaša vaše ve vedno velik velika veliki
veliko vendar ves več vi vidva vii viii visok visoka visoke
visoki vsa vsaj vsak vsaka vsakdo vsake vsaki vsakomur vse
vsega vsi vso včasih včeraj
x
z za zadaj zadnji zakaj zaprta zaprti zaprto zdaj zelo zunaj
ž že
""".split()
)

272
spacy/lang/sl/tokenizer_exceptions.py Normal file
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@ -0,0 +1,272 @@
from typing import Dict, List
from ..tokenizer_exceptions import BASE_EXCEPTIONS
from ...symbols import ORTH, NORM
from ...util import update_exc
_exc: Dict[str, List[Dict]] = {}
_other_exc = {
"t.i.": [{ORTH: "t.", NORM: "tako"}, {ORTH: "i.", NORM: "imenovano"}],
"t.j.": [{ORTH: "t.", NORM: "to"}, {ORTH: "j.", NORM: "je"}],
"T.j.": [{ORTH: "T.", NORM: "to"}, {ORTH: "j.", NORM: "je"}],
"d.o.o.": [
{ORTH: "d.", NORM: "družba"},
{ORTH: "o.", NORM: "omejeno"},
{ORTH: "o.", NORM: "odgovornostjo"},
],
"D.O.O.": [
{ORTH: "D.", NORM: "družba"},
{ORTH: "O.", NORM: "omejeno"},
{ORTH: "O.", NORM: "odgovornostjo"},
],
"d.n.o.": [
{ORTH: "d.", NORM: "družba"},
{ORTH: "n.", NORM: "neomejeno"},
{ORTH: "o.", NORM: "odgovornostjo"},
],
"D.N.O.": [
{ORTH: "D.", NORM: "družba"},
{ORTH: "N.", NORM: "neomejeno"},
{ORTH: "O.", NORM: "odgovornostjo"},
],
"d.d.": [{ORTH: "d.", NORM: "delniška"}, {ORTH: "d.", NORM: "družba"}],
"D.D.": [{ORTH: "D.", NORM: "delniška"}, {ORTH: "D.", NORM: "družba"}],
"s.p.": [{ORTH: "s.", NORM: "samostojni"}, {ORTH: "p.", NORM: "podjetnik"}],
"S.P.": [{ORTH: "S.", NORM: "samostojni"}, {ORTH: "P.", NORM: "podjetnik"}],
"l.r.": [{ORTH: "l.", NORM: "lastno"}, {ORTH: "r.", NORM: "ročno"}],
"le-te": [{ORTH: "le"}, {ORTH: "-"}, {ORTH: "te"}],
"Le-te": [{ORTH: "Le"}, {ORTH: "-"}, {ORTH: "te"}],
"le-ti": [{ORTH: "le"}, {ORTH: "-"}, {ORTH: "ti"}],
"Le-ti": [{ORTH: "Le"}, {ORTH: "-"}, {ORTH: "ti"}],
"le-to": [{ORTH: "le"}, {ORTH: "-"}, {ORTH: "to"}],
"Le-to": [{ORTH: "Le"}, {ORTH: "-"}, {ORTH: "to"}],
"le-ta": [{ORTH: "le"}, {ORTH: "-"}, {ORTH: "ta"}],
"Le-ta": [{ORTH: "Le"}, {ORTH: "-"}, {ORTH: "ta"}],
"le-tega": [{ORTH: "le"}, {ORTH: "-"}, {ORTH: "tega"}],
"Le-tega": [{ORTH: "Le"}, {ORTH: "-"}, {ORTH: "tega"}],
}
_exc.update(_other_exc)
for exc_data in [
{ORTH: "adm.", NORM: "administracija"},
{ORTH: "aer.", NORM: "aeronavtika"},
{ORTH: "agr.", NORM: "agronomija"},
{ORTH: "amer.", NORM: "ameriško"},
{ORTH: "anat.", NORM: "anatomija"},
{ORTH: "angl.", NORM: "angleški"},
{ORTH: "ant.", NORM: "antonim"},
{ORTH: "antr.", NORM: "antropologija"},
{ORTH: "apr.", NORM: "april"},
{ORTH: "arab.", NORM: "arabsko"},
{ORTH: "arheol.", NORM: "arheologija"},
{ORTH: "arhit.", NORM: "arhitektura"},
{ORTH: "avg.", NORM: "avgust"},
{ORTH: "avstr.", NORM: "avstrijsko"},
{ORTH: "avt.", NORM: "avtomobilizem"},
{ORTH: "bibl.", NORM: "biblijsko"},
{ORTH: "biokem.", NORM: "biokemija"},
{ORTH: "biol.", NORM: "biologija"},
{ORTH: "bolg.", NORM: "bolgarski"},
{ORTH: "bot.", NORM: "botanika"},
{ORTH: "cit.", NORM: "citat"},
{ORTH: "daj.", NORM: "dajalnik"},
{ORTH: "del.", NORM: "deležnik"},
{ORTH: "ed.", NORM: "ednina"},
{ORTH: "etn.", NORM: "etnografija"},
{ORTH: "farm.", NORM: "farmacija"},
{ORTH: "filat.", NORM: "filatelija"},
{ORTH: "filoz.", NORM: "filozofija"},
{ORTH: "fin.", NORM: "finančništvo"},
{ORTH: "fiz.", NORM: "fizika"},
{ORTH: "fot.", NORM: "fotografija"},
{ORTH: "fr.", NORM: "francoski"},
{ORTH: "friz.", NORM: "frizerstvo"},
{ORTH: "gastr.", NORM: "gastronomija"},
{ORTH: "geogr.", NORM: "geografija"},
{ORTH: "geol.", NORM: "geologija"},
{ORTH: "geom.", NORM: "geometrija"},
{ORTH: "germ.", NORM: "germanski"},
{ORTH: "gl.", NORM: "glej"},
{ORTH: "glag.", NORM: "glagolski"},
{ORTH: "glasb.", NORM: "glasba"},
{ORTH: "gled.", NORM: "gledališče"},
{ORTH: "gost.", NORM: "gostinstvo"},
{ORTH: "gozd.", NORM: "gozdarstvo"},
{ORTH: "gr.", NORM: "grški"},
{ORTH: "grad.", NORM: "gradbeništvo"},
{ORTH: "hebr.", NORM: "hebrejsko"},
{ORTH: "hrv.", NORM: "hrvaško"},
{ORTH: "ide.", NORM: "indoevropsko"},
{ORTH: "igr.", NORM: "igre"},
{ORTH: "im.", NORM: "imenovalnik"},
{ORTH: "iron.", NORM: "ironično"},
{ORTH: "it.", NORM: "italijanski"},
{ORTH: "itd.", NORM: "in tako dalje"},
{ORTH: "itn.", NORM: "in tako naprej"},
{ORTH: "ipd.", NORM: "in podobno"},
{ORTH: "jap.", NORM: "japonsko"},
{ORTH: "jul.", NORM: "julij"},
{ORTH: "jun.", NORM: "junij"},
{ORTH: "kit.", NORM: "kitajsko"},
{ORTH: "knj.", NORM: "knjižno"},
{ORTH: "knjiž.", NORM: "knjižno"},
{ORTH: "kor.", NORM: "koreografija"},
{ORTH: "lat.", NORM: "latinski"},
{ORTH: "les.", NORM: "lesna stroka"},
{ORTH: "lingv.", NORM: "lingvistika"},
{ORTH: "lit.", NORM: "literarni"},
{ORTH: "ljubk.", NORM: "ljubkovalno"},
{ORTH: "lov.", NORM: "lovstvo"},
{ORTH: "m.", NORM: "moški"},
{ORTH: "mak.", NORM: "makedonski"},
{ORTH: "mar.", NORM: "marec"},
{ORTH: "mat.", NORM: "matematika"},
{ORTH: "med.", NORM: "medicina"},
{ORTH: "meh.", NORM: "mehiško"},
{ORTH: "mest.", NORM: "mestnik"},
{ORTH: "mdr.", NORM: "med drugim"},
{ORTH: "min.", NORM: "mineralogija"},
{ORTH: "mitol.", NORM: "mitologija"},
{ORTH: "mn.", NORM: "množina"},
{ORTH: "mont.", NORM: "montanistika"},
{ORTH: "muz.", NORM: "muzikologija"},
{ORTH: "nam.", NORM: "namenilnik"},
{ORTH: "nar.", NORM: "narečno"},
{ORTH: "nav.", NORM: "navadno"},
{ORTH: "nedol.", NORM: "nedoločnik"},
{ORTH: "nedov.", NORM: "nedovršni"},
{ORTH: "neprav.", NORM: "nepravilno"},
{ORTH: "nepreh.", NORM: "neprehodno"},
{ORTH: "neskl.", NORM: "nesklonljiv(o)"},
{ORTH: "nestrok.", NORM: "nestrokovno"},
{ORTH: "num.", NORM: "numizmatika"},
{ORTH: "npr.", NORM: "na primer"},
{ORTH: "obrt.", NORM: "obrtništvo"},
{ORTH: "okt.", NORM: "oktober"},
{ORTH: "or.", NORM: "orodnik"},
{ORTH: "os.", NORM: "oseba"},
{ORTH: "otr.", NORM: "otroško"},
{ORTH: "oz.", NORM: "oziroma"},
{ORTH: "pal.", NORM: "paleontologija"},
{ORTH: "papir.", NORM: "papirništvo"},
{ORTH: "ped.", NORM: "pedagogika"},
{ORTH: "pisar.", NORM: "pisarniško"},
{ORTH: "pog.", NORM: "pogovorno"},
{ORTH: "polit.", NORM: "politika"},
{ORTH: "polj.", NORM: "poljsko"},
{ORTH: "poljud.", NORM: "poljudno"},
{ORTH: "preg.", NORM: "pregovor"},
{ORTH: "preh.", NORM: "prehodno"},
{ORTH: "pren.", NORM: "preneseno"},
{ORTH: "prid.", NORM: "pridevnik"},
{ORTH: "prim.", NORM: "primerjaj"},
{ORTH: "prisl.", NORM: "prislov"},
{ORTH: "psih.", NORM: "psihologija"},
{ORTH: "psiht.", NORM: "psihiatrija"},
{ORTH: "rad.", NORM: "radiotehnika"},
{ORTH: "rač.", NORM: "računalništvo"},
{ORTH: "rib.", NORM: "ribištvo"},
{ORTH: "rod.", NORM: "rodilnik"},
{ORTH: "rus.", NORM: "rusko"},
{ORTH: "s.", NORM: "srednji"},
{ORTH: "sam.", NORM: "samostalniški"},
{ORTH: "sed.", NORM: "sedanjik"},
{ORTH: "sep.", NORM: "september"},
{ORTH: "slabš.", NORM: "slabšalno"},
{ORTH: "slovan.", NORM: "slovansko"},
{ORTH: "slovaš.", NORM: "slovaško"},
{ORTH: "srb.", NORM: "srbsko"},
{ORTH: "star.", NORM: "starinsko"},
{ORTH: "stil.", NORM: "stilno"},
{ORTH: "sv.", NORM: "svet(i)"},
{ORTH: "teh.", NORM: "tehnika"},
{ORTH: "tisk.", NORM: "tiskarstvo"},
{ORTH: "tj.", NORM: "to je"},
{ORTH: "tož.", NORM: "tožilnik"},
{ORTH: "trg.", NORM: "trgovina"},
{ORTH: "ukr.", NORM: "ukrajinski"},
{ORTH: "um.", NORM: "umetnost"},
{ORTH: "vel.", NORM: "velelnik"},
{ORTH: "vet.", NORM: "veterina"},
{ORTH: "vez.", NORM: "veznik"},
{ORTH: "vn.", NORM: "visokonemško"},
{ORTH: "voj.", NORM: "vojska"},
{ORTH: "vrtn.", NORM: "vrtnarstvo"},
{ORTH: "vulg.", NORM: "vulgarno"},
{ORTH: "vznes.", NORM: "vzneseno"},
{ORTH: "zal.", NORM: "založništvo"},
{ORTH: "zastar.", NORM: "zastarelo"},
{ORTH: "zgod.", NORM: "zgodovina"},
{ORTH: "zool.", NORM: "zoologija"},
{ORTH: "čeb.", NORM: "čebelarstvo"},
{ORTH: "češ.", NORM: "češki"},
{ORTH: "člov.", NORM: "človeškost"},
{ORTH: "šah.", NORM: "šahovski"},
{ORTH: "šalj.", NORM: "šaljivo"},
{ORTH: "šp.", NORM: "španski"},
{ORTH: "špan.", NORM: "špansko"},
{ORTH: "šport.", NORM: "športni"},
{ORTH: "štev.", NORM: "števnik"},
{ORTH: "šved.", NORM: "švedsko"},
{ORTH: "švic.", NORM: "švicarsko"},
{ORTH: "ž.", NORM: "ženski"},
{ORTH: "žarg.", NORM: "žargonsko"},
{ORTH: "žel.", NORM: "železnica"},
{ORTH: "živ.", NORM: "živost"},
]:
_exc[exc_data[ORTH]] = [exc_data]
abbrv = """
Co. Ch. DIPL. DR. Dr. Ev. Inc. Jr. Kr. Mag. M. MR. Mr. Mt. Murr. Npr. OZ.
Opr. Osn. Prim. Roj. ST. Sim. Sp. Sred. St. Sv. Škofl. Tel. UR. Zb.
a. aa. ab. abc. abit. abl. abs. abt. acc. accel. add. adj. adv. aet. afr. akad. al. alban. all. alleg.
alp. alt. alter. alžir. am. an. andr. ang. anh. anon. ans. antrop. apoc. app. approx. apt. ar. arc. arch.
arh. arr. as. asist. assist. assoc. asst. astr. attn. aug. avstral. az. b. bab. bal. bbl. bd. belg. bioinf.
biomed. bk. bl. bn. borg. bp. br. braz. brit. bros. broš. bt. bu. c. ca. cal. can. cand. cantab. cap. capt.
cat. cath. cc. cca. cd. cdr. cdre. cent. cerkv. cert. cf. cfr. ch. chap. chem. chr. chs. cic. circ. civ. cl.
cm. cmd. cnr. co. cod. col. coll. colo. com. comp. con. conc. cond. conn. cons. cont. coop. corr. cost. cp.
cpl. cr. crd. cres. cresc. ct. cu. d. dan. dat. davč. ddr. dec. ded. def. dem. dent. dept. dia. dip. dipl.
dir. disp. diss. div. do. doc. dok. dol. doo. dop. dott. dr. dram. druž. družb. drž. dt. duh. dur. dvr. dwt. e.
ea. ecc. eccl. eccles. econ. edn. egipt. egr. ekon. eksp. el. em. enc. eng. eo. ep. err. esp. esq. est.
et. etc. etnogr. etnol. ev. evfem. evr. ex. exc. excl. exp. expl. ext. exx. f. fa. facs. fak. faks. fas.
fasc. fco. fcp. feb. febr. fec. fed. fem. ff. fff. fid. fig. fil. film. fiziol. fiziot. flam. fm. fo. fol. folk.
frag. fran. franc. fsc. g. ga. gal. gdč. ge. gen. geod. geog. geotehnol. gg. gimn. glas. glav. gnr. go. gor.
gosp. gp. graf. gram. gren. grš. gs. h. hab. hf. hist. ho. hort. i. ia. ib. ibid. id. idr. idridr. ill. imen.
imp. impf. impr. in. inc. incl. ind. indus. inf. inform. ing. init. ins. int. inv. inšp. inštr. inž. is. islam.
ist. ital. iur. iz. izbr. izd. izg. izgr. izr. izv. j. jak. jam. jan. jav. je. jez. jr. jsl. jud. jug.
jugoslovan. jur. juž. jv. jz. k. kal. kan. kand. kat. kdo. kem. kip. kmet. kol. kom. komp. konf. kont. kost. kov.
kp. kpfw. kr. kraj. krat. kub. kult. kv. kval. l. la. lab. lb. ld. let. lib. lik. litt. lj. ljud. ll. loc. log.
loč. lt. ma. madž. mag. manag. manjš. masc. mass. mater. max. maxmax. mb. md. mech. medic. medij. medn.
mehč. mem. menedž. mes. mess. metal. meteor. meteorol. mex. mi. mikr. mil. minn. mio. misc. miss. mit. mk.
mkt. ml. mlad. mlle. mlr. mm. mme. množ. mo. moj. moš. možn. mr. mrd. mrs. ms. msc. msgr. mt. murr. mus. mut.
n. na. nad. nadalj. nadom. nagl. nakl. namer. nan. naniz. nasl. nat. navt. nač. ned. nem. nik. nizoz. nm. nn.
no. nom. norv. notr. nov. novogr. ns. o. ob. obd. obj. oblač. obl. oblik. obr. obraz. obs. obst. obt. obč. oc.
oct. od. odd. odg. odn. odst. odv. oec. off. ok. okla. okr. ont. oo. op. opis. opp. opr. orch. ord. ore. oreg.
org. orient. orig. ork. ort. oseb. osn. ot. ozir. ošk. p. pag. par. para. parc. parl. part. past. pat. pdk.
pen. perf. pert. perz. pesn. pet. pev. pf. pfc. ph. pharm. phil. pis. pl. po. pod. podr. podaljš. pogl. pogoj. pojm.
pok. pokr. pol. poljed. poljub. polu. pom. pomen. pon. ponov. pop. por. port. pos. posl. posn. pov. pp. ppl. pr.
praet. prav. pravopis. pravosl. preb. pred. predl. predm. predp. preds. pref. pregib. prel. prem. premen. prep.
pres. pret. prev. pribl. prih. pril. primerj. primor. prip. pripor. prir. prist. priv. proc. prof. prog. proiz.
prom. pron. prop. prot. protest. prov. ps. pss. pt. publ. pz. q. qld. qu. quad. que. r. racc. rastl. razgl.
razl. razv. rd. red. ref. reg. rel. relig. rep. repr. rer. resp. rest. ret. rev. revol. rež. rim. rist. rkp. rm.
roj. rom. romun. rp. rr. rt. rud. ruš. ry. sal. samogl. san. sc. scen. sci. scr. sdv. seg. sek. sen. sept. ser.
sev. sg. sgt. sh. sig. sigg. sign. sim. sin. sing. sinh. skand. skl. sklad. sklanj. sklep. skr. sl. slik. slov.
slovak. slovn. sn. so. sob. soc. sociol. sod. sopomen. sopr. sor. sov. sovj. sp. spec. spl. spr. spreg. sq. sr.
sre. sred. sredoz. srh. ss. ssp. st. sta. stan. stanstar. stcsl. ste. stim. stol. stom. str. stroj. strok. stsl.
stud. sup. supl. suppl. svet. sz. t. tab. tech. ted. tehn. tehnol. tek. teks. tekst. tel. temp. ten. teol. ter.
term. test. th. theol. tim. tip. tisočl. tit. tl. tol. tolmač. tom. tor. tov. tr. trad. traj. trans. tren.
trib. tril. trop. trp. trž. ts. tt. tu. tur. turiz. tvor. tvorb. tč. u. ul. umet. un. univ. up. upr. ur. urad.
us. ust. utr. v. va. val. var. varn. ven. ver. verb. vest. vezal. vic. vis. viv. viz. viš. vod. vok. vol. vpr.
vrst. vrstil. vs. vv. vzd. vzg. vzh. vzor. w. wed. wg. wk. x. y. z. zah. zaim. zak. zap. zasl. zavar. zač. zb.
združ. zg. zn. znan. znanstv. zoot. zun. zv. zvd. á. é. ć. č. čas. čet. čl. člen. čustv. đ. ľ. ł. ş. ŠT. š. šir.
škofl. škot. šol. št. števil. štud. ů. ű. žen. žival.
""".split()
for orth in abbrv:
_exc[orth] = [{ORTH: orth}]
TOKENIZER_EXCEPTIONS = update_exc(BASE_EXCEPTIONS, _exc)

2
spacy/lang/uk/__init__.py
View File

@ -29,7 +29,7 @@ class Ukrainian(Language):
assigns=["token.lemma"],
default_config={
"model": None,
"mode": "pymorphy2",
"mode": "pymorphy3",
"overwrite": False,
"scorer": {"@scorers": "spacy.lemmatizer_scorer.v1"},
},

15
spacy/lang/uk/lemmatizer.py
View File

@ -14,11 +14,11 @@ class UkrainianLemmatizer(RussianLemmatizer):
model: Optional[Model],
name: str = "lemmatizer",
*,
mode: str = "pymorphy2",
mode: str = "pymorphy3",
overwrite: bool = False,
scorer: Optional[Callable] = lemmatizer_score,
) -> None:
if mode == "pymorphy2":
if mode in {"pymorphy2", "pymorphy2_lookup"}:
try:
from pymorphy2 import MorphAnalyzer
except ImportError:
@ -29,6 +29,17 @@ class UkrainianLemmatizer(RussianLemmatizer):
) from None
if getattr(self, "_morph", None) is None:
self._morph = MorphAnalyzer(lang="uk")
elif mode in {"pymorphy3", "pymorphy3_lookup"}:
try:
from pymorphy3 import MorphAnalyzer
except ImportError:
raise ImportError(
"The Ukrainian lemmatizer mode 'pymorphy3' requires the "
"pymorphy3 library and dictionaries. Install them with: "
"pip install pymorphy3 pymorphy3-dicts-uk"
) from None
if getattr(self, "_morph", None) is None:
self._morph = MorphAnalyzer(lang="uk")
super().__init__(
vocab, model, name, mode=mode, overwrite=overwrite, scorer=scorer
)

116
spacy/language.py
View File

@ -43,8 +43,7 @@ from .lookups import load_lookups
from .compat import Literal
if TYPE_CHECKING:
from .pipeline import Pipe # noqa: F401
PipeCallable = Callable[[Doc], Doc]
# This is the base config will all settings (training etc.)
@ -181,7 +180,7 @@ class Language:
self.vocab: Vocab = vocab
if self.lang is None:
self.lang = self.vocab.lang
self._components: List[Tuple[str, "Pipe"]] = []
self._components: List[Tuple[str, PipeCallable]] = []
self._disabled: Set[str] = set()
self.max_length = max_length
# Create the default tokenizer from the default config
@ -303,7 +302,7 @@ class Language:
return SimpleFrozenList(names)
@property
def components(self) -> List[Tuple[str, "Pipe"]]:
def components(self) -> List[Tuple[str, PipeCallable]]:
"""Get all (name, component) tuples in the pipeline, including the
currently disabled components.
"""
@ -322,12 +321,12 @@ class Language:
return SimpleFrozenList(names, error=Errors.E926.format(attr="component_names"))
@property
def pipeline(self) -> List[Tuple[str, "Pipe"]]:
def pipeline(self) -> List[Tuple[str, PipeCallable]]:
"""The processing pipeline consisting of (name, component) tuples. The
components are called on the Doc in order as it passes through the
pipeline.
RETURNS (List[Tuple[str, Pipe]]): The pipeline.
RETURNS (List[Tuple[str, Callable[[Doc], Doc]]]): The pipeline.
"""
pipes = [(n, p) for n, p in self._components if n not in self._disabled]
return SimpleFrozenList(pipes, error=Errors.E926.format(attr="pipeline"))
@ -527,7 +526,7 @@ class Language:
assigns: Iterable[str] = SimpleFrozenList(),
requires: Iterable[str] = SimpleFrozenList(),
retokenizes: bool = False,
func: Optional["Pipe"] = None,
func: Optional[PipeCallable] = None,
) -> Callable[..., Any]:
"""Register a new pipeline component. Can be used for stateless function
components that don't require a separate factory. Can be used as a
@ -542,7 +541,7 @@ class Language:
e.g. "token.ent_id". Used for pipeline analysis.
retokenizes (bool): Whether the component changes the tokenization.
Used for pipeline analysis.
func (Optional[Callable]): Factory function if not used as a decorator.
func (Optional[Callable[[Doc], Doc]): Factory function if not used as a decorator.
DOCS: https://spacy.io/api/language#component
"""
@ -553,11 +552,11 @@ class Language:
raise ValueError(Errors.E853.format(name=name))
component_name = name if name is not None else util.get_object_name(func)
def add_component(component_func: "Pipe") -> Callable:
def add_component(component_func: PipeCallable) -> Callable:
if isinstance(func, type): # function is a class
raise ValueError(Errors.E965.format(name=component_name))
def factory_func(nlp, name: str) -> "Pipe":
def factory_func(nlp, name: str) -> PipeCallable:
return component_func
internal_name = cls.get_factory_name(name)
@ -607,7 +606,7 @@ class Language:
print_pipe_analysis(analysis, keys=keys)
return analysis
def get_pipe(self, name: str) -> "Pipe":
def get_pipe(self, name: str) -> PipeCallable:
"""Get a pipeline component for a given component name.
name (str): Name of pipeline component to get.
@ -628,7 +627,7 @@ class Language:
config: Dict[str, Any] = SimpleFrozenDict(),
raw_config: Optional[Config] = None,
validate: bool = True,
) -> "Pipe":
) -> PipeCallable:
"""Create a pipeline component. Mostly used internally. To create and
add a component to the pipeline, you can use nlp.add_pipe.
@ -640,7 +639,7 @@ class Language:
raw_config (Optional[Config]): Internals: the non-interpolated config.
validate (bool): Whether to validate the component config against the
arguments and types expected by the factory.
RETURNS (Pipe): The pipeline component.
RETURNS (Callable[[Doc], Doc]): The pipeline component.
DOCS: https://spacy.io/api/language#create_pipe
"""
@ -695,24 +694,18 @@ class Language:
def create_pipe_from_source(
self, source_name: str, source: "Language", *, name: str
) -> Tuple["Pipe", str]:
) -> Tuple[PipeCallable, str]:
"""Create a pipeline component by copying it from an existing model.
source_name (str): Name of the component in the source pipeline.
source (Language): The source nlp object to copy from.
name (str): Optional alternative name to use in current pipeline.
RETURNS (Tuple[Callable, str]): The component and its factory name.
RETURNS (Tuple[Callable[[Doc], Doc], str]): The component and its factory name.
"""
# Check source type
if not isinstance(source, Language):
raise ValueError(Errors.E945.format(name=source_name, source=type(source)))
# Check vectors, with faster checks first
if (
self.vocab.vectors.shape != source.vocab.vectors.shape
or self.vocab.vectors.key2row != source.vocab.vectors.key2row
or self.vocab.vectors.to_bytes(exclude=["strings"])
!= source.vocab.vectors.to_bytes(exclude=["strings"])
):
if self.vocab.vectors != source.vocab.vectors:
warnings.warn(Warnings.W113.format(name=source_name))
if source_name not in source.component_names:
raise KeyError(
@ -746,7 +739,7 @@ class Language:
config: Dict[str, Any] = SimpleFrozenDict(),
raw_config: Optional[Config] = None,
validate: bool = True,
) -> "Pipe":
) -> PipeCallable:
"""Add a component to the processing pipeline. Valid components are
callables that take a `Doc` object, modify it and return it. Only one
of before/after/first/last can be set. Default behaviour is "last".
@ -769,7 +762,7 @@ class Language:
raw_config (Optional[Config]): Internals: the non-interpolated config.
validate (bool): Whether to validate the component config against the
arguments and types expected by the factory.
RETURNS (Pipe): The pipeline component.
RETURNS (Callable[[Doc], Doc]): The pipeline component.
DOCS: https://spacy.io/api/language#add_pipe
"""
@ -790,14 +783,6 @@ class Language:
factory_name, source, name=name
)
else:
if not self.has_factory(factory_name):
err = Errors.E002.format(
name=factory_name,
opts=", ".join(self.factory_names),
method="add_pipe",
lang=util.get_object_name(self),
lang_code=self.lang,
)
pipe_component = self.create_pipe(
factory_name,
name=name,
@ -883,7 +868,7 @@ class Language:
*,
config: Dict[str, Any] = SimpleFrozenDict(),
validate: bool = True,
) -> "Pipe":
) -> PipeCallable:
"""Replace a component in the pipeline.
name (str): Name of the component to replace.
@ -892,7 +877,7 @@ class Language:
component. Will be merged with default config, if available.
validate (bool): Whether to validate the component config against the
arguments and types expected by the factory.
RETURNS (Pipe): The new pipeline component.
RETURNS (Callable[[Doc], Doc]): The new pipeline component.
DOCS: https://spacy.io/api/language#replace_pipe
"""
@ -944,11 +929,11 @@ class Language:
init_cfg = self._config["initialize"]["components"].pop(old_name)
self._config["initialize"]["components"][new_name] = init_cfg
def remove_pipe(self, name: str) -> Tuple[str, "Pipe"]:
def remove_pipe(self, name: str) -> Tuple[str, PipeCallable]:
"""Remove a component from the pipeline.
name (str): Name of the component to remove.
RETURNS (tuple): A `(name, component)` tuple of the removed component.
RETURNS (Tuple[str, Callable[[Doc], Doc]]): A `(name, component)` tuple of the removed component.
DOCS: https://spacy.io/api/language#remove_pipe
"""
@ -1363,15 +1348,15 @@ class Language:
def set_error_handler(
self,
error_handler: Callable[[str, "Pipe", List[Doc], Exception], NoReturn],
error_handler: Callable[[str, PipeCallable, List[Doc], Exception], NoReturn],
):
"""Set an error handler object for all the components in the pipeline that implement
a set_error_handler function.
"""Set an error handler object for all the components in the pipeline
that implement a set_error_handler function.
error_handler (Callable[[str, Pipe, List[Doc], Exception], NoReturn]):
Function that deals with a failing batch of documents. This callable function should take in
the component's name, the component itself, the offending batch of documents, and the exception
that was thrown.
error_handler (Callable[[str, Callable[[Doc], Doc], List[Doc], Exception], NoReturn]):
Function that deals with a failing batch of documents. This callable
function should take in the component's name, the component itself,
the offending batch of documents, and the exception that was thrown.
DOCS: https://spacy.io/api/language#set_error_handler
"""
self.default_error_handler = error_handler
@ -1879,31 +1864,22 @@ class Language:
if isinstance(exclude, str):
exclude = [exclude]
def fetch_pipes_status(value: Iterable[str], key: str) -> Iterable[str]:
"""Fetch value for `enable` or `disable` w.r.t. the specified config and passed arguments passed to
.load(). If both arguments and config specified values for this field, the passed arguments take precedence
and a warning is printed.
value (Iterable[str]): Passed value for `enable` or `disable`.
key (str): Key for field in config (either "enabled" or "disabled").
RETURN (Iterable[str]):
"""
# We assume that no argument was passed if the value is the specified default value.
if id(value) == id(_DEFAULT_EMPTY_PIPES):
return config["nlp"].get(key, [])
else:
if len(config["nlp"].get(key, [])):
warnings.warn(
Warnings.W123.format(
arg=key[:-1],
arg_value=value,
config_value=config["nlp"][key],
)
# `enable` should not be merged with `enabled` (the opposite is true for `disable`/`disabled`). If the config
# specifies values for `enabled` not included in `enable`, emit warning.
if id(enable) != id(_DEFAULT_EMPTY_PIPES):
enabled = config["nlp"].get("enabled", [])
if len(enabled) and not set(enabled).issubset(enable):
warnings.warn(
Warnings.W123.format(
enable=enable,
enabled=enabled,
)
return value
)
# Ensure sets of disabled/enabled pipe names are not contradictory.
disabled_pipes = cls._resolve_component_status(
fetch_pipes_status(disable, "disabled"),
fetch_pipes_status(enable, "enabled"),
list({*disable, *config["nlp"].get("disabled", [])}),
enable,
config["nlp"]["pipeline"],
)
nlp._disabled = set(p for p in disabled_pipes if p not in exclude)
@ -2084,10 +2060,12 @@ class Language:
if enable:
if isinstance(enable, str):
enable = [enable]
to_disable = [
pipe_name for pipe_name in pipe_names if pipe_name not in enable
]
if disable and disable != to_disable:
to_disable = {
*[pipe_name for pipe_name in pipe_names if pipe_name not in enable],
*disable,
}
# If any pipe to be enabled is in to_disable, the specification is inconsistent.
if len(set(enable) & to_disable):
raise ValueError(Errors.E1042.format(enable=enable, disable=disable))
return tuple(to_disable)

17
spacy/matcher/levenshtein.pyx
View File

@ -4,6 +4,8 @@ from libc.stdint cimport int64_t
from typing import Optional
from ..util import registry
cdef extern from "polyleven.c":
int64_t polyleven(PyObject *o1, PyObject *o2, int64_t k)
@ -13,3 +15,18 @@ cpdef int64_t levenshtein(a: str, b: str, k: Optional[int] = None):
if k is None:
k = -1
return polyleven(<PyObject*>a, <PyObject*>b, k)
cpdef bint levenshtein_compare(input_text: str, pattern_text: str, fuzzy: int = -1):
if fuzzy >= 0:
max_edits = fuzzy
else:
# allow at least two edits (to allow at least one transposition) and up
# to 30% of the pattern string length
max_edits = max(2, round(0.3 * len(pattern_text)))
return levenshtein(input_text, pattern_text, max_edits) <= max_edits
@registry.misc("spacy.levenshtein_compare.v1")
def make_levenshtein_compare():
return levenshtein_compare

1
spacy/matcher/matcher.pxd
View File

@ -77,3 +77,4 @@ cdef class Matcher:
cdef public object _extensions
cdef public object _extra_predicates
cdef public object _seen_attrs
cdef public object _fuzzy_compare

7
spacy/matcher/matcher.pyi
View File

@ -5,7 +5,12 @@ from ..vocab import Vocab
from ..tokens import Doc, Span
class Matcher:
def __init__(self, vocab: Vocab, validate: bool = ...) -> None: ...
def __init__(
self,
vocab: Vocab,
validate: bool = ...,
fuzzy_compare: Callable[[str, str, int], bool] = ...,
) -> None: ...
def __reduce__(self) -> Any: ...
def __len__(self) -> int: ...
def __contains__(self, key: str) -> bool: ...

170
spacy/matcher/matcher.pyx
View File

@ -1,4 +1,4 @@
# cython: infer_types=True, cython: profile=True
# cython: binding=True, infer_types=True, profile=True
from typing import List, Iterable
from libcpp.vector cimport vector
@ -20,10 +20,12 @@ from ..tokens.token cimport Token
from ..tokens.morphanalysis cimport MorphAnalysis
from ..attrs cimport ID, attr_id_t, NULL_ATTR, ORTH, POS, TAG, DEP, LEMMA, MORPH, ENT_IOB
from .levenshtein import levenshtein_compare
from ..schemas import validate_token_pattern
from ..errors import Errors, MatchPatternError, Warnings
from ..strings import get_string_id
from ..attrs import IDS
from ..util import registry
DEF PADDING = 5
@ -36,11 +38,13 @@ cdef class Matcher:
USAGE: https://spacy.io/usage/rule-based-matching
"""
def __init__(self, vocab, validate=True):
def __init__(self, vocab, validate=True, *, fuzzy_compare=levenshtein_compare):
"""Create the Matcher.
vocab (Vocab): The vocabulary object, which must be shared with the
documents the matcher will operate on.
validate (bool): Validate all patterns added to this matcher.
fuzzy_compare (Callable[[str, str, int], bool]): The comparison method
for the FUZZY operators.
"""
self._extra_predicates = []
self._patterns = {}
@ -51,9 +55,10 @@ cdef class Matcher:
self.vocab = vocab
self.mem = Pool()
self.validate = validate
self._fuzzy_compare = fuzzy_compare
def __reduce__(self):
data = (self.vocab, self._patterns, self._callbacks)
data = (self.vocab, self._patterns, self._callbacks, self.validate, self._fuzzy_compare)
return (unpickle_matcher, data, None, None)
def __len__(self):
@ -128,7 +133,7 @@ cdef class Matcher:
for pattern in patterns:
try:
specs = _preprocess_pattern(pattern, self.vocab,
self._extensions, self._extra_predicates)
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]:
@ -326,8 +331,8 @@ cdef class Matcher:
return key
def unpickle_matcher(vocab, patterns, callbacks):
matcher = Matcher(vocab)
def unpickle_matcher(vocab, patterns, callbacks, validate, fuzzy_compare):
matcher = Matcher(vocab, validate=validate, fuzzy_compare=fuzzy_compare)
for key, pattern in patterns.items():
callback = callbacks.get(key, None)
matcher.add(key, pattern, on_match=callback)
@ -754,7 +759,7 @@ cdef attr_t get_ent_id(const TokenPatternC* pattern) nogil:
return id_attr.value
def _preprocess_pattern(token_specs, vocab, extensions_table, extra_predicates):
def _preprocess_pattern(token_specs, vocab, extensions_table, extra_predicates, fuzzy_compare):
"""This function interprets the pattern, converting the various bits of
syntactic sugar before we compile it into a struct with init_pattern.
@ -781,7 +786,7 @@ def _preprocess_pattern(token_specs, vocab, extensions_table, extra_predicates):
ops = _get_operators(spec)
attr_values = _get_attr_values(spec, string_store)
extensions = _get_extensions(spec, string_store, extensions_table)
predicates = _get_extra_predicates(spec, extra_predicates, vocab)
predicates = _get_extra_predicates(spec, extra_predicates, vocab, fuzzy_compare)
for op in ops:
tokens.append((op, list(attr_values), list(extensions), list(predicates), token_idx))
return tokens
@ -826,16 +831,45 @@ def _get_attr_values(spec, string_store):
# These predicate helper classes are used to match the REGEX, IN, >= etc
# extensions to the matcher introduced in #3173.
class _FuzzyPredicate:
operators = ("FUZZY", "FUZZY1", "FUZZY2", "FUZZY3", "FUZZY4", "FUZZY5",
"FUZZY6", "FUZZY7", "FUZZY8", "FUZZY9")
def __init__(self, i, attr, value, predicate, is_extension=False, vocab=None,
regex=False, fuzzy=None, fuzzy_compare=None):
self.i = i
self.attr = attr
self.value = value
self.predicate = predicate
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
self.fuzzy = int(fuzz) if fuzz else -1
self.fuzzy_compare = fuzzy_compare
self.key = (self.attr, self.fuzzy, self.predicate, srsly.json_dumps(value, sort_keys=True))
def __call__(self, Token token):
if self.is_extension:
value = token._.get(self.attr)
else:
value = token.vocab.strings[get_token_attr_for_matcher(token.c, self.attr)]
if self.value == value:
return True
return self.fuzzy_compare(value, self.value, self.fuzzy)
class _RegexPredicate:
operators = ("REGEX",)
def __init__(self, i, attr, value, predicate, is_extension=False, vocab=None):
def __init__(self, i, attr, value, predicate, is_extension=False, vocab=None,
regex=False, fuzzy=None, fuzzy_compare=None):
self.i = i
self.attr = attr
self.value = re.compile(value)
self.predicate = predicate
self.is_extension = is_extension
self.key = (attr, self.predicate, srsly.json_dumps(value, sort_keys=True))
self.key = (self.attr, self.predicate, srsly.json_dumps(value, sort_keys=True))
if self.predicate not in self.operators:
raise ValueError(Errors.E126.format(good=self.operators, bad=self.predicate))
@ -850,18 +884,28 @@ class _RegexPredicate:
class _SetPredicate:
operators = ("IN", "NOT_IN", "IS_SUBSET", "IS_SUPERSET", "INTERSECTS")
def __init__(self, i, attr, value, predicate, is_extension=False, vocab=None):
def __init__(self, i, attr, value, predicate, is_extension=False, vocab=None,
regex=False, fuzzy=None, fuzzy_compare=None):
self.i = i
self.attr = attr
self.vocab = vocab
self.regex = regex
self.fuzzy = fuzzy
self.fuzzy_compare = fuzzy_compare
if self.attr == MORPH:
# normalize morph strings
self.value = set(self.vocab.morphology.add(v) for v in value)
else:
self.value = set(get_string_id(v) for v in value)
if self.regex:
self.value = set(re.compile(v) for v in value)
elif self.fuzzy is not None:
# add to string store
self.value = set(self.vocab.strings.add(v) for v in value)
else:
self.value = set(get_string_id(v) for v in value)
self.predicate = predicate
self.is_extension = is_extension
self.key = (attr, self.predicate, srsly.json_dumps(value, sort_keys=True))
self.key = (self.attr, self.regex, self.fuzzy, self.predicate, srsly.json_dumps(value, sort_keys=True))
if self.predicate not in self.operators:
raise ValueError(Errors.E126.format(good=self.operators, bad=self.predicate))
@ -889,9 +933,29 @@ class _SetPredicate:
return False
if self.predicate == "IN":
return value in self.value
if self.regex:
value = self.vocab.strings[value]
return any(bool(v.search(value)) for v in self.value)
elif self.fuzzy is not None:
value = self.vocab.strings[value]
return any(self.fuzzy_compare(value, self.vocab.strings[v], self.fuzzy)
for v in self.value)
elif value in self.value:
return True
else:
return False
elif self.predicate == "NOT_IN":
return value not in self.value
if self.regex:
value = self.vocab.strings[value]
return not any(bool(v.search(value)) for v in self.value)
elif self.fuzzy is not None:
value = self.vocab.strings[value]
return not any(self.fuzzy_compare(value, self.vocab.strings[v], self.fuzzy)
for v in self.value)
elif value in self.value:
return False
else:
return True
elif self.predicate == "IS_SUBSET":
return value <= self.value
elif self.predicate == "IS_SUPERSET":
@ -906,13 +970,14 @@ class _SetPredicate:
class _ComparisonPredicate:
operators = ("==", "!=", ">=", "<=", ">", "<")
def __init__(self, i, attr, value, predicate, is_extension=False, vocab=None):
def __init__(self, i, attr, value, predicate, is_extension=False, vocab=None,
regex=False, fuzzy=None, fuzzy_compare=None):
self.i = i
self.attr = attr
self.value = value
self.predicate = predicate
self.is_extension = is_extension
self.key = (attr, self.predicate, srsly.json_dumps(value, sort_keys=True))
self.key = (self.attr, self.predicate, srsly.json_dumps(value, sort_keys=True))
if self.predicate not in self.operators:
raise ValueError(Errors.E126.format(good=self.operators, bad=self.predicate))
@ -935,7 +1000,7 @@ class _ComparisonPredicate:
return value < self.value
def _get_extra_predicates(spec, extra_predicates, vocab):
def _get_extra_predicates(spec, extra_predicates, vocab, fuzzy_compare):
predicate_types = {
"REGEX": _RegexPredicate,
"IN": _SetPredicate,
@ -949,6 +1014,16 @@ def _get_extra_predicates(spec, extra_predicates, vocab):
"<=": _ComparisonPredicate,
">": _ComparisonPredicate,
"<": _ComparisonPredicate,
"FUZZY": _FuzzyPredicate,
"FUZZY1": _FuzzyPredicate,
"FUZZY2": _FuzzyPredicate,
"FUZZY3": _FuzzyPredicate,
"FUZZY4": _FuzzyPredicate,
"FUZZY5": _FuzzyPredicate,
"FUZZY6": _FuzzyPredicate,
"FUZZY7": _FuzzyPredicate,
"FUZZY8": _FuzzyPredicate,
"FUZZY9": _FuzzyPredicate,
}
seen_predicates = {pred.key: pred.i for pred in extra_predicates}
output = []
@ -966,22 +1041,47 @@ def _get_extra_predicates(spec, extra_predicates, vocab):
attr = "ORTH"
attr = IDS.get(attr.upper())
if isinstance(value, dict):
processed = False
value_with_upper_keys = {k.upper(): v for k, v in value.items()}
for type_, cls in predicate_types.items():
if type_ in value_with_upper_keys:
predicate = cls(len(extra_predicates), attr, value_with_upper_keys[type_], type_, vocab=vocab)
# Don't create a redundant predicates.
# This helps with efficiency, as we're caching the results.
if predicate.key in seen_predicates:
output.append(seen_predicates[predicate.key])
else:
extra_predicates.append(predicate)
output.append(predicate.i)
seen_predicates[predicate.key] = predicate.i
processed = True
if not processed:
warnings.warn(Warnings.W035.format(pattern=value))
output.extend(_get_extra_predicates_dict(attr, value, vocab, predicate_types,
extra_predicates, seen_predicates, fuzzy_compare=fuzzy_compare))
return output
def _get_extra_predicates_dict(attr, value_dict, vocab, predicate_types,
extra_predicates, seen_predicates, regex=False, fuzzy=None, fuzzy_compare=None):
output = []
for type_, value in value_dict.items():
type_ = type_.upper()
cls = predicate_types.get(type_)
if cls is None:
warnings.warn(Warnings.W035.format(pattern=value_dict))
# ignore unrecognized predicate type
continue
elif cls == _RegexPredicate:
if isinstance(value, dict):
# add predicates inside regex operator
output.extend(_get_extra_predicates_dict(attr, value, vocab, predicate_types,
extra_predicates, seen_predicates,
regex=True))
continue
elif cls == _FuzzyPredicate:
if isinstance(value, dict):
# add predicates inside fuzzy operator
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,
fuzzy=fuzzy_val, fuzzy_compare=fuzzy_compare))
continue
predicate = cls(len(extra_predicates), attr, value, type_, vocab=vocab,
regex=regex, fuzzy=fuzzy, fuzzy_compare=fuzzy_compare)
# Don't create redundant predicates.
# This helps with efficiency, as we're caching the results.
if predicate.key in seen_predicates:
output.append(seen_predicates[predicate.key])
else:
extra_predicates.append(predicate)
output.append(predicate.i)
seen_predicates[predicate.key] = predicate.i
return output

37
spacy/ml/models/entity_linker.py
View File

@ -1,11 +1,12 @@
from pathlib import Path
from typing import Optional, Callable, Iterable, List, Tuple
from thinc.types import Floats2d
from thinc.api import chain, clone, list2ragged, reduce_mean, residual
from thinc.api import Model, Maxout, Linear, noop, tuplify, Ragged
from thinc.api import chain, list2ragged, reduce_mean, residual
from thinc.api import Model, Maxout, Linear, tuplify, Ragged
from ...util import registry
from ...kb import KnowledgeBase, Candidate, get_candidates
from ...kb import KnowledgeBase, InMemoryLookupKB
from ...kb import Candidate, get_candidates, get_candidates_batch
from ...vocab import Vocab
from ...tokens import Span, Doc
from ..extract_spans import extract_spans
@ -70,17 +71,18 @@ def span_maker_forward(model, docs: List[Doc], is_train) -> Tuple[Ragged, Callab
cands.append((start_token, end_token))
candidates.append(ops.asarray2i(cands))
candlens = ops.asarray1i([len(cands) for cands in candidates])
candidates = ops.xp.concatenate(candidates)
outputs = Ragged(candidates, candlens)
lengths = model.ops.asarray1i([len(cands) for cands in candidates])
out = Ragged(model.ops.flatten(candidates), lengths)
# because this is just rearranging docs, the backprop does nothing
return outputs, lambda x: []
return out, lambda x: []
@registry.misc("spacy.KBFromFile.v1")
def load_kb(kb_path: Path) -> Callable[[Vocab], KnowledgeBase]:
def kb_from_file(vocab):
kb = KnowledgeBase(vocab, entity_vector_length=1)
def load_kb(
kb_path: Path,
) -> Callable[[Vocab], KnowledgeBase]:
def kb_from_file(vocab: Vocab):
kb = InMemoryLookupKB(vocab, entity_vector_length=1)
kb.from_disk(kb_path)
return kb
@ -88,9 +90,11 @@ def load_kb(kb_path: Path) -> Callable[[Vocab], KnowledgeBase]:
@registry.misc("spacy.EmptyKB.v1")
def empty_kb(entity_vector_length: int) -> Callable[[Vocab], KnowledgeBase]:
def empty_kb_factory(vocab):
return KnowledgeBase(vocab=vocab, entity_vector_length=entity_vector_length)
def empty_kb(
entity_vector_length: int,
) -> Callable[[Vocab], KnowledgeBase]:
def empty_kb_factory(vocab: Vocab):
return InMemoryLookupKB(vocab=vocab, entity_vector_length=entity_vector_length)
return empty_kb_factory
@ -98,3 +102,10 @@ def empty_kb(entity_vector_length: int) -> Callable[[Vocab], KnowledgeBase]:
@registry.misc("spacy.CandidateGenerator.v1")
def create_candidates() -> Callable[[KnowledgeBase, Span], Iterable[Candidate]]:
return get_candidates
@registry.misc("spacy.CandidateBatchGenerator.v1")
def create_candidates_batch() -> Callable[
[KnowledgeBase, Iterable[Span]], Iterable[Iterable[Candidate]]
]:
return get_candidates_batch

82
spacy/pipeline/edit_tree_lemmatizer.py
View File

@ -1,13 +1,12 @@
from typing import cast, Any, Callable, Dict, Iterable, List, Optional
from typing import Sequence, Tuple, Union
from typing import Tuple
from collections import Counter
from copy import deepcopy
from itertools import islice
import numpy as np
import srsly
from thinc.api import Config, Model, SequenceCategoricalCrossentropy
from thinc.types import Floats2d, Ints1d, Ints2d
from thinc.api import Config, Model, SequenceCategoricalCrossentropy, NumpyOps
from thinc.types import Floats2d, Ints2d
from ._edit_tree_internals.edit_trees import EditTrees
from ._edit_tree_internals.schemas import validate_edit_tree
@ -21,6 +20,10 @@ from ..vocab import Vocab
from .. import util
# The cutoff value of *top_k* above which an alternative method is used to process guesses.
TOP_K_GUARDRAIL = 20
default_model_config = """
[model]
@architectures = "spacy.Tagger.v2"
@ -116,6 +119,7 @@ class EditTreeLemmatizer(TrainablePipe):
self.cfg: Dict[str, Any] = {"labels": []}
self.scorer = scorer
self.numpy_ops = NumpyOps()
def get_loss(
self, examples: Iterable[Example], scores: List[Floats2d]
@ -129,7 +133,7 @@ class EditTreeLemmatizer(TrainablePipe):
for (predicted, gold_lemma) in zip(
eg.predicted, eg.get_aligned("LEMMA", as_string=True)
):
if gold_lemma is None:
if gold_lemma is None or gold_lemma == "":
label = -1
else:
tree_id = self.trees.add(predicted.text, gold_lemma)
@ -145,31 +149,73 @@ class EditTreeLemmatizer(TrainablePipe):
return float(loss), d_scores
def predict(self, docs: Iterable[Doc]) -> List[Ints2d]:
if self.top_k == 1:
scores2guesses = self._scores2guesses_top_k_equals_1
elif self.top_k <= TOP_K_GUARDRAIL:
scores2guesses = self._scores2guesses_top_k_greater_1
else:
scores2guesses = self._scores2guesses_top_k_guardrail
# The behaviour of *_scores2guesses_top_k_greater_1()* is efficient for values
# of *top_k>1* that are likely to be useful when the edit tree lemmatizer is used
# for its principal purpose of lemmatizing tokens. However, the code could also
# be used for other purposes, and with very large values of *top_k* the method
# becomes inefficient. In such cases, *_scores2guesses_top_k_guardrail()* is used
# instead.
n_docs = len(list(docs))
if not any(len(doc) for doc in docs):
# Handle cases where there are no tokens in any docs.
n_labels = len(self.cfg["labels"])
guesses: List[Ints2d] = [
self.model.ops.alloc((0, n_labels), dtype="i") for doc in docs
]
guesses: List[Ints2d] = [self.model.ops.alloc2i(0, n_labels) for _ in docs]
assert len(guesses) == n_docs
return guesses
scores = self.model.predict(docs)
assert len(scores) == n_docs
guesses = self._scores2guesses(docs, scores)
guesses = scores2guesses(docs, scores)
assert len(guesses) == n_docs
return guesses
def _scores2guesses(self, docs, scores):
def _scores2guesses_top_k_equals_1(self, docs, scores):
guesses = []
for doc, doc_scores in zip(docs, scores):
if self.top_k == 1:
doc_guesses = doc_scores.argmax(axis=1).reshape(-1, 1)
else:
doc_guesses = np.argsort(doc_scores)[..., : -self.top_k - 1 : -1]
doc_guesses = doc_scores.argmax(axis=1)
doc_guesses = self.numpy_ops.asarray(doc_guesses)
if not isinstance(doc_guesses, np.ndarray):
doc_guesses = doc_guesses.get()
doc_compat_guesses = []
for i, token in enumerate(doc):
tree_id = self.cfg["labels"][doc_guesses[i]]
if self.trees.apply(tree_id, token.text) is not None:
doc_compat_guesses.append(tree_id)
else:
doc_compat_guesses.append(-1)
guesses.append(np.array(doc_compat_guesses))
return guesses
def _scores2guesses_top_k_greater_1(self, docs, scores):
guesses = []
top_k = min(self.top_k, len(self.labels))
for doc, doc_scores in zip(docs, scores):
doc_scores = self.numpy_ops.asarray(doc_scores)
doc_compat_guesses = []
for i, token in enumerate(doc):
for _ in range(top_k):
candidate = int(doc_scores[i].argmax())
candidate_tree_id = self.cfg["labels"][candidate]
if self.trees.apply(candidate_tree_id, token.text) is not None:
doc_compat_guesses.append(candidate_tree_id)
break
doc_scores[i, candidate] = np.finfo(np.float32).min
else:
doc_compat_guesses.append(-1)
guesses.append(np.array(doc_compat_guesses))
return guesses
def _scores2guesses_top_k_guardrail(self, docs, scores):
guesses = []
for doc, doc_scores in zip(docs, scores):
doc_guesses = np.argsort(doc_scores)[..., : -self.top_k - 1 : -1]
doc_guesses = self.numpy_ops.asarray(doc_guesses)
doc_compat_guesses = []
for token, candidates in zip(doc, doc_guesses):
@ -331,9 +377,9 @@ class EditTreeLemmatizer(TrainablePipe):
tree = dict(tree)
if "orig" in tree:
tree["orig"] = self.vocab.strings[tree["orig"]]
tree["orig"] = self.vocab.strings.add(tree["orig"])
if "orig" in tree:
tree["subst"] = self.vocab.strings[tree["subst"]]
tree["subst"] = self.vocab.strings.add(tree["subst"])
trees.append(tree)

184
spacy/pipeline/entity_linker.py
View File

@ -53,9 +53,11 @@ DEFAULT_NEL_MODEL = Config().from_str(default_model_config)["model"]
"incl_context": True,
"entity_vector_length": 64,
"get_candidates": {"@misc": "spacy.CandidateGenerator.v1"},
"get_candidates_batch": {"@misc": "spacy.CandidateBatchGenerator.v1"},
"overwrite": True,
"scorer": {"@scorers": "spacy.entity_linker_scorer.v1"},
"use_gold_ents": True,
"candidates_batch_size": 1,
"threshold": None,
},
default_score_weights={
@ -75,9 +77,13 @@ def make_entity_linker(
incl_context: bool,
entity_vector_length: int,
get_candidates: Callable[[KnowledgeBase, Span], Iterable[Candidate]],
get_candidates_batch: Callable[
[KnowledgeBase, Iterable[Span]], Iterable[Iterable[Candidate]]
],
overwrite: bool,
scorer: Optional[Callable],
use_gold_ents: bool,
candidates_batch_size: int,
threshold: Optional[float] = None,
):
"""Construct an EntityLinker component.
@ -90,17 +96,21 @@ def make_entity_linker(
incl_prior (bool): Whether or not to include prior probabilities from the KB in the model.
incl_context (bool): Whether or not to include the local context in the model.
entity_vector_length (int): Size of encoding vectors in the KB.
get_candidates (Callable[[KnowledgeBase, "Span"], Iterable[Candidate]]): Function that
get_candidates (Callable[[KnowledgeBase, Span], Iterable[Candidate]]): Function that
produces a list of candidates, given a certain knowledge base and a textual mention.
get_candidates_batch (
Callable[[KnowledgeBase, Iterable[Span]], Iterable[Iterable[Candidate]]], Iterable[Candidate]]
): Function that produces a list of candidates, given a certain knowledge base and several textual mentions.
scorer (Optional[Callable]): The scoring method.
use_gold_ents (bool): Whether to copy entities from gold docs or not. If false, another
component must provide entity annotations.
candidates_batch_size (int): Size of batches for entity candidate generation.
threshold (Optional[float]): Confidence threshold for entity predictions. If confidence is below the threshold,
prediction is discarded. If None, predictions are not filtered by any threshold.
"""
if not model.attrs.get("include_span_maker", False):
# The only difference in arguments here is that use_gold_ents is not available
# The only difference in arguments here is that use_gold_ents and threshold aren't available.
return EntityLinker_v1(
nlp.vocab,
model,
@ -124,9 +134,11 @@ def make_entity_linker(
incl_context=incl_context,
entity_vector_length=entity_vector_length,
get_candidates=get_candidates,
get_candidates_batch=get_candidates_batch,
overwrite=overwrite,
scorer=scorer,
use_gold_ents=use_gold_ents,
candidates_batch_size=candidates_batch_size,
threshold=threshold,
)
@ -160,9 +172,13 @@ class EntityLinker(TrainablePipe):
incl_context: bool,
entity_vector_length: int,
get_candidates: Callable[[KnowledgeBase, Span], Iterable[Candidate]],
get_candidates_batch: Callable[
[KnowledgeBase, Iterable[Span]], Iterable[Iterable[Candidate]]
],
overwrite: bool = BACKWARD_OVERWRITE,
scorer: Optional[Callable] = entity_linker_score,
use_gold_ents: bool,
candidates_batch_size: int,
threshold: Optional[float] = None,
) -> None:
"""Initialize an entity linker.
@ -178,10 +194,14 @@ class EntityLinker(TrainablePipe):
entity_vector_length (int): Size of encoding vectors in the KB.
get_candidates (Callable[[KnowledgeBase, Span], Iterable[Candidate]]): Function that
produces a list of candidates, given a certain knowledge base and a textual mention.
scorer (Optional[Callable]): The scoring method. Defaults to
Scorer.score_links.
get_candidates_batch (
Callable[[KnowledgeBase, Iterable[Span]], Iterable[Iterable[Candidate]]],
Iterable[Candidate]]
): Function that produces a list of candidates, given a certain knowledge base and several textual mentions.
scorer (Optional[Callable]): The scoring method. Defaults to Scorer.score_links.
use_gold_ents (bool): Whether to copy entities from gold docs or not. If false, another
component must provide entity annotations.
candidates_batch_size (int): Size of batches for entity candidate generation.
threshold (Optional[float]): Confidence threshold for entity predictions. If confidence is below the
threshold, prediction is discarded. If None, predictions are not filtered by any threshold.
DOCS: https://spacy.io/api/entitylinker#init
@ -204,22 +224,27 @@ class EntityLinker(TrainablePipe):
self.incl_prior = incl_prior
self.incl_context = incl_context
self.get_candidates = get_candidates
self.get_candidates_batch = get_candidates_batch
self.cfg: Dict[str, Any] = {"overwrite": overwrite}
self.distance = CosineDistance(normalize=False)
# how many neighbour sentences to take into account
# create an empty KB by default. If you want to load a predefined one, specify it in 'initialize'.
# create an empty KB by default
self.kb = empty_kb(entity_vector_length)(self.vocab)
self.scorer = scorer
self.use_gold_ents = use_gold_ents
self.candidates_batch_size = candidates_batch_size
self.threshold = threshold
if candidates_batch_size < 1:
raise ValueError(Errors.E1044)
def set_kb(self, kb_loader: Callable[[Vocab], KnowledgeBase]):
"""Define the KB of this pipe by providing a function that will
create it using this object's vocab."""
if not callable(kb_loader):
raise ValueError(Errors.E885.format(arg_type=type(kb_loader)))
self.kb = kb_loader(self.vocab)
self.kb = kb_loader(self.vocab) # type: ignore
def validate_kb(self) -> None:
# Raise an error if the knowledge base is not initialized.
@ -241,8 +266,8 @@ class EntityLinker(TrainablePipe):
get_examples (Callable[[], Iterable[Example]]): Function that
returns a representative sample of gold-standard Example objects.
nlp (Language): The current nlp object the component is part of.
kb_loader (Callable[[Vocab], KnowledgeBase]): A function that creates a KnowledgeBase from a Vocab instance.
Note that providing this argument, will overwrite all data accumulated in the current KB.
kb_loader (Callable[[Vocab], KnowledgeBase]): A function that creates a KnowledgeBase from a Vocab
instance. Note that providing this argument will overwrite all data accumulated in the current KB.
Use this only when loading a KB as-such from file.
DOCS: https://spacy.io/api/entitylinker#initialize
@ -419,66 +444,93 @@ class EntityLinker(TrainablePipe):
if len(doc) == 0:
continue
sentences = [s for s in doc.sents]
# Looping through each entity (TODO: rewrite)
for ent in doc.ents:
sent_index = sentences.index(ent.sent)
assert sent_index >= 0
if self.incl_context:
# get n_neighbour sentences, clipped to the length of the document
start_sentence = max(0, sent_index - self.n_sents)
end_sentence = min(len(sentences) - 1, sent_index + self.n_sents)
start_token = sentences[start_sentence].start
end_token = sentences[end_sentence].end
sent_doc = doc[start_token:end_token].as_doc()
# currently, the context is the same for each entity in a sentence (should be refined)
sentence_encoding = self.model.predict([sent_doc])[0]
sentence_encoding_t = sentence_encoding.T
sentence_norm = xp.linalg.norm(sentence_encoding_t)
entity_count += 1
if ent.label_ in self.labels_discard:
# ignoring this entity - setting to NIL
final_kb_ids.append(self.NIL)
else:
candidates = list(self.get_candidates(self.kb, ent))
if not candidates:
# no prediction possible for this entity - setting to NIL
final_kb_ids.append(self.NIL)
elif len(candidates) == 1 and self.threshold is None:
# shortcut for efficiency reasons: take the 1 candidate
final_kb_ids.append(candidates[0].entity_)
else:
random.shuffle(candidates)
# set all prior probabilities to 0 if incl_prior=False
prior_probs = xp.asarray([c.prior_prob for c in candidates])
if not self.incl_prior:
prior_probs = xp.asarray([0.0 for _ in candidates])
scores = prior_probs
# add in similarity from the context
if self.incl_context:
entity_encodings = xp.asarray(
[c.entity_vector for c in candidates]
)
entity_norm = xp.linalg.norm(entity_encodings, axis=1)
if len(entity_encodings) != len(prior_probs):
raise RuntimeError(
Errors.E147.format(
method="predict",
msg="vectors not of equal length",
)
)
# cosine similarity
sims = xp.dot(entity_encodings, sentence_encoding_t) / (
sentence_norm * entity_norm
)
if sims.shape != prior_probs.shape:
raise ValueError(Errors.E161)
scores = prior_probs + sims - (prior_probs * sims)
final_kb_ids.append(
candidates[scores.argmax().item()].entity_
if self.threshold is None or scores.max() >= self.threshold
else EntityLinker.NIL
# Loop over entities in batches.
for ent_idx in range(0, len(doc.ents), self.candidates_batch_size):
ent_batch = doc.ents[ent_idx : ent_idx + self.candidates_batch_size]
# Look up candidate entities.
valid_ent_idx = [
idx
for idx in range(len(ent_batch))
if ent_batch[idx].label_ not in self.labels_discard
]
batch_candidates = list(
self.get_candidates_batch(
self.kb, [ent_batch[idx] for idx in valid_ent_idx]
)
if self.candidates_batch_size > 1
else [
self.get_candidates(self.kb, ent_batch[idx])
for idx in valid_ent_idx
]
)
# Looping through each entity in batch (TODO: rewrite)
for j, ent in enumerate(ent_batch):
sent_index = sentences.index(ent.sent)
assert sent_index >= 0
if self.incl_context:
# get n_neighbour sentences, clipped to the length of the document
start_sentence = max(0, sent_index - self.n_sents)
end_sentence = min(
len(sentences) - 1, sent_index + self.n_sents
)
start_token = sentences[start_sentence].start
end_token = sentences[end_sentence].end
sent_doc = doc[start_token:end_token].as_doc()
# currently, the context is the same for each entity in a sentence (should be refined)
sentence_encoding = self.model.predict([sent_doc])[0]
sentence_encoding_t = sentence_encoding.T
sentence_norm = xp.linalg.norm(sentence_encoding_t)
entity_count += 1
if ent.label_ in self.labels_discard:
# ignoring this entity - setting to NIL
final_kb_ids.append(self.NIL)
else:
candidates = list(batch_candidates[j])
if not candidates:
# no prediction possible for this entity - setting to NIL
final_kb_ids.append(self.NIL)
elif len(candidates) == 1 and self.threshold is None:
# shortcut for efficiency reasons: take the 1 candidate
final_kb_ids.append(candidates[0].entity_)
else:
random.shuffle(candidates)
# set all prior probabilities to 0 if incl_prior=False
prior_probs = xp.asarray([c.prior_prob for c in candidates])
if not self.incl_prior:
prior_probs = xp.asarray([0.0 for _ in candidates])
scores = prior_probs
# add in similarity from the context
if self.incl_context:
entity_encodings = xp.asarray(
[c.entity_vector for c in candidates]
)
entity_norm = xp.linalg.norm(entity_encodings, axis=1)
if len(entity_encodings) != len(prior_probs):
raise RuntimeError(
Errors.E147.format(
method="predict",
msg="vectors not of equal length",
)
)
# cosine similarity
sims = xp.dot(entity_encodings, sentence_encoding_t) / (
sentence_norm * entity_norm
)
if sims.shape != prior_probs.shape:
raise ValueError(Errors.E161)
scores = prior_probs + sims - (prior_probs * sims)
final_kb_ids.append(
candidates[scores.argmax().item()].entity_
if self.threshold is None
or scores.max() >= self.threshold
else EntityLinker.NIL
)
if not (len(final_kb_ids) == entity_count):
err = Errors.E147.format(
method="predict", msg="result variables not of equal length"

24
spacy/pipeline/entityruler.py
View File

@ -11,6 +11,7 @@ from ..errors import Errors, Warnings
from ..util import ensure_path, to_disk, from_disk, SimpleFrozenList, registry
from ..tokens import Doc, Span
from ..matcher import Matcher, PhraseMatcher
from ..matcher.levenshtein import levenshtein_compare
from ..scorer import get_ner_prf
@ -23,6 +24,7 @@ PatternType = Dict[str, Union[str, List[Dict[str, Any]]]]
assigns=["doc.ents", "token.ent_type", "token.ent_iob"],
default_config={
"phrase_matcher_attr": None,
"matcher_fuzzy_compare": {"@misc": "spacy.levenshtein_compare.v1"},
"validate": False,
"overwrite_ents": False,
"ent_id_sep": DEFAULT_ENT_ID_SEP,
@ -39,6 +41,7 @@ def make_entity_ruler(
nlp: Language,
name: str,
phrase_matcher_attr: Optional[Union[int, str]],
matcher_fuzzy_compare: Callable,
validate: bool,
overwrite_ents: bool,
ent_id_sep: str,
@ -48,6 +51,7 @@ def make_entity_ruler(
nlp,
name,
phrase_matcher_attr=phrase_matcher_attr,
matcher_fuzzy_compare=matcher_fuzzy_compare,
validate=validate,
overwrite_ents=overwrite_ents,
ent_id_sep=ent_id_sep,
@ -81,6 +85,7 @@ class EntityRuler(Pipe):
name: str = "entity_ruler",
*,
phrase_matcher_attr: Optional[Union[int, str]] = None,
matcher_fuzzy_compare: Callable = levenshtein_compare,
validate: bool = False,
overwrite_ents: bool = False,
ent_id_sep: str = DEFAULT_ENT_ID_SEP,
@ -99,7 +104,10 @@ class EntityRuler(Pipe):
added. Used to disable the current entity ruler while creating
phrase patterns with the nlp object.
phrase_matcher_attr (int / str): Token attribute to match on, passed
to the internal PhraseMatcher as `attr`
to the internal PhraseMatcher as `attr`.
matcher_fuzzy_compare (Callable): The fuzzy comparison method for the
internal Matcher. Defaults to
spacy.matcher.levenshtein.levenshtein_compare.
validate (bool): Whether patterns should be validated, passed to
Matcher and PhraseMatcher as `validate`
patterns (iterable): Optional patterns to load in.
@ -117,7 +125,10 @@ class EntityRuler(Pipe):
self.token_patterns = defaultdict(list) # type: ignore
self.phrase_patterns = defaultdict(list) # type: ignore
self._validate = validate
self.matcher = Matcher(nlp.vocab, validate=validate)
self.matcher_fuzzy_compare = matcher_fuzzy_compare
self.matcher = Matcher(
nlp.vocab, validate=validate, fuzzy_compare=self.matcher_fuzzy_compare
)
self.phrase_matcher_attr = phrase_matcher_attr
self.phrase_matcher = PhraseMatcher(
nlp.vocab, attr=self.phrase_matcher_attr, validate=validate
@ -337,7 +348,11 @@ class EntityRuler(Pipe):
self.token_patterns = defaultdict(list)
self.phrase_patterns = defaultdict(list)
self._ent_ids = defaultdict(tuple)
self.matcher = Matcher(self.nlp.vocab, validate=self._validate)
self.matcher = Matcher(
self.nlp.vocab,
validate=self._validate,
fuzzy_compare=self.matcher_fuzzy_compare,
)
self.phrase_matcher = PhraseMatcher(
self.nlp.vocab, attr=self.phrase_matcher_attr, validate=self._validate
)
@ -431,7 +446,8 @@ class EntityRuler(Pipe):
self.overwrite = cfg.get("overwrite", False)
self.phrase_matcher_attr = cfg.get("phrase_matcher_attr", None)
self.phrase_matcher = PhraseMatcher(
self.nlp.vocab, attr=self.phrase_matcher_attr
self.nlp.vocab,
attr=self.phrase_matcher_attr,
)
self.ent_id_sep = cfg.get("ent_id_sep", DEFAULT_ENT_ID_SEP)
else:

5
spacy/pipeline/legacy/entity_linker.py
View File

@ -68,8 +68,7 @@ class EntityLinker_v1(TrainablePipe):
entity_vector_length (int): Size of encoding vectors in the KB.
get_candidates (Callable[[KnowledgeBase, Span], Iterable[Candidate]]): Function that
produces a list of candidates, given a certain knowledge base and a textual mention.
scorer (Optional[Callable]): The scoring method. Defaults to
Scorer.score_links.
scorer (Optional[Callable]): The scoring method. Defaults to Scorer.score_links.
DOCS: https://spacy.io/api/entitylinker#init
"""
self.vocab = vocab
@ -115,7 +114,7 @@ class EntityLinker_v1(TrainablePipe):
get_examples (Callable[[], Iterable[Example]]): Function that
returns a representative sample of gold-standard Example objects.
nlp (Language): The current nlp object the component is part of.
kb_loader (Callable[[Vocab], KnowledgeBase]): A function that creates a KnowledgeBase from a Vocab instance.
kb_loader (Callable[[Vocab], KnowledgeBase]): A function that creates an InMemoryLookupKB from a Vocab instance.
Note that providing this argument, will overwrite all data accumulated in the current KB.
Use this only when loading a KB as-such from file.

20
spacy/pipeline/span_ruler.py
View File

@ -13,6 +13,7 @@ from ..util import ensure_path, SimpleFrozenList, registry
from ..tokens import Doc, Span
from ..scorer import Scorer
from ..matcher import Matcher, PhraseMatcher
from ..matcher.levenshtein import levenshtein_compare
from .. import util
PatternType = Dict[str, Union[str, List[Dict[str, Any]]]]
@ -28,6 +29,7 @@ DEFAULT_SPANS_KEY = "ruler"
"overwrite_ents": False,
"scorer": {"@scorers": "spacy.entity_ruler_scorer.v1"},
"ent_id_sep": "__unused__",
"matcher_fuzzy_compare": {"@misc": "spacy.levenshtein_compare.v1"},
},
default_score_weights={
"ents_f": 1.0,
@ -40,6 +42,7 @@ def make_entity_ruler(
nlp: Language,
name: str,
phrase_matcher_attr: Optional[Union[int, str]],
matcher_fuzzy_compare: Callable,
validate: bool,
overwrite_ents: bool,
scorer: Optional[Callable],
@ -57,6 +60,7 @@ def make_entity_ruler(
annotate_ents=True,
ents_filter=ents_filter,
phrase_matcher_attr=phrase_matcher_attr,
matcher_fuzzy_compare=matcher_fuzzy_compare,
validate=validate,
overwrite=False,
scorer=scorer,
@ -72,6 +76,7 @@ def make_entity_ruler(
"annotate_ents": False,
"ents_filter": {"@misc": "spacy.first_longest_spans_filter.v1"},
"phrase_matcher_attr": None,
"matcher_fuzzy_compare": {"@misc": "spacy.levenshtein_compare.v1"},
"validate": False,
"overwrite": True,
"scorer": {
@ -94,6 +99,7 @@ def make_span_ruler(
annotate_ents: bool,
ents_filter: Callable[[Iterable[Span], Iterable[Span]], Iterable[Span]],
phrase_matcher_attr: Optional[Union[int, str]],
matcher_fuzzy_compare: Callable,
validate: bool,
overwrite: bool,
scorer: Optional[Callable],
@ -106,6 +112,7 @@ def make_span_ruler(
annotate_ents=annotate_ents,
ents_filter=ents_filter,
phrase_matcher_attr=phrase_matcher_attr,
matcher_fuzzy_compare=matcher_fuzzy_compare,
validate=validate,
overwrite=overwrite,
scorer=scorer,
@ -170,7 +177,7 @@ def prioritize_existing_ents_filter(
@registry.misc("spacy.prioritize_existing_ents_filter.v1")
def make_preverse_existing_ents_filter():
def make_preserve_existing_ents_filter():
return prioritize_existing_ents_filter
@ -216,6 +223,7 @@ class SpanRuler(Pipe):
[Iterable[Span], Iterable[Span]], Iterable[Span]
] = util.filter_chain_spans,
phrase_matcher_attr: Optional[Union[int, str]] = None,
matcher_fuzzy_compare: Callable = levenshtein_compare,
validate: bool = False,
overwrite: bool = False,
scorer: Optional[Callable] = partial(
@ -246,6 +254,9 @@ class SpanRuler(Pipe):
phrase_matcher_attr (Optional[Union[int, str]]): Token attribute to
match on, passed to the internal PhraseMatcher as `attr`. Defaults
to `None`.
matcher_fuzzy_compare (Callable): The fuzzy comparison method for the
internal Matcher. Defaults to
spacy.matcher.levenshtein.levenshtein_compare.
validate (bool): Whether patterns should be validated, passed to
Matcher and PhraseMatcher as `validate`.
overwrite (bool): Whether to remove any existing spans under this spans
@ -266,6 +277,7 @@ class SpanRuler(Pipe):
self.spans_filter = spans_filter
self.ents_filter = ents_filter
self.scorer = scorer
self.matcher_fuzzy_compare = matcher_fuzzy_compare
self._match_label_id_map: Dict[int, Dict[str, str]] = {}
self.clear()
@ -451,7 +463,11 @@ class SpanRuler(Pipe):
DOCS: https://spacy.io/api/spanruler#clear
"""
self._patterns: List[PatternType] = []
self.matcher: Matcher = Matcher(self.nlp.vocab, validate=self.validate)
self.matcher: Matcher = Matcher(
self.nlp.vocab,
validate=self.validate,
fuzzy_compare=self.matcher_fuzzy_compare,
)
self.phrase_matcher: PhraseMatcher = PhraseMatcher(
self.nlp.vocab,
attr=self.phrase_matcher_attr,

18
spacy/pipeline/spancat.py
View File

@ -1,7 +1,7 @@
from typing import List, Dict, Callable, Tuple, Optional, Iterable, Any, cast
from typing import List, Dict, Callable, Tuple, Optional, Iterable, Any
from thinc.api import Config, Model, get_current_ops, set_dropout_rate, Ops
from thinc.api import Optimizer
from thinc.types import Ragged, Ints2d, Floats2d, Ints1d
from thinc.types import Ragged, Ints2d, Floats2d
import numpy
@ -26,17 +26,17 @@ scorer = {"@layers": "spacy.LinearLogistic.v1"}
hidden_size = 128
[model.tok2vec]
@architectures = "spacy.Tok2Vec.v1"
@architectures = "spacy.Tok2Vec.v2"
[model.tok2vec.embed]
@architectures = "spacy.MultiHashEmbed.v1"
@architectures = "spacy.MultiHashEmbed.v2"
width = 96
rows = [5000, 2000, 1000, 1000]
attrs = ["ORTH", "PREFIX", "SUFFIX", "SHAPE"]
include_static_vectors = false
[model.tok2vec.encode]
@architectures = "spacy.MaxoutWindowEncoder.v1"
@architectures = "spacy.MaxoutWindowEncoder.v2"
width = ${model.tok2vec.embed.width}
window_size = 1
maxout_pieces = 3
@ -133,6 +133,9 @@ def make_spancat(
spans_key (str): Key of the doc.spans dict to save the spans under. During
initialization and training, the component will look for spans on the
reference document under the same key.
scorer (Optional[Callable]): The scoring method. Defaults to
Scorer.score_spans for the Doc.spans[spans_key] with overlapping
spans allowed.
threshold (float): Minimum probability to consider a prediction positive.
Spans with a positive prediction will be saved on the Doc. Defaults to
0.5.
@ -269,7 +272,10 @@ class SpanCategorizer(TrainablePipe):
DOCS: https://spacy.io/api/spancategorizer#predict
"""
indices = self.suggester(docs, ops=self.model.ops)
scores = self.model.predict((docs, indices)) # type: ignore
if indices.lengths.sum() == 0:
scores = self.model.ops.alloc2f(0, 0)
else:
scores = self.model.predict((docs, indices)) # type: ignore
return indices, scores
def set_candidates(

26
spacy/pipeline/textcat.py
View File

@ -24,8 +24,8 @@ single_label_default_config = """
[model.tok2vec.embed]
@architectures = "spacy.MultiHashEmbed.v2"
width = 64
rows = [2000, 2000, 1000, 1000, 1000, 1000]
attrs = ["ORTH", "LOWER", "PREFIX", "SUFFIX", "SHAPE", "ID"]
rows = [2000, 2000, 500, 1000, 500]
attrs = ["NORM", "LOWER", "PREFIX", "SUFFIX", "SHAPE"]
include_static_vectors = false
[model.tok2vec.encode]
@ -72,9 +72,9 @@ subword_features = true
"textcat",
assigns=["doc.cats"],
default_config={
"threshold": 0.5,
"threshold": 0.0,
"model": DEFAULT_SINGLE_TEXTCAT_MODEL,
"scorer": {"@scorers": "spacy.textcat_scorer.v1"},
"scorer": {"@scorers": "spacy.textcat_scorer.v2"},
},
default_score_weights={
"cats_score": 1.0,
@ -87,7 +87,6 @@ subword_features = true
"cats_macro_f": None,
"cats_macro_auc": None,
"cats_f_per_type": None,
"cats_macro_auc_per_type": None,
},
)
def make_textcat(
@ -118,7 +117,7 @@ def textcat_score(examples: Iterable[Example], **kwargs) -> Dict[str, Any]:
)
@registry.scorers("spacy.textcat_scorer.v1")
@registry.scorers("spacy.textcat_scorer.v2")
def make_textcat_scorer():
return textcat_score
@ -144,7 +143,8 @@ class TextCategorizer(TrainablePipe):
model (thinc.api.Model): The Thinc Model powering the pipeline component.
name (str): The component instance name, used to add entries to the
losses during training.
threshold (float): Cutoff to consider a prediction "positive".
threshold (float): Unused, not needed for single-label (exclusive
classes) classification.
scorer (Optional[Callable]): The scoring method. Defaults to
Scorer.score_cats for the attribute "cats".
@ -154,7 +154,11 @@ class TextCategorizer(TrainablePipe):
self.model = model
self.name = name
self._rehearsal_model = None
cfg = {"labels": [], "threshold": threshold, "positive_label": None}
cfg: Dict[str, Any] = {
"labels": [],
"threshold": threshold,
"positive_label": None,
}
self.cfg = dict(cfg)
self.scorer = scorer
@ -396,5 +400,9 @@ class TextCategorizer(TrainablePipe):
def _validate_categories(self, examples: Iterable[Example]):
"""Check whether the provided examples all have single-label cats annotations."""
for ex in examples:
if list(ex.reference.cats.values()).count(1.0) > 1:
vals = list(ex.reference.cats.values())
if vals.count(1.0) > 1:
raise ValueError(Errors.E895.format(value=ex.reference.cats))
for val in vals:
if not (val == 1.0 or val == 0.0):
raise ValueError(Errors.E851.format(val=val))

27
spacy/pipeline/textcat_multilabel.py
View File

@ -19,17 +19,17 @@ multi_label_default_config = """
@architectures = "spacy.TextCatEnsemble.v2"
[model.tok2vec]
@architectures = "spacy.Tok2Vec.v1"
@architectures = "spacy.Tok2Vec.v2"
[model.tok2vec.embed]
@architectures = "spacy.MultiHashEmbed.v2"
width = 64
rows = [2000, 2000, 1000, 1000, 1000, 1000]
attrs = ["ORTH", "LOWER", "PREFIX", "SUFFIX", "SHAPE", "ID"]
rows = [2000, 2000, 500, 1000, 500]
attrs = ["NORM", "LOWER", "PREFIX", "SUFFIX", "SHAPE"]
include_static_vectors = false
[model.tok2vec.encode]
@architectures = "spacy.MaxoutWindowEncoder.v1"
@architectures = "spacy.MaxoutWindowEncoder.v2"
width = ${model.tok2vec.embed.width}
window_size = 1
maxout_pieces = 3
@ -74,7 +74,7 @@ subword_features = true
default_config={
"threshold": 0.5,
"model": DEFAULT_MULTI_TEXTCAT_MODEL,
"scorer": {"@scorers": "spacy.textcat_multilabel_scorer.v1"},
"scorer": {"@scorers": "spacy.textcat_multilabel_scorer.v2"},
},
default_score_weights={
"cats_score": 1.0,
@ -87,7 +87,6 @@ subword_features = true
"cats_macro_f": None,
"cats_macro_auc": None,
"cats_f_per_type": None,
"cats_macro_auc_per_type": None,
},
)
def make_multilabel_textcat(
@ -96,8 +95,8 @@ def make_multilabel_textcat(
model: Model[List[Doc], List[Floats2d]],
threshold: float,
scorer: Optional[Callable],
) -> "TextCategorizer":
"""Create a TextCategorizer component. The text categorizer predicts categories
) -> "MultiLabel_TextCategorizer":
"""Create a MultiLabel_TextCategorizer component. The text categorizer predicts categories
over a whole document. It can learn one or more labels, and the labels are considered
to be non-mutually exclusive, which means that there can be zero or more labels
per doc).
@ -105,6 +104,7 @@ def make_multilabel_textcat(
model (Model[List[Doc], List[Floats2d]]): A model instance that predicts
scores for each category.
threshold (float): Cutoff to consider a prediction "positive".
scorer (Optional[Callable]): The scoring method.
"""
return MultiLabel_TextCategorizer(
nlp.vocab, model, name, threshold=threshold, scorer=scorer
@ -120,7 +120,7 @@ def textcat_multilabel_score(examples: Iterable[Example], **kwargs) -> Dict[str,
)
@registry.scorers("spacy.textcat_multilabel_scorer.v1")
@registry.scorers("spacy.textcat_multilabel_scorer.v2")
def make_textcat_multilabel_scorer():
return textcat_multilabel_score
@ -147,6 +147,7 @@ class MultiLabel_TextCategorizer(TextCategorizer):
name (str): The component instance name, used to add entries to the
losses during training.
threshold (float): Cutoff to consider a prediction "positive".
scorer (Optional[Callable]): The scoring method.
DOCS: https://spacy.io/api/textcategorizer#init
"""
@ -190,6 +191,8 @@ class MultiLabel_TextCategorizer(TextCategorizer):
for label in labels:
self.add_label(label)
subbatch = list(islice(get_examples(), 10))
self._validate_categories(subbatch)
doc_sample = [eg.reference for eg in subbatch]
label_sample, _ = self._examples_to_truth(subbatch)
self._require_labels()
@ -200,4 +203,8 @@ class MultiLabel_TextCategorizer(TextCategorizer):
def _validate_categories(self, examples: Iterable[Example]):
"""This component allows any type of single- or multi-label annotations.
This method overwrites the more strict one from 'textcat'."""
pass
# check that annotation values are valid
for ex in examples:
for val in ex.reference.cats.values():
if not (val == 1.0 or val == 0.0):
raise ValueError(Errors.E851.format(val=val))

20
spacy/pipeline/tok2vec.py
View File

@ -123,9 +123,6 @@ class Tok2Vec(TrainablePipe):
width = self.model.get_dim("nO")
return [self.model.ops.alloc((0, width)) for doc in docs]
tokvecs = self.model.predict(docs)
batch_id = Tok2VecListener.get_batch_id(docs)
for listener in self.listeners:
listener.receive(batch_id, tokvecs, _empty_backprop)
return tokvecs
def set_annotations(self, docs: Sequence[Doc], tokvecses) -> None:
@ -286,8 +283,19 @@ class Tok2VecListener(Model):
def forward(model: Tok2VecListener, inputs, is_train: bool):
"""Supply the outputs from the upstream Tok2Vec component."""
if is_train:
model.verify_inputs(inputs)
return model._outputs, model._backprop
# This might occur during training when the tok2vec layer is frozen / hasn't been updated.
# In that case, it should be set to "annotating" so we can retrieve the embeddings from the doc.
if model._batch_id is None:
outputs = []
for doc in inputs:
if doc.tensor.size == 0:
raise ValueError(Errors.E203.format(name="tok2vec"))
else:
outputs.append(doc.tensor)
return outputs, _empty_backprop
else:
model.verify_inputs(inputs)
return model._outputs, model._backprop
else:
# This is pretty grim, but it's hard to do better :(.
# It's hard to avoid relying on the doc.tensor attribute, because the
@ -306,7 +314,7 @@ def forward(model: Tok2VecListener, inputs, is_train: bool):
outputs.append(model.ops.alloc2f(len(doc), width))
else:
outputs.append(doc.tensor)
return outputs, lambda dX: []
return outputs, _empty_backprop
def _empty_backprop(dX): # for pickling

57
spacy/schemas.py
View File

@ -156,12 +156,40 @@ def validate_token_pattern(obj: list) -> List[str]:
class TokenPatternString(BaseModel):
REGEX: Optional[StrictStr] = Field(None, alias="regex")
REGEX: Optional[Union[StrictStr, "TokenPatternString"]] = Field(None, alias="regex")
IN: Optional[List[StrictStr]] = Field(None, alias="in")
NOT_IN: Optional[List[StrictStr]] = Field(None, alias="not_in")
IS_SUBSET: Optional[List[StrictStr]] = Field(None, alias="is_subset")
IS_SUPERSET: Optional[List[StrictStr]] = Field(None, alias="is_superset")
INTERSECTS: Optional[List[StrictStr]] = Field(None, alias="intersects")
FUZZY: Optional[Union[StrictStr, "TokenPatternString"]] = Field(None, alias="fuzzy")
FUZZY1: Optional[Union[StrictStr, "TokenPatternString"]] = Field(
None, alias="fuzzy1"
)
FUZZY2: Optional[Union[StrictStr, "TokenPatternString"]] = Field(
None, alias="fuzzy2"
)
FUZZY3: Optional[Union[StrictStr, "TokenPatternString"]] = Field(
None, alias="fuzzy3"
)
FUZZY4: Optional[Union[StrictStr, "TokenPatternString"]] = Field(
None, alias="fuzzy4"
)
FUZZY5: Optional[Union[StrictStr, "TokenPatternString"]] = Field(
None, alias="fuzzy5"
)
FUZZY6: Optional[Union[StrictStr, "TokenPatternString"]] = Field(
None, alias="fuzzy6"
)
FUZZY7: Optional[Union[StrictStr, "TokenPatternString"]] = Field(
None, alias="fuzzy7"
)
FUZZY8: Optional[Union[StrictStr, "TokenPatternString"]] = Field(
None, alias="fuzzy8"
)
FUZZY9: Optional[Union[StrictStr, "TokenPatternString"]] = Field(
None, alias="fuzzy9"
)
class Config:
extra = "forbid"
@ -181,12 +209,12 @@ class TokenPatternNumber(BaseModel):
IS_SUBSET: Optional[List[StrictInt]] = Field(None, alias="is_subset")
IS_SUPERSET: Optional[List[StrictInt]] = Field(None, alias="is_superset")
INTERSECTS: Optional[List[StrictInt]] = Field(None, alias="intersects")
EQ: Union[StrictInt, StrictFloat] = Field(None, alias="==")
NEQ: Union[StrictInt, StrictFloat] = Field(None, alias="!=")
GEQ: Union[StrictInt, StrictFloat] = Field(None, alias=">=")
LEQ: Union[StrictInt, StrictFloat] = Field(None, alias="<=")
GT: Union[StrictInt, StrictFloat] = Field(None, alias=">")
LT: Union[StrictInt, StrictFloat] = Field(None, alias="<")
EQ: Optional[Union[StrictInt, StrictFloat]] = Field(None, alias="==")
NEQ: Optional[Union[StrictInt, StrictFloat]] = Field(None, alias="!=")
GEQ: Optional[Union[StrictInt, StrictFloat]] = Field(None, alias=">=")
LEQ: Optional[Union[StrictInt, StrictFloat]] = Field(None, alias="<=")
GT: Optional[Union[StrictInt, StrictFloat]] = Field(None, alias=">")
LT: Optional[Union[StrictInt, StrictFloat]] = Field(None, alias="<")
class Config:
extra = "forbid"
@ -329,6 +357,7 @@ class ConfigSchemaTraining(BaseModel):
frozen_components: List[str] = Field(..., title="Pipeline components that shouldn't be updated during training")
annotating_components: List[str] = Field(..., title="Pipeline components that should set annotations during training")
before_to_disk: Optional[Callable[["Language"], "Language"]] = Field(..., title="Optional callback to modify nlp object after training, before it's saved to disk")
before_update: Optional[Callable[["Language", Dict[str, Any]], None]] = Field(..., title="Optional callback that is invoked at the start of each training step")
# fmt: on
class Config:
@ -430,7 +459,7 @@ class ProjectConfigAssetURL(BaseModel):
# fmt: off
dest: StrictStr = Field(..., title="Destination of downloaded asset")
url: Optional[StrictStr] = Field(None, title="URL of asset")
checksum: str = Field(None, title="MD5 hash of file", regex=r"([a-fA-F\d]{32})")
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
@ -438,7 +467,7 @@ class ProjectConfigAssetURL(BaseModel):
class ProjectConfigAssetGit(BaseModel):
# fmt: off
git: ProjectConfigAssetGitItem = Field(..., title="Git repo information")
checksum: str = Field(None, title="MD5 hash of file", regex=r"([a-fA-F\d]{32})")
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
@ -509,9 +538,9 @@ class DocJSONSchema(BaseModel):
None, title="Indices of sentences' start and end indices"
)
text: StrictStr = Field(..., title="Document text")
spans: Dict[StrictStr, List[Dict[StrictStr, Union[StrictStr, StrictInt]]]] = Field(
None, title="Span information - end/start indices, label, KB ID"
)
spans: Optional[
Dict[StrictStr, List[Dict[StrictStr, Union[StrictStr, StrictInt]]]]
] = Field(None, title="Span information - end/start indices, label, KB ID")
tokens: List[Dict[StrictStr, Union[StrictStr, StrictInt]]] = Field(
..., title="Token information - ID, start, annotations"
)
@ -520,9 +549,9 @@ class DocJSONSchema(BaseModel):
title="Any custom data stored in the document's _ attribute",
alias="_",
)
underscore_token: Optional[Dict[StrictStr, Dict[StrictStr, Any]]] = Field(
underscore_token: Optional[Dict[StrictStr, List[Dict[StrictStr, Any]]]] = Field(
None, title="Any custom data stored in the token's _ attribute"
)
underscore_span: Optional[Dict[StrictStr, Dict[StrictStr, Any]]] = Field(
underscore_span: Optional[Dict[StrictStr, List[Dict[StrictStr, Any]]]] = Field(
None, title="Any custom data stored in the span's _ attribute"
)

20
spacy/scorer.py
View File

@ -174,7 +174,7 @@ class Scorer:
prf_score.score_set(pred_spans, gold_spans)
if len(acc_score) > 0:
return {
"token_acc": acc_score.fscore,
"token_acc": acc_score.precision,
"token_p": prf_score.precision,
"token_r": prf_score.recall,
"token_f": prf_score.fscore,
@ -446,7 +446,7 @@ class Scorer:
labels (Iterable[str]): The set of possible labels. Defaults to [].
multi_label (bool): Whether the attribute allows multiple labels.
Defaults to True. When set to False (exclusive labels), missing
gold labels are interpreted as 0.0.
gold labels are interpreted as 0.0 and the threshold is set to 0.0.
positive_label (str): The positive label for a binary task with
exclusive classes. Defaults to None.
threshold (float): Cutoff to consider a prediction "positive". Defaults
@ -471,17 +471,17 @@ class Scorer:
"""
if threshold is None:
threshold = 0.5 if multi_label else 0.0
if not multi_label:
threshold = 0.0
f_per_type = {label: PRFScore() for label in labels}
auc_per_type = {label: ROCAUCScore() for label in labels}
labels = set(labels)
if labels:
for eg in examples:
labels.update(eg.predicted.cats.keys())
labels.update(eg.reference.cats.keys())
for example in examples:
# Through this loop, None in the gold_cats indicates missing label.
pred_cats = getter(example.predicted, attr)
pred_cats = {k: v for k, v in pred_cats.items() if k in labels}
gold_cats = getter(example.reference, attr)
gold_cats = {k: v for k, v in gold_cats.items() if k in labels}
for label in labels:
pred_score = pred_cats.get(label, 0.0)
@ -505,20 +505,18 @@ class Scorer:
# Get the highest-scoring for each.
pred_label, pred_score = max(pred_cats.items(), key=lambda it: it[1])
gold_label, gold_score = max(gold_cats.items(), key=lambda it: it[1])
if pred_label == gold_label and pred_score >= threshold:
if pred_label == gold_label:
f_per_type[pred_label].tp += 1
else:
f_per_type[gold_label].fn += 1
if pred_score >= threshold:
f_per_type[pred_label].fp += 1
f_per_type[pred_label].fp += 1
elif gold_cats:
gold_label, gold_score = max(gold_cats, key=lambda it: it[1])
if gold_score > 0:
f_per_type[gold_label].fn += 1
elif pred_cats:
pred_label, pred_score = max(pred_cats.items(), key=lambda it: it[1])
if pred_score >= threshold:
f_per_type[pred_label].fp += 1
f_per_type[pred_label].fp += 1
micro_prf = PRFScore()
for label_prf in f_per_type.values():
micro_prf.tp += label_prf.tp

31
spacy/tests/conftest.py
View File

@ -333,16 +333,24 @@ def ro_tokenizer():
@pytest.fixture(scope="session")
def ru_tokenizer():
pytest.importorskip("pymorphy2")
pytest.importorskip("pymorphy3")
return get_lang_class("ru")().tokenizer
@pytest.fixture
@pytest.fixture(scope="session")
def ru_lemmatizer():
pytest.importorskip("pymorphy2")
pytest.importorskip("pymorphy3")
return get_lang_class("ru")().add_pipe("lemmatizer")
@pytest.fixture(scope="session")
def ru_lookup_lemmatizer():
pytest.importorskip("pymorphy3")
return get_lang_class("ru")().add_pipe(
"lemmatizer", config={"mode": "pymorphy3_lookup"}
)
@pytest.fixture(scope="session")
def sa_tokenizer():
return get_lang_class("sa")().tokenizer
@ -411,17 +419,26 @@ def ky_tokenizer():
@pytest.fixture(scope="session")
def uk_tokenizer():
pytest.importorskip("pymorphy2")
pytest.importorskip("pymorphy3")
return get_lang_class("uk")().tokenizer
@pytest.fixture
@pytest.fixture(scope="session")
def uk_lemmatizer():
pytest.importorskip("pymorphy2")
pytest.importorskip("pymorphy2_dicts_uk")
pytest.importorskip("pymorphy3")
pytest.importorskip("pymorphy3_dicts_uk")
return get_lang_class("uk")().add_pipe("lemmatizer")
@pytest.fixture(scope="session")
def uk_lookup_lemmatizer():
pytest.importorskip("pymorphy3")
pytest.importorskip("pymorphy3_dicts_uk")
return get_lang_class("uk")().add_pipe(
"lemmatizer", config={"mode": "pymorphy3_lookup"}
)
@pytest.fixture(scope="session")
def ur_tokenizer():
return get_lang_class("ur")().tokenizer

4
spacy/tests/doc/test_array.py
View File

@ -123,14 +123,14 @@ def test_doc_from_array_heads_in_bounds(en_vocab):
# head before start
arr = doc.to_array(["HEAD"])
arr[0] = -1
arr[0] = numpy.int32(-1).astype(numpy.uint64)
doc_from_array = Doc(en_vocab, words=words)
with pytest.raises(ValueError):
doc_from_array.from_array(["HEAD"], arr)
# head after end
arr = doc.to_array(["HEAD"])
arr[0] = 5
arr[0] = numpy.int32(5).astype(numpy.uint64)
doc_from_array = Doc(en_vocab, words=words)
with pytest.raises(ValueError):
doc_from_array.from_array(["HEAD"], arr)

34
spacy/tests/doc/test_json_doc_conversion.py
View File

@ -128,7 +128,9 @@ def test_doc_to_json_with_token_span_attributes(doc):
doc._.json_test1 = "hello world"
doc._.json_test2 = [1, 2, 3]
doc[0:1]._.span_test = "span_attribute"
doc[0:2]._.span_test = "span_attribute_2"
doc[0]._.token_test = 117
doc[1]._.token_test = 118
doc.spans["span_group"] = [doc[0:1]]
json_doc = doc.to_json(
underscore=["json_test1", "json_test2", "token_test", "span_test"]
@ -139,8 +141,10 @@ def test_doc_to_json_with_token_span_attributes(doc):
assert json_doc["_"]["json_test2"] == [1, 2, 3]
assert "underscore_token" in json_doc
assert "underscore_span" in json_doc
assert json_doc["underscore_token"]["token_test"]["value"] == 117
assert json_doc["underscore_span"]["span_test"]["value"] == "span_attribute"
assert json_doc["underscore_token"]["token_test"][0]["value"] == 117
assert json_doc["underscore_token"]["token_test"][1]["value"] == 118
assert json_doc["underscore_span"]["span_test"][0]["value"] == "span_attribute"
assert json_doc["underscore_span"]["span_test"][1]["value"] == "span_attribute_2"
assert len(schemas.validate(schemas.DocJSONSchema, json_doc)) == 0
assert srsly.json_loads(srsly.json_dumps(json_doc)) == json_doc
@ -161,8 +165,8 @@ def test_doc_to_json_with_custom_user_data(doc):
assert json_doc["_"]["json_test"] == "hello world"
assert "underscore_token" in json_doc
assert "underscore_span" in json_doc
assert json_doc["underscore_token"]["token_test"]["value"] == 117
assert json_doc["underscore_span"]["span_test"]["value"] == "span_attribute"
assert json_doc["underscore_token"]["token_test"][0]["value"] == 117
assert json_doc["underscore_span"]["span_test"][0]["value"] == "span_attribute"
assert len(schemas.validate(schemas.DocJSONSchema, json_doc)) == 0
assert srsly.json_loads(srsly.json_dumps(json_doc)) == json_doc
@ -181,8 +185,8 @@ def test_doc_to_json_with_token_span_same_identifier(doc):
assert json_doc["_"]["my_ext"] == "hello world"
assert "underscore_token" in json_doc
assert "underscore_span" in json_doc
assert json_doc["underscore_token"]["my_ext"]["value"] == 117
assert json_doc["underscore_span"]["my_ext"]["value"] == "span_attribute"
assert json_doc["underscore_token"]["my_ext"][0]["value"] == 117
assert json_doc["underscore_span"]["my_ext"][0]["value"] == "span_attribute"
assert len(schemas.validate(schemas.DocJSONSchema, json_doc)) == 0
assert srsly.json_loads(srsly.json_dumps(json_doc)) == json_doc
@ -195,10 +199,9 @@ def test_doc_to_json_with_token_attributes_missing(doc):
doc[0]._.token_test = 117
json_doc = doc.to_json(underscore=["span_test"])
assert "underscore_token" in json_doc
assert "underscore_span" in json_doc
assert json_doc["underscore_span"]["span_test"]["value"] == "span_attribute"
assert "token_test" not in json_doc["underscore_token"]
assert json_doc["underscore_span"]["span_test"][0]["value"] == "span_attribute"
assert "underscore_token" not in json_doc
assert len(schemas.validate(schemas.DocJSONSchema, json_doc)) == 0
@ -283,7 +286,9 @@ def test_json_to_doc_with_token_span_attributes(doc):
doc._.json_test1 = "hello world"
doc._.json_test2 = [1, 2, 3]
doc[0:1]._.span_test = "span_attribute"
doc[0:2]._.span_test = "span_attribute_2"
doc[0]._.token_test = 117
doc[1]._.token_test = 118
json_doc = doc.to_json(
underscore=["json_test1", "json_test2", "token_test", "span_test"]
@ -295,7 +300,9 @@ def test_json_to_doc_with_token_span_attributes(doc):
assert new_doc._.json_test1 == "hello world"
assert new_doc._.json_test2 == [1, 2, 3]
assert new_doc[0]._.token_test == 117
assert new_doc[1]._.token_test == 118
assert new_doc[0:1]._.span_test == "span_attribute"
assert new_doc[0:2]._.span_test == "span_attribute_2"
assert new_doc.user_data == doc.user_data
assert new_doc.to_bytes(exclude=["user_data"]) == doc.to_bytes(
exclude=["user_data"]
@ -363,3 +370,12 @@ def test_json_to_doc_validation_error(doc):
doc_json.pop("tokens")
with pytest.raises(ValueError):
Doc(doc.vocab).from_json(doc_json, validate=True)
def test_to_json_underscore_doc_getters(doc):
def get_text_length(doc):
return len(doc.text)
Doc.set_extension("text_length", getter=get_text_length)
doc_json = doc.to_json(underscore=["text_length"])
assert doc_json["_"]["text_length"] == get_text_length(doc)

15
spacy/tests/doc/test_span_group.py
View File

@ -1,7 +1,10 @@
from typing import List
import pytest
from random import Random
from spacy.matcher import Matcher
from spacy.tokens import Span, SpanGroup
from spacy.tokens import Span, SpanGroup, Doc
from spacy.util import filter_spans
@pytest.fixture
@ -240,3 +243,13 @@ def test_span_group_extend(doc):
def test_span_group_dealloc(span_group):
with pytest.raises(AttributeError):
print(span_group.doc)
@pytest.mark.issue(11975)
def test_span_group_typing(doc: Doc):
"""Tests whether typing of `SpanGroup` as `Iterable[Span]`-like object is accepted by mypy."""
span_group: SpanGroup = doc.spans["SPANS"]
spans: List[Span] = list(span_group)
for i, span in enumerate(span_group):
assert span == span_group[i] == spans[i]
filter_spans(span_group)

18
spacy/tests/lang/grc/test_tokenizer.py Normal file
View File

@ -0,0 +1,18 @@
import pytest
# fmt: off
GRC_TOKEN_EXCEPTION_TESTS = [
("τὸ 〈τῆς〉 φιλοσοφίας ἔργον ἔνιοί φασιν ἀπὸ ⟦βαρβάρων⟧ ἄρξαι.", ["τὸ", "〈", "τῆς", "〉", "φιλοσοφίας", "ἔργον", "ἔνιοί", "φασιν", "ἀπὸ", "⟦", "βαρβάρων", "⟧", "ἄρξαι", "."]),
("τὴν δὲ τῶν Αἰγυπτίων φιλοσοφίαν εἶναι τοιαύτην περί τε †θεῶν† καὶ ὑπὲρ δικαιοσύνης.", ["τὴν", "δὲ", "τῶν", "Αἰγυπτίων", "φιλοσοφίαν", "εἶναι", "τοιαύτην", "περί", "τε", "†", "θεῶν", "†", "καὶ", "ὑπὲρ", "δικαιοσύνης", "."]),
("⸏πόσις δ' Ἐρεχθεύς ἐστί μοι σεσωσμένος⸏", ["⸏", "πόσις", "δ'", "Ἐρεχθεύς", "ἐστί", "μοι", "σεσωσμένος", "⸏"]),
("⸏ὔπνον ἴδωμεν⸎", ["⸏", "ὔπνον", "ἴδωμεν", "⸎"]),
]
# fmt: on
@pytest.mark.parametrize("text,expected_tokens", GRC_TOKEN_EXCEPTION_TESTS)
def test_grc_tokenizer(grc_tokenizer, text, expected_tokens):
tokens = grc_tokenizer(text)
token_list = [token.text for token in tokens if not token.is_space]
assert expected_tokens == token_list

29
spacy/tests/lang/ru/test_lemmatizer.py
View File

@ -78,3 +78,32 @@ def test_ru_lemmatizer_punct(ru_lemmatizer):
assert ru_lemmatizer.pymorphy2_lemmatize(doc[0]) == ['"']
doc = Doc(ru_lemmatizer.vocab, words=["»"], pos=["PUNCT"])
assert ru_lemmatizer.pymorphy2_lemmatize(doc[0]) == ['"']
def test_ru_doc_lookup_lemmatization(ru_lookup_lemmatizer):
assert ru_lookup_lemmatizer.mode == "pymorphy3_lookup"
words = ["мама", "мыла", "раму"]
pos = ["NOUN", "VERB", "NOUN"]
morphs = [
"Animacy=Anim|Case=Nom|Gender=Fem|Number=Sing",
"Aspect=Imp|Gender=Fem|Mood=Ind|Number=Sing|Tense=Past|VerbForm=Fin|Voice=Act",
"Animacy=Anim|Case=Acc|Gender=Fem|Number=Sing",
]
doc = Doc(ru_lookup_lemmatizer.vocab, words=words, pos=pos, morphs=morphs)
doc = ru_lookup_lemmatizer(doc)
lemmas = [token.lemma_ for token in doc]
assert lemmas == ["мама", "мыла", "раму"]
@pytest.mark.parametrize(
"word,lemma",
(
("бременем", "бремя"),
("будешь", "быть"),
("какая-то", "какой-то"),
),
)
def test_ru_lookup_lemmatizer(ru_lookup_lemmatizer, word, lemma):
assert ru_lookup_lemmatizer.mode == "pymorphy3_lookup"
doc = Doc(ru_lookup_lemmatizer.vocab, words=[word])
assert ru_lookup_lemmatizer(doc)[0].lemma_ == lemma

1
spacy/tests/lang/sl/test_text.py
View File

@ -20,7 +20,6 @@ od katerih so te svoboščine odvisne,
assert len(tokens) == 116
@pytest.mark.xfail
def test_ordinal_number(sl_tokenizer):
text = "10. decembra 1948"
tokens = sl_tokenizer(text)

16
spacy/tests/lang/uk/test_lemmatizer.py
View File

@ -8,4 +8,20 @@ pytestmark = pytest.mark.filterwarnings("ignore::DeprecationWarning")
def test_uk_lemmatizer(uk_lemmatizer):
"""Check that the default uk lemmatizer runs."""
doc = Doc(uk_lemmatizer.vocab, words=["a", "b", "c"])
assert uk_lemmatizer.mode == "pymorphy3"
uk_lemmatizer(doc)
assert [token.lemma for token in doc]
@pytest.mark.parametrize(
"word,lemma",
(
("якийсь", "якийсь"),
("розповідають", "розповідати"),
("розповіси", "розповісти"),
),
)
def test_uk_lookup_lemmatizer(uk_lookup_lemmatizer, word, lemma):
assert uk_lookup_lemmatizer.mode == "pymorphy3_lookup"
doc = Doc(uk_lookup_lemmatizer.vocab, words=[word])
assert uk_lookup_lemmatizer(doc)[0].lemma_ == lemma

29
spacy/tests/matcher/test_levenshtein.py
View File

@ -1,5 +1,6 @@
import pytest
from spacy.matcher import levenshtein
from spacy.matcher.levenshtein import levenshtein_compare
# empty string plus 10 random ASCII, 10 random unicode, and 2 random long tests
@ -42,3 +43,31 @@ from spacy.matcher import levenshtein
)
def test_levenshtein(dist, a, b):
assert levenshtein(a, b) == dist
@pytest.mark.parametrize(
"a,b,fuzzy,expected",
[
("a", "a", 1, True),
("a", "a", 0, True),
("a", "a", -1, True),
("a", "ab", 1, True),
("a", "ab", 0, False),
("a", "ab", -1, True),
("ab", "ac", 1, True),
("ab", "ac", -1, True),
("abc", "cde", 4, True),
("abc", "cde", -1, False),
("abcdef", "cdefgh", 4, True),
("abcdef", "cdefgh", 3, False),
("abcdef", "cdefgh", -1, False), # default (2 for length 6)
("abcdefgh", "cdefghijk", 5, True),
("abcdefgh", "cdefghijk", 4, False),
("abcdefgh", "cdefghijk", -1, False), # default (2)
("abcdefgh", "cdefghijkl", 6, True),
("abcdefgh", "cdefghijkl", 5, False),
("abcdefgh", "cdefghijkl", -1, False), # default (2)
],
)
def test_levenshtein_compare(a, b, fuzzy, expected):
assert levenshtein_compare(a, b, fuzzy) == expected

173
spacy/tests/matcher/test_matcher_api.py
View File

@ -118,6 +118,155 @@ def test_matcher_match_multi(matcher):
]
@pytest.mark.parametrize(
"rules,match_locs",
[
(
{
"GoogleNow": [[{"ORTH": {"FUZZY": "Google"}}, {"ORTH": "Now"}]],
},
[(2, 4)],
),
(
{
"Java": [[{"LOWER": {"FUZZY": "java"}}]],
},
[(5, 6)],
),
(
{
"JS": [[{"ORTH": {"FUZZY": "JavaScript"}}]],
"GoogleNow": [[{"ORTH": {"FUZZY": "Google"}}, {"ORTH": "Now"}]],
"Java": [[{"LOWER": {"FUZZY": "java"}}]],
},
[(2, 4), (5, 6), (8, 9)],
),
# only the second pattern matches (check that predicate keys used for
# caching don't collide)
(
{
"A": [[{"ORTH": {"FUZZY": "Javascripts"}}]],
"B": [[{"ORTH": {"FUZZY5": "Javascripts"}}]],
},
[(8, 9)],
),
],
)
def test_matcher_match_fuzzy(en_vocab, rules, match_locs):
words = ["They", "like", "Goggle", "Now", "and", "Jav", "but", "not", "JvvaScrpt"]
doc = Doc(en_vocab, words=words)
matcher = Matcher(en_vocab)
for key, patterns in rules.items():
matcher.add(key, patterns)
assert match_locs == [(start, end) for m_id, start, end in matcher(doc)]
@pytest.mark.parametrize("set_op", ["IN", "NOT_IN"])
def test_matcher_match_fuzzy_set_op_longest(en_vocab, set_op):
rules = {
"GoogleNow": [[{"ORTH": {"FUZZY": {set_op: ["Google", "Now"]}}, "OP": "+"}]]
}
matcher = Matcher(en_vocab)
for key, patterns in rules.items():
matcher.add(key, patterns, greedy="LONGEST")
words = ["They", "like", "Goggle", "Noo"]
doc = Doc(en_vocab, words=words)
assert len(matcher(doc)) == 1
def test_matcher_match_fuzzy_set_multiple(en_vocab):
rules = {
"GoogleNow": [
[
{
"ORTH": {"FUZZY": {"IN": ["Google", "Now"]}, "NOT_IN": ["Goggle"]},
"OP": "+",
}
]
]
}
matcher = Matcher(en_vocab)
for key, patterns in rules.items():
matcher.add(key, patterns, greedy="LONGEST")
words = ["They", "like", "Goggle", "Noo"]
doc = Doc(matcher.vocab, words=words)
assert matcher(doc) == [
(doc.vocab.strings["GoogleNow"], 3, 4),
]
@pytest.mark.parametrize("fuzzyn", range(1, 10))
def test_matcher_match_fuzzyn_all_insertions(en_vocab, fuzzyn):
matcher = Matcher(en_vocab)
matcher.add("GoogleNow", [[{"ORTH": {f"FUZZY{fuzzyn}": "GoogleNow"}}]])
# words with increasing edit distance
words = ["GoogleNow" + "a" * i for i in range(0, 10)]
doc = Doc(en_vocab, words)
assert len(matcher(doc)) == fuzzyn + 1
@pytest.mark.parametrize("fuzzyn", range(1, 6))
def test_matcher_match_fuzzyn_various_edits(en_vocab, fuzzyn):
matcher = Matcher(en_vocab)
matcher.add("GoogleNow", [[{"ORTH": {f"FUZZY{fuzzyn}": "GoogleNow"}}]])
# words with increasing edit distance of different edit types
words = [
"GoogleNow",
"GoogleNuw",
"GoogleNuew",
"GoogleNoweee",
"GiggleNuw3",
"gouggle5New",
]
doc = Doc(en_vocab, words)
assert len(matcher(doc)) == fuzzyn + 1
@pytest.mark.parametrize("greedy", ["FIRST", "LONGEST"])
@pytest.mark.parametrize("set_op", ["IN", "NOT_IN"])
def test_matcher_match_fuzzyn_set_op_longest(en_vocab, greedy, set_op):
rules = {
"GoogleNow": [[{"ORTH": {"FUZZY2": {set_op: ["Google", "Now"]}}, "OP": "+"}]]
}
matcher = Matcher(en_vocab)
for key, patterns in rules.items():
matcher.add(key, patterns, greedy=greedy)
words = ["They", "like", "Goggle", "Noo"]
doc = Doc(matcher.vocab, words=words)
spans = matcher(doc, as_spans=True)
assert len(spans) == 1
if set_op == "IN":
assert spans[0].text == "Goggle Noo"
else:
assert spans[0].text == "They like"
def test_matcher_match_fuzzyn_set_multiple(en_vocab):
rules = {
"GoogleNow": [
[
{
"ORTH": {"FUZZY1": {"IN": ["Google", "Now"]}, "NOT_IN": ["Goggle"]},
"OP": "+",
}
]
]
}
matcher = Matcher(en_vocab)
for key, patterns in rules.items():
matcher.add(key, patterns, greedy="LONGEST")
words = ["They", "like", "Goggle", "Noo"]
doc = Doc(matcher.vocab, words=words)
assert matcher(doc) == [
(doc.vocab.strings["GoogleNow"], 3, 4),
]
def test_matcher_empty_dict(en_vocab):
"""Test matcher allows empty token specs, meaning match on any token."""
matcher = Matcher(en_vocab)
@ -437,6 +586,30 @@ def test_matcher_regex(en_vocab):
assert len(matches) == 0
def test_matcher_regex_set_in(en_vocab):
matcher = Matcher(en_vocab)
pattern = [{"ORTH": {"REGEX": {"IN": [r"(?:a)", r"(?:an)"]}}}]
matcher.add("A_OR_AN", [pattern])
doc = Doc(en_vocab, words=["an", "a", "hi"])
matches = matcher(doc)
assert len(matches) == 2
doc = Doc(en_vocab, words=["bye"])
matches = matcher(doc)
assert len(matches) == 0
def test_matcher_regex_set_not_in(en_vocab):
matcher = Matcher(en_vocab)
pattern = [{"ORTH": {"REGEX": {"NOT_IN": [r"(?:a)", r"(?:an)"]}}}]
matcher.add("A_OR_AN", [pattern])
doc = Doc(en_vocab, words=["an", "a", "hi"])
matches = matcher(doc)
assert len(matches) == 1
doc = Doc(en_vocab, words=["bye"])
matches = matcher(doc)
assert len(matches) == 1
def test_matcher_regex_shape(en_vocab):
matcher = Matcher(en_vocab)
pattern = [{"SHAPE": {"REGEX": r"^[^x]+$"}}]

68
spacy/tests/pipeline/test_edit_tree_lemmatizer.py
View File

@ -60,20 +60,56 @@ def test_initialize_from_labels():
nlp2 = Language()
lemmatizer2 = nlp2.add_pipe("trainable_lemmatizer")
lemmatizer2.initialize(
get_examples=lambda: train_examples,
# We want to check that the strings in replacement nodes are
# added to the string store. Avoid that they get added through
# the examples.
get_examples=lambda: train_examples[:1],
labels=lemmatizer.label_data,
)
assert lemmatizer2.tree2label == {1: 0, 3: 1, 4: 2, 6: 3}
assert lemmatizer2.label_data == {
"trees": [
{"orig": "S", "subst": "s"},
{
"prefix_len": 1,
"suffix_len": 0,
"prefix_tree": 0,
"suffix_tree": 4294967295,
},
{"orig": "s", "subst": ""},
{
"prefix_len": 0,
"suffix_len": 1,
"prefix_tree": 4294967295,
"suffix_tree": 2,
},
{
"prefix_len": 0,
"suffix_len": 0,
"prefix_tree": 4294967295,
"suffix_tree": 4294967295,
},
{"orig": "E", "subst": "e"},
{
"prefix_len": 1,
"suffix_len": 0,
"prefix_tree": 5,
"suffix_tree": 4294967295,
},
],
"labels": (1, 3, 4, 6),
}
def test_no_data():
@pytest.mark.parametrize("top_k", (1, 5, 30))
def test_no_data(top_k):
# Test that the lemmatizer provides a nice error when there's no tagging data / labels
TEXTCAT_DATA = [
("I'm so happy.", {"cats": {"POSITIVE": 1.0, "NEGATIVE": 0.0}}),
("I'm so angry", {"cats": {"POSITIVE": 0.0, "NEGATIVE": 1.0}}),
]
nlp = English()
nlp.add_pipe("trainable_lemmatizer")
nlp.add_pipe("trainable_lemmatizer", config={"top_k": top_k})
nlp.add_pipe("textcat")
train_examples = []
@ -84,10 +120,11 @@ def test_no_data():
nlp.initialize(get_examples=lambda: train_examples)
def test_incomplete_data():
@pytest.mark.parametrize("top_k", (1, 5, 30))
def test_incomplete_data(top_k):
# Test that the lemmatizer works with incomplete information
nlp = English()
lemmatizer = nlp.add_pipe("trainable_lemmatizer")
lemmatizer = nlp.add_pipe("trainable_lemmatizer", config={"top_k": top_k})
lemmatizer.min_tree_freq = 1
train_examples = []
for t in PARTIAL_DATA:
@ -104,10 +141,25 @@ def test_incomplete_data():
assert doc[1].lemma_ == "like"
assert doc[2].lemma_ == "blue"
# Check that incomplete annotations are ignored.
scores, _ = lemmatizer.model([eg.predicted for eg in train_examples], is_train=True)
_, dX = lemmatizer.get_loss(train_examples, scores)
xp = lemmatizer.model.ops.xp
def test_overfitting_IO():
# Missing annotations.
assert xp.count_nonzero(dX[0][0]) == 0
assert xp.count_nonzero(dX[0][3]) == 0
assert xp.count_nonzero(dX[1][0]) == 0
assert xp.count_nonzero(dX[1][3]) == 0
# Misaligned annotations.
assert xp.count_nonzero(dX[1][1]) == 0
@pytest.mark.parametrize("top_k", (1, 5, 30))
def test_overfitting_IO(top_k):
nlp = English()
lemmatizer = nlp.add_pipe("trainable_lemmatizer")
lemmatizer = nlp.add_pipe("trainable_lemmatizer", config={"top_k": top_k})
lemmatizer.min_tree_freq = 1
train_examples = []
for t in TRAIN_DATA:
@ -140,7 +192,7 @@ def test_overfitting_IO():
# Check model after a {to,from}_bytes roundtrip
nlp_bytes = nlp.to_bytes()
nlp3 = English()
nlp3.add_pipe("trainable_lemmatizer")
nlp3.add_pipe("trainable_lemmatizer", config={"top_k": top_k})
nlp3.from_bytes(nlp_bytes)
doc3 = nlp3(test_text)
assert doc3[0].lemma_ == "she"

120
spacy/tests/pipeline/test_entity_linker.py
View File

@ -6,9 +6,10 @@ from numpy.testing import assert_equal
from spacy import registry, util
from spacy.attrs import ENT_KB_ID
from spacy.compat import pickle
from spacy.kb import Candidate, KnowledgeBase, get_candidates
from spacy.kb import Candidate, InMemoryLookupKB, get_candidates, KnowledgeBase
from spacy.lang.en import English
from spacy.ml import load_kb
from spacy.ml.models.entity_linker import build_span_maker
from spacy.pipeline import EntityLinker
from spacy.pipeline.legacy import EntityLinker_v1
from spacy.pipeline.tok2vec import DEFAULT_TOK2VEC_MODEL
@ -34,7 +35,7 @@ def assert_almost_equal(a, b):
def test_issue4674():
"""Test that setting entities with overlapping identifiers does not mess up IO"""
nlp = English()
kb = KnowledgeBase(nlp.vocab, entity_vector_length=3)
kb = InMemoryLookupKB(nlp.vocab, entity_vector_length=3)
vector1 = [0.9, 1.1, 1.01]
vector2 = [1.8, 2.25, 2.01]
with pytest.warns(UserWarning):
@ -51,7 +52,7 @@ def test_issue4674():
dir_path.mkdir()
file_path = dir_path / "kb"
kb.to_disk(str(file_path))
kb2 = KnowledgeBase(nlp.vocab, entity_vector_length=3)
kb2 = InMemoryLookupKB(nlp.vocab, entity_vector_length=3)
kb2.from_disk(str(file_path))
assert kb2.get_size_entities() == 1
@ -59,9 +60,9 @@ def test_issue4674():
@pytest.mark.issue(6730)
def test_issue6730(en_vocab):
"""Ensure that the KB does not accept empty strings, but otherwise IO works fine."""
from spacy.kb import KnowledgeBase
from spacy.kb.kb_in_memory import InMemoryLookupKB
kb = KnowledgeBase(en_vocab, entity_vector_length=3)
kb = InMemoryLookupKB(en_vocab, entity_vector_length=3)
kb.add_entity(entity="1", freq=148, entity_vector=[1, 2, 3])
with pytest.raises(ValueError):
@ -127,7 +128,7 @@ def test_issue7065_b():
def create_kb(vocab):
# create artificial KB
mykb = KnowledgeBase(vocab, entity_vector_length=vector_length)
mykb = InMemoryLookupKB(vocab, entity_vector_length=vector_length)
mykb.add_entity(entity="Q270853", freq=12, entity_vector=[9, 1, -7])
mykb.add_alias(
alias="No. 8",
@ -190,7 +191,7 @@ def test_no_entities():
def create_kb(vocab):
# create artificial KB
mykb = KnowledgeBase(vocab, entity_vector_length=vector_length)
mykb = InMemoryLookupKB(vocab, entity_vector_length=vector_length)
mykb.add_entity(entity="Q2146908", freq=12, entity_vector=[6, -4, 3])
mykb.add_alias("Russ Cochran", ["Q2146908"], [0.9])
return mykb
@ -231,7 +232,7 @@ def test_partial_links():
def create_kb(vocab):
# create artificial KB
mykb = KnowledgeBase(vocab, entity_vector_length=vector_length)
mykb = InMemoryLookupKB(vocab, entity_vector_length=vector_length)
mykb.add_entity(entity="Q2146908", freq=12, entity_vector=[6, -4, 3])
mykb.add_alias("Russ Cochran", ["Q2146908"], [0.9])
return mykb
@ -263,7 +264,7 @@ def test_partial_links():
def test_kb_valid_entities(nlp):
"""Test the valid construction of a KB with 3 entities and two aliases"""
mykb = KnowledgeBase(nlp.vocab, entity_vector_length=3)
mykb = InMemoryLookupKB(nlp.vocab, entity_vector_length=3)
# adding entities
mykb.add_entity(entity="Q1", freq=19, entity_vector=[8, 4, 3])
@ -292,7 +293,7 @@ def test_kb_valid_entities(nlp):
def test_kb_invalid_entities(nlp):
"""Test the invalid construction of a KB with an alias linked to a non-existing entity"""
mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
mykb = InMemoryLookupKB(nlp.vocab, entity_vector_length=1)
# adding entities
mykb.add_entity(entity="Q1", freq=19, entity_vector=[1])
@ -308,7 +309,7 @@ def test_kb_invalid_entities(nlp):
def test_kb_invalid_probabilities(nlp):
"""Test the invalid construction of a KB with wrong prior probabilities"""
mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
mykb = InMemoryLookupKB(nlp.vocab, entity_vector_length=1)
# adding entities
mykb.add_entity(entity="Q1", freq=19, entity_vector=[1])
@ -322,7 +323,7 @@ def test_kb_invalid_probabilities(nlp):
def test_kb_invalid_combination(nlp):
"""Test the invalid construction of a KB with non-matching entity and probability lists"""
mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
mykb = InMemoryLookupKB(nlp.vocab, entity_vector_length=1)
# adding entities
mykb.add_entity(entity="Q1", freq=19, entity_vector=[1])
@ -338,7 +339,7 @@ def test_kb_invalid_combination(nlp):
def test_kb_invalid_entity_vector(nlp):
"""Test the invalid construction of a KB with non-matching entity vector lengths"""
mykb = KnowledgeBase(nlp.vocab, entity_vector_length=3)
mykb = InMemoryLookupKB(nlp.vocab, entity_vector_length=3)
# adding entities
mykb.add_entity(entity="Q1", freq=19, entity_vector=[1, 2, 3])
@ -376,7 +377,7 @@ def test_kb_initialize_empty(nlp):
def test_kb_serialize(nlp):
"""Test serialization of the KB"""
mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
mykb = InMemoryLookupKB(nlp.vocab, entity_vector_length=1)
with make_tempdir() as d:
# normal read-write behaviour
mykb.to_disk(d / "kb")
@ -393,12 +394,12 @@ def test_kb_serialize(nlp):
@pytest.mark.issue(9137)
def test_kb_serialize_2(nlp):
v = [5, 6, 7, 8]
kb1 = KnowledgeBase(vocab=nlp.vocab, entity_vector_length=4)
kb1 = InMemoryLookupKB(vocab=nlp.vocab, entity_vector_length=4)
kb1.set_entities(["E1"], [1], [v])
assert kb1.get_vector("E1") == v
with make_tempdir() as d:
kb1.to_disk(d / "kb")
kb2 = KnowledgeBase(vocab=nlp.vocab, entity_vector_length=4)
kb2 = InMemoryLookupKB(vocab=nlp.vocab, entity_vector_length=4)
kb2.from_disk(d / "kb")
assert kb2.get_vector("E1") == v
@ -408,7 +409,7 @@ def test_kb_set_entities(nlp):
v = [5, 6, 7, 8]
v1 = [1, 1, 1, 0]
v2 = [2, 2, 2, 3]
kb1 = KnowledgeBase(vocab=nlp.vocab, entity_vector_length=4)
kb1 = InMemoryLookupKB(vocab=nlp.vocab, entity_vector_length=4)
kb1.set_entities(["E0"], [1], [v])
assert kb1.get_entity_strings() == ["E0"]
kb1.set_entities(["E1", "E2"], [1, 9], [v1, v2])
@ -417,7 +418,7 @@ def test_kb_set_entities(nlp):
assert kb1.get_vector("E2") == v2
with make_tempdir() as d:
kb1.to_disk(d / "kb")
kb2 = KnowledgeBase(vocab=nlp.vocab, entity_vector_length=4)
kb2 = InMemoryLookupKB(vocab=nlp.vocab, entity_vector_length=4)
kb2.from_disk(d / "kb")
assert set(kb2.get_entity_strings()) == {"E1", "E2"}
assert kb2.get_vector("E1") == v1
@ -428,7 +429,7 @@ def test_kb_serialize_vocab(nlp):
"""Test serialization of the KB and custom strings"""
entity = "MyFunnyID"
assert entity not in nlp.vocab.strings
mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
mykb = InMemoryLookupKB(nlp.vocab, entity_vector_length=1)
assert not mykb.contains_entity(entity)
mykb.add_entity(entity, freq=342, entity_vector=[3])
assert mykb.contains_entity(entity)
@ -436,14 +437,14 @@ def test_kb_serialize_vocab(nlp):
with make_tempdir() as d:
# normal read-write behaviour
mykb.to_disk(d / "kb")
mykb_new = KnowledgeBase(Vocab(), entity_vector_length=1)
mykb_new = InMemoryLookupKB(Vocab(), entity_vector_length=1)
mykb_new.from_disk(d / "kb")
assert entity in mykb_new.vocab.strings
def test_candidate_generation(nlp):
"""Test correct candidate generation"""
mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
mykb = InMemoryLookupKB(nlp.vocab, entity_vector_length=1)
doc = nlp("douglas adam Adam shrubbery")
douglas_ent = doc[0:1]
@ -481,7 +482,7 @@ def test_el_pipe_configuration(nlp):
ruler.add_patterns([pattern])
def create_kb(vocab):
kb = KnowledgeBase(vocab, entity_vector_length=1)
kb = InMemoryLookupKB(vocab, entity_vector_length=1)
kb.add_entity(entity="Q2", freq=12, entity_vector=[2])
kb.add_entity(entity="Q3", freq=5, entity_vector=[3])
kb.add_alias(alias="douglas", entities=["Q2", "Q3"], probabilities=[0.8, 0.1])
@ -500,10 +501,21 @@ def test_el_pipe_configuration(nlp):
def get_lowercased_candidates(kb, span):
return kb.get_alias_candidates(span.text.lower())
def get_lowercased_candidates_batch(kb, spans):
return [get_lowercased_candidates(kb, span) for span in spans]
@registry.misc("spacy.LowercaseCandidateGenerator.v1")
def create_candidates() -> Callable[[KnowledgeBase, "Span"], Iterable[Candidate]]:
def create_candidates() -> Callable[
[InMemoryLookupKB, "Span"], Iterable[Candidate]
]:
return get_lowercased_candidates
@registry.misc("spacy.LowercaseCandidateBatchGenerator.v1")
def create_candidates_batch() -> Callable[
[InMemoryLookupKB, Iterable["Span"]], Iterable[Iterable[Candidate]]
]:
return get_lowercased_candidates_batch
# replace the pipe with a new one with with a different candidate generator
entity_linker = nlp.replace_pipe(
"entity_linker",
@ -511,6 +523,9 @@ def test_el_pipe_configuration(nlp):
config={
"incl_context": False,
"get_candidates": {"@misc": "spacy.LowercaseCandidateGenerator.v1"},
"get_candidates_batch": {
"@misc": "spacy.LowercaseCandidateBatchGenerator.v1"
},
},
)
entity_linker.set_kb(create_kb)
@ -532,7 +547,7 @@ def test_nel_nsents(nlp):
def test_vocab_serialization(nlp):
"""Test that string information is retained across storage"""
mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
mykb = InMemoryLookupKB(nlp.vocab, entity_vector_length=1)
# adding entities
mykb.add_entity(entity="Q1", freq=27, entity_vector=[1])
@ -552,7 +567,7 @@ def test_vocab_serialization(nlp):
with make_tempdir() as d:
mykb.to_disk(d / "kb")
kb_new_vocab = KnowledgeBase(Vocab(), entity_vector_length=1)
kb_new_vocab = InMemoryLookupKB(Vocab(), entity_vector_length=1)
kb_new_vocab.from_disk(d / "kb")
candidates = kb_new_vocab.get_alias_candidates("adam")
@ -568,7 +583,7 @@ def test_vocab_serialization(nlp):
def test_append_alias(nlp):
"""Test that we can append additional alias-entity pairs"""
mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
mykb = InMemoryLookupKB(nlp.vocab, entity_vector_length=1)
# adding entities
mykb.add_entity(entity="Q1", freq=27, entity_vector=[1])
@ -599,7 +614,7 @@ def test_append_alias(nlp):
@pytest.mark.filterwarnings("ignore:\\[W036")
def test_append_invalid_alias(nlp):
"""Test that append an alias will throw an error if prior probs are exceeding 1"""
mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
mykb = InMemoryLookupKB(nlp.vocab, entity_vector_length=1)
# adding entities
mykb.add_entity(entity="Q1", freq=27, entity_vector=[1])
@ -621,7 +636,7 @@ def test_preserving_links_asdoc(nlp):
vector_length = 1
def create_kb(vocab):
mykb = KnowledgeBase(vocab, entity_vector_length=vector_length)
mykb = InMemoryLookupKB(vocab, entity_vector_length=vector_length)
# adding entities
mykb.add_entity(entity="Q1", freq=19, entity_vector=[1])
mykb.add_entity(entity="Q2", freq=8, entity_vector=[1])
@ -701,7 +716,11 @@ TRAIN_DATA = [
("Russ Cochran was a member of University of Kentucky's golf team.",
{"links": {(0, 12): {"Q7381115": 0.0, "Q2146908": 1.0}},
"entities": [(0, 12, "PERSON"), (43, 51, "LOC")],
"sent_starts": [1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]})
"sent_starts": [1, -1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0]}),
# having a blank instance shouldn't break things
("The weather is nice today.",
{"links": {}, "entities": [],
"sent_starts": [1, -1, 0, 0, 0, 0]})
]
GOLD_entities = ["Q2146908", "Q7381115", "Q7381115", "Q2146908"]
# fmt: on
@ -723,7 +742,7 @@ def test_overfitting_IO():
# create artificial KB - assign same prior weight to the two russ cochran's
# Q2146908 (Russ Cochran): American golfer
# Q7381115 (Russ Cochran): publisher
mykb = KnowledgeBase(vocab, entity_vector_length=vector_length)
mykb = InMemoryLookupKB(vocab, entity_vector_length=vector_length)
mykb.add_entity(entity="Q2146908", freq=12, entity_vector=[6, -4, 3])
mykb.add_entity(entity="Q7381115", freq=12, entity_vector=[9, 1, -7])
mykb.add_alias(
@ -805,7 +824,7 @@ def test_kb_serialization():
kb_dir = tmp_dir / "kb"
nlp1 = English()
assert "Q2146908" not in nlp1.vocab.strings
mykb = KnowledgeBase(nlp1.vocab, entity_vector_length=vector_length)
mykb = InMemoryLookupKB(nlp1.vocab, entity_vector_length=vector_length)
mykb.add_entity(entity="Q2146908", freq=12, entity_vector=[6, -4, 3])
mykb.add_alias(alias="Russ Cochran", entities=["Q2146908"], probabilities=[0.8])
assert "Q2146908" in nlp1.vocab.strings
@ -828,7 +847,7 @@ def test_kb_serialization():
def test_kb_pickle():
# Test that the KB can be pickled
nlp = English()
kb_1 = KnowledgeBase(nlp.vocab, entity_vector_length=3)
kb_1 = InMemoryLookupKB(nlp.vocab, entity_vector_length=3)
kb_1.add_entity(entity="Q2146908", freq=12, entity_vector=[6, -4, 3])
assert not kb_1.contains_alias("Russ Cochran")
kb_1.add_alias(alias="Russ Cochran", entities=["Q2146908"], probabilities=[0.8])
@ -842,7 +861,7 @@ def test_kb_pickle():
def test_nel_pickle():
# Test that a pipeline with an EL component can be pickled
def create_kb(vocab):
kb = KnowledgeBase(vocab, entity_vector_length=3)
kb = InMemoryLookupKB(vocab, entity_vector_length=3)
kb.add_entity(entity="Q2146908", freq=12, entity_vector=[6, -4, 3])
kb.add_alias(alias="Russ Cochran", entities=["Q2146908"], probabilities=[0.8])
return kb
@ -864,7 +883,7 @@ def test_nel_pickle():
def test_kb_to_bytes():
# Test that the KB's to_bytes method works correctly
nlp = English()
kb_1 = KnowledgeBase(nlp.vocab, entity_vector_length=3)
kb_1 = InMemoryLookupKB(nlp.vocab, entity_vector_length=3)
kb_1.add_entity(entity="Q2146908", freq=12, entity_vector=[6, -4, 3])
kb_1.add_entity(entity="Q66", freq=9, entity_vector=[1, 2, 3])
kb_1.add_alias(alias="Russ Cochran", entities=["Q2146908"], probabilities=[0.8])
@ -874,7 +893,7 @@ def test_kb_to_bytes():
)
assert kb_1.contains_alias("Russ Cochran")
kb_bytes = kb_1.to_bytes()
kb_2 = KnowledgeBase(nlp.vocab, entity_vector_length=3)
kb_2 = InMemoryLookupKB(nlp.vocab, entity_vector_length=3)
assert not kb_2.contains_alias("Russ Cochran")
kb_2 = kb_2.from_bytes(kb_bytes)
# check that both KBs are exactly the same
@ -897,7 +916,7 @@ def test_kb_to_bytes():
def test_nel_to_bytes():
# Test that a pipeline with an EL component can be converted to bytes
def create_kb(vocab):
kb = KnowledgeBase(vocab, entity_vector_length=3)
kb = InMemoryLookupKB(vocab, entity_vector_length=3)
kb.add_entity(entity="Q2146908", freq=12, entity_vector=[6, -4, 3])
kb.add_alias(alias="Russ Cochran", entities=["Q2146908"], probabilities=[0.8])
return kb
@ -987,7 +1006,7 @@ def test_legacy_architectures(name, config):
train_examples.append(Example.from_dict(doc, annotation))
def create_kb(vocab):
mykb = KnowledgeBase(vocab, entity_vector_length=vector_length)
mykb = InMemoryLookupKB(vocab, entity_vector_length=vector_length)
mykb.add_entity(entity="Q2146908", freq=12, entity_vector=[6, -4, 3])
mykb.add_entity(entity="Q7381115", freq=12, entity_vector=[9, 1, -7])
mykb.add_alias(
@ -1054,7 +1073,7 @@ def test_no_gold_ents(patterns):
def create_kb(vocab):
# create artificial KB
mykb = KnowledgeBase(vocab, entity_vector_length=vector_length)
mykb = InMemoryLookupKB(vocab, entity_vector_length=vector_length)
mykb.add_entity(entity="Q613241", freq=12, entity_vector=[6, -4, 3])
mykb.add_alias("Kirby", ["Q613241"], [0.9])
# Placeholder
@ -1104,7 +1123,7 @@ def test_tokenization_mismatch():
def create_kb(vocab):
# create placeholder KB
mykb = KnowledgeBase(vocab, entity_vector_length=vector_length)
mykb = InMemoryLookupKB(vocab, entity_vector_length=vector_length)
mykb.add_entity(entity="Q613241", freq=12, entity_vector=[6, -4, 3])
mykb.add_alias("Kirby", ["Q613241"], [0.9])
return mykb
@ -1121,6 +1140,12 @@ def test_tokenization_mismatch():
nlp.evaluate(train_examples)
def test_abstract_kb_instantiation():
"""Test whether instantiation of abstract KB base class fails."""
with pytest.raises(TypeError):
KnowledgeBase(None, 3)
# fmt: off
@pytest.mark.parametrize(
"meet_threshold,config",
@ -1151,7 +1176,7 @@ def test_threshold(meet_threshold: bool, config: Dict[str, Any]):
def create_kb(vocab):
# create artificial KB
mykb = KnowledgeBase(vocab, entity_vector_length=3)
mykb = InMemoryLookupKB(vocab, entity_vector_length=3)
mykb.add_entity(entity=entity_id, freq=12, entity_vector=[6, -4, 3])
mykb.add_alias(
alias="Mahler",
@ -1176,3 +1201,18 @@ def test_threshold(meet_threshold: bool, config: Dict[str, Any]):
assert len(doc.ents) == 1
assert doc.ents[0].kb_id_ == entity_id if meet_threshold else EntityLinker.NIL
def test_span_maker_forward_with_empty():
"""The forward pass of the span maker may have a doc with no entities."""
nlp = English()
doc1 = nlp("a b c")
ent = doc1[0:1]
ent.label_ = "X"
doc1.ents = [ent]
# no entities
doc2 = nlp("x y z")
# just to get a model
span_maker = build_span_maker()
span_maker([doc1, doc2], False)

37
spacy/tests/pipeline/test_entity_ruler.py
View File

@ -382,6 +382,43 @@ def test_entity_ruler_overlapping_spans(nlp, entity_ruler_factory):
assert doc.ents[0].label_ == "FOOBAR"
@pytest.mark.parametrize("entity_ruler_factory", ENTITY_RULERS)
def test_entity_ruler_fuzzy_pipe(nlp, entity_ruler_factory):
ruler = nlp.add_pipe(entity_ruler_factory, name="entity_ruler")
patterns = [{"label": "HELLO", "pattern": [{"LOWER": {"FUZZY": "hello"}}]}]
ruler.add_patterns(patterns)
doc = nlp("helloo")
assert len(doc.ents) == 1
assert doc.ents[0].label_ == "HELLO"
@pytest.mark.parametrize("entity_ruler_factory", ENTITY_RULERS)
def test_entity_ruler_fuzzy(nlp, entity_ruler_factory):
ruler = nlp.add_pipe(entity_ruler_factory, name="entity_ruler")
patterns = [{"label": "HELLO", "pattern": [{"LOWER": {"FUZZY": "hello"}}]}]
ruler.add_patterns(patterns)
doc = nlp("helloo")
assert len(doc.ents) == 1
assert doc.ents[0].label_ == "HELLO"
@pytest.mark.parametrize("entity_ruler_factory", ENTITY_RULERS)
def test_entity_ruler_fuzzy_disabled(nlp, entity_ruler_factory):
@registry.misc("test_fuzzy_compare_disabled")
def make_test_fuzzy_compare_disabled():
return lambda x, y, z: False
ruler = nlp.add_pipe(
entity_ruler_factory,
name="entity_ruler",
config={"matcher_fuzzy_compare": {"@misc": "test_fuzzy_compare_disabled"}},
)
patterns = [{"label": "HELLO", "pattern": [{"LOWER": {"FUZZY": "hello"}}]}]
ruler.add_patterns(patterns)
doc = nlp("helloo")
assert len(doc.ents) == 0
@pytest.mark.parametrize("n_process", [1, 2])
@pytest.mark.parametrize("entity_ruler_factory", ENTITY_RULERS)
def test_entity_ruler_multiprocessing(nlp, n_process, entity_ruler_factory):

18
spacy/tests/pipeline/test_pipe_methods.py
View File

@ -615,20 +615,18 @@ def test_enable_disable_conflict_with_config():
with make_tempdir() as tmp_dir:
nlp.to_disk(tmp_dir)
# Expected to fail, as config and arguments conflict.
with pytest.raises(ValueError):
spacy.load(
tmp_dir, enable=["tagger"], config={"nlp": {"disabled": ["senter"]}}
)
# Expected to succeed, as config and arguments do not conflict.
assert spacy.load(
tmp_dir, enable=["tagger"], config={"nlp": {"disabled": ["senter"]}}
).disabled == ["senter", "sentencizer"]
# Expected to succeed without warning due to the lack of a conflicting config option.
spacy.load(tmp_dir, enable=["tagger"])
# Expected to succeed with a warning, as disable=[] should override the config setting.
with pytest.warns(UserWarning):
# Expected to fail due to conflict between enable and disabled.
with pytest.raises(ValueError):
spacy.load(
tmp_dir,
enable=["tagger"],
disable=[],
config={"nlp": {"disabled": ["senter"]}},
enable=["senter"],
config={"nlp": {"disabled": ["senter", "tagger"]}},
)

43
spacy/tests/pipeline/test_spancat.py
View File

@ -372,24 +372,39 @@ def test_overfitting_IO_overlapping():
def test_zero_suggestions():
# Test with a suggester that returns 0 suggestions
# Test with a suggester that can return 0 suggestions
@registry.misc("test_zero_suggester")
def make_zero_suggester():
def zero_suggester(docs, *, ops=None):
@registry.misc("test_mixed_zero_suggester")
def make_mixed_zero_suggester():
def mixed_zero_suggester(docs, *, ops=None):
if ops is None:
ops = get_current_ops()
return Ragged(
ops.xp.zeros((0, 0), dtype="i"), ops.xp.zeros((len(docs),), dtype="i")
)
spans = []
lengths = []
for doc in docs:
if len(doc) > 0 and len(doc) % 2 == 0:
spans.append((0, 1))
lengths.append(1)
else:
lengths.append(0)
spans = ops.asarray2i(spans)
lengths_array = ops.asarray1i(lengths)
if len(spans) > 0:
output = Ragged(ops.xp.vstack(spans), lengths_array)
else:
output = Ragged(ops.xp.zeros((0, 0), dtype="i"), lengths_array)
return output
return zero_suggester
return mixed_zero_suggester
fix_random_seed(0)
nlp = English()
spancat = nlp.add_pipe(
"spancat",
config={"suggester": {"@misc": "test_zero_suggester"}, "spans_key": SPAN_KEY},
config={
"suggester": {"@misc": "test_mixed_zero_suggester"},
"spans_key": SPAN_KEY,
},
)
train_examples = make_examples(nlp)
optimizer = nlp.initialize(get_examples=lambda: train_examples)
@ -397,6 +412,16 @@ def test_zero_suggestions():
assert set(spancat.labels) == {"LOC", "PERSON"}
nlp.update(train_examples, sgd=optimizer)
# empty doc
nlp("")
# single doc with zero suggestions
nlp("one")
# single doc with one suggestion
nlp("two two")
# batch with mixed zero/one suggestions
list(nlp.pipe(["one", "two two", "three three three", "", "four four four four"]))
# batch with no suggestions
list(nlp.pipe(["", "one", "three three three"]))
def test_set_candidates():

55
spacy/tests/pipeline/test_textcat.py
View File

@ -360,6 +360,30 @@ def test_label_types(name):
nlp.initialize()
@pytest.mark.parametrize(
"name,get_examples",
[
("textcat", make_get_examples_single_label),
("textcat_multilabel", make_get_examples_multi_label),
],
)
def test_invalid_label_value(name, get_examples):
nlp = Language()
textcat = nlp.add_pipe(name)
example_getter = get_examples(nlp)
def invalid_examples():
# make one example with an invalid score
examples = example_getter()
ref = examples[0].reference
key = list(ref.cats.keys())[0]
ref.cats[key] = 2.0
return examples
with pytest.raises(ValueError):
nlp.initialize(get_examples=invalid_examples)
@pytest.mark.parametrize("name", ["textcat", "textcat_multilabel"])
def test_no_label(name):
nlp = Language()
@ -814,8 +838,8 @@ def test_textcat_loss(multi_label: bool, expected_loss: float):
textcat = nlp.add_pipe("textcat_multilabel")
else:
textcat = nlp.add_pipe("textcat")
textcat.initialize(lambda: train_examples)
assert isinstance(textcat, TextCategorizer)
textcat.initialize(lambda: train_examples)
scores = textcat.model.ops.asarray(
[[0.0, 0.0, 0.0, 1.0], [0.0, 0.0, 1.0, 1.0]], dtype="f" # type: ignore
)
@ -823,10 +847,10 @@ def test_textcat_loss(multi_label: bool, expected_loss: float):
assert loss == expected_loss
def test_textcat_threshold():
def test_textcat_multilabel_threshold():
# Ensure the scorer can be called with a different threshold
nlp = English()
nlp.add_pipe("textcat")
nlp.add_pipe("textcat_multilabel")
train_examples = []
for text, annotations in TRAIN_DATA_SINGLE_LABEL:
@ -849,7 +873,7 @@ def test_textcat_threshold():
)
pos_f = scores["cats_score"]
assert scores["cats_f_per_type"]["POSITIVE"]["r"] == 1.0
assert pos_f > macro_f
assert pos_f >= macro_f
def test_textcat_multi_threshold():
@ -871,3 +895,26 @@ def test_textcat_multi_threshold():
scores = nlp.evaluate(train_examples, scorer_cfg={"threshold": 0})
assert scores["cats_f_per_type"]["POSITIVE"]["r"] == 1.0
@pytest.mark.parametrize(
"component_name,scorer",
[
("textcat", "spacy.textcat_scorer.v1"),
("textcat_multilabel", "spacy.textcat_multilabel_scorer.v1"),
],
)
def test_textcat_legacy_scorers(component_name, scorer):
"""Check that legacy scorers are registered and produce the expected score
keys."""
nlp = English()
nlp.add_pipe(component_name, config={"scorer": {"@scorers": scorer}})
train_examples = []
for text, annotations in TRAIN_DATA_SINGLE_LABEL:
train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
nlp.initialize(get_examples=lambda: train_examples)
# score the model (it's not actually trained but that doesn't matter)
scores = nlp.evaluate(train_examples)
assert 0 <= scores["cats_score"] <= 1

91
spacy/tests/pipeline/test_tok2vec.py
View File

@ -230,6 +230,97 @@ def test_tok2vec_listener_callback():
assert get_dX(Y) is not None
def test_tok2vec_listener_overfitting():
"""Test that a pipeline with a listener properly overfits, even if 'tok2vec' is in the annotating components"""
orig_config = Config().from_str(cfg_string)
nlp = util.load_model_from_config(orig_config, auto_fill=True, validate=True)
train_examples = []
for t in TRAIN_DATA:
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
optimizer = nlp.initialize(get_examples=lambda: train_examples)
for i in range(50):
losses = {}
nlp.update(train_examples, sgd=optimizer, losses=losses, annotates=["tok2vec"])
assert losses["tagger"] < 0.00001
# test the trained model
test_text = "I like blue eggs"
doc = nlp(test_text)
assert doc[0].tag_ == "N"
assert doc[1].tag_ == "V"
assert doc[2].tag_ == "J"
assert doc[3].tag_ == "N"
# Also test the results are still the same after IO
with make_tempdir() as tmp_dir:
nlp.to_disk(tmp_dir)
nlp2 = util.load_model_from_path(tmp_dir)
doc2 = nlp2(test_text)
assert doc2[0].tag_ == "N"
assert doc2[1].tag_ == "V"
assert doc2[2].tag_ == "J"
assert doc2[3].tag_ == "N"
def test_tok2vec_frozen_not_annotating():
"""Test that a pipeline with a frozen tok2vec raises an error when the tok2vec is not annotating"""
orig_config = Config().from_str(cfg_string)
nlp = util.load_model_from_config(orig_config, auto_fill=True, validate=True)
train_examples = []
for t in TRAIN_DATA:
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
optimizer = nlp.initialize(get_examples=lambda: train_examples)
for i in range(2):
losses = {}
with pytest.raises(
ValueError, match=r"the tok2vec embedding layer is not updated"
):
nlp.update(
train_examples, sgd=optimizer, losses=losses, exclude=["tok2vec"]
)
def test_tok2vec_frozen_overfitting():
"""Test that a pipeline with a frozen & annotating tok2vec can still overfit"""
orig_config = Config().from_str(cfg_string)
nlp = util.load_model_from_config(orig_config, auto_fill=True, validate=True)
train_examples = []
for t in TRAIN_DATA:
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
optimizer = nlp.initialize(get_examples=lambda: train_examples)
for i in range(100):
losses = {}
nlp.update(
train_examples,
sgd=optimizer,
losses=losses,
exclude=["tok2vec"],
annotates=["tok2vec"],
)
assert losses["tagger"] < 0.0001
# test the trained model
test_text = "I like blue eggs"
doc = nlp(test_text)
assert doc[0].tag_ == "N"
assert doc[1].tag_ == "V"
assert doc[2].tag_ == "J"
assert doc[3].tag_ == "N"
# Also test the results are still the same after IO
with make_tempdir() as tmp_dir:
nlp.to_disk(tmp_dir)
nlp2 = util.load_model_from_path(tmp_dir)
doc2 = nlp2(test_text)
assert doc2[0].tag_ == "N"
assert doc2[1].tag_ == "V"
assert doc2[2].tag_ == "J"
assert doc2[3].tag_ == "N"
def test_replace_listeners():
orig_config = Config().from_str(cfg_string)
nlp = util.load_model_from_config(orig_config, auto_fill=True, validate=True)

8
spacy/tests/serialize/test_resource_warning.py
View File

@ -3,7 +3,7 @@ from unittest import TestCase
import pytest
import srsly
from numpy import zeros
from spacy.kb import KnowledgeBase, Writer
from spacy.kb.kb_in_memory import InMemoryLookupKB, Writer
from spacy.vectors import Vectors
from spacy.language import Language
from spacy.pipeline import TrainablePipe
@ -71,7 +71,7 @@ def entity_linker():
nlp = Language()
def create_kb(vocab):
kb = KnowledgeBase(vocab, entity_vector_length=1)
kb = InMemoryLookupKB(vocab, entity_vector_length=1)
kb.add_entity("test", 0.0, zeros((1, 1), dtype="f"))
return kb
@ -120,7 +120,7 @@ def test_writer_with_path_py35():
def test_save_and_load_knowledge_base():
nlp = Language()
kb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
kb = InMemoryLookupKB(nlp.vocab, entity_vector_length=1)
with make_tempdir() as d:
path = d / "kb"
try:
@ -129,7 +129,7 @@ def test_save_and_load_knowledge_base():
pytest.fail(str(e))
try:
kb_loaded = KnowledgeBase(nlp.vocab, entity_vector_length=1)
kb_loaded = InMemoryLookupKB(nlp.vocab, entity_vector_length=1)
kb_loaded.from_disk(path)
except Exception as e:
pytest.fail(str(e))

16
spacy/tests/serialize/test_serialize_kb.py
View File

@ -2,7 +2,7 @@ from typing import Callable
from spacy import util
from spacy.util import ensure_path, registry, load_model_from_config
from spacy.kb import KnowledgeBase
from spacy.kb.kb_in_memory import InMemoryLookupKB
from spacy.vocab import Vocab
from thinc.api import Config
@ -22,7 +22,7 @@ def test_serialize_kb_disk(en_vocab):
dir_path.mkdir()
file_path = dir_path / "kb"
kb1.to_disk(str(file_path))
kb2 = KnowledgeBase(vocab=en_vocab, entity_vector_length=3)
kb2 = InMemoryLookupKB(vocab=en_vocab, entity_vector_length=3)
kb2.from_disk(str(file_path))
# final assertions
@ -30,7 +30,7 @@ def test_serialize_kb_disk(en_vocab):
def _get_dummy_kb(vocab):
kb = KnowledgeBase(vocab, entity_vector_length=3)
kb = InMemoryLookupKB(vocab, entity_vector_length=3)
kb.add_entity(entity="Q53", freq=33, entity_vector=[0, 5, 3])
kb.add_entity(entity="Q17", freq=2, entity_vector=[7, 1, 0])
kb.add_entity(entity="Q007", freq=7, entity_vector=[0, 0, 7])
@ -104,7 +104,7 @@ def test_serialize_subclassed_kb():
custom_field = 666
"""
class SubKnowledgeBase(KnowledgeBase):
class SubInMemoryLookupKB(InMemoryLookupKB):
def __init__(self, vocab, entity_vector_length, custom_field):
super().__init__(vocab, entity_vector_length)
self.custom_field = custom_field
@ -112,9 +112,9 @@ def test_serialize_subclassed_kb():
@registry.misc("spacy.CustomKB.v1")
def custom_kb(
entity_vector_length: int, custom_field: int
) -> Callable[[Vocab], KnowledgeBase]:
) -> Callable[[Vocab], InMemoryLookupKB]:
def custom_kb_factory(vocab):
kb = SubKnowledgeBase(
kb = SubInMemoryLookupKB(
vocab=vocab,
entity_vector_length=entity_vector_length,
custom_field=custom_field,
@ -129,7 +129,7 @@ def test_serialize_subclassed_kb():
nlp.initialize()
entity_linker = nlp.get_pipe("entity_linker")
assert type(entity_linker.kb) == SubKnowledgeBase
assert type(entity_linker.kb) == SubInMemoryLookupKB
assert entity_linker.kb.entity_vector_length == 342
assert entity_linker.kb.custom_field == 666
@ -139,6 +139,6 @@ def test_serialize_subclassed_kb():
nlp2 = util.load_model_from_path(tmp_dir)
entity_linker2 = nlp2.get_pipe("entity_linker")
# After IO, the KB is the standard one
assert type(entity_linker2.kb) == KnowledgeBase
assert type(entity_linker2.kb) == InMemoryLookupKB
assert entity_linker2.kb.entity_vector_length == 342
assert not hasattr(entity_linker2.kb, "custom_field")

7
spacy/tests/serialize/test_serialize_pipeline.py
View File

@ -404,11 +404,10 @@ def test_serialize_pipeline_disable_enable():
assert nlp3.component_names == ["ner", "tagger"]
with make_tempdir() as d:
nlp3.to_disk(d)
with pytest.warns(UserWarning):
nlp4 = spacy.load(d, disable=["ner"])
assert nlp4.pipe_names == ["tagger"]
nlp4 = spacy.load(d, disable=["ner"])
assert nlp4.pipe_names == []
assert nlp4.component_names == ["ner", "tagger"]
assert nlp4.disabled == ["ner"]
assert nlp4.disabled == ["ner", "tagger"]
with make_tempdir() as d:
nlp.to_disk(d)
nlp5 = spacy.load(d, exclude=["tagger"])

360
spacy/tests/test_cli.py
View File

@ -1,8 +1,13 @@
import os
import math
from random import sample
from typing import Counter
from collections import Counter
from typing import Tuple, List, Dict, Any
import pkg_resources
import time
from pathlib import Path
import spacy
import numpy
import pytest
import srsly
from click import NoSuchOption
@ -11,10 +16,11 @@ from thinc.api import Config, ConfigValidationError
from spacy import about
from spacy.cli import info
from spacy.cli._util import is_subpath_of, load_project_config
from spacy.cli._util import is_subpath_of, load_project_config, walk_directory
from spacy.cli._util import parse_config_overrides, string_to_list
from spacy.cli._util import substitute_project_variables
from spacy.cli._util import validate_project_commands
from spacy.cli._util import upload_file, download_file
from spacy.cli.debug_data import _compile_gold, _get_labels_from_model
from spacy.cli.debug_data import _get_labels_from_spancat
from spacy.cli.debug_data import _get_distribution, _get_kl_divergence
@ -25,12 +31,16 @@ from spacy.cli.download import get_compatibility, get_version
from spacy.cli.init_config import RECOMMENDATIONS, init_config, fill_config
from spacy.cli.package import get_third_party_dependencies
from spacy.cli.package import _is_permitted_package_name
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.cli.apply import apply
from spacy.cli.find_threshold import find_threshold
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.tokens import Doc
from spacy.tokens import Doc, DocBin
from spacy.tokens.span import Span
from spacy.training import Example, docs_to_json, offsets_to_biluo_tags
from spacy.training.converters import conll_ner_to_docs, conllu_to_docs
@ -116,6 +126,25 @@ 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}"
def test_cli_info():
nlp = Dutch()
nlp.add_pipe("textcat")
@ -855,3 +884,326 @@ def test_span_length_freq_dist_output_must_be_correct():
span_freqs = _get_spans_length_freq_dist(sample_span_lengths, threshold)
assert sum(span_freqs.values()) >= threshold
assert list(span_freqs.keys()) == [3, 1, 4, 5, 2]
def test_applycli_empty_dir():
with make_tempdir() as data_path:
output = data_path / "test.spacy"
apply(data_path, output, "blank:en", "text", 1, 1)
def test_applycli_docbin():
with make_tempdir() as data_path:
output = data_path / "testout.spacy"
nlp = spacy.blank("en")
doc = nlp("testing apply cli.")
# test empty DocBin case
docbin = DocBin()
docbin.to_disk(data_path / "testin.spacy")
apply(data_path, output, "blank:en", "text", 1, 1)
docbin.add(doc)
docbin.to_disk(data_path / "testin.spacy")
apply(data_path, output, "blank:en", "text", 1, 1)
def test_applycli_jsonl():
with make_tempdir() as data_path:
output = data_path / "testout.spacy"
data = [{"field": "Testing apply cli.", "key": 234}]
data2 = [{"field": "234"}]
srsly.write_jsonl(data_path / "test.jsonl", data)
apply(data_path, output, "blank:en", "field", 1, 1)
srsly.write_jsonl(data_path / "test2.jsonl", data2)
apply(data_path, output, "blank:en", "field", 1, 1)
def test_applycli_txt():
with make_tempdir() as data_path:
output = data_path / "testout.spacy"
with open(data_path / "test.foo", "w") as ftest:
ftest.write("Testing apply cli.")
apply(data_path, output, "blank:en", "text", 1, 1)
def test_applycli_mixed():
with make_tempdir() as data_path:
output = data_path / "testout.spacy"
text = "Testing apply cli"
nlp = spacy.blank("en")
doc = nlp(text)
jsonl_data = [{"text": text}]
srsly.write_jsonl(data_path / "test.jsonl", jsonl_data)
docbin = DocBin()
docbin.add(doc)
docbin.to_disk(data_path / "testin.spacy")
with open(data_path / "test.txt", "w") as ftest:
ftest.write(text)
apply(data_path, output, "blank:en", "text", 1, 1)
# Check whether it worked
result = list(DocBin().from_disk(output).get_docs(nlp.vocab))
assert len(result) == 3
for doc in result:
assert doc.text == text
def test_applycli_user_data():
Doc.set_extension("ext", default=0)
val = ("ext", 0)
with make_tempdir() as data_path:
output = data_path / "testout.spacy"
nlp = spacy.blank("en")
doc = nlp("testing apply cli.")
doc._.ext = val
docbin = DocBin(store_user_data=True)
docbin.add(doc)
docbin.to_disk(data_path / "testin.spacy")
apply(data_path, output, "blank:en", "", 1, 1)
result = list(DocBin().from_disk(output).get_docs(nlp.vocab))
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):
thresholds = numpy.linspace(0, 1, 10)
def make_examples(nlp: Language) -> List[Example]:
docs: List[Example] = []
for t in [
(
"I am angry and confused in the Bank of America.",
{
"cats": {"ANGRY": 1.0, "CONFUSED": 1.0, "HAPPY": 0.0},
"spans": {"sc": [(31, 46, "ORG")]},
},
),
(
"I am confused but happy in New York.",
{
"cats": {"ANGRY": 0.0, "CONFUSED": 1.0, "HAPPY": 1.0},
"spans": {"sc": [(27, 35, "GPE")]},
},
),
]:
doc = nlp.make_doc(t[0])
docs.append(Example.from_dict(doc, t[1]))
return docs
def init_nlp(
components: Tuple[Tuple[str, Dict[str, Any]], ...] = ()
) -> Tuple[Language, List[Example]]:
new_nlp = English()
new_nlp.add_pipe( # type: ignore
factory_name="textcat_multilabel",
name="tc_multi",
config={"threshold": 0.9},
)
# Append additional components to pipeline.
for cfn, comp_config in components:
new_nlp.add_pipe(cfn, config=comp_config)
new_examples = make_examples(new_nlp)
new_nlp.initialize(get_examples=lambda: new_examples)
for i in range(5):
new_nlp.update(new_examples)
return new_nlp, new_examples
with make_tempdir() as docs_dir:
# Check whether find_threshold() identifies lowest threshold above 0 as (first) ideal threshold, as this matches
# the current model behavior with the examples above. This can break once the model behavior changes and serves
# mostly as a smoke test.
nlp, examples = init_nlp()
DocBin(docs=[example.reference for example in examples]).to_disk(
docs_dir / "docs.spacy"
)
with make_tempdir() as nlp_dir:
nlp.to_disk(nlp_dir)
res = find_threshold(
model=nlp_dir,
data_path=docs_dir / "docs.spacy",
pipe_name="tc_multi",
threshold_key="threshold",
scores_key="cats_macro_f",
silent=True,
)
assert res[0] != thresholds[0]
assert thresholds[0] < res[0] < thresholds[9]
assert res[1] == 1.0
assert res[2][1.0] == 0.0
# Test with spancat.
nlp, _ = init_nlp((("spancat", {}),))
with make_tempdir() as nlp_dir:
nlp.to_disk(nlp_dir)
res = find_threshold(
model=nlp_dir,
data_path=docs_dir / "docs.spacy",
pipe_name="spancat",
threshold_key="threshold",
scores_key="spans_sc_f",
silent=True,
)
assert res[0] != thresholds[0]
assert thresholds[0] < res[0] < thresholds[8]
assert res[1] >= 0.6
assert res[2][1.0] == 0.0
# Having multiple textcat_multilabel components should work, since the name has to be specified.
nlp, _ = init_nlp((("textcat_multilabel", {}),))
with make_tempdir() as nlp_dir:
nlp.to_disk(nlp_dir)
assert find_threshold(
model=nlp_dir,
data_path=docs_dir / "docs.spacy",
pipe_name="tc_multi",
threshold_key="threshold",
scores_key="cats_macro_f",
silent=True,
)
# Specifying the name of an non-existing pipe should fail.
nlp, _ = init_nlp()
with make_tempdir() as nlp_dir:
nlp.to_disk(nlp_dir)
with pytest.raises(AttributeError):
find_threshold(
model=nlp_dir,
data_path=docs_dir / "docs.spacy",
pipe_name="_",
threshold_key="threshold",
scores_key="cats_macro_f",
silent=True,
)
@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):
# 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 = [
"data1.iob",
"data2.iob",
"data3.json",
"data4.conll",
"data5.conll",
"data6.conll",
"data7.txt",
]
for f in files:
Path(d / f).touch()
assert (len(walk_directory(d))) == 7
assert (len(walk_directory(d, suffix=None))) == 7
assert (len(walk_directory(d, suffix="json"))) == 1
assert (len(walk_directory(d, suffix="iob"))) == 2
assert (len(walk_directory(d, suffix="conll"))) == 3
assert (len(walk_directory(d, suffix="pdf"))) == 0

42
spacy/tests/test_cli_app.py Normal file
View File

@ -0,0 +1,42 @@
import os
from pathlib import Path
from typer.testing import CliRunner
from spacy.cli._util import app
from .util import make_tempdir
def test_convert_auto():
with make_tempdir() as d_in, make_tempdir() as d_out:
for f in ["data1.iob", "data2.iob", "data3.iob"]:
Path(d_in / f).touch()
# ensure that "automatic" suffix detection works
result = CliRunner().invoke(app, ["convert", str(d_in), str(d_out)])
assert "Generated output file" in result.stdout
out_files = os.listdir(d_out)
assert len(out_files) == 3
assert "data1.spacy" in out_files
assert "data2.spacy" in out_files
assert "data3.spacy" in out_files
def test_convert_auto_conflict():
with make_tempdir() as d_in, make_tempdir() as d_out:
for f in ["data1.iob", "data2.iob", "data3.json"]:
Path(d_in / f).touch()
# ensure that "automatic" suffix detection warns when there are different file types
result = CliRunner().invoke(app, ["convert", str(d_in), str(d_out)])
assert "All input files must be same type" in result.stdout
out_files = os.listdir(d_out)
assert len(out_files) == 0
def test_benchmark_accuracy_alias():
# Verify that the `evaluate` alias works correctly.
result_benchmark = CliRunner().invoke(app, ["benchmark", "accuracy", "--help"])
result_evaluate = CliRunner().invoke(app, ["evaluate", "--help"])
assert result_benchmark.stdout == result_evaluate.stdout.replace(
"spacy evaluate", "spacy benchmark accuracy"
)

10
spacy/tests/test_displacy.py
View File

@ -203,6 +203,16 @@ def test_displacy_parse_spans_different_spans_key(en_vocab):
]
def test_displacy_parse_empty_spans_key(en_vocab):
"""Test that having an unset spans key doesn't raise an error"""
doc = Doc(en_vocab, words=["Welcome", "to", "the", "Bank", "of", "China"])
doc.spans["custom"] = [Span(doc, 3, 6, "BANK")]
with pytest.warns(UserWarning, match="W117"):
spans = displacy.parse_spans(doc)
assert isinstance(spans, dict)
def test_displacy_parse_ents(en_vocab):
"""Test that named entities on a Doc are converted into displaCy's format."""
doc = Doc(en_vocab, words=["But", "Google", "is", "starting", "from", "behind"])

107
spacy/tests/test_language.py
View File

@ -3,6 +3,7 @@ import logging
from unittest import mock
import pytest
from spacy.language import Language
from spacy.scorer import Scorer
from spacy.tokens import Doc, Span
from spacy.vocab import Vocab
from spacy.training import Example
@ -126,6 +127,112 @@ def test_evaluate_no_pipe(nlp):
nlp.evaluate([Example.from_dict(doc, annots)])
def test_evaluate_textcat_multilabel(en_vocab):
"""Test that evaluate works with a multilabel textcat pipe."""
nlp = Language(en_vocab)
textcat_multilabel = nlp.add_pipe("textcat_multilabel")
for label in ("FEATURE", "REQUEST", "BUG", "QUESTION"):
textcat_multilabel.add_label(label)
nlp.initialize()
annots = {"cats": {"FEATURE": 1.0, "QUESTION": 1.0}}
doc = nlp.make_doc("hello world")
example = Example.from_dict(doc, annots)
scores = nlp.evaluate([example])
labels = nlp.get_pipe("textcat_multilabel").labels
for label in labels:
assert scores["cats_f_per_type"].get(label) is not None
for key in example.reference.cats.keys():
if key not in labels:
assert scores["cats_f_per_type"].get(key) is None
def test_evaluate_multiple_textcat_final(en_vocab):
"""Test that evaluate evaluates the final textcat component in a pipeline
with more than one textcat or textcat_multilabel."""
nlp = Language(en_vocab)
textcat = nlp.add_pipe("textcat")
for label in ("POSITIVE", "NEGATIVE"):
textcat.add_label(label)
textcat_multilabel = nlp.add_pipe("textcat_multilabel")
for label in ("FEATURE", "REQUEST", "BUG", "QUESTION"):
textcat_multilabel.add_label(label)
nlp.initialize()
annots = {
"cats": {
"POSITIVE": 1.0,
"NEGATIVE": 0.0,
"FEATURE": 1.0,
"QUESTION": 1.0,
"POSITIVE": 1.0,
"NEGATIVE": 0.0,
}
}
doc = nlp.make_doc("hello world")
example = Example.from_dict(doc, annots)
scores = nlp.evaluate([example])
# get the labels from the final pipe
labels = nlp.get_pipe(nlp.pipe_names[-1]).labels
for label in labels:
assert scores["cats_f_per_type"].get(label) is not None
for key in example.reference.cats.keys():
if key not in labels:
assert scores["cats_f_per_type"].get(key) is None
def test_evaluate_multiple_textcat_separate(en_vocab):
"""Test that evaluate can evaluate multiple textcat components separately
with custom scorers."""
def custom_textcat_score(examples, **kwargs):
scores = Scorer.score_cats(
examples,
"cats",
multi_label=False,
**kwargs,
)
return {f"custom_{k}": v for k, v in scores.items()}
@spacy.registry.scorers("test_custom_textcat_scorer")
def make_custom_textcat_scorer():
return custom_textcat_score
nlp = Language(en_vocab)
textcat = nlp.add_pipe(
"textcat",
config={"scorer": {"@scorers": "test_custom_textcat_scorer"}},
)
for label in ("POSITIVE", "NEGATIVE"):
textcat.add_label(label)
textcat_multilabel = nlp.add_pipe("textcat_multilabel")
for label in ("FEATURE", "REQUEST", "BUG", "QUESTION"):
textcat_multilabel.add_label(label)
nlp.initialize()
annots = {
"cats": {
"POSITIVE": 1.0,
"NEGATIVE": 0.0,
"FEATURE": 1.0,
"QUESTION": 1.0,
"POSITIVE": 1.0,
"NEGATIVE": 0.0,
}
}
doc = nlp.make_doc("hello world")
example = Example.from_dict(doc, annots)
scores = nlp.evaluate([example])
# check custom scores for the textcat pipe
assert "custom_cats_f_per_type" in scores
labels = nlp.get_pipe("textcat").labels
assert set(scores["custom_cats_f_per_type"].keys()) == set(labels)
# check default scores for the textcat_multilabel pipe
assert "cats_f_per_type" in scores
labels = nlp.get_pipe("textcat_multilabel").labels
assert set(scores["cats_f_per_type"].keys()) == set(labels)
def vector_modification_pipe(doc):
doc.vector += 1
return doc

15
spacy/tests/test_misc.py
View File

@ -8,7 +8,7 @@ from spacy import prefer_gpu, require_gpu, require_cpu
from spacy.ml._precomputable_affine import PrecomputableAffine
from spacy.ml._precomputable_affine import _backprop_precomputable_affine_padding
from spacy.util import dot_to_object, SimpleFrozenList, import_file
from spacy.util import to_ternary_int
from spacy.util import to_ternary_int, find_available_port
from thinc.api import Config, Optimizer, ConfigValidationError
from thinc.api import get_current_ops, set_current_ops, NumpyOps, CupyOps, MPSOps
from thinc.compat import has_cupy_gpu, has_torch_mps_gpu
@ -434,3 +434,16 @@ def test_to_ternary_int():
assert to_ternary_int(-10) == -1
assert to_ternary_int("string") == -1
assert to_ternary_int([0, "string"]) == -1
def test_find_available_port():
host = "0.0.0.0"
port = 5000
assert find_available_port(port, host) == port, "Port 5000 isn't free"
from wsgiref.simple_server import make_server, demo_app
with make_server(host, port, demo_app) as httpd:
with pytest.warns(UserWarning, match="already in use"):
found_port = find_available_port(port, host, auto_select=True)
assert found_port == port + 1, "Didn't find next port"

4
spacy/tests/test_models.py
View File

@ -23,7 +23,7 @@ def get_textcat_bow_kwargs():
def get_textcat_cnn_kwargs():
return {"tok2vec": test_tok2vec(), "exclusive_classes": False, "nO": 13}
return {"tok2vec": make_test_tok2vec(), "exclusive_classes": False, "nO": 13}
def get_all_params(model):
@ -65,7 +65,7 @@ def get_tok2vec_kwargs():
}
def test_tok2vec():
def make_test_tok2vec():
return build_Tok2Vec_model(**get_tok2vec_kwargs())

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