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Merge branch 'develop' into nightly.spacy.io

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This commit is contained in:
Ines Montani 2020-10-01 22:15:59 +02:00
commit 812c15c213
161 changed files with 3333 additions and 2422 deletions
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2
Makefile
View File

@ -1,7 +1,7 @@
SHELL := /bin/bash
ifndef SPACY_EXTRAS
override SPACY_EXTRAS = spacy-lookups-data==0.4.0.dev0 jieba pkuseg==0.0.25 sudachipy sudachidict_core
override SPACY_EXTRAS = spacy-lookups-data==1.0.0rc0 jieba pkuseg==0.0.25 pickle5 sudachipy sudachidict_core
endif
ifndef PYVER

2
pyproject.toml
View File

@ -6,7 +6,7 @@ requires = [
"cymem>=2.0.2,<2.1.0",
"preshed>=3.0.2,<3.1.0",
"murmurhash>=0.28.0,<1.1.0",
"thinc>=8.0.0a35,<8.0.0a40",
"thinc>=8.0.0a42,<8.0.0a50",
"blis>=0.4.0,<0.5.0",
"pytokenizations",
"pathy"

4
requirements.txt
View File

@ -1,7 +1,7 @@
# Our libraries
cymem>=2.0.2,<2.1.0
preshed>=3.0.2,<3.1.0
thinc>=8.0.0a35,<8.0.0a40
thinc>=8.0.0a42,<8.0.0a50
blis>=0.4.0,<0.5.0
ml_datasets==0.2.0a0
murmurhash>=0.28.0,<1.1.0
@ -14,7 +14,7 @@ pathy
numpy>=1.15.0
requests>=2.13.0,<3.0.0
tqdm>=4.38.0,<5.0.0
pydantic>=1.3.0,<2.0.0
pydantic>=1.5.0,<2.0.0
pytokenizations
# Official Python utilities
setuptools

10
setup.cfg
View File

@ -34,13 +34,13 @@ setup_requires =
cymem>=2.0.2,<2.1.0
preshed>=3.0.2,<3.1.0
murmurhash>=0.28.0,<1.1.0
thinc>=8.0.0a35,<8.0.0a40
thinc>=8.0.0a42,<8.0.0a50
install_requires =
# Our libraries
murmurhash>=0.28.0,<1.1.0
cymem>=2.0.2,<2.1.0
preshed>=3.0.2,<3.1.0
thinc>=8.0.0a35,<8.0.0a40
thinc>=8.0.0a42,<8.0.0a50
blis>=0.4.0,<0.5.0
wasabi>=0.8.0,<1.1.0
srsly>=2.1.0,<3.0.0
@ -51,7 +51,7 @@ install_requires =
tqdm>=4.38.0,<5.0.0
numpy>=1.15.0
requests>=2.13.0,<3.0.0
pydantic>=1.3.0,<2.0.0
pydantic>=1.5.0,<2.0.0
pytokenizations
# Official Python utilities
setuptools
@ -65,7 +65,7 @@ console_scripts =
[options.extras_require]
lookups =
spacy_lookups_data==0.4.0.dev0
spacy_lookups_data==1.0.0rc0
cuda =
cupy>=5.0.0b4,<9.0.0
cuda80 =
@ -98,7 +98,7 @@ universal = false
formats = gztar
[flake8]
ignore = E203, E266, E501, E731, W503
ignore = E203, E266, E501, E731, W503, E741
max-line-length = 80
select = B,C,E,F,W,T4,B9
exclude =

2
spacy/about.py
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@ -1,6 +1,6 @@
# fmt: off
__title__ = "spacy-nightly"
__version__ = "3.0.0a26"
__version__ = "3.0.0a28"
__release__ = True
__download_url__ = "https://github.com/explosion/spacy-models/releases/download"
__compatibility__ = "https://raw.githubusercontent.com/explosion/spacy-models/master/compatibility.json"

2
spacy/cli/__init__.py
View File

@ -15,7 +15,7 @@ from .debug_config import debug_config # noqa: F401
from .debug_model import debug_model # noqa: F401
from .evaluate import evaluate # noqa: F401
from .convert import convert # noqa: F401
from .init_model import init_model # noqa: F401
from .init_pipeline import init_pipeline_cli # noqa: F401
from .init_config import init_config, fill_config # noqa: F401
from .validate import validate # noqa: F401
from .project.clone import project_clone # noqa: F401

53
spacy/cli/_util.py
View File

@ -10,12 +10,13 @@ from click import NoSuchOption
from click.parser import split_arg_string
from typer.main import get_command
from contextlib import contextmanager
from thinc.config import Config, ConfigValidationError
from thinc.api import Config, ConfigValidationError, require_gpu
from configparser import InterpolationError
import os
from ..schemas import ProjectConfigSchema, validate
from ..util import import_file, run_command, make_tempdir, registry, logger
from ..util import ENV_VARS
if TYPE_CHECKING:
from pathy import Pathy # noqa: F401
@ -39,7 +40,6 @@ commands to check and validate your config files, training and evaluation data,
and custom model implementations.
"""
INIT_HELP = """Commands for initializing configs and pipeline packages."""
OVERRIDES_ENV_VAR = "SPACY_CONFIG_OVERRIDES"
# Wrappers for Typer's annotations. Initially created to set defaults and to
# keep the names short, but not needed at the moment.
@ -65,7 +65,7 @@ def setup_cli() -> None:
def parse_config_overrides(
args: List[str], env_var: Optional[str] = OVERRIDES_ENV_VAR
args: List[str], env_var: Optional[str] = ENV_VARS.CONFIG_OVERRIDES
) -> Dict[str, Any]:
"""Generate a dictionary of config overrides based on the extra arguments
provided on the CLI, e.g. --training.batch_size to override
@ -226,24 +226,30 @@ def get_checksum(path: Union[Path, str]) -> str:
def show_validation_error(
file_path: Optional[Union[str, Path]] = None,
*,
title: str = "Config validation error",
title: Optional[str] = None,
desc: str = "",
show_config: Optional[bool] = None,
hint_fill: bool = True,
):
"""Helper to show custom config validation errors on the CLI.
file_path (str / Path): Optional file path of config file, used in hints.
title (str): Title of the custom formatted error.
title (str): Override title of custom formatted error.
desc (str): Override description of custom formatted error.
show_config (bool): Whether to output the config the error refers to.
hint_fill (bool): Show hint about filling config.
"""
try:
yield
except (ConfigValidationError, InterpolationError) as e:
msg.fail(title, spaced=True)
# TODO: This is kinda hacky and we should probably provide a better
# helper for this in Thinc
err_text = str(e).replace("Config validation error", "").strip()
print(err_text)
if hint_fill and "field required" in err_text:
except ConfigValidationError as e:
title = title if title is not None else e.title
if e.desc:
desc = f"{e.desc}" if not desc else f"{e.desc}\n\n{desc}"
# Re-generate a new error object with overrides
err = e.from_error(e, title="", desc=desc, show_config=show_config)
msg.fail(title)
print(err.text.strip())
if hint_fill and "value_error.missing" in err.error_types:
config_path = file_path if file_path is not None else "config.cfg"
msg.text(
"If your config contains missing values, you can run the 'init "
@ -252,6 +258,8 @@ def show_validation_error(
)
print(f"{COMMAND} init fill-config {config_path} --base {config_path}\n")
sys.exit(1)
except InterpolationError as e:
msg.fail("Config validation error", e, exits=1)
def import_code(code_path: Optional[Union[Path, str]]) -> None:
@ -267,18 +275,6 @@ def import_code(code_path: Optional[Union[Path, str]]) -> None:
msg.fail(f"Couldn't load Python code: {code_path}", e, exits=1)
def get_sourced_components(config: Union[Dict[str, Any], Config]) -> List[str]:
"""RETURNS (List[str]): All sourced components in the original config,
e.g. {"source": "en_core_web_sm"}. If the config contains a key
"factory", we assume it refers to a component factory.
"""
return [
name
for name, cfg in config.get("components", {}).items()
if "factory" not in cfg and "source" in cfg
]
def upload_file(src: Path, dest: Union[str, "Pathy"]) -> None:
"""Upload a file.
@ -450,3 +446,12 @@ def string_to_list(value: str, intify: bool = False) -> Union[List[str], List[in
p = int(p)
result.append(p)
return result
def setup_gpu(use_gpu: int) -> None:
"""Configure the GPU and log info."""
if use_gpu >= 0:
msg.info(f"Using GPU: {use_gpu}")
require_gpu(use_gpu)
else:
msg.info("Using CPU")

3
spacy/cli/convert.py
View File

@ -9,7 +9,8 @@ import sys
from ._util import app, Arg, Opt
from ..training import docs_to_json
from ..tokens import DocBin
from ..training.converters import iob_to_docs, conll_ner_to_docs, json_to_docs, conllu_to_docs
from ..training.converters import iob_to_docs, conll_ner_to_docs, json_to_docs
from ..training.converters import conllu_to_docs
# Converters are matched by file extension except for ner/iob, which are

33
spacy/cli/debug_config.py
View File

@ -2,11 +2,13 @@ from typing import Optional, Dict, Any, Union, List
from pathlib import Path
from wasabi import msg, table
from thinc.api import Config
from thinc.config import VARIABLE_RE, ConfigValidationError
from thinc.config import VARIABLE_RE
import typer
from ._util import Arg, Opt, show_validation_error, parse_config_overrides
from ._util import import_code, debug_cli
from ..schemas import ConfigSchemaTraining
from ..util import registry
from .. import util
@ -51,10 +53,11 @@ def debug_config(
msg.divider("Config validation")
with show_validation_error(config_path):
config = util.load_config(config_path, overrides=overrides)
nlp, resolved = util.load_model_from_config(config)
# Use the resolved config here in case user has one function returning
# a dict of corpora etc.
check_section_refs(resolved, ["training.dev_corpus", "training.train_corpus"])
nlp = util.load_model_from_config(config)
config = nlp.config.interpolate()
T = registry.resolve(config["training"], schema=ConfigSchemaTraining)
dot_names = [T["train_corpus"], T["dev_corpus"]]
util.resolve_dot_names(config, dot_names)
msg.good("Config is valid")
if show_vars:
variables = get_variables(config)
@ -96,23 +99,3 @@ def get_variables(config: Config) -> Dict[str, Any]:
value = util.dot_to_object(config, path)
result[variable] = repr(value)
return result
def check_section_refs(config: Config, fields: List[str]) -> None:
"""Validate fields in the config that refer to other sections or values
(e.g. in the corpora) and make sure that those references exist.
"""
errors = []
for field in fields:
# If the field doesn't exist in the config, we ignore it
try:
value = util.dot_to_object(config, field)
except KeyError:
continue
try:
util.dot_to_object(config, value)
except KeyError:
msg = f"not a valid section reference: {value}"
errors.append({"loc": field.split("."), "msg": msg})
if errors:
raise ConfigValidationError(config, errors)

77
spacy/cli/debug_data.py
View File

@ -7,10 +7,13 @@ from wasabi import Printer, MESSAGES, msg
import typer
from ._util import app, Arg, Opt, show_validation_error, parse_config_overrides
from ._util import import_code, debug_cli, get_sourced_components
from ..training import Corpus, Example
from ._util import import_code, debug_cli
from ..training import Example
from ..training.initialize import get_sourced_components
from ..schemas import ConfigSchemaTraining
from ..pipeline._parser_internals import nonproj
from ..language import Language
from ..util import registry, resolve_dot_names
from .. import util
@ -24,7 +27,7 @@ BLANK_MODEL_THRESHOLD = 2000
@debug_cli.command(
"data", context_settings={"allow_extra_args": True, "ignore_unknown_options": True},
"data", context_settings={"allow_extra_args": True, "ignore_unknown_options": True}
)
@app.command(
"debug-data",
@ -34,8 +37,6 @@ BLANK_MODEL_THRESHOLD = 2000
def debug_data_cli(
# fmt: off
ctx: typer.Context, # This is only used to read additional arguments
train_path: Path = Arg(..., help="Location of JSON-formatted training data", exists=True),
dev_path: Path = Arg(..., help="Location of JSON-formatted development data", exists=True),
config_path: Path = Arg(..., help="Path to config file", exists=True),
code_path: Optional[Path] = Opt(None, "--code-path", "-c", help="Path to Python file with additional code (registered functions) to be imported"),
ignore_warnings: bool = Opt(False, "--ignore-warnings", "-IW", help="Ignore warnings, only show stats and errors"),
@ -59,8 +60,6 @@ def debug_data_cli(
overrides = parse_config_overrides(ctx.args)
import_code(code_path)
debug_data(
train_path,
dev_path,
config_path,
config_overrides=overrides,
ignore_warnings=ignore_warnings,
@ -71,8 +70,6 @@ def debug_data_cli(
def debug_data(
train_path: Path,
dev_path: Path,
config_path: Path,
*,
config_overrides: Dict[str, Any] = {},
@ -85,56 +82,29 @@ def debug_data(
no_print=silent, pretty=not no_format, ignore_warnings=ignore_warnings
)
# Make sure all files and paths exists if they are needed
if not train_path.exists():
msg.fail("Training data not found", train_path, exits=1)
if not dev_path.exists():
msg.fail("Development data not found", dev_path, exits=1)
if not config_path.exists():
msg.fail("Config file not found", config_path, exists=1)
with show_validation_error(config_path):
cfg = util.load_config(config_path, overrides=config_overrides)
nlp, config = util.load_model_from_config(cfg)
nlp = util.load_model_from_config(cfg)
config = nlp.config.interpolate()
T = registry.resolve(config["training"], schema=ConfigSchemaTraining)
# Use original config here, not resolved version
sourced_components = get_sourced_components(cfg)
frozen_components = config["training"]["frozen_components"]
frozen_components = T["frozen_components"]
resume_components = [p for p in sourced_components if p not in frozen_components]
pipeline = nlp.pipe_names
factory_names = [nlp.get_pipe_meta(pipe).factory for pipe in nlp.pipe_names]
tag_map_path = util.ensure_path(config["training"]["tag_map"])
tag_map = {}
if tag_map_path is not None:
tag_map = srsly.read_json(tag_map_path)
morph_rules_path = util.ensure_path(config["training"]["morph_rules"])
morph_rules = {}
if morph_rules_path is not None:
morph_rules = srsly.read_json(morph_rules_path)
# Replace tag map with provided mapping
nlp.vocab.morphology.load_tag_map(tag_map)
# Load morph rules
nlp.vocab.morphology.load_morph_exceptions(morph_rules)
msg.divider("Data file validation")
# Create the gold corpus to be able to better analyze data
loading_train_error_message = ""
loading_dev_error_message = ""
with msg.loading("Loading corpus..."):
try:
train_dataset = list(Corpus(train_path)(nlp))
except ValueError as e:
loading_train_error_message = f"Training data cannot be loaded: {e}"
try:
dev_dataset = list(Corpus(dev_path)(nlp))
except ValueError as e:
loading_dev_error_message = f"Development data cannot be loaded: {e}"
if loading_train_error_message or loading_dev_error_message:
if loading_train_error_message:
msg.fail(loading_train_error_message)
if loading_dev_error_message:
msg.fail(loading_dev_error_message)
sys.exit(1)
dot_names = [T["train_corpus"], T["dev_corpus"]]
train_corpus, dev_corpus = resolve_dot_names(config, dot_names)
train_dataset = list(train_corpus(nlp))
dev_dataset = list(dev_corpus(nlp))
msg.good("Corpus is loadable")
nlp.initialize(lambda: train_dataset)
msg.good("Pipeline can be initialized with data")
# Create all gold data here to avoid iterating over the train_dataset constantly
gold_train_data = _compile_gold(train_dataset, factory_names, nlp, make_proj=True)
gold_train_unpreprocessed_data = _compile_gold(
@ -144,10 +114,10 @@ def debug_data(
train_texts = gold_train_data["texts"]
dev_texts = gold_dev_data["texts"]
frozen_components = config["training"]["frozen_components"]
frozen_components = T["frozen_components"]
msg.divider("Training stats")
msg.text(f"Language: {config['nlp']['lang']}")
msg.text(f"Language: {nlp.lang}")
msg.text(f"Training pipeline: {', '.join(pipeline)}")
if resume_components:
msg.text(f"Components from other pipelines: {', '.join(resume_components)}")
@ -354,17 +324,12 @@ def debug_data(
if "tagger" in factory_names:
msg.divider("Part-of-speech Tagging")
labels = [label for label in gold_train_data["tags"]]
tag_map = nlp.vocab.morphology.tag_map
msg.info(f"{len(labels)} label(s) in data ({len(tag_map)} label(s) in tag map)")
# TODO: does this need to be updated?
msg.info(f"{len(labels)} label(s) in data")
labels_with_counts = _format_labels(
gold_train_data["tags"].most_common(), counts=True
)
msg.text(labels_with_counts, show=verbose)
non_tagmap = [l for l in labels if l not in tag_map]
if not non_tagmap:
msg.good(f"All labels present in tag map for language '{nlp.lang}'")
for label in non_tagmap:
msg.fail(f"Label '{label}' not found in tag map for language '{nlp.lang}'")
if "parser" in factory_names:
has_low_data_warning = False

58
spacy/cli/debug_model.py
View File

@ -1,20 +1,24 @@
import warnings
from typing import Dict, Any, Optional, Iterable
from pathlib import Path
from spacy.training import Example
from spacy.util import dot_to_object
from spacy.util import resolve_dot_names
from wasabi import msg
from thinc.api import require_gpu, fix_random_seed, set_dropout_rate, Adam
from thinc.api import fix_random_seed, set_dropout_rate, Adam
from thinc.api import Model, data_validation, set_gpu_allocator
import typer
from ._util import Arg, Opt, debug_cli, show_validation_error
from ._util import parse_config_overrides, string_to_list
from ._util import parse_config_overrides, string_to_list, setup_gpu
from ..schemas import ConfigSchemaTraining
from ..util import registry
from .. import util
@debug_cli.command("model")
@debug_cli.command(
"model",
context_settings={"allow_extra_args": True, "ignore_unknown_options": True},
)
def debug_model_cli(
# fmt: off
ctx: typer.Context, # This is only used to read additional arguments
@ -38,11 +42,7 @@ def debug_model_cli(
DOCS: https://nightly.spacy.io/api/cli#debug-model
"""
if use_gpu >= 0:
msg.info("Using GPU")
require_gpu(use_gpu)
else:
msg.info("Using CPU")
setup_gpu(use_gpu)
layers = string_to_list(layers, intify=True)
print_settings = {
"dimensions": dimensions,
@ -57,14 +57,18 @@ def debug_model_cli(
}
config_overrides = parse_config_overrides(ctx.args)
with show_validation_error(config_path):
config = util.load_config(
config_path, overrides=config_overrides, interpolate=True
raw_config = util.load_config(
config_path, overrides=config_overrides, interpolate=False
)
allocator = config["training"]["gpu_allocator"]
if use_gpu >= 0 and allocator:
set_gpu_allocator(allocator)
nlp, config = util.load_model_from_config(config)
seed = config["training"]["seed"]
config = raw_config.interpolate()
allocator = config["training"]["gpu_allocator"]
if use_gpu >= 0 and allocator:
set_gpu_allocator(allocator)
with show_validation_error(config_path):
nlp = util.load_model_from_config(raw_config)
config = nlp.config.interpolate()
T = registry.resolve(config["training"], schema=ConfigSchemaTraining)
seed = T["seed"]
if seed is not None:
msg.info(f"Fixing random seed: {seed}")
fix_random_seed(seed)
@ -75,11 +79,16 @@ def debug_model_cli(
exits=1,
)
model = pipe.model
debug_model(config, nlp, model, print_settings=print_settings)
debug_model(config, T, nlp, model, print_settings=print_settings)
def debug_model(
config, nlp, model: Model, *, print_settings: Optional[Dict[str, Any]] = None
config,
resolved_train_config,
nlp,
model: Model,
*,
print_settings: Optional[Dict[str, Any]] = None,
):
if not isinstance(model, Model):
msg.fail(
@ -100,15 +109,18 @@ def debug_model(
# The output vector might differ from the official type of the output layer
with data_validation(False):
try:
train_corpus = dot_to_object(config, config["training"]["train_corpus"])
nlp.begin_training(lambda: train_corpus(nlp))
dot_names = [resolved_train_config["train_corpus"]]
with show_validation_error():
(train_corpus,) = resolve_dot_names(config, dot_names)
nlp.initialize(lambda: train_corpus(nlp))
msg.info("Initialized the model with the training corpus.")
except ValueError:
try:
_set_output_dim(nO=7, model=model)
nlp.begin_training(lambda: [Example.from_dict(x, {}) for x in X])
with show_validation_error():
nlp.initialize(lambda: [Example.from_dict(x, {}) for x in X])
msg.info("Initialized the model with dummy data.")
except:
except Exception:
msg.fail(
"Could not initialize the model: you'll have to provide a valid train_corpus argument in the config file.",
exits=1,

9
spacy/cli/evaluate.py
View File

@ -3,11 +3,11 @@ from wasabi import Printer
from pathlib import Path
import re
import srsly
from thinc.api import require_gpu, fix_random_seed
from thinc.api import fix_random_seed
from ..training import Corpus
from ..tokens import Doc
from ._util import app, Arg, Opt
from ._util import app, Arg, Opt, setup_gpu, import_code
from ..scorer import Scorer
from .. import util
from .. import displacy
@ -19,6 +19,7 @@ def evaluate_cli(
model: str = Arg(..., help="Model name or path"),
data_path: Path = Arg(..., help="Location of binary evaluation data in .spacy format", exists=True),
output: Optional[Path] = Opt(None, "--output", "-o", help="Output JSON file for metrics", dir_okay=False),
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(-1, "--gpu-id", "-g", help="GPU ID or -1 for CPU"),
gold_preproc: bool = Opt(False, "--gold-preproc", "-G", help="Use gold preprocessing"),
displacy_path: Optional[Path] = Opt(None, "--displacy-path", "-dp", help="Directory to output rendered parses as HTML", exists=True, file_okay=False),
@ -37,6 +38,7 @@ def evaluate_cli(
DOCS: https://nightly.spacy.io/api/cli#evaluate
"""
import_code(code_path)
evaluate(
model,
data_path,
@ -61,8 +63,7 @@ def evaluate(
) -> Scorer:
msg = Printer(no_print=silent, pretty=not silent)
fix_random_seed()
if use_gpu >= 0:
require_gpu(use_gpu)
setup_gpu(use_gpu)
data_path = util.ensure_path(data_path)
output_path = util.ensure_path(output)
displacy_path = util.ensure_path(displacy_path)

6
spacy/cli/init_config.py
View File

@ -88,10 +88,10 @@ def fill_config(
msg = Printer(no_print=no_print)
with show_validation_error(hint_fill=False):
config = util.load_config(base_path)
nlp, _ = util.load_model_from_config(config, auto_fill=True, validate=False)
nlp = util.load_model_from_config(config, auto_fill=True, validate=False)
# Load a second time with validation to be extra sure that the produced
# config result is a valid config
nlp, _ = util.load_model_from_config(nlp.config)
nlp = util.load_model_from_config(nlp.config)
filled = nlp.config
if pretraining:
validate_config_for_pretrain(filled, msg)
@ -169,7 +169,7 @@ def init_config(
msg.text(f"- {label}: {value}")
with show_validation_error(hint_fill=False):
config = util.load_config_from_str(base_template)
nlp, _ = util.load_model_from_config(config, auto_fill=True)
nlp = util.load_model_from_config(config, auto_fill=True)
config = nlp.config
if pretraining:
validate_config_for_pretrain(config, msg)

360
spacy/cli/init_model.py
View File

@ -1,360 +0,0 @@
from typing import Optional, List, Dict, Any, Union, IO
import math
from tqdm import tqdm
import numpy
from ast import literal_eval
from pathlib import Path
from preshed.counter import PreshCounter
import tarfile
import gzip
import zipfile
import srsly
import warnings
from wasabi import msg, Printer
import typer
from ._util import app, init_cli, Arg, Opt
from ..vectors import Vectors
from ..errors import Errors, Warnings
from ..language import Language
from ..util import ensure_path, get_lang_class, load_model, OOV_RANK
try:
import ftfy
except ImportError:
ftfy = None
DEFAULT_OOV_PROB = -20
@init_cli.command("vocab")
@app.command(
"init-model",
context_settings={"allow_extra_args": True, "ignore_unknown_options": True},
hidden=True, # hide this from main CLI help but still allow it to work with warning
)
def init_model_cli(
# fmt: off
ctx: typer.Context, # This is only used to read additional arguments
lang: str = Arg(..., help="Pipeline language"),
output_dir: Path = Arg(..., help="Pipeline output directory"),
freqs_loc: Optional[Path] = Arg(None, help="Location of words frequencies file", exists=True),
clusters_loc: Optional[Path] = Opt(None, "--clusters-loc", "-c", help="Optional location of brown clusters data", exists=True),
jsonl_loc: Optional[Path] = Opt(None, "--jsonl-loc", "-j", help="Location of JSONL-formatted attributes file", exists=True),
vectors_loc: Optional[Path] = Opt(None, "--vectors-loc", "-v", help="Optional vectors file in Word2Vec format", exists=True),
prune_vectors: int = Opt(-1, "--prune-vectors", "-V", help="Optional number of vectors to prune to"),
truncate_vectors: int = Opt(0, "--truncate-vectors", "-t", help="Optional number of vectors to truncate to when reading in vectors file"),
vectors_name: Optional[str] = Opt(None, "--vectors-name", "-vn", help="Optional name for the word vectors, e.g. en_core_web_lg.vectors"),
model_name: Optional[str] = Opt(None, "--meta-name", "-mn", help="Optional name of the package for the pipeline meta"),
base_model: Optional[str] = Opt(None, "--base", "-b", help="Name of or path to base pipeline to start with (mostly relevant for pipelines with custom tokenizers)")
# fmt: on
):
"""
Create a new blank pipeline directory with vocab and vectors from raw data.
If vectors are provided in Word2Vec format, they can be either a .txt or
zipped as a .zip or .tar.gz.
DOCS: https://nightly.spacy.io/api/cli#init-vocab
"""
if ctx.command.name == "init-model":
msg.warn(
"The init-model command is now called 'init vocab'. You can run "
"'python -m spacy init --help' for an overview of the other "
"available initialization commands."
)
init_model(
lang,
output_dir,
freqs_loc=freqs_loc,
clusters_loc=clusters_loc,
jsonl_loc=jsonl_loc,
vectors_loc=vectors_loc,
prune_vectors=prune_vectors,
truncate_vectors=truncate_vectors,
vectors_name=vectors_name,
model_name=model_name,
base_model=base_model,
silent=False,
)
def init_model(
lang: str,
output_dir: Path,
freqs_loc: Optional[Path] = None,
clusters_loc: Optional[Path] = None,
jsonl_loc: Optional[Path] = None,
vectors_loc: Optional[Path] = None,
prune_vectors: int = -1,
truncate_vectors: int = 0,
vectors_name: Optional[str] = None,
model_name: Optional[str] = None,
base_model: Optional[str] = None,
silent: bool = True,
) -> Language:
msg = Printer(no_print=silent, pretty=not silent)
if jsonl_loc is not None:
if freqs_loc is not None or clusters_loc is not None:
settings = ["-j"]
if freqs_loc:
settings.append("-f")
if clusters_loc:
settings.append("-c")
msg.warn(
"Incompatible arguments",
"The -f and -c arguments are deprecated, and not compatible "
"with the -j argument, which should specify the same "
"information. Either merge the frequencies and clusters data "
"into the JSONL-formatted file (recommended), or use only the "
"-f and -c files, without the other lexical attributes.",
)
jsonl_loc = ensure_path(jsonl_loc)
lex_attrs = srsly.read_jsonl(jsonl_loc)
else:
clusters_loc = ensure_path(clusters_loc)
freqs_loc = ensure_path(freqs_loc)
if freqs_loc is not None and not freqs_loc.exists():
msg.fail("Can't find words frequencies file", freqs_loc, exits=1)
lex_attrs = read_attrs_from_deprecated(msg, freqs_loc, clusters_loc)
with msg.loading("Creating blank pipeline..."):
nlp = create_model(lang, lex_attrs, name=model_name, base_model=base_model)
msg.good("Successfully created blank pipeline")
if vectors_loc is not None:
add_vectors(
msg, nlp, vectors_loc, truncate_vectors, prune_vectors, vectors_name
)
vec_added = len(nlp.vocab.vectors)
lex_added = len(nlp.vocab)
msg.good(
"Sucessfully compiled vocab", f"{lex_added} entries, {vec_added} vectors",
)
if not output_dir.exists():
output_dir.mkdir()
nlp.to_disk(output_dir)
return nlp
def open_file(loc: Union[str, Path]) -> IO:
"""Handle .gz, .tar.gz or unzipped files"""
loc = ensure_path(loc)
if tarfile.is_tarfile(str(loc)):
return tarfile.open(str(loc), "r:gz")
elif loc.parts[-1].endswith("gz"):
return (line.decode("utf8") for line in gzip.open(str(loc), "r"))
elif loc.parts[-1].endswith("zip"):
zip_file = zipfile.ZipFile(str(loc))
names = zip_file.namelist()
file_ = zip_file.open(names[0])
return (line.decode("utf8") for line in file_)
else:
return loc.open("r", encoding="utf8")
def read_attrs_from_deprecated(
msg: Printer, freqs_loc: Optional[Path], clusters_loc: Optional[Path]
) -> List[Dict[str, Any]]:
if freqs_loc is not None:
with msg.loading("Counting frequencies..."):
probs, _ = read_freqs(freqs_loc)
msg.good("Counted frequencies")
else:
probs, _ = ({}, DEFAULT_OOV_PROB) # noqa: F841
if clusters_loc:
with msg.loading("Reading clusters..."):
clusters = read_clusters(clusters_loc)
msg.good("Read clusters")
else:
clusters = {}
lex_attrs = []
sorted_probs = sorted(probs.items(), key=lambda item: item[1], reverse=True)
if len(sorted_probs):
for i, (word, prob) in tqdm(enumerate(sorted_probs)):
attrs = {"orth": word, "id": i, "prob": prob}
# Decode as a little-endian string, so that we can do & 15 to get
# the first 4 bits. See _parse_features.pyx
if word in clusters:
attrs["cluster"] = int(clusters[word][::-1], 2)
else:
attrs["cluster"] = 0
lex_attrs.append(attrs)
return lex_attrs
def create_model(
lang: str,
lex_attrs: List[Dict[str, Any]],
name: Optional[str] = None,
base_model: Optional[Union[str, Path]] = None,
) -> Language:
if base_model:
nlp = load_model(base_model)
# keep the tokenizer but remove any existing pipeline components due to
# potentially conflicting vectors
for pipe in nlp.pipe_names:
nlp.remove_pipe(pipe)
else:
lang_class = get_lang_class(lang)
nlp = lang_class()
for lexeme in nlp.vocab:
lexeme.rank = OOV_RANK
for attrs in lex_attrs:
if "settings" in attrs:
continue
lexeme = nlp.vocab[attrs["orth"]]
lexeme.set_attrs(**attrs)
if len(nlp.vocab):
oov_prob = min(lex.prob for lex in nlp.vocab) - 1
else:
oov_prob = DEFAULT_OOV_PROB
nlp.vocab.cfg.update({"oov_prob": oov_prob})
if name:
nlp.meta["name"] = name
return nlp
def add_vectors(
msg: Printer,
nlp: Language,
vectors_loc: Optional[Path],
truncate_vectors: int,
prune_vectors: int,
name: Optional[str] = None,
) -> None:
vectors_loc = ensure_path(vectors_loc)
if vectors_loc and vectors_loc.parts[-1].endswith(".npz"):
nlp.vocab.vectors = Vectors(data=numpy.load(vectors_loc.open("rb")))
for lex in nlp.vocab:
if lex.rank and lex.rank != OOV_RANK:
nlp.vocab.vectors.add(lex.orth, row=lex.rank)
else:
if vectors_loc:
with msg.loading(f"Reading vectors from {vectors_loc}"):
vectors_data, vector_keys = read_vectors(
msg, vectors_loc, truncate_vectors
)
msg.good(f"Loaded vectors from {vectors_loc}")
else:
vectors_data, vector_keys = (None, None)
if vector_keys is not None:
for word in vector_keys:
if word not in nlp.vocab:
nlp.vocab[word]
if vectors_data is not None:
nlp.vocab.vectors = Vectors(data=vectors_data, keys=vector_keys)
if name is None:
# TODO: Is this correct? Does this matter?
nlp.vocab.vectors.name = f"{nlp.meta['lang']}_{nlp.meta['name']}.vectors"
else:
nlp.vocab.vectors.name = name
nlp.meta["vectors"]["name"] = nlp.vocab.vectors.name
if prune_vectors >= 1:
nlp.vocab.prune_vectors(prune_vectors)
def read_vectors(msg: Printer, vectors_loc: Path, truncate_vectors: int):
f = open_file(vectors_loc)
f = ensure_shape(f)
shape = tuple(int(size) for size in next(f).split())
if truncate_vectors >= 1:
shape = (truncate_vectors, shape[1])
vectors_data = numpy.zeros(shape=shape, dtype="f")
vectors_keys = []
for i, line in enumerate(tqdm(f)):
line = line.rstrip()
pieces = line.rsplit(" ", vectors_data.shape[1])
word = pieces.pop(0)
if len(pieces) != vectors_data.shape[1]:
msg.fail(Errors.E094.format(line_num=i, loc=vectors_loc), exits=1)
vectors_data[i] = numpy.asarray(pieces, dtype="f")
vectors_keys.append(word)
if i == truncate_vectors - 1:
break
return vectors_data, vectors_keys
def ensure_shape(lines):
"""Ensure that the first line of the data is the vectors shape.
If it's not, we read in the data and output the shape as the first result,
so that the reader doesn't have to deal with the problem.
"""
first_line = next(lines)
try:
shape = tuple(int(size) for size in first_line.split())
except ValueError:
shape = None
if shape is not None:
# All good, give the data
yield first_line
yield from lines
else:
# Figure out the shape, make it the first value, and then give the
# rest of the data.
width = len(first_line.split()) - 1
captured = [first_line] + list(lines)
length = len(captured)
yield f"{length} {width}"
yield from captured
def read_freqs(
freqs_loc: Path, max_length: int = 100, min_doc_freq: int = 5, min_freq: int = 50
):
counts = PreshCounter()
total = 0
with freqs_loc.open() as f:
for i, line in enumerate(f):
freq, doc_freq, key = line.rstrip().split("\t", 2)
freq = int(freq)
counts.inc(i + 1, freq)
total += freq
counts.smooth()
log_total = math.log(total)
probs = {}
with freqs_loc.open() as f:
for line in tqdm(f):
freq, doc_freq, key = line.rstrip().split("\t", 2)
doc_freq = int(doc_freq)
freq = int(freq)
if doc_freq >= min_doc_freq and freq >= min_freq and len(key) < max_length:
try:
word = literal_eval(key)
except SyntaxError:
# Take odd strings literally.
word = literal_eval(f"'{key}'")
smooth_count = counts.smoother(int(freq))
probs[word] = math.log(smooth_count) - log_total
oov_prob = math.log(counts.smoother(0)) - log_total
return probs, oov_prob
def read_clusters(clusters_loc: Path) -> dict:
clusters = {}
if ftfy is None:
warnings.warn(Warnings.W004)
with clusters_loc.open() as f:
for line in tqdm(f):
try:
cluster, word, freq = line.split()
if ftfy is not None:
word = ftfy.fix_text(word)
except ValueError:
continue
# If the clusterer has only seen the word a few times, its
# cluster is unreliable.
if int(freq) >= 3:
clusters[word] = cluster
else:
clusters[word] = "0"
# Expand clusters with re-casing
for word, cluster in list(clusters.items()):
if word.lower() not in clusters:
clusters[word.lower()] = cluster
if word.title() not in clusters:
clusters[word.title()] = cluster
if word.upper() not in clusters:
clusters[word.upper()] = cluster
return clusters

117
spacy/cli/init_pipeline.py Normal file
View File

@ -0,0 +1,117 @@
from typing import Optional
import logging
from pathlib import Path
from wasabi import msg
import typer
import srsly
from .. import util
from ..training.initialize import init_nlp, convert_vectors
from ..language import Language
from ._util import init_cli, Arg, Opt, parse_config_overrides, show_validation_error
from ._util import import_code, setup_gpu
@init_cli.command("vectors")
def init_vectors_cli(
# fmt: off
lang: str = Arg(..., help="The language of the nlp object to create"),
vectors_loc: Path = Arg(..., help="Vectors file in Word2Vec format", exists=True),
output_dir: Path = Arg(..., help="Pipeline output directory"),
prune: int = Opt(-1, "--prune", "-p", help="Optional number of vectors to prune to"),
truncate: int = Opt(0, "--truncate", "-t", help="Optional number of vectors to truncate to when reading in vectors file"),
name: Optional[str] = Opt(None, "--name", "-n", help="Optional name for the word vectors, e.g. en_core_web_lg.vectors"),
verbose: bool = Opt(False, "--verbose", "-V", "-VV", help="Display more information for debugging purposes"),
jsonl_loc: Optional[Path] = Opt(None, "--lexemes-jsonl", "-j", help="Location of JSONL-formatted attributes file", hidden=True),
# fmt: on
):
"""Convert word vectors for use with spaCy. Will export an nlp object that
you can use in the [initialize] block of your config to initialize
a model with vectors.
"""
util.logger.setLevel(logging.DEBUG if verbose else logging.INFO)
msg.info(f"Creating blank nlp object for language '{lang}'")
nlp = util.get_lang_class(lang)()
if jsonl_loc is not None:
update_lexemes(nlp, jsonl_loc)
convert_vectors(nlp, vectors_loc, truncate=truncate, prune=prune, name=name)
msg.good(f"Successfully converted {len(nlp.vocab.vectors)} vectors")
nlp.to_disk(output_dir)
msg.good(
"Saved nlp object with vectors to output directory. You can now use the "
"path to it in your config as the 'vectors' setting in [initialize.vocab].",
output_dir.resolve(),
)
def update_lexemes(nlp: Language, jsonl_loc: Path) -> None:
# Mostly used for backwards-compatibility and may be removed in the future
lex_attrs = srsly.read_jsonl(jsonl_loc)
for attrs in lex_attrs:
if "settings" in attrs:
continue
lexeme = nlp.vocab[attrs["orth"]]
lexeme.set_attrs(**attrs)
@init_cli.command(
"nlp",
context_settings={"allow_extra_args": True, "ignore_unknown_options": True},
hidden=True,
)
def init_pipeline_cli(
# fmt: off
ctx: typer.Context, # This is only used to read additional arguments
config_path: Path = Arg(..., help="Path to config file", exists=True),
output_path: Path = Arg(..., help="Output directory for the prepared data"),
code_path: Optional[Path] = Opt(None, "--code", "-c", help="Path to Python file with additional code (registered functions) to be imported"),
verbose: bool = Opt(False, "--verbose", "-V", "-VV", help="Display more information for debugging purposes"),
use_gpu: int = Opt(-1, "--gpu-id", "-g", help="GPU ID or -1 for CPU")
# fmt: on
):
util.logger.setLevel(logging.DEBUG if verbose else logging.INFO)
overrides = parse_config_overrides(ctx.args)
import_code(code_path)
setup_gpu(use_gpu)
with show_validation_error(config_path):
config = util.load_config(config_path, overrides=overrides)
with show_validation_error(hint_fill=False):
nlp = init_nlp(config, use_gpu=use_gpu)
nlp.to_disk(output_path)
msg.good(f"Saved initialized pipeline to {output_path}")
@init_cli.command(
"labels",
context_settings={"allow_extra_args": True, "ignore_unknown_options": True},
)
def init_labels_cli(
# fmt: off
ctx: typer.Context, # This is only used to read additional arguments
config_path: Path = Arg(..., help="Path to config file", exists=True),
output_path: Path = Arg(..., help="Output directory for the labels"),
code_path: Optional[Path] = Opt(None, "--code", "-c", help="Path to Python file with additional code (registered functions) to be imported"),
verbose: bool = Opt(False, "--verbose", "-V", "-VV", help="Display more information for debugging purposes"),
use_gpu: int = Opt(-1, "--gpu-id", "-g", help="GPU ID or -1 for CPU")
# fmt: on
):
"""Generate JSON files for the labels in the data. This helps speed up the
training process, since spaCy won't have to preprocess the data to
extract the labels."""
util.logger.setLevel(logging.DEBUG if verbose else logging.INFO)
if not output_path.exists():
output_path.mkdir()
overrides = parse_config_overrides(ctx.args)
import_code(code_path)
setup_gpu(use_gpu)
with show_validation_error(config_path):
config = util.load_config(config_path, overrides=overrides)
with show_validation_error(hint_fill=False):
nlp = init_nlp(config, use_gpu=use_gpu)
for name, component in nlp.pipeline:
if getattr(component, "label_data", None) is not None:
output_file = output_path / f"{name}.json"
srsly.write_json(output_file, component.label_data)
msg.good(f"Saving {name} labels to {output_file}")
else:
msg.info(f"No labels found for {name}")

277
spacy/cli/pretrain.py
View File

@ -1,25 +1,13 @@
from typing import Optional
import numpy
import time
import re
from collections import Counter
from pathlib import Path
from thinc.api import require_gpu, set_gpu_allocator
from thinc.api import set_dropout_rate, to_categorical, fix_random_seed
from thinc.api import Config, CosineDistance, L2Distance
from wasabi import msg
import srsly
from functools import partial
import typer
import re
from ._util import app, Arg, Opt, parse_config_overrides, show_validation_error
from ._util import import_code
from ..ml.models.multi_task import build_cloze_multi_task_model
from ..ml.models.multi_task import build_cloze_characters_multi_task_model
from ..tokens import Doc
from ..attrs import ID
from .. import util
from ..util import dot_to_object
from ._util import import_code, setup_gpu
from ..training.pretrain import pretrain
from ..util import load_config
@app.command(
@ -61,25 +49,22 @@ def pretrain_cli(
config_overrides = parse_config_overrides(ctx.args)
import_code(code_path)
verify_cli_args(config_path, output_dir, resume_path, epoch_resume)
if use_gpu >= 0:
msg.info("Using GPU")
require_gpu(use_gpu)
else:
msg.info("Using CPU")
setup_gpu(use_gpu)
msg.info(f"Loading config from: {config_path}")
with show_validation_error(config_path):
config = util.load_config(
config_path, overrides=config_overrides, interpolate=True
raw_config = load_config(
config_path, overrides=config_overrides, interpolate=False
)
config = raw_config.interpolate()
if not config.get("pretraining"):
# TODO: What's the solution here? How do we handle optional blocks?
msg.fail("The [pretraining] block in your config is empty", exits=1)
if not output_dir.exists():
output_dir.mkdir()
msg.good(f"Created output directory: {output_dir}")
config.to_disk(output_dir / "config.cfg")
# Save non-interpolated config
raw_config.to_disk(output_dir / "config.cfg")
msg.good("Saved config file in the output directory")
pretrain(
@ -88,251 +73,11 @@ def pretrain_cli(
resume_path=resume_path,
epoch_resume=epoch_resume,
use_gpu=use_gpu,
silent=False,
)
def pretrain(
config: Config,
output_dir: Path,
resume_path: Optional[Path] = None,
epoch_resume: Optional[int] = None,
use_gpu: int = -1,
):
if config["training"]["seed"] is not None:
fix_random_seed(config["training"]["seed"])
allocator = config["training"]["gpu_allocator"]
if use_gpu >= 0 and allocator:
set_gpu_allocator(allocator)
nlp, config = util.load_model_from_config(config)
P_cfg = config["pretraining"]
corpus = dot_to_object(config, P_cfg["corpus"])
batcher = P_cfg["batcher"]
model = create_pretraining_model(nlp, config["pretraining"])
optimizer = config["pretraining"]["optimizer"]
# Load in pretrained weights to resume from
if resume_path is not None:
_resume_model(model, resume_path, epoch_resume)
else:
# Without '--resume-path' the '--epoch-resume' argument is ignored
epoch_resume = 0
tracker = ProgressTracker(frequency=10000)
msg.divider(f"Pre-training tok2vec layer - starting at epoch {epoch_resume}")
row_settings = {"widths": (3, 10, 10, 6, 4), "aligns": ("r", "r", "r", "r", "r")}
msg.row(("#", "# Words", "Total Loss", "Loss", "w/s"), **row_settings)
def _save_model(epoch, is_temp=False):
is_temp_str = ".temp" if is_temp else ""
with model.use_params(optimizer.averages):
with (output_dir / f"model{epoch}{is_temp_str}.bin").open("wb") as file_:
file_.write(model.get_ref("tok2vec").to_bytes())
log = {
"nr_word": tracker.nr_word,
"loss": tracker.loss,
"epoch_loss": tracker.epoch_loss,
"epoch": epoch,
}
with (output_dir / "log.jsonl").open("a") as file_:
file_.write(srsly.json_dumps(log) + "\n")
objective = create_objective(P_cfg["objective"])
# TODO: I think we probably want this to look more like the
# 'create_train_batches' function?
for epoch in range(epoch_resume, P_cfg["max_epochs"]):
for batch_id, batch in enumerate(batcher(corpus(nlp))):
docs = ensure_docs(batch)
loss = make_update(model, docs, optimizer, objective)
progress = tracker.update(epoch, loss, docs)
if progress:
msg.row(progress, **row_settings)
if P_cfg["n_save_every"] and (batch_id % P_cfg["n_save_every"] == 0):
_save_model(epoch, is_temp=True)
_save_model(epoch)
tracker.epoch_loss = 0.0
msg.good("Successfully finished pretrain")
def ensure_docs(examples_or_docs):
docs = []
for eg_or_doc in examples_or_docs:
if isinstance(eg_or_doc, Doc):
docs.append(eg_or_doc)
else:
docs.append(eg_or_doc.reference)
return docs
def _resume_model(model, resume_path, epoch_resume):
msg.info(f"Resume training tok2vec from: {resume_path}")
with resume_path.open("rb") as file_:
weights_data = file_.read()
model.get_ref("tok2vec").from_bytes(weights_data)
# Parse the epoch number from the given weight file
model_name = re.search(r"model\d+\.bin", str(resume_path))
if model_name:
# Default weight file name so read epoch_start from it by cutting off 'model' and '.bin'
epoch_resume = int(model_name.group(0)[5:][:-4]) + 1
msg.info(f"Resuming from epoch: {epoch_resume}")
else:
msg.info(f"Resuming from epoch: {epoch_resume}")
def make_update(model, docs, optimizer, objective_func):
"""Perform an update over a single batch of documents.
docs (iterable): A batch of `Doc` objects.
optimizer (callable): An optimizer.
RETURNS loss: A float for the loss.
"""
predictions, backprop = model.begin_update(docs)
loss, gradients = objective_func(model.ops, docs, predictions)
backprop(gradients)
model.finish_update(optimizer)
# Don't want to return a cupy object here
# The gradients are modified in-place by the BERT MLM,
# so we get an accurate loss
return float(loss)
def create_objective(config):
"""Create the objective for pretraining.
We'd like to replace this with a registry function but it's tricky because
we're also making a model choice based on this. For now we hard-code support
for two types (characters, vectors). For characters you can specify
n_characters, for vectors you can specify the loss.
Bleh.
"""
objective_type = config["type"]
if objective_type == "characters":
return partial(get_characters_loss, nr_char=config["n_characters"])
elif objective_type == "vectors":
if config["loss"] == "cosine":
return partial(
get_vectors_loss,
distance=CosineDistance(normalize=True, ignore_zeros=True),
)
elif config["loss"] == "L2":
return partial(
get_vectors_loss, distance=L2Distance(normalize=True, ignore_zeros=True)
)
else:
raise ValueError("Unexpected loss type", config["loss"])
else:
raise ValueError("Unexpected objective_type", objective_type)
def get_vectors_loss(ops, docs, prediction, distance):
"""Compute a loss based on a distance between the documents' vectors and
the prediction.
"""
# The simplest way to implement this would be to vstack the
# token.vector values, but that's a bit inefficient, especially on GPU.
# Instead we fetch the index into the vectors table for each of our tokens,
# and look them up all at once. This prevents data copying.
ids = ops.flatten([doc.to_array(ID).ravel() for doc in docs])
target = docs[0].vocab.vectors.data[ids]
d_target, loss = distance(prediction, target)
return loss, d_target
def get_characters_loss(ops, docs, prediction, nr_char):
"""Compute a loss based on a number of characters predicted from the docs."""
target_ids = numpy.vstack([doc.to_utf8_array(nr_char=nr_char) for doc in docs])
target_ids = target_ids.reshape((-1,))
target = ops.asarray(to_categorical(target_ids, n_classes=256), dtype="f")
target = target.reshape((-1, 256 * nr_char))
diff = prediction - target
loss = (diff ** 2).sum()
d_target = diff / float(prediction.shape[0])
return loss, d_target
def create_pretraining_model(nlp, pretrain_config):
"""Define a network for the pretraining. We simply add an output layer onto
the tok2vec input model. The tok2vec input model needs to be a model that
takes a batch of Doc objects (as a list), and returns a list of arrays.
Each array in the output needs to have one row per token in the doc.
The actual tok2vec layer is stored as a reference, and only this bit will be
serialized to file and read back in when calling the 'train' command.
"""
component = nlp.get_pipe(pretrain_config["component"])
if pretrain_config.get("layer"):
tok2vec = component.model.get_ref(pretrain_config["layer"])
else:
tok2vec = component.model
# TODO
maxout_pieces = 3
hidden_size = 300
if pretrain_config["objective"]["type"] == "vectors":
model = build_cloze_multi_task_model(
nlp.vocab, tok2vec, hidden_size=hidden_size, maxout_pieces=maxout_pieces
)
elif pretrain_config["objective"]["type"] == "characters":
model = build_cloze_characters_multi_task_model(
nlp.vocab,
tok2vec,
hidden_size=hidden_size,
maxout_pieces=maxout_pieces,
nr_char=pretrain_config["objective"]["n_characters"],
)
model.initialize(X=[nlp.make_doc("Give it a doc to infer shapes")])
set_dropout_rate(model, pretrain_config["dropout"])
return model
class ProgressTracker:
def __init__(self, frequency=1000000):
self.loss = 0.0
self.prev_loss = 0.0
self.nr_word = 0
self.words_per_epoch = Counter()
self.frequency = frequency
self.last_time = time.time()
self.last_update = 0
self.epoch_loss = 0.0
def update(self, epoch, loss, docs):
self.loss += loss
self.epoch_loss += loss
words_in_batch = sum(len(doc) for doc in docs)
self.words_per_epoch[epoch] += words_in_batch
self.nr_word += words_in_batch
words_since_update = self.nr_word - self.last_update
if words_since_update >= self.frequency:
wps = words_since_update / (time.time() - self.last_time)
self.last_update = self.nr_word
self.last_time = time.time()
loss_per_word = self.loss - self.prev_loss
status = (
epoch,
self.nr_word,
_smart_round(self.loss, width=10),
_smart_round(loss_per_word, width=6),
int(wps),
)
self.prev_loss = float(self.loss)
return status
else:
return None
def _smart_round(figure, width=10, max_decimal=4):
"""Round large numbers as integers, smaller numbers as decimals."""
n_digits = len(str(int(figure)))
n_decimal = width - (n_digits + 1)
if n_decimal <= 1:
return str(int(figure))
else:
n_decimal = min(n_decimal, max_decimal)
format_str = "%." + str(n_decimal) + "f"
return format_str % figure
def verify_cli_args(config_path, output_dir, resume_path, epoch_resume):
if not config_path or not config_path.exists():
msg.fail("Config file not found", config_path, exits=1)

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

@ -114,6 +114,6 @@ def project_document(
content = f"{before}{content}{after}"
else:
msg.warn("Replacing existing file")
with output_file.open("w") as f:
with output_file.open("w", encoding="utf8") as f:
f.write(content)
msg.good("Saved project documentation", output_file)

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

@ -134,7 +134,7 @@ def update_dvc_config(
def run_dvc_commands(
commands: Iterable[str] = SimpleFrozenList(), flags: Dict[str, bool] = {},
commands: Iterable[str] = SimpleFrozenList(), flags: Dict[str, bool] = {}
) -> None:
"""Run a sequence of DVC commands in a subprocess, in order.

16
spacy/cli/templates/quickstart_training.jinja
View File

@ -4,8 +4,8 @@ can help generate the best possible configuration, given a user's requirements.
{%- set use_transformer = (transformer_data and hardware != "cpu") -%}
{%- set transformer = transformer_data[optimize] if use_transformer else {} -%}
[paths]
train = ""
dev = ""
train = null
dev = null
[system]
{% if use_transformer -%}
@ -277,11 +277,6 @@ path = ${paths.dev}
max_length = 0
[training]
{% if use_transformer or optimize == "efficiency" or not word_vectors -%}
vectors = null
{% else -%}
vectors = "{{ word_vectors }}"
{% endif -%}
{% if use_transformer -%}
accumulate_gradient = {{ transformer["size_factor"] }}
{% endif -%}
@ -317,3 +312,10 @@ start = 100
stop = 1000
compound = 1.001
{% endif %}
[initialize]
{% if use_transformer or optimize == "efficiency" or not word_vectors -%}
vectors = null
{% else -%}
vectors = "{{ word_vectors }}"
{% endif -%}

421
spacy/cli/train.py
View File

@ -1,23 +1,14 @@
from typing import Optional, Dict, Any, Tuple, Union, Callable, List
from timeit import default_timer as timer
import srsly
import tqdm
from typing import Optional
from pathlib import Path
from wasabi import msg
import thinc
import thinc.schedules
from thinc.api import Config, Optimizer, require_gpu, fix_random_seed, set_gpu_allocator
import random
import typer
import logging
from ._util import app, Arg, Opt, parse_config_overrides, show_validation_error
from ._util import import_code, get_sourced_components
from ..language import Language
from ._util import import_code, setup_gpu
from ..training.loop import train
from ..training.initialize import init_nlp
from .. import util
from ..training.example import Example
from ..errors import Errors
from ..util import dot_to_object
@app.command(
@ -30,8 +21,7 @@ def train_cli(
output_path: Optional[Path] = Opt(None, "--output", "--output-path", "-o", help="Output directory to store trained pipeline in"),
code_path: Optional[Path] = Opt(None, "--code", "-c", help="Path to Python file with additional code (registered functions) to be imported"),
verbose: bool = Opt(False, "--verbose", "-V", "-VV", help="Display more information for debugging purposes"),
use_gpu: int = Opt(-1, "--gpu-id", "-g", help="GPU ID or -1 for CPU"),
resume: bool = Opt(False, "--resume", "-R", help="Resume training"),
use_gpu: int = Opt(-1, "--gpu-id", "-g", help="GPU ID or -1 for CPU")
# fmt: on
):
"""
@ -48,375 +38,19 @@ def train_cli(
DOCS: https://nightly.spacy.io/api/cli#train
"""
util.logger.setLevel(logging.DEBUG if verbose else logging.ERROR)
util.logger.setLevel(logging.DEBUG if verbose else logging.INFO)
verify_cli_args(config_path, output_path)
overrides = parse_config_overrides(ctx.args)
import_code(code_path)
train(
config_path,
output_path=output_path,
config_overrides=overrides,
use_gpu=use_gpu,
resume_training=resume,
)
def train(
config_path: Path,
output_path: Optional[Path] = None,
config_overrides: Dict[str, Any] = {},
use_gpu: int = -1,
resume_training: bool = False,
) -> None:
if use_gpu >= 0:
msg.info(f"Using GPU: {use_gpu}")
require_gpu(use_gpu)
else:
msg.info("Using CPU")
msg.info(f"Loading config and nlp from: {config_path}")
setup_gpu(use_gpu)
with show_validation_error(config_path):
config = util.load_config(
config_path, overrides=config_overrides, interpolate=True
)
if config["training"]["seed"] is not None:
fix_random_seed(config["training"]["seed"])
allocator = config["training"]["gpu_allocator"]
if use_gpu >= 0 and allocator:
set_gpu_allocator(allocator)
# Use original config here before it's resolved to functions
sourced_components = get_sourced_components(config)
with show_validation_error(config_path):
nlp, config = util.load_model_from_config(config)
util.load_vocab_data_into_model(nlp, lookups=config["training"]["lookups"])
if config["training"]["vectors"] is not None:
util.load_vectors_into_model(nlp, config["training"]["vectors"])
raw_text, tag_map, morph_rules, weights_data = load_from_paths(config)
T_cfg = config["training"]
optimizer = T_cfg["optimizer"]
train_corpus = dot_to_object(config, T_cfg["train_corpus"])
dev_corpus = dot_to_object(config, T_cfg["dev_corpus"])
batcher = T_cfg["batcher"]
train_logger = T_cfg["logger"]
before_to_disk = create_before_to_disk_callback(T_cfg["before_to_disk"])
# Components that shouldn't be updated during training
frozen_components = T_cfg["frozen_components"]
# Sourced components that require resume_training
resume_components = [p for p in sourced_components if p not in frozen_components]
msg.info(f"Pipeline: {nlp.pipe_names}")
if resume_components:
with nlp.select_pipes(enable=resume_components):
msg.info(f"Resuming training for: {resume_components}")
nlp.resume_training(sgd=optimizer)
with nlp.select_pipes(disable=[*frozen_components, *resume_components]):
nlp.begin_training(lambda: train_corpus(nlp), sgd=optimizer)
# Verify the config after calling 'begin_training' to ensure labels are properly initialized
verify_config(nlp)
if tag_map:
# Replace tag map with provided mapping
nlp.vocab.morphology.load_tag_map(tag_map)
if morph_rules:
# Load morph rules
nlp.vocab.morphology.load_morph_exceptions(morph_rules)
# Load pretrained tok2vec weights - cf. CLI command 'pretrain'
if weights_data is not None:
tok2vec_component = config["pretraining"]["component"]
if tok2vec_component is None:
msg.fail(
f"To use pretrained tok2vec weights, [pretraining.component] "
f"needs to specify the component that should load them.",
exits=1,
)
layer = nlp.get_pipe(tok2vec_component).model
tok2vec_layer = config["pretraining"]["layer"]
if tok2vec_layer:
layer = layer.get_ref(tok2vec_layer)
layer.from_bytes(weights_data)
msg.info(f"Loaded pretrained weights into component '{tok2vec_component}'")
# Create iterator, which yields out info after each optimization step.
msg.info("Start training")
score_weights = T_cfg["score_weights"]
training_step_iterator = train_while_improving(
nlp,
optimizer,
create_train_batches(train_corpus(nlp), batcher, T_cfg["max_epochs"]),
create_evaluation_callback(nlp, dev_corpus, score_weights),
dropout=T_cfg["dropout"],
accumulate_gradient=T_cfg["accumulate_gradient"],
patience=T_cfg["patience"],
max_steps=T_cfg["max_steps"],
eval_frequency=T_cfg["eval_frequency"],
raw_text=None,
exclude=frozen_components,
)
msg.info(f"Training. Initial learn rate: {optimizer.learn_rate}")
with nlp.select_pipes(disable=frozen_components):
print_row, finalize_logger = train_logger(nlp)
try:
progress = tqdm.tqdm(total=T_cfg["eval_frequency"], leave=False)
progress.set_description(f"Epoch 1")
for batch, info, is_best_checkpoint in training_step_iterator:
progress.update(1)
if is_best_checkpoint is not None:
progress.close()
print_row(info)
if is_best_checkpoint and output_path is not None:
with nlp.select_pipes(disable=frozen_components):
update_meta(T_cfg, nlp, info)
with nlp.use_params(optimizer.averages):
nlp = before_to_disk(nlp)
nlp.to_disk(output_path / "model-best")
progress = tqdm.tqdm(total=T_cfg["eval_frequency"], leave=False)
progress.set_description(f"Epoch {info['epoch']}")
except Exception as e:
finalize_logger()
if output_path is not None:
# We don't want to swallow the traceback if we don't have a
# specific error.
msg.warn(
f"Aborting and saving the final best model. "
f"Encountered exception: {str(e)}"
)
nlp = before_to_disk(nlp)
nlp.to_disk(output_path / "model-final")
raise e
finally:
finalize_logger()
if output_path is not None:
final_model_path = output_path / "model-final"
if optimizer.averages:
with nlp.use_params(optimizer.averages):
nlp.to_disk(final_model_path)
else:
nlp.to_disk(final_model_path)
msg.good(f"Saved pipeline to output directory {final_model_path}")
def create_train_batches(iterator, batcher, max_epochs: int):
epoch = 0
examples = list(iterator)
if not examples:
# Raise error if no data
raise ValueError(Errors.E986)
while max_epochs < 1 or epoch != max_epochs:
random.shuffle(examples)
for batch in batcher(examples):
yield epoch, batch
epoch += 1
def create_evaluation_callback(
nlp: Language, dev_corpus: Callable, weights: Dict[str, float]
) -> Callable[[], Tuple[float, Dict[str, float]]]:
weights = {key: value for key, value in weights.items() if value is not None}
def evaluate() -> Tuple[float, Dict[str, float]]:
dev_examples = list(dev_corpus(nlp))
scores = nlp.evaluate(dev_examples)
# Calculate a weighted sum based on score_weights for the main score.
# We can only consider scores that are ints/floats, not dicts like
# entity scores per type etc.
for key, value in scores.items():
if key in weights and not isinstance(value, (int, float)):
raise ValueError(Errors.E915.format(name=key, score_type=type(value)))
try:
weighted_score = sum(
scores.get(s, 0.0) * weights.get(s, 0.0) for s in weights
)
except KeyError as e:
keys = list(scores.keys())
err = Errors.E983.format(dict="score_weights", key=str(e), keys=keys)
raise KeyError(err) from None
return weighted_score, scores
return evaluate
def create_before_to_disk_callback(
callback: Optional[Callable[[Language], Language]]
) -> Callable[[Language], Language]:
def before_to_disk(nlp: Language) -> Language:
if not callback:
return nlp
modified_nlp = callback(nlp)
if not isinstance(modified_nlp, Language):
err = Errors.E914.format(name="before_to_disk", value=type(modified_nlp))
raise ValueError(err)
return modified_nlp
return before_to_disk
def train_while_improving(
nlp: Language,
optimizer: Optimizer,
train_data,
evaluate,
*,
dropout: float,
eval_frequency: int,
accumulate_gradient: int,
patience: int,
max_steps: int,
raw_text: List[Dict[str, str]],
exclude: List[str],
):
"""Train until an evaluation stops improving. Works as a generator,
with each iteration yielding a tuple `(batch, info, is_best_checkpoint)`,
where info is a dict, and is_best_checkpoint is in [True, False, None] --
None indicating that the iteration was not evaluated as a checkpoint.
The evaluation is conducted by calling the evaluate callback.
Positional arguments:
nlp: The spaCy pipeline to evaluate.
optimizer: The optimizer callable.
train_data (Iterable[Batch]): A generator of batches, with the training
data. Each batch should be a Sized[Tuple[Input, Annot]]. The training
data iterable needs to take care of iterating over the epochs and
shuffling.
evaluate (Callable[[], Tuple[float, Any]]): A callback to perform evaluation.
The callback should take no arguments and return a tuple
`(main_score, other_scores)`. The main_score should be a float where
higher is better. other_scores can be any object.
Every iteration, the function yields out a tuple with:
* batch: A list of Example objects.
* info: A dict with various information about the last update (see below).
* is_best_checkpoint: A value in None, False, True, indicating whether this
was the best evaluation so far. You should use this to save the model
checkpoints during training. If None, evaluation was not conducted on
that iteration. False means evaluation was conducted, but a previous
evaluation was better.
The info dict provides the following information:
epoch (int): How many passes over the data have been completed.
step (int): How many steps have been completed.
score (float): The main score from the last evaluation.
other_scores: : The other scores from the last evaluation.
losses: The accumulated losses throughout training.
checkpoints: A list of previous results, where each result is a
(score, step, epoch) tuple.
"""
if isinstance(dropout, float):
dropouts = thinc.schedules.constant(dropout)
else:
dropouts = dropout
results = []
losses = {}
if raw_text:
random.shuffle(raw_text)
raw_examples = [
Example.from_dict(nlp.make_doc(rt["text"]), {}) for rt in raw_text
]
raw_batches = util.minibatch(raw_examples, size=8)
words_seen = 0
start_time = timer()
for step, (epoch, batch) in enumerate(train_data):
dropout = next(dropouts)
for subbatch in subdivide_batch(batch, accumulate_gradient):
nlp.update(
subbatch, drop=dropout, losses=losses, sgd=False, exclude=exclude
)
if raw_text:
# If raw text is available, perform 'rehearsal' updates,
# which use unlabelled data to reduce overfitting.
raw_batch = list(next(raw_batches))
nlp.rehearse(raw_batch, sgd=optimizer, losses=losses, exclude=exclude)
# TODO: refactor this so we don't have to run it separately in here
for name, proc in nlp.pipeline:
if (
name not in exclude
and hasattr(proc, "model")
and proc.model not in (True, False, None)
):
proc.model.finish_update(optimizer)
optimizer.step_schedules()
if not (step % eval_frequency):
if optimizer.averages:
with nlp.use_params(optimizer.averages):
score, other_scores = evaluate()
else:
score, other_scores = evaluate()
results.append((score, step))
is_best_checkpoint = score == max(results)[0]
else:
score, other_scores = (None, None)
is_best_checkpoint = None
words_seen += sum(len(eg) for eg in batch)
info = {
"epoch": epoch,
"step": step,
"score": score,
"other_scores": other_scores,
"losses": losses,
"checkpoints": results,
"seconds": int(timer() - start_time),
"words": words_seen,
}
yield batch, info, is_best_checkpoint
if is_best_checkpoint is not None:
losses = {}
# Stop if no improvement in `patience` updates (if specified)
best_score, best_step = max(results)
if patience and (step - best_step) >= patience:
break
# Stop if we've exhausted our max steps (if specified)
if max_steps and step >= max_steps:
break
def subdivide_batch(batch, accumulate_gradient):
batch = list(batch)
batch.sort(key=lambda eg: len(eg.predicted))
sub_len = len(batch) // accumulate_gradient
start = 0
for i in range(accumulate_gradient):
subbatch = batch[start : start + sub_len]
if subbatch:
yield subbatch
start += len(subbatch)
subbatch = batch[start:]
if subbatch:
yield subbatch
def update_meta(
training: Union[Dict[str, Any], Config], nlp: Language, info: Dict[str, Any]
) -> None:
nlp.meta["performance"] = {}
for metric in training["score_weights"]:
if metric is not None:
nlp.meta["performance"][metric] = info["other_scores"].get(metric, 0.0)
for pipe_name in nlp.pipe_names:
nlp.meta["performance"][f"{pipe_name}_loss"] = info["losses"][pipe_name]
def load_from_paths(
config: Config,
) -> Tuple[List[Dict[str, str]], Dict[str, dict], bytes]:
# TODO: separate checks from loading
raw_text = util.ensure_path(config["training"]["raw_text"])
if raw_text is not None:
if not raw_text.exists():
msg.fail("Can't find raw text", raw_text, exits=1)
raw_text = list(srsly.read_jsonl(config["training"]["raw_text"]))
tag_map = {}
morph_rules = {}
weights_data = None
init_tok2vec = util.ensure_path(config["training"]["init_tok2vec"])
if init_tok2vec is not None:
if not init_tok2vec.exists():
msg.fail("Can't find pretrained tok2vec", init_tok2vec, exits=1)
with init_tok2vec.open("rb") as file_:
weights_data = file_.read()
return raw_text, tag_map, morph_rules, weights_data
config = util.load_config(config_path, overrides=overrides, interpolate=False)
msg.divider("Initializing pipeline")
with show_validation_error(config_path, hint_fill=False):
nlp = init_nlp(config, use_gpu=use_gpu)
msg.good("Initialized pipeline")
msg.divider("Training pipeline")
train(nlp, output_path, use_gpu=use_gpu, silent=False)
def verify_cli_args(config_path: Path, output_path: Optional[Path] = None) -> None:
@ -427,30 +61,3 @@ def verify_cli_args(config_path: Path, output_path: Optional[Path] = None) -> No
if not output_path.exists():
output_path.mkdir()
msg.good(f"Created output directory: {output_path}")
def verify_config(nlp: Language) -> None:
"""Perform additional checks based on the config, loaded nlp object and training data."""
# TODO: maybe we should validate based on the actual components, the list
# in config["nlp"]["pipeline"] instead?
for pipe_config in nlp.config["components"].values():
# We can't assume that the component name == the factory
factory = pipe_config["factory"]
if factory == "textcat":
verify_textcat_config(nlp, pipe_config)
def verify_textcat_config(nlp: Language, pipe_config: Dict[str, Any]) -> None:
# if 'positive_label' is provided: double check whether it's in the data and
# the task is binary
if pipe_config.get("positive_label"):
textcat_labels = nlp.get_pipe("textcat").labels
pos_label = pipe_config.get("positive_label")
if pos_label not in textcat_labels:
raise ValueError(
Errors.E920.format(pos_label=pos_label, labels=textcat_labels)
)
if len(list(textcat_labels)) != 2:
raise ValueError(
Errors.E919.format(pos_label=pos_label, labels=textcat_labels)
)

33
spacy/default_config.cfg
View File

@ -1,7 +1,8 @@
[paths]
train = ""
dev = ""
raw = null
train = null
dev = null
vectors = null
vocab_data = null
init_tok2vec = null
[system]
@ -35,6 +36,11 @@ gold_preproc = false
max_length = 0
# Limitation on number of training examples
limit = 0
# Apply some simply data augmentation, where we replace tokens with variations.
# This is especially useful for punctuation and case replacement, to help
# generalize beyond corpora that don't have smart-quotes, or only have smart
# quotes, etc.
augmenter = null
[corpora.dev]
@readers = "spacy.Corpus.v1"
@ -47,6 +53,7 @@ gold_preproc = false
max_length = 0
# Limitation on number of training examples
limit = 0
augmenter = null
# Training hyper-parameters and additional features.
[training]
@ -54,11 +61,6 @@ seed = ${system.seed}
gpu_allocator = ${system.gpu_allocator}
dropout = 0.1
accumulate_gradient = 1
# Extra resources for transfer-learning or pseudo-rehearsal
init_tok2vec = ${paths.init_tok2vec}
raw_text = ${paths.raw}
vectors = null
lookups = null
# Controls early-stopping. 0 or -1 mean unlimited.
patience = 1600
max_epochs = 0
@ -99,3 +101,18 @@ grad_clip = 1.0
use_averages = false
eps = 1e-8
learn_rate = 0.001
# The 'initialize' step is run before training or pretraining. Components and
# the tokenizer can each define their own arguments via their .initialize
# methods that are populated by the config. This lets them gather resources like
# lookup tables and build label sets, construct vocabularies, etc.
[initialize]
vocab_data = ${paths.vocab_data}
lookups = null
vectors = ${paths.vectors}
# Extra resources for transfer-learning or pseudo-rehearsal
init_tok2vec = ${paths.init_tok2vec}
# Arguments passed to the tokenizer's initialize method
tokenizer = {}
# Arguments passed to the initialize methods of the components (keyed by component name)
components = {}

5
spacy/default_config_pretraining.cfg
View File

@ -1,3 +1,6 @@
[paths]
raw_text = null
[pretraining]
max_epochs = 1000
dropout = 0.2
@ -32,7 +35,7 @@ learn_rate = 0.001
[corpora.pretrain]
@readers = "spacy.JsonlReader.v1"
path = ${paths.raw}
path = ${paths.raw_text}
min_length = 5
max_length = 500
limit = 0

28
spacy/errors.py
View File

@ -85,6 +85,7 @@ class Warnings:
"attribute or operator.")
# TODO: fix numbering after merging develop into master
W089 = ("The nlp.begin_training method has been renamed to nlp.initialize.")
W090 = ("Could not locate any {format} files in path '{path}'.")
W091 = ("Could not clean/remove the temp directory at {dir}: {msg}.")
W092 = ("Ignoring annotations for sentence starts, as dependency heads are set.")
@ -306,7 +307,7 @@ class Errors:
"settings: {opts}")
E107 = ("Value of doc._.{attr} is not JSON-serializable: {value}")
E109 = ("Component '{name}' could not be run. Did you forget to "
"call begin_training()?")
"call initialize()?")
E110 = ("Invalid displaCy render wrapper. Expected callable, got: {obj}")
E111 = ("Pickling a token is not supported, because tokens are only views "
"of the parent Doc and can't exist on their own. A pickled token "
@ -376,7 +377,7 @@ class Errors:
"provided {found}.")
E143 = ("Labels for component '{name}' not initialized. This can be fixed "
"by calling add_label, or by providing a representative batch of "
"examples to the component's begin_training method.")
"examples to the component's initialize method.")
E145 = ("Error reading `{param}` from input file.")
E146 = ("Could not access `{path}`.")
E147 = ("Unexpected error in the {method} functionality of the "
@ -476,6 +477,14 @@ class Errors:
E201 = ("Span index out of range.")
# TODO: fix numbering after merging develop into master
E912 = ("No orth_variants lookups table for data augmentation available for "
"language '{lang}'. If orth_variants are available in "
"spacy-lookups-data, make sure the package is installed and the "
"table is loaded in the [initialize.lookups] block of your config. "
"Alternatively, you can provide your own Lookups object with a "
"table orth_variants as the argument 'lookuos' of the augmenter.")
E913 = ("Corpus path can't be None. Maybe you forgot to define it in your "
"config.cfg or override it on the CLI?")
E914 = ("Executing {name} callback failed. Expected the function to "
"return the nlp object but got: {value}. Maybe you forgot to return "
"the modified object in your function?")
@ -517,7 +526,7 @@ class Errors:
"but the provided argument {loc} points to a file.")
E929 = ("A 'KnowledgeBase' could not be read from {loc} - the path does "
"not seem to exist.")
E930 = ("Received invalid get_examples callback in {name}.begin_training. "
E930 = ("Received invalid get_examples callback in {name}.initialize. "
"Expected function that returns an iterable of Example objects but "
"got: {obj}")
E931 = ("Encountered Pipe subclass without Pipe.{method} method in component "
@ -553,7 +562,10 @@ class Errors:
E953 = ("Mismatched IDs received by the Tok2Vec listener: {id1} vs. {id2}")
E954 = ("The Tok2Vec listener did not receive any valid input from an upstream "
"component.")
E955 = ("Can't find table(s) '{table}' for language '{lang}' in spacy-lookups-data.")
E955 = ("Can't find table(s) '{table}' for language '{lang}' in "
"spacy-lookups-data. If you want to initialize a blank nlp object, "
"make sure you have the spacy-lookups-data package installed or "
"remove the [initialize.lookups] block from your config.")
E956 = ("Can't find component '{name}' in [components] block in the config. "
"Available components: {opts}")
E957 = ("Writing directly to Language.factories isn't needed anymore in "
@ -670,10 +682,10 @@ class Errors:
"'{token_attrs}'.")
E999 = ("Unable to merge the `Doc` objects because they do not all share "
"the same `Vocab`.")
E1000 = ("No pkuseg model available. Provide a pkuseg model when "
"initializing the pipeline:\n"
'cfg = {"tokenizer": {"segmenter": "pkuseg", "pkuseg_model": name_or_path}}\n'
'nlp = Chinese(config=cfg)')
E1000 = ("The Chinese word segmenter is pkuseg but no pkuseg model was "
"loaded. Provide the name of a pretrained model or the path to "
"a model and initialize the pipeline:\n\n"
'nlp.tokenizer.initialize(pkuseg_model="default")')
E1001 = ("Target token outside of matched span for match with tokens "
"'{span}' and offset '{index}' matched by patterns '{patterns}'.")
E1002 = ("Span index out of range.")

1
spacy/lang/en/__init__.py
View File

@ -1,5 +1,4 @@
from typing import Optional
from thinc.api import Model
from .tokenizer_exceptions import TOKENIZER_EXCEPTIONS

3
spacy/lang/en/lemmatizer.py
View File

@ -3,8 +3,7 @@ from ...tokens import Token
class EnglishLemmatizer(Lemmatizer):
"""English lemmatizer. Only overrides is_base_form.
"""
"""English lemmatizer. Only overrides is_base_form."""
def is_base_form(self, token: Token) -> bool:
"""

2
spacy/lang/es/syntax_iterators.py
View File

@ -58,7 +58,7 @@ def noun_bounds(
doc, token, np_left_deps, np_right_deps, stop_deps
)
filter_func = lambda t: is_verb_token(t) or t.dep in stop_deps
if list(filter(filter_func, doc[left_bound.i : right.i],)):
if list(filter(filter_func, doc[left_bound.i : right.i])):
break
else:
right_bound = right

11
spacy/lang/ja/__init__.py
View File

@ -2,7 +2,6 @@ from typing import Optional, Union, Dict, Any
from pathlib import Path
import srsly
from collections import namedtuple
from thinc.api import Config
from .stop_words import STOP_WORDS
from .syntax_iterators import SYNTAX_ITERATORS
@ -12,9 +11,11 @@ from .tag_bigram_map import TAG_BIGRAM_MAP
from ...compat import copy_reg
from ...errors import Errors
from ...language import Language
from ...scorer import Scorer
from ...symbols import POS
from ...tokens import Doc
from ...util import DummyTokenizer, registry
from ...training import validate_examples
from ...util import DummyTokenizer, registry, load_config_from_str
from ... import util
@ -130,6 +131,10 @@ class JapaneseTokenizer(DummyTokenizer):
)
return sub_tokens_list
def score(self, examples):
validate_examples(examples, "JapaneseTokenizer.score")
return Scorer.score_tokenization(examples)
def _get_config(self) -> Dict[str, Any]:
return {"split_mode": self.split_mode}
@ -160,7 +165,7 @@ class JapaneseTokenizer(DummyTokenizer):
class JapaneseDefaults(Language.Defaults):
config = Config().from_str(DEFAULT_CONFIG)
config = load_config_from_str(DEFAULT_CONFIG)
stop_words = STOP_WORDS
syntax_iterators = SYNTAX_ITERATORS
writing_system = {"direction": "ltr", "has_case": False, "has_letters": False}

11
spacy/lang/ko/__init__.py
View File

@ -1,5 +1,4 @@
from typing import Optional, Any, Dict
from thinc.api import Config
from .stop_words import STOP_WORDS
from .tag_map import TAG_MAP
@ -7,8 +6,10 @@ from .lex_attrs import LEX_ATTRS
from ...language import Language
from ...tokens import Doc
from ...compat import copy_reg
from ...scorer import Scorer
from ...symbols import POS
from ...util import DummyTokenizer, registry
from ...training import validate_examples
from ...util import DummyTokenizer, registry, load_config_from_str
DEFAULT_CONFIG = """
@ -62,9 +63,13 @@ class KoreanTokenizer(DummyTokenizer):
lemma = surface
yield {"surface": surface, "lemma": lemma, "tag": tag}
def score(self, examples):
validate_examples(examples, "KoreanTokenizer.score")
return Scorer.score_tokenization(examples)
class KoreanDefaults(Language.Defaults):
config = Config().from_str(DEFAULT_CONFIG)
config = load_config_from_str(DEFAULT_CONFIG)
lex_attr_getters = LEX_ATTRS
stop_words = STOP_WORDS
writing_system = {"direction": "ltr", "has_case": False, "has_letters": False}

1
spacy/lang/ru/__init__.py
View File

@ -1,5 +1,4 @@
from typing import Optional
from thinc.api import Model
from .stop_words import STOP_WORDS

4
spacy/lang/sa/lex_attrs.py
View File

@ -108,8 +108,8 @@ _num_words = [
def like_num(text):
"""
Check if text resembles a number
"""
Check if text resembles a number
"""
if text.startswith(("+", "-", "±", "~")):
text = text[1:]
text = text.replace(",", "").replace(".", "")

6
spacy/lang/th/__init__.py
View File

@ -1,10 +1,8 @@
from thinc.api import Config
from .stop_words import STOP_WORDS
from .lex_attrs import LEX_ATTRS
from ...language import Language
from ...tokens import Doc
from ...util import DummyTokenizer, registry
from ...util import DummyTokenizer, registry, load_config_from_str
DEFAULT_CONFIG = """
@ -42,7 +40,7 @@ class ThaiTokenizer(DummyTokenizer):
class ThaiDefaults(Language.Defaults):
config = Config().from_str(DEFAULT_CONFIG)
config = load_config_from_str(DEFAULT_CONFIG)
lex_attr_getters = LEX_ATTRS
stop_words = STOP_WORDS

12
spacy/lang/vi/__init__.py
View File

@ -1,10 +1,8 @@
from thinc.api import Config
from .stop_words import STOP_WORDS
from .lex_attrs import LEX_ATTRS
from ...language import Language
from ...tokens import Doc
from .stop_words import STOP_WORDS
from ...util import DummyTokenizer, registry
from .lex_attrs import LEX_ATTRS
from ...util import DummyTokenizer, registry, load_config_from_str
DEFAULT_CONFIG = """
@ -17,7 +15,7 @@ use_pyvi = true
@registry.tokenizers("spacy.vi.VietnameseTokenizer")
def create_vietnamese_tokenizer(use_pyvi: bool = True,):
def create_vietnamese_tokenizer(use_pyvi: bool = True):
def vietnamese_tokenizer_factory(nlp):
return VietnameseTokenizer(nlp, use_pyvi=use_pyvi)
@ -55,7 +53,7 @@ class VietnameseTokenizer(DummyTokenizer):
class VietnameseDefaults(Language.Defaults):
config = Config().from_str(DEFAULT_CONFIG)
config = load_config_from_str(DEFAULT_CONFIG)
lex_attr_getters = LEX_ATTRS
stop_words = STOP_WORDS

143
spacy/lang/zh/__init__.py
View File

@ -1,21 +1,25 @@
from typing import Optional, List, Dict, Any
from typing import Optional, List, Dict, Any, Callable, Iterable
from enum import Enum
import tempfile
import srsly
import warnings
from pathlib import Path
from thinc.api import Config
from ...errors import Warnings, Errors
from ...language import Language
from ...scorer import Scorer
from ...tokens import Doc
from ...util import DummyTokenizer, registry
from ...training import validate_examples, Example
from ...util import DummyTokenizer, registry, load_config_from_str
from .lex_attrs import LEX_ATTRS
from .stop_words import STOP_WORDS
from ... import util
_PKUSEG_INSTALL_MSG = "install it with `pip install pkuseg==0.0.25` or from https://github.com/lancopku/pkuseg-python"
# fmt: off
_PKUSEG_INSTALL_MSG = "install pkuseg and pickle5 with `pip install pkuseg==0.0.25 pickle5`"
_PKUSEG_PICKLE_WARNING = "Failed to force pkuseg model to use pickle protocol 4. If you're saving this model with python 3.8, it may not work with python 3.6-3.7."
# fmt: on
DEFAULT_CONFIG = """
[nlp]
@ -23,6 +27,10 @@ DEFAULT_CONFIG = """
[nlp.tokenizer]
@tokenizers = "spacy.zh.ChineseTokenizer"
segmenter = "char"
[initialize]
[initialize.tokenizer]
pkuseg_model = null
pkuseg_user_dict = "default"
"""
@ -39,41 +47,23 @@ class Segmenter(str, Enum):
@registry.tokenizers("spacy.zh.ChineseTokenizer")
def create_chinese_tokenizer(
segmenter: Segmenter = Segmenter.char,
pkuseg_model: Optional[str] = None,
pkuseg_user_dict: Optional[str] = "default",
):
def create_chinese_tokenizer(segmenter: Segmenter = Segmenter.char,):
def chinese_tokenizer_factory(nlp):
return ChineseTokenizer(
nlp,
segmenter=segmenter,
pkuseg_model=pkuseg_model,
pkuseg_user_dict=pkuseg_user_dict,
)
return ChineseTokenizer(nlp, segmenter=segmenter)
return chinese_tokenizer_factory
class ChineseTokenizer(DummyTokenizer):
def __init__(
self,
nlp: Language,
segmenter: Segmenter = Segmenter.char,
pkuseg_model: Optional[str] = None,
pkuseg_user_dict: Optional[str] = None,
self, nlp: Language, segmenter: Segmenter = Segmenter.char,
):
self.vocab = nlp.vocab
if isinstance(segmenter, Segmenter): # we might have the Enum here
if isinstance(segmenter, Segmenter):
segmenter = segmenter.value
self.segmenter = segmenter
self.pkuseg_model = pkuseg_model
self.pkuseg_user_dict = pkuseg_user_dict
self.pkuseg_seg = None
self.jieba_seg = None
self.configure_segmenter(segmenter)
def configure_segmenter(self, segmenter: str):
if segmenter not in Segmenter.values():
warn_msg = Warnings.W103.format(
lang="Chinese",
@ -83,12 +73,21 @@ class ChineseTokenizer(DummyTokenizer):
)
warnings.warn(warn_msg)
self.segmenter = Segmenter.char
self.jieba_seg = try_jieba_import(self.segmenter)
self.pkuseg_seg = try_pkuseg_import(
self.segmenter,
pkuseg_model=self.pkuseg_model,
pkuseg_user_dict=self.pkuseg_user_dict,
)
if segmenter == Segmenter.jieba:
self.jieba_seg = try_jieba_import()
def initialize(
self,
get_examples: Optional[Callable[[], Iterable[Example]]] = None,
*,
nlp: Optional[Language] = None,
pkuseg_model: Optional[str] = None,
pkuseg_user_dict: str = "default",
):
if self.segmenter == Segmenter.pkuseg:
self.pkuseg_seg = try_pkuseg_import(
pkuseg_model=pkuseg_model, pkuseg_user_dict=pkuseg_user_dict,
)
def __call__(self, text: str) -> Doc:
if self.segmenter == Segmenter.jieba:
@ -136,17 +135,17 @@ class ChineseTokenizer(DummyTokenizer):
warn_msg = Warnings.W104.format(target="pkuseg", current=self.segmenter)
warnings.warn(warn_msg)
def score(self, examples):
validate_examples(examples, "ChineseTokenizer.score")
return Scorer.score_tokenization(examples)
def _get_config(self) -> Dict[str, Any]:
return {
"segmenter": self.segmenter,
"pkuseg_model": self.pkuseg_model,
"pkuseg_user_dict": self.pkuseg_user_dict,
}
def _set_config(self, config: Dict[str, Any] = {}) -> None:
self.segmenter = config.get("segmenter", Segmenter.char)
self.pkuseg_model = config.get("pkuseg_model", None)
self.pkuseg_user_dict = config.get("pkuseg_user_dict", "default")
def to_bytes(self, **kwargs):
pkuseg_features_b = b""
@ -157,6 +156,22 @@ class ChineseTokenizer(DummyTokenizer):
self.pkuseg_seg.feature_extractor.save(tempdir)
self.pkuseg_seg.model.save(tempdir)
tempdir = Path(tempdir)
# pkuseg saves features.pkl with pickle.HIGHEST_PROTOCOL, which
# means that it will be saved with pickle protocol 5 with
# python 3.8, which can't be reloaded with python 3.6-3.7.
# To try to make the model compatible with python 3.6+, reload
# the data with pickle5 and convert it back to protocol 4.
try:
import pickle5
with open(tempdir / "features.pkl", "rb") as fileh:
features = pickle5.load(fileh)
with open(tempdir / "features.pkl", "wb") as fileh:
pickle5.dump(features, fileh, protocol=4)
except ImportError as e:
raise e
except Exception:
warnings.warn(_PKUSEG_PICKLE_WARNING)
with open(tempdir / "features.pkl", "rb") as fileh:
pkuseg_features_b = fileh.read()
with open(tempdir / "weights.npz", "rb") as fileh:
@ -229,6 +244,18 @@ class ChineseTokenizer(DummyTokenizer):
path.mkdir(parents=True)
self.pkuseg_seg.model.save(path)
self.pkuseg_seg.feature_extractor.save(path)
# try to convert features.pkl to pickle protocol 4
try:
import pickle5
with open(path / "features.pkl", "rb") as fileh:
features = pickle5.load(fileh)
with open(path / "features.pkl", "wb") as fileh:
pickle5.dump(features, fileh, protocol=4)
except ImportError as e:
raise e
except Exception:
warnings.warn(_PKUSEG_PICKLE_WARNING)
def save_pkuseg_processors(path):
if self.pkuseg_seg:
@ -285,7 +312,7 @@ class ChineseTokenizer(DummyTokenizer):
class ChineseDefaults(Language.Defaults):
config = Config().from_str(DEFAULT_CONFIG)
config = load_config_from_str(DEFAULT_CONFIG)
lex_attr_getters = LEX_ATTRS
stop_words = STOP_WORDS
writing_system = {"direction": "ltr", "has_case": False, "has_letters": False}
@ -296,47 +323,33 @@ class Chinese(Language):
Defaults = ChineseDefaults
def try_jieba_import(segmenter: str) -> None:
def try_jieba_import() -> None:
try:
import jieba
if segmenter == Segmenter.jieba:
# segment a short text to have jieba initialize its cache in advance
list(jieba.cut("作为", cut_all=False))
# segment a short text to have jieba initialize its cache in advance
list(jieba.cut("作为", cut_all=False))
return jieba
except ImportError:
if segmenter == Segmenter.jieba:
msg = (
"Jieba not installed. To use jieba, install it with `pip "
" install jieba` or from https://github.com/fxsjy/jieba"
)
raise ImportError(msg) from None
msg = (
"Jieba not installed. To use jieba, install it with `pip "
" install jieba` or from https://github.com/fxsjy/jieba"
)
raise ImportError(msg) from None
def try_pkuseg_import(segmenter: str, pkuseg_model: str, pkuseg_user_dict: str) -> None:
def try_pkuseg_import(pkuseg_model: str, pkuseg_user_dict: str) -> None:
try:
import pkuseg
if pkuseg_model:
return pkuseg.pkuseg(pkuseg_model, pkuseg_user_dict)
elif segmenter == Segmenter.pkuseg:
msg = (
"The Chinese word segmenter is 'pkuseg' but no pkuseg model "
"was specified. Please provide the name of a pretrained model "
"or the path to a model with:\n"
'cfg = {"nlp": {"tokenizer": {"segmenter": "pkuseg", "pkuseg_model": name_or_path }}\n'
"nlp = Chinese.from_config(cfg)"
)
raise ValueError(msg)
return pkuseg.pkuseg(pkuseg_model, pkuseg_user_dict)
except ImportError:
if segmenter == Segmenter.pkuseg:
msg = "pkuseg not installed. To use pkuseg, " + _PKUSEG_INSTALL_MSG
raise ImportError(msg) from None
msg = "pkuseg not installed. To use pkuseg, " + _PKUSEG_INSTALL_MSG
raise ImportError(msg) from None
except FileNotFoundError:
if segmenter == Segmenter.pkuseg:
msg = "Unable to load pkuseg model from: " + pkuseg_model
raise FileNotFoundError(msg) from None
msg = "Unable to load pkuseg model from: " + pkuseg_model
raise FileNotFoundError(msg) from None
def _get_pkuseg_trie_data(node, path=""):

144
spacy/language.py
View File

@ -8,7 +8,7 @@ from contextlib import contextmanager
from copy import deepcopy
from pathlib import Path
import warnings
from thinc.api import Model, get_current_ops, Config, require_gpu, Optimizer
from thinc.api import Model, get_current_ops, Config, Optimizer
import srsly
import multiprocessing as mp
from itertools import chain, cycle
@ -18,8 +18,9 @@ from .tokens.underscore import Underscore
from .vocab import Vocab, create_vocab
from .pipe_analysis import validate_attrs, analyze_pipes, print_pipe_analysis
from .training import Example, validate_examples
from .training.initialize import init_vocab, init_tok2vec
from .scorer import Scorer
from .util import create_default_optimizer, registry, SimpleFrozenList
from .util import registry, SimpleFrozenList
from .util import SimpleFrozenDict, combine_score_weights, CONFIG_SECTION_ORDER
from .lang.tokenizer_exceptions import URL_MATCH, BASE_EXCEPTIONS
from .lang.punctuation import TOKENIZER_PREFIXES, TOKENIZER_SUFFIXES
@ -27,7 +28,8 @@ from .lang.punctuation import TOKENIZER_INFIXES
from .tokens import Doc
from .tokenizer import Tokenizer
from .errors import Errors, Warnings
from .schemas import ConfigSchema
from .schemas import ConfigSchema, ConfigSchemaNlp, ConfigSchemaInit
from .schemas import ConfigSchemaPretrain, validate_init_settings
from .git_info import GIT_VERSION
from . import util
from . import about
@ -166,11 +168,10 @@ class Language:
self._components = []
self._disabled = set()
self.max_length = max_length
self.resolved = {}
# Create the default tokenizer from the default config
if not create_tokenizer:
tokenizer_cfg = {"tokenizer": self._config["nlp"]["tokenizer"]}
create_tokenizer = registry.make_from_config(tokenizer_cfg)["tokenizer"]
create_tokenizer = registry.resolve(tokenizer_cfg)["tokenizer"]
self.tokenizer = create_tokenizer(self)
def __init_subclass__(cls, **kwargs):
@ -467,7 +468,7 @@ class Language:
if "nlp" not in arg_names or "name" not in arg_names:
raise ValueError(Errors.E964.format(name=name))
# Officially register the factory so we can later call
# registry.make_from_config and refer to it in the config as
# registry.resolve and refer to it in the config as
# @factories = "spacy.Language.xyz". We use the class name here so
# different classes can have different factories.
registry.factories.register(internal_name, func=factory_func)
@ -650,8 +651,9 @@ class Language:
cfg = {factory_name: config}
# We're calling the internal _fill here to avoid constructing the
# registered functions twice
resolved, filled = registry.resolve(cfg, validate=validate)
filled = Config(filled[factory_name])
resolved = registry.resolve(cfg, validate=validate)
filled = registry.fill({"cfg": cfg[factory_name]}, validate=validate)["cfg"]
filled = Config(filled)
filled["factory"] = factory_name
filled.pop("@factories", None)
# Remove the extra values we added because we don't want to keep passing
@ -1065,7 +1067,7 @@ class Language:
validate_examples(examples, "Language.update")
if sgd is None:
if self._optimizer is None:
self._optimizer = create_default_optimizer()
self._optimizer = self.create_optimizer()
sgd = self._optimizer
if component_cfg is None:
component_cfg = {}
@ -1123,7 +1125,7 @@ class Language:
validate_examples(examples, "Language.rehearse")
if sgd is None:
if self._optimizer is None:
self._optimizer = create_default_optimizer()
self._optimizer = self.create_optimizer()
sgd = self._optimizer
pipes = list(self.pipeline)
random.shuffle(pipes)
@ -1153,61 +1155,73 @@ class Language:
get_examples: Optional[Callable[[], Iterable[Example]]] = None,
*,
sgd: Optional[Optimizer] = None,
device: int = -1,
) -> Optimizer:
warnings.warn(Warnings.W089, DeprecationWarning)
return self.initialize(get_examples, sgd=sgd)
def initialize(
self,
get_examples: Optional[Callable[[], Iterable[Example]]] = None,
*,
sgd: Optional[Optimizer] = None,
) -> Optimizer:
"""Initialize the pipe for training, using data examples if available.
get_examples (Callable[[], Iterable[Example]]): Optional function that
returns gold-standard Example objects.
sgd (thinc.api.Optimizer): Optional optimizer. Will be created with
create_optimizer if it doesn't exist.
sgd (Optional[Optimizer]): An optimizer to use for updates. If not
provided, will be created using the .create_optimizer() method.
RETURNS (thinc.api.Optimizer): The optimizer.
DOCS: https://nightly.spacy.io/api/language#begin_training
DOCS: https://nightly.spacy.io/api/language#initialize
"""
if get_examples is None:
util.logger.debug(
"No 'get_examples' callback provided to 'Language.begin_training', creating dummy examples"
"No 'get_examples' callback provided to 'Language.initialize', creating dummy examples"
)
doc = Doc(self.vocab, words=["x", "y", "z"])
get_examples = lambda: [Example.from_dict(doc, {})]
# Populate vocab
if not hasattr(get_examples, "__call__"):
err = Errors.E930.format(name="Language", obj=type(get_examples))
raise ValueError(err)
valid_examples = False
for example in get_examples():
if not isinstance(example, Example):
err = Errors.E978.format(
name="Language.begin_training", types=type(example)
)
raise ValueError(err)
else:
valid_examples = True
for word in [t.text for t in example.reference]:
_ = self.vocab[word] # noqa: F841
if not valid_examples:
err = Errors.E930.format(name="Language", obj="empty list")
raise ValueError(err)
if device >= 0: # TODO: do we need this here?
require_gpu(device)
if self.vocab.vectors.data.shape[1] >= 1:
ops = get_current_ops()
self.vocab.vectors.data = ops.asarray(self.vocab.vectors.data)
if sgd is None:
sgd = create_default_optimizer()
self._optimizer = sgd
# Make sure the config is interpolated so we can resolve subsections
config = self.config.interpolate()
# These are the settings provided in the [initialize] block in the config
I = registry.resolve(config["initialize"], schema=ConfigSchemaInit)
init_vocab(
self, data=I["vocab_data"], lookups=I["lookups"], vectors=I["vectors"]
)
pretrain_cfg = config.get("pretraining")
if pretrain_cfg:
P = registry.resolve(pretrain_cfg, schema=ConfigSchemaPretrain)
init_tok2vec(self, P, I)
if self.vocab.vectors.data.shape[1] >= 1:
ops = get_current_ops()
self.vocab.vectors.data = ops.asarray(self.vocab.vectors.data)
if hasattr(self.tokenizer, "initialize"):
tok_settings = validate_init_settings(
self.tokenizer.initialize,
I["tokenizer"],
section="tokenizer",
name="tokenizer",
)
self.tokenizer.initialize(get_examples, nlp=self, **tok_settings)
for name, proc in self.pipeline:
if hasattr(proc, "begin_training"):
proc.begin_training(
get_examples, pipeline=self.pipeline, sgd=self._optimizer
if hasattr(proc, "initialize"):
p_settings = I["components"].get(name, {})
p_settings = validate_init_settings(
proc.initialize, p_settings, section="components", name=name
)
proc.initialize(get_examples, nlp=self, **p_settings)
self._link_components()
self._optimizer = sgd
if sgd is not None:
self._optimizer = sgd
elif self._optimizer is None:
self._optimizer = self.create_optimizer()
return self._optimizer
def resume_training(
self, *, sgd: Optional[Optimizer] = None, device: int = -1
) -> Optimizer:
def resume_training(self, *, sgd: Optional[Optimizer] = None) -> Optimizer:
"""Continue training a pretrained model.
Create and return an optimizer, and initialize "rehearsal" for any pipeline
@ -1216,22 +1230,20 @@ class Language:
rehearsal, collect samples of text you want the models to retain performance
on, and call nlp.rehearse() with a batch of Example objects.
sgd (Optional[Optimizer]): An optimizer.
RETURNS (Optimizer): The optimizer.
DOCS: https://nightly.spacy.io/api/language#resume_training
"""
if device >= 0: # TODO: do we need this here?
require_gpu(device)
ops = get_current_ops()
if self.vocab.vectors.data.shape[1] >= 1:
self.vocab.vectors.data = ops.asarray(self.vocab.vectors.data)
if sgd is None:
sgd = create_default_optimizer()
self._optimizer = sgd
ops = get_current_ops()
if self.vocab.vectors.data.shape[1] >= 1:
self.vocab.vectors.data = ops.asarray(self.vocab.vectors.data)
for name, proc in self.pipeline:
if hasattr(proc, "_rehearsal_model"):
proc._rehearsal_model = deepcopy(proc.model)
if sgd is not None:
self._optimizer = sgd
elif self._optimizer is None:
self._optimizer = self.create_optimizer()
return self._optimizer
def evaluate(
@ -1293,6 +1305,11 @@ class Language:
results["speed"] = n_words / (end_time - start_time)
return results
def create_optimizer(self):
"""Create an optimizer, usually using the [training.optimizer] config."""
subconfig = {"optimizer": self.config["training"]["optimizer"]}
return registry.resolve(subconfig)["optimizer"]
@contextmanager
def use_params(self, params: Optional[dict]):
"""Replace weights of models in the pipeline with those provided in the
@ -1501,7 +1518,7 @@ class Language:
).merge(config)
if "nlp" not in config:
raise ValueError(Errors.E985.format(config=config))
config_lang = config["nlp"]["lang"]
config_lang = config["nlp"].get("lang")
if config_lang is not None and config_lang != cls.lang:
raise ValueError(
Errors.E958.format(
@ -1518,15 +1535,19 @@ class Language:
config = util.copy_config(config)
orig_pipeline = config.pop("components", {})
config["components"] = {}
resolved, filled = registry.resolve(
config, validate=validate, schema=ConfigSchema
)
if auto_fill:
filled = registry.fill(config, validate=validate, schema=ConfigSchema)
else:
filled = config
filled["components"] = orig_pipeline
config["components"] = orig_pipeline
create_tokenizer = resolved["nlp"]["tokenizer"]
before_creation = resolved["nlp"]["before_creation"]
after_creation = resolved["nlp"]["after_creation"]
after_pipeline_creation = resolved["nlp"]["after_pipeline_creation"]
resolved_nlp = registry.resolve(
filled["nlp"], validate=validate, schema=ConfigSchemaNlp
)
create_tokenizer = resolved_nlp["tokenizer"]
before_creation = resolved_nlp["before_creation"]
after_creation = resolved_nlp["after_creation"]
after_pipeline_creation = resolved_nlp["after_pipeline_creation"]
lang_cls = cls
if before_creation is not None:
lang_cls = before_creation(cls)
@ -1587,7 +1608,6 @@ class Language:
disabled_pipes = [*config["nlp"]["disabled"], *disable]
nlp._disabled = set(p for p in disabled_pipes if p not in exclude)
nlp.config = filled if auto_fill else config
nlp.resolved = resolved
if after_pipeline_creation is not None:
nlp = after_pipeline_creation(nlp)
if not isinstance(nlp, cls):

25
spacy/ml/featureextractor.py Normal file
View File

@ -0,0 +1,25 @@
from typing import List, Union, Callable, Tuple
from thinc.types import Ints2d, Doc
from thinc.api import Model, registry
@registry.layers("spacy.FeatureExtractor.v1")
def FeatureExtractor(columns: List[Union[int, str]]) -> Model[List[Doc], List[Ints2d]]:
return Model("extract_features", forward, attrs={"columns": columns})
def forward(model: Model[List[Doc], List[Ints2d]], docs, is_train: bool) -> Tuple[List[Ints2d], Callable]:
columns = model.attrs["columns"]
features: List[Ints2d] = []
for doc in docs:
if hasattr(doc, "to_array"):
attrs = doc.to_array(columns)
else:
attrs = doc.doc.to_array(columns)[doc.start : doc.end]
if attrs.ndim == 1:
attrs = attrs.reshape((attrs.shape[0], 1))
features.append(model.ops.asarray2i(attrs, dtype="uint64"))
backprop: Callable[[List[Ints2d]], List] = lambda d_features: []
return features, backprop

3
spacy/ml/models/textcat.py
View File

@ -3,12 +3,13 @@ from thinc.api import Model, reduce_mean, Linear, list2ragged, Logistic
from thinc.api import chain, concatenate, clone, Dropout, ParametricAttention
from thinc.api import SparseLinear, Softmax, softmax_activation, Maxout, reduce_sum
from thinc.api import HashEmbed, with_array, with_cpu, uniqued
from thinc.api import Relu, residual, expand_window, FeatureExtractor
from thinc.api import Relu, residual, expand_window
from ...attrs import ID, ORTH, PREFIX, SUFFIX, SHAPE, LOWER
from ...util import registry
from ..extract_ngrams import extract_ngrams
from ..staticvectors import StaticVectors
from ..featureextractor import FeatureExtractor
@registry.architectures.register("spacy.TextCatCNN.v1")

8
spacy/ml/models/tok2vec.py
View File

@ -1,14 +1,14 @@
from typing import Optional, List
from thinc.api import chain, clone, concatenate, with_array, with_padded
from thinc.api import Model, noop, list2ragged, ragged2list
from thinc.api import FeatureExtractor, HashEmbed
from thinc.api import expand_window, residual, Maxout, Mish, PyTorchLSTM
from thinc.types import Floats2d
from thinc.api import chain, clone, concatenate, with_array, with_padded
from thinc.api import Model, noop, list2ragged, ragged2list, HashEmbed
from thinc.api import expand_window, residual, Maxout, Mish, PyTorchLSTM
from ...tokens import Doc
from ...util import registry
from ...ml import _character_embed
from ..staticvectors import StaticVectors
from ..featureextractor import FeatureExtractor
from ...pipeline.tok2vec import Tok2VecListener
from ...attrs import ORTH, NORM, PREFIX, SUFFIX, SHAPE

3
spacy/morphology.pyx
View File

@ -29,7 +29,8 @@ cdef class Morphology:
FEATURE_SEP = "|"
FIELD_SEP = "="
VALUE_SEP = ","
EMPTY_MORPH = "_" # not an empty string so that the PreshMap key is not 0
# not an empty string so that the PreshMap key is not 0
EMPTY_MORPH = symbols.NAMES[symbols._]
def __init__(self, StringStore strings):
self.mem = Pool()

2
spacy/pipe_analysis.py
View File

@ -78,7 +78,7 @@ def get_attr_info(nlp: "Language", attr: str) -> Dict[str, List[str]]:
def analyze_pipes(
nlp: "Language", *, keys: List[str] = DEFAULT_KEYS,
nlp: "Language", *, keys: List[str] = DEFAULT_KEYS
) -> Dict[str, Union[List[str], Dict[str, List[str]]]]:
"""Print a formatted summary for the current nlp object's pipeline. Shows
a table with the pipeline components and why they assign and require, as

17
spacy/pipeline/attributeruler.py
View File

@ -82,8 +82,7 @@ class AttributeRuler(Pipe):
matches = self.matcher(doc, allow_missing=True)
# Sort by the attribute ID, so that later rules have precendence
matches = [
(_parse_key(self.vocab.strings[m_id]), m_id, s, e)
for m_id, s, e in matches
(int(self.vocab.strings[m_id]), m_id, s, e) for m_id, s, e in matches
]
matches.sort()
for attr_id, match_id, start, end in matches:
@ -93,7 +92,7 @@ class AttributeRuler(Pipe):
try:
# The index can be negative, which makes it annoying to do
# the boundscheck. Let Span do it instead.
token = span[index]
token = span[index] # noqa: F841
except IndexError:
# The original exception is just our conditional logic, so we
# raise from.
@ -103,7 +102,7 @@ class AttributeRuler(Pipe):
span=[t.text for t in span],
index=index,
)
) from None
) from None
set_token_attrs(span[index], attrs)
return doc
@ -184,7 +183,7 @@ class AttributeRuler(Pipe):
"""
# We need to make a string here, because otherwise the ID we pass back
# will be interpreted as the hash of a string, rather than an ordinal.
key = _make_key(len(self.attrs))
key = str(len(self.attrs))
self.matcher.add(self.vocab.strings.add(key), patterns)
self._attrs_unnormed.append(attrs)
attrs = normalize_token_attrs(self.vocab, attrs)
@ -209,7 +208,7 @@ class AttributeRuler(Pipe):
all_patterns = []
for i in range(len(self.attrs)):
p = {}
p["patterns"] = self.matcher.get(_make_key(i))[1]
p["patterns"] = self.matcher.get(str(i))[1]
p["attrs"] = self._attrs_unnormed[i]
p["index"] = self.indices[i]
all_patterns.append(p)
@ -313,12 +312,6 @@ class AttributeRuler(Pipe):
return self
def _make_key(n_attr):
return f"attr_rule_{n_attr}"
def _parse_key(key):
return int(key.rsplit("_", 1)[1])
def _split_morph_attrs(attrs):
"""Split entries from a tag map or morph rules dict into to two dicts, one

4
spacy/pipeline/dep_parser.pyx
View File

@ -126,13 +126,13 @@ cdef class DependencyParser(Parser):
def add_multitask_objective(self, mt_component):
self._multitasks.append(mt_component)
def init_multitask_objectives(self, get_examples, pipeline, sgd=None, **cfg):
def init_multitask_objectives(self, get_examples, nlp=None, **cfg):
# TODO: transfer self.model.get_ref("tok2vec") to the multitask's model ?
for labeller in self._multitasks:
labeller.model.set_dim("nO", len(self.labels))
if labeller.model.has_ref("output_layer"):
labeller.model.get_ref("output_layer").set_dim("nO", len(self.labels))
labeller.begin_training(get_examples, pipeline=pipeline, sgd=sgd)
labeller.initialize(get_examples, nlp=nlp)
@property
def labels(self):

21
spacy/pipeline/entity_linker.py
View File

@ -1,5 +1,5 @@
from itertools import islice
from typing import Optional, Iterable, Callable, Dict, Iterator, Union, List, Tuple
from typing import Optional, Iterable, Callable, Dict, Iterator, Union, List
from pathlib import Path
import srsly
import random
@ -140,26 +140,20 @@ class EntityLinker(Pipe):
if len(self.kb) == 0:
raise ValueError(Errors.E139.format(name=self.name))
def begin_training(
def initialize(
self,
get_examples: Callable[[], Iterable[Example]],
*,
pipeline: Optional[List[Tuple[str, Callable[[Doc], Doc]]]] = None,
sgd: Optional[Optimizer] = None,
) -> Optimizer:
nlp: Optional[Language] = None,
):
"""Initialize the pipe for training, using a representative set
of data examples.
get_examples (Callable[[], Iterable[Example]]): Function that
returns a representative sample of gold-standard Example objects.
pipeline (List[Tuple[str, Callable]]): Optional list of pipeline
components that this component is part of. Corresponds to
nlp.pipeline.
sgd (thinc.api.Optimizer): Optional optimizer. Will be created with
create_optimizer if it doesn't exist.
RETURNS (thinc.api.Optimizer): The optimizer.
nlp (Language): The current nlp object the component is part of.
DOCS: https://nightly.spacy.io/api/entitylinker#begin_training
DOCS: https://nightly.spacy.io/api/entitylinker#initialize
"""
self._ensure_examples(get_examples)
self._require_kb()
@ -174,9 +168,6 @@ class EntityLinker(Pipe):
self.model.initialize(
X=doc_sample, Y=self.model.ops.asarray(vector_sample, dtype="float32")
)
if sgd is None:
sgd = self.create_optimizer()
return sgd
def update(
self,

2
spacy/pipeline/lemmatizer.py
View File

@ -67,7 +67,7 @@ class Lemmatizer(Pipe):
return {}
@classmethod
def load_lookups(cls, lang: str, mode: str, lookups: Optional[Lookups],) -> Lookups:
def load_lookups(cls, lang: str, mode: str, lookups: Optional[Lookups]) -> Lookups:
"""Load and validate lookups tables. If the provided lookups is None,
load the default lookups tables according to the language and mode
settings. Confirm that all required tables for the language and mode

21
spacy/pipeline/morphologizer.pyx
View File

@ -1,5 +1,5 @@
# cython: infer_types=True, profile=True, binding=True
from typing import Optional
from typing import Optional, Union, Dict
import srsly
from thinc.api import SequenceCategoricalCrossentropy, Model, Config
from itertools import islice
@ -101,6 +101,11 @@ class Morphologizer(Tagger):
"""RETURNS (Tuple[str]): The labels currently added to the component."""
return tuple(self.cfg["labels_morph"].keys())
@property
def label_data(self) -> Dict[str, Dict[str, Union[str, float, int, None]]]:
"""A dictionary with all labels data."""
return {"morph": self.cfg["labels_morph"], "pos": self.cfg["labels_pos"]}
def add_label(self, label):
"""Add a new label to the pipe.
@ -129,20 +134,15 @@ class Morphologizer(Tagger):
self.cfg["labels_pos"][norm_label] = POS_IDS[pos]
return 1
def begin_training(self, get_examples, *, pipeline=None, sgd=None):
def initialize(self, get_examples, *, nlp=None):
"""Initialize the pipe for training, using a representative set
of data examples.
get_examples (Callable[[], Iterable[Example]]): Function that
returns a representative sample of gold-standard Example objects.
pipeline (List[Tuple[str, Callable]]): Optional list of pipeline
components that this component is part of. Corresponds to
nlp.pipeline.
sgd (thinc.api.Optimizer): Optional optimizer. Will be created with
create_optimizer if it doesn't exist.
RETURNS (thinc.api.Optimizer): The optimizer.
nlp (Language): The current nlp object the component is part of.
DOCS: https://nightly.spacy.io/api/morphologizer#begin_training
DOCS: https://nightly.spacy.io/api/morphologizer#initialize
"""
self._ensure_examples(get_examples)
# First, fetch all labels from the data
@ -178,9 +178,6 @@ class Morphologizer(Tagger):
assert len(doc_sample) > 0, Errors.E923.format(name=self.name)
assert len(label_sample) > 0, Errors.E923.format(name=self.name)
self.model.initialize(X=doc_sample, Y=label_sample)
if sgd is None:
sgd = self.create_optimizer()
return sgd
def set_annotations(self, docs, batch_tag_ids):
"""Modify a batch of documents, using pre-computed scores.

12
spacy/pipeline/multitask.pyx
View File

@ -81,7 +81,7 @@ class MultitaskObjective(Tagger):
def set_annotations(self, docs, dep_ids):
pass
def begin_training(self, get_examples, pipeline=None, sgd=None):
def initialize(self, get_examples, nlp=None):
if not hasattr(get_examples, "__call__"):
err = Errors.E930.format(name="MultitaskObjective", obj=type(get_examples))
raise ValueError(err)
@ -91,9 +91,6 @@ class MultitaskObjective(Tagger):
if label is not None and label not in self.labels:
self.labels[label] = len(self.labels)
self.model.initialize() # TODO: fix initialization by defining X and Y
if sgd is None:
sgd = self.create_optimizer()
return sgd
def predict(self, docs):
tokvecs = self.model.get_ref("tok2vec")(docs)
@ -177,13 +174,10 @@ class ClozeMultitask(Pipe):
def set_annotations(self, docs, dep_ids):
pass
def begin_training(self, get_examples, pipeline=None, sgd=None):
def initialize(self, get_examples, nlp=None):
self.model.initialize() # TODO: fix initialization by defining X and Y
X = self.model.ops.alloc((5, self.model.get_ref("tok2vec").get_dim("nO")))
self.model.output_layer.begin_training(X)
if sgd is None:
sgd = self.create_optimizer()
return sgd
self.model.output_layer.initialize(X)
def predict(self, docs):
tokvecs = self.model.get_ref("tok2vec")(docs)

4
spacy/pipeline/ner.pyx
View File

@ -96,14 +96,14 @@ cdef class EntityRecognizer(Parser):
"""Register another component as a multi-task objective. Experimental."""
self._multitasks.append(mt_component)
def init_multitask_objectives(self, get_examples, pipeline, sgd=None, **cfg):
def init_multitask_objectives(self, get_examples, nlp=None, **cfg):
"""Setup multi-task objective components. Experimental and internal."""
# TODO: transfer self.model.get_ref("tok2vec") to the multitask's model ?
for labeller in self._multitasks:
labeller.model.set_dim("nO", len(self.labels))
if labeller.model.has_ref("output_layer"):
labeller.model.get_ref("output_layer").set_dim("nO", len(self.labels))
labeller.begin_training(get_examples, pipeline=pipeline)
labeller.initialize(get_examples, nlp=nlp)
@property
def labels(self):

25
spacy/pipeline/pipe.pyx
View File

@ -1,4 +1,5 @@
# cython: infer_types=True, profile=True
from typing import Optional, Tuple
import srsly
from thinc.api import set_dropout_rate, Model
@ -32,6 +33,17 @@ cdef class Pipe:
self.name = name
self.cfg = dict(cfg)
@property
def labels(self) -> Optional[Tuple[str]]:
return []
@property
def label_data(self):
"""Optional JSON-serializable data that would be sufficient to recreate
the label set if provided to the `pipe.initialize()` method.
"""
return None
def __call__(self, Doc doc):
"""Apply the pipe to one document. The document is modified in place,
and returned. This usually happens under the hood when the nlp object
@ -183,7 +195,7 @@ cdef class Pipe:
"""
return util.create_default_optimizer()
def begin_training(self, get_examples, *, pipeline=None, sgd=None):
def initialize(self, get_examples, *, nlp=None):
"""Initialize the pipe for training, using data examples if available.
This method needs to be implemented by each Pipe component,
ensuring the internal model (if available) is initialized properly
@ -191,16 +203,11 @@ cdef class Pipe:
get_examples (Callable[[], Iterable[Example]]): Function that
returns a representative sample of gold-standard Example objects.
pipeline (List[Tuple[str, Callable]]): Optional list of pipeline
components that this component is part of. Corresponds to
nlp.pipeline.
sgd (thinc.api.Optimizer): Optional optimizer. Will be created with
create_optimizer if it doesn't exist.
RETURNS (thinc.api.Optimizer): The optimizer.
nlp (Language): The current nlp object the component is part of.
DOCS: https://nightly.spacy.io/api/pipe#begin_training
DOCS: https://nightly.spacy.io/api/pipe#initialize
"""
raise NotImplementedError(Errors.E931.format(method="add_label", name=self.name))
pass
def _ensure_examples(self, get_examples):
if get_examples is None or not hasattr(get_examples, "__call__"):

2
spacy/pipeline/sentencizer.pyx
View File

@ -58,7 +58,7 @@ class Sentencizer(Pipe):
else:
self.punct_chars = set(self.default_punct_chars)
def begin_training(self, get_examples, pipeline=None, sgd=None):
def initialize(self, get_examples, nlp=None):
pass
def __call__(self, doc):

18
spacy/pipeline/senter.pyx
View File

@ -71,6 +71,10 @@ class SentenceRecognizer(Tagger):
# are 0
return tuple(["I", "S"])
@property
def label_data(self):
return self.labels
def set_annotations(self, docs, batch_tag_ids):
"""Modify a batch of documents, using pre-computed scores.
@ -124,20 +128,15 @@ class SentenceRecognizer(Tagger):
raise ValueError("nan value when computing loss")
return float(loss), d_scores
def begin_training(self, get_examples, *, pipeline=None, sgd=None):
def initialize(self, get_examples, *, nlp=None):
"""Initialize the pipe for training, using a representative set
of data examples.
get_examples (Callable[[], Iterable[Example]]): Function that
returns a representative sample of gold-standard Example objects.
pipeline (List[Tuple[str, Callable]]): Optional list of pipeline
components that this component is part of. Corresponds to
nlp.pipeline.
sgd (thinc.api.Optimizer): Optional optimizer. Will be created with
create_optimizer if it doesn't exist.
RETURNS (thinc.api.Optimizer): The optimizer.
nlp (Language): The current nlp object the component is part of.
DOCS: https://nightly.spacy.io/api/sentencerecognizer#begin_training
DOCS: https://nightly.spacy.io/api/sentencerecognizer#initialize
"""
self._ensure_examples(get_examples)
doc_sample = []
@ -151,9 +150,6 @@ class SentenceRecognizer(Tagger):
assert len(doc_sample) > 0, Errors.E923.format(name=self.name)
assert len(label_sample) > 0, Errors.E923.format(name=self.name)
self.model.initialize(X=doc_sample, Y=label_sample)
if sgd is None:
sgd = self.create_optimizer()
return sgd
def add_label(self, label, values=None):
raise NotImplementedError

40
spacy/pipeline/tagger.pyx
View File

@ -90,6 +90,11 @@ class Tagger(Pipe):
"""
return tuple(self.cfg["labels"])
@property
def label_data(self):
"""Data about the labels currently added to the component."""
return tuple(self.cfg["labels"])
def __call__(self, doc):
"""Apply the pipe to a Doc.
@ -256,31 +261,33 @@ class Tagger(Pipe):
raise ValueError("nan value when computing loss")
return float(loss), d_scores
def begin_training(self, get_examples, *, pipeline=None, sgd=None):
def initialize(self, get_examples, *, nlp=None, labels=None):
"""Initialize the pipe for training, using a representative set
of data examples.
get_examples (Callable[[], Iterable[Example]]): Function that
returns a representative sample of gold-standard Example objects..
pipeline (List[Tuple[str, Callable]]): Optional list of pipeline
components that this component is part of. Corresponds to
nlp.pipeline.
sgd (thinc.api.Optimizer): Optional optimizer. Will be created with
create_optimizer if it doesn't exist.
RETURNS (thinc.api.Optimizer): The optimizer.
nlp (Language): The current nlp object the component is part of.
labels: The labels to add to the component, typically generated by the
`init labels` command. If no labels are provided, the get_examples
callback is used to extract the labels from the data.
DOCS: https://nightly.spacy.io/api/tagger#begin_training
DOCS: https://nightly.spacy.io/api/tagger#initialize
"""
self._ensure_examples(get_examples)
if labels is not None:
for tag in labels:
self.add_label(tag)
else:
tags = set()
for example in get_examples():
for token in example.y:
if token.tag_:
tags.add(token.tag_)
for tag in sorted(tags):
self.add_label(tag)
doc_sample = []
label_sample = []
tags = set()
for example in get_examples():
for token in example.y:
if token.tag_:
tags.add(token.tag_)
for tag in sorted(tags):
self.add_label(tag)
for example in islice(get_examples(), 10):
doc_sample.append(example.x)
gold_tags = example.get_aligned("TAG", as_string=True)
@ -289,9 +296,6 @@ class Tagger(Pipe):
assert len(doc_sample) > 0, Errors.E923.format(name=self.name)
assert len(label_sample) > 0, Errors.E923.format(name=self.name)
self.model.initialize(X=doc_sample, Y=label_sample)
if sgd is None:
sgd = self.create_optimizer()
return sgd
def add_label(self, label):
"""Add a new label to the pipe.

45
spacy/pipeline/textcat.py
View File

@ -154,8 +154,16 @@ class TextCategorizer(Pipe):
@labels.setter
def labels(self, value: List[str]) -> None:
# TODO: This really shouldn't be here. I had a look and I added it when
# I added the labels property, but it's pretty nasty to have this, and
# will lead to problems.
self.cfg["labels"] = tuple(value)
@property
def label_data(self) -> List[str]:
"""RETURNS (List[str]): Information about the component's labels."""
return self.labels
def pipe(self, stream: Iterable[Doc], *, batch_size: int = 128) -> Iterator[Doc]:
"""Apply the pipe to a stream of documents. This usually happens under
the hood when the nlp object is called on a text and all components are
@ -334,43 +342,40 @@ class TextCategorizer(Pipe):
self.labels = tuple(list(self.labels) + [label])
return 1
def begin_training(
def initialize(
self,
get_examples: Callable[[], Iterable[Example]],
*,
pipeline: Optional[List[Tuple[str, Callable[[Doc], Doc]]]] = None,
sgd: Optional[Optimizer] = None,
) -> Optimizer:
nlp: Optional[Language] = None,
labels: Optional[Dict] = None,
):
"""Initialize the pipe for training, using a representative set
of data examples.
get_examples (Callable[[], Iterable[Example]]): Function that
returns a representative sample of gold-standard Example objects.
pipeline (List[Tuple[str, Callable]]): Optional list of pipeline
components that this component is part of. Corresponds to
nlp.pipeline.
sgd (thinc.api.Optimizer): Optional optimizer. Will be created with
create_optimizer if it doesn't exist.
RETURNS (thinc.api.Optimizer): The optimizer.
nlp (Language): The current nlp object the component is part of.
labels: The labels to add to the component, typically generated by the
`init labels` command. If no labels are provided, the get_examples
callback is used to extract the labels from the data.
DOCS: https://nightly.spacy.io/api/textcategorizer#begin_training
DOCS: https://nightly.spacy.io/api/textcategorizer#initialize
"""
self._ensure_examples(get_examples)
subbatch = [] # Select a subbatch of examples to initialize the model
for example in islice(get_examples(), 10):
if len(subbatch) < 2:
subbatch.append(example)
for cat in example.y.cats:
self.add_label(cat)
if labels is None:
for example in get_examples():
for cat in example.y.cats:
self.add_label(cat)
else:
for label in labels:
self.add_label(label)
subbatch = list(islice(get_examples(), 10))
doc_sample = [eg.reference for eg in subbatch]
label_sample, _ = self._examples_to_truth(subbatch)
self._require_labels()
assert len(doc_sample) > 0, Errors.E923.format(name=self.name)
assert len(label_sample) > 0, Errors.E923.format(name=self.name)
self.model.initialize(X=doc_sample, Y=label_sample)
if sgd is None:
sgd = self.create_optimizer()
return sgd
def score(self, examples: Iterable[Example], **kwargs) -> Dict[str, Any]:
"""Score a batch of examples.

16
spacy/pipeline/tok2vec.py
View File

@ -1,4 +1,4 @@
from typing import Iterator, Sequence, Iterable, Optional, Dict, Callable, List, Tuple
from typing import Iterator, Sequence, Iterable, Optional, Dict, Callable, List
from thinc.api import Model, set_dropout_rate, Optimizer, Config
from itertools import islice
@ -203,26 +203,20 @@ class Tok2Vec(Pipe):
def get_loss(self, examples, scores) -> None:
pass
def begin_training(
def initialize(
self,
get_examples: Callable[[], Iterable[Example]],
*,
pipeline: Optional[List[Tuple[str, Callable[[Doc], Doc]]]] = None,
sgd: Optional[Optimizer] = None,
nlp: Optional[Language] = None,
):
"""Initialize the pipe for training, using a representative set
of data examples.
get_examples (Callable[[], Iterable[Example]]): Function that
returns a representative sample of gold-standard Example objects.
pipeline (List[Tuple[str, Callable]]): Optional list of pipeline
components that this component is part of. Corresponds to
nlp.pipeline.
sgd (thinc.api.Optimizer): Optional optimizer. Will be created with
create_optimizer if it doesn't exist.
RETURNS (thinc.api.Optimizer): The optimizer.
nlp (Language): The current nlp object the component is part of.
DOCS: https://nightly.spacy.io/api/tok2vec#begin_training
DOCS: https://nightly.spacy.io/api/tok2vec#initialize
"""
self._ensure_examples(get_examples)
doc_sample = []

36
spacy/pipeline/transition_parser.pyx
View File

@ -1,4 +1,4 @@
# cython: infer_types=True, cdivision=True, boundscheck=False
# cython: infer_types=True, cdivision=True, boundscheck=False, binding=True
from __future__ import print_function
from cymem.cymem cimport Pool
cimport numpy as np
@ -7,6 +7,7 @@ from libcpp.vector cimport vector
from libc.string cimport memset
from libc.stdlib cimport calloc, free
import random
from typing import Optional
import srsly
from thinc.api import set_dropout_rate
@ -95,6 +96,10 @@ cdef class Parser(Pipe):
class_names = [self.moves.get_class_name(i) for i in range(self.moves.n_moves)]
return class_names
@property
def label_data(self):
return self.moves.labels
@property
def tok2vec(self):
"""Return the embedding and convolutional layer of the model."""
@ -354,7 +359,7 @@ cdef class Parser(Pipe):
# If all weights for an output are 0 in the original model, don't
# supervise that output. This allows us to add classes.
loss += (d_scores**2).sum()
backprop(d_scores, sgd=sgd)
backprop(d_scores)
# Follow the predicted action
self.transition_states(states, guesses)
states = [state for state in states if not state.is_final()]
@ -405,18 +410,20 @@ cdef class Parser(Pipe):
def set_output(self, nO):
self.model.attrs["resize_output"](self.model, nO)
def begin_training(self, get_examples, pipeline=None, sgd=None, **kwargs):
def initialize(self, get_examples, nlp=None, labels=None):
self._ensure_examples(get_examples)
self.cfg.update(kwargs)
lexeme_norms = self.vocab.lookups.get_table("lexeme_norm", {})
if len(lexeme_norms) == 0 and self.vocab.lang in util.LEXEME_NORM_LANGS:
langs = ", ".join(util.LEXEME_NORM_LANGS)
util.logger.debug(Warnings.W033.format(model="parser or NER", langs=langs))
actions = self.moves.get_actions(
examples=get_examples(),
min_freq=self.cfg['min_action_freq'],
learn_tokens=self.cfg["learn_tokens"]
)
if labels is not None:
actions = dict(labels)
else:
actions = self.moves.get_actions(
examples=get_examples(),
min_freq=self.cfg['min_action_freq'],
learn_tokens=self.cfg["learn_tokens"]
)
for action, labels in self.moves.labels.items():
actions.setdefault(action, {})
for label, freq in labels.items():
@ -425,11 +432,9 @@ cdef class Parser(Pipe):
self.moves.initialize_actions(actions)
# make sure we resize so we have an appropriate upper layer
self._resize()
if sgd is None:
sgd = self.create_optimizer()
doc_sample = []
if pipeline is not None:
for name, component in pipeline:
if nlp is not None:
for name, component in nlp.pipeline:
if component is self:
break
if hasattr(component, "pipe"):
@ -441,9 +446,8 @@ cdef class Parser(Pipe):
doc_sample.append(example.predicted)
assert len(doc_sample) > 0, Errors.E923.format(name=self.name)
self.model.initialize(doc_sample)
if pipeline is not None:
self.init_multitask_objectives(get_examples, pipeline, sgd=sgd, **self.cfg)
return sgd
if nlp is not None:
self.init_multitask_objectives(get_examples, nlp.pipeline)
def to_disk(self, path, exclude=tuple()):
serializers = {

145
spacy/schemas.py
View File

@ -1,14 +1,17 @@
from typing import Dict, List, Union, Optional, Any, Callable, Type, Tuple
from typing import Iterable, TypeVar, TYPE_CHECKING
from enum import Enum
from pydantic import BaseModel, Field, ValidationError, validator
from pydantic import BaseModel, Field, ValidationError, validator, create_model
from pydantic import StrictStr, StrictInt, StrictFloat, StrictBool
from pydantic import root_validator
from pydantic.main import ModelMetaclass
from thinc.api import Optimizer, ConfigValidationError
from thinc.config import Promise
from collections import defaultdict
from thinc.api import Optimizer
import inspect
from .attrs import NAMES
from .lookups import Lookups
from .util import is_cython_func
if TYPE_CHECKING:
# This lets us add type hints for mypy etc. without causing circular imports
@ -16,10 +19,12 @@ if TYPE_CHECKING:
from .training import Example # noqa: F401
# fmt: off
ItemT = TypeVar("ItemT")
Batcher = Callable[[Iterable[ItemT]], Iterable[List[ItemT]]]
Reader = Callable[["Language", str], Iterable["Example"]]
Logger = Callable[["Language"], Tuple[Callable[[Dict[str, Any]], None], Callable]]
Batcher = Union[Callable[[Iterable[ItemT]], Iterable[List[ItemT]]], Promise]
Reader = Union[Callable[["Language", str], Iterable["Example"]], Promise]
Logger = Union[Callable[["Language"], Tuple[Callable[[Dict[str, Any]], None], Callable]], Promise]
# fmt: on
def validate(schema: Type[BaseModel], obj: Dict[str, Any]) -> List[str]:
@ -41,6 +46,96 @@ def validate(schema: Type[BaseModel], obj: Dict[str, Any]) -> List[str]:
return [f"[{loc}] {', '.join(msg)}" for loc, msg in data.items()]
# Initialization
class ArgSchemaConfig:
extra = "forbid"
arbitrary_types_allowed = True
class ArgSchemaConfigExtra:
extra = "forbid"
arbitrary_types_allowed = True
def get_arg_model(
func: Callable,
*,
exclude: Iterable[str] = tuple(),
name: str = "ArgModel",
strict: bool = True,
) -> ModelMetaclass:
"""Generate a pydantic model for function arguments.
func (Callable): The function to generate the schema for.
exclude (Iterable[str]): Parameter names to ignore.
name (str): Name of created model class.
strict (bool): Don't allow extra arguments if no variable keyword arguments
are allowed on the function.
RETURNS (ModelMetaclass): A pydantic model.
"""
sig_args = {}
try:
sig = inspect.signature(func)
except ValueError:
# Typically happens if the method is part of a Cython module without
# binding=True. Here we just use an empty model that allows everything.
return create_model(name, __config__=ArgSchemaConfigExtra)
has_variable = False
for param in sig.parameters.values():
if param.name in exclude:
continue
if param.kind == param.VAR_KEYWORD:
# The function allows variable keyword arguments so we shouldn't
# include **kwargs etc. in the schema and switch to non-strict
# mode and pass through all other values
has_variable = True
continue
# If no annotation is specified assume it's anything
annotation = param.annotation if param.annotation != param.empty else Any
# If no default value is specified assume that it's required. Cython
# functions/methods will have param.empty for default value None so we
# need to treat them differently
default_empty = None if is_cython_func(func) else ...
default = param.default if param.default != param.empty else default_empty
sig_args[param.name] = (annotation, default)
is_strict = strict and not has_variable
sig_args["__config__"] = ArgSchemaConfig if is_strict else ArgSchemaConfigExtra
return create_model(name, **sig_args)
def validate_init_settings(
func: Callable,
settings: Dict[str, Any],
*,
section: Optional[str] = None,
name: str = "",
exclude: Iterable[str] = ("get_examples", "nlp"),
) -> Dict[str, Any]:
"""Validate initialization settings against the expected arguments in
the method signature. Will parse values if possible (e.g. int to string)
and return the updated settings dict. Will raise a ConfigValidationError
if types don't match or required values are missing.
func (Callable): The initialize method of a given component etc.
settings (Dict[str, Any]): The settings from the repsective [initialize] block.
section (str): Initialize section, for error message.
name (str): Name of the block in the section.
exclude (Iterable[str]): Parameter names to exclude from schema.
RETURNS (Dict[str, Any]): The validated settings.
"""
schema = get_arg_model(func, exclude=exclude, name="InitArgModel")
try:
return schema(**settings).dict()
except ValidationError as e:
block = "initialize" if not section else f"initialize.{section}"
title = f"Error validating initialization settings in [{block}]"
raise ConfigValidationError(
title=title, errors=e.errors(), config=settings, parent=name
) from None
# Matcher token patterns
@ -202,8 +297,6 @@ class ModelMetaSchema(BaseModel):
class ConfigSchemaTraining(BaseModel):
# fmt: off
vectors: Optional[StrictStr] = Field(..., title="Path to vectors")
lookups: Optional[Lookups] = Field(..., title="Vocab lookups")
dev_corpus: StrictStr = Field(..., title="Path in the config to the dev data")
train_corpus: StrictStr = Field(..., title="Path in the config to the training data")
batcher: Batcher = Field(..., title="Batcher for the training data")
@ -216,8 +309,6 @@ class ConfigSchemaTraining(BaseModel):
gpu_allocator: Optional[StrictStr] = Field(..., title="Memory allocator when running on GPU")
accumulate_gradient: StrictInt = Field(..., title="Whether to divide the batch up into substeps")
score_weights: Dict[StrictStr, Optional[Union[StrictFloat, StrictInt]]] = Field(..., title="Scores to report and their weights for selecting final model")
init_tok2vec: Optional[StrictStr] = Field(..., title="Path to pretrained tok2vec weights")
raw_text: Optional[StrictStr] = Field(default=None, title="Raw text")
optimizer: Optimizer = Field(..., title="The optimizer to use")
logger: Logger = Field(..., title="The logger to track training progress")
frozen_components: List[str] = Field(..., title="Pipeline components that shouldn't be updated during training")
@ -270,28 +361,42 @@ class ConfigSchemaPretrain(BaseModel):
arbitrary_types_allowed = True
class ConfigSchemaInit(BaseModel):
# fmt: off
vocab_data: Optional[StrictStr] = Field(..., title="Path to JSON-formatted vocabulary file")
lookups: Optional[Lookups] = Field(..., title="Vocabulary lookups, e.g. lexeme normalization")
vectors: Optional[StrictStr] = Field(..., title="Path to vectors")
init_tok2vec: Optional[StrictStr] = Field(..., title="Path to pretrained tok2vec weights")
tokenizer: Dict[StrictStr, Any] = Field(..., help="Arguments to be passed into Tokenizer.initialize")
components: Dict[StrictStr, Dict[StrictStr, Any]] = Field(..., help="Arguments for Pipe.initialize methods of pipeline components, keyed by component")
# fmt: on
class Config:
extra = "forbid"
arbitrary_types_allowed = True
class ConfigSchema(BaseModel):
training: ConfigSchemaTraining
nlp: ConfigSchemaNlp
pretraining: Union[ConfigSchemaPretrain, ConfigSchemaPretrainEmpty] = {}
components: Dict[str, Dict[str, Any]]
corpora: Dict[str, Reader]
@root_validator(allow_reuse=True)
def validate_config(cls, values):
"""Perform additional validation for settings with dependencies."""
pt = values.get("pretraining")
if pt and not isinstance(pt, ConfigSchemaPretrainEmpty):
if pt.objective.get("type") == "vectors" and not values["nlp"].vectors:
err = "Need nlp.vectors if pretraining.objective.type is vectors"
raise ValueError(err)
return values
initialize: ConfigSchemaInit
class Config:
extra = "allow"
arbitrary_types_allowed = True
CONFIG_SCHEMAS = {
"nlp": ConfigSchemaNlp,
"training": ConfigSchemaTraining,
"pretraining": ConfigSchemaPretrain,
"initialize": ConfigSchemaInit,
}
# Project config Schema

7
spacy/scorer.py
View File

@ -32,9 +32,7 @@ class PRFScore:
def __add__(self, other):
return PRFScore(
tp=self.tp+other.tp,
fp=self.fp+other.fp,
fn=self.fn+other.fn
tp=self.tp + other.tp, fp=self.fp + other.fp, fn=self.fn + other.fn
)
def score_set(self, cand: set, gold: set) -> None:
@ -485,7 +483,7 @@ class Scorer:
(pred_ent.start_char, pred_ent.end_char), None
)
label = gold_span.label_
if not label in f_per_type:
if label not in f_per_type:
f_per_type[label] = PRFScore()
gold = gold_span.kb_id_
# only evaluating entities that overlap between gold and pred,
@ -632,7 +630,6 @@ def get_ner_prf(examples: Iterable[Example]) -> Dict[str, PRFScore]:
continue
golds = {(e.label_, e.start, e.end) for e in eg.y.ents}
align_x2y = eg.alignment.x2y
preds = set()
for pred_ent in eg.x.ents:
if pred_ent.label_ not in scores:
scores[pred_ent.label_] = PRFScore()

1
spacy/symbols.pxd
View File

@ -466,3 +466,4 @@ cdef enum symbol_t:
ENT_ID
IDX
_

1
spacy/symbols.pyx
View File

@ -465,6 +465,7 @@ IDS = {
"acl": acl,
"LAW": LAW,
"MORPH": MORPH,
"_": _,
}

27
spacy/tests/conftest.py
View File

@ -272,22 +272,35 @@ def zh_tokenizer_char():
def zh_tokenizer_jieba():
pytest.importorskip("jieba")
config = {
"@tokenizers": "spacy.zh.ChineseTokenizer",
"segmenter": "jieba",
"nlp": {
"tokenizer": {
"@tokenizers": "spacy.zh.ChineseTokenizer",
"segmenter": "jieba",
}
}
}
nlp = get_lang_class("zh").from_config({"nlp": {"tokenizer": config}})
nlp = get_lang_class("zh").from_config(config)
return nlp.tokenizer
@pytest.fixture(scope="session")
def zh_tokenizer_pkuseg():
pytest.importorskip("pkuseg")
pytest.importorskip("pickle5")
config = {
"@tokenizers": "spacy.zh.ChineseTokenizer",
"segmenter": "pkuseg",
"pkuseg_model": "default",
"nlp": {
"tokenizer": {
"@tokenizers": "spacy.zh.ChineseTokenizer",
"segmenter": "pkuseg",
}
},
"initialize": {"tokenizer": {
"pkuseg_model": "default",
}
},
}
nlp = get_lang_class("zh").from_config({"nlp": {"tokenizer": config}})
nlp = get_lang_class("zh").from_config(config)
nlp.initialize()
return nlp.tokenizer

8
spacy/tests/doc/test_add_entities.py
View File

@ -24,9 +24,9 @@ def test_doc_add_entities_set_ents_iob(en_vocab):
"update_with_oracle_cut_size": 100,
}
cfg = {"model": DEFAULT_NER_MODEL}
model = registry.make_from_config(cfg, validate=True)["model"]
model = registry.resolve(cfg, validate=True)["model"]
ner = EntityRecognizer(en_vocab, model, **config)
ner.begin_training(lambda: [_ner_example(ner)])
ner.initialize(lambda: [_ner_example(ner)])
ner(doc)
doc.ents = [("ANIMAL", 3, 4)]
@ -46,9 +46,9 @@ def test_ents_reset(en_vocab):
"update_with_oracle_cut_size": 100,
}
cfg = {"model": DEFAULT_NER_MODEL}
model = registry.make_from_config(cfg, validate=True)["model"]
model = registry.resolve(cfg, validate=True)["model"]
ner = EntityRecognizer(en_vocab, model, **config)
ner.begin_training(lambda: [_ner_example(ner)])
ner.initialize(lambda: [_ner_example(ner)])
ner(doc)
orig_iobs = [t.ent_iob_ for t in doc]
doc.ents = list(doc.ents)

49
spacy/tests/doc/test_doc_api.py
View File

@ -19,7 +19,7 @@ def test_doc_api_init(en_vocab):
assert [t.is_sent_start for t in doc] == [True, False, True, False]
# heads override sent_starts
doc = Doc(
en_vocab, words=words, sent_starts=[True] * 4, heads=heads, deps=["dep"] * 4,
en_vocab, words=words, sent_starts=[True] * 4, heads=heads, deps=["dep"] * 4
)
assert [t.is_sent_start for t in doc] == [True, False, True, False]
@ -533,5 +533,52 @@ def test_doc_ents_setter():
assert [e.label_ for e in doc.ents] == ["HELLO", "WORLD"]
vocab = Vocab()
ents = [("HELLO", 0, 2), (vocab.strings.add("WORLD"), 3, 5)]
ents = ["B-HELLO", "I-HELLO", "O", "B-WORLD", "I-WORLD"]
doc = Doc(vocab, words=words, ents=ents)
assert [e.label_ for e in doc.ents] == ["HELLO", "WORLD"]
def test_doc_init_iob():
"""Test ents validation/normalization in Doc.__init__"""
words = ["a", "b", "c", "d", "e"]
ents = ["O"] * len(words)
doc = Doc(Vocab(), words=words, ents=ents)
assert doc.ents == ()
ents = ["B-PERSON", "I-PERSON", "O", "I-PERSON", "I-PERSON"]
doc = Doc(Vocab(), words=words, ents=ents)
assert len(doc.ents) == 2
ents = ["B-PERSON", "I-PERSON", "O", "I-PERSON", "I-GPE"]
doc = Doc(Vocab(), words=words, ents=ents)
assert len(doc.ents) == 3
# None is missing
ents = ["B-PERSON", "I-PERSON", "O", None, "I-GPE"]
doc = Doc(Vocab(), words=words, ents=ents)
assert len(doc.ents) == 2
# empty tag is missing
ents = ["", "B-PERSON", "O", "B-PERSON", "I-PERSON"]
doc = Doc(Vocab(), words=words, ents=ents)
assert len(doc.ents) == 2
# invalid IOB
ents = ["Q-PERSON", "I-PERSON", "O", "I-PERSON", "I-GPE"]
with pytest.raises(ValueError):
doc = Doc(Vocab(), words=words, ents=ents)
# no dash
ents = ["OPERSON", "I-PERSON", "O", "I-PERSON", "I-GPE"]
with pytest.raises(ValueError):
doc = Doc(Vocab(), words=words, ents=ents)
# no ent type
ents = ["O", "B-", "O", "I-PERSON", "I-GPE"]
with pytest.raises(ValueError):
doc = Doc(Vocab(), words=words, ents=ents)
# not strings or None
ents = [0, "B-", "O", "I-PERSON", "I-GPE"]
with pytest.raises(ValueError):
doc = Doc(Vocab(), words=words, ents=ents)

18
spacy/tests/doc/test_retokenize_merge.py
View File

@ -201,6 +201,12 @@ def test_doc_retokenize_spans_entity_merge(en_tokenizer):
heads = [1, 2, 2, 4, 6, 4, 2, 8, 6, 8, 9, 8, 8, 14, 12, 2, 15]
tags = ["NNP", "NNP", "VBZ", "DT", "VB", "RP", "NN", "WP", "VBZ", "IN", "NNP", "CC", "VBZ", "NNP", "NNP", ".", "SP"]
ents = [("PERSON", 0, 2), ("GPE", 10, 11), ("PERSON", 13, 15)]
ents = ["O"] * len(heads)
ents[0] = "B-PERSON"
ents[1] = "I-PERSON"
ents[10] = "B-GPE"
ents[13] = "B-PERSON"
ents[14] = "I-PERSON"
# fmt: on
tokens = en_tokenizer(text)
doc = Doc(
@ -269,7 +275,11 @@ def test_doc_retokenize_spans_entity_merge_iob(en_vocab):
# if there is a parse, span.root provides default values
words = ["a", "b", "c", "d", "e", "f", "g", "h", "i"]
heads = [0, 0, 3, 0, 0, 0, 5, 0, 0]
ents = [("ent-de", 3, 5), ("ent-fg", 5, 7)]
ents = ["O"] * len(words)
ents[3] = "B-ent-de"
ents[4] = "I-ent-de"
ents[5] = "B-ent-fg"
ents[6] = "I-ent-fg"
deps = ["dep"] * len(words)
en_vocab.strings.add("ent-de")
en_vocab.strings.add("ent-fg")
@ -292,7 +302,11 @@ def test_doc_retokenize_spans_entity_merge_iob(en_vocab):
# check that B is preserved if span[start] is B
words = ["a", "b", "c", "d", "e", "f", "g", "h", "i"]
heads = [0, 0, 3, 4, 0, 0, 5, 0, 0]
ents = [("ent-de", 3, 5), ("ent-de", 5, 7)]
ents = ["O"] * len(words)
ents[3] = "B-ent-de"
ents[4] = "I-ent-de"
ents[5] = "B-ent-de"
ents[6] = "I-ent-de"
deps = ["dep"] * len(words)
doc = Doc(en_vocab, words=words, heads=heads, deps=deps, ents=ents)
with doc.retokenize() as retokenizer:

2
spacy/tests/doc/test_to_json.py
View File

@ -9,7 +9,7 @@ def doc(en_vocab):
tags = ["VBP", "NN", "NN"]
heads = [0, 0, 0]
deps = ["ROOT", "dobj", "dobj"]
ents = [("ORG", 1, 2)]
ents = ["O", "B-ORG", "O"]
return Doc(
en_vocab, words=words, pos=pos, tags=tags, heads=heads, deps=deps, ents=ents
)

3
spacy/tests/lang/de/test_noun_chunks.py
View File

@ -2,8 +2,7 @@ import pytest
def test_noun_chunks_is_parsed_de(de_tokenizer):
"""Test that noun_chunks raises Value Error for 'de' language if Doc is not parsed.
"""
"""Test that noun_chunks raises Value Error for 'de' language if Doc is not parsed."""
doc = de_tokenizer("Er lag auf seinem")
with pytest.raises(ValueError):
list(doc.noun_chunks)

3
spacy/tests/lang/el/test_noun_chunks.py
View File

@ -2,8 +2,7 @@ import pytest
def test_noun_chunks_is_parsed_el(el_tokenizer):
"""Test that noun_chunks raises Value Error for 'el' language if Doc is not parsed.
"""
"""Test that noun_chunks raises Value Error for 'el' language if Doc is not parsed."""
doc = el_tokenizer("είναι χώρα της νοτιοανατολικής")
with pytest.raises(ValueError):
list(doc.noun_chunks)

3
spacy/tests/lang/en/test_noun_chunks.py
View File

@ -7,8 +7,7 @@ import pytest
def test_noun_chunks_is_parsed(en_tokenizer):
"""Test that noun_chunks raises Value Error for 'en' language if Doc is not parsed.
"""
"""Test that noun_chunks raises Value Error for 'en' language if Doc is not parsed."""
doc = en_tokenizer("This is a sentence")
with pytest.raises(ValueError):
list(doc.noun_chunks)

3
spacy/tests/lang/es/test_noun_chunks.py
View File

@ -2,8 +2,7 @@ import pytest
def test_noun_chunks_is_parsed_es(es_tokenizer):
"""Test that noun_chunks raises Value Error for 'es' language if Doc is not parsed.
"""
"""Test that noun_chunks raises Value Error for 'es' language if Doc is not parsed."""
doc = es_tokenizer("en Oxford este verano")
with pytest.raises(ValueError):
list(doc.noun_chunks)

3
spacy/tests/lang/fa/test_noun_chunks.py
View File

@ -2,8 +2,7 @@ import pytest
def test_noun_chunks_is_parsed_fa(fa_tokenizer):
"""Test that noun_chunks raises Value Error for 'fa' language if Doc is not parsed.
"""
"""Test that noun_chunks raises Value Error for 'fa' language if Doc is not parsed."""
doc = fa_tokenizer("این یک جمله نمونه می باشد.")
with pytest.raises(ValueError):

4
spacy/tests/lang/fr/test_exceptions.py
View File

@ -36,9 +36,7 @@ def test_fr_tokenizer_infix_exceptions(fr_tokenizer, text):
assert len(tokens) == 1
@pytest.mark.parametrize(
"text", ["janv.", "juill.", "Dr.", "av.", "sept."],
)
@pytest.mark.parametrize("text", ["janv.", "juill.", "Dr.", "av.", "sept."])
def test_fr_tokenizer_handles_abbr(fr_tokenizer, text):
tokens = fr_tokenizer(text)
assert len(tokens) == 1

3
spacy/tests/lang/fr/test_noun_chunks.py
View File

@ -2,8 +2,7 @@ import pytest
def test_noun_chunks_is_parsed_fr(fr_tokenizer):
"""Test that noun_chunks raises Value Error for 'fr' language if Doc is not parsed.
"""
"""Test that noun_chunks raises Value Error for 'fr' language if Doc is not parsed."""
doc = fr_tokenizer("trouver des travaux antérieurs")
with pytest.raises(ValueError):
list(doc.noun_chunks)

3
spacy/tests/lang/id/test_noun_chunks.py
View File

@ -2,8 +2,7 @@ import pytest
def test_noun_chunks_is_parsed_id(id_tokenizer):
"""Test that noun_chunks raises Value Error for 'id' language if Doc is not parsed.
"""
"""Test that noun_chunks raises Value Error for 'id' language if Doc is not parsed."""
doc = id_tokenizer("sebelas")
with pytest.raises(ValueError):
list(doc.noun_chunks)

2
spacy/tests/lang/ja/test_tokenizer.py
View File

@ -112,7 +112,7 @@ def test_ja_tokenizer_split_modes(ja_tokenizer, text, len_a, len_b, len_c):
@pytest.mark.parametrize(
"text,sub_tokens_list_a,sub_tokens_list_b,sub_tokens_list_c", SUB_TOKEN_TESTS,
"text,sub_tokens_list_a,sub_tokens_list_b,sub_tokens_list_c", SUB_TOKEN_TESTS
)
def test_ja_tokenizer_sub_tokens(
ja_tokenizer, text, sub_tokens_list_a, sub_tokens_list_b, sub_tokens_list_c

3
spacy/tests/lang/nb/test_noun_chunks.py
View File

@ -2,8 +2,7 @@ import pytest
def test_noun_chunks_is_parsed_nb(nb_tokenizer):
"""Test that noun_chunks raises Value Error for 'nb' language if Doc is not parsed.
"""
"""Test that noun_chunks raises Value Error for 'nb' language if Doc is not parsed."""
doc = nb_tokenizer("Smørsausen brukes bl.a. til")
with pytest.raises(ValueError):
list(doc.noun_chunks)

2
spacy/tests/lang/ne/test_text.py
View File

@ -8,7 +8,7 @@ def test_ne_tokenizer_handlers_long_text(ne_tokenizer):
@pytest.mark.parametrize(
"text,length", [("समय जान कति पनि बेर लाग्दैन ।", 7), ("म ठूलो हुँदै थिएँ ।", 5)],
"text,length", [("समय जान कति पनि बेर लाग्दैन ।", 7), ("म ठूलो हुँदै थिएँ ।", 5)]
)
def test_ne_tokenizer_handles_cnts(ne_tokenizer, text, length):
tokens = ne_tokenizer(text)

2
spacy/tests/lang/sa/test_text.py
View File

@ -10,7 +10,7 @@ def test_sa_tokenizer_handles_long_text(sa_tokenizer):
@pytest.mark.parametrize(
"text,length",
[
("श्री भगवानुवाच पश्य मे पार्थ रूपाणि शतशोऽथ सहस्रशः।", 9,),
("श्री भगवानुवाच पश्य मे पार्थ रूपाणि शतशोऽथ सहस्रशः।", 9),
("गुणान् सर्वान् स्वभावो मूर्ध्नि वर्तते ।", 6),
],
)

3
spacy/tests/lang/sv/test_noun_chunks.py
View File

@ -3,8 +3,7 @@ from spacy.tokens import Doc
def test_noun_chunks_is_parsed_sv(sv_tokenizer):
"""Test that noun_chunks raises Value Error for 'sv' language if Doc is not parsed.
"""
"""Test that noun_chunks raises Value Error for 'sv' language if Doc is not parsed."""
doc = sv_tokenizer("Studenten läste den bästa boken")
with pytest.raises(ValueError):
list(doc.noun_chunks)

19
spacy/tests/lang/zh/test_serialize.py
View File

@ -27,9 +27,18 @@ def test_zh_tokenizer_serialize_jieba(zh_tokenizer_jieba):
@pytest.mark.slow
def test_zh_tokenizer_serialize_pkuseg_with_processors(zh_tokenizer_pkuseg):
nlp = Chinese(
meta={
"tokenizer": {"config": {"segmenter": "pkuseg", "pkuseg_model": "medicine"}}
}
)
config = {
"nlp": {
"tokenizer": {
"@tokenizers": "spacy.zh.ChineseTokenizer",
"segmenter": "pkuseg",
}
},
"initialize": {"tokenizer": {
"pkuseg_model": "medicine",
}
},
}
nlp = Chinese.from_config(config)
nlp.initialize()
zh_tokenizer_serialize(nlp.tokenizer)

2
spacy/tests/lang/zh/test_tokenizer.py
View File

@ -1,6 +1,6 @@
import pytest
from spacy.lang.zh import Chinese, _get_pkuseg_trie_data
from thinc.config import ConfigValidationError
from thinc.api import ConfigValidationError
# fmt: off

10
spacy/tests/parser/test_add_label.py
View File

@ -23,7 +23,7 @@ def parser(vocab):
"update_with_oracle_cut_size": 100,
}
cfg = {"model": DEFAULT_PARSER_MODEL}
model = registry.make_from_config(cfg, validate=True)["model"]
model = registry.resolve(cfg, validate=True)["model"]
parser = DependencyParser(vocab, model, **config)
return parser
@ -35,7 +35,7 @@ def test_init_parser(parser):
def _train_parser(parser):
fix_random_seed(1)
parser.add_label("left")
parser.begin_training(lambda: [_parser_example(parser)], **parser.cfg)
parser.initialize(lambda: [_parser_example(parser)])
sgd = Adam(0.001)
for i in range(5):
@ -82,12 +82,12 @@ def test_add_label_deserializes_correctly():
"update_with_oracle_cut_size": 100,
}
cfg = {"model": DEFAULT_NER_MODEL}
model = registry.make_from_config(cfg, validate=True)["model"]
model = registry.resolve(cfg, validate=True)["model"]
ner1 = EntityRecognizer(Vocab(), model, **config)
ner1.add_label("C")
ner1.add_label("B")
ner1.add_label("A")
ner1.begin_training(lambda: [_ner_example(ner1)])
ner1.initialize(lambda: [_ner_example(ner1)])
ner2 = EntityRecognizer(Vocab(), model, **config)
# the second model needs to be resized before we can call from_bytes
@ -111,7 +111,7 @@ def test_add_label_get_label(pipe_cls, n_moves, model_config):
splitting the move names.
"""
labels = ["A", "B", "C"]
model = registry.make_from_config({"model": model_config}, validate=True)["model"]
model = registry.resolve({"model": model_config}, validate=True)["model"]
config = {
"learn_tokens": False,
"min_action_freq": 30,

2
spacy/tests/parser/test_arc_eager_oracle.py
View File

@ -127,7 +127,7 @@ def test_get_oracle_actions():
"update_with_oracle_cut_size": 100,
}
cfg = {"model": DEFAULT_PARSER_MODEL}
model = registry.make_from_config(cfg, validate=True)["model"]
model = registry.resolve(cfg, validate=True)["model"]
parser = DependencyParser(doc.vocab, model, **config)
parser.moves.add_action(0, "")
parser.moves.add_action(1, "")

20
spacy/tests/parser/test_ner.py
View File

@ -202,7 +202,7 @@ def test_train_empty():
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
ner = nlp.add_pipe("ner", last=True)
ner.add_label("PERSON")
nlp.begin_training()
nlp.initialize()
for itn in range(2):
losses = {}
batches = util.minibatch(train_examples, size=8)
@ -213,7 +213,7 @@ def test_train_empty():
def test_overwrite_token():
nlp = English()
nlp.add_pipe("ner")
nlp.begin_training()
nlp.initialize()
# The untrained NER will predict O for each token
doc = nlp("I live in New York")
assert [token.ent_iob_ for token in doc] == ["O", "O", "O", "O", "O"]
@ -235,7 +235,7 @@ def test_empty_ner():
nlp = English()
ner = nlp.add_pipe("ner")
ner.add_label("MY_LABEL")
nlp.begin_training()
nlp.initialize()
doc = nlp("John is watching the news about Croatia's elections")
# if this goes wrong, the initialization of the parser's upper layer is probably broken
result = ["O", "O", "O", "O", "O", "O", "O", "O", "O"]
@ -254,7 +254,7 @@ def test_ruler_before_ner():
# 2: untrained NER - should set everything else to O
untrained_ner = nlp.add_pipe("ner")
untrained_ner.add_label("MY_LABEL")
nlp.begin_training()
nlp.initialize()
doc = nlp("This is Antti Korhonen speaking in Finland")
expected_iobs = ["B", "O", "O", "O", "O", "O", "O"]
expected_types = ["THING", "", "", "", "", "", ""]
@ -269,7 +269,7 @@ def test_ner_before_ruler():
# 1: untrained NER - should set everything to O
untrained_ner = nlp.add_pipe("ner", name="uner")
untrained_ner.add_label("MY_LABEL")
nlp.begin_training()
nlp.initialize()
# 2 : Entity Ruler - should set "this" to B and keep everything else O
patterns = [{"label": "THING", "pattern": "This"}]
@ -290,7 +290,7 @@ def test_block_ner():
nlp.add_pipe("blocker", config={"start": 2, "end": 5})
untrained_ner = nlp.add_pipe("ner")
untrained_ner.add_label("MY_LABEL")
nlp.begin_training()
nlp.initialize()
doc = nlp("This is Antti L Korhonen speaking in Finland")
expected_iobs = ["O", "O", "B", "B", "B", "O", "O", "O"]
expected_types = ["", "", "", "", "", "", "", ""]
@ -307,7 +307,7 @@ def test_overfitting_IO():
train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
for ent in annotations.get("entities"):
ner.add_label(ent[2])
optimizer = nlp.begin_training()
optimizer = nlp.initialize()
for i in range(50):
losses = {}
@ -340,13 +340,13 @@ def test_ner_warns_no_lookups(caplog):
assert not len(nlp.vocab.lookups)
nlp.add_pipe("ner")
with caplog.at_level(logging.DEBUG):
nlp.begin_training()
nlp.initialize()
assert "W033" in caplog.text
caplog.clear()
nlp.vocab.lookups.add_table("lexeme_norm")
nlp.vocab.lookups.get_table("lexeme_norm")["a"] = "A"
with caplog.at_level(logging.DEBUG):
nlp.begin_training()
nlp.initialize()
assert "W033" not in caplog.text
@ -358,5 +358,5 @@ class BlockerComponent1:
self.name = name
def __call__(self, doc):
doc.set_ents([], blocked=[doc[self.start:self.end]], default="unmodified")
doc.set_ents([], blocked=[doc[self.start : self.end]], default="unmodified")
return doc

8
spacy/tests/parser/test_neural_parser.py
View File

@ -25,7 +25,7 @@ def arc_eager(vocab):
@pytest.fixture
def tok2vec():
cfg = {"model": DEFAULT_TOK2VEC_MODEL}
tok2vec = registry.make_from_config(cfg, validate=True)["model"]
tok2vec = registry.resolve(cfg, validate=True)["model"]
tok2vec.initialize()
return tok2vec
@ -38,14 +38,14 @@ def parser(vocab, arc_eager):
"update_with_oracle_cut_size": 100,
}
cfg = {"model": DEFAULT_PARSER_MODEL}
model = registry.make_from_config(cfg, validate=True)["model"]
model = registry.resolve(cfg, validate=True)["model"]
return Parser(vocab, model, moves=arc_eager, **config)
@pytest.fixture
def model(arc_eager, tok2vec, vocab):
cfg = {"model": DEFAULT_PARSER_MODEL}
model = registry.make_from_config(cfg, validate=True)["model"]
model = registry.resolve(cfg, validate=True)["model"]
model.attrs["resize_output"](model, arc_eager.n_moves)
model.initialize()
return model
@ -72,7 +72,7 @@ def test_build_model(parser, vocab):
"update_with_oracle_cut_size": 100,
}
cfg = {"model": DEFAULT_PARSER_MODEL}
model = registry.make_from_config(cfg, validate=True)["model"]
model = registry.resolve(cfg, validate=True)["model"]
parser.model = Parser(vocab, model=model, moves=parser.moves, **config).model
assert parser.model is not None

2
spacy/tests/parser/test_parse.py
View File

@ -191,7 +191,7 @@ def test_overfitting_IO():
train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
for dep in annotations.get("deps", []):
parser.add_label(dep)
optimizer = nlp.begin_training()
optimizer = nlp.initialize()
for i in range(100):
losses = {}
nlp.update(train_examples, sgd=optimizer, losses=losses)

4
spacy/tests/parser/test_preset_sbd.py
View File

@ -28,13 +28,13 @@ def parser(vocab):
"update_with_oracle_cut_size": 100,
}
cfg = {"model": DEFAULT_PARSER_MODEL}
model = registry.make_from_config(cfg, validate=True)["model"]
model = registry.resolve(cfg, validate=True)["model"]
parser = DependencyParser(vocab, model, **config)
parser.cfg["token_vector_width"] = 4
parser.cfg["hidden_width"] = 32
# parser.add_label('right')
parser.add_label("left")
parser.begin_training(lambda: [_parser_example(parser)], **parser.cfg)
parser.initialize(lambda: [_parser_example(parser)])
sgd = Adam(0.001)
for i in range(10):

16
spacy/tests/pipeline/test_entity_linker.py
View File

@ -134,7 +134,7 @@ def test_kb_undefined(nlp):
"""Test that the EL can't train without defining a KB"""
entity_linker = nlp.add_pipe("entity_linker", config={})
with pytest.raises(ValueError):
entity_linker.begin_training(lambda: [])
entity_linker.initialize(lambda: [])
def test_kb_empty(nlp):
@ -143,7 +143,7 @@ def test_kb_empty(nlp):
entity_linker = nlp.add_pipe("entity_linker", config=config)
assert len(entity_linker.kb) == 0
with pytest.raises(ValueError):
entity_linker.begin_training(lambda: [])
entity_linker.initialize(lambda: [])
def test_kb_serialize(nlp):
@ -254,14 +254,12 @@ def test_vocab_serialization(nlp):
mykb = KnowledgeBase(nlp.vocab, entity_vector_length=1)
# adding entities
q1_hash = mykb.add_entity(entity="Q1", freq=27, entity_vector=[1])
mykb.add_entity(entity="Q1", freq=27, entity_vector=[1])
q2_hash = mykb.add_entity(entity="Q2", freq=12, entity_vector=[2])
q3_hash = mykb.add_entity(entity="Q3", freq=5, entity_vector=[3])
mykb.add_entity(entity="Q3", freq=5, entity_vector=[3])
# adding aliases
douglas_hash = mykb.add_alias(
alias="douglas", entities=["Q2", "Q3"], probabilities=[0.4, 0.1]
)
mykb.add_alias(alias="douglas", entities=["Q2", "Q3"], probabilities=[0.4, 0.1])
adam_hash = mykb.add_alias(alias="adam", entities=["Q2"], probabilities=[0.9])
candidates = mykb.get_alias_candidates("adam")
@ -360,7 +358,7 @@ def test_preserving_links_asdoc(nlp):
ruler.add_patterns(patterns)
el_config = {"kb_loader": {"@misc": "myLocationsKB.v1"}, "incl_prior": False}
entity_linker = nlp.add_pipe("entity_linker", config=el_config, last=True)
nlp.begin_training()
nlp.initialize()
assert entity_linker.model.get_dim("nO") == vector_length
# test whether the entity links are preserved by the `as_doc()` function
@ -463,7 +461,7 @@ def test_overfitting_IO():
)
# train the NEL pipe
optimizer = nlp.begin_training(get_examples=lambda: train_examples)
optimizer = nlp.initialize(get_examples=lambda: train_examples)
assert entity_linker.model.get_dim("nO") == vector_length
assert entity_linker.model.get_dim("nO") == entity_linker.kb.entity_vector_length

69
spacy/tests/pipeline/test_initialize.py Normal file
View File

@ -0,0 +1,69 @@
import pytest
from spacy.language import Language
from spacy.lang.en import English
from spacy.training import Example
from thinc.api import ConfigValidationError
from pydantic import StrictBool
def test_initialize_arguments():
name = "test_initialize_arguments"
class CustomTokenizer:
def __init__(self, tokenizer):
self.tokenizer = tokenizer
self.from_initialize = None
def __call__(self, text):
return self.tokenizer(text)
def initialize(self, get_examples, nlp, custom: int):
self.from_initialize = custom
class Component:
def __init__(self):
self.from_initialize = None
def initialize(
self, get_examples, nlp, custom1: str, custom2: StrictBool = False
):
self.from_initialize = (custom1, custom2)
Language.factory(name, func=lambda nlp, name: Component())
nlp = English()
nlp.tokenizer = CustomTokenizer(nlp.tokenizer)
example = Example.from_dict(nlp("x"), {})
get_examples = lambda: [example]
nlp.add_pipe(name)
# The settings here will typically come from the [initialize] block
init_cfg = {"tokenizer": {"custom": 1}, "components": {name: {}}}
nlp.config["initialize"].update(init_cfg)
with pytest.raises(ConfigValidationError) as e:
# Empty config for component, no required custom1 argument
nlp.initialize(get_examples)
errors = e.value.errors
assert len(errors) == 1
assert errors[0]["loc"] == ("custom1",)
assert errors[0]["type"] == "value_error.missing"
init_cfg = {
"tokenizer": {"custom": 1},
"components": {name: {"custom1": "x", "custom2": 1}},
}
nlp.config["initialize"].update(init_cfg)
with pytest.raises(ConfigValidationError) as e:
# Wrong type of custom 2
nlp.initialize(get_examples)
errors = e.value.errors
assert len(errors) == 1
assert errors[0]["loc"] == ("custom2",)
assert errors[0]["type"] == "value_error.strictbool"
init_cfg = {
"tokenizer": {"custom": 1},
"components": {name: {"custom1": "x"}},
}
nlp.config["initialize"].update(init_cfg)
nlp.initialize(get_examples)
assert nlp.tokenizer.from_initialize == 1
pipe = nlp.get_pipe(name)
assert pipe.from_initialize == ("x", False)

20
spacy/tests/pipeline/test_morphologizer.py
View File

@ -33,7 +33,7 @@ def test_no_label():
nlp = Language()
nlp.add_pipe("morphologizer")
with pytest.raises(ValueError):
nlp.begin_training()
nlp.initialize()
def test_implicit_label():
@ -42,7 +42,7 @@ def test_implicit_label():
train_examples = []
for t in TRAIN_DATA:
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
nlp.begin_training(get_examples=lambda: train_examples)
nlp.initialize(get_examples=lambda: train_examples)
def test_no_resize():
@ -50,13 +50,13 @@ def test_no_resize():
morphologizer = nlp.add_pipe("morphologizer")
morphologizer.add_label("POS" + Morphology.FIELD_SEP + "NOUN")
morphologizer.add_label("POS" + Morphology.FIELD_SEP + "VERB")
nlp.begin_training()
nlp.initialize()
# this throws an error because the morphologizer can't be resized after initialization
with pytest.raises(ValueError):
morphologizer.add_label("POS" + Morphology.FIELD_SEP + "ADJ")
def test_begin_training_examples():
def test_initialize_examples():
nlp = Language()
morphologizer = nlp.add_pipe("morphologizer")
morphologizer.add_label("POS" + Morphology.FIELD_SEP + "NOUN")
@ -64,12 +64,12 @@ def test_begin_training_examples():
for t in TRAIN_DATA:
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
# you shouldn't really call this more than once, but for testing it should be fine
nlp.begin_training()
nlp.begin_training(get_examples=lambda: train_examples)
with pytest.raises(TypeError):
nlp.begin_training(get_examples=lambda: None)
nlp.initialize()
nlp.initialize(get_examples=lambda: train_examples)
with pytest.raises(ValueError):
nlp.begin_training(get_examples=train_examples)
nlp.initialize(get_examples=lambda: None)
with pytest.raises(ValueError):
nlp.initialize(get_examples=train_examples)
def test_overfitting_IO():
@ -79,7 +79,7 @@ def test_overfitting_IO():
train_examples = []
for inst in TRAIN_DATA:
train_examples.append(Example.from_dict(nlp.make_doc(inst[0]), inst[1]))
optimizer = nlp.begin_training(get_examples=lambda: train_examples)
optimizer = nlp.initialize(get_examples=lambda: train_examples)
for i in range(50):
losses = {}

3
spacy/tests/pipeline/test_pipe_factories.py
View File

@ -4,8 +4,7 @@ from spacy.lang.en import English
from spacy.lang.de import German
from spacy.tokens import Doc
from spacy.util import registry, SimpleFrozenDict, combine_score_weights
from thinc.api import Model, Linear
from thinc.config import ConfigValidationError
from thinc.api import Model, Linear, ConfigValidationError
from pydantic import StrictInt, StrictStr
from ..util import make_tempdir

14
spacy/tests/pipeline/test_senter.py
View File

@ -31,19 +31,19 @@ TRAIN_DATA = [
]
def test_begin_training_examples():
def test_initialize_examples():
nlp = Language()
nlp.add_pipe("senter")
train_examples = []
for t in TRAIN_DATA:
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
# you shouldn't really call this more than once, but for testing it should be fine
nlp.begin_training()
nlp.begin_training(get_examples=lambda: train_examples)
with pytest.raises(TypeError):
nlp.begin_training(get_examples=lambda: None)
nlp.initialize()
nlp.initialize(get_examples=lambda: train_examples)
with pytest.raises(ValueError):
nlp.begin_training(get_examples=train_examples)
nlp.initialize(get_examples=lambda: None)
with pytest.raises(ValueError):
nlp.initialize(get_examples=train_examples)
def test_overfitting_IO():
@ -58,7 +58,7 @@ def test_overfitting_IO():
train_examples[1].reference[11].is_sent_start = False
nlp.add_pipe("senter")
optimizer = nlp.begin_training()
optimizer = nlp.initialize()
for i in range(200):
losses = {}

34
spacy/tests/pipeline/test_tagger.py
View File

@ -15,14 +15,14 @@ def test_label_types():
tagger.add_label(9)
def test_tagger_begin_training_tag_map():
"""Test that Tagger.begin_training() without gold tuples does not clobber
def test_tagger_initialize_tag_map():
"""Test that Tagger.initialize() without gold tuples does not clobber
the tag map."""
nlp = Language()
tagger = nlp.add_pipe("tagger")
orig_tag_count = len(tagger.labels)
tagger.add_label("A")
nlp.begin_training()
nlp.initialize()
assert orig_tag_count + 1 == len(nlp.get_pipe("tagger").labels)
@ -38,7 +38,7 @@ def test_no_label():
nlp = Language()
nlp.add_pipe("tagger")
with pytest.raises(ValueError):
nlp.begin_training()
nlp.initialize()
def test_no_resize():
@ -47,7 +47,7 @@ def test_no_resize():
tagger.add_label("N")
tagger.add_label("V")
assert tagger.labels == ("N", "V")
nlp.begin_training()
nlp.initialize()
assert tagger.model.get_dim("nO") == 2
# this throws an error because the tagger can't be resized after initialization
with pytest.raises(ValueError):
@ -60,10 +60,10 @@ def test_implicit_label():
train_examples = []
for t in TRAIN_DATA:
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
nlp.begin_training(get_examples=lambda: train_examples)
nlp.initialize(get_examples=lambda: train_examples)
def test_begin_training_examples():
def test_initialize_examples():
nlp = Language()
tagger = nlp.add_pipe("tagger")
train_examples = []
@ -72,16 +72,16 @@ def test_begin_training_examples():
for t in TRAIN_DATA:
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
# you shouldn't really call this more than once, but for testing it should be fine
nlp.begin_training()
nlp.begin_training(get_examples=lambda: train_examples)
with pytest.raises(TypeError):
nlp.begin_training(get_examples=lambda: None)
with pytest.raises(TypeError):
nlp.begin_training(get_examples=lambda: train_examples[0])
nlp.initialize()
nlp.initialize(get_examples=lambda: train_examples)
with pytest.raises(ValueError):
nlp.begin_training(get_examples=lambda: [])
nlp.initialize(get_examples=lambda: None)
with pytest.raises(TypeError):
nlp.initialize(get_examples=lambda: train_examples[0])
with pytest.raises(ValueError):
nlp.begin_training(get_examples=train_examples)
nlp.initialize(get_examples=lambda: [])
with pytest.raises(ValueError):
nlp.initialize(get_examples=train_examples)
def test_overfitting_IO():
@ -91,7 +91,7 @@ def test_overfitting_IO():
train_examples = []
for t in TRAIN_DATA:
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
optimizer = nlp.begin_training(get_examples=lambda: train_examples)
optimizer = nlp.initialize(get_examples=lambda: train_examples)
assert tagger.model.get_dim("nO") == len(TAGS)
for i in range(50):
@ -122,4 +122,4 @@ def test_tagger_requires_labels():
nlp = English()
nlp.add_pipe("tagger")
with pytest.raises(ValueError):
nlp.begin_training()
nlp.initialize()

53
spacy/tests/pipeline/test_textcat.py
View File

@ -9,10 +9,10 @@ from spacy.pipeline import TextCategorizer
from spacy.tokens import Doc
from spacy.pipeline.tok2vec import DEFAULT_TOK2VEC_MODEL
from spacy.scorer import Scorer
from spacy.training import Example
from spacy.training.initialize import verify_textcat_config
from ..util import make_tempdir
from ...cli.train import verify_textcat_config
from ...training import Example
TRAIN_DATA = [
@ -26,7 +26,7 @@ def test_simple_train():
nlp = Language()
textcat = nlp.add_pipe("textcat")
textcat.add_label("answer")
nlp.begin_training()
nlp.initialize()
for i in range(5):
for text, answer in [
("aaaa", 1.0),
@ -56,7 +56,7 @@ def test_textcat_learns_multilabel():
textcat = TextCategorizer(nlp.vocab, width=8)
for letter in letters:
textcat.add_label(letter)
optimizer = textcat.begin_training(lambda: [])
optimizer = textcat.initialize(lambda: [])
for i in range(30):
losses = {}
examples = [Example.from_dict(doc, {"cats": cats}) for doc, cat in docs]
@ -86,7 +86,7 @@ def test_no_label():
nlp = Language()
nlp.add_pipe("textcat")
with pytest.raises(ValueError):
nlp.begin_training()
nlp.initialize()
def test_implicit_label():
@ -95,7 +95,7 @@ def test_implicit_label():
train_examples = []
for t in TRAIN_DATA:
train_examples.append(Example.from_dict(nlp.make_doc(t[0]), t[1]))
nlp.begin_training(get_examples=lambda: train_examples)
nlp.initialize(get_examples=lambda: train_examples)
def test_no_resize():
@ -103,14 +103,14 @@ def test_no_resize():
textcat = nlp.add_pipe("textcat")
textcat.add_label("POSITIVE")
textcat.add_label("NEGATIVE")
nlp.begin_training()
nlp.initialize()
assert textcat.model.get_dim("nO") == 2
# this throws an error because the textcat can't be resized after initialization
with pytest.raises(ValueError):
textcat.add_label("NEUTRAL")
def test_begin_training_examples():
def test_initialize_examples():
nlp = Language()
textcat = nlp.add_pipe("textcat")
train_examples = []
@ -119,12 +119,12 @@ def test_begin_training_examples():
for label, value in annotations.get("cats").items():
textcat.add_label(label)
# you shouldn't really call this more than once, but for testing it should be fine
nlp.begin_training()
nlp.begin_training(get_examples=lambda: train_examples)
with pytest.raises(TypeError):
nlp.begin_training(get_examples=lambda: None)
nlp.initialize()
nlp.initialize(get_examples=lambda: train_examples)
with pytest.raises(ValueError):
nlp.begin_training(get_examples=train_examples)
nlp.initialize(get_examples=lambda: None)
with pytest.raises(ValueError):
nlp.initialize(get_examples=train_examples)
def test_overfitting_IO():
@ -139,7 +139,7 @@ def test_overfitting_IO():
train_examples = []
for text, annotations in TRAIN_DATA:
train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
optimizer = nlp.begin_training(get_examples=lambda: train_examples)
optimizer = nlp.initialize(get_examples=lambda: train_examples)
assert textcat.model.get_dim("nO") == 2
for i in range(50):
@ -195,7 +195,7 @@ def test_textcat_configs(textcat_config):
train_examples.append(Example.from_dict(nlp.make_doc(text), annotations))
for label, value in annotations.get("cats").items():
textcat.add_label(label)
optimizer = nlp.begin_training()
optimizer = nlp.initialize()
for i in range(5):
losses = {}
nlp.update(train_examples, sgd=optimizer, losses=losses)
@ -226,6 +226,7 @@ def test_positive_class_not_binary():
with pytest.raises(ValueError):
verify_textcat_config(nlp, pipe_config)
def test_textcat_evaluation():
train_examples = []
nlp = English()
@ -241,15 +242,17 @@ def test_textcat_evaluation():
pred2.cats = {"winter": 1.0, "summer": 0.0, "spring": 0.0, "autumn": 1.0}
train_examples.append(Example(pred2, ref2))
scores = Scorer().score_cats(train_examples, "cats", labels=["winter", "summer", "spring", "autumn"])
assert scores["cats_f_per_type"]["winter"]["p"] == 1/2
assert scores["cats_f_per_type"]["winter"]["r"] == 1/1
scores = Scorer().score_cats(
train_examples, "cats", labels=["winter", "summer", "spring", "autumn"]
)
assert scores["cats_f_per_type"]["winter"]["p"] == 1 / 2
assert scores["cats_f_per_type"]["winter"]["r"] == 1 / 1
assert scores["cats_f_per_type"]["summer"]["p"] == 0
assert scores["cats_f_per_type"]["summer"]["r"] == 0/1
assert scores["cats_f_per_type"]["spring"]["p"] == 1/1
assert scores["cats_f_per_type"]["spring"]["r"] == 1/2
assert scores["cats_f_per_type"]["autumn"]["p"] == 2/2
assert scores["cats_f_per_type"]["autumn"]["r"] == 2/2
assert scores["cats_f_per_type"]["summer"]["r"] == 0 / 1
assert scores["cats_f_per_type"]["spring"]["p"] == 1 / 1
assert scores["cats_f_per_type"]["spring"]["r"] == 1 / 2
assert scores["cats_f_per_type"]["autumn"]["p"] == 2 / 2
assert scores["cats_f_per_type"]["autumn"]["r"] == 2 / 2
assert scores["cats_micro_p"] == 4/5
assert scores["cats_micro_r"] == 4/6
assert scores["cats_micro_p"] == 4 / 5
assert scores["cats_micro_r"] == 4 / 6

11
spacy/tests/pipeline/test_tok2vec.py
View File

@ -73,8 +73,7 @@ def test_tok2vec_configs(width, embed_arch, embed_config, encode_arch, encode_co
encode_config["width"] = width
docs = get_batch(3)
tok2vec = build_Tok2Vec_model(
embed_arch(**embed_config),
encode_arch(**encode_config)
embed_arch(**embed_config), encode_arch(**encode_config)
)
tok2vec.initialize(docs)
vectors, backprop = tok2vec.begin_update(docs)
@ -88,7 +87,7 @@ def test_init_tok2vec():
nlp = English()
tok2vec = nlp.add_pipe("tok2vec")
assert tok2vec.listeners == []
nlp.begin_training()
nlp.initialize()
assert tok2vec.model.get_dim("nO")
@ -139,7 +138,7 @@ TRAIN_DATA = [
def test_tok2vec_listener():
orig_config = Config().from_str(cfg_string)
nlp, config = util.load_model_from_config(orig_config, auto_fill=True, validate=True)
nlp = util.load_model_from_config(orig_config, auto_fill=True, validate=True)
assert nlp.pipe_names == ["tok2vec", "tagger"]
tagger = nlp.get_pipe("tagger")
tok2vec = nlp.get_pipe("tok2vec")
@ -154,7 +153,7 @@ def test_tok2vec_listener():
# Check that the Tok2Vec component finds it listeners
assert tok2vec.listeners == []
optimizer = nlp.begin_training(lambda: train_examples)
optimizer = nlp.initialize(lambda: train_examples)
assert tok2vec.listeners == [tagger_tok2vec]
for i in range(5):
@ -173,7 +172,7 @@ def test_tok2vec_listener():
def test_tok2vec_listener_callback():
orig_config = Config().from_str(cfg_string)
nlp, config = util.load_model_from_config(orig_config, auto_fill=True, validate=True)
nlp = util.load_model_from_config(orig_config, auto_fill=True, validate=True)
assert nlp.pipe_names == ["tok2vec", "tagger"]
tagger = nlp.get_pipe("tagger")
tok2vec = nlp.get_pipe("tok2vec")

2
spacy/tests/regression/test_issue1-1000.py
View File

@ -428,7 +428,7 @@ def test_issue999():
for _, offsets in TRAIN_DATA:
for start, end, label in offsets:
ner.add_label(label)
nlp.begin_training()
nlp.initialize()
for itn in range(20):
random.shuffle(TRAIN_DATA)
for raw_text, entity_offsets in TRAIN_DATA:

2
spacy/tests/regression/test_issue1501-2000.py
View File

@ -250,7 +250,7 @@ def test_issue1915():
ner = nlp.add_pipe("ner")
ner.add_label("answer")
with pytest.raises(ValueError):
nlp.begin_training(**cfg)
nlp.initialize(**cfg)
def test_issue1945():

2
spacy/tests/regression/test_issue2001-2500.py
View File

@ -30,7 +30,7 @@ def test_issue2179():
nlp = Italian()
ner = nlp.add_pipe("ner")
ner.add_label("CITIZENSHIP")
nlp.begin_training()
nlp.initialize()
nlp2 = Italian()
nlp2.add_pipe("ner")
assert len(nlp2.get_pipe("ner").labels) == 0

4
spacy/tests/regression/test_issue2501-3000.py
View File

@ -18,7 +18,7 @@ def test_issue2564():
nlp = Language()
tagger = nlp.add_pipe("tagger")
tagger.add_label("A")
nlp.begin_training()
nlp.initialize()
doc = nlp("hello world")
assert doc.has_annotation("TAG")
docs = nlp.pipe(["hello", "world"])
@ -149,7 +149,7 @@ def test_issue2800():
ner = nlp.add_pipe("ner")
for entity_type in list(entity_types):
ner.add_label(entity_type)
optimizer = nlp.begin_training()
optimizer = nlp.initialize()
for i in range(20):
losses = {}
random.shuffle(train_data)

8
spacy/tests/regression/test_issue3001-3500.py
View File

@ -59,7 +59,7 @@ def test_issue3012(en_vocab):
words = ["This", "is", "10", "%", "."]
tags = ["DT", "VBZ", "CD", "NN", "."]
pos = ["DET", "VERB", "NUM", "NOUN", "PUNCT"]
ents = [("PERCENT", 2, 4)]
ents = ["O", "O", "B-PERCENT", "I-PERCENT", "O"]
doc = Doc(en_vocab, words=words, tags=tags, pos=pos, ents=ents)
assert doc.has_annotation("TAG")
expected = ("10", "NUM", "CD", "PERCENT")
@ -92,7 +92,7 @@ def test_issue3209():
nlp = English()
ner = nlp.add_pipe("ner")
ner.add_label("ANIMAL")
nlp.begin_training()
nlp.initialize()
move_names = ["O", "B-ANIMAL", "I-ANIMAL", "L-ANIMAL", "U-ANIMAL"]
assert ner.move_names == move_names
nlp2 = English()
@ -195,7 +195,7 @@ def test_issue3345():
"update_with_oracle_cut_size": 100,
}
cfg = {"model": DEFAULT_NER_MODEL}
model = registry.make_from_config(cfg, validate=True)["model"]
model = registry.resolve(cfg, validate=True)["model"]
ner = EntityRecognizer(doc.vocab, model, **config)
# Add the OUT action. I wouldn't have thought this would be necessary...
ner.moves.add_action(5, "")
@ -239,7 +239,7 @@ def test_issue3456():
nlp = English()
tagger = nlp.add_pipe("tagger")
tagger.add_label("A")
nlp.begin_training()
nlp.initialize()
list(nlp.pipe(["hi", ""]))

27
spacy/tests/regression/test_issue3501-4000.py
View File

@ -223,15 +223,13 @@ def test_issue3611():
textcat.add_label(label)
# training the network
with nlp.select_pipes(enable="textcat"):
optimizer = nlp.begin_training()
optimizer = nlp.initialize()
for i in range(3):
losses = {}
batches = minibatch(train_data, size=compounding(4.0, 32.0, 1.001))
for batch in batches:
nlp.update(
examples=batch, sgd=optimizer, drop=0.1, losses=losses,
)
nlp.update(examples=batch, sgd=optimizer, drop=0.1, losses=losses)
def test_issue3625():
@ -264,13 +262,11 @@ def test_issue3830_no_subtok():
"min_action_freq": 30,
"update_with_oracle_cut_size": 100,
}
model = registry.make_from_config({"model": DEFAULT_PARSER_MODEL}, validate=True)[
"model"
]
model = registry.resolve({"model": DEFAULT_PARSER_MODEL}, validate=True)["model"]
parser = DependencyParser(Vocab(), model, **config)
parser.add_label("nsubj")
assert "subtok" not in parser.labels
parser.begin_training(lambda: [_parser_example(parser)])
parser.initialize(lambda: [_parser_example(parser)])
assert "subtok" not in parser.labels
@ -281,13 +277,11 @@ def test_issue3830_with_subtok():
"min_action_freq": 30,
"update_with_oracle_cut_size": 100,
}
model = registry.make_from_config({"model": DEFAULT_PARSER_MODEL}, validate=True)[
"model"
]
model = registry.resolve({"model": DEFAULT_PARSER_MODEL}, validate=True)["model"]
parser = DependencyParser(Vocab(), model, **config)
parser.add_label("nsubj")
assert "subtok" not in parser.labels
parser.begin_training(lambda: [_parser_example(parser)])
parser.initialize(lambda: [_parser_example(parser)])
assert "subtok" in parser.labels
@ -346,7 +340,7 @@ def test_issue3880():
nlp.add_pipe("parser").add_label("dep")
nlp.add_pipe("ner").add_label("PERSON")
nlp.add_pipe("tagger").add_label("NN")
nlp.begin_training()
nlp.initialize()
for doc in nlp.pipe(texts):
pass
@ -394,7 +388,7 @@ def test_issue3959():
def test_issue3962(en_vocab):
""" Ensure that as_doc does not result in out-of-bound access of tokens.
"""Ensure that as_doc does not result in out-of-bound access of tokens.
This is achieved by setting the head to itself if it would lie out of the span otherwise."""
# fmt: off
words = ["He", "jests", "at", "scars", ",", "that", "never", "felt", "a", "wound", "."]
@ -432,7 +426,7 @@ def test_issue3962(en_vocab):
def test_issue3962_long(en_vocab):
""" Ensure that as_doc does not result in out-of-bound access of tokens.
"""Ensure that as_doc does not result in out-of-bound access of tokens.
This is achieved by setting the head to itself if it would lie out of the span otherwise."""
# fmt: off
words = ["He", "jests", "at", "scars", ".", "They", "never", "felt", "a", "wound", "."]
@ -467,8 +461,7 @@ def test_issue3962_long(en_vocab):
def test_issue3972(en_vocab):
"""Test that the PhraseMatcher returns duplicates for duplicate match IDs.
"""
"""Test that the PhraseMatcher returns duplicates for duplicate match IDs."""
matcher = PhraseMatcher(en_vocab)
matcher.add("A", [Doc(en_vocab, words=["New", "York"])])
matcher.add("B", [Doc(en_vocab, words=["New", "York"])])

19
spacy/tests/regression/test_issue4001-4500.py
View File

@ -19,8 +19,7 @@ from ..util import make_tempdir
def test_issue4002(en_vocab):
"""Test that the PhraseMatcher can match on overwritten NORM attributes.
"""
"""Test that the PhraseMatcher can match on overwritten NORM attributes."""
matcher = PhraseMatcher(en_vocab, attr="NORM")
pattern1 = Doc(en_vocab, words=["c", "d"])
assert [t.norm_ for t in pattern1] == ["c", "d"]
@ -66,15 +65,13 @@ def test_issue4030():
textcat.add_label(label)
# training the network
with nlp.select_pipes(enable="textcat"):
optimizer = nlp.begin_training()
optimizer = nlp.initialize()
for i in range(3):
losses = {}
batches = minibatch(train_data, size=compounding(4.0, 32.0, 1.001))
for batch in batches:
nlp.update(
examples=batch, sgd=optimizer, drop=0.1, losses=losses,
)
nlp.update(examples=batch, sgd=optimizer, drop=0.1, losses=losses)
# processing of an empty doc should result in 0.0 for all categories
doc = nlp("")
assert doc.cats["offensive"] == 0.0
@ -87,7 +84,7 @@ def test_issue4042():
# add ner pipe
ner = nlp.add_pipe("ner")
ner.add_label("SOME_LABEL")
nlp.begin_training()
nlp.initialize()
# Add entity ruler
patterns = [
{"label": "MY_ORG", "pattern": "Apple"},
@ -118,7 +115,7 @@ def test_issue4042_bug2():
# add ner pipe
ner1 = nlp1.add_pipe("ner")
ner1.add_label("SOME_LABEL")
nlp1.begin_training()
nlp1.initialize()
# add a new label to the doc
doc1 = nlp1("What do you think about Apple ?")
assert len(ner1.labels) == 1
@ -244,7 +241,7 @@ def test_issue4267():
nlp = English()
ner = nlp.add_pipe("ner")
ner.add_label("PEOPLE")
nlp.begin_training()
nlp.initialize()
assert "ner" in nlp.pipe_names
# assert that we have correct IOB annotations
doc1 = nlp("hi")
@ -299,7 +296,7 @@ def test_issue4313():
config = {}
ner = nlp.create_pipe("ner", config=config)
ner.add_label("SOME_LABEL")
ner.begin_training(lambda: [])
ner.initialize(lambda: [])
# add a new label to the doc
doc = nlp("What do you think about Apple ?")
assert len(ner.labels) == 1
@ -327,7 +324,7 @@ def test_issue4348():
TRAIN_DATA = [example, example]
tagger = nlp.add_pipe("tagger")
tagger.add_label("A")
optimizer = nlp.begin_training()
optimizer = nlp.initialize()
for i in range(5):
losses = {}
batches = minibatch(TRAIN_DATA, size=compounding(4.0, 32.0, 1.001))

2
spacy/tests/regression/test_issue4501-5000.py
View File

@ -180,7 +180,7 @@ def test_issue4725_2():
vocab.set_vector("dog", data[1])
nlp = English(vocab=vocab)
nlp.add_pipe("ner")
nlp.begin_training()
nlp.initialize()
docs = ["Kurt is in London."] * 10
for _ in nlp.pipe(docs, batch_size=2, n_process=2):
pass

4
spacy/tests/regression/test_issue5230.py
View File

@ -64,7 +64,7 @@ def tagger():
# 1. no model leads to error in serialization,
# 2. the affected line is the one for model serialization
tagger.add_label("A")
nlp.begin_training()
nlp.initialize()
return tagger
@ -85,7 +85,7 @@ def entity_linker():
# need to add model for two reasons:
# 1. no model leads to error in serialization,
# 2. the affected line is the one for model serialization
nlp.begin_training()
nlp.initialize()
return entity_linker

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