mirror of
https://github.com/explosion/spaCy.git
synced 2024-09-21 19:39:13 +03:00
79d460e3a2
* quick test as part of train script * train_logger in config, default ConsoleLogger in loggers catalogue * entitiy typo * add wandb_logger * cleanup * Update spacy/cli/train_logger.py Co-authored-by: Ines Montani <ines@ines.io> * move loggers to gold.loggers Co-authored-by: Ines Montani <ines@ines.io>
359 lines
14 KiB
Python
359 lines
14 KiB
Python
from typing import Dict, List, Union, Optional, Sequence, 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 StrictStr, StrictInt, StrictFloat, StrictBool
|
|
from pydantic import root_validator
|
|
from collections import defaultdict
|
|
from thinc.api import Optimizer
|
|
|
|
from .attrs import NAMES
|
|
|
|
if TYPE_CHECKING:
|
|
# This lets us add type hints for mypy etc. without causing circular imports
|
|
from .language import Language # noqa: F401
|
|
from .gold import Example # noqa: F401
|
|
|
|
|
|
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]]
|
|
|
|
|
|
def validate(schema: Type[BaseModel], obj: Dict[str, Any]) -> List[str]:
|
|
"""Validate data against a given pydantic schema.
|
|
|
|
obj (Dict[str, Any]): JSON-serializable data to validate.
|
|
schema (pydantic.BaseModel): The schema to validate against.
|
|
RETURNS (List[str]): A list of error messages, if available.
|
|
"""
|
|
try:
|
|
schema(**obj)
|
|
return []
|
|
except ValidationError as e:
|
|
errors = e.errors()
|
|
data = defaultdict(list)
|
|
for error in errors:
|
|
err_loc = " -> ".join([str(p) for p in error.get("loc", [])])
|
|
data[err_loc].append(error.get("msg"))
|
|
return [f"[{loc}] {', '.join(msg)}" for loc, msg in data.items()]
|
|
|
|
|
|
# Matcher token patterns
|
|
|
|
|
|
def validate_token_pattern(obj: list) -> List[str]:
|
|
# Try to convert non-string keys (e.g. {ORTH: "foo"} -> {"ORTH": "foo"})
|
|
get_key = lambda k: NAMES[k] if isinstance(k, int) and k < len(NAMES) else k
|
|
if isinstance(obj, list):
|
|
converted = []
|
|
for pattern in obj:
|
|
if isinstance(pattern, dict):
|
|
pattern = {get_key(k): v for k, v in pattern.items()}
|
|
converted.append(pattern)
|
|
obj = converted
|
|
return validate(TokenPatternSchema, {"pattern": obj})
|
|
|
|
|
|
class TokenPatternString(BaseModel):
|
|
REGEX: Optional[StrictStr]
|
|
IN: Optional[List[StrictStr]]
|
|
NOT_IN: Optional[List[StrictStr]]
|
|
|
|
class Config:
|
|
extra = "forbid"
|
|
|
|
@validator("*", pre=True, each_item=True, allow_reuse=True)
|
|
def raise_for_none(cls, v):
|
|
if v is None:
|
|
raise ValueError("None / null is not allowed")
|
|
return v
|
|
|
|
|
|
class TokenPatternNumber(BaseModel):
|
|
REGEX: Optional[StrictStr] = None
|
|
IN: Optional[List[StrictInt]] = None
|
|
NOT_IN: Optional[List[StrictInt]] = None
|
|
EQ: Union[StrictInt, StrictFloat] = Field(None, alias="==")
|
|
NEQ: Union[StrictInt, StrictFloat] = Field(None, alias="!=")
|
|
GEQ: Union[StrictInt, StrictFloat] = Field(None, alias=">=")
|
|
LEQ: Union[StrictInt, StrictFloat] = Field(None, alias="<=")
|
|
GT: Union[StrictInt, StrictFloat] = Field(None, alias=">")
|
|
LT: Union[StrictInt, StrictFloat] = Field(None, alias="<")
|
|
|
|
class Config:
|
|
extra = "forbid"
|
|
|
|
@validator("*", pre=True, each_item=True, allow_reuse=True)
|
|
def raise_for_none(cls, v):
|
|
if v is None:
|
|
raise ValueError("None / null is not allowed")
|
|
return v
|
|
|
|
|
|
class TokenPatternOperator(str, Enum):
|
|
plus: StrictStr = "+"
|
|
start: StrictStr = "*"
|
|
question: StrictStr = "?"
|
|
exclamation: StrictStr = "!"
|
|
|
|
|
|
StringValue = Union[TokenPatternString, StrictStr]
|
|
NumberValue = Union[TokenPatternNumber, StrictInt, StrictFloat]
|
|
UnderscoreValue = Union[
|
|
TokenPatternString, TokenPatternNumber, str, int, float, list, bool,
|
|
]
|
|
|
|
|
|
class TokenPattern(BaseModel):
|
|
orth: Optional[StringValue] = None
|
|
text: Optional[StringValue] = None
|
|
lower: Optional[StringValue] = None
|
|
pos: Optional[StringValue] = None
|
|
tag: Optional[StringValue] = None
|
|
dep: Optional[StringValue] = None
|
|
lemma: Optional[StringValue] = None
|
|
shape: Optional[StringValue] = None
|
|
ent_type: Optional[StringValue] = None
|
|
norm: Optional[StringValue] = None
|
|
length: Optional[NumberValue] = None
|
|
spacy: Optional[StrictBool] = None
|
|
is_alpha: Optional[StrictBool] = None
|
|
is_ascii: Optional[StrictBool] = None
|
|
is_digit: Optional[StrictBool] = None
|
|
is_lower: Optional[StrictBool] = None
|
|
is_upper: Optional[StrictBool] = None
|
|
is_title: Optional[StrictBool] = None
|
|
is_punct: Optional[StrictBool] = None
|
|
is_space: Optional[StrictBool] = None
|
|
is_bracket: Optional[StrictBool] = None
|
|
is_quote: Optional[StrictBool] = None
|
|
is_left_punct: Optional[StrictBool] = None
|
|
is_right_punct: Optional[StrictBool] = None
|
|
is_currency: Optional[StrictBool] = None
|
|
is_stop: Optional[StrictBool] = None
|
|
is_sent_start: Optional[StrictBool] = None
|
|
sent_start: Optional[StrictBool] = None
|
|
like_num: Optional[StrictBool] = None
|
|
like_url: Optional[StrictBool] = None
|
|
like_email: Optional[StrictBool] = None
|
|
op: Optional[TokenPatternOperator] = None
|
|
underscore: Optional[Dict[StrictStr, UnderscoreValue]] = Field(None, alias="_")
|
|
|
|
class Config:
|
|
extra = "forbid"
|
|
allow_population_by_field_name = True
|
|
alias_generator = lambda value: value.upper()
|
|
|
|
@validator("*", pre=True, allow_reuse=True)
|
|
def raise_for_none(cls, v):
|
|
if v is None:
|
|
raise ValueError("None / null is not allowed")
|
|
return v
|
|
|
|
|
|
class TokenPatternSchema(BaseModel):
|
|
pattern: List[TokenPattern] = Field(..., minItems=1)
|
|
|
|
class Config:
|
|
extra = "forbid"
|
|
|
|
|
|
# Model meta
|
|
|
|
|
|
class ModelMetaSchema(BaseModel):
|
|
# fmt: off
|
|
lang: StrictStr = Field(..., title="Two-letter language code, e.g. 'en'")
|
|
name: StrictStr = Field(..., title="Model name")
|
|
version: StrictStr = Field(..., title="Model version")
|
|
spacy_version: StrictStr = Field("", title="Compatible spaCy version identifier")
|
|
parent_package: StrictStr = Field("spacy", title="Name of parent spaCy package, e.g. spacy or spacy-nightly")
|
|
pipeline: List[StrictStr] = Field([], title="Names of pipeline components")
|
|
description: StrictStr = Field("", title="Model description")
|
|
license: StrictStr = Field("", title="Model license")
|
|
author: StrictStr = Field("", title="Model author name")
|
|
email: StrictStr = Field("", title="Model author email")
|
|
url: StrictStr = Field("", title="Model author URL")
|
|
sources: Optional[Union[List[StrictStr], List[Dict[str, str]]]] = Field(None, title="Training data sources")
|
|
vectors: Dict[str, Any] = Field({}, title="Included word vectors")
|
|
labels: Dict[str, Dict[str, List[str]]] = Field({}, title="Component labels, keyed by component name")
|
|
accuracy: Dict[str, Union[float, Dict[str, float]]] = Field({}, title="Accuracy numbers")
|
|
speed: Dict[str, Union[float, int]] = Field({}, title="Speed evaluation numbers")
|
|
spacy_git_version: StrictStr = Field("", title="Commit of spaCy version used")
|
|
# fmt: on
|
|
|
|
|
|
# Config schema
|
|
# We're not setting any defaults here (which is too messy) and are making all
|
|
# fields required, so we can raise validation errors for missing values. To
|
|
# provide a default, we include a separate .cfg file with all values and
|
|
# check that against this schema in the test suite to make sure it's always
|
|
# up to date.
|
|
|
|
|
|
class ConfigSchemaTraining(BaseModel):
|
|
# fmt: off
|
|
vectors: Optional[StrictStr] = Field(..., title="Path to vectors")
|
|
train_corpus: Reader = Field(..., title="Reader for the training data")
|
|
dev_corpus: Reader = Field(..., title="Reader for the dev data")
|
|
batcher: Batcher = Field(..., title="Batcher for the training data")
|
|
dropout: StrictFloat = Field(..., title="Dropout rate")
|
|
patience: StrictInt = Field(..., title="How many steps to continue without improvement in evaluation score")
|
|
max_epochs: StrictInt = Field(..., title="Maximum number of epochs to train for")
|
|
max_steps: StrictInt = Field(..., title="Maximum number of update steps to train for")
|
|
eval_frequency: StrictInt = Field(..., title="How often to evaluate during training (steps)")
|
|
seed: Optional[StrictInt] = Field(..., title="Random seed")
|
|
accumulate_gradient: StrictInt = Field(..., title="Whether to divide the batch up into substeps")
|
|
score_weights: Dict[StrictStr, 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")
|
|
# fmt: on
|
|
|
|
class Config:
|
|
extra = "forbid"
|
|
arbitrary_types_allowed = True
|
|
|
|
|
|
class ConfigSchemaNlp(BaseModel):
|
|
# fmt: off
|
|
lang: StrictStr = Field(..., title="The base language to use")
|
|
pipeline: List[StrictStr] = Field(..., title="The pipeline component names in order")
|
|
tokenizer: Callable = Field(..., title="The tokenizer to use")
|
|
load_vocab_data: StrictBool = Field(..., title="Whether to load additional vocab data from spacy-lookups-data")
|
|
before_creation: Optional[Callable[[Type["Language"]], Type["Language"]]] = Field(..., title="Optional callback to modify Language class before initialization")
|
|
after_creation: Optional[Callable[["Language"], "Language"]] = Field(..., title="Optional callback to modify nlp object after creation and before the pipeline is constructed")
|
|
after_pipeline_creation: Optional[Callable[["Language"], "Language"]] = Field(..., title="Optional callback to modify nlp object after the pipeline is constructed")
|
|
# fmt: on
|
|
|
|
class Config:
|
|
extra = "forbid"
|
|
arbitrary_types_allowed = True
|
|
|
|
|
|
class ConfigSchemaPretrainEmpty(BaseModel):
|
|
class Config:
|
|
extra = "forbid"
|
|
|
|
|
|
class ConfigSchemaPretrain(BaseModel):
|
|
# fmt: off
|
|
max_epochs: StrictInt = Field(..., title="Maximum number of epochs to train for")
|
|
min_length: StrictInt = Field(..., title="Minimum length of examples")
|
|
max_length: StrictInt = Field(..., title="Maximum length of examples")
|
|
dropout: StrictFloat = Field(..., title="Dropout rate")
|
|
n_save_every: Optional[StrictInt] = Field(..., title="Saving frequency")
|
|
batch_size: Union[Sequence[int], int] = Field(..., title="The batch size or batch size schedule")
|
|
seed: Optional[StrictInt] = Field(..., title="Random seed")
|
|
use_pytorch_for_gpu_memory: StrictBool = Field(..., title="Allocate memory via PyTorch")
|
|
tok2vec_model: StrictStr = Field(..., title="tok2vec model in config, e.g. components.tok2vec.model")
|
|
optimizer: Optimizer = Field(..., title="The optimizer to use")
|
|
# TODO: use a more detailed schema for this?
|
|
objective: Dict[str, Any] = Field(..., title="Pretraining objective")
|
|
# 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]]
|
|
|
|
@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
|
|
|
|
class Config:
|
|
extra = "allow"
|
|
arbitrary_types_allowed = True
|
|
|
|
|
|
# Project config Schema
|
|
|
|
|
|
class ProjectConfigAssetGitItem(BaseModel):
|
|
# fmt: off
|
|
repo: StrictStr = Field(..., title="URL of Git repo to download from")
|
|
path: StrictStr = Field(..., title="File path or sub-directory to download (used for sparse checkout)")
|
|
branch: StrictStr = Field("master", title="Branch to clone from")
|
|
# fmt: on
|
|
|
|
|
|
class ProjectConfigAssetURL(BaseModel):
|
|
# fmt: off
|
|
dest: StrictStr = Field(..., title="Destination of downloaded asset")
|
|
url: Optional[StrictStr] = Field(None, title="URL of asset")
|
|
checksum: str = Field(None, title="MD5 hash of file", regex=r"([a-fA-F\d]{32})")
|
|
description: StrictStr = Field("", title="Description of asset")
|
|
# fmt: on
|
|
|
|
|
|
class ProjectConfigAssetGit(BaseModel):
|
|
# fmt: off
|
|
git: ProjectConfigAssetGitItem = Field(..., title="Git repo information")
|
|
checksum: str = Field(None, title="MD5 hash of file", regex=r"([a-fA-F\d]{32})")
|
|
description: Optional[StrictStr] = Field(None, title="Description of asset")
|
|
# fmt: on
|
|
|
|
|
|
class ProjectConfigCommand(BaseModel):
|
|
# fmt: off
|
|
name: StrictStr = Field(..., title="Name of command")
|
|
help: Optional[StrictStr] = Field(None, title="Command description")
|
|
script: List[StrictStr] = Field([], title="List of CLI commands to run, in order")
|
|
deps: List[StrictStr] = Field([], title="File dependencies required by this command")
|
|
outputs: List[StrictStr] = Field([], title="Outputs produced by this command")
|
|
outputs_no_cache: List[StrictStr] = Field([], title="Outputs not tracked by DVC (DVC only)")
|
|
no_skip: bool = Field(False, title="Never skip this command, even if nothing changed")
|
|
# fmt: on
|
|
|
|
class Config:
|
|
title = "A single named command specified in a project config"
|
|
extra = "forbid"
|
|
|
|
|
|
class ProjectConfigSchema(BaseModel):
|
|
# fmt: off
|
|
vars: Dict[StrictStr, Any] = Field({}, title="Optional variables to substitute in commands")
|
|
assets: List[Union[ProjectConfigAssetURL, ProjectConfigAssetGit]] = Field([], title="Data assets")
|
|
workflows: Dict[StrictStr, List[StrictStr]] = Field({}, title="Named workflows, mapped to list of project commands to run in order")
|
|
commands: List[ProjectConfigCommand] = Field([], title="Project command shortucts")
|
|
title: Optional[str] = Field(None, title="Project title")
|
|
# fmt: on
|
|
|
|
class Config:
|
|
title = "Schema for project configuration file"
|
|
|
|
|
|
# Recommendations for init config workflows
|
|
|
|
|
|
class RecommendationTrfItem(BaseModel):
|
|
name: str
|
|
size_factor: int
|
|
|
|
|
|
class RecommendationTrf(BaseModel):
|
|
efficiency: RecommendationTrfItem
|
|
accuracy: RecommendationTrfItem
|
|
|
|
|
|
class RecommendationSchema(BaseModel):
|
|
word_vectors: Optional[str] = None
|
|
transformer: Optional[RecommendationTrf] = None
|
|
has_letters: bool = True
|