spaCy/spacy/schemas.py

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from typing import Dict, List, Union, Optional, Sequence
from enum import Enum
from pydantic import BaseModel, Field, ValidationError, validator
from pydantic import StrictStr, StrictInt, StrictFloat, StrictBool, FilePath
from collections import defaultdict
from thinc.api import Model
from .attrs import NAMES
def validate(schema, obj):
"""Validate data against a given pydantic schema.
obj (dict): JSON-serializable data to validate.
schema (pydantic.BaseModel): The schema to validate against.
RETURNS (list): 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):
# 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, whole=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="==")
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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, whole=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
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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
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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)
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: Optional[StrictStr] = Field(None, title="Compatible spaCy version identifier")
parent_package: Optional[StrictStr] = Field("spacy", title="Name of parent spaCy package, e.g. spacy or spacy-nightly")
pipeline: Optional[List[StrictStr]] = Field([], title="Names of pipeline components")
description: Optional[StrictStr] = Field(None, title="Model description")
license: Optional[StrictStr] = Field(None, title="Model license")
author: Optional[StrictStr] = Field(None, title="Model author name")
email: Optional[StrictStr] = Field(None, title="Model author email")
url: Optional[StrictStr] = Field(None, title="Model author URL")
sources: Optional[Union[List[StrictStr], Dict[str, str]]] = Field(None, title="Training data sources")
vectors: Optional[Dict[str, int]] = Field(None, title="Included word vectors")
accuracy: Optional[Dict[str, Union[float, int]]] = Field(None, title="Accuracy numbers")
speed: Optional[Dict[str, Union[float, int]]] = Field(None, title="Speed evaluation numbers")
# fmt: on
# JSON training format
class PipelineComponent(BaseModel):
factory: str
model: Model
class Config:
arbitrary_types_allowed = True
class ConfigSchema(BaseModel):
optimizer: Optional["Optimizer"]
class training(BaseModel):
patience: int = 10
eval_frequency: int = 100
dropout: float = 0.2
init_tok2vec: Optional[FilePath] = None
max_epochs: int = 100
orth_variant_level: float = 0.0
gold_preproc: bool = False
max_length: int = 0
use_gpu: int = 0
scores: List[str] = ["ents_p", "ents_r", "ents_f"]
score_weights: Dict[str, Union[int, float]] = {"ents_f": 1.0}
limit: int = 0
batch_size: Union[Sequence[int], int]
class nlp(BaseModel):
lang: str
vectors: Optional[str]
pipeline: Optional[Dict[str, PipelineComponent]]
class Config:
extra = "allow"
class TrainingSchema(BaseModel):
# TODO: write
class Config:
title = "Schema for training data in spaCy's JSON format"
extra = "forbid"
# Project config Schema
class ProjectConfigAsset(BaseModel):
dest: StrictStr = Field(..., title="Destination of downloaded asset")
url: StrictStr = Field(..., title="URL of asset")
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")
dvc_deps: List[StrictStr] = Field([], title="Data Version Control dependencies")
dvc_outputs: List[StrictStr] = Field([], title="Data Version Control outputs")
dvc_outputs_no_cache: List[StrictStr] = Field([], title="Data Version Control outputs (no cache)")
# fmt: on
class ProjectConfigSchema(BaseModel):
# fmt: off
variables: Dict[StrictStr, Union[str, int, float, bool]] = Field({}, title="Optional variables to substitute in commands")
assets: List[ProjectConfigAsset] = Field([], title="Data assets")
run: List[StrictStr] = Field([], title="Names of project commands to execute, in order")
commands: List[ProjectConfigCommand] = Field([], title="Project command shortucts")
# fmt: on
class Config:
title = "Schema for project configuration file"