mirror of https://github.com/explosion/spaCy.git
248 lines
8.4 KiB
Python
248 lines
8.4 KiB
Python
from typing import Dict, List, Union, Optional, Sequence, Any
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from enum import Enum
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from pydantic import BaseModel, Field, ValidationError, validator
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from pydantic import StrictStr, StrictInt, StrictFloat, StrictBool, FilePath
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from collections import defaultdict
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from thinc.api import Model
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from .attrs import NAMES
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def validate(schema, obj):
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"""Validate data against a given pydantic schema.
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obj (dict): JSON-serializable data to validate.
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schema (pydantic.BaseModel): The schema to validate against.
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RETURNS (list): A list of error messages, if available.
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"""
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try:
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schema(**obj)
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return []
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except ValidationError as e:
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errors = e.errors()
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data = defaultdict(list)
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for error in errors:
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err_loc = " -> ".join([str(p) for p in error.get("loc", [])])
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data[err_loc].append(error.get("msg"))
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return [f"[{loc}] {', '.join(msg)}" for loc, msg in data.items()]
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# Matcher token patterns
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def validate_token_pattern(obj):
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# Try to convert non-string keys (e.g. {ORTH: "foo"} -> {"ORTH": "foo"})
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get_key = lambda k: NAMES[k] if isinstance(k, int) and k < len(NAMES) else k
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if isinstance(obj, list):
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converted = []
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for pattern in obj:
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if isinstance(pattern, dict):
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pattern = {get_key(k): v for k, v in pattern.items()}
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converted.append(pattern)
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obj = converted
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return validate(TokenPatternSchema, {"pattern": obj})
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class TokenPatternString(BaseModel):
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REGEX: Optional[StrictStr]
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IN: Optional[List[StrictStr]]
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NOT_IN: Optional[List[StrictStr]]
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class Config:
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extra = "forbid"
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@validator("*", pre=True, whole=True)
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def raise_for_none(cls, v):
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if v is None:
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raise ValueError("None / null is not allowed")
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return v
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class TokenPatternNumber(BaseModel):
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REGEX: Optional[StrictStr] = None
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IN: Optional[List[StrictInt]] = None
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NOT_IN: Optional[List[StrictInt]] = None
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EQ: Union[StrictInt, StrictFloat] = Field(None, alias="==")
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NEQ: Union[StrictInt, StrictFloat] = Field(None, alias="!=")
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GEQ: Union[StrictInt, StrictFloat] = Field(None, alias=">=")
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LEQ: Union[StrictInt, StrictFloat] = Field(None, alias="<=")
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GT: Union[StrictInt, StrictFloat] = Field(None, alias=">")
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LT: Union[StrictInt, StrictFloat] = Field(None, alias="<")
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class Config:
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extra = "forbid"
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@validator("*", pre=True, whole=True)
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def raise_for_none(cls, v):
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if v is None:
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raise ValueError("None / null is not allowed")
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return v
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class TokenPatternOperator(str, Enum):
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plus: StrictStr = "+"
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start: StrictStr = "*"
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question: StrictStr = "?"
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exclamation: StrictStr = "!"
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StringValue = Union[TokenPatternString, StrictStr]
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NumberValue = Union[TokenPatternNumber, StrictInt, StrictFloat]
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UnderscoreValue = Union[
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TokenPatternString, TokenPatternNumber, str, int, float, list, bool,
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]
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class TokenPattern(BaseModel):
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orth: Optional[StringValue] = None
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text: Optional[StringValue] = None
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lower: Optional[StringValue] = None
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pos: Optional[StringValue] = None
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tag: Optional[StringValue] = None
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dep: Optional[StringValue] = None
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lemma: Optional[StringValue] = None
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shape: Optional[StringValue] = None
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ent_type: Optional[StringValue] = None
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norm: Optional[StringValue] = None
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length: Optional[NumberValue] = None
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spacy: Optional[StrictBool] = None
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is_alpha: Optional[StrictBool] = None
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is_ascii: Optional[StrictBool] = None
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is_digit: Optional[StrictBool] = None
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is_lower: Optional[StrictBool] = None
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is_upper: Optional[StrictBool] = None
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is_title: Optional[StrictBool] = None
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is_punct: Optional[StrictBool] = None
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is_space: Optional[StrictBool] = None
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is_bracket: Optional[StrictBool] = None
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is_quote: Optional[StrictBool] = None
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is_left_punct: Optional[StrictBool] = None
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is_right_punct: Optional[StrictBool] = None
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is_currency: Optional[StrictBool] = None
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is_stop: Optional[StrictBool] = None
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is_sent_start: Optional[StrictBool] = None
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sent_start: Optional[StrictBool] = None
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like_num: Optional[StrictBool] = None
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like_url: Optional[StrictBool] = None
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like_email: Optional[StrictBool] = None
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op: Optional[TokenPatternOperator] = None
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underscore: Optional[Dict[StrictStr, UnderscoreValue]] = Field(None, alias="_")
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class Config:
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extra = "forbid"
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allow_population_by_field_name = True
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alias_generator = lambda value: value.upper()
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@validator("*", pre=True)
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def raise_for_none(cls, v):
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if v is None:
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raise ValueError("None / null is not allowed")
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return v
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class TokenPatternSchema(BaseModel):
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pattern: List[TokenPattern] = Field(..., minItems=1)
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class Config:
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extra = "forbid"
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# Model meta
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class ModelMetaSchema(BaseModel):
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# fmt: off
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lang: StrictStr = Field(..., title="Two-letter language code, e.g. 'en'")
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name: StrictStr = Field(..., title="Model name")
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version: StrictStr = Field(..., title="Model version")
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spacy_version: Optional[StrictStr] = Field(None, title="Compatible spaCy version identifier")
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parent_package: Optional[StrictStr] = Field("spacy", title="Name of parent spaCy package, e.g. spacy or spacy-nightly")
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pipeline: Optional[List[StrictStr]] = Field([], title="Names of pipeline components")
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description: Optional[StrictStr] = Field(None, title="Model description")
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license: Optional[StrictStr] = Field(None, title="Model license")
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author: Optional[StrictStr] = Field(None, title="Model author name")
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email: Optional[StrictStr] = Field(None, title="Model author email")
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url: Optional[StrictStr] = Field(None, title="Model author URL")
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sources: Optional[Union[List[StrictStr], Dict[str, str]]] = Field(None, title="Training data sources")
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vectors: Optional[Dict[str, Any]] = Field(None, title="Included word vectors")
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accuracy: Optional[Dict[str, Union[float, int]]] = Field(None, title="Accuracy numbers")
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speed: Optional[Dict[str, Union[float, int]]] = Field(None, title="Speed evaluation numbers")
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# fmt: on
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# JSON training format
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class PipelineComponent(BaseModel):
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factory: str
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model: Model
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class Config:
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arbitrary_types_allowed = True
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class ConfigSchema(BaseModel):
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optimizer: Optional["Optimizer"]
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class training(BaseModel):
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patience: int = 10
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eval_frequency: int = 100
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dropout: float = 0.2
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init_tok2vec: Optional[FilePath] = None
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max_epochs: int = 100
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orth_variant_level: float = 0.0
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gold_preproc: bool = False
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max_length: int = 0
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use_gpu: int = 0
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scores: List[str] = ["ents_p", "ents_r", "ents_f"]
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score_weights: Dict[str, Union[int, float]] = {"ents_f": 1.0}
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limit: int = 0
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batch_size: Union[Sequence[int], int]
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class nlp(BaseModel):
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lang: str
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vectors: Optional[str]
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pipeline: Optional[Dict[str, PipelineComponent]]
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class Config:
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extra = "allow"
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class TrainingSchema(BaseModel):
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# TODO: write
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class Config:
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title = "Schema for training data in spaCy's JSON format"
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extra = "forbid"
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# Project config Schema
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class ProjectConfigAsset(BaseModel):
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dest: StrictStr = Field(..., title="Destination of downloaded asset")
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url: StrictStr = Field(..., title="URL of asset")
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class ProjectConfigCommand(BaseModel):
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# fmt: off
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name: StrictStr = Field(..., title="Name of command")
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help: Optional[StrictStr] = Field(None, title="Command description")
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script: List[StrictStr] = Field([], title="List of CLI commands to run, in order")
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dvc_deps: List[StrictStr] = Field([], title="Data Version Control dependencies")
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dvc_outputs: List[StrictStr] = Field([], title="Data Version Control outputs")
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dvc_outputs_no_cache: List[StrictStr] = Field([], title="Data Version Control outputs (no cache)")
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# fmt: on
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class ProjectConfigSchema(BaseModel):
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# fmt: off
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variables: Dict[StrictStr, Union[str, int, float, bool]] = Field({}, title="Optional variables to substitute in commands")
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assets: List[ProjectConfigAsset] = Field([], title="Data assets")
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run: List[StrictStr] = Field([], title="Names of project commands to execute, in order")
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commands: List[ProjectConfigCommand] = Field([], title="Project command shortucts")
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# fmt: on
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class Config:
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title = "Schema for project configuration file"
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