mirror of https://github.com/explosion/spaCy.git
331 lines
13 KiB
Python
331 lines
13 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
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from pydantic import FilePath, DirectoryPath, root_validator
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from collections import defaultdict
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from thinc.api import Model, Optimizer
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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 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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# Config schema
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# We're not setting any defaults here (which is too messy) and are making all
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# fields required, so we can raise validation errors for missing values. To
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# provide a default, we include a separate .cfg file with all values and
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# check that against this schema in the test suite to make sure it's always
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# up to date.
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class ConfigSchemaTraining(BaseModel):
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# fmt: off
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gold_preproc: StrictBool = Field(..., title="Whether to train on gold-standard sentences and tokens")
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max_length: StrictInt = Field(..., title="Maximum length of examples (longer examples are divided into sentences if possible)")
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limit: StrictInt = Field(..., title="Number of examples to use (0 for all)")
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orth_variant_level: StrictFloat = Field(..., title="Orth variants for data augmentation")
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dropout: StrictFloat = Field(..., title="Dropout rate")
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patience: StrictInt = Field(..., title="How many steps to continue without improvement in evaluation score")
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max_epochs: StrictInt = Field(..., title="Maximum number of epochs to train for")
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max_steps: StrictInt = Field(..., title="Maximum number of update steps to train for")
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eval_frequency: StrictInt = Field(..., title="How often to evaluate during training (steps)")
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seed: Optional[StrictInt] = Field(..., title="Random seed")
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accumulate_gradient: StrictInt = Field(..., title="Whether to divide the batch up into substeps")
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use_pytorch_for_gpu_memory: StrictBool = Field(..., title="Allocate memory via PyTorch")
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use_gpu: StrictInt = Field(..., title="GPU ID or -1 for CPU")
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scores: List[StrictStr] = Field(..., title="Score types to be printed in overview")
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score_weights: Dict[StrictStr, Union[StrictFloat, StrictInt]] = Field(..., title="Weights of each score type for selecting final model")
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init_tok2vec: Optional[FilePath] = Field(..., title="Path to pretrained tok2vec weights")
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discard_oversize: StrictBool = Field(..., title="Whether to skip examples longer than batch size")
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omit_extra_lookups: StrictBool = Field(..., title="Don't include extra lookups in model")
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batch_by: StrictStr = Field(..., title="Batch examples by type")
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raw_text: Optional[FilePath] = Field(..., title="Raw text")
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tag_map: Optional[FilePath] = Field(..., title="Path to JSON-formatted tag map")
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batch_size: Union[Sequence[int], int] = Field(..., title="The batch size or batch size schedule")
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optimizer: Optimizer = Field(..., title="The optimizer to use")
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# fmt: on
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class Config:
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extra = "forbid"
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arbitrary_types_allowed = True
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class ConfigSchemaNlpComponent(BaseModel):
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factory: StrictStr = Field(..., title="Component factory name")
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model: Model = Field(..., title="Component model")
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# TODO: add config schema / types for components so we can fill and validate
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# component options like learn_tokens, min_action_freq etc.
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class Config:
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extra = "allow"
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arbitrary_types_allowed = True
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class ConfigSchemaPipeline(BaseModel):
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__root__: Dict[str, ConfigSchemaNlpComponent]
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class Config:
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extra = "allow"
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class ConfigSchemaNlp(BaseModel):
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lang: StrictStr = Field(..., title="The base language to use")
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base_model: Optional[StrictStr] = Field(..., title="The base model to use")
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vectors: Optional[DirectoryPath] = Field(..., title="Path to vectors")
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pipeline: Optional[ConfigSchemaPipeline]
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class Config:
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extra = "forbid"
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arbitrary_types_allowed = True
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class ConfigSchemaPretrain(BaseModel):
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# fmt: off
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max_epochs: StrictInt = Field(..., title="Maximum number of epochs to train for")
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min_length: StrictInt = Field(..., title="Minimum length of examples")
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max_length: StrictInt = Field(..., title="Maximum length of examples")
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dropout: StrictFloat = Field(..., title="Dropout rate")
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n_save_every: Optional[StrictInt] = Field(..., title="Saving frequency")
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batch_size: Union[Sequence[int], int] = Field(..., title="The batch size or batch size schedule")
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seed: Optional[StrictInt] = Field(..., title="Random seed")
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use_pytorch_for_gpu_memory: StrictBool = Field(..., title="Allocate memory via PyTorch")
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tok2vec_model: StrictStr = Field(..., title="tok2vec model in config, e.g. nlp.pipeline.tok2vec.model")
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optimizer: Optimizer = Field(..., title="The optimizer to use")
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# TODO: use a more detailed schema for this?
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objective: Dict[str, Any] = Field(..., title="Pretraining objective")
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# fmt: on
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class Config:
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extra = "forbid"
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arbitrary_types_allowed = True
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class ConfigSchema(BaseModel):
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training: ConfigSchemaTraining
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nlp: ConfigSchemaNlp
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pretraining: Optional[ConfigSchemaPretrain]
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@root_validator
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def validate_config(cls, values):
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"""Perform additional validation for settings with dependencies."""
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pt = values.get("pretraining")
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if pt and pt.objective.get("type") == "vectors" and not values["nlp"].vectors:
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err = "Need nlp.vectors if pretraining.objective.type is vectors"
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raise ValueError(err)
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return values
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class Config:
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extra = "allow"
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arbitrary_types_allowed = True
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# Project config Schema
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class ProjectConfigAsset(BaseModel):
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# fmt: off
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dest: StrictStr = Field(..., title="Destination of downloaded asset")
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url: Optional[StrictStr] = Field(None, title="URL of asset")
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checksum: str = Field(None, title="MD5 hash of file", regex=r"([a-fA-F\d]{32})")
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# fmt: on
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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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deps: List[StrictStr] = Field([], title="File dependencies required by this command")
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outputs: List[StrictStr] = Field([], title="Outputs produced by this command")
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outputs_no_cache: List[StrictStr] = Field([], title="Outputs not tracked by DVC (DVC only)")
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no_skip: bool = Field(False, title="Never skip this command, even if nothing changed")
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# fmt: on
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class Config:
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title = "A single named command specified in a project config"
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extra = "forbid"
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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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workflows: Dict[StrictStr, List[StrictStr]] = Field({}, title="Named workflows, mapped to list of project commands to run 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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