mirror of
https://github.com/explosion/spaCy.git
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a4b32b9552
* Handle missing reference values in scorer Handle missing values in reference doc during scoring where it is possible to detect an unset state for the attribute. If no reference docs contain annotation, `None` is returned instead of a score. `spacy evaluate` displays `-` for missing scores and the missing scores are saved as `None`/`null` in the metrics. Attributes without unset states: * `token.head`: relies on `token.dep` to recognize unset values * `doc.cats`: unable to handle missing annotation Additional changes: * add optional `has_annotation` check to `score_scans` to replace `doc.sents` hack * update `score_token_attr_per_feat` to handle missing and empty morph representations * fix bug in `Doc.has_annotation` for normalization of `IS_SENT_START` vs. `SENT_START` * Fix import * Update return types
335 lines
13 KiB
Python
335 lines
13 KiB
Python
from typing import List, Dict, Union, Iterable, Any, Optional, Callable
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from typing import Tuple
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import srsly
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from pathlib import Path
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from .pipe import Pipe
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from ..errors import Errors
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from ..training import validate_examples, Example
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from ..language import Language
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from ..matcher import Matcher
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from ..scorer import Scorer
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from ..symbols import IDS, TAG, POS, MORPH, LEMMA
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from ..tokens import Doc, Span
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from ..tokens._retokenize import normalize_token_attrs, set_token_attrs
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from ..vocab import Vocab
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from ..util import SimpleFrozenList
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from .. import util
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MatcherPatternType = List[Dict[Union[int, str], Any]]
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AttributeRulerPatternType = Dict[str, Union[MatcherPatternType, Dict, int]]
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TagMapType = Dict[str, Dict[Union[int, str], Union[int, str]]]
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MorphRulesType = Dict[str, Dict[str, Dict[Union[int, str], Union[int, str]]]]
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@Language.factory("attribute_ruler", default_config={"validate": False})
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def make_attribute_ruler(nlp: Language, name: str, validate: bool):
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return AttributeRuler(nlp.vocab, name, validate=validate)
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class AttributeRuler(Pipe):
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"""Set token-level attributes for tokens matched by Matcher patterns.
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Additionally supports importing patterns from tag maps and morph rules.
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DOCS: https://nightly.spacy.io/api/attributeruler
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"""
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def __init__(
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self, vocab: Vocab, name: str = "attribute_ruler", *, validate: bool = False
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) -> None:
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"""Create the AttributeRuler. After creation, you can add patterns
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with the `.initialize()` or `.add_patterns()` methods, or load patterns
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with `.from_bytes()` or `.from_disk()`. Loading patterns will remove
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any patterns you've added previously.
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vocab (Vocab): The vocab.
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name (str): The pipe name. Defaults to "attribute_ruler".
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RETURNS (AttributeRuler): The AttributeRuler component.
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DOCS: https://nightly.spacy.io/api/attributeruler#init
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"""
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self.name = name
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self.vocab = vocab
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self.matcher = Matcher(self.vocab, validate=validate)
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self.validate = validate
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self.attrs = []
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self._attrs_unnormed = [] # store for reference
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self.indices = []
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def clear(self) -> None:
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"""Reset all patterns."""
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self.matcher = Matcher(self.vocab, validate=self.validate)
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self.attrs = []
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self._attrs_unnormed = []
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self.indices = []
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def initialize(
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self,
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get_examples: Optional[Callable[[], Iterable[Example]]],
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*,
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nlp: Optional[Language] = None,
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patterns: Optional[Iterable[AttributeRulerPatternType]] = None,
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tag_map: Optional[TagMapType] = None,
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morph_rules: Optional[MorphRulesType] = None,
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) -> None:
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"""Initialize the attribute ruler by adding zero or more patterns.
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Rules can be specified as a sequence of dicts using the `patterns`
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keyword argument. You can also provide rules using the "tag map" or
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"morph rules" formats supported by spaCy prior to v3.
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"""
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self.clear()
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if patterns:
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self.add_patterns(patterns)
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if tag_map:
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self.load_from_tag_map(tag_map)
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if morph_rules:
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self.load_from_morph_rules(morph_rules)
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def __call__(self, doc: Doc) -> Doc:
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"""Apply the AttributeRuler to a Doc and set all attribute exceptions.
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doc (Doc): The document to process.
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RETURNS (Doc): The processed Doc.
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DOCS: https://nightly.spacy.io/api/attributeruler#call
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"""
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matches = self.matcher(doc, allow_missing=True)
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# Sort by the attribute ID, so that later rules have precendence
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matches = [
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(int(self.vocab.strings[m_id]), m_id, s, e) for m_id, s, e in matches
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]
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matches.sort()
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for attr_id, match_id, start, end in matches:
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span = Span(doc, start, end, label=match_id)
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attrs = self.attrs[attr_id]
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index = self.indices[attr_id]
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try:
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# The index can be negative, which makes it annoying to do
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# the boundscheck. Let Span do it instead.
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token = span[index] # noqa: F841
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except IndexError:
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# The original exception is just our conditional logic, so we
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# raise from.
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raise ValueError(
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Errors.E1001.format(
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patterns=self.matcher.get(span.label),
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span=[t.text for t in span],
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index=index,
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)
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) from None
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set_token_attrs(span[index], attrs)
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return doc
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def load_from_tag_map(
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self, tag_map: Dict[str, Dict[Union[int, str], Union[int, str]]]
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) -> None:
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"""Load attribute ruler patterns from a tag map.
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tag_map (dict): The tag map that maps fine-grained tags to
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coarse-grained tags and morphological features.
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DOCS: https://nightly.spacy.io/api/attributeruler#load_from_morph_rules
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"""
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for tag, attrs in tag_map.items():
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pattern = [{"TAG": tag}]
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attrs, morph_attrs = _split_morph_attrs(attrs)
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if "MORPH" not in attrs:
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morph = self.vocab.morphology.add(morph_attrs)
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attrs["MORPH"] = self.vocab.strings[morph]
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else:
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morph = self.vocab.morphology.add(attrs["MORPH"])
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attrs["MORPH"] = self.vocab.strings[morph]
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self.add([pattern], attrs)
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def load_from_morph_rules(
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self, morph_rules: Dict[str, Dict[str, Dict[Union[int, str], Union[int, str]]]]
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) -> None:
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"""Load attribute ruler patterns from morph rules.
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morph_rules (dict): The morph rules that map token text and
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fine-grained tags to coarse-grained tags, lemmas and morphological
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features.
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DOCS: https://nightly.spacy.io/api/attributeruler#load_from_morph_rules
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"""
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for tag in morph_rules:
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for word in morph_rules[tag]:
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pattern = [{"ORTH": word, "TAG": tag}]
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attrs = morph_rules[tag][word]
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attrs, morph_attrs = _split_morph_attrs(attrs)
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if "MORPH" in attrs:
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morph = self.vocab.morphology.add(attrs["MORPH"])
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attrs["MORPH"] = self.vocab.strings[morph]
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elif morph_attrs:
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morph = self.vocab.morphology.add(morph_attrs)
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attrs["MORPH"] = self.vocab.strings[morph]
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self.add([pattern], attrs)
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def add(
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self, patterns: Iterable[MatcherPatternType], attrs: Dict, index: int = 0
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) -> None:
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"""Add Matcher patterns for tokens that should be modified with the
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provided attributes. The token at the specified index within the
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matched span will be assigned the attributes.
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patterns (Iterable[List[Dict]]): A list of Matcher patterns.
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attrs (Dict): The attributes to assign to the target token in the
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matched span.
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index (int): The index of the token in the matched span to modify. May
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be negative to index from the end of the span. Defaults to 0.
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DOCS: https://nightly.spacy.io/api/attributeruler#add
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"""
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# We need to make a string here, because otherwise the ID we pass back
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# will be interpreted as the hash of a string, rather than an ordinal.
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key = str(len(self.attrs))
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self.matcher.add(self.vocab.strings.add(key), patterns)
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self._attrs_unnormed.append(attrs)
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attrs = normalize_token_attrs(self.vocab, attrs)
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self.attrs.append(attrs)
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self.indices.append(index)
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def add_patterns(self, patterns: Iterable[AttributeRulerPatternType]) -> None:
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"""Add patterns from a list of pattern dicts with the keys as the
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arguments to AttributeRuler.add.
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patterns (Iterable[dict]): A list of pattern dicts with the keys
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as the arguments to AttributeRuler.add (patterns/attrs/index) to
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add as patterns.
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DOCS: https://nightly.spacy.io/api/attributeruler#add_patterns
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"""
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for p in patterns:
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self.add(**p)
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@property
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def patterns(self) -> List[AttributeRulerPatternType]:
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"""All the added patterns."""
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all_patterns = []
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for i in range(len(self.attrs)):
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p = {}
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p["patterns"] = self.matcher.get(str(i))[1]
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p["attrs"] = self._attrs_unnormed[i]
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p["index"] = self.indices[i]
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all_patterns.append(p)
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return all_patterns
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def score(self, examples: Iterable[Example], **kwargs) -> Dict[str, Any]:
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"""Score a batch of examples.
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examples (Iterable[Example]): The examples to score.
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RETURNS (Dict[str, Any]): The scores, produced by
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Scorer.score_token_attr for the attributes "tag", "pos", "morph"
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and "lemma" for the target token attributes.
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DOCS: https://nightly.spacy.io/api/tagger#score
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"""
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def morph_key_getter(token, attr):
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return getattr(token, attr).key
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validate_examples(examples, "AttributeRuler.score")
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results = {}
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attrs = set()
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for token_attrs in self.attrs:
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attrs.update(token_attrs)
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for attr in attrs:
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if attr == TAG:
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results.update(Scorer.score_token_attr(examples, "tag", **kwargs))
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elif attr == POS:
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results.update(Scorer.score_token_attr(examples, "pos", **kwargs))
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elif attr == MORPH:
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results.update(Scorer.score_token_attr(examples, "morph", getter=morph_key_getter, **kwargs))
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results.update(Scorer.score_token_attr_per_feat(examples, "morph", getter=morph_key_getter, **kwargs))
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elif attr == LEMMA:
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results.update(Scorer.score_token_attr(examples, "lemma", **kwargs))
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return results
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def to_bytes(self, exclude: Iterable[str] = SimpleFrozenList()) -> bytes:
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"""Serialize the AttributeRuler to a bytestring.
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exclude (Iterable[str]): String names of serialization fields to exclude.
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RETURNS (bytes): The serialized object.
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DOCS: https://nightly.spacy.io/api/attributeruler#to_bytes
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"""
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serialize = {}
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serialize["vocab"] = self.vocab.to_bytes
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serialize["patterns"] = lambda: srsly.msgpack_dumps(self.patterns)
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return util.to_bytes(serialize, exclude)
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def from_bytes(
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self, bytes_data: bytes, exclude: Iterable[str] = SimpleFrozenList()
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) -> "AttributeRuler":
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"""Load the AttributeRuler from a bytestring.
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bytes_data (bytes): The data to load.
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exclude (Iterable[str]): String names of serialization fields to exclude.
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returns (AttributeRuler): The loaded object.
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DOCS: https://nightly.spacy.io/api/attributeruler#from_bytes
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"""
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def load_patterns(b):
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self.add_patterns(srsly.msgpack_loads(b))
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deserialize = {
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"vocab": lambda b: self.vocab.from_bytes(b),
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"patterns": load_patterns,
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}
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util.from_bytes(bytes_data, deserialize, exclude)
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return self
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def to_disk(
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self, path: Union[Path, str], exclude: Iterable[str] = SimpleFrozenList()
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) -> None:
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"""Serialize the AttributeRuler to disk.
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path (Union[Path, str]): A path to a directory.
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exclude (Iterable[str]): String names of serialization fields to exclude.
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DOCS: https://nightly.spacy.io/api/attributeruler#to_disk
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"""
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serialize = {
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"vocab": lambda p: self.vocab.to_disk(p),
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"patterns": lambda p: srsly.write_msgpack(p, self.patterns),
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}
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util.to_disk(path, serialize, exclude)
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def from_disk(
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self, path: Union[Path, str], exclude: Iterable[str] = SimpleFrozenList()
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) -> "AttributeRuler":
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"""Load the AttributeRuler from disk.
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path (Union[Path, str]): A path to a directory.
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exclude (Iterable[str]): String names of serialization fields to exclude.
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RETURNS (AttributeRuler): The loaded object.
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DOCS: https://nightly.spacy.io/api/attributeruler#from_disk
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"""
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def load_patterns(p):
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self.add_patterns(srsly.read_msgpack(p))
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deserialize = {
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"vocab": lambda p: self.vocab.from_disk(p),
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"patterns": load_patterns,
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}
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util.from_disk(path, deserialize, exclude)
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return self
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def _split_morph_attrs(attrs: dict) -> Tuple[dict, dict]:
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"""Split entries from a tag map or morph rules dict into to two dicts, one
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with the token-level features (POS, LEMMA) and one with the remaining
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features, which are presumed to be individual MORPH features."""
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other_attrs = {}
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morph_attrs = {}
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for k, v in attrs.items():
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if k in "_" or k in IDS.keys() or k in IDS.values():
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other_attrs[k] = v
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else:
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morph_attrs[k] = v
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return other_attrs, morph_attrs
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