mirror of https://github.com/explosion/spaCy.git
422 lines
16 KiB
Python
422 lines
16 KiB
Python
from typing import Optional, Union, List, Dict, Tuple, Iterable, Any
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from collections import defaultdict
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from pathlib import Path
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import srsly
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from ..language import Language
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from ..errors import Errors
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from ..util import ensure_path, to_disk, from_disk, SimpleFrozenList
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from ..tokens import Doc, Span
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from ..matcher import Matcher, PhraseMatcher
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from ..scorer import Scorer
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from ..training import validate_examples
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DEFAULT_ENT_ID_SEP = "||"
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PatternType = Dict[str, Union[str, List[Dict[str, Any]]]]
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@Language.factory(
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"entity_ruler",
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assigns=["doc.ents", "token.ent_type", "token.ent_iob"],
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default_config={
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"phrase_matcher_attr": None,
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"validate": False,
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"overwrite_ents": False,
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"ent_id_sep": DEFAULT_ENT_ID_SEP,
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},
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scores=["ents_p", "ents_r", "ents_f", "ents_per_type"],
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default_score_weights={"ents_f": 1.0, "ents_p": 0.0, "ents_r": 0.0},
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)
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def make_entity_ruler(
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nlp: Language,
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name: str,
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phrase_matcher_attr: Optional[Union[int, str]],
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validate: bool,
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overwrite_ents: bool,
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ent_id_sep: str,
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):
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return EntityRuler(
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nlp,
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name,
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phrase_matcher_attr=phrase_matcher_attr,
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validate=validate,
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overwrite_ents=overwrite_ents,
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ent_id_sep=ent_id_sep,
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)
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class EntityRuler:
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"""The EntityRuler lets you add spans to the `Doc.ents` using token-based
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rules or exact phrase matches. It can be combined with the statistical
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`EntityRecognizer` to boost accuracy, or used on its own to implement a
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purely rule-based entity recognition system. After initialization, the
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component is typically added to the pipeline using `nlp.add_pipe`.
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DOCS: https://nightly.spacy.io/api/entityruler
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USAGE: https://nightly.spacy.io/usage/rule-based-matching#entityruler
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"""
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def __init__(
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self,
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nlp: Language,
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name: str = "entity_ruler",
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*,
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phrase_matcher_attr: Optional[Union[int, str]] = None,
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validate: bool = False,
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overwrite_ents: bool = False,
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ent_id_sep: str = DEFAULT_ENT_ID_SEP,
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patterns: Optional[List[PatternType]] = None,
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) -> None:
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"""Initialize the entity ruler. If patterns are supplied here, they
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need to be a list of dictionaries with a `"label"` and `"pattern"`
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key. A pattern can either be a token pattern (list) or a phrase pattern
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(string). For example: `{'label': 'ORG', 'pattern': 'Apple'}`.
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nlp (Language): The shared nlp object to pass the vocab to the matchers
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and process phrase patterns.
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name (str): Instance name of the current pipeline component. Typically
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passed in automatically from the factory when the component is
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added. Used to disable the current entity ruler while creating
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phrase patterns with the nlp object.
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phrase_matcher_attr (int / str): Token attribute to match on, passed
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to the internal PhraseMatcher as `attr`
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validate (bool): Whether patterns should be validated, passed to
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Matcher and PhraseMatcher as `validate`
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patterns (iterable): Optional patterns to load in.
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overwrite_ents (bool): If existing entities are present, e.g. entities
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added by the model, overwrite them by matches if necessary.
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ent_id_sep (str): Separator used internally for entity IDs.
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DOCS: https://nightly.spacy.io/api/entityruler#init
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"""
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self.nlp = nlp
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self.name = name
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self.overwrite = overwrite_ents
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self.token_patterns = defaultdict(list)
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self.phrase_patterns = defaultdict(list)
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self.matcher = Matcher(nlp.vocab, validate=validate)
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if phrase_matcher_attr is not None:
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if phrase_matcher_attr.upper() == "TEXT":
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phrase_matcher_attr = "ORTH"
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self.phrase_matcher_attr = phrase_matcher_attr
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self.phrase_matcher = PhraseMatcher(
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nlp.vocab, attr=self.phrase_matcher_attr, validate=validate
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)
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else:
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self.phrase_matcher_attr = None
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self.phrase_matcher = PhraseMatcher(nlp.vocab, validate=validate)
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self.ent_id_sep = ent_id_sep
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self._ent_ids = defaultdict(dict)
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if patterns is not None:
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self.add_patterns(patterns)
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def __len__(self) -> int:
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"""The number of all patterns added to the entity ruler."""
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n_token_patterns = sum(len(p) for p in self.token_patterns.values())
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n_phrase_patterns = sum(len(p) for p in self.phrase_patterns.values())
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return n_token_patterns + n_phrase_patterns
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def __contains__(self, label: str) -> bool:
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"""Whether a label is present in the patterns."""
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return label in self.token_patterns or label in self.phrase_patterns
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def __call__(self, doc: Doc) -> Doc:
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"""Find matches in document and add them as entities.
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doc (Doc): The Doc object in the pipeline.
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RETURNS (Doc): The Doc with added entities, if available.
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DOCS: https://nightly.spacy.io/api/entityruler#call
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"""
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matches = list(self.matcher(doc)) + list(self.phrase_matcher(doc))
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matches = set(
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[(m_id, start, end) for m_id, start, end in matches if start != end]
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)
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get_sort_key = lambda m: (m[2] - m[1], -m[1])
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matches = sorted(matches, key=get_sort_key, reverse=True)
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entities = list(doc.ents)
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new_entities = []
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seen_tokens = set()
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for match_id, start, end in matches:
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if any(t.ent_type for t in doc[start:end]) and not self.overwrite:
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continue
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# check for end - 1 here because boundaries are inclusive
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if start not in seen_tokens and end - 1 not in seen_tokens:
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if match_id in self._ent_ids:
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label, ent_id = self._ent_ids[match_id]
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span = Span(doc, start, end, label=label)
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if ent_id:
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for token in span:
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token.ent_id_ = ent_id
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else:
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span = Span(doc, start, end, label=match_id)
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new_entities.append(span)
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entities = [
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e for e in entities if not (e.start < end and e.end > start)
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]
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seen_tokens.update(range(start, end))
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doc.ents = entities + new_entities
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return doc
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@property
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def labels(self) -> Tuple[str, ...]:
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"""All labels present in the match patterns.
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RETURNS (set): The string labels.
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DOCS: https://nightly.spacy.io/api/entityruler#labels
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"""
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keys = set(self.token_patterns.keys())
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keys.update(self.phrase_patterns.keys())
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all_labels = set()
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for l in keys:
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if self.ent_id_sep in l:
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label, _ = self._split_label(l)
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all_labels.add(label)
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else:
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all_labels.add(l)
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return tuple(all_labels)
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@property
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def ent_ids(self) -> Tuple[str, ...]:
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"""All entity ids present in the match patterns `id` properties
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RETURNS (set): The string entity ids.
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DOCS: https://nightly.spacy.io/api/entityruler#ent_ids
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"""
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keys = set(self.token_patterns.keys())
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keys.update(self.phrase_patterns.keys())
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all_ent_ids = set()
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for l in keys:
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if self.ent_id_sep in l:
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_, ent_id = self._split_label(l)
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all_ent_ids.add(ent_id)
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return tuple(all_ent_ids)
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@property
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def patterns(self) -> List[PatternType]:
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"""Get all patterns that were added to the entity ruler.
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RETURNS (list): The original patterns, one dictionary per pattern.
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DOCS: https://nightly.spacy.io/api/entityruler#patterns
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"""
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all_patterns = []
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for label, patterns in self.token_patterns.items():
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for pattern in patterns:
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ent_label, ent_id = self._split_label(label)
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p = {"label": ent_label, "pattern": pattern}
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if ent_id:
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p["id"] = ent_id
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all_patterns.append(p)
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for label, patterns in self.phrase_patterns.items():
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for pattern in patterns:
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ent_label, ent_id = self._split_label(label)
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p = {"label": ent_label, "pattern": pattern.text}
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if ent_id:
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p["id"] = ent_id
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all_patterns.append(p)
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return all_patterns
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def add_patterns(self, patterns: List[PatternType]) -> None:
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"""Add patterns to the entity ruler. A pattern can either be a token
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pattern (list of dicts) or a phrase pattern (string). For example:
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{'label': 'ORG', 'pattern': 'Apple'}
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{'label': 'GPE', 'pattern': [{'lower': 'san'}, {'lower': 'francisco'}]}
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patterns (list): The patterns to add.
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DOCS: https://nightly.spacy.io/api/entityruler#add_patterns
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"""
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# disable the nlp components after this one in case they hadn't been initialized / deserialised yet
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try:
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current_index = self.nlp.pipe_names.index(self.name)
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subsequent_pipes = [
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pipe for pipe in self.nlp.pipe_names[current_index + 1 :]
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]
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except ValueError:
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subsequent_pipes = []
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with self.nlp.select_pipes(disable=subsequent_pipes):
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token_patterns = []
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phrase_pattern_labels = []
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phrase_pattern_texts = []
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phrase_pattern_ids = []
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for entry in patterns:
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if isinstance(entry["pattern"], str):
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phrase_pattern_labels.append(entry["label"])
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phrase_pattern_texts.append(entry["pattern"])
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phrase_pattern_ids.append(entry.get("id"))
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elif isinstance(entry["pattern"], list):
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token_patterns.append(entry)
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phrase_patterns = []
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for label, pattern, ent_id in zip(
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phrase_pattern_labels,
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self.nlp.pipe(phrase_pattern_texts),
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phrase_pattern_ids,
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):
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phrase_pattern = {"label": label, "pattern": pattern, "id": ent_id}
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if ent_id:
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phrase_pattern["id"] = ent_id
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phrase_patterns.append(phrase_pattern)
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for entry in token_patterns + phrase_patterns:
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label = entry["label"]
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if "id" in entry:
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ent_label = label
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label = self._create_label(label, entry["id"])
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key = self.matcher._normalize_key(label)
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self._ent_ids[key] = (ent_label, entry["id"])
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pattern = entry["pattern"]
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if isinstance(pattern, Doc):
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self.phrase_patterns[label].append(pattern)
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elif isinstance(pattern, list):
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self.token_patterns[label].append(pattern)
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else:
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raise ValueError(Errors.E097.format(pattern=pattern))
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for label, patterns in self.token_patterns.items():
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self.matcher.add(label, patterns)
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for label, patterns in self.phrase_patterns.items():
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self.phrase_matcher.add(label, patterns)
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def clear(self) -> None:
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"""Reset all patterns."""
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self.token_patterns = defaultdict(list)
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self.phrase_patterns = defaultdict(list)
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self._ent_ids = defaultdict(dict)
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def _split_label(self, label: str) -> Tuple[str, str]:
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"""Split Entity label into ent_label and ent_id if it contains self.ent_id_sep
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label (str): The value of label in a pattern entry
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RETURNS (tuple): ent_label, ent_id
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"""
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if self.ent_id_sep in label:
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ent_label, ent_id = label.rsplit(self.ent_id_sep, 1)
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else:
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ent_label = label
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ent_id = None
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return ent_label, ent_id
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def _create_label(self, label: str, ent_id: str) -> str:
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"""Join Entity label with ent_id if the pattern has an `id` attribute
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label (str): The label to set for ent.label_
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ent_id (str): The label
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RETURNS (str): The ent_label joined with configured `ent_id_sep`
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"""
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if isinstance(ent_id, str):
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label = f"{label}{self.ent_id_sep}{ent_id}"
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return label
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def score(self, examples, **kwargs):
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validate_examples(examples, "EntityRuler.score")
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return Scorer.score_spans(examples, "ents", **kwargs)
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def from_bytes(
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self, patterns_bytes: bytes, *, exclude: Iterable[str] = SimpleFrozenList()
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) -> "EntityRuler":
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"""Load the entity ruler from a bytestring.
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patterns_bytes (bytes): The bytestring to load.
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RETURNS (EntityRuler): The loaded entity ruler.
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DOCS: https://nightly.spacy.io/api/entityruler#from_bytes
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"""
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cfg = srsly.msgpack_loads(patterns_bytes)
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self.clear()
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if isinstance(cfg, dict):
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self.add_patterns(cfg.get("patterns", cfg))
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self.overwrite = cfg.get("overwrite", False)
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self.phrase_matcher_attr = cfg.get("phrase_matcher_attr", None)
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if self.phrase_matcher_attr is not None:
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self.phrase_matcher = PhraseMatcher(
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self.nlp.vocab, attr=self.phrase_matcher_attr
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)
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self.ent_id_sep = cfg.get("ent_id_sep", DEFAULT_ENT_ID_SEP)
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else:
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self.add_patterns(cfg)
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return self
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def to_bytes(self, *, exclude: Iterable[str] = SimpleFrozenList()) -> bytes:
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"""Serialize the entity ruler patterns to a bytestring.
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RETURNS (bytes): The serialized patterns.
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DOCS: https://nightly.spacy.io/api/entityruler#to_bytes
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"""
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serial = {
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"overwrite": self.overwrite,
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"ent_id_sep": self.ent_id_sep,
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"phrase_matcher_attr": self.phrase_matcher_attr,
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"patterns": self.patterns,
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}
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return srsly.msgpack_dumps(serial)
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def from_disk(
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self, path: Union[str, Path], *, exclude: Iterable[str] = SimpleFrozenList()
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) -> "EntityRuler":
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"""Load the entity ruler from a file. Expects a file containing
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newline-delimited JSON (JSONL) with one entry per line.
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path (str / Path): The JSONL file to load.
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RETURNS (EntityRuler): The loaded entity ruler.
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DOCS: https://nightly.spacy.io/api/entityruler#from_disk
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"""
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path = ensure_path(path)
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self.clear()
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depr_patterns_path = path.with_suffix(".jsonl")
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if depr_patterns_path.is_file():
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patterns = srsly.read_jsonl(depr_patterns_path)
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self.add_patterns(patterns)
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else:
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cfg = {}
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deserializers_patterns = {
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"patterns": lambda p: self.add_patterns(
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srsly.read_jsonl(p.with_suffix(".jsonl"))
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)
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}
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deserializers_cfg = {"cfg": lambda p: cfg.update(srsly.read_json(p))}
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from_disk(path, deserializers_cfg, {})
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self.overwrite = cfg.get("overwrite", False)
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self.phrase_matcher_attr = cfg.get("phrase_matcher_attr")
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self.ent_id_sep = cfg.get("ent_id_sep", DEFAULT_ENT_ID_SEP)
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if self.phrase_matcher_attr is not None:
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self.phrase_matcher = PhraseMatcher(
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self.nlp.vocab, attr=self.phrase_matcher_attr
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)
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from_disk(path, deserializers_patterns, {})
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return self
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def to_disk(
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self, path: Union[str, Path], *, exclude: Iterable[str] = SimpleFrozenList()
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) -> None:
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"""Save the entity ruler patterns to a directory. The patterns will be
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saved as newline-delimited JSON (JSONL).
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path (str / Path): The JSONL file to save.
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DOCS: https://nightly.spacy.io/api/entityruler#to_disk
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"""
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path = ensure_path(path)
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cfg = {
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"overwrite": self.overwrite,
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"phrase_matcher_attr": self.phrase_matcher_attr,
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"ent_id_sep": self.ent_id_sep,
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}
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serializers = {
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"patterns": lambda p: srsly.write_jsonl(
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p.with_suffix(".jsonl"), self.patterns
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),
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"cfg": lambda p: srsly.write_json(p, cfg),
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}
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if path.suffix == ".jsonl": # user wants to save only JSONL
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srsly.write_jsonl(path, self.patterns)
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else:
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to_disk(path, serializers, {})
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