import warnings from typing import Optional, Union, List, Dict, Tuple, Iterable, Any, Callable, Sequence from collections import defaultdict from pathlib import Path import srsly import warnings from .pipe import Pipe from ..training import Example from ..language import Language from ..errors import Errors, Warnings from ..util import ensure_path, to_disk, from_disk, SimpleFrozenList from ..tokens import Doc, Span from ..matcher import Matcher, PhraseMatcher from ..scorer import get_ner_prf from ..training import validate_examples DEFAULT_ENT_ID_SEP = "||" PatternType = Dict[str, Union[str, List[Dict[str, Any]]]] @Language.factory( "entity_ruler", assigns=["doc.ents", "token.ent_type", "token.ent_iob"], default_config={ "phrase_matcher_attr": None, "validate": False, "overwrite_ents": False, "ent_id_sep": DEFAULT_ENT_ID_SEP, }, default_score_weights={ "ents_f": 1.0, "ents_p": 0.0, "ents_r": 0.0, "ents_per_type": None, }, ) def make_entity_ruler( nlp: Language, name: str, phrase_matcher_attr: Optional[Union[int, str]], validate: bool, overwrite_ents: bool, ent_id_sep: str, ): return EntityRuler( nlp, name, phrase_matcher_attr=phrase_matcher_attr, validate=validate, overwrite_ents=overwrite_ents, ent_id_sep=ent_id_sep, ) class EntityRuler(Pipe): """The EntityRuler lets you add spans to the `Doc.ents` using token-based rules or exact phrase matches. It can be combined with the statistical `EntityRecognizer` to boost accuracy, or used on its own to implement a purely rule-based entity recognition system. After initialization, the component is typically added to the pipeline using `nlp.add_pipe`. DOCS: https://spacy.io/api/entityruler USAGE: https://spacy.io/usage/rule-based-matching#entityruler """ def __init__( self, nlp: Language, name: str = "entity_ruler", *, phrase_matcher_attr: Optional[Union[int, str]] = None, validate: bool = False, overwrite_ents: bool = False, ent_id_sep: str = DEFAULT_ENT_ID_SEP, patterns: Optional[List[PatternType]] = None, ) -> None: """Initialize the entity ruler. If patterns are supplied here, they need to be a list of dictionaries with a `"label"` and `"pattern"` key. A pattern can either be a token pattern (list) or a phrase pattern (string). For example: `{'label': 'ORG', 'pattern': 'Apple'}`. nlp (Language): The shared nlp object to pass the vocab to the matchers and process phrase patterns. name (str): Instance name of the current pipeline component. Typically passed in automatically from the factory when the component is added. Used to disable the current entity ruler while creating phrase patterns with the nlp object. phrase_matcher_attr (int / str): Token attribute to match on, passed to the internal PhraseMatcher as `attr` validate (bool): Whether patterns should be validated, passed to Matcher and PhraseMatcher as `validate` patterns (iterable): Optional patterns to load in. overwrite_ents (bool): If existing entities are present, e.g. entities added by the model, overwrite them by matches if necessary. ent_id_sep (str): Separator used internally for entity IDs. DOCS: https://spacy.io/api/entityruler#init """ self.nlp = nlp self.name = name self.overwrite = overwrite_ents self.token_patterns = defaultdict(list) self.phrase_patterns = defaultdict(list) self._validate = validate self.matcher = Matcher(nlp.vocab, validate=validate) self.phrase_matcher_attr = phrase_matcher_attr self.phrase_matcher = PhraseMatcher( nlp.vocab, attr=self.phrase_matcher_attr, validate=validate ) self.ent_id_sep = ent_id_sep self._ent_ids = defaultdict(dict) if patterns is not None: self.add_patterns(patterns) def __len__(self) -> int: """The number of all patterns added to the entity ruler.""" n_token_patterns = sum(len(p) for p in self.token_patterns.values()) n_phrase_patterns = sum(len(p) for p in self.phrase_patterns.values()) return n_token_patterns + n_phrase_patterns def __contains__(self, label: str) -> bool: """Whether a label is present in the patterns.""" return label in self.token_patterns or label in self.phrase_patterns def __call__(self, doc: Doc) -> Doc: """Find matches in document and add them as entities. doc (Doc): The Doc object in the pipeline. RETURNS (Doc): The Doc with added entities, if available. DOCS: https://spacy.io/api/entityruler#call """ error_handler = self.get_error_handler() try: matches = self.match(doc) self.set_annotations(doc, matches) return doc except Exception as e: error_handler(self.name, self, [doc], e) def match(self, doc: Doc): self._require_patterns() with warnings.catch_warnings(): warnings.filterwarnings("ignore", message="\\[W036") matches = list(self.matcher(doc)) + list(self.phrase_matcher(doc)) matches = set( [(m_id, start, end) for m_id, start, end in matches if start != end] ) get_sort_key = lambda m: (m[2] - m[1], -m[1]) matches = sorted(matches, key=get_sort_key, reverse=True) return matches def set_annotations(self, doc, matches): """Modify the document in place""" entities = list(doc.ents) new_entities = [] seen_tokens = set() for match_id, start, end in matches: if any(t.ent_type for t in doc[start:end]) and not self.overwrite: continue # check for end - 1 here because boundaries are inclusive if start not in seen_tokens and end - 1 not in seen_tokens: if match_id in self._ent_ids: label, ent_id = self._ent_ids[match_id] span = Span(doc, start, end, label=label) if ent_id: for token in span: token.ent_id_ = ent_id else: span = Span(doc, start, end, label=match_id) new_entities.append(span) entities = [ e for e in entities if not (e.start < end and e.end > start) ] seen_tokens.update(range(start, end)) doc.ents = entities + new_entities @property def labels(self) -> Tuple[str, ...]: """All labels present in the match patterns. RETURNS (set): The string labels. DOCS: https://spacy.io/api/entityruler#labels """ keys = set(self.token_patterns.keys()) keys.update(self.phrase_patterns.keys()) all_labels = set() for l in keys: if self.ent_id_sep in l: label, _ = self._split_label(l) all_labels.add(label) else: all_labels.add(l) return tuple(sorted(all_labels)) def initialize( self, get_examples: Callable[[], Iterable[Example]], *, nlp: Optional[Language] = None, patterns: Optional[Sequence[PatternType]] = None, ): """Initialize the pipe for training. get_examples (Callable[[], Iterable[Example]]): Function that returns a representative sample of gold-standard Example objects. nlp (Language): The current nlp object the component is part of. patterns Optional[Iterable[PatternType]]: The list of patterns. DOCS: https://spacy.io/api/entityruler#initialize """ self.clear() if patterns: self.add_patterns(patterns) @property def ent_ids(self) -> Tuple[str, ...]: """All entity ids present in the match patterns `id` properties RETURNS (set): The string entity ids. DOCS: https://spacy.io/api/entityruler#ent_ids """ keys = set(self.token_patterns.keys()) keys.update(self.phrase_patterns.keys()) all_ent_ids = set() for l in keys: if self.ent_id_sep in l: _, ent_id = self._split_label(l) all_ent_ids.add(ent_id) return tuple(all_ent_ids) @property def patterns(self) -> List[PatternType]: """Get all patterns that were added to the entity ruler. RETURNS (list): The original patterns, one dictionary per pattern. DOCS: https://spacy.io/api/entityruler#patterns """ all_patterns = [] for label, patterns in self.token_patterns.items(): for pattern in patterns: ent_label, ent_id = self._split_label(label) p = {"label": ent_label, "pattern": pattern} if ent_id: p["id"] = ent_id all_patterns.append(p) for label, patterns in self.phrase_patterns.items(): for pattern in patterns: ent_label, ent_id = self._split_label(label) p = {"label": ent_label, "pattern": pattern.text} if ent_id: p["id"] = ent_id all_patterns.append(p) return all_patterns def add_patterns(self, patterns: List[PatternType]) -> None: """Add patterns to the entity ruler. A pattern can either be a token pattern (list of dicts) or a phrase pattern (string). For example: {'label': 'ORG', 'pattern': 'Apple'} {'label': 'GPE', 'pattern': [{'lower': 'san'}, {'lower': 'francisco'}]} patterns (list): The patterns to add. DOCS: https://spacy.io/api/entityruler#add_patterns """ # disable the nlp components after this one in case they hadn't been initialized / deserialised yet try: current_index = -1 for i, (name, pipe) in enumerate(self.nlp.pipeline): if self == pipe: current_index = i break subsequent_pipes = [ pipe for pipe in self.nlp.pipe_names[current_index :] ] except ValueError: subsequent_pipes = [] with self.nlp.select_pipes(disable=subsequent_pipes): token_patterns = [] phrase_pattern_labels = [] phrase_pattern_texts = [] phrase_pattern_ids = [] for entry in patterns: if isinstance(entry["pattern"], str): phrase_pattern_labels.append(entry["label"]) phrase_pattern_texts.append(entry["pattern"]) phrase_pattern_ids.append(entry.get("id")) elif isinstance(entry["pattern"], list): token_patterns.append(entry) phrase_patterns = [] for label, pattern, ent_id in zip( phrase_pattern_labels, self.nlp.pipe(phrase_pattern_texts), phrase_pattern_ids, ): phrase_pattern = {"label": label, "pattern": pattern} if ent_id: phrase_pattern["id"] = ent_id phrase_patterns.append(phrase_pattern) for entry in token_patterns + phrase_patterns: label = entry["label"] if "id" in entry: ent_label = label label = self._create_label(label, entry["id"]) key = self.matcher._normalize_key(label) self._ent_ids[key] = (ent_label, entry["id"]) pattern = entry["pattern"] if isinstance(pattern, Doc): self.phrase_patterns[label].append(pattern) self.phrase_matcher.add(label, [pattern]) elif isinstance(pattern, list): self.token_patterns[label].append(pattern) self.matcher.add(label, [pattern]) else: raise ValueError(Errors.E097.format(pattern=pattern)) def clear(self) -> None: """Reset all patterns.""" self.token_patterns = defaultdict(list) self.phrase_patterns = defaultdict(list) self._ent_ids = defaultdict(dict) self.matcher = Matcher(self.nlp.vocab, validate=self._validate) self.phrase_matcher = PhraseMatcher( self.nlp.vocab, attr=self.phrase_matcher_attr, validate=self._validate ) def _require_patterns(self) -> None: """Raise a warning if this component has no patterns defined.""" if len(self) == 0: warnings.warn(Warnings.W036.format(name=self.name)) def _split_label(self, label: str) -> Tuple[str, str]: """Split Entity label into ent_label and ent_id if it contains self.ent_id_sep label (str): The value of label in a pattern entry RETURNS (tuple): ent_label, ent_id """ if self.ent_id_sep in label: ent_label, ent_id = label.rsplit(self.ent_id_sep, 1) else: ent_label = label ent_id = None return ent_label, ent_id def _create_label(self, label: str, ent_id: str) -> str: """Join Entity label with ent_id if the pattern has an `id` attribute label (str): The label to set for ent.label_ ent_id (str): The label RETURNS (str): The ent_label joined with configured `ent_id_sep` """ if isinstance(ent_id, str): label = f"{label}{self.ent_id_sep}{ent_id}" return label def score(self, examples, **kwargs): validate_examples(examples, "EntityRuler.score") return get_ner_prf(examples) def from_bytes( self, patterns_bytes: bytes, *, exclude: Iterable[str] = SimpleFrozenList() ) -> "EntityRuler": """Load the entity ruler from a bytestring. patterns_bytes (bytes): The bytestring to load. RETURNS (EntityRuler): The loaded entity ruler. DOCS: https://spacy.io/api/entityruler#from_bytes """ cfg = srsly.msgpack_loads(patterns_bytes) self.clear() if isinstance(cfg, dict): self.add_patterns(cfg.get("patterns", cfg)) self.overwrite = cfg.get("overwrite", False) self.phrase_matcher_attr = cfg.get("phrase_matcher_attr", None) self.phrase_matcher = PhraseMatcher( self.nlp.vocab, attr=self.phrase_matcher_attr ) self.ent_id_sep = cfg.get("ent_id_sep", DEFAULT_ENT_ID_SEP) else: self.add_patterns(cfg) return self def to_bytes(self, *, exclude: Iterable[str] = SimpleFrozenList()) -> bytes: """Serialize the entity ruler patterns to a bytestring. RETURNS (bytes): The serialized patterns. DOCS: https://spacy.io/api/entityruler#to_bytes """ serial = { "overwrite": self.overwrite, "ent_id_sep": self.ent_id_sep, "phrase_matcher_attr": self.phrase_matcher_attr, "patterns": self.patterns, } return srsly.msgpack_dumps(serial) def from_disk( self, path: Union[str, Path], *, exclude: Iterable[str] = SimpleFrozenList() ) -> "EntityRuler": """Load the entity ruler from a file. Expects a file containing newline-delimited JSON (JSONL) with one entry per line. path (str / Path): The JSONL file to load. RETURNS (EntityRuler): The loaded entity ruler. DOCS: https://spacy.io/api/entityruler#from_disk """ path = ensure_path(path) self.clear() depr_patterns_path = path.with_suffix(".jsonl") if depr_patterns_path.is_file(): patterns = srsly.read_jsonl(depr_patterns_path) self.add_patterns(patterns) else: cfg = {} deserializers_patterns = { "patterns": lambda p: self.add_patterns( srsly.read_jsonl(p.with_suffix(".jsonl")) ) } deserializers_cfg = {"cfg": lambda p: cfg.update(srsly.read_json(p))} from_disk(path, deserializers_cfg, {}) self.overwrite = cfg.get("overwrite", False) self.phrase_matcher_attr = cfg.get("phrase_matcher_attr") self.ent_id_sep = cfg.get("ent_id_sep", DEFAULT_ENT_ID_SEP) self.phrase_matcher = PhraseMatcher( self.nlp.vocab, attr=self.phrase_matcher_attr ) from_disk(path, deserializers_patterns, {}) return self def to_disk( self, path: Union[str, Path], *, exclude: Iterable[str] = SimpleFrozenList() ) -> None: """Save the entity ruler patterns to a directory. The patterns will be saved as newline-delimited JSON (JSONL). path (str / Path): The JSONL file to save. DOCS: https://spacy.io/api/entityruler#to_disk """ path = ensure_path(path) cfg = { "overwrite": self.overwrite, "phrase_matcher_attr": self.phrase_matcher_attr, "ent_id_sep": self.ent_id_sep, } serializers = { "patterns": lambda p: srsly.write_jsonl( p.with_suffix(".jsonl"), self.patterns ), "cfg": lambda p: srsly.write_json(p, cfg), } if path.suffix == ".jsonl": # user wants to save only JSONL srsly.write_jsonl(path, self.patterns) else: to_disk(path, serializers, {})