edsnlp.pipelines.qualifiers.base

Qualifier

Bases: BaseComponent

Implements the NegEx algorithm.

PARAMETER	DESCRIPTION
nlp	spaCy nlp pipeline to use for matching. TYPE: Language
attr	spaCy's attribute to use: a string with the value "TEXT" or "NORM", or a dict with the key 'term_attr' we can also add a key for each regex. TYPE: str
on_ents_only	Whether to look for matches around detected entities only. Useful for faster inference in downstream tasks. If True, will look in all ents located in doc.ents only If an iterable of string is passed, will additionally look in doc.spans[key] for each key in the iterable TYPE: Union[bool, str, List[str], Set[str]]
explain	Whether to keep track of cues for each entity. TYPE: bool
**terms	Terms to look for. TYPE: Dict[str, Optional[List[str]]] DEFAULT: {}

Source code in edsnlp/pipelines/qualifiers/base.py

class Qualifier(BaseComponent):
    """
    Implements the NegEx algorithm.

    Parameters
    ----------
    nlp : Language
        spaCy nlp pipeline to use for matching.
    attr : str
        spaCy's attribute to use:
        a string with the value "TEXT" or "NORM", or a dict with the key 'term_attr'
        we can also add a key for each regex.
    on_ents_only : Union[bool, str, List[str], Set[str]]
        Whether to look for matches around detected entities only.
        Useful for faster inference in downstream tasks.

        - If True, will look in all ents located in `doc.ents` only
        - If an iterable of string is passed, will additionally look in `doc.spans[key]`
        for each key in the iterable
    explain : bool
        Whether to keep track of cues for each entity.
    **terms : Dict[str, Optional[List[str]]]
        Terms to look for.
    """

    defaults = dict()

    def __init__(
        self,
        nlp: Language,
        attr: str,
        on_ents_only: Union[bool, str, List[str], Set[str]],
        explain: bool,
        **terms: Dict[str, Optional[List[str]]],
    ):

        if attr.upper() == "NORM":
            check_normalizer(nlp)

        self.phrase_matcher = EDSPhraseMatcher(vocab=nlp.vocab, attr=attr)
        self.phrase_matcher.build_patterns(nlp=nlp, terms=terms)

        self.on_ents_only = on_ents_only

        assert isinstance(on_ents_only, (list, str, set, bool)), (
            "The `on_ents_only` argument should be a "
            "string, a bool, a list or a set of string"
        )

        if isinstance(on_ents_only, list):
            on_ents_only = set(on_ents_only)
        elif isinstance(on_ents_only, str):
            on_ents_only = set([on_ents_only])
        self.on_ents_only = on_ents_only
        self.explain = explain

    def get_defaults(self, **kwargs: Optional[List[str]]) -> Dict[str, List[str]]:
        """
        Merge terms with their defaults. Null keys are replaced with defaults.

        Returns
        -------
        Dict[str, List[str]]
            Merged dictionary
        """
        # Filter out empty keys
        kwargs = {k: v for k, v in kwargs.items() if v is not None}

        # Update defaults
        terms = self.defaults.copy()
        terms.update(kwargs)

        return terms

    def get_matches(self, doc: Doc) -> List[Span]:
        """
        Extract matches.

        Parameters
        ----------
        doc : Doc
            spaCy `Doc` object.
        Returns
        -------
        List[Span]
            List of detected spans
        """

        if self.on_ents_only:
            sents = set([ent.sent for ent in self.get_spans(doc)])

            match_iterator = map(
                lambda sent: self.phrase_matcher(sent, as_spans=True), sents
            )

            matches = chain.from_iterable(match_iterator)

        else:

            matches = self.phrase_matcher(doc, as_spans=True)

        return list(matches)

    def __call__(self, doc: Doc) -> Doc:
        return self.process(doc)

get_defaults(**kwargs)

Merge terms with their defaults. Null keys are replaced with defaults.

RETURNS	DESCRIPTION
Dict[str, List[str]]	Merged dictionary

Source code in edsnlp/pipelines/qualifiers/base.py

def get_defaults(self, **kwargs: Optional[List[str]]) -> Dict[str, List[str]]:
    """
    Merge terms with their defaults. Null keys are replaced with defaults.

    Returns
    -------
    Dict[str, List[str]]
        Merged dictionary
    """
    # Filter out empty keys
    kwargs = {k: v for k, v in kwargs.items() if v is not None}

    # Update defaults
    terms = self.defaults.copy()
    terms.update(kwargs)

    return terms

get_matches(doc)

Extract matches.

PARAMETER	DESCRIPTION
doc	spaCy Doc object. TYPE: Doc
Returns
List	List of detected spans

Source code in edsnlp/pipelines/qualifiers/base.py

def get_matches(self, doc: Doc) -> List[Span]:
    """
    Extract matches.

    Parameters
    ----------
    doc : Doc
        spaCy `Doc` object.
    Returns
    -------
    List[Span]
        List of detected spans
    """

    if self.on_ents_only:
        sents = set([ent.sent for ent in self.get_spans(doc)])

        match_iterator = map(
            lambda sent: self.phrase_matcher(sent, as_spans=True), sents
        )

        matches = chain.from_iterable(match_iterator)

    else:

        matches = self.phrase_matcher(doc, as_spans=True)

    return list(matches)

get_qualifier_extensions(nlp)

Check for all qualifiers present in the pipe and return its corresponding extension

Source code in edsnlp/pipelines/qualifiers/base.py

def get_qualifier_extensions(nlp: Language):
    """
    Check for all qualifiers present in the pipe and return its corresponding extension
    """
    return {
        name: nlp.get_pipe_meta(name).assigns[0].split("span.")[-1]
        for name, pipe in nlp.pipeline
        if isinstance(pipe, Qualifier)
    }