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edsnlp.pipelines.core.matcher.matcher

GenericTermMatcher

Bases: str, Enum

Source code in edsnlp/pipelines/core/matcher/matcher.py 
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class GenericTermMatcher(str, Enum):
    exact = "exact"
    simstring = "simstring"

exact = 'exact' class-attribute

simstring = 'simstring' class-attribute

GenericMatcher

Bases: BaseComponent

Provides a generic matcher component.

PARAMETER DESCRIPTION
nlp

The spaCy object.

TYPE: Language

terms

A dictionary of terms.

TYPE: Optional[Patterns]

regex

A dictionary of regular expressions.

TYPE: Optional[Patterns]

attr

The default attribute to use for matching. Can be overridden using the terms and regex configurations.

TYPE: str

ignore_excluded

Whether to skip excluded tokens (requires an upstream pipeline to mark excluded tokens).

TYPE: bool

term_matcher

The matcher to use for matching phrases ? One of (exact, simstring)
term_matcher_config

Parameters of the matcher class
Source code in edsnlp/pipelines/core/matcher/matcher.py 
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class GenericMatcher(BaseComponent):
    """
    Provides a generic matcher component.

    Parameters
    ----------
    nlp : Language
        The spaCy object.
    terms : Optional[Patterns]
        A dictionary of terms.
    regex : Optional[Patterns]
        A dictionary of regular expressions.
    attr : str
        The default attribute to use for matching.
        Can be overridden using the `terms` and `regex` configurations.
    ignore_excluded : bool
        Whether to skip excluded tokens (requires an upstream
        pipeline to mark excluded tokens).
    term_matcher: GenericTermMatcher
        The matcher to use for matching phrases ?
        One of (exact, simstring)
    term_matcher_config: Dict[str,Any]
        Parameters of the matcher class
    """

    def __init__(
        self,
        nlp: Language,
        terms: Optional[Patterns],
        regex: Optional[Patterns],
        attr: str,
        ignore_excluded: bool,
        term_matcher: GenericTermMatcher = GenericTermMatcher.exact,
        term_matcher_config: Dict[str, Any] = None,
    ):

        self.nlp = nlp

        self.attr = attr

        if term_matcher == GenericTermMatcher.exact:
            self.phrase_matcher = EDSPhraseMatcher(
                self.nlp.vocab,
                attr=attr,
                ignore_excluded=ignore_excluded,
                **(term_matcher_config or {}),
            )
        elif term_matcher == GenericTermMatcher.simstring:
            self.phrase_matcher = SimstringMatcher(
                self.nlp.vocab,
                attr=attr,
                ignore_excluded=ignore_excluded,
                **(term_matcher_config or {}),
            )
        else:
            raise ValueError(
                f"Algorithm {repr(term_matcher)} does not belong to"
                f" known matcher [exact, simstring]."
            )

        self.regex_matcher = RegexMatcher(
            attr=attr,
            ignore_excluded=ignore_excluded,
        )

        self.phrase_matcher.build_patterns(nlp=nlp, terms=terms)
        self.regex_matcher.build_patterns(regex=regex)

        self.set_extensions()

    def process(self, doc: Doc) -> List[Span]:
        """
        Find matching spans in doc.

        Parameters
        ----------
        doc:
            spaCy Doc object.

        Returns
        -------
        spans:
            List of Spans returned by the matchers.
        """

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

        spans = list(matches) + list(regex_matches)

        return spans

    def __call__(self, doc: Doc) -> Doc:
        """
        Adds spans to document.

        Parameters
        ----------
        doc:
            spaCy Doc object

        Returns
        -------
        doc:
            spaCy Doc object, annotated for extracted terms.
        """
        matches = self.process(doc)

        for span in matches:
            if span.label_ not in doc.spans:
                doc.spans[span.label_] = []
            doc.spans[span.label_].append(span)

        ents, discarded = filter_spans(list(doc.ents) + matches, return_discarded=True)

        doc.ents = ents

        if "discarded" not in doc.spans:
            doc.spans["discarded"] = []
        doc.spans["discarded"].extend(discarded)

        return doc

nlp = nlp instance-attribute

attr = attr instance-attribute

phrase_matcher = EDSPhraseMatcher(self.nlp.vocab, attr=attr, ignore_excluded=ignore_excluded, None=term_matcher_config or {}) instance-attribute

regex_matcher = RegexMatcher(attr=attr, ignore_excluded=ignore_excluded) instance-attribute

__init__(nlp, terms, regex, attr, ignore_excluded, term_matcher=GenericTermMatcher.exact, term_matcher_config=None)

Source code in edsnlp/pipelines/core/matcher/matcher.py 
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def __init__(
    self,
    nlp: Language,
    terms: Optional[Patterns],
    regex: Optional[Patterns],
    attr: str,
    ignore_excluded: bool,
    term_matcher: GenericTermMatcher = GenericTermMatcher.exact,
    term_matcher_config: Dict[str, Any] = None,
):

    self.nlp = nlp

    self.attr = attr

    if term_matcher == GenericTermMatcher.exact:
        self.phrase_matcher = EDSPhraseMatcher(
            self.nlp.vocab,
            attr=attr,
            ignore_excluded=ignore_excluded,
            **(term_matcher_config or {}),
        )
    elif term_matcher == GenericTermMatcher.simstring:
        self.phrase_matcher = SimstringMatcher(
            self.nlp.vocab,
            attr=attr,
            ignore_excluded=ignore_excluded,
            **(term_matcher_config or {}),
        )
    else:
        raise ValueError(
            f"Algorithm {repr(term_matcher)} does not belong to"
            f" known matcher [exact, simstring]."
        )

    self.regex_matcher = RegexMatcher(
        attr=attr,
        ignore_excluded=ignore_excluded,
    )

    self.phrase_matcher.build_patterns(nlp=nlp, terms=terms)
    self.regex_matcher.build_patterns(regex=regex)

    self.set_extensions()

process(doc)

Find matching spans in doc.

PARAMETER DESCRIPTION
doc

spaCy Doc object.

TYPE: Doc

RETURNS DESCRIPTION
spans

List of Spans returned by the matchers.
Source code in edsnlp/pipelines/core/matcher/matcher.py 
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def process(self, doc: Doc) -> List[Span]:
    """
    Find matching spans in doc.

    Parameters
    ----------
    doc:
        spaCy Doc object.

    Returns
    -------
    spans:
        List of Spans returned by the matchers.
    """

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

    spans = list(matches) + list(regex_matches)

    return spans

__call__(doc)

Adds spans to document.

PARAMETER DESCRIPTION
doc

spaCy Doc object

TYPE: Doc

RETURNS DESCRIPTION
doc

spaCy Doc object, annotated for extracted terms.
Source code in edsnlp/pipelines/core/matcher/matcher.py 
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def __call__(self, doc: Doc) -> Doc:
    """
    Adds spans to document.

    Parameters
    ----------
    doc:
        spaCy Doc object

    Returns
    -------
    doc:
        spaCy Doc object, annotated for extracted terms.
    """
    matches = self.process(doc)

    for span in matches:
        if span.label_ not in doc.spans:
            doc.spans[span.label_] = []
        doc.spans[span.label_].append(span)

    ents, discarded = filter_spans(list(doc.ents) + matches, return_discarded=True)

    doc.ents = ents

    if "discarded" not in doc.spans:
        doc.spans["discarded"] = []
    doc.spans["discarded"].extend(discarded)

    return doc