edsnlp.pipelines.ner.scores.emergency

gemsa

patterns

regex = ['\\bgemsa\\b'] module-attribute

after_extract = 'gemsa.*?[\\n\\W]*?(\\d+)' module-attribute

score_normalization_str = 'score_normalization.gemsa' module-attribute

score_normalization(extracted_score)

GEMSA score normalization. If available, returns the integer value of the GEMSA score.

Source code in edsnlp/pipelines/ner/scores/emergency/gemsa/patterns.py

@spacy.registry.misc(score_normalization_str)
def score_normalization(extracted_score: Union[str, None]):
    """
    GEMSA score normalization.
    If available, returns the integer value of the GEMSA score.
    """
    score_range = [1, 2, 3, 4, 5, 6]
    if (extracted_score is not None) and (int(extracted_score) in score_range):
        return int(extracted_score)

factory

DEFAULT_CONFIG = dict(regex=patterns.regex, after_extract=patterns.after_extract, score_normalization=patterns.score_normalization_str, attr='NORM', window=20, verbose=0, ignore_excluded=False) module-attribute

create_component(nlp, name, regex, after_extract, score_normalization, attr, window, verbose, ignore_excluded)

Source code in edsnlp/pipelines/ner/scores/emergency/gemsa/factory.py

@deprecated_factory(
    "emergency.gemsa", "eds.emergency.gemsa", default_config=DEFAULT_CONFIG
)
@Language.factory("eds.emergency.gemsa", default_config=DEFAULT_CONFIG)
def create_component(
    nlp: Language,
    name: str,
    regex: List[str],
    after_extract: str,
    score_normalization: Union[str, Callable[[Union[str, None]], Any]],
    attr: str,
    window: int,
    verbose: int,
    ignore_excluded: bool,
):
    return Score(
        nlp,
        score_name=name,
        regex=regex,
        after_extract=after_extract,
        score_normalization=score_normalization,
        attr=attr,
        window=window,
        verbose=verbose,
        ignore_excluded=ignore_excluded,
    )

priority

patterns

regex = ['\\bpriorite\\b'] module-attribute

after_extract = 'priorite.*?[\\n\\W]*?(\\d+)' module-attribute

score_normalization_str = 'score_normalization.priority' module-attribute

score_normalization(extracted_score)

Priority score normalization. If available, returns the integer value of the priority score.

Source code in edsnlp/pipelines/ner/scores/emergency/priority/patterns.py

@spacy.registry.misc(score_normalization_str)
def score_normalization(extracted_score: Union[str, None]):
    """
    Priority score normalization.
    If available, returns the integer value of the priority score.
    """
    score_range = list(range(0, 6))
    if (extracted_score is not None) and (int(extracted_score) in score_range):
        return int(extracted_score)

factory

DEFAULT_CONFIG = dict(regex=patterns.regex, after_extract=patterns.after_extract, score_normalization=patterns.score_normalization_str, attr='NORM', window=7, verbose=0, ignore_excluded=False) module-attribute

create_component(nlp, name, regex, after_extract, score_normalization, attr, window, verbose, ignore_excluded)

Source code in edsnlp/pipelines/ner/scores/emergency/priority/factory.py

@deprecated_factory(
    "emergency.priority", "eds.emergency.priority", default_config=DEFAULT_CONFIG
)
@Language.factory("eds.emergency.priority", default_config=DEFAULT_CONFIG)
def create_component(
    nlp: Language,
    name: str,
    regex: List[str],
    after_extract: str,
    score_normalization: Union[str, Callable[[Union[str, None]], Any]],
    attr: str,
    window: int,
    verbose: int,
    ignore_excluded: bool,
):
    return Score(
        nlp,
        score_name=name,
        regex=regex,
        after_extract=after_extract,
        score_normalization=score_normalization,
        attr=attr,
        window=window,
        verbose=verbose,
        ignore_excluded=ignore_excluded,
    )

ccmu

patterns

regex = ['\\bccmu\\b'] module-attribute

after_extract = 'ccmu.*?[\\n\\W]*?(\\d+)' module-attribute

score_normalization_str = 'score_normalization.ccmu' module-attribute

score_normalization(extracted_score)

CCMU score normalization. If available, returns the integer value of the CCMU score.

Source code in edsnlp/pipelines/ner/scores/emergency/ccmu/patterns.py

@spacy.registry.misc(score_normalization_str)
def score_normalization(extracted_score: Union[str, None]):
    """
    CCMU score normalization.
    If available, returns the integer value of the CCMU score.
    """
    score_range = [1, 2, 3, 4, 5]
    if (extracted_score is not None) and (int(extracted_score) in score_range):
        return int(extracted_score)

factory

DEFAULT_CONFIG = dict(regex=patterns.regex, after_extract=patterns.after_extract, score_normalization=patterns.score_normalization_str, attr='NORM', window=20, verbose=0, ignore_excluded=False) module-attribute

create_component(nlp, name, regex, after_extract, score_normalization, attr, window, verbose, ignore_excluded)

Source code in edsnlp/pipelines/ner/scores/emergency/ccmu/factory.py

@deprecated_factory(
    "emergency.ccmu", "eds.emergency.ccmu", default_config=DEFAULT_CONFIG
)
@Language.factory("eds.emergency.ccmu", default_config=DEFAULT_CONFIG)
def create_component(
    nlp: Language,
    name: str,
    regex: List[str],
    after_extract: str,
    score_normalization: Union[str, Callable[[Union[str, None]], Any]],
    attr: str,
    window: int,
    verbose: int,
    ignore_excluded: bool,
):
    return Score(
        nlp,
        score_name=name,
        regex=regex,
        after_extract=after_extract,
        score_normalization=score_normalization,
        attr=attr,
        window=window,
        verbose=verbose,
        ignore_excluded=ignore_excluded,
    )