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edsnlp.pipelines.ner.scores.tnm.models

TnmEnum

Bases: Enum

Source code in edsnlp/pipelines/ner/scores/tnm/models.py 
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class TnmEnum(Enum):
    def __str__(self) -> str:
        return self.value

__str__()

Source code in edsnlp/pipelines/ner/scores/tnm/models.py 
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def __str__(self) -> str:
    return self.value

Prefix

Bases: TnmEnum

Source code in edsnlp/pipelines/ner/scores/tnm/models.py 
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class Prefix(TnmEnum):
    clinical = "c"
    histopathology = "p"
    histopathology2 = "P"
    neoadjuvant_therapy = "y"
    recurrent = "r"
    autopsy = "a"
    ultrasonography = "u"
    multifocal = "m"
    py = "yp"
    mp = "mp"

clinical = 'c' class-attribute

histopathology = 'p' class-attribute

histopathology2 = 'P' class-attribute

neoadjuvant_therapy = 'y' class-attribute

recurrent = 'r' class-attribute

autopsy = 'a' class-attribute

ultrasonography = 'u' class-attribute

multifocal = 'm' class-attribute

py = 'yp' class-attribute

mp = 'mp' class-attribute

Tumour

Bases: TnmEnum

Source code in edsnlp/pipelines/ner/scores/tnm/models.py 
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class Tumour(TnmEnum):
    unknown = "x"
    in_situ = "is"
    score_0 = "0"
    score_1 = "1"
    score_2 = "2"
    score_3 = "3"
    score_4 = "4"
    o = "o"

unknown = 'x' class-attribute

in_situ = 'is' class-attribute

score_0 = '0' class-attribute

score_1 = '1' class-attribute

score_2 = '2' class-attribute

score_3 = '3' class-attribute

score_4 = '4' class-attribute

o = 'o' class-attribute

Specification

Bases: TnmEnum

Source code in edsnlp/pipelines/ner/scores/tnm/models.py 
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class Specification(TnmEnum):
    a = "a"
    b = "b"
    c = "c"
    d = "d"
    mi = "mi"
    x = "x"

a = 'a' class-attribute

b = 'b' class-attribute

c = 'c' class-attribute

d = 'd' class-attribute

mi = 'mi' class-attribute

x = 'x' class-attribute

Node

Bases: TnmEnum

Source code in edsnlp/pipelines/ner/scores/tnm/models.py 
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class Node(TnmEnum):
    unknown = "x"
    score_0 = "0"
    score_1 = "1"
    score_2 = "2"
    score_3 = "3"
    o = "o"

unknown = 'x' class-attribute

score_0 = '0' class-attribute

score_1 = '1' class-attribute

score_2 = '2' class-attribute

score_3 = '3' class-attribute

o = 'o' class-attribute

Metastasis

Bases: TnmEnum

Source code in edsnlp/pipelines/ner/scores/tnm/models.py 
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class Metastasis(TnmEnum):
    unknown = "x"
    score_0 = "0"
    score_1 = "1"
    o = "o"
    score_1x = "1x"
    score_2x = "2x"
    ox = "ox"

unknown = 'x' class-attribute

score_0 = '0' class-attribute

score_1 = '1' class-attribute

o = 'o' class-attribute

score_1x = '1x' class-attribute

score_2x = '2x' class-attribute

ox = 'ox' class-attribute

TNM

Bases: BaseModel

Source code in edsnlp/pipelines/ner/scores/tnm/models.py 
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class TNM(BaseModel):

    prefix: Optional[Prefix] = None
    tumour: Optional[Tumour] = None
    tumour_specification: Optional[Specification] = None
    tumour_suffix: Optional[str] = None
    node: Optional[Node] = None
    node_specification: Optional[Specification] = None
    node_suffix: Optional[str] = None
    metastasis: Optional[Metastasis] = None
    resection_completeness: Optional[int] = None
    version: Optional[str] = None
    version_year: Optional[int] = None

    @validator("*", pre=True)
    def coerce_o(cls, v):
        if isinstance(v, str):
            v = v.replace("o", "0")
        return v

    @validator("version_year")
    def validate_year(cls, v):
        if v is None:
            return v

        if v < 40:
            v += 2000
        elif v < 100:
            v += 1900

        return v

    def norm(self) -> str:
        norm = []

        if self.prefix is not None:
            norm.append(str(self.prefix))

        if (
            (self.tumour is not None)
            | (self.tumour_specification is not None)
            | (self.tumour_suffix is not None)
        ):
            norm.append(f"T{str(self.tumour or '')}")
            norm.append(f"{str(self.tumour_specification or '')}")
            norm.append(f"{str(self.tumour_suffix or '')}")

        if (
            (self.node is not None)
            | (self.node_specification is not None)
            | (self.node_suffix is not None)
        ):
            norm.append(f"N{str(self.node or '')}")
            norm.append(f"{str(self.node_specification or '')}")
            norm.append(f"{str(self.node_suffix or '')}")

        if self.metastasis is not None:
            norm.append(f"M{self.metastasis}")

        if self.resection_completeness is not None:
            norm.append(f"R{self.resection_completeness}")

        if self.version is not None and self.version_year is not None:
            norm.append(f" ({self.version.upper()} {self.version_year})")

        return "".join(norm)

    def dict(
        self,
        *,
        include: Union["AbstractSetIntStr", "MappingIntStrAny"] = None,
        exclude: Union["AbstractSetIntStr", "MappingIntStrAny"] = None,
        by_alias: bool = False,
        skip_defaults: bool = None,
        exclude_unset: bool = False,
        exclude_defaults: bool = False,
        exclude_none: bool = False,
    ) -> "DictStrAny":
        """
        Generate a dictionary representation of the model,
        optionally specifying which fields to include or exclude.

        """
        if skip_defaults is not None:
            warnings.warn(
                f"""{self.__class__.__name__}.dict(): "skip_defaults"
                is deprecated and replaced by "exclude_unset" """,
                DeprecationWarning,
            )
            exclude_unset = skip_defaults

        d = dict(
            self._iter(
                to_dict=True,
                by_alias=by_alias,
                include=include,
                exclude=exclude,
                exclude_unset=exclude_unset,
                exclude_defaults=exclude_defaults,
                exclude_none=exclude_none,
            )
        )
        set_keys = set(d.keys())
        for k in set_keys.intersection(
            {
                "prefix",
                "tumour",
                "node",
                "metastasis",
                "tumour_specification",
                "node_specification",
                "tumour_suffix",
                "node_suffix",
            }
        ):
            v = d[k]
            if isinstance(v, TnmEnum):
                d[k] = v.value

        return d

prefix: Optional[Prefix] = None class-attribute

tumour: Optional[Tumour] = None class-attribute

tumour_specification: Optional[Specification] = None class-attribute

tumour_suffix: Optional[str] = None class-attribute

node: Optional[Node] = None class-attribute

node_specification: Optional[Specification] = None class-attribute

node_suffix: Optional[str] = None class-attribute

metastasis: Optional[Metastasis] = None class-attribute

resection_completeness: Optional[int] = None class-attribute

version: Optional[str] = None class-attribute

version_year: Optional[int] = None class-attribute

coerce_o(v)

Source code in edsnlp/pipelines/ner/scores/tnm/models.py 
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@validator("*", pre=True)
def coerce_o(cls, v):
    if isinstance(v, str):
        v = v.replace("o", "0")
    return v

validate_year(v)

Source code in edsnlp/pipelines/ner/scores/tnm/models.py 
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@validator("version_year")
def validate_year(cls, v):
    if v is None:
        return v

    if v < 40:
        v += 2000
    elif v < 100:
        v += 1900

    return v

norm()

Source code in edsnlp/pipelines/ner/scores/tnm/models.py 
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def norm(self) -> str:
    norm = []

    if self.prefix is not None:
        norm.append(str(self.prefix))

    if (
        (self.tumour is not None)
        | (self.tumour_specification is not None)
        | (self.tumour_suffix is not None)
    ):
        norm.append(f"T{str(self.tumour or '')}")
        norm.append(f"{str(self.tumour_specification or '')}")
        norm.append(f"{str(self.tumour_suffix or '')}")

    if (
        (self.node is not None)
        | (self.node_specification is not None)
        | (self.node_suffix is not None)
    ):
        norm.append(f"N{str(self.node or '')}")
        norm.append(f"{str(self.node_specification or '')}")
        norm.append(f"{str(self.node_suffix or '')}")

    if self.metastasis is not None:
        norm.append(f"M{self.metastasis}")

    if self.resection_completeness is not None:
        norm.append(f"R{self.resection_completeness}")

    if self.version is not None and self.version_year is not None:
        norm.append(f" ({self.version.upper()} {self.version_year})")

    return "".join(norm)

dict(*, include=None, exclude=None, by_alias=False, skip_defaults=None, exclude_unset=False, exclude_defaults=False, exclude_none=False)

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Source code in edsnlp/pipelines/ner/scores/tnm/models.py 
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def dict(
    self,
    *,
    include: Union["AbstractSetIntStr", "MappingIntStrAny"] = None,
    exclude: Union["AbstractSetIntStr", "MappingIntStrAny"] = None,
    by_alias: bool = False,
    skip_defaults: bool = None,
    exclude_unset: bool = False,
    exclude_defaults: bool = False,
    exclude_none: bool = False,
) -> "DictStrAny":
    """
    Generate a dictionary representation of the model,
    optionally specifying which fields to include or exclude.

    """
    if skip_defaults is not None:
        warnings.warn(
            f"""{self.__class__.__name__}.dict(): "skip_defaults"
            is deprecated and replaced by "exclude_unset" """,
            DeprecationWarning,
        )
        exclude_unset = skip_defaults

    d = dict(
        self._iter(
            to_dict=True,
            by_alias=by_alias,
            include=include,
            exclude=exclude,
            exclude_unset=exclude_unset,
            exclude_defaults=exclude_defaults,
            exclude_none=exclude_none,
        )
    )
    set_keys = set(d.keys())
    for k in set_keys.intersection(
        {
            "prefix",
            "tumour",
            "node",
            "metastasis",
            "tumour_specification",
            "node_specification",
            "tumour_suffix",
            "node_suffix",
        }
    ):
        v = d[k]
        if isinstance(v, TnmEnum):
            d[k] = v.value

    return d