Utilities

Utility functions and classes.

lydata.utils.get_default_column_map() → _ColumnMap

Get the default column map.

This map defines which short column names can be used to access columns in the DataFrames.

>>> from lydata import accessor, loader
>>> df = next(loader.load_datasets(institution="usz"))
>>> df.ly.surgery   
0      False
...
286    False
Name: (patient, #, neck_dissection), Length: 287, dtype: bool
>>> df.ly.smoke   
0       True
...
286     True
Name: (patient, #, nicotine_abuse), Length: 287, dtype: bool

class lydata.utils.ModalityConfig(*, spec: Annotated[float, Ge(ge=0.5), Le(le=1.0)], sens: Annotated[float, Ge(ge=0.5), Le(le=1.0)], kind: Literal['clinical', 'pathological'] = 'clinical')

Define a diagnostic or pathological modality.

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}

A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {}

Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

model_fields: ClassVar[Dict[str, FieldInfo]] = {'kind': FieldInfo(annotation=Literal['clinical', 'pathological'], required=False, default='clinical', description='Clinical modalities cannot detect microscopic disease.'), 'sens': FieldInfo(annotation=float, required=True, description='Sensitivity of the modality.', metadata=[Ge(ge=0.5), Le(le=1.0)]), 'spec': FieldInfo(annotation=float, required=True, description='Specificity of the modality.', metadata=[Ge(ge=0.5), Le(le=1.0)])}

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

lydata.utils.get_default_modalities() → dict[str, ModalityConfig]

Get defaults values for sensitivities and specificities of modalities.

Taken from de Bondt et al. (2007) and Kyzas et al. (2008).