langtest.utils.custom_types.sample.ClinicalSample#

class ClinicalSample(*, patient_info_A: str, patient_info_B: str, diagnosis: str, clinical_domain: str, treatment_plan_A: str = None, treatment_plan_B: str = None, state: str = None, dataset_name: str = None, task: str = None, category: str = None, test_type: str = None, ran_pass: tuple = None)#

Bases: BaseModel

A class Representing a sample for clinical task.

patient_info_A#

The information of patient A.

Type:

str

patient_info_B#

The information of patient B.

Type:

str

diagnosis#

The diagnosis for the patient.

Type:

str

treatment_plan_A#

The treatment prescribed for patient A.

Type:

str

treatment_plan_B#

The treatment prescribed for patient B.

Type:

str

state#

The state of the sample.

Type:

str

dataset_name#

The name of the dataset the sample belongs to.

Type:

str

task#

The task associated with the sample.

Type:

str

category#

The category of the sample.

Type:

str

test_type#

The type of test the sample belongs to.

Type:

str

__init__(**data)#

Create a new model by parsing and validating input data from keyword arguments.

Raises ValidationError if the input data cannot be parsed to form a valid model.

Methods

__init__(**data)	Create a new model by parsing and validating input data from keyword arguments.
construct([_fields_set])	Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data.
copy(*[, include, exclude, update, deep])	Duplicate a model, optionally choose which fields to include, exclude and change.
dict(*[, include, exclude, by_alias, ...])	Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.
from_orm(obj)
is_pass()
json(*[, include, exclude, by_alias, ...])	Generate a JSON representation of the model, include and exclude arguments as per dict().
parse_file(path, *[, content_type, ...])
parse_obj(obj)
parse_raw(b, *[, content_type, encoding, ...])
run(model, **kwargs)
schema([by_alias, ref_template])
schema_json(*[, by_alias, ref_template])
to_dict()	Converts the ClinicalSample object to a dictionary.
update_forward_refs(**localns)	Try to update ForwardRefs on fields based on this Model, globalns and localns.
validate(value)

Attributes

patient_info_A
patient_info_B
diagnosis
clinical_domain
treatment_plan_A
treatment_plan_B
state
dataset_name
task
category
test_type
ran_pass

classmethod construct(_fields_set: SetStr | None = None, **values: Any) → Model#

Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed. Behaves as if Config.extra = ‘allow’ was set since it adds all passed values

copy(*, include: AbstractSetIntStr | MappingIntStrAny | None = None, exclude: AbstractSetIntStr | MappingIntStrAny | None = None, update: DictStrAny | None = None, deep: bool = False) → Model#

Duplicate a model, optionally choose which fields to include, exclude and change.

Parameters:

• include – fields to include in new model

• exclude – fields to exclude from new model, as with values this takes precedence over include

• update – values to change/add in the new model. Note: the data is not validated before creating the new model: you should trust this data

• deep – set to True to make a deep copy of the model

Returns:

new model instance

dict(*, include: AbstractSetIntStr | MappingIntStrAny | None = None, exclude: AbstractSetIntStr | MappingIntStrAny | None = None, by_alias: bool = False, skip_defaults: bool | None = 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.

json(*, include: AbstractSetIntStr | MappingIntStrAny | None = None, exclude: AbstractSetIntStr | MappingIntStrAny | None = None, by_alias: bool = False, skip_defaults: bool | None = None, exclude_unset: bool = False, exclude_defaults: bool = False, exclude_none: bool = False, encoder: Callable[[Any], Any] | None = None, models_as_dict: bool = True, **dumps_kwargs: Any) → unicode#

Generate a JSON representation of the model, include and exclude arguments as per dict().

encoder is an optional function to supply as default to json.dumps(), other arguments as per json.dumps().

to_dict() → Dict[str, Any]#

Converts the ClinicalSample object to a dictionary.

Returns:

A dictionary representation of the ClinicalSample object.

Return type:

Dict[str, Any]

classmethod update_forward_refs(**localns: Any) → None#

Try to update ForwardRefs on fields based on this Model, globalns and localns.