measurement

mlte/measurement/measurement.py

Superclass for all measurements.

Measurement

The superclass for all model measurements.

Source code in mlte/measurement/measurement.py

class Measurement(metaclass=abc.ABCMeta):
    """
    The superclass for all model measurements.
    """

    @classmethod
    def __subclasshook__(cls, subclass):
        """Define the interface for all concrete measurements."""
        return meta.has_callables(subclass, "__call__")

    def __init__(
        self, instance: Measurement, identifier: str, info: Optional[str] = None
    ):
        """
        Initialize a new Measurement instance.

        :param instance: The invoking instance (Measurement subclass)
        :param identifier: A unique identifier for the instance
        """
        self.metadata = EvidenceMetadata(
            measurement_type=type(instance).__name__,
            identifier=Identifier(name=identifier),
            info=info,
        )
        """The metadata for the measurement instance."""

    @abc.abstractmethod
    def __call__(self, *args, **kwargs) -> Value:
        """Evaluate a measurement and return a value semantics."""
        raise NotImplementedError("Cannot evaluate abstract measurement.")

    def evaluate(self, *args, **kwargs) -> Value:
        """
        Evaluate a measurement and return a value with semantics.

        :return: The resulting value of measurement execution, with semantics
        :rtype: Value
        """
        # Evaluate the measurement
        return self.__call__(*args, **kwargs)

    @classmethod
    def value(cls) -> Type[Value]:
        """Returns the class type object for the Value produced by the Measurement."""
        # Opaque is the default Value type.
        return Opaque

    def __str__(self) -> str:
        """Return a string representation of a Measurement."""
        return f"{self.metadata}"

metadata = EvidenceMetadata(measurement_type=type(instance).__name__, identifier=Identifier(name=identifier), info=info) instance-attribute

The metadata for the measurement instance.

__call__(*args, **kwargs) abstractmethod

Evaluate a measurement and return a value semantics.

Source code in mlte/measurement/measurement.py

@abc.abstractmethod
def __call__(self, *args, **kwargs) -> Value:
    """Evaluate a measurement and return a value semantics."""
    raise NotImplementedError("Cannot evaluate abstract measurement.")

__init__(instance, identifier, info=None)

Initialize a new Measurement instance.

Parameters:

Name	Type	Description	Default
instance	Measurement	The invoking instance (Measurement subclass)	required
identifier	str	A unique identifier for the instance	required

Source code in mlte/measurement/measurement.py

def __init__(
    self, instance: Measurement, identifier: str, info: Optional[str] = None
):
    """
    Initialize a new Measurement instance.

    :param instance: The invoking instance (Measurement subclass)
    :param identifier: A unique identifier for the instance
    """
    self.metadata = EvidenceMetadata(
        measurement_type=type(instance).__name__,
        identifier=Identifier(name=identifier),
        info=info,
    )
    """The metadata for the measurement instance."""

__str__()

Return a string representation of a Measurement.

Source code in mlte/measurement/measurement.py

def __str__(self) -> str:
    """Return a string representation of a Measurement."""
    return f"{self.metadata}"

__subclasshook__(subclass) classmethod

Define the interface for all concrete measurements.

Source code in mlte/measurement/measurement.py

@classmethod
def __subclasshook__(cls, subclass):
    """Define the interface for all concrete measurements."""
    return meta.has_callables(subclass, "__call__")

evaluate(*args, **kwargs)

Evaluate a measurement and return a value with semantics.

Returns:

Type	Description
Value	The resulting value of measurement execution, with semantics

Source code in mlte/measurement/measurement.py

def evaluate(self, *args, **kwargs) -> Value:
    """
    Evaluate a measurement and return a value with semantics.

    :return: The resulting value of measurement execution, with semantics
    :rtype: Value
    """
    # Evaluate the measurement
    return self.__call__(*args, **kwargs)

value() classmethod

Returns the class type object for the Value produced by the Measurement.

Source code in mlte/measurement/measurement.py

@classmethod
def value(cls) -> Type[Value]:
    """Returns the class type object for the Value produced by the Measurement."""
    # Opaque is the default Value type.
    return Opaque