Class swarmauri_standard.metrics.AbsoluteValueMetric.AbsoluteValueMetric

swarmauri_standard.metrics.AbsoluteValueMetric.AbsoluteValueMetric

Bases: MetricBase

Implementation of a metric based on absolute value difference.

This is the simplest valid metric, using subtraction and the absolute value to calculate distance between real numbers.

Attributes

type : Literal["AbsoluteValueMetric"] The type identifier for this metric implementation. resource : str, optional The resource type, defaults to METRIC.

type class-attribute instance-attribute

type = 'AbsoluteValueMetric'

model_config class-attribute instance-attribute

model_config = ConfigDict(
    extra="allow", arbitrary_types_allowed=True
)

id class-attribute instance-attribute

id = Field(default_factory=generate_id)

members class-attribute instance-attribute

members = None

owners class-attribute instance-attribute

owners = None

host class-attribute instance-attribute

host = None

default_logger class-attribute

default_logger = None

logger class-attribute instance-attribute

logger = None

name class-attribute instance-attribute

name = None

resource class-attribute instance-attribute

resource = METRIC.value

version class-attribute instance-attribute

version = '0.1.0'

distance

distance(x, y)

Calculate the distance between two scalar values using absolute difference.

Parameters

x : MetricInput First scalar value y : MetricInput Second scalar value

Returns

float The absolute difference between x and y

Raises

TypeError If inputs are not scalar numeric values

Source code in swarmauri_standard/metrics/AbsoluteValueMetric.py

def distance(self, x: MetricInput, y: MetricInput) -> float:
    """
    Calculate the distance between two scalar values using absolute difference.

    Parameters
    ----------
    x : MetricInput
        First scalar value
    y : MetricInput
        Second scalar value

    Returns
    -------
    float
        The absolute difference between x and y

    Raises
    ------
    TypeError
        If inputs are not scalar numeric values
    """
    logger.debug(f"Calculating absolute value distance between {x} and {y}")

    # Validate inputs are scalar numeric values
    if not isinstance(x, (int, float)) or not isinstance(y, (int, float)):
        logger.error(f"Invalid input types: x is {type(x)}, y is {type(y)}")
        raise TypeError("AbsoluteValueMetric requires scalar numeric inputs")

    # Calculate absolute difference
    return abs(x - y)

distances

distances(x, y)

Calculate distances between collections of scalar values.

Parameters

x : Union[MetricInput, MetricInputCollection] First collection of scalar values y : Union[MetricInput, MetricInputCollection] Second collection of scalar values

Returns

Union[List[float], IVector, IMatrix] Vector of distances if one of the inputs is a single value, or a matrix of pairwise distances otherwise

Raises

TypeError If inputs are not collections of scalar values ValueError If input collections have incompatible shapes

Source code in swarmauri_standard/metrics/AbsoluteValueMetric.py

def distances(
    self,
    x: Union[MetricInput, MetricInputCollection],
    y: Union[MetricInput, MetricInputCollection],
) -> Union[List[float], IVector, IMatrix]:
    """
    Calculate distances between collections of scalar values.

    Parameters
    ----------
    x : Union[MetricInput, MetricInputCollection]
        First collection of scalar values
    y : Union[MetricInput, MetricInputCollection]
        Second collection of scalar values

    Returns
    -------
    Union[List[float], IVector, IMatrix]
        Vector of distances if one of the inputs is a single value,
        or a matrix of pairwise distances otherwise

    Raises
    ------
    TypeError
        If inputs are not collections of scalar values
    ValueError
        If input collections have incompatible shapes
    """
    logger.debug("Calculating distances between collections")

    # Convert inputs to lists for uniform processing
    x_values = self._to_list(x)
    y_values = self._to_list(y)

    # If one input is a single value and the other is a collection
    if len(x_values) == 1 and len(y_values) > 1:
        return [self.distance(x_values[0], y_val) for y_val in y_values]
    elif len(y_values) == 1 and len(x_values) > 1:
        return [self.distance(x_val, y_values[0]) for x_val in x_values]

    # Both inputs are collections - return matrix of pairwise distances
    return [
        [self.distance(x_val, y_val) for y_val in y_values] for x_val in x_values
    ]

check_non_negativity

check_non_negativity(x, y)

Check if the metric satisfies the non-negativity axiom: d(x,y) ≥ 0.

This is always true for absolute value difference.

Parameters

x : MetricInput First scalar value y : MetricInput Second scalar value

Returns

bool True (absolute value is always non-negative)

Source code in swarmauri_standard/metrics/AbsoluteValueMetric.py

def check_non_negativity(self, x: MetricInput, y: MetricInput) -> bool:
    """
    Check if the metric satisfies the non-negativity axiom: d(x,y) ≥ 0.

    This is always true for absolute value difference.

    Parameters
    ----------
    x : MetricInput
        First scalar value
    y : MetricInput
        Second scalar value

    Returns
    -------
    bool
        True (absolute value is always non-negative)
    """
    logger.debug(f"Checking non-negativity axiom for {x} and {y}")
    dist = self.distance(x, y)
    # Absolute value is always non-negative
    return dist >= 0

check_identity_of_indiscernibles

check_identity_of_indiscernibles(x, y)

Check if the metric satisfies the identity of indiscernibles axiom: d(x,y) = 0 if and only if x = y.

Parameters

x : MetricInput First scalar value y : MetricInput Second scalar value

Returns

bool True if the axiom is satisfied

Source code in swarmauri_standard/metrics/AbsoluteValueMetric.py

def check_identity_of_indiscernibles(self, x: MetricInput, y: MetricInput) -> bool:
    """
    Check if the metric satisfies the identity of indiscernibles axiom:
    d(x,y) = 0 if and only if x = y.

    Parameters
    ----------
    x : MetricInput
        First scalar value
    y : MetricInput
        Second scalar value

    Returns
    -------
    bool
        True if the axiom is satisfied
    """
    logger.debug(f"Checking identity of indiscernibles axiom for {x} and {y}")
    dist = self.distance(x, y)

    # Check if distance is 0 iff x equals y
    return (dist == 0 and x == y) or (dist > 0 and x != y)

check_symmetry

check_symmetry(x, y)

Check if the metric satisfies the symmetry axiom: d(x,y) = d(y,x).

Parameters

x : MetricInput First scalar value y : MetricInput Second scalar value

Returns

bool True if the axiom is satisfied

Source code in swarmauri_standard/metrics/AbsoluteValueMetric.py

def check_symmetry(self, x: MetricInput, y: MetricInput) -> bool:
    """
    Check if the metric satisfies the symmetry axiom: d(x,y) = d(y,x).

    Parameters
    ----------
    x : MetricInput
        First scalar value
    y : MetricInput
        Second scalar value

    Returns
    -------
    bool
        True if the axiom is satisfied
    """
    logger.debug(f"Checking symmetry axiom for {x} and {y}")

    # Calculate distances in both directions
    dist_xy = self.distance(x, y)
    dist_yx = self.distance(y, x)

    # Check if distances are equal (allowing for small floating-point errors)
    return abs(dist_xy - dist_yx) < 1e-10

check_triangle_inequality

check_triangle_inequality(x, y, z)

Check if the metric satisfies the triangle inequality axiom: d(x,z) ≤ d(x,y) + d(y,z).

Parameters

x : MetricInput First scalar value y : MetricInput Second scalar value z : MetricInput Third scalar value

Returns

bool True if the axiom is satisfied

Source code in swarmauri_standard/metrics/AbsoluteValueMetric.py

def check_triangle_inequality(
    self, x: MetricInput, y: MetricInput, z: MetricInput
) -> bool:
    """
    Check if the metric satisfies the triangle inequality axiom:
    d(x,z) ≤ d(x,y) + d(y,z).

    Parameters
    ----------
    x : MetricInput
        First scalar value
    y : MetricInput
        Second scalar value
    z : MetricInput
        Third scalar value

    Returns
    -------
    bool
        True if the axiom is satisfied
    """
    logger.debug(f"Checking triangle inequality axiom for {x}, {y}, and {z}")

    # Calculate all three distances
    dist_xy = self.distance(x, y)
    dist_yz = self.distance(y, z)
    dist_xz = self.distance(x, z)

    # Check triangle inequality (with small tolerance for floating-point errors)
    return dist_xz <= dist_xy + dist_yz + 1e-10

register_model classmethod

register_model()

Decorator to register a base model in the unified registry.

RETURNS	DESCRIPTION
Callable	A decorator function that registers the model class. TYPE: Callable[[Type[BaseModel]], Type[BaseModel]]

Source code in swarmauri_base/DynamicBase.py

@classmethod
def register_model(cls) -> Callable[[Type[BaseModel]], Type[BaseModel]]:
    """
    Decorator to register a base model in the unified registry.

    Returns:
        Callable: A decorator function that registers the model class.
    """

    def decorator(model_cls: Type[BaseModel]):
        """Register ``model_cls`` as a base model."""
        model_name = model_cls.__name__
        if model_name in cls._registry:
            glogger.warning(
                "Model '%s' is already registered; skipping duplicate.", model_name
            )
            return model_cls

        cls._registry[model_name] = {"model_cls": model_cls, "subtypes": {}}
        glogger.debug("Registered base model '%s'.", model_name)
        DynamicBase._recreate_models()
        return model_cls

    return decorator

register_type classmethod

register_type(resource_type=None, type_name=None)

Decorator to register a subtype under one or more base models in the unified registry.

PARAMETER	DESCRIPTION
resource_type	The base model(s) under which to register the subtype. If None, all direct base classes (except DynamicBase) are used. TYPE: Optional[Union[Type[T], List[Type[T]]]] DEFAULT: None
type_name	An optional custom type name for the subtype. TYPE: Optional[str] DEFAULT: None

RETURNS	DESCRIPTION
Callable	A decorator function that registers the subtype. TYPE: Callable[[Type[DynamicBase]], Type[DynamicBase]]

Source code in swarmauri_base/DynamicBase.py

@classmethod
def register_type(
    cls,
    resource_type: Optional[Union[Type[T], List[Type[T]]]] = None,
    type_name: Optional[str] = None,
) -> Callable[[Type["DynamicBase"]], Type["DynamicBase"]]:
    """
    Decorator to register a subtype under one or more base models in the unified registry.

    Parameters:
        resource_type (Optional[Union[Type[T], List[Type[T]]]]):
            The base model(s) under which to register the subtype. If None, all direct base classes (except DynamicBase)
            are used.
        type_name (Optional[str]): An optional custom type name for the subtype.

    Returns:
        Callable: A decorator function that registers the subtype.
    """

    def decorator(subclass: Type["DynamicBase"]):
        """Register ``subclass`` as a subtype."""
        if resource_type is None:
            resource_types = [
                base for base in subclass.__bases__ if base is not cls
            ]
        elif not isinstance(resource_type, list):
            resource_types = [resource_type]
        else:
            resource_types = resource_type

        for rt in resource_types:
            if not issubclass(subclass, rt):
                raise TypeError(
                    f"'{subclass.__name__}' must be a subclass of '{rt.__name__}'."
                )
            final_type_name = type_name or getattr(
                subclass, "_type", subclass.__name__
            )
            base_model_name = rt.__name__

            if base_model_name not in cls._registry:
                cls._registry[base_model_name] = {"model_cls": rt, "subtypes": {}}
                glogger.debug(
                    "Created new registry entry for base model '%s'.",
                    base_model_name,
                )

            subtypes_dict = cls._registry[base_model_name]["subtypes"]
            if final_type_name in subtypes_dict:
                glogger.warning(
                    "Type '%s' already exists under '%s'; skipping duplicate.",
                    final_type_name,
                    base_model_name,
                )
                continue

            subtypes_dict[final_type_name] = subclass
            glogger.debug(
                "Registered '%s' as '%s' under '%s'.",
                subclass.__name__,
                final_type_name,
                base_model_name,
            )

        DynamicBase._recreate_models()
        return subclass

    return decorator

model_validate_toml classmethod

model_validate_toml(toml_data)

Validate a model from a TOML string.

Source code in swarmauri_base/TomlMixin.py

@classmethod
def model_validate_toml(cls, toml_data: str):
    """Validate a model from a TOML string."""
    try:
        # Parse TOML into a Python dictionary
        toml_content = tomllib.loads(toml_data)

        # Convert the dictionary to JSON and validate using Pydantic
        return cls.model_validate_json(json.dumps(toml_content))
    except tomllib.TOMLDecodeError as e:
        raise ValueError(f"Invalid TOML data: {e}")
    except ValidationError as e:
        raise ValueError(f"Validation failed: {e}")

model_dump_toml

model_dump_toml(
    fields_to_exclude=None, api_key_placeholder=None
)

Return a TOML representation of the model.

Source code in swarmauri_base/TomlMixin.py

def model_dump_toml(self, fields_to_exclude=None, api_key_placeholder=None):
    """Return a TOML representation of the model."""
    if fields_to_exclude is None:
        fields_to_exclude = []

    # Load the JSON string into a Python dictionary
    json_data = json.loads(self.model_dump_json())

    # Function to recursively remove specific keys and handle api_key placeholders
    def process_fields(data, fields_to_exclude):
        """Recursively filter fields and apply placeholders."""
        if isinstance(data, dict):
            return {
                key: (
                    api_key_placeholder
                    if key == "api_key" and api_key_placeholder is not None
                    else process_fields(value, fields_to_exclude)
                )
                for key, value in data.items()
                if key not in fields_to_exclude
            }
        elif isinstance(data, list):
            return [process_fields(item, fields_to_exclude) for item in data]
        else:
            return data

    # Filter the JSON data
    filtered_data = process_fields(json_data, fields_to_exclude)

    # Convert the filtered data into TOML
    return toml.dumps(filtered_data)

model_validate_yaml classmethod

model_validate_yaml(yaml_data)

Validate a model from a YAML string.

Source code in swarmauri_base/YamlMixin.py

@classmethod
def model_validate_yaml(cls, yaml_data: str):
    """Validate a model from a YAML string."""
    try:
        # Parse YAML into a Python dictionary
        yaml_content = yaml.safe_load(yaml_data)

        # Convert the dictionary to JSON and validate using Pydantic
        return cls.model_validate_json(json.dumps(yaml_content))
    except yaml.YAMLError as e:
        raise ValueError(f"Invalid YAML data: {e}")
    except ValidationError as e:
        raise ValueError(f"Validation failed: {e}")

model_dump_yaml

model_dump_yaml(
    fields_to_exclude=None, api_key_placeholder=None
)

Return a YAML representation of the model.

Source code in swarmauri_base/YamlMixin.py

def model_dump_yaml(self, fields_to_exclude=None, api_key_placeholder=None):
    """Return a YAML representation of the model."""
    if fields_to_exclude is None:
        fields_to_exclude = []

    # Load the JSON string into a Python dictionary
    json_data = json.loads(self.model_dump_json())

    # Function to recursively remove specific keys and handle api_key placeholders
    def process_fields(data, fields_to_exclude):
        """Recursively filter fields and apply placeholders."""
        if isinstance(data, dict):
            return {
                key: (
                    api_key_placeholder
                    if key == "api_key" and api_key_placeholder is not None
                    else process_fields(value, fields_to_exclude)
                )
                for key, value in data.items()
                if key not in fields_to_exclude
            }
        elif isinstance(data, list):
            return [process_fields(item, fields_to_exclude) for item in data]
        else:
            return data

    # Filter the JSON data
    filtered_data = process_fields(json_data, fields_to_exclude)

    # Convert the filtered data into YAML using safe mode
    return yaml.safe_dump(filtered_data, default_flow_style=False)

model_post_init

model_post_init(logger=None)

Assign a logger instance after model initialization.

Source code in swarmauri_base/LoggerMixin.py

def model_post_init(self, logger: Optional[FullUnion[LoggerBase]] = None) -> None:
    """Assign a logger instance after model initialization."""

    # Directly assign the provided FullUnion[LoggerBase] or fallback to the
    # class-level default.
    self.logger = self.logger or logger or self.default_logger