Class swarmauri_base.pseudometrics.PseudometricBase.PseudometricBase

swarmauri_base.pseudometrics.PseudometricBase.PseudometricBase

Bases: IPseudometric, ComponentBase

Abstract base class implementing pseudometric behavior.

A pseudometric satisfies: 1. Non-negativity: d(x,y) ≥ 0 2. Symmetry: d(x,y) = d(y,x) 3. Triangle inequality: d(x,z) ≤ d(x,y) + d(y,z)

Unlike a metric, a pseudometric allows d(x,y) = 0 for x ≠ y, meaning it may not distinguish between distinct points.

This base class provides skeleton implementations for all required methods of the IPseudometric interface. Derived classes should override these methods with actual implementations.

resource class-attribute instance-attribute

resource = Field(default=PSEUDOMETRIC.value)

type class-attribute instance-attribute

type = 'ComponentBase'

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

version class-attribute instance-attribute

version = '0.1.0'

distance

distance(x, y)

Calculate the pseudometric distance between two objects.

Parameters

x : Union[VectorType, MatrixType, Sequence[T], str, Callable] The first object y : Union[VectorType, MatrixType, Sequence[T], str, Callable] The second object

Returns

float The distance between x and y

Raises

TypeError If inputs are of incompatible types ValueError If inputs have incompatible dimensions NotImplementedError If the method is not implemented by a derived class

Source code in swarmauri_base/pseudometrics/PseudometricBase.py

def distance(
    self,
    x: Union[VectorType, MatrixType, Sequence[T], str, Callable],
    y: Union[VectorType, MatrixType, Sequence[T], str, Callable],
) -> float:
    """
    Calculate the pseudometric distance between two objects.

    Parameters
    ----------
    x : Union[VectorType, MatrixType, Sequence[T], str, Callable]
        The first object
    y : Union[VectorType, MatrixType, Sequence[T], str, Callable]
        The second object

    Returns
    -------
    float
        The distance between x and y

    Raises
    ------
    TypeError
        If inputs are of incompatible types
    ValueError
        If inputs have incompatible dimensions
    NotImplementedError
        If the method is not implemented by a derived class
    """
    logger.error("distance method not implemented")
    raise NotImplementedError(
        "The distance method must be implemented by derived classes"
    )

distances

distances(xs, ys)

Calculate the pairwise distances between two collections of objects.

Parameters

xs : Sequence[Union[VectorType, MatrixType, Sequence[T], str, Callable]] The first collection of objects ys : Sequence[Union[VectorType, MatrixType, Sequence[T], str, Callable]] The second collection of objects

Returns

List[List[float]] A matrix of distances where distances[i][j] is the distance between xs[i] and ys[j]

Raises

TypeError If inputs contain incompatible types ValueError If inputs have incompatible dimensions NotImplementedError If the method is not implemented by a derived class

Source code in swarmauri_base/pseudometrics/PseudometricBase.py

def distances(
    self,
    xs: Sequence[Union[VectorType, MatrixType, Sequence[T], str, Callable]],
    ys: Sequence[Union[VectorType, MatrixType, Sequence[T], str, Callable]],
) -> List[List[float]]:
    """
    Calculate the pairwise distances between two collections of objects.

    Parameters
    ----------
    xs : Sequence[Union[VectorType, MatrixType, Sequence[T], str, Callable]]
        The first collection of objects
    ys : Sequence[Union[VectorType, MatrixType, Sequence[T], str, Callable]]
        The second collection of objects

    Returns
    -------
    List[List[float]]
        A matrix of distances where distances[i][j] is the distance between xs[i] and ys[j]

    Raises
    ------
    TypeError
        If inputs contain incompatible types
    ValueError
        If inputs have incompatible dimensions
    NotImplementedError
        If the method is not implemented by a derived class
    """
    logger.error("distances method not implemented")
    raise NotImplementedError(
        "The distances method must be implemented by derived classes"
    )

check_non_negativity

check_non_negativity(x, y)

Check if the distance function satisfies the non-negativity property.

Parameters

x : Union[VectorType, MatrixType, Sequence[T], str, Callable] The first object y : Union[VectorType, MatrixType, Sequence[T], str, Callable] The second object

Returns

bool True if d(x,y) ≥ 0, False otherwise

Raises

NotImplementedError If the method is not implemented by a derived class

Source code in swarmauri_base/pseudometrics/PseudometricBase.py

def check_non_negativity(
    self,
    x: Union[VectorType, MatrixType, Sequence[T], str, Callable],
    y: Union[VectorType, MatrixType, Sequence[T], str, Callable],
) -> bool:
    """
    Check if the distance function satisfies the non-negativity property.

    Parameters
    ----------
    x : Union[VectorType, MatrixType, Sequence[T], str, Callable]
        The first object
    y : Union[VectorType, MatrixType, Sequence[T], str, Callable]
        The second object

    Returns
    -------
    bool
        True if d(x,y) ≥ 0, False otherwise

    Raises
    ------
    NotImplementedError
        If the method is not implemented by a derived class
    """
    logger.error("check_non_negativity method not implemented")
    raise NotImplementedError(
        "The check_non_negativity method must be implemented by derived classes"
    )

check_symmetry

check_symmetry(x, y, tolerance=1e-10)

Check if the distance function satisfies the symmetry property.

Parameters

x : Union[VectorType, MatrixType, Sequence[T], str, Callable] The first object y : Union[VectorType, MatrixType, Sequence[T], str, Callable] The second object tolerance : float, optional The tolerance for floating-point comparisons, by default 1e-10

Returns

bool True if d(x,y) = d(y,x) within tolerance, False otherwise

Raises

NotImplementedError If the method is not implemented by a derived class

Source code in swarmauri_base/pseudometrics/PseudometricBase.py

def check_symmetry(
    self,
    x: Union[VectorType, MatrixType, Sequence[T], str, Callable],
    y: Union[VectorType, MatrixType, Sequence[T], str, Callable],
    tolerance: float = 1e-10,
) -> bool:
    """
    Check if the distance function satisfies the symmetry property.

    Parameters
    ----------
    x : Union[VectorType, MatrixType, Sequence[T], str, Callable]
        The first object
    y : Union[VectorType, MatrixType, Sequence[T], str, Callable]
        The second object
    tolerance : float, optional
        The tolerance for floating-point comparisons, by default 1e-10

    Returns
    -------
    bool
        True if d(x,y) = d(y,x) within tolerance, False otherwise

    Raises
    ------
    NotImplementedError
        If the method is not implemented by a derived class
    """
    logger.error("check_symmetry method not implemented")
    raise NotImplementedError(
        "The check_symmetry method must be implemented by derived classes"
    )

check_triangle_inequality

check_triangle_inequality(x, y, z, tolerance=1e-10)

Check if the distance function satisfies the triangle inequality.

Parameters

x : Union[VectorType, MatrixType, Sequence[T], str, Callable] The first object y : Union[VectorType, MatrixType, Sequence[T], str, Callable] The second object z : Union[VectorType, MatrixType, Sequence[T], str, Callable] The third object tolerance : float, optional The tolerance for floating-point comparisons, by default 1e-10

Returns

bool True if d(x,z) ≤ d(x,y) + d(y,z) within tolerance, False otherwise

Raises

NotImplementedError If the method is not implemented by a derived class

Source code in swarmauri_base/pseudometrics/PseudometricBase.py

def check_triangle_inequality(
    self,
    x: Union[VectorType, MatrixType, Sequence[T], str, Callable],
    y: Union[VectorType, MatrixType, Sequence[T], str, Callable],
    z: Union[VectorType, MatrixType, Sequence[T], str, Callable],
    tolerance: float = 1e-10,
) -> bool:
    """
    Check if the distance function satisfies the triangle inequality.

    Parameters
    ----------
    x : Union[VectorType, MatrixType, Sequence[T], str, Callable]
        The first object
    y : Union[VectorType, MatrixType, Sequence[T], str, Callable]
        The second object
    z : Union[VectorType, MatrixType, Sequence[T], str, Callable]
        The third object
    tolerance : float, optional
        The tolerance for floating-point comparisons, by default 1e-10

    Returns
    -------
    bool
        True if d(x,z) ≤ d(x,y) + d(y,z) within tolerance, False otherwise

    Raises
    ------
    NotImplementedError
        If the method is not implemented by a derived class
    """
    logger.error("check_triangle_inequality method not implemented")
    raise NotImplementedError(
        "The check_triangle_inequality method must be implemented by derived classes"
    )

check_weak_identity

check_weak_identity(x, y)

Check if the distance function satisfies the weak identity property.

In a pseudometric, d(x,y) = 0 is allowed even when x ≠ y. This method verifies that this property is properly handled.

Parameters

x : Union[VectorType, MatrixType, Sequence[T], str, Callable] The first object y : Union[VectorType, MatrixType, Sequence[T], str, Callable] The second object

Returns

bool True if the pseudometric properly handles the weak identity property

Raises

NotImplementedError If the method is not implemented by a derived class

Source code in swarmauri_base/pseudometrics/PseudometricBase.py

def check_weak_identity(
    self,
    x: Union[VectorType, MatrixType, Sequence[T], str, Callable],
    y: Union[VectorType, MatrixType, Sequence[T], str, Callable],
) -> bool:
    """
    Check if the distance function satisfies the weak identity property.

    In a pseudometric, d(x,y) = 0 is allowed even when x ≠ y.
    This method verifies that this property is properly handled.

    Parameters
    ----------
    x : Union[VectorType, MatrixType, Sequence[T], str, Callable]
        The first object
    y : Union[VectorType, MatrixType, Sequence[T], str, Callable]
        The second object

    Returns
    -------
    bool
        True if the pseudometric properly handles the weak identity property

    Raises
    ------
    NotImplementedError
        If the method is not implemented by a derived class
    """
    logger.error("check_weak_identity method not implemented")
    raise NotImplementedError(
        "The check_weak_identity method must be implemented by derived classes"
    )

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