Class swarmauri_standard.pseudometrics.EquivalenceRelationPseudometric.EquivalenceRelationPseudometric

swarmauri_standard.pseudometrics.EquivalenceRelationPseudometric.EquivalenceRelationPseudometric

EquivalenceRelationPseudometric(
    equivalence_relation, **kwargs
)

Bases: PseudometricBase

Implements a pseudometric based on equivalence relations.

This pseudometric assigns distance 0 to points that are equivalent under a given equivalence relation, and distance 1 to points that are not. This effectively creates a quotient space where points in the same equivalence class are treated as identical.

The equivalence relation must satisfy: 1. Reflexivity: x ~ x for all x 2. Symmetry: if x ~ y, then y ~ x 3. Transitivity: if x ~ y and y ~ z, then x ~ z

Initialize the EquivalenceRelationPseudometric with an equivalence relation.

Parameters

equivalence_relation : Callable[[Any, Any], bool] A function that takes two arguments and returns True if they are equivalent, False otherwise. kwargs : Any Additional keyword arguments for the base class.

Source code in swarmauri_standard/pseudometrics/EquivalenceRelationPseudometric.py

def __init__(self, equivalence_relation: Callable[[Any, Any], bool], **kwargs):
    """
    Initialize the EquivalenceRelationPseudometric with an equivalence relation.

    Parameters
    ----------
    equivalence_relation : Callable[[Any, Any], bool]
        A function that takes two arguments and returns True if they are equivalent,
        False otherwise.
    kwargs : Any
        Additional keyword arguments for the base class.
    """
    super().__init__(**kwargs, equivalence_relation=equivalence_relation)

type class-attribute instance-attribute

type = 'EquivalenceRelationPseudometric'

equivalence_relation class-attribute instance-attribute

equivalence_relation = Field(
    ..., description="Equivalence relation function"
)

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 = Field(default=PSEUDOMETRIC.value)

version class-attribute instance-attribute

version = '0.1.0'

distance

distance(x, y)

Calculate the pseudometric distance based on equivalence relation.

Returns 0 if x is equivalent to y, 1 otherwise.

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 0.0 if x and y are equivalent, 1.0 otherwise

Source code in swarmauri_standard/pseudometrics/EquivalenceRelationPseudometric.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 based on equivalence relation.

    Returns 0 if x is equivalent to y, 1 otherwise.

    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
        0.0 if x and y are equivalent, 1.0 otherwise
    """
    try:
        # Apply the equivalence relation to determine if x and y are equivalent
        if self.equivalence_relation(x, y):
            return 0.0
        else:
            return 1.0
    except Exception as e:
        logger.error(f"Error calculating distance: {str(e)}")
        raise ValueError(f"Failed to calculate distance: {str(e)}")

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]

Source code in swarmauri_standard/pseudometrics/EquivalenceRelationPseudometric.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]
    """
    try:
        result = []
        for x in xs:
            row = []
            for y in ys:
                row.append(self.distance(x, y))
            result.append(row)
        return result
    except Exception as e:
        logger.error(f"Error calculating distances matrix: {str(e)}")
        raise ValueError(f"Failed to calculate distances matrix: {str(e)}")

check_non_negativity

check_non_negativity(x, y)

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

For an equivalence relation pseudometric, this is always true as the distance is either 0 or 1, both of which are non-negative.

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 Always True for this pseudometric

Source code in swarmauri_standard/pseudometrics/EquivalenceRelationPseudometric.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.

    For an equivalence relation pseudometric, this is always true as the distance
    is either 0 or 1, both of which are non-negative.

    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
        Always True for this pseudometric
    """
    # Distance is always either 0 or 1, so it's always non-negative
    return True

check_symmetry

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

Check if the distance function satisfies the symmetry property.

This checks if the equivalence relation is symmetric, i.e., if x ~ y then y ~ x.

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

Source code in swarmauri_standard/pseudometrics/EquivalenceRelationPseudometric.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.

    This checks if the equivalence relation is symmetric, i.e., if x ~ y then y ~ x.

    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
    """
    try:
        # Check if the distance from x to y equals the distance from y to x
        d_xy = self.distance(x, y)
        d_yx = self.distance(y, x)
        return abs(d_xy - d_yx) <= tolerance
    except Exception as e:
        logger.error(f"Error checking symmetry: {str(e)}")
        raise ValueError(f"Failed to check symmetry: {str(e)}")

check_triangle_inequality

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

Check if the distance function satisfies the triangle inequality.

For an equivalence relation pseudometric, this is always true: - If x ~ z, then d(x,z) = 0 ≤ d(x,y) + d(y,z) for any y - If x !~ z, then d(x,z) = 1, and there are two cases: * If x ~ y and y ~ z, transitivity of the equivalence relation would imply x ~ z, contradicting our assumption. So this case is impossible. * If x !~ y or y !~ z, then d(x,y) + d(y,z) ≥ 1 = d(x,z)

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

Source code in swarmauri_standard/pseudometrics/EquivalenceRelationPseudometric.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.

    For an equivalence relation pseudometric, this is always true:
    - If x ~ z, then d(x,z) = 0 ≤ d(x,y) + d(y,z) for any y
    - If x !~ z, then d(x,z) = 1, and there are two cases:
      * If x ~ y and y ~ z, transitivity of the equivalence relation would imply x ~ z,
        contradicting our assumption. So this case is impossible.
      * If x !~ y or y !~ z, then d(x,y) + d(y,z) ≥ 1 = d(x,z)

    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
    """
    try:
        d_xz = self.distance(x, z)
        d_xy = self.distance(x, y)
        d_yz = self.distance(y, z)

        # Check triangle inequality: d(x,z) ≤ d(x,y) + d(y,z)
        return d_xz <= d_xy + d_yz + tolerance
    except Exception as e:
        logger.error(f"Error checking triangle inequality: {str(e)}")
        raise ValueError(f"Failed to check triangle inequality: {str(e)}")

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. For an equivalence relation pseudometric, d(x,y) = 0 exactly when x ~ y.

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

Source code in swarmauri_standard/pseudometrics/EquivalenceRelationPseudometric.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.
    For an equivalence relation pseudometric, d(x,y) = 0 exactly when x ~ y.

    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
    """
    try:
        # For this pseudometric, d(x,y) = 0 if and only if x ~ y
        # So we check if the equivalence relation and distance are consistent
        are_equivalent = self.equivalence_relation(x, y)
        d_xy = self.distance(x, y)

        # If they're equivalent, distance should be 0; if not, distance should be 1
        return (are_equivalent and d_xy == 0.0) or (
            not are_equivalent and d_xy == 1.0
        )
    except Exception as e:
        logger.error(f"Error checking weak identity: {str(e)}")
        raise ValueError(f"Failed to check weak identity: {str(e)}")

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